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nature of the internet inherently means that by the time you read this text, some of the things we discuss will be outdated.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/")
head(soc_staff)
#> $node
#> <pointer: 0x000001bb138032a0>
#> 
#> $doc
#> <pointer: 0x000001bb0eeec390>

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: 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 8.2: 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 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


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 8.3: 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 department 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 8.4: 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 department 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 8.5: 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 department Health, family and work verbakel ellen radboud university
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] 3034
#> 
#> $h_index
#> [1] 26
#> 
#> $i10_index
#> [1] 41
#> 
#> $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"     "Nan Dirk de Graaf"   "Sara Kinsbergen"     "dr. Natascha Notten"
#> [13] "Tobias H. Stark"     "Thomas Feliciani"    "Andreas Flache"      "Carlijn Bussemakers"
#> [17] "Lincoln Quillian"    "René Veenstra"       "Ioana Van Deurzen"   "Ariana Need"        
#> 
#> $available
#> [1] 13
#> 
#> $not_available
#> [1] 1

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                                                                           When do people report crime to the police? Results from a factorial survey design in the Netherlands, 2010
#> 8                                                         Education and cultural integration among ethnic minorities and natives in the Netherlands: A test of the integration paradox
#> 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              Neighbourhood ethnic composition and voting for the radical right in The Netherlands. The role of perceived neighbourhood threat and interethnic neighbourhood contact
#> 13                                                       The impact of adolescents' classroom and neighborhood ethnic diversity on same‐and cross‐ethnic friendships within classrooms
#> 14         Losing Wallets, Retaining Trust? The Relationship Between Ethnic Heterogeneity and Trusting Coethnic and Non-coethnic Neighbours and Non-neighbours to Return a Lost Wallet
#> 15                                  Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data
#> 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                                                                                                                                                          Naar een open samenleving?
#> 22                                                                                       How, when and where can spatial segregation induce opinion polarization? Two competing models
#> 23                                                                               Explaining monetary donations to international development organisations: A factorial survey approach
#> 24     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
#> 25  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 …
#> 26                                                                                           Social origin and occupational success at labour market entry in The Netherlands, 1931–80
#> 27                      Perceptions as the crucial link? The mediating role of neighborhood perceptions in the relationship between the neighborhood context and neighborhood cohesion
#> 28                                                    Where does ethnic concentration matter for populist radical right support? An analysis of geographical scale and the halo effect
#> 29                                                                                          Explaining natives' interethnic friendship and contact with colleagues in European regions
#> 30                                                       Combating hooliganism in the Netherlands: An evaluation of measures to combat hooliganism with longitudinal registration data
#> 31                                                                                       De burger als rechter: een onderzoek naar geprefereerde sancties voor misdrijven in Nederland
#> 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       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
#> 35     De onderwijskansen van allochtone en autochtone Nederlanders vergeleken: Een cohort-design [Ethnic inequality of educational opportunities in the Netherlands: A cohort design]
#> 36                                                                            Like two peas in a pod? Explaining friendship selection processes related to victimization and offending
#> 37                                                                                        Netherlands Longitudinal Lifecourse Study-NELLS Panel Wave 1 2009 and Wave 2 2013-versie 1.2
#> 38                                                                            Aangiftebereidheid: Welke overwegingen spelen een rol bij de beslissing om wel of niet aangifte te doen?
#> 39                                                                                                                     Preferences for work arrangements: A discrete choice experiment
#> 40                                                                                                                De aangifte van delicten bij de multichannelstrategie van de politie
#> 41                                                                                                         Opleiding als sociale scheidslijn. Een nieuw perspectief op een oude kloof.
#> 42                                                                                                                                                   Opleiding als sociale scheidslijn
#> 43                                                                                                                        Taakstraffen langs de lat: strafopvattingen van Nederlanders
#> 44                                                                      Exposure to asylum seekers and changing support for the radical right: A natural experiment in the Netherlands
#> 45                                                                                                                                                      Dader, slachtoffer, of beiden?
#> 46                                                        Parents, television and children's weight status: On lasting effects of parental television socialization in the Netherlands
#> 47                                                                                      Vrijheid versus veiligheid: Wie steunt vrijheidsbeperking omwille van veiligheid in Nederland?
#> 48                                                                                            Kudos make you run! How runners influence each other on the online social network Strava
#> 49                                                                                                                              Running together: How sports partners keep you running
#> 50                                                                                                    Archeologische verwachtings-en beleidskaart buitengebied gemeente Midden-Drenthe
#> 51                                                                                                                     Education's impact on explanations of radical right-wing voting
#> 52                                                                                                       The social outcomes of psychosocial support: A grey literature scoping review
#> 53                                                                                                                                                               De burger als rechter
#> 54                                                                                                         Household dysfunction and child development: Do financial resources matter?
#> 55                                                                                                      Social origin and inequality in educational returns in the Dutch labour market
#> 56                                                   Integratie en depressie-De relatie tussen sociaal-culturele integratie en depressieklachten bij Turkse en Marokkaanse Nederlander
#> 57                                                                                                Ontwikkelingen in de maatschappelijke positie van middelbaar opgeleiden in Nederland
#> 58                                                                                                                                                             Integratie en depressie
#> 59                                                                       Onderwijsexpansie en opleidingsrichting: Trends in de intergenerationele overdracht van onderwijsongelijkheid
#> 60                                                                                                             Trends in de opleidingskloof op verschillende maatschappelijke domeinen
#> 61                                               Similarity and differences in age, gender, ethnicity, and education as explanatory factors of tie loss in the core discussion network
#> 62                                                                               The Well-Being of Undocumented Migrants in The Netherlands: Learning about Post-Migration Experiences
#> 63                                                                   Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation
#> 64                                                                                                             Variation in the educational consequences of parental death and divorce
#> 65                                                                                                                     VIA pilot participatietraject gemeente Nijmegen: Eindrapportage
#> 66                                                                                                                     Preferences for work arrangements: A discrete choice experiment
#> 67                                                          Fairly paid but dissatisfied? Determinants of pay fairness and pay satisfaction: Evidence from Germany and the Netherlands
#> 68                                                                                       In hoeverre verklaart de etnische samenstelling van de buurt de kans om te stemmen op de PVV?
#> 69                                                                        In de etalage: In hoeverre verklaart de etnische samenstelling van de buurt de kans om te stemmen op de PVV?
#> 70                                                                                                                                            Family Survey Dutch Population 2017/2018
#> 71                                                                                   Soort zoekt soort: vriendschapselectieprocessen met betrekking tot slachtofferschap en daderschap
#> 72                                                                                                                                           Joran Laméris Radboud University Nijmegen
#> 73                                     Modeling opinion dynamics in a real city: How realistic spatial patterns of demographic attributes affect the emergence of opinion polarization
#> 74                       Summary of “How, When and Where Can Spatial Segregation Induce Opinion Polarization? Two Competing Models”: Paper Under Review as JASSS Fast Track Submission
#> 75                                                                                                                                     ICS Alumni revisited [Brochure tbv Lustrum ICS]
#> 76                                                                                                                                                ICS Alumni Survey 2016 [Databestand]
#> 77                                       Modeling opinion dynamics in a simulated city. Realistic spatial patterns of demographic attributes and the emergence of opinion polarization
#> 78                                                                                                                           Opleiding als nieuwe sociale scheidslijn: een tegengeluid
#> 79                                                                                   Opleiding als sociale scheidslijn: aanleiding, probleemstelling, bestaande inzichten en werkwijze
#> 80                                                                                                                    De samenhang in het opleidingsniveau tussen (huwelijks) partners
#> 81                                                                                                 Sociale herkomst en ongelijkheid in de opbrengsten van diploma's op de arbeidsmarkt
#> 82                                                                                                       De rol van opleidingsniveau voor het starten en stoppen met vrijwilligerswerk
#> 83                                                                                  Was, is of wordt opleiding de sociale scheidslijn? Een terugblik, stand van zaken en toekomstvisie
#> 84                                                                                                Klein en vrijwillig of groot en ervaren? Een analyse van de voorkeuren van donateurs
#> 85                                                                     De burger als rechter, onderzoek naar geprefereerde sancties voor misdrijven in Nederland (projectnummer 1933B)
#> 86                                                                           Aangiftebereidheid: Welke overwegingen spelen een rol bij de beslissing om wel of niet aangifte te doen?|
#> 87                                                                                                                                   Onderwijsexpansie veroorzaakt nieuwe ongelijkheid
#> 88                                                                                                                  Sociale daling schaadt vertrouwen: Effecten van sociale mobiliteit
#> 89                                                                                                                                        Toenemende gelijkheid is nog geen verheffing
#> 90                    Over ouders, televisiekijken en (over) gewicht: Een studie naar de langetermijneffecten van ouderlijke televisiesocialisatie op het lichaamsgewicht van kinderen
#> 91                                                                                                                                         Nijmegen School Study 2010-TNSS 2010-wave 1
#> 92                                                                                      Does Intergenerational Social Mobility affect Antagonistic Attitudes towards Ethnic Minorities
#> 93                                                             De invloed van lands-, gemeente-en buurtkenmerken op sociaal kapitaal: Putnam's hypothese getest in Europa en Nederland
#> 94                                                                                       Lokale en regionale instituties in verandering: een onderzoek naar de ervaringen in Duitsland
#> 95                         Volume 46-Article 20| Pages 581–618 Variation in the educational consequences of parental death and divorce: The role of family and country characteristics
#> 96                                       An Investigation into the Impact of Social Mobility Experiences, the Local Living Environment and Educational Attainment on Ethnic Hostility.
