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Return migration of German-affiliated
researchers: Analyzing departure and return
by gender, cohort, and discipline using
Scopus bibliometric data 1996-2020
Xinyi Zhao1,2, Samin Aref3,1*, Emilio Zagheni1and Guy
Stecklov4
1Lab of Digital and Computational Demography, Max Planck Institute for Demographic
Research, Konrad-Zuse-Str. 1, Rostock, 18057, Mecklenburg-Vorpommern, Germany.
2Leverhulme Centre for Demographic Science, Department of Sociology, University of
Oxford, 42-43 Park End Street, Oxford, OX1 1JD, UK.
3Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s
College Rd, Toronto, M5S 3G8, ON, Canada.
4Department of Sociology, University of British Columbia, 2329 West Mall, Vancouver,
V6T 1Z4, BC, Canada.
*Corresponding author. E-mail: aref@mie.utoronto.ca;
Abstract
The international migration of researchers is an important dimension of
scientific mobility, and has been the subject of considerable policy debate.
However, tracking the migration life courses of researchers is challeng-
ing due to data limitations. In this study, we use Scopus bibliometric
data on eight million publications from 1.1 million researchers who have
published at least once with an affiliation address from Germany in 1996-
2020. We construct the partial life histories of published researchers in
this period and explore both their out-migration and the subsequent
return of a subset of this group: the returnees. Our analyses shed light on
the career stages and gender disparities between researchers who remain
in Germany, those who emigrate, and those who eventually return. We
find that the return migration streams are even more gender imbal-
anced, which points to the need for additional efforts to encourage female
researchers to come back to Germany. We document a slightly declining
trend in return migration among more recent cohorts of researchers who
left Germany, which, for most disciplines, was associated with a decrease
in the German collaborative ties of these researchers. Moreover, we find
that the gender disparities for the most gender imbalanced disciplines are
1
arXiv:2110.08340v2 [cs.DL] 1 Feb 2022
2Return migration in academia
unlikely to be mitigated by return migration given the gender composi-
tions of the cohorts of researchers who have left Germany and of those
who have returned. This analysis uncovers new dimensions of migra-
tion among scholars by investigating the return migration of published
researchers, which is critical for the development of science policy.
Keywords: High-skilled migration, Return migration, Computational
demography, Scholarly migration, Gender disparities, Science of science,
Scientometrics
1 Introduction
To ensure the dynamic flow of scientific ideas and expertise, and to promote
and facilitate knowledge production, national science systems rely on the inter-
national exchange of scholars (Moed et al,2013;Conchi and Michels,2014;
Robinson-Garcia et al,2019). While the globalization of research has numerous
benefits that have been widely acknowledged in the literature (Appelt et al,
2015;Bauder,2015;Franzoni et al,2015;Netz and Jaksztat,2017), the undeni-
able downside of academic mobility is the potential loss of talent for countries
that train and export more researchers than they receive from other countries.
Global competition for talent has led to the introduction of a range of policies
and economic incentives aimed at encouraging balanced flows of researchers.
However, little attention has been paid to returnees who stay in other coun-
tries temporarily, and then return to their country of academic origin. These
returnees usually bring with them additional skills, newly established con-
nections and collaborative ties, and complementary expertise acquired abroad
(OECD,2008;Appelt et al,2015). Equally importantly, there is evidence that
these returnees tend to receive far more citations than their stationary counter-
parts (Guthrie et al,2017), or their internationally mobile counterparts who do
not return (Zhao et al,2021). Thus, for countries that are facing the challenge
of the out-migration of researchers exceeding the in-migration of researchers
(Zhao et al,2021), facilitating the return migration of scholars and taking
steps to rebalance these trends to their benefit are extremely critical.
Despite being recognized as a science powerhouse, Germany has been send-
ing more highly qualified individuals (including researchers) abroad than it has
been receiving according to some reports (OECD,2008;Schiller and Cordes,
2016;Zhao et al,2021). In recent years, Germany has implemented a range of
policies and programs designed to attract students and researchers from other
countries (Bardin,2016;Eule,2016;Dvell,2019). There are several return
migration programs aimed at maintaining and strengthening ties with previ-
ously German-affiliated researchers in order to facilitate their re-integration
into the German science system (Conchi and Michels,2014). For example,
the German Academic International Network (GAIN)1supports current and
1www.gain-network.org
Return migration in academia 3
prospective returnees, and facilitates cooperation between researchers in Ger-
many and North America. The German Scholars Organization (GSO)2is
another initiative aimed at reversing Germany’s “brain drain” and turning it
into a “brain gain” by offering several services to academic professionals in
Germany. Given the practical relevance of this issue to policy development
and strategic decisions at a national level, a better understanding of the tra-
jectories and migration trends of researchers formerly affiliated with German
institutions and academic returnees to Germany is urgently needed.
Return migration also has potentially large implications for the persistence
of gender inequalities in academia (Zippel,2017). The issue of gender dispar-
ities in academia has been extensively documented across many disciplines
and in most countries (Larivi`ere et al,2013;Huang et al,2020;Zhao et al,
2021). Although it has been suggested that breaking the glass ceiling that hin-
ders women’s advancement is especially challenging for internationally mobile
academics (Zippel,2017;Zhao et al,2021), the heterogeneity in the levels
of gender disparity among former and current German-affiliated researchers,
and particularly among returnees, is not clear. Exploring this topic is the first
step towards achieving more balanced gender representation in academia, and
ensuring the sustainability of academic careers for women researchers (Weert,
2013;Zhao et al,2021). Due to the implementation of a wide range of measures
and policies aimed at promoting gender equality in academia, female repre-
sentation in various disciplines have been increasing (Macaluso et al,2016;
Zippel,2017;Huang et al,2020). Given these developments, studying the tem-
poral trends at the intersection of gender and international mobility can have
important administrative and policy implications. Moreover, the question of
how the representation of female returnees in more recent cohorts has changed
in response to these policies and developments deserves more attention. An
extensive analysis of return migration among German-affiliated researchers
disaggregated by discipline, gender, and cohort is critical for making progress
towards transforming the German science system into an inclusive and diverse
system with balanced migration flows of scholars.
