Chapter 8 Webscraping for Social Scientists

8.1 Chapter overview

“[The] technological revolution in mobile, Web, and Internet communications has the potential to revolutionize our understanding of ourselves and how we interact. Merton was right: Social science has still not found its Kepler. But three hundred years after Alexander Pope argued that the proper study of mankind should lie not in the heavens but in ourselves, we have finally found our telescope. Let the revolution begin…” (Watts 2011: 266)

Watts’ already-famous quote predicts a revolution in the social sciences. He and others (see also Lazer et al. 2009) essentially argue that social science will be revolutionized by the unprecedented use of of the social internet. Given that people overwhelmingly adopted internet technologies and given that many of the platforms that offer these technologies automatically archive all kinds of behavior (Spiro 2016) such as clicks, messages, social media relationships, and so forth, there may be a treasure trove of data on the internet that social scientists can use for their research on social processes. In this chapter, we discuss some of the promises and pitfalls of webscraping so-called “digital trace” data (Golder and Macy 2014) on the internet for social network analysis. We are then going to discuss some different techniques that are often used for webscraping. Note that the fast-pace nature of the internet inherently means that by the time you read this text, some of the things we discuss will be outdated. (Which can be argued to be one of the pitfalls of social science research with webscraping!) We are also getting our hands dirty with a hands-on example of digital trace data that we are going to collect ourselves. So by the end of this chapter, you will be familiar with some of the unique opportunities and difficulties of webscraped (social network) data, have a birds-eye perspective on the different techniques for scraping the web for your own research, have knowledge on the ethics surrounding webscraping, and have more in-depth experience on one specific package for webscraping bibliometric data in R. In short, you will have firsthand knowledge on the current state-of-the-art in sociological data collection. There are really good, exhaustive resources for webscraping and computational sociology. See, for instance, the book by Robert Ackland (Ackland 2013). Yet, to get up to speed for this chapter, you can read the first chapter of Bas Hofstra’s dissertation (Hofstra 2017), Golder and Macy’s Annual Review of Sociology article (Golder and Macy 2014), and Lazer and colleagues’ Science article (Lazer et al. 2009). A very nice introduction to the field of computational social science can be found in Salganik’s text book (Salganik 2019). An overview with recent applications is written by Edelman and colleagues (Edelmann et al. 2020).

8.1.1 Definitions

Webscraping

The process by which you collect data from the internet. This can entail different routes: manual data collection, automated data collection via code, use of application programming interfaces, and so forth.

Digital footprints

Automatically logged behavioral signals that actors – broadly construed: individuals, companies, organizations, groups, etc. – leave on the internet. This may imply many things, including the messages one leaves on Instagram posts, back-and-forth conversations on Whatsapp, companies’ job advertisements, university course texts, and so forth. All of these signals can capture some social process: networking on social media, signalling specific job requirements, or university course prerequisites. This also means that digital footprints can contain a lot of different and sometimes unstructured data types. Social network data is obvious: who is friends with whom on Facebook, who Tweets to whom, and so forth. Network data (not social) is also obvious. For instance, which website links to what other websites. (Sidenote: Google’s page-rank algorithm made them succesful, and this page-rank algorithm is based on network centrality that essentially filters out “influential” websites quickly. In other words, Google became such an influential company because of network analyses.) It can also contain (unstructured) text data, which in itself signals a lot of interesting social processes that one may consider.

Computational sociology

Problem-driven, empirical sociology, but with the empirical part specifically containing some form of digital footprint data and/or some new methodological technique. Sociologists are usually (necessarily?) interested in digital footprints concerning some social process. Because digital footprints are often related to social network processes (e.g., befriending on Facebook, messaging on Twitter, etc.), a lot of computational sociology includes some form of social network analysis. Because this is often, though not always, the case, discussing webscraping in the context of this book on social network analyses makes perfect sense. Some claim Agent-Based Modelling to be part of computational sociology too, others not. Again others claim performing RSiena analyses is part of computational sociology, others not. Note that this definition-issue is somewhat of a useless moving target. Computational sociology’s definition will be different next week depending on who you ask. In this book, we use a pragmatic definition. This means that you are a computational sociologists if you use digital footprint data and/or use relatively new methodological techniques in your research. Also note that there is a certain cause-effect sequence in the three definions above: using webscraping techniques to gather digital footprint data to study social problems makes you a computational sociologist.

8.2 Promises and pitfalls

Like every data source in the social sciences and beyond, there are unique features as well as difficult challenges to webscraped data. In this subsection, we will discuss some of these advantages and challenges of webscraping and, by extension, digital footprint data. Like we discussed before, most of the research using webscraped digital footprint data concerns social networks and so we situate these promises and pitfalls in the context of social network analysis. Note, however, that some of the promises and pitfalls generalize to other types of digital footprint data too.

8.2.1 Promises

(Social) networks

One of the key advantages of scraping data from the internet is that it is relatively easy to collect sociologically interesting network data. This may sound like a surprising thing to note in a book on social network analyses. But imagine a world without the internet, and then imagine that you are a social scientist interested in weak tie dynamics. What toolset do you have available to collect data on and then study those weak ties? You would probably think about qualitative interviews or collecting survey data. For the purpose of studying weak ties, however, both of these methods of data collection suffer from some some weaknesses. For instance, it is incredibly hard for respondents to recall those social ties that are weakly related. So asking about weak ties will likely not yield very reliable and valid results if you not somehow account for that. That is, respondents will mostly acquiesce to naming those ties that they met recently or which are relatively stronger. (There are some techniques to circumvent some of these issues, but those have their own drawbacks too (see Hofstra, Corten, and Tubergen 2021). And those techniques are suited mostly to ego networks and not full social networks.) Many surveys are also restricted, in that respondents can only name five social ties. It is possible to collect sociometric data of entire contexts in surveys, for instance by presenting respondents with a class-roster or department-roster and asking them who their trustees, friends, etc. This book even devotes an entire section to such data. Yet, such a design is pretty expensive to set up – i.e., you may be limited to a fixed set of network contexts – and may be quite taxing to respondents.

In contrast, an inherent feature of many places on the social internet is that individuals curate themselves who their connections are over a long time-span (e.g., friendships on Facebook) or leave many traces of interactions (e.g., mentioning someone on Twitter). Sometimes networks online are even pushed towards (triadic) network closure by recommendation systems on platforms like Facebook or Instagram. With some creativity on the scientists’ part, such data are relatively easy and cheap to collect. This may often lead to large and complete networks that are not restricted by relationship type (e.g., strong or weak), social context (e.g., family or school friends), respondent recall (acquiesce to strong ties), or social desirability bias (e.g., only nominating the popular kid in a class). An exemplary paper that circumvents some of the “regular” biases of social network research mentioned above is Hofstra et al. (2017) or Wimmer and Lewis (2010) who analyze segregation among weak ties by means of Facebook data. The benefits mentioned here for the analyses of social networks are also prime reasons as to why much research using digital traces incorporates some type of social network analysis.

Dynamics

A second advantage of digital trace data is that these data are often time-stamped (and sometimes geo-stamped). This means that the the researcher knows exactly when (and where) the digital trace – e.g., the social interaction on Twitter – occurred. So the researcher can potentially perform some sort of longitudinal analyses so as to come closer to causal estimates in inferential statistical models. In the context of webscraped social networks this is particularly useful so as to separate selection from influence in larger social networks. Gathering such longitudinal sociometric data for many social foci (e.g., school classes) is difficult (yet, definitely not impossible!), whereas collecting time-stamped social interactions on the internet may be somewhat easier. Note also that social network data collected in, for instance, school classes often puts the same time-stamp on a given network (e.g., the time that class was surveyed), whereas networks online may contain more-detailed time-stamps. These time-stamped (network) data can in some cases be considered relational events (Butts 2008).

Signals

A third advantage of webscraped data is that it can potentially capture behavioral and/or attitudinal signals that are otherwise hard to come by. Say you want to know about social network dynamics among drug traders. Those drug traders probably won’t indicate in a survey that they are engaged in such illicit activities. Scraping data from the so-called “dark web” may be one of the only ways to study networks among drug traders (see Norbutas 2018 who does just that) apart from stitching together police reports which are likely to be confidential. Furthermore, survey respondents may be a bit hesitant to write about their own attitudes that are perceived to be socially undesirable (like severe ethnic prejudice). In that case, one may collect digital trace data on Twitter, where you can observe and then operationalize ethnic prejudice happening in real time. Not all online behaviors and/or attitudes are accurate proxies for offline attitudes/behavior of interest and are very particular to online settings. Therefore, the researcher must be aware, theorize, and ideally empirically show where and how their online data proxies their behavior-of-interest offline.

Size

Finally, and we discuss why this is both a blessing and a curse (see below), webscraping can lead to data that can contain a lot of observations (into the many millions!) or variables. Note that this is in and of itself not an advantage. More data are not always better data if they are biased. Yet, the sheer size of webscraped data – under appropriate sampling! – may make it easier to observe relationships between the variables of interest when they are small in magnitude. Such small effects may in smaller samples be swamped by random variability (cf. Golder and Macy 2014: 132) This does not mean one can go look into their big dataset for random relationships between variables, these relationships should be problematized and theorized first (just like any other problem-driven, hypothesis-testing social science study).

8.2.2 Pitfalls

Sampling

Scientists using digital trace data should think carefully about their target population vis-a-vis their sampling frame and realized sample. This is something that is not unique to digital trace data. Yet, it is easy to be so impressed by the sheer data size in studies using digital trace data that questions about generalizability of results sometimes tend to get overshadowed. That is not to say that it doesn’t tell us anything informative, just that we do not necessarily know to what target population such results generalize. All types of selectivity can crawl into the data. For instance, if you want to study Facebook/Instagram/TikTok, you should be aware that such platforms tend to get disproportionately populated by certain demographics. If among 5 million Twitter users those in geographical region a display some behavior y more so than those in region b, it does not necessarily mean that regions a and b differ in y. It may be that Twitter is perceived to be a particularly good platform in a to display y, whereas in b people are indifferent to display y on Twitter or elsewhere. Selection into Twitter thus plays an important role in this example. This may happen for many digital trace data sources: biased selection into certain platforms, biased selection into privacy settings which can obscure what you can observe, biased selection into what people display online, and so forth. Ideally, you would have some anchor data set from which you know that it generalizes to a given target population and link that to some source of digital trace data. On the other end, one could attempt to study an entire population such that you are pretty certain that you can generalize your results to that population.

Size

Like we described above, data size is an advantage of digital trace data, yet simultaneously it is also a pitfall. Huge numbers of observations (again, into the many if not hundreds of millions) may be pretty difficult to manipulate and analyze. In some cases, the data become so large that it is necessary to move to computing clusters because your laptop’s memory cannot handle it anymore. Dependent on what type of data you analyze you thus might need to adjust your data workflow. Not many educational programs prepare students for storing, manipulating, and analyzing large data sets, and this requires slightly different programming/statistical skills than what we’re used to. It may be a solution to sample from these huge data sets, such that you, say, only analyze a random sample of 5%. Yet, sampling from social networks is especially hard because of the interdependent nature of networks; some of the inherently clustered structure of networks is lost when you only draw a subset of agents from the network.

Data structure

This point relates to the point above. Webscraped digital trace data is usually structured very differently compared to the “flat” data files social scientists are used to working with. Usually, we open a dataset with columns (variables) and rows (observations). Webscraped data is usually stored in nested structured such as XML or JSON or contains text data. Therefore, additional manipulation is needed before we arrive at the data formats that standard statistical packages can read/analyze. Sometimes the networks-of-interest are stored in text data, for instance if you’re interested in letter-writing relationships. Hence, if you want to manipulate and analyze these data at scale, some form of programming becomes nearly unavoidable. (Luckily, we provide hands-on tools and examples in this book!)

Unobserved variables

Finally, webscraped digital trace data often do not contain the detailed demographic information that surveys (often) do provide. And this information often contains the key (control) variables in social science analyses. Imagine you scraped all customer reviews on the Etsy website because you want study how women and men reviewers judge the products of women and men creators differently. That is an important research question because it may show how gender dynamics in reviewing may (re)produce inequality between women and men creators. Yet, how do we know which reviewers are women or men? These labels do not come with the scraped data and some additional manipulation is needed. You could, for instance, attempt to predict whether a reviewer is a women or men by their first name. This is because first names are signals that relate strongly to gender. Yet, not every reviewer provides their first name but only a rather uninformative screenname. Furthermore, naming habits also vary between countries and the data do not inform you from which countries these reviewers originate. And what about age? What about income? Hence, there could be many factors related to the outcome you intend to study that are not readily available in digital trace data. This again requires some creativity on the researchers side, for instance by matching survey data with digital trace data, enrich data with other data sources, and so forth.

8.3 Where does this lead us?

So if we sum up what we have learned thus far, what are the overarching benefits of webscraped digital trace data compared to more-traditional data sources? We can think of at least three of those:

1. New tests of old social science hypotheses made possible by the availability of digital footprint data.
2. Tests of newly derived social science hypotheses made possible by the availability of digital footprint data.
3. Tests of new theories about “the internet” as social phenomenon by itself.

Note that these three points are not mutually exclusive: a newly derived social science hypothesis might just as well be about the internet as social phenomenon by itself. Yet, for analytic purposes it is convenient to list these three as separate. An interesting example related to point (1) is the question whether social networks are “small-worlds” – i.e., highly clustered yet having a short average path length. A popular adage derives from this feature of social networks: individuals are all separated by approximately six degrees. This was traditionally studied by considering letter chains. Now one could study this with the entire Facebook network, and so one could actually test this hypothesis at much larger and complete scale than before. With respect to point (2): you could derive new hypotheses about the conversational nature of massive collaborative projects by scraping and then studying all Wikipedia edits (something that was hard to study before). More recently, a lot of studies emerged on the role of fake news and echo chambers with respect to individual attitudes and polarization, which relates to point (3): how the internet as social phenomenon by itself can influence behavior/attitudes. The strongest computational social science papers leverage the strengths of digital trace data but simultaneously account for (or at the very least acknowledge) some of its weaknesses as we list above.

8.4 Ethics

In webscraping digital footprints, it is essential to consider ethics. In an ideal situation, a researcher has informed consent from the research subjects they intend to study In practice, the nature of webscraping digital footprints makes it very difficult to obtaining such consent. In many cases the researchers only have a pseudonym of the research subjects which makes it hard to contact them. In other cases the scale of the data collection makes it hard to build logistics for informed consent. Imagine asking ten million website forum users for their consent when they don’t list their name on the website. This is arguably a very difficult task. Ethical review boards, a collection of persons who apply research ethics to the research designs that scholars propose, are only now beginning to think about how to review research designs with unconventional data sources such as digital footprints. I found the overview found in the paper by (Ferretti et al. 2021) very useful.

8.5 Webscraping techniques

[This section is heavily under construction, but important to remember that there are several ways to gather digital footprints]

8.5.1 Manual

  • Army of research assistants looking up information on the web, saving that systematically.

8.5.2 APIs

  • Application Programming Interface: Utilizing existing structures in place, usually a connection between a computer/program to another computer/program that makes use of an interface to provide some service. For instance, through Twitter’s API you can scrape a limited amount of data via a dedicated connection for that.

8.5.3 Crawling

  • You can also design your own webscraping crawlers. That is how we proceed in the tutorial of this chapter.

8.5.4 Industry-Science Collaboration

  • You can also collaborate with companies like Facebook or Twitter such that they provide data for researchers (which has been done in the past).

8.6 Hands-on webscraping

Now that we learned about computational social science, webscraped digital trace data, and some of its techniques, it is time to get our hands dirty ourselves. In what follows is a short tutorial on webscraping where we will be collecting data from webpages on the internet. We will use the specific use-case of sociology staff at Radboud University. What do they publish? Where? And with whom do they collaborate? We assume you followed the R tutorial in this book, or that you otherwise have at least some experience with coding in R. In the rest of this tutorial, we will switch between base R and Tidyverse (just a bit), whatever is most convenient. (Note that this will happen sometimes if you become an applied computational sociologist.)

What are we are going to scrape specifically? This is a social network book so we’re obviously going to scrape social networks. Specifically, the social network of co-authors on the scientific papers of Radboud University’s Department of Sociology Staff. Co-authors on a paper are the set of scholars who publish a scientific paper together in a journal. Who do sociology staff publish with? And are those co-authors connected with one another too? Are these social networks clustered in some way? To get at such data we need several things: a list of RU’s sociology staff, a repository with their publication,s and the meta-information (titles, authors, etc.) of those publications. Substantively, this is key to look at things like how often scholars are cited or how social network dynamics in science work. (Not there is a whole body of research on the so-called “Sociology of Science” or the “Science of Science”. Because scientists are particularly good at documenting things, sociology/science of science type-of-work is some of the earliest work that you can label computational social science.)

In the remainder of this chapter, we thus provide a tutorial in which we explain the packages needed to do what we write above. We will shortly describe the scholar and other packages in R, the data sources, and the (network) data structures you encounter and how to deal with them. Yet, there are many ways code-wise or data-wise with which you can do very similar things like we do in this tutorial.

8.6.1 Staging your script

  • Open a new R-script (via file –> new –> RScript (or simply hit Ctrl+Shift+N or Cmd+Shift+N if you work on Mac)
  • Before you start scraping and analyzing, take all the precautionary steps noted in Appendix A.

So for this tutorial, your starting script will look something like this:

######################################### Title: Webscraping in R Author: Bas Hofstra Version:
######################################### 29-07-2021

# start with clean workspace
rm(list = ls())

# install.packages('data.table')
library(data.table)  # mainly for faster data handling
library(tidyverse)  # I assume you already installed this one!
# install.packages('httr') # we don't need this for now require(httr)
install.packages("xml2")
require(xml2)
install.packages("rvest")
require(rvest)
install.packages("devtools")
require(devtools)
# Note we're doing something different here. We're installing a *latest* version directly from
# GitHub This is because the released version of this packages contains some errors!
devtools::install_github("jkeirstead/scholar")

require(scholar)

# define workdirectory, note the double *backslashes* if you're on windows setwd('/yourpathhere)'

So what are these different packages? You already know the tidyverse from Appendix A. The packages xml2, rvest, devtools, and scholar are probably new to you. We introduce the scholar package later, and devtools is shortly explained in the annotated code above. What do the two other packages we use do?

  • rvest: This is the explanation the writers of the package give: “rvest is new package that makes it easy to scrape (or harvest) data from html web pages.” Seems like something we need, a package that stores information from webpages into relatively structured data that we can then query/manipulate.
  • xml2: This is what the writers of the package say about it: “Work with XML files using a simple, consistent interface.” So we can manipulate the data scraped with rvest, using the xml2 package functions.

8.6.2 Getting “anchor” data

A first step is to get the “anchor” data. The anchor data are the first data we scrape with which we then link to further data sources. Our goal is to get to know (i) who the Radboud University Department of Sociology staff is, (ii) what they publish with respect to scientific work, and (iii) who they collaborate with. So that means at least three data sources we need to collect from somewhere. What would be a nice starting (read: anchor) point be? First, we have to know who is on the sociology staff. Let’s check out the Radboud sociology website. There is lots of intriguing information, but not on who is who. There is, however, a specific link to the research staff. Here we do see a nice list on who is on the sociology staff! How do we get that data? It is actually quite simple, the package xml2 has a very nice function html_read() which simply extracts the source html of a webpage:

# Let's first get the staff page read_html is a function that simply extracts html webpages and
# puts them in xml format
soc_staff <- read_html("https://www.ru.nl/sociology/research/staff/")
#> Error in open.connection(x, "rb"): HTTP error 404.

Whoops, this gives an error because as of 2024 this website does not exist anymore. This happens frequently with webscraping. In our Sunbelt workshop we show how to scrape the new website.

Here we will use a trick, namely the wayback machine.

soc_staff <- read_html("https://web.archive.org/web/20230528153336/https://www.ru.nl/sociology/research/staff/")
head(soc_staff)
#> $node
#> <pointer: 0x00000252619fe250>
#> 
#> $doc
#> <pointer: 0x000002525ecec880>

That looks kinda weird. What type of object did we store it by putting the html into soc_staff?

class(soc_staff)
#> [1] "xml_document" "xml_node"

So it is is stored in something that R calls an XML object. Remember when we talked about that in the text above? Extensible Markup Language, XML, is a nested data structure where in each next sublayer of that structure new information is stored. Not important for now what that means specifically. But it is important to extract the relevant table that we saw on the sociology staff website. How do we do that? Go to the https://www.ru.nl/sociology/research/staff/ in Google Chrome and then press “Inspect” on the webpage (right click–>Inspect). You should see something like the screenshot below (Figure 8.1), right?

Inspect element

Figure 8.1: Inspect element

Look at the screenshot below (Figure 8.2), you should be able to see something like this. In the html code we extracted from the Radboud website, we need to go to one of the nodes first. If you move your cursor over “body” in the html code on the right-hand side of your screen, the entire “body” of the page should become some shade of blue. This means that the elements encapsulated in the “body” node captures everything that turned blue. This is essentially the nested data structure we mentioned above.

Website 'body' node

Figure 8.2: Website ‘body’ node

Next, we need to look at the specific elements on the page that we need to extract. Somewhat by informed trial and error, looking for the correct code, we can select the elements we want. In the screenshot below (Figure 8.3), you see that the “td” elements actually are the ones we need. So we need code that looks for the node “body” and the “td” elements in the xml object and then extract those elements in it. Note that you can click on the arrows once you are in the “Inspect” mode in the web browser to trial-and-error to get at the correct elements.

Note that you can identify elements visually with extensions for browsers like selectorgadget for Google Chrome: if you activate the extension you can move your cursor over the webpage elements and see the “div” path.)


Element 'td' on website

Figure 8.3: Element ‘td’ on website

Something like the code below should do just that:

# so we need to find WHERE the table is located in the html 'inspect element' in mozilla firefox or
# 'view page source' and you see that everything AFTER /td in the 'body' of the page seems to be
# the table we do need
soc_staff <- soc_staff %>%
    rvest::html_nodes("body") %>%
    xml2::xml_find_all("//td") %>%
    rvest::html_text()

Question: What happens in the code above? Why do we specify search for ‘body’ and ‘//td’?

Let us check out what happened to the soc_staff object now:

soc_staff  # looks much better!!
Table 8.1: Table 8.2: Sociology staff
x
Staff:
Expertise:
Batenburg, prof. dr. R. (Ronald)
Healthcare, labour market and healthcare professions and training
Begall, dr. K.H. (Katia)
Family, life course, labour market participation, division of household tasks and gender norms
Bekhuis, dr. H. (Hidde)
Welfare state, nationalism and sports
Berg, dr. L. van den (Lonneke)
Family, life course and transition to adulthood
Blommaert, dr. L. (Lieselotte)
Discrimination and inequality on the labour market
Damman, dr. M. (Marleen)
Labour market, life course, older workers, retirement and solo self-employed
Eisinga, prof. dr. R.N. (Rob)
Methods of research and statistics
Gesthuizen, dr. M.J.W. (Maurice)
Poverty en social cohesion
Glas, dr. S. (Saskia)
Islam, gender attitudes and sexuality
Hek, dr. M. van (Margriet)
Educational inequality, gender inequality, organizational sociology and culture
Hoekman, dr. R. H. A.(Remco)
Sports and policy sociology
Hofstra, dr. B. (Bas)
Diversity, inequality and innovation
Kraaykamp, prof. dr. G.L.M. (Gerbert)
Educational inequality, culture and health
Meuleman, dr. (Roza)
Culture and nationalism
Savelkoul, dr. M.J. (Michael)
Ethnic diversity, prejudice and social cohesion
Scheepers, prof. dr. P.L.H. (Peer)
Comparative research, social cohesion and diversity
Spierings, dr. C.H.B.M. (Niels)
Islam, gender, populism, social media, Middle East and migration
Tolsma, dr. J. (Jochem)
Inequality, criminology and ethnic diversity
Verbakel, prof. dr. C.M.C. (Ellen) Head of the department
Health, family and work
Visser, dr. M. (Mark)
Older workers, radicalism and social cohesion
Wolbers, prof. dr. M.H.J. (Maarten)
Educational inequality and labour market inequality
PhD:
Expertise:
Bussemakers, C. (Carlijn) MSc
Adverse youth experiences and social inequality
Franken, R. (Rob) MSc
Sport networks and motivation for sustainable sports participation
Firat, M. (Mustafa) MSc
Social inequality, older workers, life course and retirement
Geurts, P.G. (Nella) MSc
Integration and migration
Hendriks, I.P. (Inge) MSc
Resistance to refugees and social cohesion
Jeroense, T.M.G. (Thijmen) MSc
Political participation, segregation, opinion polarization and voting behaviour
Linders, N. (Nik) MSc
Populism, gender, masculinity and sexuality
Loh, S.M. (Renae) MSc
Educational sociology, social stratification, gender inequality and information communication technology (ICT)
Meijeren, M. (Maikel) MSc
Social capital, volunteer work and diversity
Mensvoort, C.A. van (Carly) MSc
Gender, leadership and social norms
Müller, K. (Katrin) MSc
Opinions about discrimination, migration and inequality
Raiber, K. (Klara) MSc
Informal care, employment, social inequality and gender
Ramaekers, M.J.M. (Marlou) MSc
Prosocial behaviour and family
Wiertsema, S. (Sara) MSc
Inequality in sports and physical activity, school-to-work transition and employment
External PhD:
Expertise:
Betkó, drs. J.G. (János)
Social assistance benefit, poverty, reintegration, RCT and social experiment
Houten, J. (Jasper) van MSc
Sports
Middendorp J. (Jansje) van MSc
Home administration
Vis, E. (Elize) MSc
Healthcare, labour market, healthcare professions and training, health and social capital
Weber, T. (Tijmen) MSc
International student mobility and the internationalization of higher education
Guest researchers:
Expertise:
Sterkens, dr. C.J.A. (Carl)
Religious conflicts, cohesion, religion and the philosophy of life
Vermeer, dr. P.A.D.M. (Paul)
Socialization processes, secularisation, religion and the philosophy of life


So it looks much nicer but does not seem to be in the entirely correct order. We have odd rows and even rows: odd rows are names, even rows have the expertise of staff. We need to get a bit creative to put the data in a nicer format. The %% operator gives a “remainder” of integers (whole numbers). So 10/2=5 with no remainder, but 11/2=5 with a remainder of 1. This means that we can derive odd or even with a function with that operator. Remember functions from Appendix A.13.5?

fodd <- function(x) x%%2 != 0
feven <- function(x) x%%2 == 0

Question: Do you understand what this function does?

How long are the data?

nstaf <- length(soc_staff)
nstaf
#> [1] 92

Alright, can we get the odd rows out of there?

# Do you understand why we need the nstaf? What it does?
soc_names <- soc_staff[fodd(1:nstaf)]  # in the 1 until 94st number, get the odd elements
head(soc_names)
#> [1] "Staff:"                           "Batenburg, prof. dr. R. (Ronald)"
#> [3] "Begall, dr. K.H. (Katia)"         "Bekhuis, dr. H. (Hidde)"         
#> [5] "Berg, dr. L. van den (Lonneke)"   "Blommaert, dr. L. (Lieselotte)"

And how about people’s expertise?

soc_experts <- soc_staff[feven(1:nstaf)]  # in the 1 until 94st number, get the even elements
head(soc_experts)
#> [1] "Expertise:"                                                                                    
#> [2] "Healthcare, labour market and healthcare professions and training"                             
#> [3] "Family, life course, labour market participation, division of household tasks and gender norms"
#> [4] "Welfare state, nationalism and sports"                                                         
#> [5] "Family, life course and transition to adulthood"                                               
#> [6] "Discrimination and inequality on the labour market"

Finally, can we merge those two vectors?

soc_df <- data.frame(cbind(soc_names, soc_experts))  # columnbind those and we have a DF for soc staff!

How does that look?

soc_df  # pretty nice!
Table 8.3: Table 8.4: Sociology staff cleaner
soc_names soc_experts
Staff: Expertise:
Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training
Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms
Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports
Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood
Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market
Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed
Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics
Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion
Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality
Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture
Hoekman, dr. R. H. A.(Remco) Sports and policy sociology
Hofstra, dr. B. (Bas) Diversity, inequality and innovation
Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health
Meuleman, dr. (Roza) Culture and nationalism
Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion
Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity
Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration
Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity
Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work
Visser, dr. M. (Mark) Older workers, radicalism and social cohesion
Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality
PhD: Expertise:
Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality
Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation
Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement
Geurts, P.G. (Nella) MSc Integration and migration
Hendriks, I.P. (Inge) MSc Resistance to refugees and social cohesion
Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour
Linders, N. (Nik) MSc Populism, gender, masculinity and sexuality
Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT)
Meijeren, M. (Maikel) MSc Social capital, volunteer work and diversity
Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms
Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality
Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender
Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family
Wiertsema, S. (Sara) MSc Inequality in sports and physical activity, school-to-work transition and employment
External PhD: Expertise:
Betkó, drs. J.G. (János) Social assistance benefit, poverty, reintegration, RCT and social experiment
Houten, J. (Jasper) van MSc Sports
Middendorp J. (Jansje) van MSc Home administration
Vis, E. (Elize) MSc Healthcare, labour market, healthcare professions and training, health and social capital
Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education
Guest researchers: Expertise:
Sterkens, dr. C.J.A. (Carl) Religious conflicts, cohesion, religion and the philosophy of life
Vermeer, dr. P.A.D.M. (Paul) Socialization processes, secularisation, religion and the philosophy of life


That looks much better! Now we only need to remove the redundant rows that state “expertise”, “staff,” and so forth.

# inspect again, and remove the rows we don't need (check for yourself to be certain!)

delrows <- which(soc_df$soc_names == "Staff:" | soc_df$soc_names == "PhD:" | soc_df$soc_names == "External PhD:" |
    soc_df$soc_names == "Guest researchers:" | soc_df$soc_names == "Other researchers:")

soc_df <- soc_df[-delrows, ]

Let’s check it out

soc_df  # even better
Table 8.5: Table 8.6: Sociology staff even cleaner
soc_names soc_experts
2 Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training
3 Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms
4 Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports
5 Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood
6 Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market
7 Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed
8 Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics
9 Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion
10 Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality
11 Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture
12 Hoekman, dr. R. H. A.(Remco) Sports and policy sociology
13 Hofstra, dr. B. (Bas) Diversity, inequality and innovation
14 Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health
15 Meuleman, dr. (Roza) Culture and nationalism
16 Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion
17 Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity
18 Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration
19 Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity
20 Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work
21 Visser, dr. M. (Mark) Older workers, radicalism and social cohesion
22 Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality
24 Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality
25 Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation
26 Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement
27 Geurts, P.G. (Nella) MSc Integration and migration
28 Hendriks, I.P. (Inge) MSc Resistance to refugees and social cohesion
29 Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour
30 Linders, N. (Nik) MSc Populism, gender, masculinity and sexuality
31 Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT)
32 Meijeren, M. (Maikel) MSc Social capital, volunteer work and diversity
33 Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms
34 Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality
35 Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender
36 Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family
37 Wiertsema, S. (Sara) MSc Inequality in sports and physical activity, school-to-work transition and employment
39 Betkó, drs. J.G. (János) Social assistance benefit, poverty, reintegration, RCT and social experiment
40 Houten, J. (Jasper) van MSc Sports
41 Middendorp J. (Jansje) van MSc Home administration
42 Vis, E. (Elize) MSc Healthcare, labour market, healthcare professions and training, health and social capital
43 Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education
45 Sterkens, dr. C.J.A. (Carl) Religious conflicts, cohesion, religion and the philosophy of life
46 Vermeer, dr. P.A.D.M. (Paul) Socialization processes, secularisation, religion and the philosophy of life


Now we have a nice relatively clean dataset with all sociology staff and their expterise. But there is yet some work to do before we can move on. We need to do some data cleaning. Ideally, we have staff their first and last names in clean columns. So the last name seems easy, everything before the comma. Do you understand the code below? gsub is a function that remove something and replaces it with something else. In the code below it replaces everything that’s behind a comma with nothing in the column soc_names in the data frame soc_df.