#> 97                                                                                                                                                     Parental Discouragement Measure
#> 98                                                                                                                                                      Complete Networks Small worlds
#> 99                                                                                                                                                            Causes of dyads (theory)
#> 100                                                                                                                                                                Egocentric Networks
#> 101                                                 De invloed van lands-, gemeente-en buurtkenmerken op sociaal kapitaal Gesthuizen, M.; Scheepers, P.; Tolsma, J.; Meer, TWG van der
#> 102                                                                                            The Role of Recent Migrants’ Country of Origin Engagement in Dutch Language Proficiency
#> 103                                                                                                                                                M1-102: Social capital and networks
#> 104                                                                                                   Social origin and occupational success at labour market entry in the Netherlands
#>                                                          author
#> 1                                      T 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, J Blaauw, M Te Grotenhuis
#> 8                              J Tolsma, M Lubbers, M Gijsberts
#> 9                               J Tolsma, M Coenders, M Lubbers
#> 10                            J Tolsma, ND De Graaf, L Quillian
#> 11                                  J Tolsma, A Need, U De Jong
#> 12                             M Savelkoul, J Laméris, J Tolsma
#> 13                 A Munniksma, P Scheepers, TH Stark, J Tolsma
#> 14                                   J Tolsma, TWG van der Meer
#> 15                         C Bussemakers, G Kraaykamp, J Tolsma
#> 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                                        J Tolsma, MHJ Wolbers
#> 22                              T Feliciani, A Flache, J Tolsma
#> 23                                       S Kinsbergen, J Tolsma
#> 24                                        J Tolsma, MHJ Wolbers
#> 25                                                     J Tolsma
#> 26                                        J Tolsma, MHJ Wolbers
#> 27                                 J Laméris, JR Hipp, J Tolsma
#> 28                                 D van Wijk, G Bolt, J Tolsma
#> 29                           M Savelkoul, J Tolsma, P Scheepers
#> 30                       D Schaap, M Postma, L Jansen, J Tolsma
#> 31     S Ruiter, J Tolsma, M de Hoon, H Elffers, P van der Laan
#> 32                          R van der Zwan, J Tolsma, M Lubbers
#> 33                                   J Tolsma, TWG Van der Meer
#> 34                   J Laméris, G Kraaykamp, S Ruiter, J Tolsma
#> 35                            J Tolsma, MTA Coenders, M Lubbers
#> 36                   JJ Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 37    J Tolsma, GLM Kraaykamp, DM de Graaf, M Kalmijn, C Monden
#> 38                                                     J Tolsma
#> 39                                   P Valet, C Sauer, J Tolsma
#> 40                                      PFM Boekhoorn, J Tolsma
#> 41                            M de Lange, J Tolsma, MHJ Wolbers
#> 42                               M Lange, J Tolsma, MHJ Wolbers
#> 43                                           S Ruiter, J Tolsma
#> 44                             J Tolsma, J Lameris, M Savelkoul
#> 45                                 J Rokven, S Ruiter, J Tolsma
#> 46                              N Notten, G Kraaykamp, J Tolsma
#> 47                              G Jansen, J Tolsma, ND de Graaf
#> 48                               R Franken, H Bekhuis, J Tolsma
#> 49                               R Franken, H Bekhuis, J Tolsma
#> 50                                       MG Marinelli, J Tolsma
#> 51                                          M Lubbers, J Tolsma
#> 52                     T Ubels, S Kinsbergen, J Tolsma, DJ Koch
#> 53                S Ruiter, J Tolsma, M Hoon, H Elffers, P Laan
#> 54               C Bussemakers, G Kraaykamp, I Schoon, J Tolsma
#> 55                                        J Tolsma, MHJ Wolbers
#> 56                                    R Zwan, van der, J Tolsma
#> 57                                        J Tolsma, MHJ Wolbers
#> 58                                     R van der Zwan, J Tolsma
#> 59                         GLM Kraaykamp, J Tolsma, MHJ Wolbers
#> 60                                            M Lange, J Tolsma
#> 61                            T Jeroense, N Spierings, J Tolsma
#> 62                     T Ubels, S Kinsbergen, DJ Koch, J Tolsma
#> 63                              T Feliciani, J Tolsma, A Flache
#> 64                         C Bussemakers, G Kraaykamp, J Tolsma
#> 65                 N Spierings, LM Werner, PJ Beckers, J Tolsma
#> 66                                            C Sauer, J Tolsma
#> 67                               J Adriaans, CG Sauer, J Tolsma
#> 68                             M Savelkoul, J Laméris, J Tolsma
#> 69                           MJ Savelkoul, JG Laméris, J Tolsma
#> 70                          R Meuleman, J Tolsma, GLM Kraaykamp
#> 71                    J Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 72                                            JR Hipp, J Tolsma
#> 73                              T Feliciani, A Flache, J Tolsma
#> 74                              T Feliciani, A Flache, J Tolsma
#> 75                 PE Thijs, GLM Kraaykamp, M Scholte, J Tolsma
#> 76                 GLM Kraaykamp, M Scholte, PE Thijs, J Tolsma
#> 77                      T Feliciani, A Flache, J Tolsma, M Maes
#> 78                                                     J Tolsma
#> 79                            M de Lange, J Tolsma, MHJ Wolbers
#> 80                                        J Tolsma, ND de Graaf
#> 81                                        J Tolsma, MHJ Wolbers
#> 82                              D Wiertz, J Tolsma, ND De Graaf
#> 83                                        J Tolsma, MHJ Wolbers
#> 84                                       S Kinsbergen, J Tolsma
#> 85                S Ruiter, J Tolsma, M Hoon, H Elffers, D Laan
#> 86                                                     J Tolsma
#> 87                         GLM Kraaykamp, MHJ Wolbers, J Tolsma
#> 88                                        J Tolsma, MHJ Wolbers
#> 89                                        MHJ Wolbers, J Tolsma
#> 90                            N Notten, GLM Kraaykamp, J Tolsma
#> 91                                              J Tolsma, I Pop
#> 92                             N de Graaf, J Tolsma, L Quillian
#> 93    MJW Gesthuizen, PLH Scheepers, J Tolsma, TWG van der Meer
#> 94         GH Hagelstein, G Morssinkhof, JML Tolsma, JBJM Berge
#> 95                         C Bussemakers, G Kraaykamp, J Tolsma
#> 96                                                     J TOLSMA
#> 97                   JJ Rokven, J Tolsma, S Ruiter, G Kraaykamp
#> 98                                                     J Tolsma
#> 99                                                     J Tolsma
#> 100                                                    J Tolsma
#> 101                                                M Gesthuizen
#> 102                                          N Geurts, J Tolsma
#> 103                                 J Laméris, J Tolsma, J Hipp
#> 104                                       J Tolsma, MHJ Wolbers
#>                                                                              journal
#> 1                                                         Annual review of sociology
#> 2                                                       European sociological review
#> 3                                                                      Acta Politica
#> 4                                                       European Sociological Review
#> 5                                                                    Social Networks
#> 6                                                       European Sociological Review
#> 7                                                Journal of experimental criminology
#> 8                                            Journal of Ethnic and Migration Studies
#> 9                                                       European Sociological Review
#> 10                                                  The British Journal of Sociology
#> 11                                                      European Sociological Review
#> 12                                                      European Sociological Review
#> 13                                                Journal of Research on Adolescence
#> 14                                                        Social Indicators Research
#> 15                                                             Child Abuse & Neglect
#> 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                                                                                  
#> 22                             Journal of Artificial Societies and Social Simulation
#> 23                                                           Social science research