Previous studies have suggested that academic returnees tend to main-
tain collaborative ties with their previous host countries (Ackers and Gill,
2005;Conchi and Michels,2014;Franzoni et al,2014;Guthrie et al,2017). As
these ties are critical components of knowledge transfers, it is clear that schol-
arly migration is no longer a zero-sum game. Returnee researchers may face
challenges when attempting to incorporate the knowledge they have acquired
abroad into another context, and in re-establishing their career in their aca-
demic home country (Melin and Janson,2006;Weert,2013;Fernndez-Zubieta
et al,2015). This may be partly because researchers are disconnected from
their home country’s academic networks while abroad, which may limit their
access to the information and support they would need to find a job in their
academic home country. This erosion of connections may, in turn, reduce the
2www.gsonet.org
4Return migration in academia
willingness of researchers to return (Ackers and Gill,2005;Baruffaldi and Lan-
doni,2012). Previous work on this topic has shown that there is a gap in
macro-level quantitative research on collaboration and migration among schol-
ars. In particular, the interactions between scholars’ collaborative ties with
their academic home countries and return migration have not been previously
investigated. Therefore, a comprehensive analysis that examines these rela-
tionships can provide useful insights into return migration among academics.
This can facilitate the development of policies that create additional paths for
returnees to re-integrate professionally into their academic home countries.
Motivated by the observations above, this paper relies on large-scale digi-
tized bibliometric data from Scopus (Burnham,2006;Mongeon and Paul-Hus,
2016) to investigate the trajectories and migration trends of internationally
mobile researchers in Germany as well as their German academic links during
the period of being outside of Germany. We analyze German-affiliated pub-
lished researchers during the 1996-2020 period, while taking each researcher’s
years of experience, gender, discipline, and cohort into account. Specifically,
this paper aims to address the following research questions (which are both
methodological and empirical), with a focus on the return migration of scholars
to Germany:
1. What is the composition of returning researchers based on their gender,
years of experience, and previous host countries? (Subsection 3.1 and 3.2)
2. How does the gender ratio vary by discipline and cohort among researchers
who leave Germany, and among researchers who return? (Subsection 3.3
and 3.4)
3. How does the association between return migration to Germany and col-
laboration with German institutions vary across disciplines and cohorts?
(Subsection 3.5)
2 Materials and methods
2.1 Authorship records of German-affiliated researchers
Scopus is an abstract and citation database of scientific literature (Burnham,
2006) that covers over 77 million publications, according to its 2020 cover-
age guide (Elsevier,2020). From the Scopus database, we have obtained the
authorship records (linkages between an author’s affiliation and a publication)
of more than 1.1 million researchers for our analysis, which involves over eight
million publications. All of these researchers had used a German affiliation
address in at least one publication at some point during the 1996-2020 period
(ending in April 2020).
2.2 Pre-processing of raw bibliometric data
To ensure the reliability of our results, we pre-process the raw bibliometric data
from Scopus using three sequential steps. These three steps are: handling the
missing countries in the dataset (discussed in subsection 2.2.1), disambiguating
Return migration in academia 5
the author profiles (discussed in subsection 2.2.2), and inferring gender from
the authors’ first names (discussed in subsection 2.2.3).
2.2.1 Handling missing countries in the dataset
First, there are 74,430 (5%) authorship records for which the country variable
is missing. To handle the missing values systematically, we have developed a
neural network algorithm inspired by Miranda-Gonz´alez et al (2020) that pre-
dicts the missing countries with a high degree of accuracy. This supervised
learning algorithm takes an affiliation address as the input, and predicts the
country as the output. The data used to make the prediction are city, insti-
tution, and address. These strings are combined using a bag-of-words method
with frequencies (of a given word in a sample) that are normalized (relative to
the frequency in the whole dataset) using a term frequency inverse document
frequency (tfidf) approach (Tokunaga and Makoto,1994). A simple and stan-
dard architecture is used to develop the neural network (deep feed forward
neural network) with non-linear activation functions. We use a random set
of one million authorship records drawn from our dataset that contain coun-
try information and split it into training data (80%) and testing data (20%).
Other technical details of the development of the neural network have been
explained in (Miranda-Gonz´alez et al,2020). The predictions made based on
the test dataset show that the neural network can correctly predict the coun-
try for 98.4% of records, which is a level of accuracy we consider acceptable
for handling missing country data.
2.2.2 Author name disambiguation
The second step of our data pre-processing helps us overcome the problems
associated with using Scopus author IDs to identify unique authors. It has been
shown that Scopus author IDs have high levels of precision and completeness
(Kawashima and Tomizawa,2015;Paturi and Loktev,2020). Precision mea-
sures the percentage of author IDs that are associated with the publications
of a single individual only. Completeness measures the percentage of author
IDs that are associated with all of the Scopus publications of an individual.
The results of an evaluation of the accuracy of Scopus author IDs conducted
in August 2020 showed that the precision and the completeness of Scopus
author profiles are 98.3% and 90.6%, respectively (Paturi and Loktev,2020).
However, while it appears that the quality of individual-level Scopus data is
sufficiently high to enable us to study the migration of researchers (Kawashima
and Tomizawa,2015;Aman,2018a), there are several notable limitations to
keep in mind when using Scopus data for migration research. The precision
limits in Scopus author IDs imply that 1.7% of Scopus author IDs may be
associated with the publications of more than one person, which could affect
the accuracy of the migration events detected by looking at changes in affil-
iation countries per author ID. Accordingly, as the second step in our data
6Return migration in academia
pre-processing, a subset of authorship records that are more likely to have suf-
fered from the precision flaws of Scopus author IDs are analyzed using our
conservative author name disambiguation algorithm.
Our author disambiguation algorithm is inspired by the state-of-the-art
methods in author name disambiguation (D’Angelo and van Eck,2020).