The first name is trickier, we need some more difficult expressions to extract first names from this string. It’s not necessary for now to exactly know how the expressions below work, but if you want to get into it, here’s a nice resource. The important part of the code below is that it extracts everything that’s in between the brackets.

# Last name seems to be everything before the comma
soc_df$last_name <- gsub(",.*$", "", soc_df$soc_names)

# first name is everything between brackets
soc_df$first_name <- str_extract_all(soc_df$soc_names, "(?<=\\().+?(?=\\))", simplify = TRUE)
Table 8.7: Table 8.8: Sociology staff cleaner yet
soc_names soc_experts last_name first_name
2 Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training Batenburg Ronald
3 Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms Begall Katia
4 Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports Bekhuis Hidde
5 Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood Berg Lonneke
6 Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market Blommaert Lieselotte
7 Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed Damman Marleen
8 Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics Eisinga Rob
9 Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion Gesthuizen Maurice
10 Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality Glas Saskia
11 Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture Hek Margriet
12 Hoekman, dr. R. H. A.(Remco) Sports and policy sociology Hoekman Remco
13 Hofstra, dr. B. (Bas) Diversity, inequality and innovation Hofstra Bas
14 Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health Kraaykamp Gerbert
15 Meuleman, dr. (Roza) Culture and nationalism Meuleman Roza
16 Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion Savelkoul Michael
17 Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity Scheepers Peer
18 Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration Spierings Niels
19 Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity Tolsma Jochem
20 Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work |Verbakel |Ellen
21 Visser, dr. M. (Mark) Older workers, radicalism and social cohesion Visser Mark
22 Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality Wolbers Maarten
24 Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality Bussemakers Carlijn
25 Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation Franken Rob
26 Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement Firat Mustafa
27 Geurts, P.G. (Nella) MSc Integration and migration Geurts Nella
28 Hendriks, I.P. (Inge) MSc Resistance to refugees and social cohesion Hendriks Inge
29 Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour Jeroense Thijmen
30 Linders, N. (Nik) MSc Populism, gender, masculinity and sexuality Linders Nik
31 Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT) Loh Renae
32 Meijeren, M. (Maikel) MSc Social capital, volunteer work and diversity Meijeren Maikel
33 Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms Mensvoort Carly
34 Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality Müller Katrin
35 Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender Raiber Klara
36 Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family Ramaekers Marlou
37 Wiertsema, S. (Sara) MSc Inequality in sports and physical activity, school-to-work transition and employment Wiertsema Sara
39 Betkó, drs. J.G. (János) Social assistance benefit, poverty, reintegration, RCT and social experiment Betkó János
40 Houten, J. (Jasper) van MSc Sports Houten Jasper
41 Middendorp J. (Jansje) van MSc Home administration Middendorp J. (Jansje) van MSc Jansje
42 Vis, E. (Elize) MSc Healthcare, labour market, healthcare professions and training, health and social capital Vis Elize
43 Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education Weber Tijmen
45 Sterkens, dr. C.J.A. (Carl) Religious conflicts, cohesion, religion and the philosophy of life Sterkens Carl
46 Vermeer, dr. P.A.D.M. (Paul) Socialization processes, secularisation, religion and the philosophy of life Vermeer Paul


So we need yet to do some manual cleaning, one name seemed to be inconsistent with how the other names were listed on the webpage. As data get bigger, this becomes impossible to do manually and we simply have to accept this as noise.

soc_df$last_name <- gsub(" J. \\(Jansje\\) van MSc", "", soc_df$last_name)
soc_df$first_name <- tolower(soc_df$first_name)  # everything to lower!
soc_df$last_name <- tolower(soc_df$last_name)

Not quite there yet. To be sure, we’ll trim some white space in the variables we know created. This means we remove spaces before and after strings. Usually, with a much larger dataset which you cannot immediately observe, you can further clean the data by removing weird characters.

# trimws looses all spacing before and after (if you specify 'both') a character string
soc_df$last_name <- trimws(soc_df$last_name, which = c("both"), whitespace = "[ \t\r\n]")
soc_df$first_name <- trimws(soc_df$first_name, which = c("both"), whitespace = "[ \t\r\n]")
soc_df$soc_experts <- trimws(soc_df$soc_experts, which = c("both"), whitespace = "[ \t\r\n]")
soc_df$soc_names <- trimws(soc_df$soc_names, which = c("both"), whitespace = "[ \t\r\n]")

Finally, because we’re quite sure that all these staff are in some way affiliated with Radboud University (why would they otherwise be on the Radboud website?), we simply create a variable that contains a character string “radboud university” for all.

# set affiliation to radboud, comes in handy for querying google scholar
soc_df$affiliation <- "radboud university"

How do the data look?

soc_df
Table 3.5: Table 3.5: Sociology staff cleanest?
soc_names soc_experts last_name first_name affiliation
2 Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training batenburg ronald radboud university
3 Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms begall katia radboud university
4 Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports bekhuis hidde radboud university
5 Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood berg lonneke radboud university
6 Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market blommaert lieselotte radboud university
7 Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed damman marleen radboud university
8 Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics eisinga rob radboud university
9 Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion gesthuizen maurice radboud university
10 Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality glas saskia radboud university
11 Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture hek margriet radboud university
12 Hoekman, dr. R. H. A.(Remco) Sports and policy sociology hoekman remco radboud university
13 Hofstra, dr. B. (Bas) Diversity, inequality and innovation hofstra bas radboud university
14 Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health kraaykamp gerbert radboud university
15 Meuleman, dr. (Roza) Culture and nationalism meuleman roza radboud university
16 Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion savelkoul michael radboud university
17 Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity scheepers peer radboud university
18 Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration spierings niels radboud university
19 Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity tolsma jochem radboud university
20 Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work |verbakel |ellen |radboud universit
21 Visser, dr. M. (Mark) Older workers, radicalism and social cohesion visser mark radboud university
22 Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality wolbers maarten radboud university
24 Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality bussemakers carlijn radboud university
25 Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation franken rob radboud university
26 Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement firat mustafa radboud university
27 Geurts, P.G. (Nella) MSc Integration and migration geurts nella radboud university
28 Hendriks, I.P. (Inge) MSc Resistance to refugees and social cohesion hendriks inge radboud university
29 Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour jeroense thijmen radboud university
30 Linders, N. (Nik) MSc Populism, gender, masculinity and sexuality linders nik radboud university
31 Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT) loh renae radboud university
32 Meijeren, M. (Maikel) MSc Social capital, volunteer work and diversity meijeren maikel radboud university
33 Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms mensvoort carly radboud university
34 Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality müller katrin radboud university
35 Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender raiber klara radboud university
36 Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family ramaekers marlou radboud university
37 Wiertsema, S. (Sara) MSc Inequality in sports and physical activity, school-to-work transition and employment wiertsema sara radboud university
39 Betkó, drs. J.G. (János) Social assistance benefit, poverty, reintegration, RCT and social experiment betkó jános radboud university
40 Houten, J. (Jasper) van MSc Sports houten jasper radboud university
41 Middendorp J. (Jansje) van MSc Home administration middendorp jansje radboud university
42 Vis, E. (Elize) MSc Healthcare, labour market, healthcare professions and training, health and social capital vis elize radboud university
43 Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education weber tijmen radboud university
45 Sterkens, dr. C.J.A. (Carl) Religious conflicts, cohesion, religion and the philosophy of life sterkens carl radboud university
46 Vermeer, dr. P.A.D.M. (Paul) Socialization processes, secularisation, religion and the philosophy of life vermeer paul radboud university


Pretty good, so I think we can move on to the next section.

8.6.3 Google Scholar Profiles and Publications

What we now have is a data frame of sociology staff members. So we successfully gathered the anchor data set we can move on with. Next, we need to find out whether these staff have a Google Scholar profile. I imagine you have accessed Google Scholar many times during your studies for finding scientists or publications. The nice thing about Google Scholar is that it lists collaborators, publications, and citations on profiles. So what we first need to do is look for Google Scholar profiles among sociology staff. Luckily, we cleaned first and last names and have their affiliation. That makes looking them up much easier. So we need to do this for every person in our data frame. Before we query Google Scholar, we first need to go back to the neat trick of for loops (remember them from Appendix A.13.2?). Can you follow the code below? We can thus do all kinds of things automatically in a for loop.

# The 'for loop': for every i in a vector (can be numbers, strings, etc.), say 1 to 10, you can do
# 'something'
for (i in 1:10) {
    print(i)  # So for every i from 1 to 10, we print i, see what happens!
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
#> [1] 6
#> [1] 7
#> [1] 8
#> [1] 9
#> [1] 10
# # or do something more complicated p <- rnorm(10, 0, 1) # draw 10 normally distributed numbers
# with mean 0 and SD 1 (so z-scores, essentially) plot(density(p)) # relatively, normal, right?  u
# <- 0 # make an element we can fill up in the loop below for (i in 1:10) { u[i] <- p[i]*p[i] # get
# p-squared for every i-th element in vector p print(u[i]) # and print that squared element }

Now that we know how to implement for loops in our workflow, we can utilize them to do slightly more complicated stuff. We want to know the identifying link on Google Scholar for each sociology staff member. We first set an empty identifier in our data frame so that we can “fill up” that data column later.

soc_df$gs_id <- ""  # we set an empty identifier

So let’s move on with attempting to find Google Scholar profiles. The package scholar has a range of very nice functions! What type of functions does scholar have? Take a look by ?scholar or ??scholar. It includes all kinds of interesting functions like comparing scholars’ careers, getting scholar citations, getting profiles, and so forth. Using get_scholar_id seems appropriate to find out what the profile ID is of Jochem’s Google Scholar page. If you write get_scholar_id and then click ctrl or cmd together with your right mouse button, you can actually see precisely what the function does (it’s quite complicated!). Note that this package does not use an API, but simple wrote code to extract Google Scholar pages from the internet. They then wrapped the code in much simpler functions for you to use. The function get_scholar_id needs a last name, first name, and affiliation. Luckily, we already found those on the Radboud University website! So we can fill in those. Let’s try it for one staff member first.

Please download function here: get_scholar_id_fix.

source("addfiles/function_fix.R")  # Put the function_fix.R in your working directory, we need this first line.
get_scholar_id_fix(last_name = "tolsma", first_name = "jochem", affiliation = "radboud university")
#> [1] "Iu23-90AAAAJ"

We now know that Jochem’s Scholar ID is “Iu23-90AAAAJ”. That’s very convenient, because now we can use the package scholar again to extract a range of useful other information from his Google Scholar profile. Let’s try it out on his profile first. Notice the nice function get_profiles. We simply have to input his Google Scholar ID and it shows everything on the profile.

get_profile("Iu23-90AAAAJ")  # Jochem's profile
#> $id
#> [1] "Iu23-90AAAAJ"
#> 
#> $name
#> [1] "Jochem Tolsma"
#> 
#> $affiliation
#> [1] "Professor, Radboud University Nijmegen / University of Groningen"
#> 
#> $total_cites
#> [1] 3392
#> 
#> $h_index
#> [1] 27
#> 
#> $i10_index
#> [1] 43
#> 
#> $fields
#> [1] "social divisions between groups" "segregation"                    
#> [3] "inequality"                     
#> 
#> $homepage
#> [1] "http://www.jochemtolsma.nl/"
#> 
#> $coauthors
#>  [1] "Gerbert Kraaykamp"   "Tom van der Meer"    "Maarten HJ Wolbers"  "Michael Savelkoul"  
#>  [5] "Stijn Ruiter"        "peer scheepers"      "Marcel Lubbers"      "Joran Lameris"      
#>  [9] "Marcel Coenders"     "Sara Kinsbergen"     "Nan Dirk de Graaf"   "dr. Natascha Notten"
#> [13] "Carlijn Bussemakers" "Tobias H. Stark"     "Thomas Feliciani"    "Andreas Flache"     
#> [17] "Matthijs Kalmijn"    "Lincoln Quillian"    "Ariana Need"         "René Veenstra"      
#> 
#> $available
#> [1] 13
#> 
#> $not_available
#> [1] 0

A lot of useful information! Next up, Jochem’s publications. Notice how not everything is in a nice data frame format yet, we’ll get to that later.