#> 24                                                       Tijdschrift voor Sociologie
#> 25                                                 [Sl]: sn [ICS dissertation series
#> 26                                                                  Acta Sociologica
#> 27                                                           Social Science Research
#> 28                                                               Political Geography
#> 29                                           Journal of Ethnic and Migration Studies
#> 30                                  European Journal on Criminal Policy and Research
#> 31                                                    Den Haag, Boom Lemma Uitgevers
#> 32                                                               Political Geography
#> 33                                                           Social science research
#> 34                                  International Journal of Intercultural Relations
#> 35                                                                                  
#> 36                                                   European Journal of Criminology
#> 37                                                                         DANS EASY
#> 38                                  Proces-verbaal, aangifte en forensisch onderzoek
#> 39                                                                          PloS one
#> 40           Apeldoorn; Nijmegen: Politie & Wetenschap; BBSO en Radboud Universiteit
#> 41                                                                             Maklu
#> 42                                                                                  
#> 43                                                                                  
#> 44                                                                          Plos one
#> 45                                                     Tijdschrift voor Criminologie
#> 46                                                     Journal of Children and Media
#> 47                                                              Mens en maatschappij
#> 48                                                                   Social Networks
#> 49                                             Frontiers in Sports and Active Living
#> 50                                                            Oranjewoud, Heerenveen
#> 51    Interdisciplinary Conference on Migration, Economic Change, Social Challenge …
#> 52                                                                 SSM-mental health
#> 53                                                                              NSCR
#> 54                                                  Advances in Life Course Research
#> 55          Education, Occupation and Social Origin: A Comparative Analysis of the …
#> 56                                                              Mens en maatschappij
#> 57                                                                     Den Haag: WRR
#> 58                                                         De relatie tussen sociaal
#> 59                                                                    Amsterdam: AUP
#> 60                                                       Antwerpen/Apeldoorn: Garant
#> 61                                                                   Social Networks
#> 62                                            Journal of Immigrant & Refugee Studies
#> 63                                           Journal of Computational Social Science
#> 64                                                              Demographic Research
#> 65                                                            Nijmegen: RUNOMI; RSCR
#> 66                                                                                  
#> 67                      New York, NY: Society for the Advancement of Socio-Economics
#> 68                                                              Mens en Maatschappij
#> 69                                                                                  
#> 70                                                                         DANS EASY
#> 71                                                              Mens en maatschappij
#> 72                                                                                  
#> 73                                                                                  
#> 74      International Conference on Principles and Practice of Multi-Agent Systems …
#> 75  Nijmegen: Interuniversity Center for Social Science Theory and Methodology (ICS)
#> 76  Nijmegen: Interuniversity Center for Social Science Theory and Methodology (ICS)
#> 77                                                      Social Simulation Conference
#> 78                                                                        Sociologos
#> 79        Opleiding als sociale scheidslijn. Een nieuw perspectief op een oude kloof
#> 80                                                       Antwerpen/Apeldoorn: Garant
#> 81                                                       Antwerpen/Apeldoorn: Garant
#> 82                                                                  Garant Uitgevers
#> 83             Lange, M. de; Tolsma, J.; Wolbers, MHJ (ed.), Opleiding als sociale …
#> 84                                                                   Amsterdam: NCDO
#> 85                                                                         DANS EASY
#> 86                                                            Cahiers Politiestudies
#> 87                                                        [Sl]: Sociale Vraagstukken
#> 88                                                                                  
#> 89                                                        [Sl]: Sociale Vraagstukken
#> 90                                                                 Assen: Van Gorcum
#> 91                                                                         DANS EASY
#> 92                                                                   Wiley Blackwell
#> 93                                                        Den Haag/Nijmegen: SCP-NSV
#> 94                     Nederlands Instituut voor Sociaal en Economisch Recht (NISER)
#> 95                                                                                  
#> 96                                                                                  
#> 97                                                   European Journal of Criminology
#> 98                                                                                  
#> 99                                                                                  
#> 100                                                                                 
#> 101                                                                                 
#> 102                                                                                 
#> 103                                                                Book of Abstracts
#> 104                                                                                 
#>                                                                    number cites year
#> 1                                                         40 (1), 459-478   629 2014
#> 2                                                         27 (6), 741-758   345 2011
#> 3                                                                  44 (3)   317 2009
#> 4                                                         27 (3), 291-306   148 2011
#> 5                                                           35 (1), 51-61   143 2013
#> 6                                                         24 (2), 215-230   132 2008
#> 7                                                              8, 117-134    96 2012
#> 8                                                         38 (5), 793-813    89 2012
#> 9                                                         23 (3), 325-339    82 2007
#> 10                                                        60 (2), 257-277    78 2009
#> 11                                                        26 (2), 235-252    69 2010
#> 12                                                        33 (2), 209-224    56 2017
#> 13                                                          27 (1), 20-33    56 2017
#> 14                                                                           46 2016
#> 15                                                             98, 104185    43 2019
#> 16                                                      34 (5-6), 888-906    43 2013
#> 17                                                          42 (1), 59-83    39 2013
#> 18                                                        14 (6), 697-719    38 2017
#> 19                                                              54, 80-95    38 2015
#> 20                                                                           34 2014
#> 21                                                                           33 2010
#> 22                                                              20 (2), 6    32 2017
#> 23                                                      42 (6), 1571-1586    32 2013
#> 24                                                        31 (3), 239-259    31 2010
#> 25                                                                   155]    28 2009
#> 26                                                        57 (3), 253-269    27 2014
#> 27                                                              72, 53-68    24 2018