It assumes that every two authorship records are from distinct individuals
(despite sharing a Scopus author ID), unless sufficient evidence is found to the
contrary using a rule-based scoring approach and a clustering method. We first
calculate the similarity score of each pair of authorship records belonging to
the same author ID. The similarity is measured based on author names, coau-
thor names, subjects, funding information, and grant numbers. The author
disambiguation algorithm makes all pairwise comparisons between authorship
records with the same author ID, and creates a distance matrix based on
similarities and dissimilarities in the aforementioned features for each pair of
records. A clustering algorithm is then used to process the distance matrices,
and to cluster similar authorship records. We then issue revised author IDs
based on the resulting clusters. We use the agglomerative clustering algorithm
from the scikit-learn Python library (Pedregosa et al,2011) to cluster author-
ship records. This algorithm belongs to the family of hierarchical clustering
methods. Supporting our conservative approach, it first places each record in
its own cluster, and then merges pairs of clusters successively if doing so min-
imally increases a given linkage distance (Pedregosa et al,2011). As well as
being compatible with our conservative approach, agglomerative clustering has
the advantage of offering us the flexibility to process any pairwise distance
matrix.
We examine the author profiles that are outliers in terms of the number
of affiliation countries or the number of publications. In particular, there are
30,715 (2%) author profiles that are associated with more than six countries
of affiliation, or more than 292 publications3(an average of more than one
publication per month across a period of 24 years and four months). These
author profiles are more likely than others to be affected by the precision
flaws of Scopus author IDs. For example, each Scopus author profile could
contain records from more than one individual researcher. Based on these
criteria, 25,000 author IDs are classified as suspicious. These author IDs are
associated with 2,242,797 publications. After disambiguation, revised author
IDs are issued for these records according to their clusters, and are then merged
with the rest of the data in preparation for the third pre-processing step.
2.2.3 Inferring gender from first names
The last step of our data pre-processing is inferring gender from first names,
which involves looking up first names in a large database of names and genders
called Genderize (genderize.io). After performing basic text operations (like
removing middle initials from the first name field), we obtained the gender
3These two thresholds are chosen empirically so that a subset of outliers of a size compatible
with the results on Scopus author ID precision flaws (Paturi and Loktev,2020) can be extracted.
Return migration in academia 7
for 1,117,813 author profiles in our dataset. For the remaining profiles, we
manually searched for public author information to determine the gender by
checking the individuals’ personal homepages, curricula vitae, online profiles,
and biographies in publications, as well as other online sources. Using this
manual approach, we were able to determine the genders for 3,139 additional
author profiles. Finally, the most likely gender for 77% of the author profiles
in our dataset was determined through either algorithmic or manual gender
detection. For our analyses that involve gender (e.g., measuring gender ratios),
we set aside the 23% of author profiles whose gender could not be determined
either algorithmically or manually.
2.3 Migration events, mobility types, and career stages
The international mobility of researchers is determined by identifying the
changes in the affiliation addresses of authors across different publications over
time. To more reliably detect migration events, the most frequent (mode) coun-
try(ies) of affiliation is extracted for each researcher in each year. A migration
event is considered to have happened only if the mode country of affiliation
changes for the researcher across different years (Subbotin and Aref,2021).
Accordingly, the country of academic origin (country of academic destination)
is defined as the mode country during the first (last) year of publishing. Based
on the individual’s migration events or the lack thereof, each researcher can
be assigned to one of the following four categories:
1. Non-mover (with Germany being the researcher’s mode country in all
years);
2. Immigrants and transients (origin: not Germany; but with Germany being
the researcher’s mode country at some point in time);
3. Outward (origin: Germany; current country: not Germany); and
4. Returnee (origin and current country: Germany; but with another country
being the researcher’s mode country at some point in time).
Except for non-movers, researchers may move between the categories over
time, as an individual’s status depends on the time period being examined.
For example, an individual identified as an outward researcher will become a
returnee at the next point in time if a move to Germany is detected.
We define the academic age (age) of a researcher as the number of years
since his/her first publication. Furthermore, we classify researchers as early-
career (senior) if their academic age is seven years or less (14 years or more).
Researchers with an academic age between seven and 14 years are categorized
as mid-career (Aref et al,2019). As our dataset covers only the 1996-2020
period, our analysis of some temporal dimensions of the data or cohorts of
researchers may suffer from left truncation and/or right censoring. We explain
in Section 3some of the resulting limitations of our dataset.
8Return migration in academia
2.4 Inferring disciplines using a topic model
The Science Journal Classification (ASJC) codes in our bibliometric dataset
indicate the fields and disciplines (subfields) of publication venues, which could
be used as proxies for determining the disciplines of researchers (Zhao et al,
2021). However, because the links between the disciplines associated with jour-
nals and the disciplines of authors are indirect, we use a data-driven method to
infer the disciplines of individual researchers. Topic modelling, which is a com-
mon unsupervised learning approach for natural language processing, can be
used to determine the disciplines of researchers by inferring the latent topical
structure of textual bibliometric data (Blei,2012;Gerlach et al,2018).
As a flexible topic model, Latent Dirichlet Allocation (LDA) is in essence
a generative probabilistic model with three layers: document, topic, and word
(Pritchard et al,2000;Blei et al,2003). It assumes that each topic is a mixture
of an underlying set of words, and that each document is a mixture of a set of
topic probabilities (Blei et al,2003;Gerlach et al,2018). LDA has been shown
to perform reliably in automatically identifying semantic topic information
from large-scale textual data (Dahal et al,2019).
From the publications authored by each researcher in our dataset, we
extract publication titles, journal titles, and keywords to generate the individ-
ual’s text corpus (document in LDA terminology). We then tokenize the text
by removing all punctuation, and making all of the words lower-case to improve
the cohesion of the documents. The remaining words in each text corpus are
then lemmatized and stemmed to their root form. This includes being trans-
ferred to the first person and the present tense if needed. For some common
phases (e.g., machine learning) that are related to discipline topics, we use the
multi-word expressions, two-gram collocations, and three-gram collocations
from all of the text documents according to their frequency of occurrence. The
tokenized and lemmatized texts are then abstracted to a bag of words, which
records the indices of words and the number of times each word appears in an
author’s LDA document. In LDA, each document can be considered as a mix-
ture of latent topics, each of which is characterized by a distribution of words
(Blei et al,2003). The topic coherence score is a measurement of the seman-
tic similarity between the high scoring words in each topic, and represents the
interpretability of the topics.