get_publications("Iu23-90AAAAJ")  # Jochem's pubs
#>                                                                                                                                                                                  title
#> 1                                                                                                                                  Ethnic diversity and its effects on social cohesion
#> 2                                     Anti-Muslim attitudes in the Netherlands: Tests of contradictory hypotheses derived from ethnic competition theory and intergroup contact theory
#> 3                                                                                   The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands
#> 4             The effects of parental reading socialization and early school involvement on children’s academic performance: A panel study of primary school pupils in the Netherlands
#> 5                               Who is bullying whom in ethnically diverse primary schools? Exploring links between bullying, ethnicity, and ethnic diversity in Dutch primary schools
#> 6                                                                                 Ethnic competition and opposition to ethnic intermarriage in the Netherlands: A multi-level approach
#> 7                                                         Education and cultural integration among ethnic minorities and natives in the Netherlands: A test of the integration paradox
#> 8                                                                           When do people report crime to the police? Results from a factorial survey design in the Netherlands, 2010
#> 9                                                                                                        Trends in ethnic educational inequalities in the Netherlands: a cohort design
#> 10                                                                                     Does intergenerational social mobility affect antagonistic attitudes towards ethnic minorities?
#> 11                                   Explaining participation differentials in Dutch higher education: the impact of subjective success probabilities on level choice and field choice
#> 12                                                       The impact of adolescents' classroom and neighborhood ethnic diversity on same‐and cross‐ethnic friendships within classrooms
#> 13              Neighbourhood ethnic composition and voting for the radical right in The Netherlands. The role of perceived neighbourhood threat and interethnic neighbourhood contact
#> 14                                  Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data
#> 15         Losing Wallets, Retaining Trust? The Relationship Between Ethnic Heterogeneity and Trusting Coethnic and Non-coethnic Neighbours and Non-neighbours to Return a Lost Wallet
#> 16                                                 Educational expansion and field of study: trends in the intergenerational transmission of educational inequality in the Netherlands
#> 17                                                                        Bringing the beneficiary closer: Explanations for volunteering time in Dutch private development initiatives
#> 18                                                                                                   How friends’ involvement in crime affects the risk of offending and victimization
#> 19                                                                                           At which geographic scale does ethnic diversity affect intra-neighborhood social capital?
#> 20                                                                                                              The NEtherlands Longitudinal Lifecourse Study (NELLS, Panel): Codebook
#> 21                                                                               Explaining monetary donations to international development organisations: A factorial survey approach
#> 22     Onderwijs als nieuwe sociale scheidslijn? De gevolgen van onderwijsexpansie voor sociale mobiliteit, de waarde van diploma's en het relatieve belang van opleiding in Nederland
#> 23                                                                                       How, when and where can spatial segregation induce opinion polarization? Two competing models
#> 24                                                                                                                                                          Naar een open samenleving?
#> 25                                                    Where does ethnic concentration matter for populist radical right support? An analysis of geographical scale and the halo effect
#> 26                                                                                           Social origin and occupational success at labour market entry in The Netherlands, 1931–80
#> 27  Ethnic hostility among ethnic majority and minority groups in the Netherlands: an investigation into the impact of social mobility experiences, the local living environment and …
#> 28                                                                                            Kudos make you run! How runners influence each other on the online social network Strava
#> 29                      Perceptions as the crucial link? The mediating role of neighborhood perceptions in the relationship between the neighborhood context and neighborhood cohesion
#> 30                                                       Combating hooliganism in the Netherlands: An evaluation of measures to combat hooliganism with longitudinal registration data
#> 31                                                                                                                     Preferences for work arrangements: A discrete choice experiment
#> 32      Under what conditions do ethnic minority candidates attract the ethnic minority vote? How neighbourhood and candidate characteristics affected ethnic affinity voting in the …
#> 33                                                                                                 Trust and contact in diverse neighbourhoods: An interplay of four ethnicity effects
#> 34                                                                                          Explaining natives' interethnic friendship and contact with colleagues in European regions
#> 35                                                                                                                                                               De burger als rechter
#> 36                                                                                                         Opleiding als sociale scheidslijn. Een nieuw perspectief op een oude kloof.
#> 37       Size is in the eye of the beholder: How differences between neighbourhoods and individuals explain variation in estimations of the ethnic out-group size in the neighbourhood
#> 38                                                                                          De onderwijskansen van allochtone en autochtone Nederlanders vergeleken: een cohort-design
#> 39                                                                      Exposure to asylum seekers and changing support for the radical right: A natural experiment in the Netherlands
#> 40                                                                            Like two peas in a pod? Explaining friendship selection processes related to victimization and offending
#> 41                                                                                        Netherlands Longitudinal Lifecourse Study-NELLS Panel Wave 1 2009 and Wave 2 2013-versie 1.2
#> 42                                                                            Aangiftebereidheid: Welke overwegingen spelen een rol bij de beslissing om wel of niet aangifte te doen?
#> 43                                                                                                                              Running together: How sports partners keep you running
#> 44                                                                                                                        Taakstraffen langs de lat: strafopvattingen van Nederlanders
#> 45                                                                                                             Variation in the educational consequences of parental death and divorce
#> 46                                                                                                                De aangifte van delicten bij de multichannelstrategie van de politie
#> 47                                                        Parents, television and children's weight status: On lasting effects of parental television socialization in the Netherlands
#> 48                                                                                                         Household dysfunction and child development: Do financial resources matter?
#> 49                                                                                                       The social outcomes of psychosocial support: A grey literature scoping review
#> 50                                                                                                      Social origin and inequality in educational returns in the Dutch labour market
#> 51                                                                                        Dader, slachtoffer, of beiden? De samenhang tussen daderschap en slachtofferschap onderzocht
#> 52                                                                                                                     Education's impact on explanations of radical right-wing voting
#> 53                                                                                                    Archeologische verwachtings-en beleidskaart buitengebied gemeente Midden-Drenthe
#> 54                                                                                      Vrijheid versus veiligheid: Wie steunt vrijheidsbeperking omwille van veiligheid in Nederland?
#> 55                                                     Twitter and divides in the Dutch parliament: social and political segregation in the following,@-mentions and retweets networks
#> 56                                               Similarity and differences in age, gender, ethnicity, and education as explanatory factors of tie loss in the core discussion network
#> 57                                                                                                                                                      Dader, slachtoffer, of beiden?
#> 58                                                                               The Well-Being of Undocumented Migrants in The Netherlands: Learning about Post-Migration Experiences
#> 59                                                   Integratie en depressie-De relatie tussen sociaal-culturele integratie en depressieklachten bij Turkse en Marokkaanse Nederlander
#> 60                                                                                     A matter of time? Gender and ethnic inequality in the academic publishing careers of Dutch PhDs
#> 61                                                                                                Ontwikkelingen in de maatschappelijke positie van middelbaar opgeleiden in Nederland
#> 62                                                                                                                                                             Integratie en depressie
#> 63                                              Size and ethnic homogeneity of extended social networks in the Netherlands: Differences between migrant groups and migrant generations
#> 64                                                                                                             Trends in de opleidingskloof op verschillende maatschappelijke domeinen
#> 65                                                                       Choosing Your Sports Partners: Assessing Selection Preferences Through Observational and Experimental Studies
#> 66                                                                                    The social outcomes of community-based sociotherapy for refugees in nakivale settlements, Uganda
#> 67                                                                   Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation
#> 68                                                                                                 SCP, onderzoek liever hoe we kansenongelijkheid in het onderwijs kunnen verminderen
#> 69                                                                                                                     Sociale scheidslijnen tussen groepen: Wat valt er te verklaren?
#> 70                                                                                            NEtherlands Longitudinal Lifecourse Study 2022-Versie 1.0 [dataverzameling and codebook]
#> 71                                                                                                                     VIA pilot participatietraject gemeente Nijmegen: Eindrapportage
#> 72                                                          Fairly Paid but Dissatisfied? Determinants of Pay Fairness and Pay Satisfaction: Evidence from Germany and the Netherlands
#> 73                                                                                       In hoeverre verklaart de etnische samenstelling van de buurt de kans om te stemmen op de PVV?
#> 74                                                                        In de etalage: In hoeverre verklaart de etnische samenstelling van de buurt de kans om te stemmen op de PVV?
#> 75                                                                                                                                            Family Survey Dutch Population 2017/2018
#> 76                                                                                   Soort zoekt soort: vriendschapselectieprocessen met betrekking tot slachtofferschap en daderschap
#> 77                                     Modeling opinion dynamics in a real city: How realistic spatial patterns of demographic attributes affect the emergence of opinion polarization
#> 78                       Summary of “How, When and Where Can Spatial Segregation Induce Opinion Polarization? Two Competing Models”: Paper Under Review as JASSS Fast Track Submission
#> 79                                                                                                                                     ICS Alumni revisited [Brochure tbv Lustrum ICS]
#> 80                                                                                                                                                ICS Alumni Survey 2016 [Databestand]
#> 81                                       Modeling opinion dynamics in a simulated city. Realistic spatial patterns of demographic attributes and the emergence of opinion polarization
#> 82                                                                                                                           Opleiding als nieuwe sociale scheidslijn: een tegengeluid
#> 83                                                                                                                                  Opleiding als sociale scheidslijn: Een tegengeluid
#> 84                                                                                   Opleiding als sociale scheidslijn: aanleiding, probleemstelling, bestaande inzichten en werkwijze
#> 85                                                                                                                    De samenhang in het opleidingsniveau tussen (huwelijks) partners
#> 86                                                                                                 Sociale herkomst en ongelijkheid in de opbrengsten van diploma's op de arbeidsmarkt
#> 87                                                                                                       De rol van opleidingsniveau voor het starten en stoppen met vrijwilligerswerk
#> 88                                                                                  Was, is of wordt opleiding de sociale scheidslijn? Een terugblik, stand van zaken en toekomstvisie
#> 89                                                                                                Klein en vrijwillig of groot en ervaren? Een analyse van de voorkeuren van donateurs
#> 90                                                 Integratie en depressie: De relatie tussen sociaal-culturele integratie en depressieklachten bij Turkse en Marokkaanse Nederlanders
#> 91                                                                     De burger als rechter, onderzoek naar geprefereerde sancties voor misdrijven in Nederland (projectnummer 1933B)
#> 92                                                                                                                                   Onderwijsexpansie veroorzaakt nieuwe ongelijkheid
#> 93                                                                                                                  Sociale daling schaadt vertrouwen: Effecten van sociale mobiliteit
#> 94                                                                                                                                        Toenemende gelijkheid is nog geen verheffing
#> 95                    Over ouders, televisiekijken en (over) gewicht: Een studie naar de langetermijneffecten van ouderlijke televisiesocialisatie op het lichaamsgewicht van kinderen
#> 96                                                                       Onderwijsexpansie en opleidingsrichting: Trends in de intergenerationele overdracht van onderwijsongelijkheid
#> 97                                                                                                                                         Nijmegen School Study 2010-TNSS 2010-wave 1
#> 98                                                             On trouvera sous cette rubrique les sommaires les plus récents des grandes revues internationales de science politique.
#> 99                                                                                      Does Intergenerational Social Mobility affect Antagonistic Attitudes towards Ethnic Minorities
#> 100                                                            De invloed van lands-, gemeente-en buurtkenmerken op sociaal kapitaal: Putnam's hypothese getest in Europa en Nederland
#> 101      Vrijheid versus veiligheid: Wie steunt vrijheidsbeperking omwille van veiligheid in Nederland?[Liberty versus security: Who supports the restriction of personal freedom by …
#> 102    De onderwijskansen van allochtone en autochtone Nederlanders vergeleken: Een cohort-design [Ethnic inequality of educational opportunities in the Netherlands: A cohort design]
#> 103                                                                                      Lokale en regionale instituties in verandering: een onderzoek naar de ervaringen in Duitsland
#> 104                                                                                                                        Mentoring for Gender Equality in Early-Career Grant Receipt
#> 105                                                                                                                   Educational consequences of parental divorce and death in Europe
#> 106                        Volume 46-Article 20| Pages 581–618 Variation in the educational consequences of parental death and divorce: The role of family and country characteristics
#> 107                                      An Investigation into the Impact of Social Mobility Experiences, the Local Living Environment and Educational Attainment on Ethnic Hostility.
#> 108                                                                                                                                                    Parental Discouragement Measure
#> 109                                                                                                                                                     Complete Networks Small worlds
#> 110                                                                                                                                                           Causes of dyads (theory)
#> 111                                                                                                                                                                Egocentric Networks
#> 112                                                                                            The Role of Recent Migrants’ Country of Origin Engagement in Dutch Language Proficiency
#> 113                                                                                                                                                M1-102: Social capital and networks
#> 114                                                                                                   Social origin and occupational success at labour market entry in the Netherlands
#>                                                          author
#> 1                                    TWG van der Meer, J Tolsma
#> 2              M Savelkoul, P Scheepers, J Tolsma, L Hagendoorn
#> 3                        J Tolsma, T van der Meer, M Gesthuizen
#> 4                R Kloosterman, N Notten, J Tolsma, G Kraaykamp
#> 5                 J Tolsma, I van Deurzen, TH Stark, R Veenstra
#> 6                               J Tolsma, M Lubbers, M Coenders
#> 7                              J Tolsma, M Lubbers, M Gijsberts
#> 8                           J Tolsma, J Blaauw, M Te Grotenhuis
#> 9                               J Tolsma, M Coenders, M Lubbers
#> 10                            J Tolsma, ND De Graaf, L Quillian
#> 11                                  J Tolsma, A Need, U De Jong
#> 12                 A Munniksma, P Scheepers, TH Stark, J Tolsma
#> 13                             M Savelkoul, J Laméris, J Tolsma
#> 14                         C Bussemakers, G Kraaykamp, J Tolsma
#> 15                                   J Tolsma, TWG van der Meer
#> 16                           G Kraaykamp, J Tolsma, MHJ Wolbers
#> 17                             S Kinsbergen, J Tolsma, S Ruiter
#> 18                     JJ Rokven, G de Boer, J Tolsma, S Ruiter
#> 19                             R Sluiter, J Tolsma, P Scheepers
#> 20  J Tolsma, GLM Kraaykamp, PM De Graaf, M Kalmijn, CWS Monden
#> 21                                       S Kinsbergen, J Tolsma
#> 22                                        J Tolsma, MHJ Wolbers
#> 23                              T Feliciani, A Flache, J Tolsma
#> 24                                        J Tolsma, MHJ Wolbers
#> 25                                 D van Wijk, G Bolt, J Tolsma
#> 26                                        J Tolsma, MHJ Wolbers
#> 27                                                     J Tolsma
#> 28                               R Franken, H Bekhuis, J Tolsma
#> 29                                 J Laméris, JR Hipp, J Tolsma
#> 30                       D Schaap, M Postma, L Jansen, J Tolsma
#> 31                                   P Valet, C Sauer, J Tolsma
#> 32                          R Van der Zwan, J Tolsma, M Lubbers
#> 33                                   J Tolsma, TWG Van der Meer
#> 34                           M Savelkoul, J Tolsma, P Scheepers
#> 35                S Ruiter, J Tolsma, M Hoon, H Elffers, P Laan
#> 36                            M De Lange, J Tolsma, MHJ Wolbers
#> 37                   J Laméris, G Kraaykamp, S Ruiter, J Tolsma
#> 38                              J Tolsma, M Coenders, M Lubbers
#> 39                             J Tolsma, J Lameris, M Savelkoul
#> 40                   JJ Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 41    J Tolsma, GLM Kraaykamp, DM de Graaf, M Kalmijn, C Monden
#> 42                                                     J Tolsma
#> 43                               R Franken, H Bekhuis, J Tolsma
#> 44                                           S Ruiter, J Tolsma
#> 45                         C Bussemakers, G Kraaykamp, J Tolsma
#> 46                                      PFM Boekhoorn, J Tolsma
#> 47                              N Notten, G Kraaykamp, J Tolsma
#> 48               C Bussemakers, G Kraaykamp, I Schoon, J Tolsma
#> 49                     T Ubels, S Kinsbergen, J Tolsma, DJ Koch
#> 50                                        J Tolsma, MHJ Wolbers
#> 51                                JJ Rokven, S Ruiter, J Tolsma
#> 52                                          M Lubbers, J Tolsma
#> 53                                       MG Marinelli, J Tolsma
#> 54                              G Jansen, J Tolsma, ND de Graaf
#> 55                                        J Tolsma, N Spierings
#> 56                            T Jeroense, N Spierings, J Tolsma
#> 57                                 J Rokven, S Ruiter, J Tolsma
#> 58                     T Ubels, S Kinsbergen, DJ Koch, J Tolsma
#> 59                                    R Zwan, van der, J Tolsma
#> 60                              AM Mulders, B Hofstra, J Tolsma
#> 61                                        J Tolsma, MHJ Wolbers
#> 62                                     R van der Zwan, J Tolsma
#> 63                 T Jeroense, B Hofstra, N Spierings, J Tolsma
#> 64                                            M Lange, J Tolsma
#> 65                               R Franken, H Bekhuis, J Tolsma
#> 66                     T Ubels, S Kinsbergen, J Tolsma, DJ Koch
#> 67                              T Feliciani, J Tolsma, A Flache
#> 68                                                     J Tolsma
#> 69                                                     J Tolsma
#> 70             TMG Jeroense, J Tolsma, M Kalmijn, GLM Kraaykamp
#> 71                   N Spierings, L Werner, P Beckers, J Tolsma
#> 72                                J Adriaans, C Sauer, J Tolsma
#> 73                             M Savelkoul, J Laméris, J Tolsma
#> 74                           MJ Savelkoul, JG Laméris, J Tolsma
#> 75                          R Meuleman, J Tolsma, GLM Kraaykamp
#> 76                    J Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 77                              T Feliciani, A Flache, J Tolsma
#> 78                              T Feliciani, A Flache, J Tolsma
#> 79                 PE Thijs, GLM Kraaykamp, M Scholte, J Tolsma
#> 80                 GLM Kraaykamp, M Scholte, PE Thijs, J Tolsma
#> 81                      T Feliciani, A Flache, J Tolsma, M Maes
#> 82                                                     J Tolsma
#> 83                                                     J Tolsma
#> 84                            M de Lange, J Tolsma, MHJ Wolbers
#> 85                                        J Tolsma, ND de Graaf
#> 86                                        J Tolsma, MHJ Wolbers
#> 87                              D Wiertz, J Tolsma, ND De Graaf
#> 88                                        J Tolsma, MHJ Wolbers
#> 89                                       S Kinsbergen, J Tolsma
#> 90                                             R Zwan, J Tolsma
#> 91                S Ruiter, J Tolsma, M Hoon, H Elffers, D Laan
#> 92                         GLM Kraaykamp, MHJ Wolbers, J Tolsma
#> 93                                        J Tolsma, MHJ Wolbers
#> 94                                        MHJ Wolbers, J Tolsma
#> 95                            N Notten, GLM Kraaykamp, J Tolsma
#> 96                         GLM Kraaykamp, J Tolsma, MHJ Wolbers
#> 97                                              J Tolsma, I Pop
#> 98            NA Allern, J Tolsma, T van der Meer, M Gesthuizen
#> 99                             N de Graaf, J Tolsma, L Quillian
#> 100   MJW Gesthuizen, PLH Scheepers, J Tolsma, TWG van der Meer
#> 101                             G Jansen, J Tolsma, ND de Graaf
#> 102                           J Tolsma, MTA Coenders, M Lubbers
#> 103        GH Hagelstein, G Morssinkhof, JML Tolsma, JBJM Berge
#> 104                             AM Mulders, B Hofstra, J Tolsma
#> 105                        C Bussemakers, G Kraaykamp, J Tolsma
#> 106                        C Bussemakers, G Kraaykamp, J Tolsma
#> 107                                                    J TOLSMA
#> 108                  JJ Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 109                                                    J Tolsma
#> 110                                                    J Tolsma
#> 111                                                    J Tolsma
#> 112                                          N Geurts, J Tolsma
#> 113                                 J Laméris, J Tolsma, J Hipp
#> 114                                       J Tolsma, MHJ Wolbers
#>                                                                              journal
#> 1                                                                                   
#> 2                                                       European sociological review
#> 3                                                                      Acta Politica
#> 4                                                       European Sociological Review
#> 5                                                                    Social Networks
#> 6                                                       European Sociological Review
#> 7                                            Journal of Ethnic and Migration Studies
#> 8                                                Journal of experimental criminology
#> 9                                                       European Sociological Review
#> 10                                                  The British Journal of Sociology
#> 11                                                      European Sociological Review
#> 12                                                Journal of Research on Adolescence
#> 13                                                      European Sociological Review
#> 14                                                             Child Abuse & Neglect
#> 15                                                        Social Indicators Research
#> 16                                         British Journal of Sociology of Education
#> 17                                          Nonprofit and Voluntary Sector Quarterly
#> 18                                                   European journal of criminology
#> 19                                                           Social science research
#> 20              Nijmegen; Tilburg; Amsterdam: Radboud University Nijmegen; Tilburg …
#> 21                                                           Social science research
#> 22                                                       Tijdschrift voor Sociologie
#> 23                             Journal of Artificial Societies and Social Simulation
#> 24                                                                                  
#> 25                                                               Political Geography
#> 26                                                                  Acta Sociologica
#> 27                                                                                  
#> 28                                                                   Social Networks
#> 29                                                           Social Science Research
#> 30                                  European Journal on Criminal Policy and Research
#> 31                                                                          PloS one
#> 32                                                               Political Geography
#> 33                                                           Social science research
#> 34                                           Journal of Ethnic and Migration Studies
#> 35                                                                              NSCR
#> 36                                                                             Maklu
#> 37                                  International Journal of Intercultural Relations
#> 38                                                               Mens & Maatschappij
#> 39                                                                          PloS one
#> 40                                                   European Journal of Criminology
#> 41                                                                         DANS EASY
#> 42                                  Proces-verbaal, aangifte en forensisch onderzoek
#> 43                                             Frontiers in Sports and Active Living
#> 44                                                                                  
#> 45                                                              Demographic Research
#> 46           Apeldoorn; Nijmegen: Politie & Wetenschap; BBSO en Radboud Universiteit
#> 47                                                     Journal of Children and Media
#> 48                                                  Advances in Life Course Research
#> 49                                                                 SSM-mental health
#> 50                                           Education, Occupation and Social Origin
#> 51                                                                                  
#> 52                                                 London: University College London
#> 53                                                            Oranjewoud, Heerenveen
#> 54                                                               Mens & Maatschappij
#> 55                                              Information, Communication & Society
#> 56                                                                   Social Networks
#> 57                                                     Tijdschrift voor Criminologie
#> 58                                            Journal of Immigrant & Refugee Studies
#> 59                                                              Mens en maatschappij
#> 60                                                      Quantitative Science Studies
#> 61                                                                     Den Haag: WRR
#> 62                                                         De relatie tussen sociaal
#> 63                                                           International Migration
#> 64                                                       Antwerpen/Apeldoorn: Garant
#> 65                                         Research Quarterly for Exercise and Sport
#> 66                                          TROPICAL MEDICINE & INTERNATIONAL HEALTH
#> 67                                           Journal of Computational Social Science
#> 68                                                                                  
#> 69                                                     University of Groningen Press
#> 70                                      Nijmegen; Den Haag: Radboud University; NIDI
#> 71                                                            Nijmegen: RUNOMI; RSCR
#> 72                                                                                  
#> 73                                                              Mens en Maatschappij
#> 74                                                                                  
#> 75                                                                         DANS EASY
#> 76                                                              Mens en maatschappij
#> 77                                                                                  
#> 78      International Conference on Principles and Practice of Multi-Agent Systems …
#> 79  Nijmegen: Interuniversity Center for Social Science Theory and Methodology (ICS)
#> 80  Nijmegen: Interuniversity Center for Social Science Theory and Methodology (ICS)
#> 81                                                      Social Simulation Conference
#> 82                                                                        Sociologos
#> 83                                                                                  
#> 84        Opleiding als sociale scheidslijn. Een nieuw perspectief op een oude kloof
#> 85                                                       Antwerpen/Apeldoorn: Garant
#> 86                                                       Antwerpen/Apeldoorn: Garant
#> 87                                                                  Garant Uitgevers
#> 88                                                       Antwerpen/Apeldoorn: Garant
#> 89                                                                   Amsterdam: NCDO
#> 90                                                                                  
#> 91                                                                         DANS EASY
#> 92                                                        [Sl]: Sociale Vraagstukken
#> 93                                                                                  
#> 94                                                        [Sl]: Sociale Vraagstukken
#> 95                                                                 Assen: Van Gorcum
#> 96                                                                    Amsterdam: AUP
#> 97                                                                         DANS EASY
#> 98                                                                     Acta politica
#> 99                                                                   Wiley Blackwell
#> 100                                                       Den Haag/Nijmegen: SCP-NSV
#> 101                                                                                 
#> 102                                                                                 
#> 103                    Nederlands Instituut voor Sociaal en Economisch Recht (NISER)
#> 104                                                                Available at SSRN
#> 105                                                                                 
#> 106                                                                                 
#> 107                                                                                 
#> 108                                                  European Journal of Criminology
#> 109                                                                                 
#> 110                                                                                 
#> 111                                                                                 
#> 112                                                                                 
#> 113                                                                Book of Abstracts
#> 114                                                                                 
#>                                                                    number cites year
#> 1                                                                           712 2014
#> 2                                                         27 (6), 741-758   372 2011
#> 3                                                                  44 (3)   328 2009
#> 4                                                         27 (3), 291-306   158 2011
#> 5                                                           35 (1), 51-61   154 2013
#> 6                                                         24 (2), 215-230   135 2008
#> 7                                                         38 (5), 793-813   104 2012
#> 8                                                              8, 117-134   103 2012
#> 9                                                         23 (3), 325-339    86 2007
#> 10                                                        60 (2), 257-277    84 2009
#> 11                                                        26 (2), 235-252    82 2010
#> 12                                                          27 (1), 20-33    69 2017
#> 13                                                        33 (2), 209-224    68 2017
#> 14                                                             98, 104185    54 2019
#> 15                                                                           53 2016
#> 16                                                      34 (5-6), 888-906    51 2013
#> 17                                                          42 (1), 59-83    44 2013
#> 18                                                        14 (6), 697-719    42 2017
#> 19                                                              54, 80-95    40 2015
#> 20                                                                           38 2014
#> 21                                                      42 (6), 1571-1586    38 2013
#> 22                                                        31 (3), 239-259    37 2010
#> 23                                                              20 (2), 6    33 2017
#> 24                                                                           33 2010
#> 25                                                             77, 102097    31 2020
#> 26                                                        57 (3), 253-269    30 2014
#> 27                                                                           28 2009
#> 28                                                            72, 151-164    23 2023
#> 29                                                              72, 53-68    23 2018
#> 30                                                              21, 83-97    23 2015
#> 31                                                       16 (7), e0254483    22 2021
#> 32                                                             77, 102098    22 2020
#> 33                                                             73, 92-106    22 2018
#> 34                                                        41 (5), 683-709    22 2015
#> 35                                                                           20 2011
#> 36                                                                           19 2015
#> 37                                                              63, 80-94    17 2018
#> 38                                                            82 (2), 133    15 2007
#> 39                                                       16 (2), e0245644    14 2021
#> 40                                                        13 (2), 231-256    13 2016
#> 41                                                                           13 2014
#> 42                                                                     11    12 2011
#> 43                                                              4, 643150    10 2022
#> 44                                                                            9 2010
#> 45                                                            46, 581-618     8 2022
#> 46                                                                            8 2016
#> 47                                                         7 (2), 235-252     8 2013
#> 48                                                             51, 100447     6 2022
#> 49                                                              2, 100074     5 2022
#> 50                                                                114-131     5 2016
#> 51                                                                            5 2013
#> 52                                                                            5 2011
#> 53                                                                            5 2009
#> 54                                                             83 (1), 47     5 2008
#> 55                                                                   1-20     4 2024
#> 56                                                            76, 135-149     4 2024
#> 57                                                            55 (3), 278     4 2013
#> 58                                                                   1-14     3 2023
#> 59                                                        88 (2), 177-205     3 2013
#> 60                                                                   1-29     2 2024
#> 61                                                                            2 2017
#> 62                                                                            2 2013
#> 63                                                                            1 2024
#> 64                                                                            1 2015
#> 65                                                                   1-15     0 2024
#> 66                                                            28, 266-266     0 2023
#> 67                                                         6 (2), 877-921     0 2023
#> 68                                                                            0 2023
#> 69                                                                            0 2023
#> 70                                                                            0 2023
#> 71                                                                            0 2021
#> 72                                                    31st Annual Meeting     0 2019
#> 73                                                          93 (1), 82-85     0 2018
#> 74                                                                            0 2018
#> 75                                                                            0 2018
#> 76                                                        92 (3), 327-329     0 2017
#> 77  9th Conference of the International Network of Analytical Sociology …     0 2016
#> 78                                                                            0 2016
#> 79                                                                            0 2016
#> 80                                                                            0 2016
#> 81                                                                   2016     0 2016
#> 82                                                                 36 (3)     0 2015
#> 83                                                                            0 2015
#> 84                                                                   9-32     0 2015
#> 85                                                                            0 2015
#> 86                                                                            0 2015
#> 87                                                                            0 2015
#> 88                                                                            0 2015
#> 89                                                                            0 2014
#> 90                                                                            0 2013
#> 91                                                                            0 2013
#> 92                                                                            0 2011
#> 93                                                                            0 2011
#> 94                                                                            0 2011
#> 95                                                                            0 2011
#> 96                                                                            0 2011
#> 97                                                                            0 2010
#> 98                                                                 44 (3)     0 2009
#> 99                                                                            0 2009
#> 100                                                                           0 2009
#> 101                                                                           0 2008
#> 102                                                                           0 2007
#> 103                                                                           0 1991
#> 104                                                                486625     0   NA
#> 105                                                                           0   NA
#> 106                                                                           0   NA
#> 107                                                                           0   NA
#> 108                                                                           0   NA
#> 109                                                                           0   NA
#> 110                                                                           0   NA
#> 111                                                                           0   NA
#> 112                                                                           0   NA
#> 113                                                                     1     0   NA
#> 114                                                                           0   NA
#>                                                                cid        pubid
#> 1      17240473400423700490,461159763596233481,1315542974843119305 UxriW0iASnsC
#> 2                                              9140218593636983243 9yKSN-GCB0IC
#> 3                                               203105297399726489 UeHWp8X0CEIC
#> 4                                              9327830809512404486 qjMakFHDy7sC
#> 5                                             15442728615805262127 kNdYIx-mwKoC
#> 6                                             17191703704621608544 u5HHmVD_uO8C
#> 7                                             16121967639591190378 eQOLeE2rZwMC
#> 8                                              3147100585201897138 UebtZRa9Y70C
#> 9                                              5904489841843560927 d1gkVwhDpl0C
#> 10                                            10446633547221929964 2osOgNQ5qMEC
#> 11  18143881066769803140,18233438384904663264,12975380653095517868 Tyk-4Ss8FVUC
#> 12                                            18309594979069207516 maZDTaKrznsC
#> 13                                             4894344398065441656 ldfaerwXgEUC
#> 14                                              163003866819331000 CHSYGLWDkRkC
#> 15                                             2251620908592189324 BqipwSGYUEgC
#> 16                                             2401615506068930127 7PzlFSSx8tAC
#> 17                                             2112276567018030922 _FxGoFyzp5QC
#> 18                                            13322468554278639475 vV6vV6tmYwMC
#> 19                                             7670225499012303854 e5wmG9Sq2KIC
#> 20                                             8792123396141403739 xtRiw3GOFMkC
#> 21                                            10149692484122806616 aqlVkmm33-oC
#> 22                                            17130607869100522312 YsMSGLbcyi4C
#> 23                                             6880814424039971499 g5m5HwL7SMYC
#> 24                                             2539524527836644253 Y0pCki6q_DkC
#> 25                                             4092382021694339447 SP6oXDckpogC
#> 26                                             8248470043986462984 M3ejUd6NZC8C
#> 27                                            10378332126833599949 IjCSPb-OGe4C
#> 28                                            12114155743971894213 9Nmd_mFXekcC
#> 29                                            16357054384393453824 D03iK_w7-QYC
#> 30                                             9528443224826780083 ZeXyd9-uunAC
#> 31                                            14800116132549688497 tKAzc9rXhukC
#> 32                                             7114430646392466648 uWQEDVKXjbEC
#> 33                                             8349908030823257502 pyW8ca7W8N0C
#> 34                                            18182577779862774305 -f6ydRqryjwC
#> 35                                            16411127097378483929 KlAtU1dfN6UC
#> 36                        6027896113597554400,15089097845718498290 O3NaXMp0MMsC
#> 37                                             1627288244325129498 a0OBvERweLwC
#> 38                                            12951311005934288487 ILKRHgRFtOwC
#> 39                                            10820360089296230361 Fu2w8maKXqMC
#> 40                                            16075774780598089063 k_IJM867U9cC
#> 41                                             7914543636642646054 mB3voiENLucC
#> 42                                            10745397192148013810 LkGwnXOMwfcC
#> 43                                             6762471506678500061 evX43VCCuoAC
#> 44                                            15258532569899652859 W7OEmFMy1HYC
#> 45                                            18414208192975515577 35r97b3x0nAC
#> 46                                            14256154602665082067 JV2RwH3_ST0C
#> 47                                              818925813101569366 Se3iqnhoufwC
#> 48                                             3664663685279702514 ZfRJV9d4-WMC
#> 49                                            12792330965359094918 2KloaMYe4IUC
#> 50                                            10107474183324844052 YFjsv_pBGBYC
#> 51                                             3722991261384213184 BwyfMAYsbu0C
#> 52                                            10658172101302530460 4TOpqqG69KYC
#> 53                                              641362829363743487 kzcrU_BdoSEC
#> 54                                             6273244451878075724 ufrVoPGSRksC
#> 55                                            12757691501254196820 4MWp96NkSFoC
#> 56                                             3733534889800546258 AvfA0Oy_GE0C
#> 57                                            10200636729873805270 QIV2ME_5wuYC
#> 58                                            16962548993069978185 uWiczbcajpAC
#> 59                            199361376786756491,53571672086000399 35N4QoGY0k4C
#> 60                                            10208965116913196309 TIZ-Mc8IlK0C
#> 61                                            12961270881488694754 u_35RYKgDlwC
#> 62                                            12114359551598956835 f2IySw72cVMC
#> 63                                            17141958553841870840 KUbvn5osdkgC
#> 64                                            12139749723307797145 j3f4tGmQtD8C
#> 65                                                            <NA> b1wdh0AR-JQC
#> 66                                                            <NA> tYavs44e6CUC
#> 67                                                            <NA> vDijr-p_gm4C
#> 68                                                            <NA> g3aElNc5_aQC
#> 69                                                            <NA> ML0RJ9NH7IQC
#> 70                                                            <NA> Z5m8FVwuT1cC
#> 71                                                            <NA> yB1At4FlUx8C
#> 72                                                            <NA> OU6Ihb5iCvQC
#> 73                                                            <NA> b0M2c_1WBrUC
#> 74                                                            <NA> ipzZ9siozwsC
#> 75                                                            <NA> j8SEvjWlNXcC
#> 76                                                            <NA> dfsIfKJdRG4C
#> 77                                                            <NA> 7T2F9Uy0os0C
#> 78                                                            <NA> NJ774b8OgUMC
#> 79                                                            <NA> lSLTfruPkqcC
#> 80                                                            <NA> RYcK_YlVTxYC
#> 81                                                            <NA> NaGl4SEjCO4C
#> 82                                                            <NA> NMxIlDl6LWMC
#> 83                                                            <NA> EYYDruWGBe4C
#> 84                                                            <NA> TFP_iSt0sucC
#> 85                                                            <NA> bEWYMUwI8FkC
#> 86                                                            <NA> iH-uZ7U-co4C
#> 87                                                            <NA> r0BpntZqJG4C
#> 88                                                            <NA> 4JMBOYKVnBMC
#> 89                                                            <NA> yD5IFk8b50cC
#> 90                                                            <NA> L7CI7m0gUJcC
#> 91                                                            <NA> 738O_yMBCRsC
#> 92                                                            <NA> _kc_bZDykSQC
#> 93                                                            <NA> ULOm3_A8WrAC
#> 94                                                            <NA> Zph67rFs4hoC
#> 95                                                            <NA> 3fE2CSJIrl8C
#> 96                                                            <NA> 5nxA0vEk-isC
#> 97                                                            <NA> uc_IGeMz5qoC
#> 98                                                            <NA> tuHXwOkdijsC
#> 99                                                            <NA> dshw04ExmUIC
#> 100                                                           <NA> YOwf2qJgpHMC
#> 101                                                           <NA> hMsQuOkrut0C
#> 102                                                           <NA> zYLM7Y9cAGgC
#> 103                                                           <NA> zLWjf1WUPmwC
#> 104                                                           <NA> gsN89kCJA0AC
#> 105                                                           <NA> BUYA1_V_uYcC
#> 106                                                           <NA> URolC5Kub84C
#> 107                                                           <NA> epqYDVWIO7EC
#> 108                                                           <NA> tzM49s52ZIMC
#> 109                                                           <NA> _Re3VWB3Y0AC
#> 110                                                           <NA> WbkHhVStYXYC
#> 111                                                           <NA> Tiz5es2fbqcC
#> 112                                                           <NA> 4OULZ7Gr8RgC
#> 113                                                           <NA> M05iB0D1s5AC
#> 114                                                           <NA> 70eg2SAEIzsC

When and how often was Jochem cited? Seems like an increasing trend line!

get_citation_history("Iu23-90AAAAJ")  # Jochem's citation history
#>    year cites
#> 1  2008    12
#> 2  2009    23
#> 3  2010    32
#> 4  2011    77
#> 5  2012    81
#> 6  2013   124
#> 7  2014   167
#> 8  2015   218
#> 9  2016   226
#> 10 2017   228
#> 11 2018   288
#> 12 2019   311
#> 13 2020   300
#> 14 2021   329
#> 15 2022   334
#> 16 2023   334
#> 17 2024   259

And now most importantly, Jochem’s collaborators, and the collaborators of those collaborators (note the n_deep = 1, can you find out what that does?). So essentially a “one-step-further-than-Jochem” network.

jochem_coauthors <- get_coauthors("Iu23-90AAAAJ", n_coauthors = 50, n_deep = 1)  # Jochem's collaborators and their co-authors!
jochem_coauthors
Table 8.9: Table 8.10: Sociology staff cleanest?
author coauthors
1 Jochem Tolsma Gerbert Kraaykamp
2 Jochem Tolsma Tom Van Der Meer
3 Jochem Tolsma Maarten Hj Wolbers
4 Jochem Tolsma Michael Savelkoul
5 Jochem Tolsma Stijn Ruiter
6 Jochem Tolsma Peer Scheepers
7 Jochem Tolsma Marcel Lubbers
8 Jochem Tolsma Joran Lameris
9 Jochem Tolsma Marcel Coenders
10 Jochem Tolsma Sara Kinsbergen
11 Jochem Tolsma Nan Dirk De Graaf
12 Jochem Tolsma Dr. Natascha Notten
13 Jochem Tolsma Carlijn Bussemakers
14 Jochem Tolsma Tobias H. Stark
15 Jochem Tolsma Thomas Feliciani
16 Jochem Tolsma Andreas Flache
17 Jochem Tolsma Matthijs Kalmijn
18 Jochem Tolsma Lincoln Quillian
19 Jochem Tolsma Ariana Need
20 Jochem Tolsma René Veenstra
24 Jochem Tolsma About Scholar
25 Jochem Tolsma Search Help
1100 Gerbert Kraaykamp Nan Dirk De Graaf
2100 Gerbert Kraaykamp Paul M. De Graaf
335 Gerbert Kraaykamp Matthijs Kalmijn
410 Gerbert Kraaykamp Tim Huijts
510 Gerbert Kraaykamp Roza Meuleman
610 Gerbert Kraaykamp Maarten Hj Wolbers
710 Gerbert Kraaykamp Jochem Tolsma
810 Gerbert Kraaykamp Mark Visser
910 Gerbert Kraaykamp Christiaan Monden
1010 Gerbert Kraaykamp Margriet Van Hek
1110 Gerbert Kraaykamp Maurice Gesthuizen
1210 Gerbert Kraaykamp Mark Levels
1310 Gerbert Kraaykamp Wout Ultee
1410 Gerbert Kraaykamp Herman G. Van De Werfhorst
1510 Gerbert Kraaykamp Koen Van Eijck
1610 Gerbert Kraaykamp Ellen Verbakel
1710 Gerbert Kraaykamp Remco Hoekman
1810 Gerbert Kraaykamp Stéfanie André
1910 Gerbert Kraaykamp Carlijn Bussemakers
2010 Gerbert Kraaykamp Niels Blom
2410 Gerbert Kraaykamp About Scholar
2510 Gerbert Kraaykamp Search Help
26 Tom Van Der Meer Paul Dekker
27 Tom Van Der Meer Wouter Van Der Brug
28 Tom Van Der Meer Peer Scheepers
29 Tom Van Der Meer Eefje Steenvoorden
30 Tom Van Der Meer Jochem Tolsma
31 Tom Van Der Meer Manfred Te Grotenhuis
32 Tom Van Der Meer Erika Van Elsas
33 Tom Van Der Meer Sarah L. De Lange
34 Tom Van Der Meer Armen Hakhverdian
35 Tom Van Der Meer Maurice Gesthuizen
36 Tom Van Der Meer Eelco Harteveld
37 Tom Van Der Meer Huib Pellikaan
38 Tom Van Der Meer Erik Van Ingen
39 Tom Van Der Meer Tim Reeskens
40 Tom Van Der Meer Marcel Lubbers
41 Tom Van Der Meer Sonja Zmerli
42 Tom Van Der Meer Mérove Gijsberts
43 Tom Van Der Meer Kristof Jacobs
44 Tom Van Der Meer Ben Pelzer
45 Tom Van Der Meer Loes Aaldering
49 Tom Van Der Meer About Scholar
50 Tom Van Der Meer Search Help
51 Maarten Hj Wolbers Marloes De Lange
52 Maarten Hj Wolbers Gerbert Kraaykamp
53 Maarten Hj Wolbers Jochem Tolsma
54 Maarten Hj Wolbers Wout Ultee
55 Maarten Hj Wolbers Mark Visser
56 Maarten Hj Wolbers Paul M. De Graaf
57 Maarten Hj Wolbers Emer Smyth
58 Maarten Hj Wolbers Jaap Dronkers
59 Maarten Hj Wolbers Muja Ardita
60 Maarten Hj Wolbers Ruud Luijkx
61 Maarten Hj Wolbers Dr. Lieselotte Blommaert
62 Maarten Hj Wolbers Walter Müller
63 Maarten Hj Wolbers Tanja Traag
64 Maarten Hj Wolbers Don Westerheijden
65 Maarten Hj Wolbers Renze Kolster
66 Maarten Hj Wolbers Giampiero Passaretta
67 Maarten Hj Wolbers Nicole Tieben
68 Maarten Hj Wolbers Margriet Van Hek
69 Maarten Hj Wolbers Andries De Grip
70 Maarten Hj Wolbers Selina Mccoy
74 Maarten Hj Wolbers About Scholar
75 Maarten Hj Wolbers Search Help
76 Michael Savelkoul Peer Scheepers
77 Michael Savelkoul Jochem Tolsma
78 Michael Savelkoul Maurice Gesthuizen
79 Michael Savelkoul Miles Hewstone
80 Michael Savelkoul William M. Van Der Veld
81 Michael Savelkoul Jasper Van Assche
82 Michael Savelkoul Dietlind Stolle
83 Michael Savelkoul Marcel Lubbers
87 Michael Savelkoul About Scholar
88 Michael Savelkoul Search Help
89 Stijn Ruiter Wim Bernasco
90 Stijn Ruiter Nan Dirk De Graaf
91 Stijn Ruiter Jochem Tolsma
92 Stijn Ruiter Gerbert Kraaykamp
93 Stijn Ruiter Frank Van Tubergen
94 Stijn Ruiter Wouter Steenbeek
95 Stijn Ruiter Shane D Johnson
96 Stijn Ruiter Jean-Louis Van Gelder
97 Stijn Ruiter Hidde Bekhuis
98 Stijn Ruiter Marcel Coenders
99 Stijn Ruiter Michael Townsley
100 Stijn Ruiter Frank Weerman
101 Stijn Ruiter Paul M. De Graaf
102 Stijn Ruiter Marieke Van De Rakt
103 Stijn Ruiter Paul Nieuwbeerta
104 Stijn Ruiter Gentry White
105 Stijn Ruiter Daniel Birks
106 Stijn Ruiter Chantal Van Den Berg
107 Stijn Ruiter Marleen Weulen Kranenbarg
108 Stijn Ruiter Lukas Norbutas
112 Stijn Ruiter About Scholar
113 Stijn Ruiter Search Help
114 Peer Scheepers Marcel Lubbers
115 Peer Scheepers Marcel Coenders
116 Peer Scheepers Rob Eisinga
117 Peer Scheepers Manfred Te Grotenhuis
118 Peer Scheepers Mérove Gijsberts
119 Peer Scheepers Maurice Gesthuizen
120 Peer Scheepers Michael Savelkoul
121 Peer Scheepers Tom Van Der Meer
122 Peer Scheepers Ank De Jonge
123 Peer Scheepers Jaak Billiet
124 Peer Scheepers Maurice Vergeer
125 Peer Scheepers Hans De Witte
126 Peer Scheepers Agnieszka Kanas
127 Peer Scheepers Karin M Van Der Pal-De Bruin
128 Peer Scheepers Jochem Tolsma
129 Peer Scheepers Niels Spierings
130 Peer Scheepers Paula Thijs
131 Peer Scheepers Frans Van Der Slik
132 Peer Scheepers Pytrik Schafraad
133 Peer Scheepers Mark Visser
137 Peer Scheepers About Scholar
138 Peer Scheepers Search Help
139 Marcel Lubbers Peer Scheepers
140 Marcel Lubbers Marcel Coenders
141 Marcel Lubbers Mérove Gijsberts
142 Marcel Lubbers Eva Jaspers
143 Marcel Lubbers Niels Spierings
144 Marcel Lubbers Roza Meuleman
145 Marcel Lubbers Jochem Tolsma
146 Marcel Lubbers Nella Geurts
147 Marcel Lubbers Maykel Verkuyten
148 Marcel Lubbers Nan Dirk De Graaf
149 Marcel Lubbers Tim Immerzeel
150 Marcel Lubbers Hidde Bekhuis
151 Marcel Lubbers Rob Eisinga
152 Marcel Lubbers Roos Van Der Zwan
153 Marcel Lubbers Claudia Diehl
154 Marcel Lubbers Mark Visser
155 Marcel Lubbers Jaak Billiet
156 Marcel Lubbers Maurice Vergeer
157 Marcel Lubbers Jeanette A.j. Renema
158 Marcel Lubbers Hilde Coffe
162 Marcel Lubbers About Scholar
163 Marcel Lubbers Search Help
167 Joran Lameris About Scholar
168 Joran Lameris Search Help
172 Marcel Coenders About Scholar
173 Marcel Coenders Search Help
177 Sara Kinsbergen About Scholar
178 Sara Kinsbergen Search Help
179 Nan Dirk De Graaf Gerbert Kraaykamp
180 Nan Dirk De Graaf Paul M. De Graaf
181 Nan Dirk De Graaf Ariana Need
182 Nan Dirk De Graaf Paul Nieuwbeerta
183 Nan Dirk De Graaf Stijn Ruiter
184 Nan Dirk De Graaf Anthony F Heath
185 Nan Dirk De Graaf Geoffrey Evans
186 Nan Dirk De Graaf Manfred Te Grotenhuis
187 Nan Dirk De Graaf Giedo Jansen
188 Nan Dirk De Graaf Herman G. Van De Werfhorst
189 Nan Dirk De Graaf Ayse Guveli
190 Nan Dirk De Graaf Christiaan Monden
191 Nan Dirk De Graaf Jonathan Kelley
192 Nan Dirk De Graaf Harry Bg Ganzeboom
193 Nan Dirk De Graaf Jochem Tolsma
194 Nan Dirk De Graaf Marcel Lubbers
195 Nan Dirk De Graaf Eva Jaspers
196 Nan Dirk De Graaf René Bekkers
197 Nan Dirk De Graaf Ruud Luijkx
198 Nan Dirk De Graaf Lincoln Quillian
202 Nan Dirk De Graaf About Scholar
203 Nan Dirk De Graaf Search Help
204 Dr. Natascha Notten Gerbert Kraaykamp
205 Dr. Natascha Notten Herman G. Van De Werfhorst
206 Dr. Natascha Notten Bram Lancee
207 Dr. Natascha Notten Jochem Tolsma
208 Dr. Natascha Notten Ruben Konig
209 Dr. Natascha Notten Wiemer Salverda
210 Dr. Natascha Notten Patti M. Valkenburg
211 Dr. Natascha Notten Ingrid K. Van Dijk
212 Dr. Natascha Notten Paula Thijs
213 Dr. Natascha Notten Harry Bg Ganzeboom
214 Dr. Natascha Notten Ellen Verbakel
215 Dr. Natascha Notten Hidde Bekhuis
216 Dr. Natascha Notten Horn Daniel
217 Dr. Natascha Notten Manfred Te Grotenhuis
221 Dr. Natascha Notten About Scholar
222 Dr. Natascha Notten Search Help
223 Carlijn Bussemakers Gerbert Kraaykamp
224 Carlijn Bussemakers Jochem Tolsma
225 Carlijn Bussemakers Niels Spierings
226 Carlijn Bussemakers Kars Van Oosterhout
230 Carlijn Bussemakers About Scholar
231 Carlijn Bussemakers Search Help
232 Tobias H. Stark Maykel Verkuyten
233 Tobias H. Stark René Veenstra
234 Tobias H. Stark Anke Munniksma
235 Tobias H. Stark Josh Pasek
236 Tobias H. Stark Trevor Tompson
237 Tobias H. Stark Jochem Tolsma
238 Tobias H. Stark J. Ashwin Rambaran, Ph.d.
239 Tobias H. Stark Ioana Van Deurzen
243 Tobias H. Stark About Scholar
244 Tobias H. Stark Search Help
248 Thomas Feliciani About Scholar
249 Thomas Feliciani Search Help
250 Andreas Flache Michael Macy
251 Andreas Flache Michael Mäs
252 Andreas Flache Tobias H. Stark
253 Andreas Flache Guillaume Deffuant
254 Andreas Flache Rainer Hegselmann
255 Andreas Flache René Veenstra
256 Andreas Flache Károly Takács
257 Andreas Flache Rafael Wittek
258 Andreas Flache Dirk Helbing
259 Andreas Flache Anke Munniksma
260 Andreas Flache James A Kitts
261 Andreas Flache Maykel Verkuyten
262 Andreas Flache Nigel Gilbert
263 Andreas Flache Maxi San Miguel
264 Andreas Flache Rosaria Conte
265 Andreas Flache Andrzej Nowak
266 Andreas Flache Josep M. Pujol
267 Andreas Flache André Grow
268 Andreas Flache Werner Raub
269 Andreas Flache Rene Torenvlied
273 Andreas Flache About Scholar
274 Andreas Flache Search Help
275 Matthijs Kalmijn Paul M. De Graaf
276 Matthijs Kalmijn Kène Henkens
277 Matthijs Kalmijn Gerbert Kraaykamp
278 Matthijs Kalmijn Frank Van Tubergen
279 Matthijs Kalmijn Aart C. Liefbroer
280 Matthijs Kalmijn Katya Ivanova
281 Matthijs Kalmijn Wilfred Uunk
282 Matthijs Kalmijn Marleen Damman
283 Matthijs Kalmijn Christiaan Monden
284 Matthijs Kalmijn Anne-Rigt Poortman
285 Matthijs Kalmijn Harry Bg Ganzeboom
286 Matthijs Kalmijn Jornt J. Mandemakers
287 Matthijs Kalmijn Ellen Verbakel
288 Matthijs Kalmijn Ruud Luijkx
289 Matthijs Kalmijn Jaap Dronkers
290 Matthijs Kalmijn Marjolein Broese Van Groenou
291 Matthijs Kalmijn Tanja Van Der Lippe
292 Matthijs Kalmijn Erik Van Ingen
293 Matthijs Kalmijn Nells - Netherlands Longitudinal Lif…
297 Matthijs Kalmijn About Scholar
298 Matthijs Kalmijn Search Help
299 Lincoln Quillian Jochem Tolsma
303 Lincoln Quillian About Scholar
304 Lincoln Quillian Search Help
308 Ariana Need About Scholar
309 Ariana Need Search Help
310 René Veenstra Ormel, Johan (Hans)
311 René Veenstra Frank Verhulst
312 René Veenstra Siegwart Lindenberg
313 René Veenstra Jan Kornelis Dijkstra
314 René Veenstra Tineke Oldehinkel
315 René Veenstra Gijs Huitsing
316 René Veenstra Christian Steglich
317 René Veenstra Christina Salmivalli
318 René Veenstra Wilma Vollebergh
319 René Veenstra Tina Kretschmer
320 René Veenstra Lydia Laninga-Wijnen
321 René Veenstra Miranda Sentse
322 René Veenstra Rozemarijn Van Der Ploeg
323 René Veenstra Jelle Sijtsema
324 René Veenstra Catharina A. Hartman
325 René Veenstra Beau Oldenburg
326 René Veenstra Sijmen A Reijneveld
327 René Veenstra Miia Sainio
328 René Veenstra Marijtje A.j. Van Duijn
329 René Veenstra Z Harakeh
333 René Veenstra About Scholar
334 René Veenstra Search Help