#> 28                                                             77, 102097    23 2020
#> 29                                                        41 (5), 683-709    20 2015
#> 30                                                              21, 83-97    20 2015
#> 31                                                                           18 2011
#> 32                                                             77, 102098    17 2020
#> 33                                                             73, 92-106    16 2018
#> 34                                                              63, 80-94    15 2018
#> 35                                                                           14 2007
#> 36                                                        13 (2), 231-256    13 2016
#> 37                                                                           13 2014
#> 38                                                                     11    12 2011
#> 39                                                       16 (7), e0254483    11 2021
#> 40                                                                           11 2016
#> 41                                                                           11 2015
#> 42                                                                            9 2016
#> 43                                                                            8 2010
#> 44                                                       16 (2), e0245644     7 2021
#> 45                                                            55 (3), 278     7 2013
#> 46                                                         7 (2), 235-252     7 2013
#> 47                                                          83 (1), 47-69     7 2008
#> 48                                                            72, 151-164     6 2023
#> 49                                                              4, 643150     6 2022
#> 50                                                                            6 2009
#> 51                                                                            5 2011
#> 52                                                              2, 100074     4 2022
#> 53                                                                            4 2011
#> 54                                                             51, 100447     3 2022
#> 55                                                                            3 2016
#> 56                                                        88 (2), 177-205     3 2013
#> 57                                                                            2 2017
#> 58                                                                            2 2013
#> 59                                                                            2 2011
#> 60                                                                            1 2015
#> 61                                                            76, 135-149     0 2024
#> 62                                                                   1-14     0 2023
#> 63                                                                   1-45     0 2023
#> 64                                                            46, 581-618     0 2022
#> 65                                                                            0 2021
#> 66                                                                            0 2021
#> 67                                                                            0 2019
#> 68                                                          93 (1), 82-85     0 2018
#> 69                                                                            0 2018
#> 70                                                                            0 2018
#> 71                                                        92 (3), 327-329     0 2017
#> 72                                                                            0 2017
#> 73  9th Conference of the International Network of Analytical Sociology …     0 2016
#> 74                                                                            0 2016
#> 75                                                                            0 2016
#> 76                                                                            0 2016
#> 77                                                                   2016     0 2016
#> 78                                                                 36 (3)     0 2015
#> 79                                                                   9-32     0 2015
#> 80                                                                            0 2015
#> 81                                                                            0 2015
#> 82                                                                            0 2015
#> 83                                                                            0 2015
#> 84                                                                            0 2014
#> 85                                                                            0 2013
#> 86                                                              2 (4), 11     0 2012
#> 87                                                                            0 2011
#> 88                                                                            0 2011
#> 89                                                                            0 2011
#> 90                                                                            0 2011
#> 91                                                                            0 2010
#> 92                                                                            0 2009
#> 93                                                                            0 2009
#> 94                                                                            0 1991
#> 95                                                                            0   NA
#> 96                                                                            0   NA
#> 97                                                                            0   NA
#> 98                                                                            0   NA
#> 99                                                                            0   NA
#> 100                                                                           0   NA
#> 101                                                                           0   NA
#> 102                                                                           0   NA
#> 103                                                                     1     0   NA
#> 104                                                                           0   NA
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#> 17                                             2112276567018030922 _FxGoFyzp5QC
#> 18                                            13322468554278639475 vV6vV6tmYwMC
#> 19                                             7670225499012303854 e5wmG9Sq2KIC
#> 20                                             8792123396141403739 xtRiw3GOFMkC
#> 21                                             2539524527836644253 Y0pCki6q_DkC
#> 22                                             6880814424039971499 g5m5HwL7SMYC
#> 23                                            10149692484122806616 aqlVkmm33-oC
#> 24                                            17130607869100522312 YsMSGLbcyi4C
#> 25                                            10378332126833599949 IjCSPb-OGe4C
#> 26                                             8248470043986462984 M3ejUd6NZC8C
#> 27                                            16357054384393453824 D03iK_w7-QYC
#> 28                                             4092382021694339447 SP6oXDckpogC
#> 29                                            18182577779862774305 -f6ydRqryjwC
#> 30                                             9528443224826780083 ZeXyd9-uunAC
#> 31                                             7211129999202990486 KlAtU1dfN6UC
#> 32                                             7114430646392466648 uWQEDVKXjbEC
#> 33                                             8349908030823257502 pyW8ca7W8N0C
#> 34                                             1627288244325129498 a0OBvERweLwC
#> 35                                               41511425553822262 zYLM7Y9cAGgC
#> 36                                            16075774780598089063 k_IJM867U9cC
#> 37                                             7914543636642646054 mB3voiENLucC
#> 38                                            10745397192148013810 LkGwnXOMwfcC
#> 39                                            14800116132549688497 tKAzc9rXhukC
#> 40                                            14256154602665082067 JV2RwH3_ST0C
#> 41                                             6027896113597554400 isC4tDSrTZIC
#> 42                                             4589290607551316207 O3NaXMp0MMsC
#> 43                                            15258532569899652859 W7OEmFMy1HYC
#> 44                                            10820360089296230361 Fu2w8maKXqMC
#> 45                                            10200636729873805270 QIV2ME_5wuYC
#> 46                                              818925813101569366 Se3iqnhoufwC
#> 47                                             6273244451878075724 ufrVoPGSRksC
#> 48                                            12114155743971894213 9Nmd_mFXekcC
#> 49                                             6762471506678500061 evX43VCCuoAC
#> 50                                              641362829363743487 kzcrU_BdoSEC