After the implementation of the steps above, the average topic coherence
score of all topics is maximized at 0.67 (through trial and error) when we
allow 30 topics for the whole text corpus. Each topic is composed of a set of
vocabularies and the corresponding weights that indicate their contributions to
the topic. The topic with the highest probability among all of the initial topics
is assigned to each author’s LDA document as the intermediate result. We
manually interpret the topics based on their most frequent terms and assign
titles to them accordingly. When different topics include the similar or highly
relevant keywords, we combine them into a single discipline. For example, a
topic involving “space” and “earth” and a topic involving “galaxy” and “star”
are combined to the same discipline: “Earth and Planetary Sciences.” Using
Return migration in academia 9
this approach, we produce 17 distinguishable disciplines4to represent the main
discipline of each researcher according to their publications. Detailed results on
the 30 topics and their mapping to 17 disciplines are provided in the appendix.
We consider an author’s LDA document to be “Multidisciplinary” if it does
not have any contribution percentage for any topic that exceeds 0.3.
2.5 Cohorts leaving Germany and returning to Germany
A cohort is a group of people who have experienced a common event in a
selected period, such as birth (Reilly et al,2005;Rothman,2012). In our
analysis, we use the time of first publication as the common event for defining
cohorts of researchers. To reduce the impact of left-truncated data on cohorts,
which is more likely for the first few years of our dataset, we use the following
three cohorts: 1998-2001, 2002-2005, and 2006-2009.
Person-time rate is an index commonly used in epidemiology and demog-
raphy to express an incidence rate: i.e., the number of incidents (migration
events) per person-time in a population during a period (Rothman,2012). The
denominator of a person-time rate is the total amount of time that the study
members are at risk of a certain incident during a period. One key advantage
of using person-time rate for migration is that it enables us to consider that
different individuals are exposed to migration events for varying amounts of
time. The incidents we are interested in are: (1) leaving Germany for the group
of all researchers in Germany, and (2) returning to Germany for the group of
all outward researchers. Given a specific period of time t, the departure rate
per 1,000 person-years for a cohort cis defined in Eq. (1).
Rdeparture(c,t)=Ndeparture(c,t)/P Tin Germany(c,t)×1000 (1)
In Eq. (1), Ndeparture(c,t)represents the number of researchers from cohort
cleaving Germany during time period t, and P Tin Germany(c,t)represents the
sum of the number of years each researcher from cohort cstays in Germany
(and is exposed to leaving Germany) during period t. The denominator of the
departure rate takes all of the researchers who are in Germany into considera-
tion as the population exposed to leaving Germany. Similarly, given a specific
period of time t, the return rate per 1,000 person-years for a cohort cis defined
in Eq. (2).
Rreturn(c,t)=Nreturn(c,t)/P Toutside Germany(c,t)×1000 (2)
In Eq. (2), Nreturn(c,t)is the number of returnees from cohort cduring
period t, and P Toutside Germany(c,t)is the sum of the number of years each
outward researcher stays outside of Germany (and is exposed to returning to
Germany) during period t. The denominator of the return rate only involves
4“Agricultural, Biological and Environmental Sciences,” “Biochemistry, Genetics and Molec-
ular Biology,” “Chemistry and Chemical Engineering,” “Computer Science,” “Earth and
Planetary Sciences,” “Economics and Social Science,” “Engineering,” “Energy,” “Health Pro-
fessions,” “Immunology and Microbiology,” “Materials Science,” “Mathematics,” “Medicine,”
“Neuroscience,” “Pharmacology, Toxicology and Pharmaceutics,” “Physics and Astronomy,” and
“Psychology.”
10 Return migration in academia
researchers who have left Germany as the population exposed to returning
to Germany. We compute the departure rates and the return rates separately
for male and female researchers in the three cohorts (1998-2001, 2002-2005,
and 2006-2009). Specifically, we consider the departure rates of researchers of
different cohorts who leave Germany at the academic ages of one to five, and
the corresponding return rates during the first five years after their departure
from Germany. Taking the 1998-2001 cohort as an example, the departure rate
at academic age one refers to the outward researchers who were “academically
born” in 1998 (1999, 2000, 2001) and leave Germany in 1999 (correspondingly,
2000, 2001, 2002). For the same cohort, the return rate refers to the researchers
who returned during the 2000-2004 (2001-2005, 2002-2006, 2003-2007) period
among the outward researchers who left Germany in 1999 (correspondingly,
2000, 2001, 2002).
2.6 Collaborations with Germany while away
We define and use the variable collaborative ratio to distinguish the strength of
the academic linkages with Germany for outward researchers during the period
when they were away from Germany. For the outward researcher, i, during the
period when s/he was away from Germany (denoted by t), we calculate his/her
collaborative ratio using a simple fraction: CR(i)=D(i,t)/N(i,t). The numera-
tor, D(i,t), counts the publications of outward researcher iin period tthat list
a German affiliation for ior for his/her co-authors. The denominator, N(i,t),
is the number of all publications of outward researcher iduring period t. If a
publication authored by iduring period thas at least one author with a Ger-
man affiliation, it contributes to the collaborative ratio CR(i). Furthermore,
the average collaborative ratio for all researchers in each discipline (cohort) is
calculated to measure the average strength of the academic collaboration with
Germany maintained by the outward researchers in that discipline (cohort).
3 Results
Using cleaned and processed bibliometric data associated with over eight mil-
lion Scopus-indexed publications over 1996-2020 from more than one million
German-affiliated researchers, we analyze data on 375,288 female researchers
associated with 2,665,139 publications and 745,664 male researchers associ-
ated with 6,516,016 publications. Among these researchers, there are 50,803
female mobile researchers (associated with 1,007,606 publications) and 119,298
male mobile researchers (associated with 2,760,282 publications) who have ever
migrated between Germany and 194 other countries in our dataset. There are
103,573±48610 researchers in each discipline, with medicine having the largest
number of researchers (199,658) and health professions having the smallest
number of researchers (26,398).