Notice, however, that we could easily plot Jochem’s collaboration network already! This is thus a one-deeper network where we plot the co-authors of Jochem and who they collaborate with, there is some overlap, but not always.

plot_coauthors(get_coauthors("Iu23-90AAAAJ", n_coauthors = 20, n_deep = 1), size_labels = 2)  # Doesn't look like much yet, but we'll make it prettier later.

So let’s gather these data, but now for all sociology staff simultaneously! For this, we use the for loop again. The for loop I make below is a bit more complicated, but follows the same logic as before. For each row (i) in soc_df, we attempt to query Google Scholar on the basis of the first name, last name, and affiliation listed in that row in the data frame. We use some handy subsetting, e.g., soc_df[i, 3] means we input last_name= with the last name (which is the third column) found in the i-th row in the data frame. The same goes for first name and affiliation. We fill up gs_id in the data frame with the Google Scholar IDs we’ll hopefully find. The for (i in nrow(soc_df)) simply means we let i run for however many rows the data frame has. Finally, the tryCatch({}) function makes that we can continue the loop even though we may encounter errors for a given row. Here, that probably means that not every row (i.e., sociology staff member) can be found on Google Scholar. We print the error, but continue the for loop with the tryCatch({}) function. In the final rows of the code below. We simply drop those rows that we cannot identify on Google Scholar.

# because we don't wanna 'Rate limit' google scholar, they throw you out if you make to many
# requests, we randomize request time do you understand the code below?
for (i in 1:10) {
    time <- runif(1, 0, 5)
    Sys.sleep(time)
    print(paste(i, ": R slept for", round(time, 1), "seconds"))
}
# for every number from 1 to 10 we draw one number from 0 to 5 from a uniform distribution we put
# the wrapper sys.sleep around it that we put R to sleep for the drawn number
# Look throught get_scholar_id_fix(last_name, first_name, affiliation) 
# if we can find google scholar profiles of sociology staff!
for (i in 1:nrow(soc_df)) {
  
  time <- runif(1, 0, 5)
  Sys.sleep(time)
  
  tryCatch({
     soc_df[i,c("gs_id")] <- get_scholar_id_fix(last_name = soc_df[i, c("last_name")], # so search on last_name of staff (third column)
                                             first_name = soc_df[i, c("first_name")],  # search on first_name of staff (fourth column)
                                             affiliation = soc_df[i,c("affiliation")]) # search on affiliation of each staff (fifth column)

    }, error=function(e){cat("ERROR :", conditionMessage(e), "\n")}) # continue on error, but print the error
  }

# remove those without pubs from the df
# seems we're left with about 34 sociology staff members!
soc_df <- soc_df[!soc_df$gs_id == "", ]
soc_df
Table 8.11: Table 8.12: Sociology staff with Scholar IDs
soc_names soc_experts last_name first_name affiliation gs_id
2 Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training Batenburg Ronald radboud university UK7nVSEAAAAJ
3 Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms Begall Katia radboud university e7zfTqMAAAAJ
4 Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports Bekhuis Hidde radboud university Q4saWX8AAAAJ
5 Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood Berg Lonneke radboud university vzBNQ1kAAAAJ
6 Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market Blommaert Lieselotte radboud university RG54uasAAAAJ
7 Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed Damman Marleen radboud university MEv-V_YAAAAJ
8 Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics Eisinga Rob radboud university GDHdsXAAAAAJ
9 Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion Gesthuizen Maurice radboud university n6hiblQAAAAJ
10 Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality Glas Saskia radboud university ZMc0j2YAAAAJ
11 Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture Hek Margriet radboud university ZvLlx2EAAAAJ
12 Hoekman, dr. R. H. A.(Remco) Sports and policy sociology Hoekman Remco radboud university LsMimOEAAAAJ
13 Hofstra, dr. B. (Bas) Diversity, inequality and innovation Hofstra Bas radboud university Nx7pDywAAAAJ
14 Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health Kraaykamp Gerbert radboud university l8aM4jAAAAAJ
15 Meuleman, dr. (Roza) Culture and nationalism Meuleman Roza radboud university iKs_5WkAAAAJ
16 Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion Savelkoul Michael radboud university _f3krXUAAAAJ
17 Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity Scheepers Peer radboud university hPeXxvEAAAAJ
18 Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration Spierings Niels radboud university cy3Ye6sAAAAJ
19 Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity Tolsma Jochem radboud university Iu23-90AAAAJ
20 Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work |Verbakel |Ellen |radboud universit |w2McVJAAAAA
21 Visser, dr. M. (Mark) Older workers, radicalism and social cohesion Visser Mark radboud university ItITloQAAAAJ
22 Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality Wolbers Maarten radboud university TqKrXnMAAAAJ
24 Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality Bussemakers Carlijn radboud university bDPtkIoAAAAJ
25 Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation Franken Rob radboud university p3IwtT4AAAAJ
26 Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement Firat Mustafa radboud university _ukytQYAAAAJ
27 Geurts, P.G. (Nella) MSc Integration and migration Geurts Nella radboud university VCTvbTkAAAAJ
29 Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour Jeroense Thijmen radboud university izq-KNUAAAAJ
31 Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT) Loh Renae radboud university tFaMPOQAAAAJ
33 Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms Mensvoort Carly radboud university z6iMs-UAAAAJ
34 Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality Müller Katrin radboud university lkVq32sAAAAJ
35 Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender Raiber Klara radboud university xE65HUcAAAAJ
36 Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family Ramaekers Marlou radboud university fp99JAQAAAAJ
40 Houten, J. (Jasper) van MSc Sports Houten Jasper radboud university iR4UIwwAAAAJ
41 Middendorp J. (Jansje) van MSc Home administration Middendorp Jansje radboud university gs0li6MAAAAJ
43 Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education Weber Tijmen radboud university KfLALRIAAAAJ


It works! So what is left to do is to get the data we already extracted for Jochem (citation, publications, and so forth), but now for all sociology staff. For that, we need a bunch of for loops. Let’s first gather the profiles and publications. We store those in a list() which is an object in which you can store multiple data frames, vectors, matrices, and so forth. This is particularly good for for loops because you can store information that is – at first sight – not necessarily compatible. For instance, matrices of different length. Note that we bind a Google Scholar ID to the publications too which is important to be able match to the soc_df later on.

soc_list_profiles <- list()  # first we create an empty list that we then fill up with the for loop
soc_list_publications <- list()

for (i in 1:nrow(soc_df)) {

    time <- runif(1, 0, 5)
    Sys.sleep(time)

    # note how you call different elements in a list '[[]]', fill in the i-th element
    soc_list_profiles[[i]] <- get_profile(soc_df[i, c("gs_id")])  # Note how we call row i (remember how to call rows in a DF/Matrix) and then the associated scholar id
    soc_list_publications[[i]] <- get_publications(soc_df[i, c("gs_id")])
    soc_list_publications[[i]][, c("gs_id")] <- soc_df[i, c("gs_id")]  # note that we again attach an id
    # so both functions here call the entire profile and pubs for an author, based on google
    # scholar ids

}
# Notice how fast the data blow up! The 34 RU sociology scholars publish ~3000 papers
soc_df_publications <- bind_rows(soc_list_publications)

Note how soc_list_profiles contains all Google Scholar profile information in a list of 34 elements. We need to do some relatively involved data handling to attach the Google Scholar profiles in soc_list_profiles to the soc_df. That is, we want some information from soc_list_profiles to be attached to soc_df. Specifically, we want several things we want from the profiles: id, name (as reported on Scholar), affiliation, tot_cites, h_index, i10index, fields, and homepage.

Some profile elements can contain more than one row. For instance, co-authors are stored in long format per profile that do not easily merge to a data frame where each staff member is a row. For instance, say Bas has two co-authors; one needs to first concatonate those co-authors before you can merge them in one row for Bas. So we need to get only the profile elements that have a single element per profile element (e.g., total_cites). Seems these are the first 8 elements in a list element. So we need to get those out of the lists and store those in a new dataframe. This involves several steps:

  • We first unlist the 1 tot 8-th elements in a list element.
  • We then make it a data frame.
  • Then transpose the data such that each row contains 8 columns.
  • We then use the function bind_rows() to simply make a data frame from the list elements.
  • We then merge it to soc_df. So what we end up with is a sociology staff data frame with much more information than before: citations, indices, expertise listed on Google Scholar, and so forth.

Note how we can do this at once, or all separately (I commented out the code that does the same as the next three rows). Which has your preference?

soc_profiles_df <- list()
for (i in 1:length(soc_list_profiles)) {
    # soc_profiles_df[[i]] <- data.frame(t(unlist(soc_list_profiles[[i]][1:8]))) #some annyoing
    # data handling
    soc_profiles_df[[i]] <- unlist(soc_list_profiles[[i]][1:8])
    soc_profiles_df[[i]] <- data.frame(soc_profiles_df[[i]])
    soc_profiles_df[[i]] <- t(soc_profiles_df[[i]])

}
soc_profiles_df <- bind_rows(soc_profiles_df)
soc_df <- left_join(soc_df, soc_profiles_df, by = c(gs_id = "id"))  # merge data with soc_df
soc_df  # notice all the new information we were able to get from the scholar profiles!
Table 8.13: Table 8.14: Sociology staff with Scholar info
soc_names soc_experts last_name first_name affiliation.x gs_id name affiliation.y total_cites h_index i10_index fields homepage
Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training Batenburg Ronald radboud university UK7nVSEAAAAJ Ronald Batenburg Programmaleider NIVEL en bijzonder hoogleraar Radboud Universiteit Nijmegen 3608 30 87 verified email at nivel.nl - homepage https://www.nivel.nl/nl/ronald-batenburg
Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms Begall Katia radboud university e7zfTqMAAAAJ Katia Begall Radboud University Nijmegen 936 9 9 verified email at maw.ru.nl NA
Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports Bekhuis Hidde radboud university Q4saWX8AAAAJ Hidde Bekhuis Post Doc Sociology, Radboud University Nijmegen 348 8 7 verified email at ru.nl NA
Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood Berg Lonneke radboud university vzBNQ1kAAAAJ Lonneke van den Berg Radboud University 34 3 2 verified email at maw.ru.nl - homepage https://www.ru.nl/personen/berg-l-van-den-lonneke/
Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market Blommaert Lieselotte radboud university RG54uasAAAAJ Lieselotte Blommaert Sociology/Social Cultural Research, Radboud University, Nijmegen, the 317 7 7 verified email at ru.nl - homepage http://www.ru.nl/english/people/blommaert-e/
Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed Damman Marleen radboud university MEv-V_YAAAAJ Marleen Damman Assistant Professor, Utrecht University 515 10 12 verified email at uu.nl - homepage https://www.uu.nl/staff/MDamman
Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics Eisinga Rob radboud university GDHdsXAAAAAJ Rob Eisinga Professor social science research methods, Radboud University Nijmegen 4994 33 77 verified email at ru.nl - homepage http://robeisinga.ruhosting.nl/
Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion Gesthuizen Maurice radboud university n6hiblQAAAAJ Maurice Gesthuizen Sociology, Radboud University Nijmegen, the Netherland - Assistant Professor 2425 24 41 verified email at maw.ru.nl - homepage http://www.ru.nl/methodenentechnieken/methoden-technieken/medewerkers/vm_medewerkers/maurice_gesthuizen/
Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality Glas Saskia radboud university ZMc0j2YAAAAJ Saskia Glas PhD student, Radboud University 70 4 2 verified email at ru.nl NA
Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture Hek Margriet radboud university ZvLlx2EAAAAJ Margriet van Hek Radboud University 262 8 7 verified email at maw.ru.nl NA
Hoekman, dr. R. H. A.(Remco) Sports and policy sociology Hoekman Remco radboud university LsMimOEAAAAJ Remco Hoekman Director, Mulier Institute / Senior researcher, Radboud University 610 12 15 verified email at mulierinstituut.nl - homepage https://www.mulierinstituut.nl/over-mulier/medewerkers/remco-hoekman/
Hofstra, dr. B. (Bas) Diversity, inequality and innovation Hofstra Bas radboud university Nx7pDywAAAAJ Bas Hofstra Assistant Professor, Radboud University 384 7 7 verified email at ru.nl - homepage http://www.bashofstra.com/
Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health Kraaykamp Gerbert radboud university l8aM4jAAAAAJ Gerbert Kraaykamp Professor of Sociology, Radboud Universiteit Nijmegen 7724 46 98 verified email at maw.ru.nl - homepage https://www.ru.nl/english/people/kraaykamp-g/
Meuleman, dr. (Roza) Culture and nationalism Meuleman Roza radboud university iKs_5WkAAAAJ Roza Meuleman Assistant Professor - Sociology - Radboud University Nijmegen 214 8 6 verified email at ru.nl NA
Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion Savelkoul Michael radboud university _f3krXUAAAAJ Michael Savelkoul Assistant Professor - Sociology, Radboud University Nijmegen, the Netherlands 580 8 7 verified email at maw.ru.nl NA
Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity Scheepers Peer radboud university hPeXxvEAAAAJ peer scheepers hoogleraar methodologie, faculteit der sociale wetenschappen radboud universiteit 14399 61 180 verified email at maw.ru.nl NA
Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration Spierings Niels radboud university cy3Ye6sAAAAJ Niels Spierings Associate Professor of Sociology, Radboud University 1662 22 33 verified email at ru.nl - homepage https://www.ru.nl/english/people/spierings-c/
Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity Tolsma Jochem radboud university Iu23-90AAAAJ Jochem Tolsma Professor, Radboud University Nijmegen / University of Groningen 2260 22 33 verified email at ru.nl - homepage http://www.jochemtolsma.nl/
Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work |Verbakel |Ellen |radboud universit |w2McVJAAAAA |Ellen Verbakel |Professor of Sociology, Department of Sociology, Radboud University Nijmegen |1474 |24 |32 |verified email at maw.ru.nl - homepage |http://www.ellenverbakel.nl/
Visser, dr. M. (Mark) Older workers, radicalism and social cohesion Visser Mark radboud university ItITloQAAAAJ Mark Visser Assistant Professor, Radboud University 381 9 8 verified email at ru.nl - homepage https://www.researchgate.net/profile/Mark_Visser
Wolbers, prof. dr. M.H.J. (Maarten) Educational inequality and labour market inequality Wolbers Maarten radboud university TqKrXnMAAAAJ Maarten HJ Wolbers Professor of Sociology, Radboud University, Nijmegen 3624 29 58 verified email at ru.nl - homepage http://www.socsci.ru.nl/maartenw/
Bussemakers, C. (Carlijn) MSc Adverse youth experiences and social inequality Bussemakers Carlijn radboud university bDPtkIoAAAAJ Carlijn Bussemakers Department of Sociology, Radboud University 37 3 1 verified email at maw.ru.nl NA
Franken, R. (Rob) MSc Sport networks and motivation for sustainable sports participation Franken Rob radboud university p3IwtT4AAAAJ Rob JM Franken Unknown affiliation 1219 11 12 no verified email NA
Firat, M. (Mustafa) MSc Social inequality, older workers, life course and retirement Firat Mustafa radboud university _ukytQYAAAAJ mustafa Inc firat university 5298 34 173 verified email at firat.edu.tr NA
Geurts, P.G. (Nella) MSc Integration and migration Geurts Nella radboud university VCTvbTkAAAAJ Nella Geurts Department of Sociology, Radboud University 32 3 1 verified email at ru.nl NA
Jeroense, T.M.G. (Thijmen) MSc Political participation, segregation, opinion polarization and voting behaviour Jeroense Thijmen radboud university izq-KNUAAAAJ Thijmen Jeroense PhD candidate, Radboud University Nijmegen 1 1 0 verified email at ru.nl - homepage https://www.ru.nl/personen/jeroense-t/
Loh, S.M. (Renae) MSc Educational sociology, social stratification, gender inequality and information communication technology (ICT) Loh Renae radboud university tFaMPOQAAAAJ Renae Sze Ming Loh PhD candidate, Radboud University 70 2 2 verified email at ru.nl - homepage http://renaeloh.com/
Mensvoort, C.A. van (Carly) MSc Gender, leadership and social norms Mensvoort Carly radboud university z6iMs-UAAAAJ Carly van Mensvoort Radboud University 35 2 2 verified email at ru.nl - homepage https://www.ru.nl/english/people/mensvoort-c-van/
Müller, K. (Katrin) MSc Opinions about discrimination, migration and inequality Müller Katrin radboud university lkVq32sAAAAJ Kathrin Friederike Müller Post-Doc, Universtität Rostock/CAIS NRW 201 9 9 verified email at uni-rostock.de - homepage https://www.imf.uni-rostock.de/institut/mitarbeiterinnen/lehrende/dr-kathrin-friederike-mueller/
Raiber, K. (Klara) MSc Informal care, employment, social inequality and gender Raiber Klara radboud university xE65HUcAAAAJ Klara Raiber PhD candidate, Radboud University Nijmegen 4 1 0 verified email at maw.ru.nl - homepage https://www.ru.nl/english/people/raiber-k/
Ramaekers, M.J.M. (Marlou) MSc Prosocial behaviour and family Ramaekers Marlou radboud university fp99JAQAAAAJ Marlou Ramaekers PhD Candidate, Radboud University NA NA NA verified email at ru.nl NA
Houten, J. (Jasper) van MSc Sports Houten Jasper radboud university iR4UIwwAAAAJ Jasper van Houten PhD Candidate, HAN Institute of Sport and Exercise Studies (Hogeschool van Arnhem en Nijmegen 31 4 1 verified email at ru.nl - homepage https://www.researchgate.net/profile/Jasper_Houten
Middendorp J. (Jansje) van MSc Home administration Middendorp Jansje radboud university gs0li6MAAAAJ Jansje van Middendorp Buitenpromovendus Radboud Universiteit 3 1 0 verified email at ru.nl NA
Weber, T. (Tijmen) MSc International student mobility and the internationalization of higher education Weber Tijmen radboud university KfLALRIAAAAJ Tijmen Weber Lecturer Statistics and Research, HAN University of Applied Sciences 42 2 2 verified email at han.nl NA


So we have papers and profiles. Remember how we got Jochem’s citation history? We want that for each staff member too. Yet again, we use a for loop. We first store the citation history in a list. But notice the if statement! We only continue the for loop for that i-th element if some statement is TRUE. Here, we attempt to find out if the i-th element, the citation history of the staff member, has a length than is larger than 0. Some staff members are never cited (which happens all the time if papers are only just published), and so for these staff members that is no list element that contains information. We only attach a Google Scholar ID for those staff members that are cited at least once. We bind the rows again and end up with a data frame in long format: three columns with years, cites, and Google Scholar ID. Therefore, there is more than one row per staff member.

# get citation history of a scholar
soc_staff_cit <- list()
for (i in 1:nrow(soc_df)) {

    soc_staff_cit[[i]] <- get_citation_history(soc_df[i, c("gs_id")])