#> 51                                            10658172101302530460 4TOpqqG69KYC
#> 52                                            12792330965359094918 2KloaMYe4IUC
#> 53                                            16411127097378483929 ns9cj8rnVeAC
#> 54                                             3664663685279702514 ZfRJV9d4-WMC
#> 55                                            10107474183324844052 YFjsv_pBGBYC
#> 56                            199361376786756491,53571672086000399 35N4QoGY0k4C
#> 57                                            12961270881488694754 u_35RYKgDlwC
#> 58                                            12114359551598956835 f2IySw72cVMC
#> 59                                            12146662344897831656 5nxA0vEk-isC
#> 60                                            12139749723307797145 j3f4tGmQtD8C
#> 61                                                            <NA> AvfA0Oy_GE0C
#> 62                                                            <NA> uWiczbcajpAC
#> 63                                                            <NA> vDijr-p_gm4C
#> 64                                                            <NA> 35r97b3x0nAC
#> 65                                                            <NA> yB1At4FlUx8C
#> 66                                                            <NA> fEOibwPWpKIC
#> 67                                                            <NA> OU6Ihb5iCvQC
#> 68                                                            <NA> b0M2c_1WBrUC
#> 69                                                            <NA> ipzZ9siozwsC
#> 70                                                            <NA> j8SEvjWlNXcC
#> 71                                                            <NA> dfsIfKJdRG4C
#> 72                                                            <NA> u9iWguZQMMsC
#> 73                                                            <NA> 7T2F9Uy0os0C
#> 74                                                            <NA> NJ774b8OgUMC
#> 75                                                            <NA> lSLTfruPkqcC
#> 76                                                            <NA> RYcK_YlVTxYC
#> 77                                                            <NA> NaGl4SEjCO4C
#> 78                                                            <NA> NMxIlDl6LWMC
#> 79                                                            <NA> TFP_iSt0sucC
#> 80                                                            <NA> bEWYMUwI8FkC
#> 81                                                            <NA> iH-uZ7U-co4C
#> 82                                                            <NA> r0BpntZqJG4C
#> 83                                                            <NA> 4JMBOYKVnBMC
#> 84                                                            <NA> yD5IFk8b50cC
#> 85                                                            <NA> 738O_yMBCRsC
#> 86                                                            <NA> P5F9QuxV20EC
#> 87                                                            <NA> _kc_bZDykSQC
#> 88                                                            <NA> ULOm3_A8WrAC
#> 89                                                            <NA> Zph67rFs4hoC
#> 90                                                            <NA> 3fE2CSJIrl8C
#> 91                                                            <NA> uc_IGeMz5qoC
#> 92                                                            <NA> dshw04ExmUIC
#> 93                                                            <NA> YOwf2qJgpHMC
#> 94                                                            <NA> zLWjf1WUPmwC
#> 95                                                            <NA> URolC5Kub84C
#> 96                                                            <NA> epqYDVWIO7EC
#> 97                                                            <NA> tzM49s52ZIMC
#> 98                                                            <NA> _Re3VWB3Y0AC
#> 99                                                            <NA> WbkHhVStYXYC
#> 100                                                           <NA> Tiz5es2fbqcC
#> 101                                                           <NA> cFHS6HbyZ2cC
#> 102                                                           <NA> 4OULZ7Gr8RgC
#> 103                                                           <NA> M05iB0D1s5AC
#> 104                                                           <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    76
#> 5  2012    81
#> 6  2013   118
#> 7  2014   163
#> 8  2015   214
#> 9  2016   230
#> 10 2017   227
#> 11 2018   280
#> 12 2019   309
#> 13 2020   297
#> 14 2021   335
#> 15 2022   332
#> 16 2023   262

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 8.6: 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 Nan Dirk De Graaf
11 Jochem Tolsma Sara Kinsbergen
12 Jochem Tolsma Dr. Natascha Notten
13 Jochem Tolsma Tobias H. Stark
14 Jochem Tolsma Thomas Feliciani
15 Jochem Tolsma Andreas Flache
16 Jochem Tolsma Carlijn Bussemakers
17 Jochem Tolsma Lincoln Quillian
18 Jochem Tolsma René Veenstra
19 Jochem Tolsma Ioana Van Deurzen
20 Jochem Tolsma Ariana Need
24 Jochem Tolsma About Scholar
25 Jochem Tolsma Search Help
1100 Gerbert Kraaykamp Nan Dirk De Graaf
2100 Gerbert Kraaykamp Paul M. De Graaf
321 Gerbert Kraaykamp Matthijs Kalmijn
410 Gerbert Kraaykamp Tim Huijts
510 Gerbert Kraaykamp Jochem Tolsma
610 Gerbert Kraaykamp Maarten Hj Wolbers
710 Gerbert Kraaykamp Roza Meuleman
810 Gerbert Kraaykamp Mark Levels
910 Gerbert Kraaykamp Maurice Gesthuizen
1010 Gerbert Kraaykamp Christiaan Monden
1110 Gerbert Kraaykamp Mark Visser
1210 Gerbert Kraaykamp Koen Van Eijck
1310 Gerbert Kraaykamp Herman G. Van De Werfhorst
1410 Gerbert Kraaykamp Wout Ultee
1510 Gerbert Kraaykamp Margriet Van Hek
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 Peer Scheepers
28 Tom Van Der Meer Wouter Van Der Brug
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 Eelco Harteveld
36 Tom Van Der Meer Huib Pellikaan
37 Tom Van Der Meer Erik Van Ingen
38 Tom Van Der Meer Tim Reeskens
39 Tom Van Der Meer Mérove Gijsberts
40 Tom Van Der Meer Ben Pelzer
41 Tom Van Der Meer Sonja Zmerli
42 Tom Van Der Meer Marcel Lubbers
43 Tom Van Der Meer Loes Aaldering
44 Tom Van Der Meer Jan W. Van Deth
45 Tom Van Der Meer Kristof Jacobs
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 Jaap Dronkers
58 Maarten Hj Wolbers Emer Smyth
59 Maarten Hj Wolbers Muja Ardita
60 Maarten Hj Wolbers Ruud Luijkx
61 Maarten Hj Wolbers Walter Müller
62 Maarten Hj Wolbers Tanja Traag
63 Maarten Hj Wolbers Renze Kolster
64 Maarten Hj Wolbers Lieselotte Blommaert
65 Maarten Hj Wolbers Don Westerheijden
66 Maarten Hj Wolbers Giampiero Passaretta
67 Maarten Hj Wolbers Nicole Tieben
68 Maarten Hj Wolbers Andries De Grip
69 Maarten Hj Wolbers Margriet Van Hek
70 Maarten Hj Wolbers Richard Layte
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 William M. Van Der Veld
80 Michael Savelkoul Miles Hewstone
81 Michael Savelkoul Dietlind Stolle
82 Michael Savelkoul Jasper Van Assche
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 Shane D Johnson
95 Stijn Ruiter Michael Townsley
96 Stijn Ruiter Jean-Louis Van Gelder
97 Stijn Ruiter Paul M. De Graaf
98 Stijn Ruiter Hidde Bekhuis
99 Stijn Ruiter Marcel Coenders
100 Stijn Ruiter Gentry White
101 Stijn Ruiter Daniel Birks
102 Stijn Ruiter Wouter Steenbeek
103 Stijn Ruiter Frank Weerman
104 Stijn Ruiter Marieke Van De Rakt
105 Stijn Ruiter Paul Nieuwbeerta
106 Stijn Ruiter Chantal Van Den Berg
107 Stijn Ruiter Marleen Weulen Kranenbarg
108 Stijn Ruiter Lieven J.r. Pauwels
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 Hans De Witte
125 Peer Scheepers Maurice Vergeer
126 Peer Scheepers Agnieszka Kanas
127 Peer Scheepers Karin M Van Der Pal-De Bruin
128 Peer Scheepers Jochem Tolsma
129 Peer Scheepers Paula Thijs
130 Peer Scheepers Frans Van Der Slik
131 Peer Scheepers Niels Spierings
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 Maykel Verkuyten
147 Marcel Lubbers Nan Dirk De Graaf
148 Marcel Lubbers Hidde Bekhuis
149 Marcel Lubbers Tim Immerzeel
150 Marcel Lubbers Rob Eisinga
151 Marcel Lubbers Nella Geurts
152 Marcel Lubbers Claudia Diehl
153 Marcel Lubbers Roos Van Der Zwan
154 Marcel Lubbers Jaak Billiet
155 Marcel Lubbers Mark Visser
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
174 Nan Dirk De Graaf Gerbert Kraaykamp
175 Nan Dirk De Graaf Paul M. De Graaf
176 Nan Dirk De Graaf Ariana Need
177 Nan Dirk De Graaf Paul Nieuwbeerta
178 Nan Dirk De Graaf Anthony F Heath
179 Nan Dirk De Graaf Stijn Ruiter
180 Nan Dirk De Graaf Geoffrey Evans
181 Nan Dirk De Graaf Manfred Te Grotenhuis
182 Nan Dirk De Graaf Giedo Jansen
183 Nan Dirk De Graaf Herman G. Van De Werfhorst
184 Nan Dirk De Graaf Ayse Guveli
185 Nan Dirk De Graaf Christiaan Monden
186 Nan Dirk De Graaf Jonathan Kelley
187 Nan Dirk De Graaf Marcel Lubbers
188 Nan Dirk De Graaf Eva Jaspers
189 Nan Dirk De Graaf Harry Bg Ganzeboom
190 Nan Dirk De Graaf René Bekkers
191 Nan Dirk De Graaf Jochem Tolsma
192 Nan Dirk De Graaf Ruud Luijkx
193 Nan Dirk De Graaf Lincoln Quillian
197 Nan Dirk De Graaf About Scholar
198 Nan Dirk De Graaf Search Help
202 Sara Kinsbergen About Scholar
203 Sara Kinsbergen 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 Patti M. Valkenburg
210 Dr. Natascha Notten Wiemer Salverda
211 Dr. Natascha Notten Harry Bg Ganzeboom
212 Dr. Natascha Notten Ingrid K. Van Dijk
213 Dr. Natascha Notten Paula Thijs
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 Tobias H. Stark Anke Munniksma