Based on pre-processed data, we provide five analyses to describe differ-
ent aspects of the emigration and the return migration of these researchers. In
Return migration in academia 11
Subsections 3.1 and 3.2, we track their career life courses from a temporal per-
spective, and their geographic trajectories from a spatial perspective. We then
compare the departure rates and the return rates of female and male scholars
to explore the gender differences across cohorts and disciplines (Subsections
3.3 and 3.4). Finally, we look at the association between the return rates of
outward researchers and the strength of their collaborative ties to Germany in
Subsection 3.5. Our inferred migration events dataset is publicly available in
a FigShare data repository (Zhao et al,2022).
3.1 Age and gender composition of researchers
We compare the age and gender compositions of three groups of researchers:
non-movers, outward researchers, and returnees. Figure 1compares the age
and gender distribution of these three groups using population pyramids, which
include individuals who survive as an active researcher up to a certain date
(researchers whose latest publication was in 2010 or later). Ignoring the trun-
cated top age, we can see that both female and male non-movers have a notable
and pronounced bulge at the transition from early-career ages to middle-career
ages, which is presented as an expansive pattern. However, the non-mover age
pyramid shows considerably small proportions at other ages, with a pattern
characterized by a sharp decline to age 21, followed by a stable increase until
age 25+. The median ages for female and male non-movers are nine and 10
years, respectively. In the categories of outward researchers and returnees, the
overall length of academic trajectories has been considerably prolonged for
both female and male researchers. Specifically, the median ages of female out-
ward researchers and returnees are 12 and 14, respectively; and the median
ages of male outward researchers and returnees are 13 and 16, respectively.
15k 10k 5k 0 0
5
10
15
20
25+
Female: Mid-career Senior
Early-career
0 5k 10k 15k
(a) Non-movers
0
5
10
15
20
25+
Male: Mid-career Senior
Early-career
0 500 1500 2500
2500 1500 500 0
(b) Outward researchers
1000 500 0
0
5
10
15
20
25+
0 500 1000
(c) Returnee researchers
Fig. 1 Composition of academic age and gender for non-movers, outward researchers, and
returnees
Overall, these findings suggest that both the male and the female returnees
stayed in academia longer than both the outward researchers and the non-
movers. As more evidence on return migrants emerges, the strengths of
returnees are increasingly being seen as valuable. It has, for example, been
shown that from a historical perspective, returnees tend to make important
contributions to local economies and to be relatively successful, both in com-
parison to people who never migrated and to people who emigrated but did not
return (Abramitzky et al,2019). The findings on the positive impact of return
12 Return migration in academia
migration are encouraging, and suggest that Germany, as well as other send-
ing countries, should embrace international mobility and the return migration
of scholars. While we find that both the male and the female scholars bene-
fited from returning, we also observe that returning to Germany had a more
positive impact on the careers of male than of female researchers. For exam-
ple, the results show that 64.57% of male returnees, but only 51.13% of female
returnees, had become senior professionals (see detailed age composition in
1(c)). The smaller benefits found for women are not surprising, and point to
the ongoing challenges women in academia face.
3.2 Out-migration and return migration by geography
Figure 2illustrates from a geographic perspective the interplay between out-
flows of researchers from Germany and the corresponding rates of return to
Germany, through a density equalizing cartogram (Dorling et al,2006-09;Houle
et al,2009). Here, the shape of the map polygons is transformed proportionally
to the outflows to different countries. The colors represent the differences in the
countries’ return rates, as further explained in the legend. The most common
host country for researchers from Germany was the United States (US), which
received around 24% of the outward researchers from Germany. Next came
Switzerland and the United Kingdom (UK), which together attracted 22% of
the outward researchers from Germany. In total, these three countries received
nearly half of the outward researchers from Germany, and had thus become the
most appealing options for German researchers interested in pursuing an inter-
national academic career. These estimates are also consistent with previous
findings that the US, the UK, and Switzerland are the most common origin and
destination countries for scholarly migration to and from Germany (OECD,
2015;Zhao et al,2021). The observed pattern for the European countries that
received researchers from Germany indicates that the countries that neighbor
Germany and German-speaking countries were among the most popular host
countries for scholars who began their publishing activity in Germany.
As the colors on the map show, the rates of return from the most com-
mon receiving countries that are larger in size were all below 36%; meaning
that about one-third of German-affiliated researchers moved back to Germany,
while nearly two-thirds continued their research abroad. While the US hosted
the largest share of researchers from Germany, the rate of return to Germany
from the US was also relatively high, at 34%. Similarly, while the UK and
France were among the top host countries for researchers from Germany, the
rates of return to Germany from these countries were also high, at 30% and
29%, respectively. By contrast, the rates of return to Germany were below
one-quarter for German-affiliated researchers in Switzerland, Sweden, Austria,
and Australia; and the return rate was especially low for German researchers
in Switzerland, at only 20%. It thus appears that researchers who moved from
Germany to these four countries were comparatively less likely to return. The
lower propensity to return may be partly explained by the higher spending
on Research & Development (R&D) in these countries. In 2017, Switzerland,
Return migration in academia 13
USA
FRA
ITA
GBR
USA
CHE AUT CHN
CAN
AUS
RUS
SWE
ESP
BEL
NLD
IND
BRA
NOR
POL
CZE
EGY
MEX
FIN
SAU
ZAF
SVN
JPN
TUR
IRL
IRN
HRV
HUN
CAN DNK
ARG
CAN
PRT
CHL
ROU
THA
NZL
GRC
KOR
ETH
LBY
COL
JOR
IDN
SDN
VEN
IDN
JPN
CAN ISL
KEN
IDN
DZA
NZL
TZA
VNM
MOZ
NAM
PER
NGA
MAR
MYS
IDN
OMN
LTU
MMR
KAZ
LUX
GHA
CIV
JPN
CAF IDN
CUB
TUN
SEN
PRK
PHL
NIC
DNK
YEM
DNK
DNK
BOL
JPN
PAN
LKA
GTM
CHL
LIE
FJI
IDN
PRI
TTO
GUM
ÜLegend
Return rate
0.03 - 0.07
0.08 - 0.10
0.11 - 0.12
0.13 - 0.16
0.17 - 0.18
0.19 - 0.20
0.21 - 0.25
0.26 - 0.29
0.30 - 0.36
0.37 - 0.75
Fig. 2 Outward flows (from Germany) and respective return rates across countries. The
sizes of the countries are proportional to the flows of outward researchers from Germany.