    if (nrow(soc_staff_cit[[i]]) > 0) {
        soc_staff_cit[[i]][, c("gs_id")] <- soc_df[i, c("gs_id")]  # again attach the gs_id as third column
    }
}
soc_staff_cit <- bind_rows(soc_staff_cit)
colnames(soc_staff_cit)[3] <- "gs_id"
Table 8.15: Table 8.16: Sociology staff citation by year
year cites gs_id
1999 14 UK7nVSEAAAAJ
2000 25 UK7nVSEAAAAJ
2001 21 UK7nVSEAAAAJ
2002 18 UK7nVSEAAAAJ
2003 35 UK7nVSEAAAAJ
2004 24 UK7nVSEAAAAJ
2005 53 UK7nVSEAAAAJ
2006 64 UK7nVSEAAAAJ
2007 52 UK7nVSEAAAAJ
2008 80 UK7nVSEAAAAJ
2009 115 UK7nVSEAAAAJ
2010 134 UK7nVSEAAAAJ
2011 158 UK7nVSEAAAAJ
2012 186 UK7nVSEAAAAJ
2013 219 UK7nVSEAAAAJ
2014 207 UK7nVSEAAAAJ
2015 257 UK7nVSEAAAAJ
2016 336 UK7nVSEAAAAJ
2017 307 UK7nVSEAAAAJ
2018 358 UK7nVSEAAAAJ
2019 314 UK7nVSEAAAAJ
2020 335 UK7nVSEAAAAJ
2021 183 UK7nVSEAAAAJ
2008 5 e7zfTqMAAAAJ
2009 18 e7zfTqMAAAAJ
2010 28 e7zfTqMAAAAJ
2011 29 e7zfTqMAAAAJ
2012 32 e7zfTqMAAAAJ
2013 48 e7zfTqMAAAAJ
2014 51 e7zfTqMAAAAJ
2015 61 e7zfTqMAAAAJ
2016 77 e7zfTqMAAAAJ
2017 120 e7zfTqMAAAAJ
2018 99 e7zfTqMAAAAJ
2019 119 e7zfTqMAAAAJ
2020 137 e7zfTqMAAAAJ
2021 102 e7zfTqMAAAAJ
2008 1 Q4saWX8AAAAJ
2009 4 Q4saWX8AAAAJ
2010 7 Q4saWX8AAAAJ
2011 7 Q4saWX8AAAAJ
2012 17 Q4saWX8AAAAJ
2013 22 Q4saWX8AAAAJ
2014 36 Q4saWX8AAAAJ
2015 29 Q4saWX8AAAAJ
2016 37 Q4saWX8AAAAJ
2017 25 Q4saWX8AAAAJ
2018 33 Q4saWX8AAAAJ
2019 50 Q4saWX8AAAAJ
2020 40 Q4saWX8AAAAJ
2021 32 Q4saWX8AAAAJ
2018 1 vzBNQ1kAAAAJ
2019 6 vzBNQ1kAAAAJ
2020 9 vzBNQ1kAAAAJ
2021 15 vzBNQ1kAAAAJ
2012 3 RG54uasAAAAJ
2013 3 RG54uasAAAAJ
2014 8 RG54uasAAAAJ
2015 24 RG54uasAAAAJ
2016 19 RG54uasAAAAJ
2017 34 RG54uasAAAAJ
2018 41 RG54uasAAAAJ
2019 58 RG54uasAAAAJ
2020 72 RG54uasAAAAJ
2021 51 RG54uasAAAAJ
2011 2 MEv-V_YAAAAJ
2012 7 MEv-V_YAAAAJ
2013 15 MEv-V_YAAAAJ
2014 19 MEv-V_YAAAAJ
2015 30 MEv-V_YAAAAJ
2016 60 MEv-V_YAAAAJ
2017 65 MEv-V_YAAAAJ
2018 78 MEv-V_YAAAAJ
2019 88 MEv-V_YAAAAJ
2020 85 MEv-V_YAAAAJ
2021 60 MEv-V_YAAAAJ
1991 18 GDHdsXAAAAAJ
1992 13 GDHdsXAAAAAJ
1993 14 GDHdsXAAAAAJ
1994 41 GDHdsXAAAAAJ
1995 38 GDHdsXAAAAAJ
1996 37 GDHdsXAAAAAJ
1997 26 GDHdsXAAAAAJ
1998 24 GDHdsXAAAAAJ
1999 36 GDHdsXAAAAAJ
2000 47 GDHdsXAAAAAJ
2001 50 GDHdsXAAAAAJ
2002 44 GDHdsXAAAAAJ
2003 42 GDHdsXAAAAAJ
2004 35 GDHdsXAAAAAJ
2005 48 GDHdsXAAAAAJ
2006 63 GDHdsXAAAAAJ
2007 58 GDHdsXAAAAAJ
2008 83 GDHdsXAAAAAJ
2009 120 GDHdsXAAAAAJ
2010 98 GDHdsXAAAAAJ
2011 104 GDHdsXAAAAAJ
2012 143 GDHdsXAAAAAJ
2013 184 GDHdsXAAAAAJ
2014 277 GDHdsXAAAAAJ
2015 345 GDHdsXAAAAAJ
2016 411 GDHdsXAAAAAJ
2017 441 GDHdsXAAAAAJ
2018 527 GDHdsXAAAAAJ
2019 495 GDHdsXAAAAAJ
2020 591 GDHdsXAAAAAJ
2021 447 GDHdsXAAAAAJ
2005 10 n6hiblQAAAAJ
2006 19 n6hiblQAAAAJ
2007 28 n6hiblQAAAAJ
2008 47 n6hiblQAAAAJ
2009 54 n6hiblQAAAAJ
2010 67 n6hiblQAAAAJ
2011 128 n6hiblQAAAAJ
2012 121 n6hiblQAAAAJ
2013 175 n6hiblQAAAAJ
2014 200 n6hiblQAAAAJ
2015 231 n6hiblQAAAAJ
2016 252 n6hiblQAAAAJ
2017 237 n6hiblQAAAAJ
2018 206 n6hiblQAAAAJ
2019 208 n6hiblQAAAAJ
2020 210 n6hiblQAAAAJ
2021 186 n6hiblQAAAAJ
2018 2 ZMc0j2YAAAAJ
2019 13 ZMc0j2YAAAAJ
2020 29 ZMc0j2YAAAAJ
2021 26 ZMc0j2YAAAAJ
2014 3 ZvLlx2EAAAAJ
2015 4 ZvLlx2EAAAAJ
2016 19 ZvLlx2EAAAAJ
2017 15 ZvLlx2EAAAAJ
2018 31 ZvLlx2EAAAAJ
2019 55 ZvLlx2EAAAAJ
2020 78 ZvLlx2EAAAAJ
2021 55 ZvLlx2EAAAAJ
2010 4 LsMimOEAAAAJ
2011 7 LsMimOEAAAAJ
2012 12 LsMimOEAAAAJ
2013 24 LsMimOEAAAAJ
2014 40 LsMimOEAAAAJ
2015 36 LsMimOEAAAAJ
2016 53 LsMimOEAAAAJ
2017 76 LsMimOEAAAAJ
2018 81 LsMimOEAAAAJ
2019 49 LsMimOEAAAAJ
2020 122 LsMimOEAAAAJ
2021 98 LsMimOEAAAAJ
2014 2 Nx7pDywAAAAJ
2015 2 Nx7pDywAAAAJ
2016 15 Nx7pDywAAAAJ
2017 25 Nx7pDywAAAAJ
2018 33 Nx7pDywAAAAJ
2019 28 Nx7pDywAAAAJ
2020 105 Nx7pDywAAAAJ
2021 162 Nx7pDywAAAAJ
1997 22 l8aM4jAAAAAJ
1998 19 l8aM4jAAAAAJ
1999 31 l8aM4jAAAAAJ
2000 49 l8aM4jAAAAAJ
2001 77 l8aM4jAAAAAJ
2002 87 l8aM4jAAAAAJ
2003 98 l8aM4jAAAAAJ
2004 116 l8aM4jAAAAAJ
2005 126 l8aM4jAAAAAJ
2006 176 l8aM4jAAAAAJ
2007 205 l8aM4jAAAAAJ
2008 256 l8aM4jAAAAAJ
2009 246 l8aM4jAAAAAJ
2010 303 l8aM4jAAAAAJ
2011 360 l8aM4jAAAAAJ
2012 363 l8aM4jAAAAAJ
2013 460 l8aM4jAAAAAJ
2014 474 l8aM4jAAAAAJ
2015 512 l8aM4jAAAAAJ
2016 614 l8aM4jAAAAAJ
2017 581 l8aM4jAAAAAJ
2018 655 l8aM4jAAAAAJ
2019 621 l8aM4jAAAAAJ
2020 662 l8aM4jAAAAAJ
2021 440 l8aM4jAAAAAJ
2012 1 iKs_5WkAAAAJ
2013 5 iKs_5WkAAAAJ
2014 14 iKs_5WkAAAAJ
2015 19 iKs_5WkAAAAJ
2016 23 iKs_5WkAAAAJ
2017 30 iKs_5WkAAAAJ
2018 31 iKs_5WkAAAAJ
2019 39 iKs_5WkAAAAJ
2020 23 iKs_5WkAAAAJ
2021 20 iKs_5WkAAAAJ
2011 10 _f3krXUAAAAJ
2012 24 _f3krXUAAAAJ
2013 32 _f3krXUAAAAJ
2014 51 _f3krXUAAAAJ
2015 67 _f3krXUAAAAJ
2016 54 _f3krXUAAAAJ
2017 63 _f3krXUAAAAJ
2018 80 _f3krXUAAAAJ
2019 64 _f3krXUAAAAJ
2020 70 _f3krXUAAAAJ
2021 54 _f3krXUAAAAJ
1994 60 hPeXxvEAAAAJ
1995 35 hPeXxvEAAAAJ
1996 39 hPeXxvEAAAAJ
1997 33 hPeXxvEAAAAJ
1998 35 hPeXxvEAAAAJ
1999 47 hPeXxvEAAAAJ
2000 74 hPeXxvEAAAAJ
2001 122 hPeXxvEAAAAJ
2002 107 hPeXxvEAAAAJ
2003 153 hPeXxvEAAAAJ
2004 170 hPeXxvEAAAAJ
2005 180 hPeXxvEAAAAJ
2006 253 hPeXxvEAAAAJ
2007 336 hPeXxvEAAAAJ
2008 439 hPeXxvEAAAAJ
2009 515 hPeXxvEAAAAJ
2010 511 hPeXxvEAAAAJ
2011 622 hPeXxvEAAAAJ
2012 767 hPeXxvEAAAAJ
2013 782 hPeXxvEAAAAJ
2014 935 hPeXxvEAAAAJ
2015 1129 hPeXxvEAAAAJ
2016 1076 hPeXxvEAAAAJ
2017 1182 hPeXxvEAAAAJ
2018 1206 hPeXxvEAAAAJ
2019 1163 hPeXxvEAAAAJ
2020 1235 hPeXxvEAAAAJ
2021 863 hPeXxvEAAAAJ
2011 12 cy3Ye6sAAAAJ
2012 21 cy3Ye6sAAAAJ
2013 42 cy3Ye6sAAAAJ
2014 55 cy3Ye6sAAAAJ
2015 74 cy3Ye6sAAAAJ
2016 141 cy3Ye6sAAAAJ
2017 140 cy3Ye6sAAAAJ
2018 223 cy3Ye6sAAAAJ
2019 285 cy3Ye6sAAAAJ
2020 346 cy3Ye6sAAAAJ
2021 287 cy3Ye6sAAAAJ
2008 12 Iu23-90AAAAJ
2009 21 Iu23-90AAAAJ
2010 26 Iu23-90AAAAJ
2011 79 Iu23-90AAAAJ
2012 79 Iu23-90AAAAJ
2013 116 Iu23-90AAAAJ
2014 151 Iu23-90AAAAJ
2015 204 Iu23-90AAAAJ
2016 228 Iu23-90AAAAJ
2017 223 Iu23-90AAAAJ
2018 267 Iu23-90AAAAJ
2019 297 Iu23-90AAAAJ
2020 305 Iu23-90AAAAJ
2021 228 Iu23-90AAAAJ
2007 7 w2McVJAAAAAJ
2008 3 w2McVJAAAAAJ
2009 14 w2McVJAAAAAJ
2010 19 w2McVJAAAAAJ
2011 19 w2McVJAAAAAJ
2012 19 w2McVJAAAAAJ
2013 51 w2McVJAAAAAJ
2014 50 w2McVJAAAAAJ
2015 76 w2McVJAAAAAJ
2016 113 w2McVJAAAAAJ
2017 138 w2McVJAAAAAJ
2018 175 w2McVJAAAAAJ
2019 229 w2McVJAAAAAJ
2020 312 w2McVJAAAAAJ
2021 220 w2McVJAAAAAJ
2012 1 ItITloQAAAAJ
2013 5 ItITloQAAAAJ
2014 12 ItITloQAAAAJ
2015 15 ItITloQAAAAJ
2016 38 ItITloQAAAAJ
2017 38 ItITloQAAAAJ
2018 57 ItITloQAAAAJ
2019 71 ItITloQAAAAJ
2020 74 ItITloQAAAAJ
2021 60 ItITloQAAAAJ
1999 11 TqKrXnMAAAAJ
2000 17 TqKrXnMAAAAJ
2001 28 TqKrXnMAAAAJ
2002 33 TqKrXnMAAAAJ
2003 44 TqKrXnMAAAAJ
2004 41 TqKrXnMAAAAJ
2005 61 TqKrXnMAAAAJ
2006 64 TqKrXnMAAAAJ
2007 83 TqKrXnMAAAAJ
2008 109 TqKrXnMAAAAJ
2009 102 TqKrXnMAAAAJ
2010 148 TqKrXnMAAAAJ
2011 196 TqKrXnMAAAAJ
2012 129 TqKrXnMAAAAJ
2013 222 TqKrXnMAAAAJ
2014 236 TqKrXnMAAAAJ
2015 251 TqKrXnMAAAAJ
2016 305 TqKrXnMAAAAJ
2017 301 TqKrXnMAAAAJ
2018 295 TqKrXnMAAAAJ
2019 308 TqKrXnMAAAAJ
2020 299 TqKrXnMAAAAJ
2021 259 TqKrXnMAAAAJ
2017 1 bDPtkIoAAAAJ
2018 4 bDPtkIoAAAAJ
2019 8 bDPtkIoAAAAJ
2020 13 bDPtkIoAAAAJ
2021 10 bDPtkIoAAAAJ
2003 12 p3IwtT4AAAAJ
2004 26 p3IwtT4AAAAJ
2005 35 p3IwtT4AAAAJ
2006 39 p3IwtT4AAAAJ
2007 76 p3IwtT4AAAAJ
2008 54 p3IwtT4AAAAJ
2009 78 p3IwtT4AAAAJ
2010 75 p3IwtT4AAAAJ
2011 81 p3IwtT4AAAAJ
2012 92 p3IwtT4AAAAJ
2013 74 p3IwtT4AAAAJ
2014 87 p3IwtT4AAAAJ
2015 75 p3IwtT4AAAAJ
2016 85 p3IwtT4AAAAJ
2017 62 p3IwtT4AAAAJ
2018 73 p3IwtT4AAAAJ
2019 65 p3IwtT4AAAAJ
2020 70 p3IwtT4AAAAJ
2021 49 p3IwtT4AAAAJ
2005 17 _ukytQYAAAAJ
2006 19 _ukytQYAAAAJ
2007 34 _ukytQYAAAAJ
2008 73 _ukytQYAAAAJ
2009 56 _ukytQYAAAAJ
2010 82 _ukytQYAAAAJ
2011 40 _ukytQYAAAAJ
2012 59 _ukytQYAAAAJ
2013 60 _ukytQYAAAAJ
2014 87 _ukytQYAAAAJ
2015 73 _ukytQYAAAAJ
2016 89 _ukytQYAAAAJ
2017 314 _ukytQYAAAAJ
2018 750 _ukytQYAAAAJ
2019 922 _ukytQYAAAAJ
2020 1100 _ukytQYAAAAJ
2021 1461 _ukytQYAAAAJ
2017 4 VCTvbTkAAAAJ
2018 4 VCTvbTkAAAAJ
2019 7 VCTvbTkAAAAJ
2020 5 VCTvbTkAAAAJ
2021 12 VCTvbTkAAAAJ
2019 10 tFaMPOQAAAAJ
2020 29 tFaMPOQAAAAJ
2021 31 tFaMPOQAAAAJ
2016 4 z6iMs-UAAAAJ
2017 9 z6iMs-UAAAAJ
2018 6 z6iMs-UAAAAJ
2019 6 z6iMs-UAAAAJ
2020 6 z6iMs-UAAAAJ
2021 3 z6iMs-UAAAAJ
2010 6 lkVq32sAAAAJ
2011 3 lkVq32sAAAAJ
2012 12 lkVq32sAAAAJ
2013 8 lkVq32sAAAAJ
2014 27 lkVq32sAAAAJ
2015 10 lkVq32sAAAAJ
2016 15 lkVq32sAAAAJ
2017 11 lkVq32sAAAAJ
2018 13 lkVq32sAAAAJ
2019 33 lkVq32sAAAAJ
2020 39 lkVq32sAAAAJ
2021 17 lkVq32sAAAAJ
2013 1 iR4UIwwAAAAJ
2014 1 iR4UIwwAAAAJ
2015 2 iR4UIwwAAAAJ
2016 4 iR4UIwwAAAAJ
2017 4 iR4UIwwAAAAJ
2018 3 iR4UIwwAAAAJ
2019 3 iR4UIwwAAAAJ
2020 6 iR4UIwwAAAAJ
2021 7 iR4UIwwAAAAJ
2019 1 gs0li6MAAAAJ
2020 0 gs0li6MAAAAJ
2021 2 gs0li6MAAAAJ
2017 3 KfLALRIAAAAJ
2018 4 KfLALRIAAAAJ
2019 10 KfLALRIAAAAJ
2020 11 KfLALRIAAAAJ
2021 11 KfLALRIAAAAJ


8.6.4 Getting collaborators

Next, we get the collaborators. Note that on Google Scholar, people add co-authors manually. Unfortunately, the Scholar package only scrapes the first twenty. Some scholars have more co-authors listed, but for that you would need to “click” on “VIEW ALL” on Google Scholar. The scholar package does not do that for us. However, we wrote the code for you to get more than twenty co-authors! You could take a look at the underlying code of fcollabs.R and attempt understand what is happening there. Because scholars add their own co-authors, it is a directed tie. For instance, I could list Albert Einstein as my co-author, but Albert probably doesn’t reciprocate that tie. The for loop should be clear by now. We get collaborators for a given Google Scholar ID in soc_df. We then bind_rows again, and remove those staff members that did not list any collaborator.

Please download function here: fcollabs.

source("addfiles/fcollabs.R")  # Change path if necessary. 
# first the soc collaborators note how we already build a function (fcollabs()) for you you need to
# input a google scholar id and a 1 (if you want to find collabs) or 0 (only extracting names)
# fcollabs --> you can check it out if you're interested
soc_collabs <- list()
for (i in 1:nrow(soc_df)) {

    time <- runif(1, 0, 5)
    Sys.sleep(time)

    soc_collabs[[i]] <- fcollabs(soc_df[i, c("gs_id")], 1)

}
soc_collabs <- bind_rows(soc_collabs)  # bind rows, get the unique ones!
soc_collabs_unique <- unique(soc_collabs[, 3])  # so 229 unique collaborators for RU staff?
soc_collabs_unique <- soc_collabs_unique[!is.na(soc_collabs_unique)]
save(soc_collabs, file = "addfiles/soc_df_collabs1.RData")  # you notice this takes a while, so we save the data here.
Table 8.17: Table 8.18: Sociology staff collaborators
gs_id name coauth coauth_id
UK7nVSEAAAAJ Ronald Batenburg NA NA
e7zfTqMAAAAJ Katia Begall Melinda C. Mills HX9KQ5MAAAAJ
e7zfTqMAAAAJ Katia Begall Patrick Präg MW-rFm4AAAAJ
e7zfTqMAAAAJ Katia Begall Letizia Mencarini PUlRJy0AAAAJ
e7zfTqMAAAAJ Katia Begall Tanturri Maria Letizia xN3XevQAAAAJ
e7zfTqMAAAAJ Katia Begall Harry BG Ganzeboom -abxxmUAAAAJ
e7zfTqMAAAAJ Katia Begall Anne-Rigt Poortman fuS31Y0AAAAJ
e7zfTqMAAAAJ Katia Begall Leonie van Breeschoten MjYWorQAAAAJ
e7zfTqMAAAAJ Katia Begall Tanja van der Lippe Rn56SIMAAAAJ
e7zfTqMAAAAJ Katia Begall Katya Ivanova EgMYeqMAAAAJ
e7zfTqMAAAAJ Katia Begall Laura den Dulk aB03M_IAAAAJ
Q4saWX8AAAAJ Hidde Bekhuis NA NA
vzBNQ1kAAAAJ Lonneke van den Berg Thomas Leopold Vip84JAAAAAJ
vzBNQ1kAAAAJ Lonneke van den Berg Matthijs Kalmijn OEMqUeAAAAAJ
vzBNQ1kAAAAJ Lonneke van den Berg Ruben van Gaalen ienj2p4AAAAJ
RG54uasAAAAJ Lieselotte Blommaert Marcel Coenders HH4PPBwAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Frank van Tubergen YU1DcNIAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Maarten HJ Wolbers TqKrXnMAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Maurice Gesthuizen n6hiblQAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Muja Ardita 1o7Njf0AAAAJ
RG54uasAAAAJ Lieselotte Blommaert Stijn Ruiter GCHDgZcAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Tanja van der Lippe Rn56SIMAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Marieke van den Brink Z-TxZ5wAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Roza Meuleman iKs_5WkAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Anete Butkēviča mV1GwWgAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Niels Spierings cy3Ye6sAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Ineke Maas lHZlqNsAAAAJ
RG54uasAAAAJ Lieselotte Blommaert Eva Jaspers Ss6I3eAAAAAJ
MEv-V_YAAAAJ Marleen Damman NA NA
GDHdsXAAAAAJ Rob Eisinga peer scheepers hPeXxvEAAAAJ
GDHdsXAAAAAJ Rob Eisinga ben pelzer aLppskkAAAAJ
GDHdsXAAAAAJ Rob Eisinga Manfred te Grotenhuis sIIqmAcAAAAJ
GDHdsXAAAAAJ Rob Eisinga Christine Teelken bwfxxhoAAAAJ
GDHdsXAAAAAJ Rob Eisinga Philip Hans Franses HWrD14UAAAAJ
GDHdsXAAAAAJ Rob Eisinga tatjana van strien EVCcnugAAAAJ
GDHdsXAAAAAJ Rob Eisinga Ruben Konig UoJeIDEAAAAJ
GDHdsXAAAAAJ Rob Eisinga Rutger Engels UVCmUC4AAAAJ
GDHdsXAAAAAJ Rob Eisinga Sophie Bolt Opgt0BQAAAAJ
GDHdsXAAAAAJ Rob Eisinga Dr. ing. Peter O. Gerrits, senior anatomist qY5xm1UAAAAJ
GDHdsXAAAAAJ Rob Eisinga Jaak Billiet RZUS_2gAAAAJ
GDHdsXAAAAAJ Rob Eisinga Eric Venbrux ylyxea4AAAAJ
GDHdsXAAAAAJ Rob Eisinga Tom Heskes 3jUymSMAAAAJ
GDHdsXAAAAAJ Rob Eisinga Marcel Coenders HH4PPBwAAAAJ
GDHdsXAAAAAJ Rob Eisinga Rense Nieuwenhuis KA_HjwEAAAAJ
GDHdsXAAAAAJ Rob Eisinga Rainer Breitling sOtfTK8AAAAJ
GDHdsXAAAAAJ Rob Eisinga Marcel Lubbers 078qsZoAAAAJ
GDHdsXAAAAAJ Rob Eisinga Alexander W. Schmidt-Catran wf6XxagAAAAJ
GDHdsXAAAAAJ Rob Eisinga Paul Hendriks Vettehen hN9Jc2gAAAAJ
GDHdsXAAAAAJ Rob Eisinga Mariska Kleemans sCRhG8YAAAAJ
GDHdsXAAAAAJ Rob Eisinga Machteld Ouwens GJLyMhwAAAAJ
GDHdsXAAAAAJ Rob Eisinga Nan Dirk de Graaf nTqzQ0oAAAAJ
GDHdsXAAAAAJ Rob Eisinga Ariana Need AmFGGWEAAAAJ
GDHdsXAAAAAJ Rob Eisinga Maurice Vergeer V6HscJIAAAAJ
GDHdsXAAAAAJ Rob Eisinga Andris Jankevics uOW7tEYAAAAJ
GDHdsXAAAAAJ Rob Eisinga Fangxin Hong M-lG3qsAAAAJ
GDHdsXAAAAAJ Rob Eisinga Ad A Vermulst kuUJ7NgAAAAJ
GDHdsXAAAAAJ Rob Eisinga James J. Gross MlMKkPUAAAAJ
GDHdsXAAAAAJ Rob Eisinga Marius Ooms AY7Ijg0AAAAJ
GDHdsXAAAAAJ Rob Eisinga Jacqueline M. Vink VOGcRMsAAAAJ
GDHdsXAAAAAJ Rob Eisinga Otto Swank 9cFGhwEAAAAJ
GDHdsXAAAAAJ Rob Eisinga mieke verloo mlIXgUcAAAAJ
GDHdsXAAAAAJ Rob Eisinga Dick van Dijk ogxQxIEAAAAJ
GDHdsXAAAAAJ Rob Eisinga Roos van der Zwan sKXOwkoAAAAJ
GDHdsXAAAAAJ Rob Eisinga Stef van Buuren _3y5C0UAAAAJ
GDHdsXAAAAAJ Rob Eisinga Maaike Verhagen Drw4KUMAAAAJ
GDHdsXAAAAAJ Rob Eisinga Nina van den Broek cd5WuWYAAAAJ
GDHdsXAAAAAJ Rob Eisinga Michael Savelkoul _f3krXUAAAAJ
GDHdsXAAAAAJ Rob Eisinga jac vennix i0jYlF4AAAAJ
GDHdsXAAAAAJ Rob Eisinga Mark Visser ItITloQAAAAJ
GDHdsXAAAAAJ Rob Eisinga Gary King 5sz_jBoAAAAJ
GDHdsXAAAAAJ Rob Eisinga Martin A. Tanner prOk5EcAAAAJ
GDHdsXAAAAAJ Rob Eisinga Hubert Korzilius B3JxrJIAAAAJ
GDHdsXAAAAAJ Rob Eisinga Inge Bleijenbergh BaUO69MAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen peer scheepers hPeXxvEAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Gerbert Kraaykamp l8aM4jAAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Marloes de Lange bC8tl_MAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Tom van der Meer K51iiIAAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Mark Visser ItITloQAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Michael Savelkoul _f3krXUAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen “Heike Solga” or “H. Solga” lXJuWe8AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Jochem Tolsma Iu23-90AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Bram Steijn Nw8yFOkAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Ariana Need AmFGGWEAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Paul M. de Graaf WvaJ-9IAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Ellen Verbakel w2McVJAAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Tim Huijts -imfMCIAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Stéfanie André n_NQoe8AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Jasper van Houten iR4UIwwAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Geert Driessen uTUCuH0AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Beate Volker v6V0y2cAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen William M. van der Veld G_FILmcAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Jan Paul Heisig CX9g1hsAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen JC Vrooman sweM218AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen mieke verloo mlIXgUcAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Hans-Peter Blossfeld F0kCgy8AAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Herman G. van de Werfhorst amYIKXQAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Christiaan Monden 96E8EncAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Bram Lancee WLYKP4sAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Ruben (RS) Gowricharn ui–XoYAAAAJ
n6hiblQAAAAJ Maurice Gesthuizen Evelien Tonkens qtn_EOQAAAAJ
ZMc0j2YAAAAJ Saskia Glas NA NA
ZvLlx2EAAAAJ Margriet van Hek Gerbert Kraaykamp l8aM4jAAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Maarten HJ Wolbers TqKrXnMAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Tanja van der Lippe Rn56SIMAAAAJ
ZvLlx2EAAAAJ Margriet van Hek ben pelzer aLppskkAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Claudia Buchmann pOC360YAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Leonie van Breeschoten MjYWorQAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Anke Heyder pScIkdUAAAAJ
ZvLlx2EAAAAJ Margriet van Hek Mieke Van Houtte 0tOowAIAAAAJ
LsMimOEAAAAJ Remco Hoekman NA NA
Nx7pDywAAAAJ Bas Hofstra Rense Corten pehgQfIAAAAJ
Nx7pDywAAAAJ Bas Hofstra Frank van Tubergen YU1DcNIAAAAJ
Nx7pDywAAAAJ Bas Hofstra Daniel McFarland nx8clgcAAAAJ
Nx7pDywAAAAJ Bas Hofstra Sebastian Munoz-Najar Galvez QP4EDyUAAAAJ
Nx7pDywAAAAJ Bas Hofstra Bryan He VnvkluIAAAAJ
Nx7pDywAAAAJ Bas Hofstra Dan Jurafsky uZg9l58AAAAJ
Nx7pDywAAAAJ Bas Hofstra Vivek Kulkarni bCwtPMUAAAAJ
Nx7pDywAAAAJ Bas Hofstra Nicole B. Ellison P0XTYBEAAAAJ
Nx7pDywAAAAJ Bas Hofstra Niek C. de Schipper PEuawIgAAAAJ
Nx7pDywAAAAJ Bas Hofstra Vincent Buskens ITE9bHMAAAAJ
Nx7pDywAAAAJ Bas Hofstra Antonie Knigge rYbhEhgAAAAJ
Nx7pDywAAAAJ Bas Hofstra Matthew E. Brashears MPwIyzkAAAAJ
Nx7pDywAAAAJ Bas Hofstra Beate Volker v6V0y2cAAAAJ
Nx7pDywAAAAJ Bas Hofstra Jesper Jelle Rözer NZdQUWwAAAAJ
Nx7pDywAAAAJ Bas Hofstra Joseph B. Bayer fBiKnVYAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Nan Dirk de Graaf nTqzQ0oAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Paul M. de Graaf WvaJ-9IAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Matthijs Kalmijn OEMqUeAAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Tim Huijts -imfMCIAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Maarten HJ Wolbers TqKrXnMAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Christiaan Monden 96E8EncAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Maurice Gesthuizen n6hiblQAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Mark Levels 1KVq6W0AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Jochem Tolsma Iu23-90AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp wout ultee vfhKSjgAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Herman G. van de Werfhorst amYIKXQAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Roza Meuleman iKs_5WkAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Mark Visser ItITloQAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Koen van Eijck kiyeZQUAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Margriet van Hek ZvLlx2EAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Ellen Verbakel w2McVJAAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Stéfanie André n_NQoe8AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Jesper Jelle Rözer NZdQUWwAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Niels Blom wJRAXQQAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Marcel Lubbers 078qsZoAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Carlijn Bussemakers bDPtkIoAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp peer scheepers hPeXxvEAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Remco Hoekman LsMimOEAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Tanja van der Lippe Rn56SIMAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Eva Jaspers Ss6I3eAAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Kène Henkens feAtftcAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp ben pelzer aLppskkAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Jasper van Houten iR4UIwwAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Jale Tosun jr42m50AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Ineke Stoop QC8OV4UAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Patti M. Valkenburg a_8Wf-MAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp ZEYNEP CEMALCILAR yQJmOe0AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Carly van Mensvoort z6iMs-UAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Niels Spierings cy3Ye6sAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Claudia Buchmann pOC360YAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Hidde Bekhuis Q4saWX8AAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Marlou Ramaekers fp99JAQAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Marieke van den Brink Z-TxZ5wAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Ronald Batenburg UK7nVSEAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Ingrid Schoon w4Fm8OEAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp NELLS - NEtherlands Longitudinal Lifecourse Study 2M7h07cAAAAJ
l8aM4jAAAAAJ Gerbert Kraaykamp Harry BG Ganzeboom -abxxmUAAAAJ
iKs_5WkAAAAJ Roza Meuleman Gerbert Kraaykamp l8aM4jAAAAAJ
iKs_5WkAAAAJ Roza Meuleman Marcel Lubbers 078qsZoAAAAJ
iKs_5WkAAAAJ Roza Meuleman Stéfanie André n_NQoe8AAAAJ
iKs_5WkAAAAJ Roza Meuleman Mike Savage 9r4Ase4AAAAJ
iKs_5WkAAAAJ Roza Meuleman Hidde Bekhuis Q4saWX8AAAAJ
iKs_5WkAAAAJ Roza Meuleman Ellen Verbakel w2McVJAAAAAJ
iKs_5WkAAAAJ Roza Meuleman peer scheepers hPeXxvEAAAAJ
iKs_5WkAAAAJ Roza Meuleman Maykel Verkuyten R3QcZZIAAAAJ
iKs_5WkAAAAJ Roza Meuleman Lieselotte Blommaert RG54uasAAAAJ
iKs_5WkAAAAJ Roza Meuleman Jeanette AJ Renema bvFEqBoAAAAJ
iKs_5WkAAAAJ Roza Meuleman Marieke van den Brink Z-TxZ5wAAAAJ
iKs_5WkAAAAJ Roza Meuleman Carly van Mensvoort z6iMs-UAAAAJ
iKs_5WkAAAAJ Roza Meuleman Ronald Batenburg UK7nVSEAAAAJ
_f3krXUAAAAJ Michael Savelkoul peer scheepers hPeXxvEAAAAJ
_f3krXUAAAAJ Michael Savelkoul Maurice Gesthuizen n6hiblQAAAAJ
_f3krXUAAAAJ Michael Savelkoul Jochem Tolsma Iu23-90AAAAJ
_f3krXUAAAAJ Michael Savelkoul William M. van der Veld G_FILmcAAAAJ
_f3krXUAAAAJ Michael Savelkoul Dietlind Stolle mjqHdG4AAAAJ
_f3krXUAAAAJ Michael Savelkoul Miles Hewstone VI0ny3gAAAAJ
hPeXxvEAAAAJ peer scheepers Marcel Coenders HH4PPBwAAAAJ
hPeXxvEAAAAJ peer scheepers Marcel Lubbers 078qsZoAAAAJ
hPeXxvEAAAAJ peer scheepers Rob Eisinga GDHdsXAAAAAJ
hPeXxvEAAAAJ peer scheepers Manfred te Grotenhuis sIIqmAcAAAAJ
hPeXxvEAAAAJ peer scheepers Mérove Gijsberts QngQH7sAAAAJ
hPeXxvEAAAAJ peer scheepers Maurice Gesthuizen n6hiblQAAAAJ
hPeXxvEAAAAJ peer scheepers Tom van der Meer K51iiIAAAAAJ
hPeXxvEAAAAJ peer scheepers Michael Savelkoul _f3krXUAAAAJ
hPeXxvEAAAAJ peer scheepers Jaak Billiet RZUS_2gAAAAJ
hPeXxvEAAAAJ peer scheepers Hans De Witte Zjjb6TAAAAAJ
hPeXxvEAAAAJ peer scheepers Maurice Vergeer V6HscJIAAAAJ
hPeXxvEAAAAJ peer scheepers Karin M van der Pal-de Bruin xSd4Wp8AAAAJ
hPeXxvEAAAAJ peer scheepers Agnieszka Kanas wTAuHRkAAAAJ
hPeXxvEAAAAJ peer scheepers Jochem Tolsma Iu23-90AAAAJ
hPeXxvEAAAAJ peer scheepers Ruben Konig UoJeIDEAAAAJ
hPeXxvEAAAAJ peer scheepers Pytrik Schafraad 6wS1wGQAAAAJ
hPeXxvEAAAAJ peer scheepers Frans van der Slik 1zOc13kAAAAJ
hPeXxvEAAAAJ peer scheepers Mark Visser ItITloQAAAAJ
hPeXxvEAAAAJ peer scheepers Paula Thijs Ni3PX2wAAAAJ
hPeXxvEAAAAJ peer scheepers ben pelzer aLppskkAAAAJ
hPeXxvEAAAAJ peer scheepers Rozemarijn van der Ploeg -TUCif4AAAAJ
hPeXxvEAAAAJ peer scheepers Elmar Schlueter z6CfxrUAAAAJ
hPeXxvEAAAAJ peer scheepers Niels Spierings cy3Ye6sAAAAJ
hPeXxvEAAAAJ peer scheepers Peter Sleegers fk3eIQcAAAAJ
hPeXxvEAAAAJ peer scheepers Paul Dekker gHuTzXcAAAAJ
hPeXxvEAAAAJ peer scheepers Jan W. van Deth ABvk6xUAAAAJ
hPeXxvEAAAAJ peer scheepers Jan Baars 0juqFroAAAAJ
hPeXxvEAAAAJ peer scheepers Menandro S. Abanes WvfwF84AAAAJ
hPeXxvEAAAAJ peer scheepers Gerbert Kraaykamp l8aM4jAAAAAJ
hPeXxvEAAAAJ peer scheepers Tim Huijts -imfMCIAAAAJ
hPeXxvEAAAAJ peer scheepers William M. van der Veld G_FILmcAAAAJ
hPeXxvEAAAAJ peer scheepers Hilde Tobi vdyXK7EAAAAJ
hPeXxvEAAAAJ peer scheepers Maykel Verkuyten R3QcZZIAAAAJ
hPeXxvEAAAAJ peer scheepers Rik Linssen qaQ4OP8AAAAJ
hPeXxvEAAAAJ peer scheepers Saskia Glas ZMc0j2YAAAAJ
hPeXxvEAAAAJ peer scheepers Sigrid Roßteutscher fXpk8r0AAAAJ
hPeXxvEAAAAJ peer scheepers Daniel Stegmueller 6omT8KIAAAAJ
hPeXxvEAAAAJ peer scheepers marlies rijnders JWc2u14AAAAJ
hPeXxvEAAAAJ peer scheepers Tery Setiawan lrwpBzsAAAAJ
hPeXxvEAAAAJ peer scheepers Marijn Scholte wOJP4PMAAAAJ
hPeXxvEAAAAJ peer scheepers Ad A Vermulst kuUJ7NgAAAAJ
hPeXxvEAAAAJ peer scheepers Wilma Vollebergh NNDEktAAAAAJ
hPeXxvEAAAAJ peer scheepers Roderick Sluiter oFYyNMIAAAAJ
hPeXxvEAAAAJ peer scheepers Stéfanie André n_NQoe8AAAAJ
hPeXxvEAAAAJ peer scheepers Jaco Dagevos _d-TPGkAAAAJ
hPeXxvEAAAAJ peer scheepers Tobias H. Stark UH6UvgYAAAAJ
hPeXxvEAAAAJ peer scheepers Anke Munniksma WRIjGkYAAAAJ
hPeXxvEAAAAJ peer scheepers Edwin de Jong feum8g0AAAAJ
hPeXxvEAAAAJ peer scheepers Geert Driessen uTUCuH0AAAAJ
hPeXxvEAAAAJ peer scheepers Peter Ester 7S7E170AAAAJ
hPeXxvEAAAAJ peer scheepers Agnes Zenaida V. Camacho fBSe7FwAAAAJ
hPeXxvEAAAAJ peer scheepers Paul Hendriks Vettehen hN9Jc2gAAAAJ
hPeXxvEAAAAJ peer scheepers Leen d’Haenens YKF5ig8AAAAJ
hPeXxvEAAAAJ peer scheepers Cahyo Pamungkas 2JhSd3YAAAAJ
hPeXxvEAAAAJ peer scheepers Beate Volker v6V0y2cAAAAJ
hPeXxvEAAAAJ peer scheepers Roza Meuleman iKs_5WkAAAAJ
hPeXxvEAAAAJ peer scheepers Dietlind Stolle mjqHdG4AAAAJ
hPeXxvEAAAAJ peer scheepers Miles Hewstone VI0ny3gAAAAJ
hPeXxvEAAAAJ peer scheepers Eva Jaspers Ss6I3eAAAAAJ
hPeXxvEAAAAJ peer scheepers Wilma otten V6STY8QAAAAJ
hPeXxvEAAAAJ peer scheepers Mark Levels 1KVq6W0AAAAJ
hPeXxvEAAAAJ peer scheepers Marieke van den Brink Z-TxZ5wAAAAJ
hPeXxvEAAAAJ peer scheepers Maarten HJ Wolbers TqKrXnMAAAAJ
hPeXxvEAAAAJ peer scheepers Tri Subagya t6pUex4AAAAJ
hPeXxvEAAAAJ peer scheepers mieke verloo mlIXgUcAAAAJ
hPeXxvEAAAAJ peer scheepers Janos Betko Cvdrl6AAAAAJ
hPeXxvEAAAAJ peer scheepers Moniek Buijzen iCD9WCwAAAAJ
hPeXxvEAAAAJ peer scheepers Niels Blom wJRAXQQAAAAJ
hPeXxvEAAAAJ peer scheepers Maartje Cobussen LPwkdtgAAAAJ
hPeXxvEAAAAJ peer scheepers Ellen Verbakel w2McVJAAAAAJ
hPeXxvEAAAAJ peer scheepers Jarl K. Kampen iyoW6JMAAAAJ
hPeXxvEAAAAJ peer scheepers jac vennix i0jYlF4AAAAJ
hPeXxvEAAAAJ peer scheepers Mariska Kleemans sCRhG8YAAAAJ
hPeXxvEAAAAJ peer scheepers Harry BG Ganzeboom -abxxmUAAAAJ
hPeXxvEAAAAJ peer scheepers Phalet Karen r8_cHO0AAAAJ
cy3Ye6sAAAAJ Niels Spierings Kristof Jacobs p_n5i1EAAAAJ
cy3Ye6sAAAAJ Niels Spierings Harry BG Ganzeboom -abxxmUAAAAJ
cy3Ye6sAAAAJ Niels Spierings Bernhard Nauck 1fZTMKIAAAAJ
cy3Ye6sAAAAJ Niels Spierings Lucinda Platt hLc_ptsAAAAJ
cy3Ye6sAAAAJ Niels Spierings Sait Bayrakdar KPFh08IAAAAJ
cy3Ye6sAAAAJ Niels Spierings Efe Kerem Sozeri 34pCKYMAAAAJ
cy3Ye6sAAAAJ Niels Spierings mieke verloo mlIXgUcAAAAJ
cy3Ye6sAAAAJ Niels Spierings Jeroen Smits 28aIODkAAAAJ
cy3Ye6sAAAAJ Niels Spierings Marcel Lubbers 078qsZoAAAAJ
cy3Ye6sAAAAJ Niels Spierings Sarah L. de Lange DKeDYmYAAAAJ
cy3Ye6sAAAAJ Niels Spierings Liza Mügge (née Liza Nell) N-4d9d8AAAAJ
cy3Ye6sAAAAJ Niels Spierings Gerbert Kraaykamp l8aM4jAAAAAJ
cy3Ye6sAAAAJ Niels Spierings Stéfanie André n_NQoe8AAAAJ
cy3Ye6sAAAAJ Niels Spierings Caroline Dewilde Nl6ZO5cAAAAJ
cy3Ye6sAAAAJ Niels Spierings Ruud Luijkx 2vaELsQAAAAJ
cy3Ye6sAAAAJ Niels Spierings Amy Mazur bdQ-JCsAAAAJ
Iu23-90AAAAJ Jochem Tolsma Tom van der Meer K51iiIAAAAAJ
Iu23-90AAAAJ Jochem Tolsma Gerbert Kraaykamp l8aM4jAAAAAJ
Iu23-90AAAAJ Jochem Tolsma Maarten HJ Wolbers TqKrXnMAAAAJ
Iu23-90AAAAJ Jochem Tolsma peer scheepers hPeXxvEAAAAJ
Iu23-90AAAAJ Jochem Tolsma Michael Savelkoul _f3krXUAAAAJ
Iu23-90AAAAJ Jochem Tolsma Stijn Ruiter GCHDgZcAAAAJ
Iu23-90AAAAJ Jochem Tolsma Marcel Lubbers 078qsZoAAAAJ
Iu23-90AAAAJ Jochem Tolsma Maurice Gesthuizen n6hiblQAAAAJ
Iu23-90AAAAJ Jochem Tolsma Marcel Coenders HH4PPBwAAAAJ
Iu23-90AAAAJ Jochem Tolsma Nan Dirk de Graaf nTqzQ0oAAAAJ
Iu23-90AAAAJ Jochem Tolsma Tobias H. Stark UH6UvgYAAAAJ
Iu23-90AAAAJ Jochem Tolsma Sara Kinsbergen cgrJ8q8AAAAJ
Iu23-90AAAAJ Jochem Tolsma Christiaan Monden 96E8EncAAAAJ
Iu23-90AAAAJ Jochem Tolsma Matthijs Kalmijn OEMqUeAAAAAJ
Iu23-90AAAAJ Jochem Tolsma Lincoln Quillian z6JOvkAAAAAJ
Iu23-90AAAAJ Jochem Tolsma Thomas Feliciani YuAjBUEAAAAJ
Iu23-90AAAAJ Jochem Tolsma Andreas Flache dt9jPJ0AAAAJ
Iu23-90AAAAJ Jochem Tolsma Marloes de Lange bC8tl_MAAAAJ
Iu23-90AAAAJ Jochem Tolsma Ariana Need AmFGGWEAAAAJ
Iu23-90AAAAJ Jochem Tolsma René Veenstra _9OVrqMAAAAJ
Iu23-90AAAAJ Jochem Tolsma Ioana Van Deurzen m8LjNPcAAAAJ
Iu23-90AAAAJ Jochem Tolsma Paul M. de Graaf WvaJ-9IAAAAJ
Iu23-90AAAAJ Jochem Tolsma Manfred te Grotenhuis sIIqmAcAAAAJ
Iu23-90AAAAJ Jochem Tolsma Mérove Gijsberts QngQH7sAAAAJ
Iu23-90AAAAJ Jochem Tolsma Anke Munniksma WRIjGkYAAAAJ
Iu23-90AAAAJ Jochem Tolsma John R. Hipp 0T-dsp0AAAAJ
Iu23-90AAAAJ Jochem Tolsma Roderick Sluiter oFYyNMIAAAAJ
Iu23-90AAAAJ Jochem Tolsma Carlijn Bussemakers bDPtkIoAAAAJ
Iu23-90AAAAJ Jochem Tolsma Roos van der Zwan sKXOwkoAAAAJ
Iu23-90AAAAJ Jochem Tolsma Gideon Bolt 0UbclGIAAAAJ
Iu23-90AAAAJ Jochem Tolsma N Notten NakYfX4AAAAJ
Iu23-90AAAAJ Jochem Tolsma NELLS - NEtherlands Longitudinal Lifecourse Study 2M7h07cAAAAJ
w2McVJAAAAAJ Ellen Verbakel Paul M. de Graaf WvaJ-9IAAAAJ
w2McVJAAAAAJ Ellen Verbakel Gerbert Kraaykamp l8aM4jAAAAAJ
w2McVJAAAAAJ Ellen Verbakel Inge Sieben uONmLWwAAAAJ
w2McVJAAAAAJ Ellen Verbakel Matthijs Kalmijn OEMqUeAAAAAJ
w2McVJAAAAAJ Ellen Verbakel Eva Jaspers Ss6I3eAAAAAJ
w2McVJAAAAAJ Ellen Verbakel Maurice Gesthuizen n6hiblQAAAAJ
w2McVJAAAAAJ Ellen Verbakel Ruud Luijkx 2vaELsQAAAAJ
w2McVJAAAAAJ Ellen Verbakel Marcel Lubbers 078qsZoAAAAJ
w2McVJAAAAAJ Ellen Verbakel peer scheepers hPeXxvEAAAAJ
ItITloQAAAAJ Mark Visser Gerbert Kraaykamp l8aM4jAAAAAJ
ItITloQAAAAJ Mark Visser Maurice Gesthuizen n6hiblQAAAAJ
ItITloQAAAAJ Mark Visser Maarten HJ Wolbers TqKrXnMAAAAJ
ItITloQAAAAJ Mark Visser peer scheepers hPeXxvEAAAAJ
ItITloQAAAAJ Mark Visser Eva Jaspers Ss6I3eAAAAAJ
ItITloQAAAAJ Mark Visser Marcel Lubbers 078qsZoAAAAJ
ItITloQAAAAJ Mark Visser Marijn Scholte wOJP4PMAAAAJ
ItITloQAAAAJ Mark Visser Anette Eva Fasang dy3yBVAAAAAJ
ItITloQAAAAJ Mark Visser Tanja van der Lippe Rn56SIMAAAAJ
ItITloQAAAAJ Mark Visser Jelle Lössbroek qMK0bm4AAAAJ
ItITloQAAAAJ Mark Visser Ellen Verbakel w2McVJAAAAAJ
ItITloQAAAAJ Mark Visser Klara Raiber xE65HUcAAAAJ
ItITloQAAAAJ Mark Visser Jasper van Houten iR4UIwwAAAAJ
ItITloQAAAAJ Mark Visser wout ultee vfhKSjgAAAAJ
ItITloQAAAAJ Mark Visser Michael Savelkoul _f3krXUAAAAJ
ItITloQAAAAJ Mark Visser Rob Eisinga GDHdsXAAAAAJ
ItITloQAAAAJ Mark Visser Maaike van der Vleuten _SAvPogAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Maurice Gesthuizen n6hiblQAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Marloes de Lange bC8tl_MAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Gerbert Kraaykamp l8aM4jAAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers wout ultee vfhKSjgAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Jochem Tolsma Iu23-90AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Paul M. de Graaf WvaJ-9IAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Mark Visser ItITloQAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers jaap dronkers p0ABWjAAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Emer Smyth ksHGzSYAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Ruud Luijkx 2vaELsQAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Walter Müller zETR4LEAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Renze Kolster 1YImw-wAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Tanja Traag UWhszHQAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Don Westerheijden MR3GtuUAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Muja Ardita 1o7Njf0AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Nicole Tieben v6Q7Xe0AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Andries De Grip b7N_BiEAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Lieselotte Blommaert RG54uasAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Richard Layte rF6qjPkAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Selina McCoy JzmAe9oAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Giampiero Passaretta D8Od9W0AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Margriet van Hek ZvLlx2EAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Tim Huijts -imfMCIAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Frank Kalter 8dnLY2UAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Lex Thijssen EvxAs5gAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Geert Driessen uTUCuH0AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Ben Jongbloed AHBT3I8AAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Barbara Belfi JOZG3IMAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Riccardo Welters rIHZQVIAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Madelon Jacobs t3A1A9QAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Jasper van Houten iR4UIwwAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Kim van Broekhoven 6vHMK9wAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers peer scheepers hPeXxvEAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Babs Jacobs aWbM9sIAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Luis Ortiz-Gervasi 2nbUBWsAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Rick de Graaff UjiKsNYAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Robert Tholen sG-KL7EAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Jos Sanders se-dJjwAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Manfred te Grotenhuis sIIqmAcAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Lex Borghans peeNxMAAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Ilse Laurijssen xpooTDUAAAAJ
TqKrXnMAAAAJ Maarten HJ Wolbers Marcel Lubbers 078qsZoAAAAJ
bDPtkIoAAAAJ Carlijn Bussemakers Gerbert Kraaykamp l8aM4jAAAAAJ
bDPtkIoAAAAJ Carlijn Bussemakers Niels Spierings cy3Ye6sAAAAJ
bDPtkIoAAAAJ Carlijn Bussemakers Kars van Oosterhout n9dm8b8AAAAJ
bDPtkIoAAAAJ Carlijn Bussemakers Jochem Tolsma Iu23-90AAAAJ
p3IwtT4AAAAJ Rob JM Franken NA NA
_ukytQYAAAAJ mustafa Inc NA NA
VCTvbTkAAAAJ Nella Geurts Marcel Lubbers 078qsZoAAAAJ
VCTvbTkAAAAJ Nella Geurts Niels Spierings cy3Ye6sAAAAJ
VCTvbTkAAAAJ Nella Geurts Tine Davids kDdV29EAAAAJ
izq-KNUAAAAJ Thijmen Jeroense NA NA
tFaMPOQAAAAJ Renae Sze Ming Loh NA NA
z6iMs-UAAAAJ Carly van Mensvoort NA NA
lkVq32sAAAAJ Kathrin Friederike Müller Margreth Lünenborg 9fX4DJoAAAAJ
lkVq32sAAAAJ Kathrin Friederike Müller Claudia Riesmeyer QBLbtAsAAAAJ
lkVq32sAAAAJ Kathrin Friederike Müller Annika Sehl TOqJjZEAAAAJ
lkVq32sAAAAJ Kathrin Friederike Müller Stephanie Geise R8A3In0AAAAJ
lkVq32sAAAAJ Kathrin Friederike Müller Melanie Magin WJel050AAAAJ
xE65HUcAAAAJ Klara Raiber Dorothée Behr twI7W8UAAAAJ
xE65HUcAAAAJ Klara Raiber LYDIA REPKE 4N3QXXUAAAAJ
fp99JAQAAAAJ Marlou Ramaekers NA NA
iR4UIwwAAAAJ Jasper van Houten NA NA
gs0li6MAAAAJ Jansje van Middendorp NA NA
KfLALRIAAAAJ Tijmen Weber NA NA