224 Tobias H. Stark René Veenstra
225 Tobias H. Stark Maykel Verkuyten
226 Tobias H. Stark Josh Pasek
227 Tobias H. Stark Trevor Tompson
228 Tobias H. Stark Jochem Tolsma
229 Tobias H. Stark J. Ashwin Rambaran, Ph.d.
230 Tobias H. Stark Ioana Van Deurzen
234 Tobias H. Stark About Scholar
235 Tobias H. Stark Search Help
239 Thomas Feliciani About Scholar
240 Thomas Feliciani Search Help
241 Andreas Flache Michael Macy
242 Andreas Flache Michael Mäs
243 Andreas Flache Tobias H. Stark
244 Andreas Flache Rainer Hegselmann
245 Andreas Flache René Veenstra
246 Andreas Flache Károly Takács
247 Andreas Flache Rafael Wittek
248 Andreas Flache Guillaume Deffuant
249 Andreas Flache Anke Munniksma
250 Andreas Flache Dirk Helbing
251 Andreas Flache James A Kitts
252 Andreas Flache Maykel Verkuyten
253 Andreas Flache Josep M. Pujol
254 Andreas Flache Nigel Gilbert
255 Andreas Flache Maxi San Miguel
256 Andreas Flache Rosaria Conte
257 Andreas Flache Andrzej Nowak
258 Andreas Flache André Grow
259 Andreas Flache Werner Raub
260 Andreas Flache Rene Torenvlied
264 Andreas Flache About Scholar
265 Andreas Flache Search Help
266 Carlijn Bussemakers Gerbert Kraaykamp
267 Carlijn Bussemakers Jochem Tolsma
268 Carlijn Bussemakers Niels Spierings
269 Carlijn Bussemakers Kars Van Oosterhout
273 Carlijn Bussemakers About Scholar
274 Carlijn Bussemakers Search Help
275 Lincoln Quillian Jochem Tolsma
279 Lincoln Quillian About Scholar
280 Lincoln Quillian Search Help
281 René Veenstra Ormel, Johan (Hans)
282 René Veenstra Frank Verhulst
283 René Veenstra Siegwart Lindenberg
284 René Veenstra Jan Kornelis Dijkstra
285 René Veenstra Tineke Oldehinkel
286 René Veenstra Gijs Huitsing
287 René Veenstra Christian Steglich
288 René Veenstra Wilma Vollebergh
289 René Veenstra Christina Salmivalli
290 René Veenstra Tina Kretschmer
291 René Veenstra Miranda Sentse
292 René Veenstra Jelle Sijtsema
293 René Veenstra Rozemarijn Van Der Ploeg
294 René Veenstra Lydia Laninga-Wijnen
295 René Veenstra Sijmen A Reijneveld
296 René Veenstra Catharina A. Hartman
297 René Veenstra Beau Oldenburg
298 René Veenstra Miia Sainio
299 René Veenstra Marijtje A.j. Van Duijn
300 René Veenstra Z Harakeh
304 René Veenstra About Scholar
305 René Veenstra Search Help
306 Ioana Van Deurzen Wim Van Oorschot
307 Ioana Van Deurzen Erik Van Ingen
308 Ioana Van Deurzen Tobias H. Stark
309 Ioana Van Deurzen René Veenstra
310 Ioana Van Deurzen Jochem Tolsma
314 Ioana Van Deurzen About Scholar
315 Ioana Van Deurzen Search Help
319 Ariana Need About Scholar
320 Ariana Need 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 8.7: 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 department Health, family and work Verbakel Ellen radboud university w2McVJAAAAAJ
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 8.8: 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 department Health, family and work Verbakel Ellen radboud university w2McVJAAAAAJ 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 8.9: 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 8.10: 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 8.11: 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
Bram Lancee WLYKP4sAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Martin Kreidl l36sAacAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Marie-Louise Damen 1aZwmskAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Siegwart Lindenberg aPEP4mMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Anton Kramberger kq1LvS0AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Melinda C. Mills HX9KQ5MAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Katia Begall e7zfTqMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Peter Kerkhof zlVfv04AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Catrin Finkenauer ZouuGtQAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
jaap dronkers p0ABWjAAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Ronald Batenburg UK7nVSEAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Mérove Gijsberts QngQH7sAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Liset Van Dijk J6i2ABQAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Gerbert Kraaykamp l8aM4jAAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Cor van Dijkum X2OCg_4AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Richard Breen peGwkbcAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Jörg Rössel 4DO_94IAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Jan de Leeuw Ccp–gIAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
N Notten NakYfX4AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Christof Wolf -cDBb70AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Yossi Shavit brfWXKMAAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
Hans-Peter Blossfeld F0kCgy8AAAAJ -abxxmUAAAAJ Harry BG Ganzeboom
NA NA fuS31Y0AAAAJ Anne-Rigt Poortman
Tanja van der Lippe Rn56SIMAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Nikki van Gerwen nTFmcScAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Jelle Lössbroek qMK0bm4AAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Zoltán Lippényi 1Y-FSfsAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Margriet van Hek ZvLlx2EAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Anne-Rigt Poortman fuS31Y0AAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Katia Begall e7zfTqMAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Marie Evertsson XK2aLOwAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Anne Roeters -GHvKqQAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Laura den Dulk aB03M_IAAAAJ MjYWorQAAAAJ Leonie van Breeschoten
Yvonne Kops 3ehPBuAAAAAJ Rn56SIMAAAAJ Tanja van der Lippe
Agnieszka Kanas wTAuHRkAAAAJ Rn56SIMAAAAJ Tanja van der Lippe
Gerbert Kraaykamp l8aM4jAAAAAJ Rn56SIMAAAAJ Tanja van der Lippe
Jan Skopek Mmo1hFkAAAAJ Rn56SIMAAAAJ Tanja van der Lippe
Tally Katz-Gerro Kigyu04AAAAJ Rn56SIMAAAAJ Tanja van der Lippe
NA NA EgMYeqMAAAAJ Katya Ivanova
NA NA aB03M_IAAAAJ Laura den Dulk
Jan Skopek Mmo1hFkAAAAJ Vip84JAAAAAJ Thomas Leopold
Marcel Raab y2gQ3DUAAAAJ Vip84JAAAAAJ Thomas Leopold
Matthijs Kalmijn OEMqUeAAAAAJ Vip84JAAAAAJ Thomas Leopold
Sebastian Pink 2cUWmhUAAAAJ Vip84JAAAAAJ Thomas Leopold
Clemens Lechner rcTXPfEAAAAJ Vip84JAAAAAJ Thomas Leopold
Liliya Leopold cqIXj1oAAAAJ Vip84JAAAAAJ Thomas Leopold
Thijs Bol nMP0iF4AAAAJ Vip84JAAAAAJ Thomas Leopold
Hans-Peter Blossfeld F0kCgy8AAAAJ Vip84JAAAAAJ Thomas Leopold
Florian Schulz MWCt6hQAAAAJ Vip84JAAAAAJ Thomas Leopold
Dragana Stojmenovska 52rdSagAAAAJ Vip84JAAAAAJ Thomas Leopold
Manon A. van Scheppingen TF5olT4AAAAJ Vip84JAAAAAJ Thomas Leopold
Knut Wenzig yGaux80AAAAJ Vip84JAAAAAJ Thomas Leopold
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Simone Lederer t0MAgncAAAAJ 3jUymSMAAAAJ Tom Heskes
Kees Albers MJM6M1YAAAAJ 3jUymSMAAAAJ Tom Heskes
hans knoop auxDXNMAAAAJ 3jUymSMAAAAJ Tom Heskes
Fabian Gieseke g3ZiieoAAAAJ 3jUymSMAAAAJ Tom Heskes
Markus Barth f1G1FLIAAAAJ 3jUymSMAAAAJ Tom Heskes
Nanda Rommelse pj0deloAAAAJ 3jUymSMAAAAJ Tom Heskes
Clara I. Sánchez YxMfdYEAAAAJ 3jUymSMAAAAJ Tom Heskes
Geert Litjens neXK8tMAAAAJ 3jUymSMAAAAJ Tom Heskes
Bernhard Schölkopf DZ-fHPgAAAAJ 3jUymSMAAAAJ Tom Heskes
Dominik Janzing O-3bc_EAAAAJ 3jUymSMAAAAJ Tom Heskes