The colors indicate the differences in the return rates of the German-affiliated researchers
returning to Germany from each country.
Sweden, Austria, and Australia spent about 3.18%, 3.36%, 3.00%, and 3.08%
of their GDP, respectively, on R&D far above the OECD average of 2.67%,
ahead of the US (2.85%), the UK (1.68%), and on levels competing with
Germany (3.07%) (OECD,2021). In addition, the lower return rate of Ger-
man researchers in Switzerland is broadly consistent with our expectations,
given that approximately 1.2% of all scientific papers worldwide are produced
by Swiss-affiliated researchers, which is remarkable given the country’s small
population (Turney,2019).
3.3 Rates of departure and return across cohorts
Figure 3illustrates the departure rates (left) and the return rates (right) per
1,000 person-years, disaggregated by cohort and gender. The academic age at
departure is on the y-axis for both outward researchers and returnees. For
returnees, the length of time away from Germany is also reported by the use
of ombre colors. Taking the cohort 1998-2001 as an example, out of 1,000
researchers, around eight women and nine men with a German academic origin
in this cohort moved abroad at academic age one. For every 1,000 outward
researchers who left Germany at academic age one, around 215 women and
278 men had returned to Germany within five years. Among them, 74 women
and 88 men had returned to Germany after one year, making the first year the
most likely year of return for that cohort. In general, there was a slight but
stable decline in the departure rates with academic age for all three cohorts.
However, the most striking pattern is observed for the 2006-2009 cohort: the
departure rates of female researchers exceeded those of male researchers for
most ages, especially at academic ages one, two, and three. Specifically, we
find that 11 out of 1,000 female researchers in this cohort left Germany at
14 Return migration in academia
female
male
female
male
female
male
Years abroad: 5
Years abroad: 3
Years abroad: 4
Years abroad: 1
Years abroad: 2
8 4 0
1
2
3
4
5
2006-2009
0 50 100 150 200 250 300
8 4 0
1
2
3
4
5
2002-2005
0 50 100 150 200 250 300
8 4 0
1
2
3
4
5
1998-2001
0 50 100 150 200 250 300
female
male
female
Outward:
male
female
male
Age at departure
Age at departure
Age at departure
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
Departure rate (per 1000 person-years in Germany) Return rate (per 1000 person-years outside Germany)
Fig. 3 The rates of leaving Germany within first 5 years since first publication per 1000
person-years (left), and the rates of return to Germany within the first 5 years after departure
per 1000 person-years (right).
academic age one, while only nine out of 1,000 male researchers left Germany
at that age. This result indicates that in this cohort, more female than male
researchers chose to migrate early in their careers. Meanwhile, the return rates
of the female researchers of all three cohorts were much lower than those of
their male counterparts. This difference may be partly related to the longer
average length of academic life for male returnees, as Figure 1shows. Taken
together, these results indicate that female outward researchers had a greater
tendency than their male counterparts to remain abroad for longer periods or
possibly to settle down in other countries, which may have exacerbated the
gender disparities in the German science system. Thus, the findings suggest
that out-migration trends may increase gender disparities within the German
academic system unless further action is taken.
We also observe that the return rates were generally higher for researchers
who moved out of Germany in their later years, and tended to increase with
academic age. This trend is more noticeable among male researchers and in
the two latest cohorts. The more pronounced increase in return migration at
later academic ages for men than for women suggests that there are structural
processes that operate at specific moments of the academic life course, and
that these processes could further extend the gender differences in German
academia.
3.4 Gender composition of outward and return streams
by discipline and cohort
Considering that the male-to-female ratios of researchers vary across disciplines
(Zhao et al,2021), we take a further look at the gender disparities disaggre-
gated by discipline for the three cohorts, as shown in Figure 4. The colors
Return migration in academia 15
in the heat map show that the representation of female researchers varies by
discipline in the horizontal dimension, and by cohort in the vertical dimen-
sion. The bottom row of the map represents the overall proportion of female
researchers in each discipline in Germany over the 1996-2020 period, as a base-
line for comparing the variability in the representation of females among the
researchers who left and returned over time.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
All (1996-2020)
outward (1998-2001)
return(1998-2001)
outward (2002-2005)
return(2002-2005)
outward (2006-2009)
return(2006-2009)
0.1
0.2
0.3
0.4
0.5
Female proportion
Discipline
1:Agricultural, Biological and Environmental Sciences; 2:Biochemistry, Genetics and Molecular Biology;
3:Chemistry and Chemical Engineering;4:Computer Science; 5:Earth and Planetary Sciences; 6:Economics and Social Science;
7:Engineering; 8:Energy; 9:Health Professions; 10:Immunology and Microbiology;11:Materials Science; 12:Mathematics;13:Medicine;
14:Multidisciplinary;15:Neuroscience; 16:Pharmacology, Toxicology and Pharmaceutics; 17:Physics and Astronomy; 18:Psychology
Fig. 4 Proportion of female researchers in different groups by discipline and cohort
Compared to the baseline, almost all disciplines appear to be more male-
dominated over time among both outward and returnee researchers, albeit to
varying degrees. One exception is the field of mathematics (12), in which female
researchers accounted for a higher proportion of each of these two migrant
categories in the latest cohort (2006-2009), relative to the long-term pattern.
Despite the lower representation of female researchers in both the outward and
the returnee groups, for the majority of the disciplines, we see an increasing
trend in the proportion of female researchers with each successive cohort, in
line with our earlier discussion in Subsection 3.3.