Pretty nice! We now have a dataframe with the gs_id ID of the focal author and their name plus their co-authors’ names (if they have chosen to list any) and their Google Scholar ID. In turn, we can use these Google Scholar IDs to go even one layer deeper. In other words, to get the co-authors’ co-authors. Note that we can also render this to be a 1.5 network if we drop all those nodes that are not immediately connected to the focal sociology staff members. Therefore, we need the unique co-author IDs from soc_collabs above, otherwise we would search for the same persons multiple times on Google Scholar. Therefore, we selected the unique authors above. We then loop again to find their co-authors. We also include and if statement: if we already know the collaboration network of a given author (if they are in the soc_collabs dataframe already), we don’t include those in the loop to spare some time scraping these data.

# then the names of those collaborators plus THEIR collaborators understand that we don't have
# names of them yet from the code above?
collabs_1deep <- list()
for (i in 1:length(soc_collabs_unique)) {

    time <- runif(1, 0, 3)
    Sys.sleep(time)

    if (!soc_collabs_unique[i] %in% soc_df$gs_id) {
        collabs_1deep[[i]] <- fcollabs(soc_collabs_unique[i], 1)

    }
}
collabs_1deep <- bind_rows(collabs_1deep)
collabs_1deep_unique <- unique(collabs_1deep[, 2])
collabs_1deep_unique <- collabs_1deep_unique[!is.na(collabs_1deep_unique)]
save(collabs_1deep, file = "addfiles/soc_collabs2.RData")  # you notice this takes a while, so we save the data here.
Table 8.19: Table 8.20: Sociology staff collaborators of collaborators
coauth coauth_id gs_id name
Hans-Peter Blossfeld F0kCgy8AAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Nicola Barban cspszw4AAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Felix Tropf dNMTdw0AAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Harold Snieder t034HooAAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Nicoletta Balbo Us2QJz4AAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Katia Begall e7zfTqMAAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Tanturri Maria Letizia xN3XevQAAAAJ HX9KQ5MAAAAJ Melinda C. Mills
René Veenstra _9OVrqMAAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Francesco C. Billari -JR6yo4AAAAJ HX9KQ5MAAAAJ Melinda C. Mills
Melinda C. Mills HX9KQ5MAAAAJ MW-rFm4AAAAJ Patrick Präg
Rafael Wittek nqv-il8AAAAJ MW-rFm4AAAAJ Patrick Präg
Katia Begall e7zfTqMAAAAJ MW-rFm4AAAAJ Patrick Präg
Andreas Baierl ypSrADgAAAAJ MW-rFm4AAAAJ Patrick Präg
Lea Ellwardt o_fMo3UAAAAJ MW-rFm4AAAAJ Patrick Präg
Christiaan Monden 96E8EncAAAAJ MW-rFm4AAAAJ Patrick Präg
Lindsay Richards DOu7U1MAAAAJ MW-rFm4AAAAJ Patrick Präg
Alexi Gugushvili i8GjBdQAAAAJ MW-rFm4AAAAJ Patrick Präg
Aleksi Karhula MGofGgwAAAAJ MW-rFm4AAAAJ Patrick Präg
Kieron Barclay SpfiACwAAAAJ MW-rFm4AAAAJ Patrick Präg
Jani Erola JF5aci0AAAAJ MW-rFm4AAAAJ Patrick Präg
Torkild Hovde Lyngstad 1wrj9nQAAAAJ MW-rFm4AAAAJ Patrick Präg
Dalton Conley 7y6DzMgAAAAJ MW-rFm4AAAAJ Patrick Präg
Michael Grätz mHQvWYgAAAAJ MW-rFm4AAAAJ Patrick Präg
Suzan Lewis f7wByDcAAAAJ MW-rFm4AAAAJ Patrick Präg
Marii Paskov JcP3024AAAAJ MW-rFm4AAAAJ Patrick Präg
Seongsoo Choi W11hVBYAAAAJ MW-rFm4AAAAJ Patrick Präg
Cecilia Potente FT772yoAAAAJ MW-rFm4AAAAJ Patrick Präg
Evelyn Ersanilli PxAjCaEAAAAJ MW-rFm4AAAAJ Patrick Präg
Moritz Hess nU0BeUAAAAAJ MW-rFm4AAAAJ Patrick Präg
Jana Mäcken qi7v3mYAAAAJ MW-rFm4AAAAJ Patrick Präg
Roland Verwiebe 20qavD4AAAAJ MW-rFm4AAAAJ Patrick Präg
Steffen Mau 9yk-jFMAAAAJ MW-rFm4AAAAJ Patrick Präg
Arnstein Aassve Y3mB0zUAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Tanturri Maria Letizia xN3XevQAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Daniele Vignoli PvqdS2UAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Stefano Mazzuco V8xXooEAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Ariane Pailhé Jh7OEykAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Anne Solaz OttZB_YAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Marco Le Moglie N4wGm40AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Dominique Anxo oJ6G5B4AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Giulia Fuochi ev7MVJwAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Gianni Betti mScVT04AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Chiara Rapallini VL957IcAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Lennart Flood 6m9g7BIAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Maria Sironi pHZrPycAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Francesco Mattioli n7X_ahgAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Fabrizia Mealli ukAwOdAAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Alexia Fürnkranz-Prskawetz sHpdNIsAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Kedir Iy72KPwAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Melinda C. Mills HX9KQ5MAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Katia Begall e7zfTqMAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Daria Mendola YzmMS8EAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Gianpiero Dalla Zuanna fNlpkCoAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Pierluigi Conzo YGFMQg8AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Marco Albertini GpTyFYoAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Francesca Luppi YlA8gnQAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Man Yee Kan K3771wkAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Francesco C. Billari -JR6yo4AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Viviana Patti Th0j7coAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Daniela Del Boca EWgvcP8AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Jani Erola JF5aci0AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Torkild Hovde Lyngstad 1wrj9nQAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Anne-Rigt Poortman fuS31Y0AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Lynn Prince Cooke io_0p9gAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Marika Jalovaara dz4_gLgAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Ettore Recchi gQI-qNcAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Elena Claudia Meroni PnEdEuYAAAAJ PUlRJy0AAAAJ Letizia Mencarini
James Raymo 5ibGwv0AAAAJ PUlRJy0AAAAJ Letizia Mencarini
Teresa Castro Martin xwnAuqsAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Mario Ricciardi GyrUqsQAAAAJ PUlRJy0AAAAJ Letizia Mencarini
Chiara Seghieri 2HNDNMYAAAAJ xN3XevQAAAAJ Tanturri Maria Letizia
Cheti Nicoletti smWWJQQAAAAJ xN3XevQAAAAJ Tanturri Maria Letizia
Donald Treiman DYZUS1wAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Paul M. de Graaf WvaJ-9IAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Ruud Luijkx 2vaELsQAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
wout ultee vfhKSjgAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Ineke Nagel L0UP8CYAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Niels Spierings cy3Ye6sAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Bernhard Nauck 1fZTMKIAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Lucinda Platt hLc_ptsAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
paul nieuwbeerta gm1vodsAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Efe Kerem Sozeri 34pCKYMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Sait Bayrakdar KPFh08IAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Matthijs Kalmijn OEMqUeAAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Tanja van der Lippe Rn56SIMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Karin Sanders l1BiM7EAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Nan Dirk de Graaf nTqzQ0oAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Bart Bakker XOaI_CIAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Aart C. Liefbroer mui-7rAAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Carolina V. Zuccotti rMNQk5AAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Inge Sieben uONmLWwAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Wilfred Uunk uMbCVn0AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Irma Mooi-Reci or Irma Mooi-Reçi or Irma Reci m5bG7jkAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Ineke Maas lHZlqNsAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Peter GM van der Heijden KFJz8nMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Herman G. van de Werfhorst amYIKXQAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
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Richard W Horobin ZQUo84wAAAAJ qY5xm1UAAAAJ Dr. ing. Peter O. Gerrits, senior anatomist
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Van Der Want 3fbE7PgAAAAJ qY5xm1UAAAAJ Dr. ing. Peter O. Gerrits, senior anatomist
Bart Meuleman WHsV0tYAAAAJ RZUS_2gAAAAJ Jaak Billiet
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Eldad Davidov Ic1jyiYAAAAJ RZUS_2gAAAAJ Jaak Billiet
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Peter Schmidt sHzMqs0AAAAJ RZUS_2gAAAAJ Jaak Billiet
Ineke Stoop QC8OV4UAAAAJ RZUS_2gAAAAJ Jaak Billiet
peer scheepers hPeXxvEAAAAJ RZUS_2gAAAAJ Jaak Billiet
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Piërre van de Laar p8iCm_oAAAAJ 3jUymSMAAAAJ Tom Heskes
Mohsen Ghafoorian 989WL-wAAAAJ 3jUymSMAAAAJ Tom Heskes
Bram Platel, PhD W13cBesAAAAJ 3jUymSMAAAAJ Tom Heskes
Ole Jensen ADvWcoUAAAAJ 3jUymSMAAAAJ Tom Heskes
Ali Bahramisharif ythtuiMAAAAJ 3jUymSMAAAAJ Tom Heskes
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Floris de Lange 5z1S51MAAAAJ 3jUymSMAAAAJ Tom Heskes
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Benjamin Neale OFVWBK0AAAAJ 3jUymSMAAAAJ Tom Heskes
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Florence d’Alché-Buc -qbL7z0AAAAJ 3jUymSMAAAAJ Tom Heskes
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Jan AJG van den Brand men37bsAAAAJ 3jUymSMAAAAJ Tom Heskes
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Atsuko Takashima VVtjfA8AAAAJ 3jUymSMAAAAJ Tom Heskes
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Cosmin Oancea RpmM52IAAAAJ 3jUymSMAAAAJ Tom Heskes
Kai Lars Polsterer GlE_sY0AAAAJ 3jUymSMAAAAJ Tom Heskes
Sinead Williamson Lt9LpLMAAAAJ 3jUymSMAAAAJ Tom Heskes
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jean-charles Languilaire D6MdwLgAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
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bea cantillon ovPLLuYAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
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Susanne Alm 2jGKzF0AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Flaviana Palmisano UAFtaAQAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Sebastian Sirén qqXKNLkAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Bernhard Ebbinghaus 3ppVQCMAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Anne Grete Tøge IVxXVSAAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Joakim Palme GndQYMIAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Jonathan Bradshaw 6fY5YFoAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Joël Machado QjGoeF0AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Jürgen Schupp 0Xl_7oUAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Ellen Ernst Kossek 3XF30OoAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
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Maria Metzing zl5hiUwAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Pär Dalén 4VMpGawfr0UC KA_HjwEAAAAJ Rense Nieuwenhuis
Tim Goedemé d-5pU4YAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Eriko Takano wU8IvRIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Marnix Medema BByKtkUAAAAJ sOtfTK8AAAAJ Rainer Breitling
ritsert jansen TfqoYWEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Andris Jankevics uOW7tEYAAAAJ sOtfTK8AAAAJ Rainer Breitling
David Gilbert DS-hZXgAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Tilmann Weber KuOZZSAAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Nigel Scrutton JZpcyMEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Darren Creek JXDhpZsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Yang Li, PhD hPuPdggAAAAJ sOtfTK8AAAAJ Rainer Breitling
David George Watson hZOzsxQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Michael A Fischbach HCtL9MYAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Monika Heiner esKBj4UAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Mohammad Tauqeer Alam r5ink-sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Simon Rogers 5EiC_wEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Pablo Carbonell 5tmd-LYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jingyuan Fu, PhD 7OHBkYMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Fiona Achcar ihLNP4AAAAAJ sOtfTK8AAAAJ Rainer Breitling
Adrian J Jervis 9yf56LcAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Jean-Claude Dujardin erTuBr4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Mark Girolami BSoawXwAAAAJ sOtfTK8AAAAJ Rainer Breitling
Morris Swertz TM9ZkeAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Neil Swainston bc32feUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Roy Goodacre Dszi0U8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Achuthanunni Chokkathukalam B88EQzQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Fangxin Hong M-lG3qsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Leonid Bystrykh tG0ZoVcAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jean-Loup Faulon 9j3cIHIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Francesco Del Carratore OhnD0rgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Barbara Bakker 9PIuH7gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jan Kammenga -2gNhzMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ellen Nollen laVWK6cAAAAJ sOtfTK8AAAAJ Rainer Breitling
vonk rj oqo_xqUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Katherine Anne Hollywood lDcCXRQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Martijn Dijkstra 2mxdV8sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Douglas Kell DOrAFFEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Daniel Krug 2t5k3wgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Cunyu Yan -Q8hQmEAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Nicholas JW Rattray T6iJhIoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tjakko van Ham Hw4_BhUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Marc G Chevrette VX3Laf8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Douglas A Mitchell arWBqN8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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Maria Vinaixa MBX-5O8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Joanne Chory EeEtku8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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Ben Wittner yNFHGG0AAAAJ sOtfTK8AAAAJ Rainer Breitling
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Poolman B GQObZz0AAAAJ sOtfTK8AAAAJ Rainer Breitling
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Matthias Mann NjzvH3kAAAAJ sOtfTK8AAAAJ Rainer Breitling
Harald Stenmark xHghKiQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Blagoy Blagoev UwIjITQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tobias Bauer mvIOGq8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Pjotr Prins 5ijHQRIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Rónán Daly JWTsNqgAAAAJ sOtfTK8AAAAJ Rainer Breitling
De Vos xZ64zKYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Twan America dVMXadkAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jane Ward 8lA5hOcAAAAJ sOtfTK8AAAAJ Rainer Breitling
M Koornneef mXptns8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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Andrew I. Su xPGhKkoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Chunlei Wu EmD8988AAAAJ sOtfTK8AAAAJ Rainer Breitling
Tim Wiltshire gosZHB8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Keith Matthews IVSIEvgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Christine Clayton Z9nx7UsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Manu Vanaerschot VqipSpgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Andriy Kovalchuk nHQZve8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Pieter C Dorrestein IsfIfVsAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Basten Snoek oYdq4PwAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Fabien Jourdan pbvEidYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Charles Ebikeme iz_YU64AAAAJ sOtfTK8AAAAJ Rainer Breitling
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EJ Blom m6Qw4Y4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Sacha van Hijum 3fgwGNgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jos Roerdink jCFYHlkAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Xusheng Wang, Ph.D.  eM3dr7gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Kirstyn Brunker e2aCxEMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Stefan Weidt QEBvbRAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Frédéric Bringaud tXSPCWMAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Ziga Zebec, PhD aTTVTdYAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Stefan Kol aieqQ5oAAAAJ sOtfTK8AAAAJ Rainer Breitling
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Konrad Zych DZAWbbwAAAAJ sOtfTK8AAAAJ Rainer Breitling
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mikael benson YAnoorQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Rainer Bischoff ABg1LxgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Frank Suits QmsJl0AAAAAJ sOtfTK8AAAAJ Rainer Breitling
PL Horvatovich T5-3_nQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ian Gilbert ucHYISkAAAAJ sOtfTK8AAAAJ Rainer Breitling
Helen S. Toogood 4a7UDmcAAAAJ sOtfTK8AAAAJ Rainer Breitling
Alan R Williams txPAhv0AAAAJ sOtfTK8AAAAJ Rainer Breitling
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NA NA prOk5EcAAAAJ Martin A. Tanner
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Irma Mooi-Reci or Irma Mooi-Reçi or Irma Reci m5bG7jkAAAAJ BaUO69MAAAAJ Inge Bleijenbergh
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Channah Herschberg SYyIm_YAAAAJ BaUO69MAAAAJ Inge Bleijenbergh
NA NA bC8tl_MAAAAJ Marloes de Lange
Paul Dekker gHuTzXcAAAAJ K51iiIAAAAAJ Tom van der Meer
peer scheepers hPeXxvEAAAAJ K51iiIAAAAAJ Tom van der Meer
Wouter van der Brug cJQGmHsAAAAJ K51iiIAAAAAJ Tom van der Meer
Jochem Tolsma Iu23-90AAAAJ K51iiIAAAAAJ Tom van der Meer
Manfred te Grotenhuis sIIqmAcAAAAJ K51iiIAAAAAJ Tom van der Meer
Erika van Elsas c77jhDkAAAAJ K51iiIAAAAAJ Tom van der Meer
Maurice Gesthuizen n6hiblQAAAAJ K51iiIAAAAAJ Tom van der Meer
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Armen Hakhverdian 1p8uveMAAAAJ K51iiIAAAAAJ Tom van der Meer
Eelco Harteveld D-95uTAAAAAJ K51iiIAAAAAJ Tom van der Meer
Erik van Ingen xK_MDL8AAAAJ K51iiIAAAAAJ Tom van der Meer
Huib Pellikaan mytLO24AAAAJ K51iiIAAAAAJ Tom van der Meer
Mérove Gijsberts QngQH7sAAAAJ K51iiIAAAAAJ Tom van der Meer
ben pelzer aLppskkAAAAJ K51iiIAAAAAJ Tom van der Meer
Jan W. van Deth ABvk6xUAAAAJ K51iiIAAAAAJ Tom van der Meer
Jaco Dagevos _d-TPGkAAAAJ K51iiIAAAAAJ Tom van der Meer
Sonja Zmerli vgR7YCAAAAAJ K51iiIAAAAAJ Tom van der Meer
Loes Aaldering shEs2hAAAAAJ K51iiIAAAAAJ Tom van der Meer
Tim Reeskens LwvZcWoAAAAJ K51iiIAAAAAJ Tom van der Meer
Henk van der Kolk SjieyBcAAAAJ K51iiIAAAAAJ Tom van der Meer
Martin Elff x6K1LBUAAAAJ K51iiIAAAAAJ Tom van der Meer
Mark Bovens 1iXhFlAAAAAJ K51iiIAAAAAJ Tom van der Meer
Patrick van Erkel EU2Waw0AAAAJ K51iiIAAAAAJ Tom van der Meer
Eric M. Uslaner M1Uj9qwAAAAJ K51iiIAAAAAJ Tom van der Meer
Catherine E. De Vries kK4r0GUAAAAJ K51iiIAAAAAJ Tom van der Meer
Marcel Coenders HH4PPBwAAAAJ K51iiIAAAAAJ Tom van der Meer
Marcel Lubbers 078qsZoAAAAJ K51iiIAAAAAJ Tom van der Meer
Sofie Marien S4pJrrQAAAAJ K51iiIAAAAAJ Tom van der Meer
Staffan Kumlin OjDQ5SYAAAAJ K51iiIAAAAAJ Tom van der Meer
Kristof Jacobs p_n5i1EAAAAJ K51iiIAAAAAJ Tom van der Meer
Floris Vermeulen pbp6BdIAAAAJ K51iiIAAAAAJ Tom van der Meer
Debra Minkoff vpM0XisAAAAJ K51iiIAAAAAJ Tom van der Meer
Emily M. Miltenburg u2cImq4AAAAJ K51iiIAAAAAJ Tom van der Meer
Lisanne de Blok eAp6k2oAAAAJ K51iiIAAAAAJ Tom van der Meer
Ruud Koole AYQAexkAAAAJ K51iiIAAAAAJ Tom van der Meer
Irene de Goede etoUcakAAAAJ K51iiIAAAAAJ Tom van der Meer
William A Maloney 6Ha5AtAAAAAJ K51iiIAAAAAJ Tom van der Meer
Marijn van Klingeren n992i-UAAAAJ K51iiIAAAAAJ Tom van der Meer
Rudy Andeweg taq-vsMAAAAJ K51iiIAAAAAJ Tom van der Meer
Rob Eisinga GDHdsXAAAAAJ K51iiIAAAAAJ Tom van der Meer
Roderik Rekker KSV2VJ4AAAAJ K51iiIAAAAAJ Tom van der Meer
Annemarie Walter rXhMUixG8fkC K51iiIAAAAAJ Tom van der Meer
Peter Van Aelst c-bB2qsAAAAJ K51iiIAAAAAJ Tom van der Meer
Loek Halman 0a0gqnwAAAAJ K51iiIAAAAAJ Tom van der Meer
Daphne van der Pas oCZMZ1MAAAAJ K51iiIAAAAAJ Tom van der Meer
Kai Arzheimer sq2lFhsAAAAJ K51iiIAAAAAJ Tom van der Meer
Jocelyn Evans iZhi03wAAAAJ K51iiIAAAAAJ Tom van der Meer
Myles Gould ndjbBv8AAAAJ K51iiIAAAAAJ Tom van der Meer
Theresa Kuhn BSz64J0AAAAJ K51iiIAAAAAJ Tom van der Meer
Henk LM Kox MfkjF3oAAAAJ K51iiIAAAAAJ Tom van der Meer
Maria Kranendonk Hr1xATMAAAAJ K51iiIAAAAAJ Tom van der Meer
Paula Thijs Ni3PX2wAAAAJ K51iiIAAAAAJ Tom van der Meer
Herman G. van de Werfhorst amYIKXQAAAAJ K51iiIAAAAAJ Tom van der Meer
Anna Kern vt2f6jMAAAAJ K51iiIAAAAAJ Tom van der Meer
René Bekkers K4nsiugAAAAJ K51iiIAAAAAJ Tom van der Meer
Philip van Praag 97UDV4cAAAAJ K51iiIAAAAAJ Tom van der Meer
Kees Aarts hiBD68oAAAAJ K51iiIAAAAAJ Tom van der Meer
Claes H de Vreese ddz5VQYAAAAJ K51iiIAAAAAJ Tom van der Meer
Joop van Holsteyn 4_Kvv0oAAAAJ K51iiIAAAAAJ Tom van der Meer
Inge Sieben uONmLWwAAAAJ K51iiIAAAAAJ Tom van der Meer
Carolien van Ham FZ66iE4AAAAJ K51iiIAAAAAJ Tom van der Meer
NA NA lXJuWe8AAAAJ “Heike Solga” or “H. Solga”
Victor Bekkers NotNGQUAAAAJ Nw8yFOkAAAAJ Bram Steijn
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J. Edelenbos psdMw_AAAAAJ Nw8yFOkAAAAJ Bram Steijn
Peter Leisink yt6i3iwAAAAJ Nw8yFOkAAAAJ Bram Steijn
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Ben S. Kuipers 322gAxkAAAAJ Nw8yFOkAAAAJ Bram Steijn
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Ariana Need AmFGGWEAAAAJ Nw8yFOkAAAAJ Bram Steijn
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wout ultee vfhKSjgAAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
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Maarten HJ Wolbers TqKrXnMAAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
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Pearl Dykstra LB5T-_0AAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