Marcos Zampieri vAx7VsoAAAAJ 3jUymSMAAAAJ Tom Heskes
Alejandro Arias Vasquez YwoMKYcAAAAJ 3jUymSMAAAAJ Tom Heskes
Robert Oostenveld eEbaa0UAAAAJ 3jUymSMAAAAJ Tom Heskes
Bert de Vries x3EIIHEAAAAJ 3jUymSMAAAAJ Tom Heskes
Herman Geuvers hbnF9xUAAAAJ 3jUymSMAAAAJ Tom Heskes
Catharina A. Hartman tNp9BUsAAAAJ 3jUymSMAAAAJ Tom Heskes
Jean de la Rosette pFZS7VcAAAAJ 3jUymSMAAAAJ Tom Heskes
Bart Kiemeney k_sMDv4AAAAJ 3jUymSMAAAAJ Tom Heskes
Daniel von Rhein noOctzYAAAAJ 3jUymSMAAAAJ Tom Heskes
Thomas Natschläger O1ix5uYAAAAJ 3jUymSMAAAAJ Tom Heskes
Thomas Grubinger emxAoI0AAAAJ 3jUymSMAAAAJ Tom Heskes
Twan van Laarhoven DOecs_8AAAAJ 3jUymSMAAAAJ Tom Heskes
Francesco Del Carratore OhnD0rgAAAAJ 3jUymSMAAAAJ Tom Heskes
Manfred te Grotenhuis sIIqmAcAAAAJ 3jUymSMAAAAJ Tom Heskes
ben pelzer aLppskkAAAAJ 3jUymSMAAAAJ Tom Heskes
Max A Little qHG9PB8AAAAJ 3jUymSMAAAAJ Tom Heskes
Ole Winther 7VAwhzUAAAAJ 3jUymSMAAAAJ Tom Heskes
Martine Hoogman mOIto-wAAAAJ 3jUymSMAAAAJ Tom Heskes
Darren Monckton UrBOZZ8AAAAJ 3jUymSMAAAAJ Tom Heskes
Luca Ambrogioni J9IABpQAAAAJ 3jUymSMAAAAJ Tom Heskes
Guido Sanguinetti R4zLUksAAAAJ 3jUymSMAAAAJ Tom Heskes
Lodewyk Wessels khuAybgAAAAJ 3jUymSMAAAAJ Tom Heskes
Bram Thijssen fVH6c_sAAAAJ 3jUymSMAAAAJ Tom Heskes
Andrew Zammit Mangion PHYtnUUAAAAJ 3jUymSMAAAAJ Tom Heskes
Florence d’Alché-Buc -qbL7z0AAAAJ 3jUymSMAAAAJ Tom Heskes
Christian F. Beckmann pl8kfJwAAAAJ 3jUymSMAAAAJ Tom Heskes
Steven Bloemen fpvnlBIAAAAJ 3jUymSMAAAAJ Tom Heskes
Valério ARM Ribeiro WfY32DoAAAAJ 3jUymSMAAAAJ Tom Heskes
Paul Groot nkZri-wAAAAJ 3jUymSMAAAAJ Tom Heskes
Jan van Roestel vMg1cvQAAAAJ 3jUymSMAAAAJ Tom Heskes
Matthias Ekman BTv-_UYAAAAJ 3jUymSMAAAAJ Tom Heskes
Tapio Pahikkala ROQQmPEAAAAJ 3jUymSMAAAAJ Tom Heskes
Christian Igel d-jF4zIAAAAJ 3jUymSMAAAAJ Tom Heskes
Rita Verhelst Fi_zsEoAAAAJ 3jUymSMAAAAJ Tom Heskes
Joseph Meletiadis z-Sa7y4AAAAJ 3jUymSMAAAAJ Tom Heskes
Zenas C. Chao 42UJugkAAAAJ 3jUymSMAAAAJ Tom Heskes
Concha Bielza zwSj1n8AAAAJ 3jUymSMAAAAJ Tom Heskes
Diego Vidaurre krbBtukAAAAJ 3jUymSMAAAAJ Tom Heskes
Jan AJG van den Brand men37bsAAAAJ 3jUymSMAAAAJ Tom Heskes
Wolfgang Ketter XkFVi8cAAAAJ 3jUymSMAAAAJ Tom Heskes
Onno Crasborn x6DoSOgAAAAJ 3jUymSMAAAAJ Tom Heskes
Peter Kok oxeunkoAAAAJ 3jUymSMAAAAJ Tom Heskes
José Miguel Hernández-Lobato BEBccCQAAAAJ 3jUymSMAAAAJ Tom Heskes
Tapio Salakoski t2jierQAAAAJ 3jUymSMAAAAJ Tom Heskes
Koen Vijverberg 6T7oaF8AAAAJ 3jUymSMAAAAJ Tom Heskes
Jill Naaijen rhrlaS8AAAAJ 3jUymSMAAAAJ Tom Heskes
Michael Biehl 493MsTwAAAAJ 3jUymSMAAAAJ Tom Heskes
Eric Maris YnX5NBQAAAAJ 3jUymSMAAAAJ Tom Heskes
John Quinn d4_ktUIAAAAJ 3jUymSMAAAAJ Tom Heskes
Moritz Schauer pzH_fSYAAAAJ 3jUymSMAAAAJ Tom Heskes
Stephen V. Faraone PtJmhRwAAAAJ 3jUymSMAAAAJ Tom Heskes
Annabeth P. Groenman sOPtXh4AAAAJ 3jUymSMAAAAJ Tom Heskes
Jaap Oosterlaan 0eQWbSwAAAAJ 3jUymSMAAAAJ Tom Heskes
Atsuko Takashima VVtjfA8AAAAJ 3jUymSMAAAAJ Tom Heskes
Marijn Huijbregts GOXdNLkAAAAJ 3jUymSMAAAAJ Tom Heskes
Arno Solin U_fJCnAAAAAJ 3jUymSMAAAAJ Tom Heskes
Simo Särkkä QVhmc9cAAAAJ 3jUymSMAAAAJ Tom Heskes
Katja Hofmann bHsjbLwAAAAJ 3jUymSMAAAAJ Tom Heskes
Zoubin Ghahramani 0uTu7fYAAAAJ 3jUymSMAAAAJ Tom Heskes
Jan-Mathijs Schoffelen _o1t7fsAAAAJ 3jUymSMAAAAJ Tom Heskes
Paauw Armand ljLwf1IAAAAJ 3jUymSMAAAAJ Tom Heskes
Yuka O. Okazaki heaIp6kAAAAJ 3jUymSMAAAAJ Tom Heskes
Cosmin Oancea RpmM52IAAAAJ 3jUymSMAAAAJ Tom Heskes
Kai Lars Polsterer GlE_sY0AAAAJ 3jUymSMAAAAJ Tom Heskes
Sinead Williamson Lt9LpLMAAAAJ 3jUymSMAAAAJ Tom Heskes
Maytal Saar-Tsechansky SUAbOcgAAAAJ 3jUymSMAAAAJ Tom Heskes
David Barber dqJPZHEAAAAJ 3jUymSMAAAAJ Tom Heskes
Matthias Treder iEBE0qgAAAAJ 3jUymSMAAAAJ Tom Heskes
Ali Taylan Cemgil X3ZFZ7AAAAAJ 3jUymSMAAAAJ Tom Heskes
Sybert Stroeve dkgOmUsAAAAJ 3jUymSMAAAAJ Tom Heskes
Céline Brouard EznKrYEAAAAJ 3jUymSMAAAAJ Tom Heskes
Laurens van de Wiel pmvYGJcAAAAJ 3jUymSMAAAAJ Tom Heskes
Tom de Ruijter xWoK0GYAAAAJ 3jUymSMAAAAJ Tom Heskes
Tomi Kauppinen XwyKrbQAAAAJ 3jUymSMAAAAJ Tom Heskes
Marten den Uyl J9Us62EAAAAJ 3jUymSMAAAAJ Tom Heskes
Pasi Jylänki SQPOa14AAAAJ 3jUymSMAAAAJ Tom Heskes
David van Leeuwen yW_0v-kAAAAJ 3jUymSMAAAAJ Tom Heskes
Antal van den Bosch LCuzJyYAAAAJ 3jUymSMAAAAJ Tom Heskes
Manfred te Grotenhuis sIIqmAcAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
ben pelzer aLppskkAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Ariana Need AmFGGWEAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Laurie C. Maldonado urXwGoEAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Henk van der Kolk SjieyBcAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Kenneth Nelson AS6E4z8AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Wim Van Lancker VvCWQf8AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Rob Eisinga GDHdsXAAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Ruben Konig UoJeIDEAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Mark Levels 1KVq6W0AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Alexander W. Schmidt-Catran wf6XxagAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Mara A. Yerkes N_b-99cAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Minna van Gerven sIUWGjYAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
wout ultee vfhKSjgAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Richard J. Petts 6ayGbAcAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Rebecca Lawthom ez36f54AAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Katherine Runswick-Cole b7l0jagAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
jean-charles Languilaire D6MdwLgAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Jenny Fisher brVeeLkAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Diego Collado 6HsnWNsAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
bea cantillon ovPLLuYAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
Roderick Sluiter oFYyNMIAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
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
Tuba Bircan zYVT4kAAAAAJ KA_HjwEAAAAJ Rense Nieuwenhuis
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
Richard Scheltema _PA0rK4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Pawel Herzyk S0FmkHUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tilmann Weber KuOZZSAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Kai Blin b8ihZ-oAAAAJ sOtfTK8AAAAJ Rainer Breitling
Patrick Armengaud zQzi72kAAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Karl Burgess r1VzxrUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Monika Heiner esKBj4UAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ronald Plasterk 6p9IFm8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Sang Yup Lee uSXESZIAAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Desmond Higham DHQy3wcHP4kC sOtfTK8AAAAJ Rainer Breitling
Marcel P. de Vries Y8ucDdMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Peter Cimermančič VoDxiRAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Victor de Jager s8i7LaAAAAAJ sOtfTK8AAAAJ Rainer Breitling