When comparing the categories of outward researchers and returnees in the
same discipline and cohort, we observe that the proportion of female returnees
was generally smaller than the proportion of female outward researchers. For
example, when we look at the latest cohort of researchers in the field of energy,
we find that the female proportion among returnees was much smaller than the
female proportion among outward researchers. The overall impression provided
by these data is that most disciplines are experiencing rising gender dispari-
ties, in part because female scientists who leave Germany are less likely than
their male counterparts to return. Despite significant efforts to increase gen-
der equality in academia, gender disparities seem to remain substantial across
disciplines.
3.5 Collaborative ties with Germany and rates of return
In this section, we examine the association between the levels of academic
collaboration with Germany researchers maintained while abroad and the cor-
responding rates of return to Germany. Figure 5shows a scatter plot of the
16 Return migration in academia
return rates (y-axis) and the average collaborative ratios (x-axis) for each dis-
cipline. Note that the collaborative ratio is the fraction of publications of an
outward researcher (during the period outside of Germany) with a German
affiliation. The horizontal (vertical) line indicates the overall average return
rates (average collaborative ratio) for outward researchers across all disciplines.
The number of returnees for each discipline is represented by the size of the
circles. Overall, the Pearson correlation coefficient between the collaborative
ratio and the return rates is 0.45, indicating a moderate positive associa-
tion. Researchers in most health science and life science disciplines, including
medicine, health professions, and psychology, were more likely to return to Ger-
many than researchers in other disciplines, as indicated by the higher return
rate over the average rate. When we look at the returnees’ levels of academic
collaboration with Germany while abroad, we see that health science returnees,
as well as researchers in some physical science disciplines, like earth and plan-
etary science, were more likely to maintain academic ties with Germany, as
shown by collaborative ratios that exceed the mean values of 33%. Specifically,
we observe that health science researchers maintained stronger collaborative
ties with Germany, and were more likely to return; whereas researchers in
STEM fields, who tended to leave Germany without maintaining as many
collaborative ties, were less likely to return.
Next, we look at the association between collaborative ties and return rates
among outward researchers by cohort. The results disaggregated by cohort are
shown in Figure 6, with the average return rates and collaborative ratios in each
cohort represented by the horizontal lines and the vertical lines, respectively.
Our results show an overall decreasing trend in rates of return by cohort, but
the left-truncation of the data complicates the reliable investigation of trends
involving the first cohort. Despite the general trend, researchers in health pro-
fessions and medicine were more likely to return than researchers in other
disciplines. The collaborative ratios grew slowly but steadily with each cohort;
thus, researchers in the latest cohort maintained relatively strong collabora-
tive ties to Germany. Similar patterns can be observed separately for most
disciplines.
The correlations found between the collaborative ratios and return rates
in the first two cohorts are in line with the overall pattern shown in Figure
5, with Pearson correlation coefficients of 0.43 and 0.41, respectively. This
association becomes much weaker (the correlation coefficient was 0.29) in the
latest cohort, whose discipline averages appear to be scattered widely across
the four quadrants. Between cohorts 2 and 3, neuroscience drops from quadrant
1 to quadrant 4, indicating a sharp decrease in return rates, despite an increase
in academic links with Germany. Between cohorts 2 and 3, we see an increase
in collaborative ratios among the outward researchers in the fields of chemistry
and chemical engineering, accompanied by stable return rates. For most other
disciplines, however, the return rates tended to decrease, as shown in Figure 6.
Return migration in academia 17
1.Agricultu
3.Chemistry
4.Computer 5.Earth and
6.Economics
8.Engineeri
9.Health Pr
10.Immunolog
11.Materials
12.Mathemati
15.Neuroscie
16.Pharmacol
18.Psycholog
0.2 0.25 0.3 0.35 0.4 0.45 0.5
0.2
0.24
0.28
0.32
0.36
Average collaborative ratio
Return rate
12.Medicine
7.Energy
14.Multidisc
17.Physics a
2.Biochemis
Fig. 5 Return rates and collaborative ratios across disciplines
1
25
67
8
10
12
14
15
16
17
18
0.2 0.25 0.3 0.35 0.4 0.45 0.5
0.2
0.24
0.28
0.32
0.36
Average collaborative ratio
Return rate
3
4
11
13
9
1
3
45
67
8
9
11
12 13
14
15
16
17 18
0.2 0.25 0.3 0.35 0.4 0.45 0.5
1
3
4
5
6
7
9
10
11
12
13
14 16
17
18
0.2 0.25 0.3 0.35 0.4 0.45 0.5
1998-2001 2002-2005
1:Agricultural, Biological and Environmental Sciences; 2:Biochemistry, Genetics and Molecular Biology; 3:Chemistry and Chemical Engineering; 4:Computer Science; 5:Earth and Planetary Sciences;
6:Economics and Social Science; 7:Engineering; 8:Energy; 9:Health Professions; 10:Immunology and Microbiology;11:Materials Science; 12:Mathematics;13:Medicine; 14:Multidisciplinary;15:Neuroscience;
16:Pharmacology, Toxicology and Pharmaceutics; 17:Physics and Astronomy; 18:Psychology
2006-2009
Fig. 6 Return rates and collaborative ratios by discipline and cohort
4 Discussion and future directions
As “science brokers,” researchers develop innovative ideas and make scien-
tific contributions by combining information and resources in various domains
using specialized skills and knowledge, which they acquire at different insti-
tutions and geographical locations (Williams,2007). International experience
can play a substantial role in helping researchers accumulate knowledge, infor-
mation, and capital, and can thus contribute to their scientific research and
academic careers (Teichler,2015;Wang,2020). Our previous study found that
internationally mobile researchers accounted for over 16% of the population
of Scopus-published researchers who had affiliation ties to Germany over the
1996-2020 period (Zhao et al,2021). We also observed that despite repre-
senting a minority in the German science system, mobile researchers make
substantial contributions, as evidenced by the finding that compared to non-
mover researchers in Germany, they have higher annual citation rates (Zhao
et al,2021). Because of their more nuanced trajectories and international expe-
rience, returnees can make important contributions to the German science
system. Here, we have analyzed the return migration of researchers to Ger-
many from several perspectives; i.e., by taking into account their disciplines,
cohorts, genders, and levels of collaboration with Germany while abroad.