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Malina Voicu 00qYHA4AAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
NELLS - NEtherlands Longitudinal Lifecourse Study 2M7h07cAAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
Dominique Joye opHFWPYAAAAJ WvaJ-9IAAAAJ Paul M. de Graaf
NA NA -imfMCIAAAAJ Tim Huijts
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Caroline Dewilde Nl6ZO5cAAAAJ n_NQoe8AAAAJ Stéfanie André
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Mara A. Yerkes N_b-99cAAAAJ n_NQoe8AAAAJ Stéfanie André
Peter M Kruyen 4RvhI-AAAAAJ n_NQoe8AAAAJ Stéfanie André
Janna Besamusca VMZzBdkAAAAJ n_NQoe8AAAAJ Stéfanie André
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EPWA Jansen -xFLiHkAAAAJ n_NQoe8AAAAJ Stéfanie André
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Roos van der Zwan sKXOwkoAAAAJ n_NQoe8AAAAJ Stéfanie André
Maurice Gesthuizen n6hiblQAAAAJ n_NQoe8AAAAJ Stéfanie André
peer scheepers hPeXxvEAAAAJ n_NQoe8AAAAJ Stéfanie André
Ruud Muffels qvZa5TkAAAAJ n_NQoe8AAAAJ Stéfanie André
Niels Spierings cy3Ye6sAAAAJ n_NQoe8AAAAJ Stéfanie André
M. Helms-Lorenz qDfIWAwAAAAJ n_NQoe8AAAAJ Stéfanie André
R. Maulana 0f29mzkAAAAJ n_NQoe8AAAAJ Stéfanie André
Thelma de Jager 20S3xZYAAAAJ n_NQoe8AAAAJ Stéfanie André
Dr.Yulia Irnidayanti, M. Si UeuxVOEAAAAJ n_NQoe8AAAAJ Stéfanie André
Carmen Mª Fernández García V-ng6yIAAAAJ n_NQoe8AAAAJ Stéfanie André
Mª de las Mercedes Inda Caro YluGU20AAAAJ n_NQoe8AAAAJ Stéfanie André
Paul de Beer sXmSaLcAAAAJ n_NQoe8AAAAJ Stéfanie André
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James Ko DSNrJnUAAAAJ n_NQoe8AAAAJ Stéfanie André
Ronald Batenburg UK7nVSEAAAAJ n_NQoe8AAAAJ Stéfanie André
Hyungjun Suh (서형준) |kR vlpIAAAAJ |n_ Qoe8AAAAJ |St fanie André |
Jacques van der Meer _7CeOL4AAAAJ n_NQoe8AAAAJ Stéfanie André
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Michael S. Merry qIAG-WkAAAAJ uTUCuH0AAAAJ Geert Driessen
Kees de Bot I5Y4Bs8AAAAJ uTUCuH0AAAAJ Geert Driessen
Frans van der Slik 1zOc13kAAAAJ uTUCuH0AAAAJ Geert Driessen
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Roel J. Bosker -U1TQ5gAAAAJ uTUCuH0AAAAJ Geert Driessen
Ed Smeets NHWvbt0AAAAJ uTUCuH0AAAAJ Geert Driessen
Professor Jeff Bezemer 9l7FvoEAAAAJ uTUCuH0AAAAJ Geert Driessen
Eddie Denessen OojPCnoAAAAJ uTUCuH0AAAAJ Geert Driessen
Pim Valkenberg f92k5XwAAAAJ uTUCuH0AAAAJ Geert Driessen
Christine Teelken bwfxxhoAAAAJ uTUCuH0AAAAJ Geert Driessen
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MPC Werf, van der lJczIxUAAAAJ uTUCuH0AAAAJ Geert Driessen
Ludo Verhoeven fog_H1UAAAAJ uTUCuH0AAAAJ Geert Driessen
Janet van Hell NNGhm40AAAAJ uTUCuH0AAAAJ Geert Driessen
peer scheepers hPeXxvEAAAAJ uTUCuH0AAAAJ Geert Driessen
Maurice Gesthuizen n6hiblQAAAAJ uTUCuH0AAAAJ Geert Driessen
Cees Klaassen 5JiRQIwAAAAJ uTUCuH0AAAAJ Geert Driessen
PP Groenewegen EvRbWEAAAAAJ v6V0y2cAAAAJ Beate Volker
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martin van der gaag t8rRl2sAAAAJ v6V0y2cAAAAJ Beate Volker
Fenne M. Pinkster POpqk6oAAAAJ v6V0y2cAAAAJ Beate Volker
paul nieuwbeerta gm1vodsAAAAJ v6V0y2cAAAAJ Beate Volker
Jaap Nieuwenhuis 64i-jc8AAAAJ v6V0y2cAAAAJ Beate Volker
Kevin M Leander aroDUJgAAAAJ v6V0y2cAAAAJ Beate Volker
Maurice Gesthuizen n6hiblQAAAAJ v6V0y2cAAAAJ Beate Volker
Lieven JR Pauwels UNUREpMAAAAJ v6V0y2cAAAAJ Beate Volker
Wim Bernasco pVTfrE8AAAAJ v6V0y2cAAAAJ Beate Volker
FMHM DRIESSEN SS8jsBYAAAAJ v6V0y2cAAAAJ Beate Volker
stefan bogaerts duk9ShAAAAAJ v6V0y2cAAAAJ Beate Volker
NA NA G_FILmcAAAAJ William M. van der Veld
Merlin Schaeffer 4GibFFUAAAAJ CX9g1hsAAAAJ Jan Paul Heisig
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Bradley P. Turnwald, PhD oWP3JDEAAAAJ uZg9l58AAAAJ Dan Jurafsky
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Elijah Mayfield fjeYWzkAAAAJ uZg9l58AAAAJ Dan Jurafsky
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Alana Conner, PhD tYkklmMAAAAJ uZg9l58AAAAJ Dan Jurafsky
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Daniel Levy canwhPgAAAAJ uZg9l58AAAAJ Dan Jurafsky
Chu-Ren Huang 黄居仁 |zP DNqgAAAAJ |uZ 9l58AAAAJ |Da Jurafsky |
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Lecia Barker fzNnkykAAAAJ uZg9l58AAAAJ Dan Jurafsky
Steven Skiena fnE2dSoAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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Salvatore Giorgi 0O7xQLkAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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Yatin Chaudhary g4m2iUAAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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Hinrich Schütze qIL9dWUAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
Hang Jiang 9qvTNQ4AAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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Abdellah Fourtassi d4LZM90AAAAJ bCwtPMUAAAAJ Vivek Kulkarni
Yuxing Chen r5mtDzoAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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Dzmitry Bahdanau Nq0dVMcAAAAJ bCwtPMUAAAAJ Vivek Kulkarni
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danah boyd BkGE4AsAAAAJ P0XTYBEAAAAJ Nicole B. Ellison
Jessica Vitak RC9bN7kAAAAJ P0XTYBEAAAAJ Nicole B. Ellison
Rebecca Gray, PhD KR505DcAAAAJ P0XTYBEAAAAJ Nicole B. Ellison
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Jennifer Gibbs lQYOM9AAAAAJ P0XTYBEAAAAJ Nicole B. Ellison
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Chih-Hui Lai 0DtgZGAAAAAJ P0XTYBEAAAAJ Nicole B. Ellison
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peer scheepers hPeXxvEAAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
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Pablo Gracia kCjyRt8AAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
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Beate Volker v6V0y2cAAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
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Jasper van de Pol oo9-eU0AAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
Arjen Leerkes a_n_Ng8AAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
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N Notten NakYfX4AAAAJ 2M7h07cAAAAJ NELLS - NEtherlands Longitudinal Lifecourse Study
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alan warde 9N0Au5kAAAAJ 9r4Ase4AAAAJ Mike Savage
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Jelle Sijtsema ZvVEsxUAAAAJ _9OVrqMAAAAJ René Veenstra
Miranda Sentse NCvDHakAAAAJ _9OVrqMAAAAJ René Veenstra
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Catharina A. Hartman tNp9BUsAAAAJ _9OVrqMAAAAJ René Veenstra
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Henning Tiemeier yFFtlX4AAAAJ _9OVrqMAAAAJ René Veenstra
Antonius Cillessen bbGSOyoAAAAJ _9OVrqMAAAAJ René Veenstra
Tom Snijders xqefLxQAAAAJ _9OVrqMAAAAJ René Veenstra
Andreas Flache dt9jPJ0AAAAJ _9OVrqMAAAAJ René Veenstra
z harakeh NwvhRKcAAAAJ _9OVrqMAAAAJ René Veenstra
Marina Verlinden 85bYk7oAAAAJ _9OVrqMAAAAJ René Veenstra
Lydia Laninga-Wijnen 2QMRfbsAAAAJ _9OVrqMAAAAJ René Veenstra
Dennis Raven ms_bnhEAAAAJ _9OVrqMAAAAJ René Veenstra
PW Jansen KJWU9WsAAAAJ _9OVrqMAAAAJ René Veenstra
Anke Munniksma WRIjGkYAAAAJ _9OVrqMAAAAJ René Veenstra
Maja Dekovic 6a3cMAwAAAAJ _9OVrqMAAAAJ René Veenstra
J. Ashwin Rambaran, Ph.D.  zq5jpCQAAAAJ _9OVrqMAAAAJ René Veenstra
Tessa ML Kaufman _LsY5vQAAAAJ _9OVrqMAAAAJ René Veenstra
Judith Rosmalen mJJu06wAAAAJ _9OVrqMAAAAJ René Veenstra
Tobias H. Stark UH6UvgYAAAAJ _9OVrqMAAAAJ René Veenstra
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Katya Ivanova EgMYeqMAAAAJ _9OVrqMAAAAJ René Veenstra
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Noona Kiuru aqQiTV8AAAAJ _9OVrqMAAAAJ René Veenstra
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Harriette Riese z_DDrmYAAAAJ _9OVrqMAAAAJ René Veenstra
YHM van den Berg fZDdqnkAAAAJ _9OVrqMAAAAJ René Veenstra
Coby van Niejenhuis gQRqn_oAAAAJ _9OVrqMAAAAJ René Veenstra
Claire F. Garandeau tXAteoUAAAAJ _9OVrqMAAAAJ René Veenstra
Wim Meeus 66GFo2kAAAAJ _9OVrqMAAAAJ René Veenstra
Patricia H. Hawley HgsHwUcAAAAJ _9OVrqMAAAAJ René Veenstra
Christian Berger -5qMVbcAAAAJ _9OVrqMAAAAJ René Veenstra
Aart Franken ooL741EAAAAJ _9OVrqMAAAAJ René Veenstra
Noel Card MwjtZS8AAAAJ _9OVrqMAAAAJ René Veenstra
Catherine Herba I6OKxFwAAAAJ _9OVrqMAAAAJ René Veenstra
Theo Stijnen 2Em2YS8AAAAJ _9OVrqMAAAAJ René Veenstra
Derek Kreager 9c6_ChYAAAAJ _9OVrqMAAAAJ René Veenstra
Peng Wang FWgnwbIAAAAJ _9OVrqMAAAAJ René Veenstra
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Jaana Juvonen lCmz818AAAAJ _9OVrqMAAAAJ René Veenstra
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Davide Barrera QGE9fJUAAAAJ _9OVrqMAAAAJ René Veenstra
Bram Orobio de Castro 5wirA5UAAAAJ _9OVrqMAAAAJ René Veenstra
Sabine Stoltz hAxa8b0AAAAJ _9OVrqMAAAAJ René Veenstra
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Marianne Hooijsma 6taQYiAAAAAJ _9OVrqMAAAAJ René Veenstra
Ank Ringoot, PhD dnY6tHcAAAAJ _9OVrqMAAAAJ René Veenstra
Bertus F. Jeronimus Wxaac_gAAAAJ _9OVrqMAAAAJ René Veenstra
Scott D. Gest NDUj_mUAAAAJ _9OVrqMAAAAJ René Veenstra
Akhgar Ghassabian TN2uiOoAAAAJ _9OVrqMAAAAJ René Veenstra
Diego Palacios o3EWqIoAAAAJ _9OVrqMAAAAJ René Veenstra
JM Buil prrTM18AAAAJ _9OVrqMAAAAJ René Veenstra
Robert Althoff m-DOgLkAAAAJ _9OVrqMAAAAJ René Veenstra
scholte rhj 4SeWqfwAAAAJ _9OVrqMAAAAJ René Veenstra
Arunima Roy JxcfZ-0AAAAJ _9OVrqMAAAAJ René Veenstra
Allison Ryan cfGakS0AAAAJ _9OVrqMAAAAJ René Veenstra
Vincent Busch qxG2m2gAAAAJ _9OVrqMAAAAJ René Veenstra
Judith JM Rijnhart X1h5OKIAAAAJ _9OVrqMAAAAJ René Veenstra
Herman G. van de Werfhorst amYIKXQAAAAJ _9OVrqMAAAAJ René Veenstra
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Susan Branje JAIJnFgAAAAJ _9OVrqMAAAAJ René Veenstra
Margot Peeters omLxo6MAAAAJ _9OVrqMAAAAJ René Veenstra
jaap dronkers p0ABWjAAAAAJ _9OVrqMAAAAJ René Veenstra
Brett Laursen yVoiZ1kAAAAJ _9OVrqMAAAAJ René Veenstra
Renske Verweij vAFgHR4AAAAJ _9OVrqMAAAAJ René Veenstra
Dorottya Kisfalusi ztJJkGIAAAAJ _9OVrqMAAAAJ René Veenstra
Esther Bouma ETFH65EAAAAJ _9OVrqMAAAAJ René Veenstra
Jennifer Watling Neal kcD2PeYAAAAJ _9OVrqMAAAAJ René Veenstra
Rosario Ortega-Ruiz IMx49nMAAAAJ _9OVrqMAAAAJ René Veenstra
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Ana Bravo WfiHTfUAAAAJ _9OVrqMAAAAJ René Veenstra
Nardi Steverink CJUasEkAAAAJ _9OVrqMAAAAJ René Veenstra
Eleonora Marucci lBmx9J_iGqIC _9OVrqMAAAAJ René Veenstra
Bernadette Paula Luengo Kanacri dyvEELIAAAAJ _9OVrqMAAAAJ René Veenstra
Cristóbal Villalobos Dintrans 2Luja0YAAAAJ _9OVrqMAAAAJ René Veenstra
Ernesto Trevino JFvRc2sAAAAJ _9OVrqMAAAAJ René Veenstra
Harold Snieder t034HooAAAAJ _9OVrqMAAAAJ René Veenstra
Felix Tropf dNMTdw0AAAAJ _9OVrqMAAAAJ René Veenstra
Katariina Salmela-Aro Wo7Pk4wAAAAJ _9OVrqMAAAAJ René Veenstra
William Browne yEHJg0AAAAAJ _9OVrqMAAAAJ René Veenstra
Laura Baams FexSK88AAAAJ _9OVrqMAAAAJ René Veenstra
Wouter Kiekens rGpsLoYAAAAJ _9OVrqMAAAAJ René Veenstra
DD Bergen, van oO6wL9YAAAAJ _9OVrqMAAAAJ René Veenstra
Marloes Hendrickx ordB1YQAAAAJ _9OVrqMAAAAJ René Veenstra
Maarten Tim Mainhard mgRXYMwAAAAJ _9OVrqMAAAAJ René Veenstra
Danelien van Aalst I0-TFHcAAAAJ _9OVrqMAAAAJ René Veenstra
Wim Van den Noortgate W9bVpfEAAAAJ _9OVrqMAAAAJ René Veenstra
Berna Güroğlu MMfN78QAAAAJ _9OVrqMAAAAJ René Veenstra
Willem van Rhenen MpmQkngAAAAJ _9OVrqMAAAAJ René Veenstra
Stefania A. Barzeva BlE8bL4AAAAJ _9OVrqMAAAAJ René Veenstra
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Simon Venema bcYVTYEAAAAJ _9OVrqMAAAAJ René Veenstra
Marieke Haan qTbDPO4AAAAJ _9OVrqMAAAAJ René Veenstra
Melanie J. Zimmer-Gembeck SDw2OdgAAAAJ _9OVrqMAAAAJ René Veenstra
TRAILS mhFIkAMAAAAJ _9OVrqMAAAAJ René Veenstra
Floor van Oort 7Gz01bcAAAAJ _9OVrqMAAAAJ René Veenstra
stefan bogaerts duk9ShAAAAAJ _9OVrqMAAAAJ René Veenstra
Mitch Prinstein, Ph.D.  Jbdt0H4AAAAJ _9OVrqMAAAAJ René Veenstra
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Jonathan Kelley 2h8EhAIAAAAJ p0ABWjAAAAAJ jaap dronkers
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Frans van Vught FlE9wJkAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Bjørn Stensaker IS2qWfEAAAAJ MR3GtuUAAAAJ Don Westerheijden
Jürgen Enders nJtreYUAAAAJ MR3GtuUAAAAJ Don Westerheijden
Leon Cremonini AxN6V80AAAAJ MR3GtuUAAAAJ Don Westerheijden
Harry de Boer 5ZIKbfwAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Liudvika Leišytė zWjzPnQAAAAJ MR3GtuUAAAAJ Don Westerheijden
Maria João Rosa aWcsUlQAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Frans Kaiser Gau8JrIAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Paul Benneworth p4ks57gAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Maarten HJ Wolbers TqKrXnMAAAAJ MR3GtuUAAAAJ Don Westerheijden
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Myroslava Hladchenko (Мирослава Гладченко, Мирослава Авраменко) JK6X6kcAAAAJ MR3GtuUAAAAJ Don Westerheijden
alberto amaral 3IFSQ6oAAAAJ MR3GtuUAAAAJ Don Westerheijden
Airi Rovio-Johansson p6W8pUEAAAAJ MR3GtuUAAAAJ Don Westerheijden
Tibor Csizmadia ER75T98AAAAJ MR3GtuUAAAAJ Don Westerheijden
Eric Beerkens UrVsYxAAAAAJ MR3GtuUAAAAJ Don Westerheijden
Dominik Antonowicz ugRoza0AAAAJ MR3GtuUAAAAJ Don Westerheijden
Dave Huitema fbmO4aMAAAAJ MR3GtuUAAAAJ Don Westerheijden
Andrea Kottmann BEp4XOwAAAAJ MR3GtuUAAAAJ Don Westerheijden
Paradeise Catherine wje3AuoAAAAJ MR3GtuUAAAAJ Don Westerheijden
Jussi Välimaa VH3NkdwAAAAJ MR3GtuUAAAAJ Don Westerheijden
Anne Corbett IoRt3UYAAAAJ MR3GtuUAAAAJ Don Westerheijden
Nadine Zeeman lGWVyyYAAAAJ MR3GtuUAAAAJ Don Westerheijden
Manuel Souto-Otero hiYwxFQAAAAJ MR3GtuUAAAAJ Don Westerheijden
Guy Neave GUoFYmkAAAAJ MR3GtuUAAAAJ Don Westerheijden
Aleš Vlk hN2TIOcAAAAJ MR3GtuUAAAAJ Don Westerheijden
Maarja Beerkens RUG4vO8AAAAJ MR3GtuUAAAAJ Don Westerheijden
Harry Boer vGiFYU8AAAAJ MR3GtuUAAAAJ Don Westerheijden
Emanuela Reale 0InLmQIAAAAJ MR3GtuUAAAAJ Don Westerheijden
Marita Jacob 0SsL15IAAAAJ v6Q7Xe0AAAAJ Nicole Tieben
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Nicole Biedinger uEGdTWMAAAAJ v6Q7Xe0AAAAJ Nicole Tieben
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Cathal McCrory RXRDjeUAAAAJ rF6qjPkAAAAJ Richard Layte
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Janas Harrington iL4EQPMAAAAJ rF6qjPkAAAAJ Richard Layte
Karen Morgan 0Kd3UhUAAAAJ rF6qjPkAAAAJ Richard Layte
Ronán M Conroy qdUHYc8AAAAJ rF6qjPkAAAAJ Richard Layte
Michal Molcho tY25SyEAAAAJ rF6qjPkAAAAJ Richard Layte
Margaret M Barry djBWy9cAAAAJ rF6qjPkAAAAJ Richard Layte
Anne Hickey cRUYxzQAAAAJ rF6qjPkAAAAJ Richard Layte
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Brenda Gannon yyXvNsgAAAAJ rF6qjPkAAAAJ Richard Layte
Eithne Sexton XurhxOYAAAAJ rF6qjPkAAAAJ Richard Layte
Mark Ward LLIJaeUAAAAJ rF6qjPkAAAAJ Richard Layte
Patricia Kearney H5MRdLEAAAAJ rF6qjPkAAAAJ Richard Layte
Charles Normand _G3AHZYAAAAJ rF6qjPkAAAAJ Richard Layte
Dr Eimear Keane Y3HV8IwAAAAJ rF6qjPkAAAAJ Richard Layte
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Paolo Vineis h0SlOB4AAAAJ rF6qjPkAAAAJ Richard Layte
Didier Fouarge U7oKjvsAAAAJ rF6qjPkAAAAJ Richard Layte
Aoife Brick kSXvBfkAAAAJ rF6qjPkAAAAJ Richard Layte
Tony Fahey Pdg2JyEAAAAJ rF6qjPkAAAAJ Richard Layte
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Grainne Cousins bzatrFkAAAAJ rF6qjPkAAAAJ Richard Layte
Mika Kivimaki NrvVnX4AAAAJ rF6qjPkAAAAJ Richard Layte
Silvia Stringhini w6EeUCQAAAAJ rF6qjPkAAAAJ Richard Layte
Mauricio Avendano 923pgOUAAAAJ rF6qjPkAAAAJ Richard Layte
Emilie Courtin VkyklK8AAAAJ rF6qjPkAAAAJ Richard Layte
Emer Smyth ksHGzSYAAAAJ JzmAe9oAAAAJ Selina McCoy
Merike Darmody V30ZLvMAAAAJ JzmAe9oAAAAJ Selina McCoy
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Richard Layte rF6qjPkAAAAJ JzmAe9oAAAAJ Selina McCoy
Elish Kelly 8NE91hcAAAAJ JzmAe9oAAAAJ Selina McCoy
Michael Shevlin 7tsUxlsAAAAJ JzmAe9oAAAAJ Selina McCoy
Seamus Mcguinness jKPstIgAAAAJ JzmAe9oAAAAJ Selina McCoy
Adele Bergin X-ZCTSUAAAAJ JzmAe9oAAAAJ Selina McCoy
Elizabeth Nixon P5y-_E0AAAAJ JzmAe9oAAAAJ Selina McCoy
Lorraine Swords AOhGfHAAAAAJ JzmAe9oAAAAJ Selina McCoy
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Adele Whelan HCDTRYUAAAAJ JzmAe9oAAAAJ Selina McCoy
Ciarán Mac Domhnaill UKFnlPYAAAAJ JzmAe9oAAAAJ Selina McCoy
John Cullinan jf69VSYAAAAJ JzmAe9oAAAAJ Selina McCoy
Darragh Flannery FLCZCpUAAAAJ JzmAe9oAAAAJ Selina McCoy
Helen Russell aGGfrogAAAAJ JzmAe9oAAAAJ Selina McCoy
Gillian Kingston tBmsnRIAAAAJ JzmAe9oAAAAJ Selina McCoy
Gretta Mohan t3jCRNoAAAAJ JzmAe9oAAAAJ Selina McCoy
Tony Fahey Pdg2JyEAAAAJ JzmAe9oAAAAJ Selina McCoy
Christopher Whelan -CjQfgoAAAAJ JzmAe9oAAAAJ Selina McCoy
Seraphim Dempsey 4oB0L2oAAAAJ JzmAe9oAAAAJ Selina McCoy
Naomi Tyrrell WNeuuEMAAAAJ JzmAe9oAAAAJ Selina McCoy
Richard Rose fa9NwWEAAAAJ JzmAe9oAAAAJ Selina McCoy
Ann Marcus-Quinn PhD TNLhoUQAAAAJ JzmAe9oAAAAJ Selina McCoy
Tríona Hourigan PhD l9l5buIAAAAJ JzmAe9oAAAAJ Selina McCoy
Mary Silles 5iJb7psAAAAJ JzmAe9oAAAAJ Selina McCoy
Raffaele Grotti P56272MAAAAJ JzmAe9oAAAAJ Selina McCoy
Marie Hyland CmVXirwAAAAJ JzmAe9oAAAAJ Selina McCoy
Kyriakides, L. -u7iAfEAAAAJ JzmAe9oAAAAJ Selina McCoy
Paul F Conway OspYBoEAAAAJ JzmAe9oAAAAJ Selina McCoy
Philip Lane 8iXVkrEAAAAJ JzmAe9oAAAAJ Selina McCoy
John Walsh rocNORwAAAAJ JzmAe9oAAAAJ Selina McCoy
Bryan Coyne sN75zjEAAAAJ JzmAe9oAAAAJ Selina McCoy
Jan Skopek Mmo1hFkAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Moris Triventi c6QxiwkAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Maarten HJ Wolbers TqKrXnMAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Mark Visser ItITloQAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Paolo Trivellato cCB-WG4AAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Giorgio Vittadini 1ExmxDkAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Stefano Verzillo _Fmv6cQAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Jesper Jelle Rözer NZdQUWwAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
Herman G. van de Werfhorst amYIKXQAAAAJ D8Od9W0AAAAJ Giampiero Passaretta
NA NA 8dnLY2UAAAAJ Frank Kalter
Bram Lancee WLYKP4sAAAAJ EvxAs5gAAAAJ Lex Thijssen
Marcel Coenders HH4PPBwAAAAJ EvxAs5gAAAAJ Lex Thijssen
Susanne Veit G6YLha0AAAAJ EvxAs5gAAAAJ Lex Thijssen
Maarten HJ Wolbers TqKrXnMAAAAJ EvxAs5gAAAAJ Lex Thijssen
Maurice Gesthuizen n6hiblQAAAAJ EvxAs5gAAAAJ Lex Thijssen
Hans Vossensteyn C6l60q8AAAAJ AHBT3I8AAAAJ Ben Jongbloed
Frans Kaiser Gau8JrIAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Paul Benneworth p4ks57gAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Harry de Boer 5ZIKbfwAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Don Westerheijden MR3GtuUAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Jon File gybMFYIAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Benedetto Lepori -J6opjAAAAAJ AHBT3I8AAAAJ Ben Jongbloed
alberto amaral 3IFSQ6oAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Pedro Teixeira Vl2eQegAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Elmer Sterken 0HANvIwAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Guy Neave GUoFYmkAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Eric Beerkens UrVsYxAAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Robert JW Tijssen GbQBFhAAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Peter van der Sijde NcW5-SYAAAAJ AHBT3I8AAAAJ Ben Jongbloed
Harry Boer vGiFYU8AAAAJ AHBT3I8AAAAJ Ben Jongbloed
NA NA JOZG3IMAAAAJ Barbara Belfi
NA NA rIHZQVIAAAAJ Riccardo Welters
NA NA t3A1A9QAAAAJ Madelon Jacobs
NA NA 6vHMK9wAAAAJ Kim van Broekhoven
NA NA aWbM9sIAAAAJ Babs Jacobs
NA NA 2nbUBWsAAAAJ Luis Ortiz-Gervasi
NA NA UjiKsNYAAAAJ Rick de Graaff
NA NA sG-KL7EAAAAJ Robert Tholen
NA NA se-dJjwAAAAJ Jos Sanders
NA NA peeNxMAAAAAJ Lex Borghans
NA NA xpooTDUAAAAJ Ilse Laurijssen
NA NA n9dm8b8AAAAJ Kars van Oosterhout
Francien Th. M. van Driel d309S4sAAAAJ kDdV29EAAAAJ Tine Davids
Ruerd Ruben kjKAqKgAAAAJ kDdV29EAAAAJ Tine Davids
Melissa Siegel H40wmpAAAAAJ kDdV29EAAAAJ Tine Davids
Hein de Haas nJno7ZAAAAAJ kDdV29EAAAAJ Tine Davids
karin willemse HUqQ8rQAAAAJ kDdV29EAAAAJ Tine Davids
Lothar Smith JvsznqgAAAAJ kDdV29EAAAAJ Tine Davids
Marianne H. Marchand RHULjqIAAAAJ kDdV29EAAAAJ Tine Davids
Lau Schulpen 65LuxrMAAAAJ kDdV29EAAAAJ Tine Davids
NA NA 9fX4DJoAAAAJ Margreth Lünenborg
NA NA QBLbtAsAAAAJ Claudia Riesmeyer
Alessio Cornia KNvolXcAAAAJ TOqJjZEAAAAJ Annika Sehl
Rasmus Kleis Nielsen Q_SVygEAAAAJ TOqJjZEAAAAJ Annika Sehl
Teresa Naab 1MnEjW0AAAAJ TOqJjZEAAAAJ Annika Sehl
Dr. Stephan Weichert G-4ArzsAAAAJ TOqJjZEAAAAJ Annika Sehl
Felix M. Simon JBiGIG0AAAAJ TOqJjZEAAAAJ Annika Sehl
Claudia Riesmeyer QBLbtAsAAAAJ TOqJjZEAAAAJ Annika Sehl
Richard Fletcher g-1qDAUAAAAJ TOqJjZEAAAAJ Annika Sehl
Kathrin Friederike Müller lkVq32sAAAAJ TOqJjZEAAAAJ Annika Sehl
Melanie Magin WJel050AAAAJ TOqJjZEAAAAJ Annika Sehl
Lucas Graves zYci-JsAAAAJ TOqJjZEAAAAJ Annika Sehl
Robert G. Picard bHM4E_EAAAAJ TOqJjZEAAAAJ Annika Sehl
Stephanie Geise R8A3In0AAAAJ TOqJjZEAAAAJ Annika Sehl
Ulrike Klinger JMclx94AAAAJ TOqJjZEAAAAJ Annika Sehl
Schroeder, Ralph f6vRNDcAAAAJ TOqJjZEAAAAJ Annika Sehl
Sarah Anne Ganter 606WCH0AAAAJ TOqJjZEAAAAJ Annika Sehl
NA NA R8A3In0AAAAJ Stephanie Geise
Pascal Jürgens 15fp21UAAAAJ WJel050AAAAJ Melanie Magin
Stefan Geiss NQHP14MAAAAJ WJel050AAAAJ Melanie Magin
Joerg Hassler -CwlQGcAAAAJ WJel050AAAAJ Melanie Magin
Uta Russmann Tx7uQk4AAAAJ WJel050AAAAJ Melanie Magin
Olaf Jandura EXG-yNEAAAAJ WJel050AAAAJ Melanie Magin
Marcus Maurer q1-nBsgAAAAJ WJel050AAAAJ Melanie Magin
Peter Maurer TuzELewAAAAJ WJel050AAAAJ Melanie Magin
Annika Sehl TOqJjZEAAAAJ WJel050AAAAJ Melanie Magin
Claudia Riesmeyer QBLbtAsAAAAJ WJel050AAAAJ Melanie Magin
Kathrin Friederike Müller lkVq32sAAAAJ WJel050AAAAJ Melanie Magin
Vicente Fenoll yl_NfQ4AAAAJ WJel050AAAAJ Melanie Magin
Uwe Hasebrink lCl8D_sAAAAJ WJel050AAAAJ Melanie Magin
Dominique Heinbach ftQa33gAAAAJ WJel050AAAAJ Melanie Magin
Linards Udris fi9HZpUAAAAJ WJel050AAAAJ Melanie Magin
Sven Engesser eK0M_mAAAAAJ WJel050AAAAJ Melanie Magin
Marton Bene x9mctikAAAAJ WJel050AAAAJ Melanie Magin
Petter Bae Brandtzaeg lOSmypYAAAAJ WJel050AAAAJ Melanie Magin
Bjørn Magnus Mathisen nVAJ8o4AAAAJ WJel050AAAAJ Melanie Magin
Christian Oggolder gJ6pDQkAAAAJ WJel050AAAAJ Melanie Magin
Paweł Baranowski MGaFcYsAAAAJ WJel050AAAAJ Melanie Magin
Simon Kruschinski kdLjzS4AAAAJ WJel050AAAAJ Melanie Magin
David Nicolas Hopmann z0fOkewAAAAJ WJel050AAAAJ Melanie Magin
Stephanie Geise R8A3In0AAAAJ WJel050AAAAJ Melanie Magin
Ulrike Klinger JMclx94AAAAJ WJel050AAAAJ Melanie Magin
Andrea Ceron VrhaWUUAAAAJ WJel050AAAAJ Melanie Magin
Darren Lilleker R4tKadAAAAAJ WJel050AAAAJ Melanie Magin
Martin Krieg nu9pmrEAAAAJ WJel050AAAAJ Melanie Magin
NA NA twI7W8UAAAAJ Dorothée Behr
NA NA 4N3QXXUAAAAJ LYDIA REPKE