NJ Turner 1mP1Q1sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Eduard J. Kerkhoven rqNuEj4AAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Satria A Kautsar y6sm7j8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Christopher J. Schwalen dqtASCoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Xiaowen Lu VelOyNAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jeroen S. Dickschat PV1_aI0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Emzo de los Santos kIMqnM4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Maria Vinaixa MBX-5O8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Joanne Chory EeEtku8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Jennifer Nemhauser tgAGQm0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Ben Wittner yNFHGG0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Tejas Gandhi tnfT1lMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Perdita Barran 8mOEDVIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tom Heskes 3jUymSMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Rob Eisinga GDHdsXAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Thomas Hankemeier 8rLa3BMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Mark R. Viant XLN4i1YAAAAJ sOtfTK8AAAAJ Rainer Breitling
Steffen Neumann EcQVenkAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ralf Weber A4IK57kAAAAJ sOtfTK8AAAAJ Rainer Breitling
Warwick Dunn oE2lZlAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Joachim Kopka CPIxfloAAAAJ sOtfTK8AAAAJ Rainer Breitling
Richard Orton MJBIgMEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Daniel Speidel h3IErXMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Poolman B GQObZz0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Hjalmar Permentier _Vx2EHcAAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Colin Campbell rqHh-U8AAAAJ sOtfTK8AAAAJ Rainer Breitling
DJ Slotboom gCJjJ14AAAAJ sOtfTK8AAAAJ Rainer Breitling
Philip Shapira tbmaHr0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Hans Westerhoff UJCCkB8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Don Nguyen F_WOwMYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Robin Donaldson QnJNl5oAAAAJ sOtfTK8AAAAJ Rainer Breitling
Basten Snoek oYdq4PwAAAAJ sOtfTK8AAAAJ Rainer Breitling
K. Joeri van der Velde Dpdzh_cAAAAJ sOtfTK8AAAAJ Rainer Breitling
Theo Blick iNcay1gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Joe Wandy K6nz3aoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Andrew Pitt gjyZD7gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Thorsten Stiewe 5NhIqGQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Fabien Jourdan pbvEidYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Charles Ebikeme iz_YU64AAAAJ sOtfTK8AAAAJ Rainer Breitling
Anas Kamleh -xQU6FkAAAAJ sOtfTK8AAAAJ Rainer Breitling
EJ Blom m6Qw4Y4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Sacha van Hijum 3fgwGNgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jos Roerdink jCFYHlkAAAAJ sOtfTK8AAAAJ Rainer Breitling
OP Kuipers fWA7-G8AAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Donal Fellows 5BYTqwQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ziga Zebec, PhD aTTVTdYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Carole Goble 2G4kqPYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Stefan Kol aieqQ5oAAAAJ sOtfTK8AAAAJ Rainer Breitling
William Schafer rpGmiKsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Konrad Zych DZAWbbwAAAAJ sOtfTK8AAAAJ Rainer Breitling
Fred Opperdoes GET-hUAAAAAJ sOtfTK8AAAAJ Rainer Breitling
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
Kay Nieselt Ty-5Q7sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Andrzej M. Kierzek uPn9tEkAAAAJ sOtfTK8AAAAJ Rainer Breitling
Claus Bendtsen Vvh1YD0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Marina Wright Muelas ulEb2J8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Tatyana Suhan AC8M0ncAAAAJ sOtfTK8AAAAJ Rainer Breitling
Pedro Mendes lDWW5J0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Sophia Ananiadou quhi-K0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Riza Batista-Navarro fRBJmp9gk_cC sOtfTK8AAAAJ Rainer Breitling
Paul Dobson L2JKzNUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Fabrizia Fusetti 82qAoj0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Han Roelofsen GdIaDFYAAAAJ sOtfTK8AAAAJ Rainer Breitling
J Bijzet UFz-MMsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Syed Murtuza Baker HPQ_NvwAAAAJ sOtfTK8AAAAJ Rainer Breitling
Michael Biehl 493MsTwAAAAJ sOtfTK8AAAAJ Rainer Breitling
Gonzalo Vera A0cgc5sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Howbeer Muhamadali 5Ha0hK0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Rosa Morra IejH-_gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Neil Dixon ZS85RP0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Adelinde Uhrmacher NIyd6aoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Harm-Jan Westra REHR8RQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Christian Johannes Gloeckner nwJiIBUAAAAJ sOtfTK8AAAAJ Rainer Breitling
Stephan Feldmeier DcaBqAkAAAAJ sOtfTK8AAAAJ Rainer Breitling
Henrik Krehenwinkel 1vCvXzgAAAAJ sOtfTK8AAAAJ Rainer Breitling
Jurgen R Haanstra l_ZK4WIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Theodore Papamarkou ydMfbhAAAAAJ sOtfTK8AAAAJ Rainer Breitling
Abeer Fadda SXHNTN8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Alejandro E. Leroux JTEngzYAAAAJ sOtfTK8AAAAJ Rainer Breitling
Robert Kelsh REDpJ5sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tamás Székely EU5mIF8AAAAJ sOtfTK8AAAAJ Rainer Breitling
Frank J Bruggeman CXRLPG0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Walter P. Pfliegler 8T1ExG0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Prof Gregory L Challis sTdzECIAAAAJ sOtfTK8AAAAJ Rainer Breitling
Mohamed A. Marahiel sVX44K0AAAAJ sOtfTK8AAAAJ Rainer Breitling
Anna Dominiczak N7Hqdj4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Peter Leadlay jcwHIesAAAAJ sOtfTK8AAAAJ Rainer Breitling
Philip I. Buckland 4ph6iocAAAAJ sOtfTK8AAAAJ Rainer Breitling
Joel S. Bader -dyxvHoAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tom Ellis 3j1vzfQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Christian Allen 51p1B7gAAAAJ sOtfTK8AAAAJ Rainer Breitling
Claude Ferec i54izBMAAAAJ sOtfTK8AAAAJ Rainer Breitling
Tim Downing Z6dNzgsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Ulrike K Müller NcXqEk4AAAAJ sOtfTK8AAAAJ Rainer Breitling
Loretta C Johnson i2Wn9EQAAAAJ sOtfTK8AAAAJ Rainer Breitling
Luca Cardelli npBTgSsAAAAJ sOtfTK8AAAAJ Rainer Breitling
Gary Churchill lfdvCjEAAAAJ sOtfTK8AAAAJ Rainer Breitling
Hideo Imamura V3eCT8sAAAAJ sOtfTK8AAAAJ Rainer Breitling
Thomas Wolf -TdraGkAAAAJ sOtfTK8AAAAJ Rainer Breitling
W Gaze OR WH Gaze OR William Gaze OR STREAM consortium yDLp-W4AAAAJ sOtfTK8AAAAJ Rainer Breitling
peer scheepers hPeXxvEAAAAJ 078qsZoAAAAJ Marcel Lubbers
Marcel Coenders HH4PPBwAAAAJ 078qsZoAAAAJ Marcel Lubbers
Mérove Gijsberts QngQH7sAAAAJ 078qsZoAAAAJ Marcel Lubbers
Eva Jaspers Ss6I3eAAAAAJ 078qsZoAAAAJ Marcel Lubbers
Roza Meuleman iKs_5WkAAAAJ 078qsZoAAAAJ Marcel Lubbers
Jochem Tolsma Iu23-90AAAAJ 078qsZoAAAAJ Marcel Lubbers
Rob Eisinga GDHdsXAAAAAJ 078qsZoAAAAJ Marcel Lubbers
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Hyunjoon Park DpuRLdsAAAAJ 28aIODkAAAAJ Jeroen Smits
mieke verloo mlIXgUcAAAAJ 28aIODkAAAAJ Jeroen Smits
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Huib Pellikaan