Now we have a data frame with the self-declared co-authors of sociology staff and a dataframe of co-authors’ their co-authors. Together, these can make a big edgelist of co-authors. Note, however, that the combinations of these dataframes make for a rather large network. If we add the sociology staff, their collaborators, and then their “one-deep” co-authors, we end up with around 2,6K authors! Perhaps a bit too big of a network to visualize? We shall see later. Nevertheless, we can now go provide some nice statistics about this network.

By just eyeballing the data there are some suspicious outliers in soc_df: Muller and Firat and Franken have suspiciously many citations for PhD candidates. By looking up their Google Scholar profiles, we find that both have the wrong Google Scholar ID attached to them (errors that are more likely to occur with common names when querying Google Scholar). So we deleted them from the data. The rest seems to have worked fine, the amount of data corruption is thus 2/34. You can also compare the Google Scholar profile names to the last_name and first_name columns so as to see which scraped profiles do not match the anchor dataframe.

for (i in c("_ukytQYAAAAJ", "lkVq32sAAAAJ", "p3IwtT4AAAAJ")) {
    # drop google scholar ids that look suspiciously productive
    soc_df <- soc_df[!soc_df$gs_id == i, ]
    soc_df_publications <- soc_df_publications[!(soc_df_publications$gs_id == i), ]
    soc_staff_cit <- soc_staff_cit[!(soc_staff_cit$gs_id == i), ]
    soc_collabs <- soc_collabs[!(soc_collabs$gs_id == i), ]
}

8.6.5 Data exploration

How about the network stuff? We actually have data that are already in a pretty nice format, the edgelist. Can we show a “friendship paradox” in these data? Let’s first get some additional information in the data frame with the collaborators. We first find the number of co-authors of sociology staff. Then, we find the average number of co-authors of sociology staff’s co-authors. Finally, we provide averages of that: seems no friendship paradox here for the 20 sociology staff members that list their co-authors on Google Scholar!

Friendship paradox: Your friends have more friends than you do (Feld 1991).

# get num co-authors of soc staff
num_coauth_soc <- soc_collabs[!is.na(soc_collabs$coauth), ]  # drop the NAs
num_coauth_soc <- data.frame(table(num_coauth_soc$gs_id))  # Number of coauthors per google scholar id of soc staff

# get average number of co-authors of soc staff's co-authors
num_coauth_col <- data.frame(table(collabs_1deep$gs_id))  # Number of coauthors per google scholar id of coauthors
friend_par <- left_join(soc_collabs, num_coauth_col, by = c(coauth_id = "Var1"))
friend_par <- friend_par[!is.na(friend_par$coauth), ]  # drop NAs again
friend_par <- friend_par[!is.na(friend_par$Freq), ]
friend_par <- setDT(friend_par)[, mean(Freq), by = gs_id]  # average per gs_id of soc staff

friend_par <- left_join(friend_par, num_coauth_soc, by = c(gs_id = "Var1"))  # left join to num_coauth_soc
names(friend_par) <- c("gs_id", "col_collabs", "soc_collabs")  # better column names

# And find the differences! Seems no friendship paradox here: that is, no more friends of friends
# than my own friends
summary(friend_par$soc_collabs)  # their collaborators' co-authors, 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    2.00    7.00   13.00   20.05   29.50   75.00
summary(friend_par$col_collabs)  # staff members
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   9.328  13.600  13.700  17.962  37.533
t.test(friend_par$col_collabs, friend_par$soc_collabs)  # no paradox it seems...
#> 
#>  Welch Two Sample t-test
#> 
#> data:  friend_par$col_collabs and friend_par$soc_collabs
#> t = -1.3256, df = 24.43, p-value = 0.1972
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#>  -16.23330   3.52866
#> sample estimates:
#> mean of x mean of y 
#>  13.70031  20.05263


Who among sociology staff is cited often? Note how we start with making total_cites numeric. Then we call a function in ggplot2 to make a nice plot. Note how I call a theme where I rotate the x-axis labels with 90 degrees, otherwise they would overlap heavily. Note how we use the function ggplot, a function of the package ggplot2 in which you can visualize data with very consistent code.

soc_df$total_cites <- as.numeric(soc_df$total_cites)
ggplot(soc_df) + geom_bar(aes(x = reorder(last_name, -total_cites), y = total_cites), stat = "identity") +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

Wow, Peer is sure cited a lot. Can we drop those often cited folks? Say, those cited more than 2K times?

ggplot(soc_df[!soc_df$total_cites>2000,]) + # note the subsetting of the data!
      geom_bar(aes(x=reorder(last_name, -total_cites), y=total_cites), stat = "identity") +
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Is citation related to number of publications? Let’s visualize that correlation. Can you follow the code below pertaining to the subsetting of the data? By now, these are all pretty standard operations.

num_pubs <- data.frame(table(soc_df_publications$gs_id))  # A table per gs_id, so counting the pubs per staff member
colnames(num_pubs) <- c("gs_id", "num_pubs")  # better column names
num_pubs <- left_join(num_pubs, soc_df[, c("total_cites", "gs_id")], by = c(gs_id = "gs_id"))  # left join on num_pubs
num_pubs$total_cites[is.na(num_pubs$total_cites)] <- 0  # if there are pubs but no cites, those pubs aren't cited yet

# pretty obvious correlation, right?
ggplot(num_pubs, aes(x = num_pubs, y = total_cites)) + geom_point() + geom_smooth(method = lm)

How about number of citations by both number of publications and the years since first publication (so proxying career age)? We need to first get years since first publication. Given the low number of observations and the high correlation between career age and number of publications, the regression outcome below does not tell us that much.

soc_df_publications <- data.table(soc_df_publications)  # make it a DT
min_year <- soc_df_publications[, .SD[which.min(year)], by = gs_id]  # find the lowest publication year in the list of publications of soc staff
num_pubs <- left_join(num_pubs, min_year[, c("gs_id", "year")], by = c(gs_id = "gs_id"))  # merge those data
num_pubs$career_age <- 2021 - num_pubs$year  # find career age
summary(lm(total_cites ~ num_pubs + career_age, data = num_pubs))  # and the regression model
#> 
#> Call:
#> lm(formula = total_cites ~ num_pubs + career_age, data = num_pubs)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3547.7  -912.4   103.5   748.2  4105.1 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -1525.443    462.250  -3.300 0.002640 ** 
#> num_pubs        4.917      4.179   1.177 0.249233    
#> career_age    186.917     43.635   4.284 0.000196 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 1401 on 28 degrees of freedom
#> Multiple R-squared:  0.7928, Adjusted R-squared:  0.778 
#> F-statistic: 53.55 on 2 and 28 DF,  p-value: 2.696e-10

Now let’s look at the network. How about the simplest thing: outdegree, or the number nominated co-authors by the staff member. Does that help folks get cited? Seems so, even independent of number of publications. Obviously, these things are highly interdependent and likely have undue influence on each other in the regression estimation below, especially with so few observations (N = 34).

num_coauth <- soc_collabs[!is.na(soc_collabs$coauth), ]
num_coauth <- data.frame(table(num_coauth$gs_id))  # Number of coauthors per google scholar id
names(num_coauth) <- c("gs_id", "num_coauth")  # better column names
num_coauth$num_coauth <- as.numeric(num_coauth$num_coauth)
num_pubs <- left_join(num_pubs, num_coauth, by = c(gs_id = "gs_id"))  # merge those dfs
num_pubs$num_coauth[is.na(num_pubs$num_coauth)] <- 0
summary(lm(total_cites ~ num_pubs + career_age + num_coauth, data = num_pubs))  # and the regression model!
#> 
#> Call:
#> lm(formula = total_cites ~ num_pubs + career_age + num_coauth, 
#>     data = num_pubs)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2515.24  -528.51   -40.68   572.48  2440.56 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -1068.728    332.158  -3.218  0.00335 ** 
#> num_pubs        5.856      2.914   2.010  0.05456 .  
#> career_age     76.632     36.303   2.111  0.04418 *  
#> num_coauth     85.277     15.372   5.548 7.02e-06 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 975 on 27 degrees of freedom
#> Multiple R-squared:  0.9032, Adjusted R-squared:  0.8924 
#> F-statistic: 83.93 on 3 and 27 DF,  p-value: 8.337e-14

And how about reciprocated ties? Let’s calculate the share of ties that is reciprocated and whether that correlates with citation. This means we have to find which staff’s coauthors also list them and have to merge on two identifiers. Do you follow the code below?

soc_recip <- soc_collabs[!is.na(soc_collabs$coauth), ]  # drop those without coauthors

# notice how we switch these ids around to find reciprocated ties!
soc_recip <- left_join(soc_recip, collabs_1deep, by = c(gs_id = "coauth_id", coauth_id = "gs_id"))
soc_recip <- soc_recip[!is.na(soc_recip$coauth.y), ]  #drop those coauthors who don't reciprocate
soc_recip <- data.frame(table(soc_recip$gs_id))  # count coauthors that have reciprocated

num_pubs <- left_join(num_pubs, soc_recip, by = c(gs_id = "Var1"))  # Join on gs_id for both
num_pubs$reciprocity <- num_pubs$Freq/num_pubs$num_coauth  # calculate share reciprocated 
num_pubs$reciprocity[is.na(num_pubs$reciprocity)] <- 0  # zero if no ties send or received
cor(num_pubs$reciprocity, num_pubs$num_coauth)  # correlation is decent
#> [1] 0.4831148

How does reciprocity relate to citation?

summary(lm(total_cites ~ num_pubs + career_age + reciprocity, data = num_pubs))  # and the regression model!
#> 
#> Call:
#> lm(formula = total_cites ~ num_pubs + career_age + reciprocity, 
#>     data = num_pubs)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3352.1  -801.7   -93.1   681.8  4103.0 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -1626.194    470.117  -3.459 0.001814 ** 
#> num_pubs        5.964      4.277   1.395 0.174503    
#> career_age    171.193     45.858   3.733 0.000893 ***
#> reciprocity  1424.659   1315.167   1.083 0.288275    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 1396 on 27 degrees of freedom
#> Multiple R-squared:  0.8014, Adjusted R-squared:  0.7793 
#> F-statistic: 36.31 on 3 and 27 DF,  p-value: 1.282e-09

And how does it relate to the career age, number of co-authors, and number of publication variables?

summary(lm(reciprocity ~ num_pubs + career_age + num_coauth, data = num_pubs))  # and the regression model!
#> 
#> Call:
#> lm(formula = reciprocity ~ num_pubs + career_age + num_coauth, 
#>     data = num_pubs)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.15176 -0.11857 -0.10546  0.09528  0.42224 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)  
#> (Intercept)  0.1127696  0.0614716   1.834   0.0776 .
#> num_pubs    -0.0006486  0.0005393  -1.203   0.2396  
#> career_age   0.0008831  0.0067185   0.131   0.8964  
#> num_coauth   0.0078515  0.0028448   2.760   0.0103 *
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.1804 on 27 degrees of freedom
#> Multiple R-squared:  0.3051, Adjusted R-squared:  0.2279 
#> F-statistic: 3.951 on 3 and 27 DF,  p-value: 0.01856

Finally, we check whether gender correlates with citation, controlled for the network and other covariates above or whether women are reciprocated less in terms of listed coauthorships. Let’s first merge num_pubs with soc_df to get folks’ names back to the variables we just studied. For now we can easily assign people’s gender on the basis of their names, just 31 isn’t that much work to label.

soc_df <- left_join(soc_df, num_pubs, by = c("gs_id"))
soc_df$total_cites <- soc_df$total_cites.x  # some double columns names because of joining datasets with some of the same columns
soc_df$total_cites.x <- NULL
soc_df$total_cites.y <- NULL

Let’s check out the sociology staff once more to match names to perceived gender signals using names.

Table 8.21: Table 8.22: Sociology staff
soc_names soc_experts last_name first_name affiliation.x gs_id name affiliation.y h_index i10_index fields homepage num_pubs year career_age num_coauth Freq reciprocity total_cites
Batenburg, prof. dr. R. (Ronald) Healthcare, labour market and healthcare professions and training Batenburg Ronald radboud university UK7nVSEAAAAJ Ronald Batenburg Programmaleider NIVEL en bijzonder hoogleraar Radboud Universiteit Nijmegen 30 87 verified email at nivel.nl - homepage https://www.nivel.nl/nl/ronald-batenburg 435 1986 35 0 NA 0.0000000 3608
Begall, dr. K.H. (Katia) Family, life course, labour market participation, division of household tasks and gender norms Begall Katia radboud university e7zfTqMAAAAJ Katia Begall Radboud University Nijmegen 9 9 verified email at maw.ru.nl NA 23 2006 15 10 5 0.5000000 936
Bekhuis, dr. H. (Hidde) Welfare state, nationalism and sports Bekhuis Hidde radboud university Q4saWX8AAAAJ Hidde Bekhuis Post Doc Sociology, Radboud University Nijmegen 8 7 verified email at ru.nl NA 25 2005 16 0 NA 0.0000000 348
Berg, dr. L. van den (Lonneke) Family, life course and transition to adulthood Berg Lonneke radboud university vzBNQ1kAAAAJ Lonneke van den Berg Radboud University 3 2 verified email at maw.ru.nl - homepage https://www.ru.nl/personen/berg-l-van-den-lonneke/ 11 2018 3 3 NA 0.0000000 34
Blommaert, dr. L. (Lieselotte) Discrimination and inequality on the labour market Blommaert Lieselotte radboud university RG54uasAAAAJ Lieselotte Blommaert Sociology/Social Cultural Research, Radboud University, Nijmegen, the 7 7 verified email at ru.nl - homepage http://www.ru.nl/english/people/blommaert-e/ 20 2012 9 13 3 0.2307692 317
Damman, dr. M. (Marleen) Labour market, life course, older workers, retirement and solo self-employed Damman Marleen radboud university MEv-V_YAAAAJ Marleen Damman Assistant Professor, Utrecht University 10 12 verified email at uu.nl - homepage https://www.uu.nl/staff/MDamman 68 2007 14 0 NA 0.0000000 515
Eisinga, prof. dr. R.N. (Rob) Methods of research and statistics Eisinga Rob radboud university GDHdsXAAAAAJ Rob Eisinga Professor social science research methods, Radboud University Nijmegen 33 77 verified email at ru.nl - homepage http://robeisinga.ruhosting.nl/ 101 1986 35 44 14 0.3181818 4994
Gesthuizen, dr. M.J.W. (Maurice) Poverty en social cohesion Gesthuizen Maurice radboud university n6hiblQAAAAJ Maurice Gesthuizen Sociology, Radboud University Nijmegen, the Netherland - Assistant Professor 24 41 verified email at maw.ru.nl - homepage http://www.ru.nl/methodenentechnieken/methoden-technieken/medewerkers/vm_medewerkers/maurice_gesthuizen/ 153 1999 22 27 9 0.3333333 2425
Glas, dr. S. (Saskia) Islam, gender attitudes and sexuality Glas Saskia radboud university ZMc0j2YAAAAJ Saskia Glas PhD student, Radboud University 4 2 verified email at ru.nl NA 10 2018 3 0 NA 0.0000000 70
Hek, dr. M. van (Margriet) Educational inequality, gender inequality, organizational sociology and culture Hek Margriet radboud university ZvLlx2EAAAAJ Margriet van Hek Radboud University 8 7 verified email at maw.ru.nl NA 13 2013 8 8 3 0.3750000 262
Hoekman, dr. R. H. A.(Remco) Sports and policy sociology Hoekman Remco radboud university LsMimOEAAAAJ Remco Hoekman Director, Mulier Institute / Senior researcher, Radboud University 12 15 verified email at mulierinstituut.nl - homepage https://www.mulierinstituut.nl/over-mulier/medewerkers/remco-hoekman/ 159 2007 14 0 NA 0.0000000 610
Hofstra, dr. B. (Bas) Diversity, inequality and innovation Hofstra Bas radboud university Nx7pDywAAAAJ Bas Hofstra Assistant Professor, Radboud University 7 7 verified email at ru.nl - homepage http://www.bashofstra.com/ 15 2013 8 15 9 0.6000000 384
Kraaykamp, prof. dr. G.L.M. (Gerbert) Educational inequality, culture and health Kraaykamp Gerbert radboud university l8aM4jAAAAAJ Gerbert Kraaykamp Professor of Sociology, Radboud Universiteit Nijmegen 46 98 verified email at maw.ru.nl - homepage https://www.ru.nl/english/people/kraaykamp-g/ 177 1989 32 42 14 0.3333333 7724
Meuleman, dr. (Roza) Culture and nationalism Meuleman Roza radboud university iKs_5WkAAAAJ Roza Meuleman Assistant Professor - Sociology - Radboud University Nijmegen 8 6 verified email at ru.nl NA 28 2011 10 13 4 0.3076923 214
Savelkoul, dr. M.J. (Michael) Ethnic diversity, prejudice and social cohesion Savelkoul Michael radboud university _f3krXUAAAAJ Michael Savelkoul Assistant Professor - Sociology, Radboud University Nijmegen, the Netherlands 8 7 verified email at maw.ru.nl NA 27 2011 10 6 NA 0.0000000 580
Scheepers, prof. dr. P.L.H. (Peer) Comparative research, social cohesion and diversity Scheepers Peer radboud university hPeXxvEAAAAJ peer scheepers hoogleraar methodologie, faculteit der sociale wetenschappen radboud universiteit 61 180 verified email at maw.ru.nl NA 465 1970 51 75 22 0.2933333 14399
Spierings, dr. C.H.B.M. (Niels) Islam, gender, populism, social media, Middle East and migration Spierings Niels radboud university cy3Ye6sAAAAJ Niels Spierings Associate Professor of Sociology, Radboud University 22 33 verified email at ru.nl - homepage https://www.ru.nl/english/people/spierings-c/ 74 2007 14 16 10 0.6250000 1662
Tolsma, dr. J. (Jochem) Inequality, criminology and ethnic diversity Tolsma Jochem radboud university Iu23-90AAAAJ Jochem Tolsma Professor, Radboud University Nijmegen / University of Groningen 22 33 verified email at ru.nl - homepage http://www.jochemtolsma.nl/ 93 2007 14 32 12 0.3750000 2260
Verbakel, prof. dr. C.M.C. (Ellen) Head of the departmen |Health, family and work |Verbakel |Ellen |radboud universit |w2McVJAAAAA |Ellen Verbakel |Professor of Sociology, Department of Sociology, Radboud University Nijmegen |24 |32 |verified email at maw.ru.nl - homepage |http://www.ellenverbakel.nl/


Seems this is correct below? Important note: It may be that other scholars have been added to the sociology staff list in the time that this code was written. That means you have to carefully check whether the list of genders below is still correct.

soc_df$gender <- c("man", "woman", "man", "woman", "woman", #1-5
                    "woman", "man", "man", "woman", "woman", #6-10
                    "man", "man", "man", "woman", "man", #11-15
                    "man", "man", "man", "woman", "man", #16-20
                    "man", "woman", "woman", #21-23
                    "man", "woman", "woman",  "woman", #24-27
                    "woman", "man", "woman",  "man") #28-31

The code below shows there seem to be some qualitative differences between man and woman in reciprocity and citation, but the variation and sample size is not sufficient to conclude that these differences statistically deviate from zero.

t.test(reciprocity ~ gender, data = soc_df)
#> 
#>  Welch Two Sample t-test
#> 
#> data:  reciprocity by gender
#> t = 0.6955, df = 28.92, p-value = 0.4923
#> alternative hypothesis: true difference in means between group man and group woman is not equal to 0
#> 95 percent confidence interval:
#>  -0.1001049  0.2032516
#> sample estimates:
#>   mean in group man mean in group woman 
#>           0.1976560           0.1460826
summary(lm(total_cites ~ career_age + num_coauth + as.factor(gender), data = soc_df))  # and the regression model!
#> 
#> Call:
#> lm(formula = total_cites ~ career_age + num_coauth + as.factor(gender), 
#>     data = soc_df)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1911.31  -636.96   -68.24   603.54  2647.94 
#> 
#> Coefficients:
#>                        Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)            -1677.57     452.74  -3.705    0.001 ** 
#> career_age               138.73      26.56   5.224 1.86e-05 ***
#> num_coauth                84.71      16.48   5.139 2.33e-05 ***
#> as.factor(gender)woman   291.89     430.52   0.678    0.504    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 1037 on 26 degrees of freedom
#>   (1 observation deleted due to missingness)
#> Multiple R-squared:  0.8935, Adjusted R-squared:  0.8812 
#> F-statistic: 72.73 on 3 and 26 DF,  p-value: 8.983e-13

Let’s save the data we have thus far.

# save the DFs thus far
save(soc_df, file = "addfiles/soc_df.rda")
save(soc_df_publications, file = "addfiles/soc_df_publications.rda")
save(soc_collabs, file = "addfiles/soc_collabs1.rda")
save(collabs_1deep, file = "addfiles/soc_collabs2.rda")
save(soc_staff_cit, file = "addfiles/soc_staff_cit.rda")
# save(soc_art_cit, 'addfiles/soc_art_cit.rda')) We didn't gather this one, takes too much time!

Nicely done, this was the webscraping tutorial for bibliometric science data. We gathered useful information about sociology staff:

  • 1.1 who actually is the staff on the RU website?
  • 1.2 staff Google Scholar profiles (we merged 1.1 and 1.2)
  • 2 publications and total cites per publication
  • 3.1 collaborators
  • 3.2 plus their collaborators (“friends-of-friends”)
  • (3.3 a 1.5 degree network of 3.1 and 3.2)
  • 4 citation history of scholars themselves (cites per year)

8.7 Assignments

8.7.1 Assignment 1 - Theoretical

  1. Find a paper that you can label as computational sociology using the definition at the beginning of this chapter. Perhaps your favorite computational sociology paper if you have one?
    • Read that paper and write a short review (say, 500-600 words/two pages) on how you see the promises and pitfalls of computational sociology described in this chapter reflected in that paper.
    • Also provide some intuitions as to how you would address some of the pitfalls you find. In other words, maybe you have a great idea for a solution to that pitfall.
    • Prepare a short presentation, 5-10 minutes, for Wednesday where you present the content of that review. In that presentation take at most 2 minutes to summarize the paper and then predominantly talk about the content of your review.

8.7.2 Assignment 2 - Practical

  1. Redo the tutorial – really should go much quicker now that you’ve got the hang of it! – but with the sociology staff of the University of Groningen.
    • All of it?! Yes, the goal is to get to the regression of citations on number of publications, career age, gender, and number of co-authors.
    • Upload the associated code (with a summary of the regression results in comment blocks in the code) for that exercise.

If all else fails, you can find the necessary files to work with here:
soc_df
soc_list_profiles
soc_df_publications
soc_collabs
collabs_1deep
soc_staff_cit
onefivedegree


References

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