Our quantitative results for Scopus-published researchers with ties to Ger-
many provide further evidence to support previous findings. The results of our
comprehensive analysis of emigration and return migration as two outcomes
for researchers who left the German science system indicate that the age and
gender compositions of outward researchers and returnee researchers differed
from those of non-movers. The median age for returnee researchers was up to
18 Return migration in academia
six years higher than that of non-movers, which suggests that there were sub-
stantial differences in their levels of experience. All three groups of researchers
differentiated by their levels of experience, from early-career to senior, were
heavily dominated by men. The ongoing gender disparities we found through-
out the academic life cycle were in line with the findings of previous studies
(V´as´arhelyi et al,2021). In particular, we observed that the publishing careers
of male returnees were, on average, longer than those of other groups, with
more than half of them being in their senior career stage.
The countries receiving the largest flows of researchers from Germany were
shown to have some of the highest return rates as well. However, we also
found that of the large numbers of German researchers who moved to Switzer-
land, Sweden, Austria, and Australia, relatively small proportions returned to
Germany. Three of these countries have linguistic, cultural, and geographic
proximity to Germany. Moreover, they all have higher R&D spending per GDP
(OECD,2021) than the UK and the US (and three have higher R&D spending
than Germany), which has enabled them to succeed in attracting and retaining
published researchers from Germany.
Supporting the representation of female researchers in academia through
equitable policies is imperative for Germany (Lutter and Schr¨oder,2020), and
for other countries (Morgan et al,2021). The trajectories of internationally
mobile female researchers is a particularly important dimension in evaluating
a national science system. We analyzed the gender differences among outward
researchers and returnees. Our results indicate that the gender disparities in
the German science system tend to be intensified over cohorts. Consistent with
evidence showing that the representation of female researchers in academia
has rise over time (Huang et al,2020), we found that the proportion of female
researchers has increased among both outward researchers and returnees across
cohorts, taking into account the number of years between first publication,
departure from Germany, and return to Germany. However, the proportion of
female researchers among those who returned to Germany was lower than it
was among those who left, which indicates that female outward researchers
have a greater tendency than their male counterparts to live abroad for longer
periods, or possibly to settle down in other countries. When we looked at
the proportions of female researchers in the two subpopulations of interest
disaggregated by cohort and discipline, we found that both the outward and the
returnee subpopulations in most disciplines were more male-dominated than
the overall population of researchers in that discipline, in line with the greater
gender disparities observed among all German-affiliated migrant researchers
in most disciplines (Zhao et al,2021). These findings suggest that the gender
imbalance in the German science system (with respect to scholars who started
publishing in Germany) may be intensified by the subgroups who are returning
to Germany being more male-dominated than the subgroups who are leaving
Germany.
Finally, we looked at the interplay between the degree to which researchers
continued to collaborate with German institutions while abroad, and their
Return migration in academia 19
corresponding return rates. The results showed a positive moderate associa-
tion between collaboration and return rates across disciplines. After cohorts
were introduced into the analysis, the return rates decreased with successive
cohorts, while the collaborative ratios increased on average. In the fields of
medicine, health professions, physics, and psychology, the likelihood of collab-
orating with Germany and of returning to Germany were both higher than
the total averages. In contrast, researchers in the fields of engineering, com-
puter science, and economics had both lower collaboration and lower return
rates that the total average. To tackle the challenge of talent loss in STEM
fields, and to attract and retain STEM researchers from abroad, Germany –
which already has a large number of initiatives for international researchers,
like GAIN and GSO– would likely benefit from developing additional programs
focused on STEM fields (OECD,2015).
Our study has several limitations, which can be addressed only through
ongoing work and additional efforts. Our bibliometric analysis was based on
the higher quality signals for researchers who have higher publication rates.
Therefore, the reliability of our findings may not be the same for all fields, given
that their average publication rates vary (e.g., physics vs. history). Another
limitation is that we could not analyze migration events that were not captured
in the publication data. In addition, because of the possible differences between
publication years and migration years, the temporal patterns of the data should
be interpreted with caution.
We recognize that bibliometric data, like other sources of big data, are not
produced for use as research data, and are therefore susceptible to potential
biases or errors. In our materials and methods, we outlined a series of pre-
processing steps for systematically dealing with some of the data quality issues
in our application context. Additional scientometrics research is needed to
better identify the potential quality problems with bibliometric data, and to
find systematic and effective remedies for addressing them.
As well as contributing to the literature on the migration of researchers
(Moed and Halevi,2014;Aman,2018a,b;Aref et al,2019;Andrey and Elena,
2019;Robinson-Garcia et al,2019;Miranda-Gonz´alez et al,2020;Subbotin and
Aref,2021;El-Ouahi et al,2021) in the context of Germany (Netz and Jaksztat,
2014;Parey et al,2017;Zhao et al,2021), more importantly, our research fills
a critical gap in the research on the return migration of scholars, which is a
novel subject in the bibliometric analysis of academic migration. This work,
which represents a continuation of Zhao et al (2021), was aimed to provide a
policy-relevant descriptive analysis of return migration among researchers by
taking their levels of experience, gender, disciplines, and cohorts into account.
Obtaining insights into researchers who have left Germany, including into their
age, gender, and characteristics that could influence their potential return to
Germany, is a key step towards understanding migration among scholars as a
concept that is more nuanced than a one-off relocation event.
20 Return migration in academia
A number of interesting questions still remain to be investigated, including
the question of what personal and professional factors drive the interna-
tional migration of researchers. Differences in levels of support for parenthood
between Germany (Gangl and Ziefle,2009;Lutter and Schr¨oder,2020) and
other countries (Morgan et al,2021) may have a bearing on some of the
observed gender disparities. Combining different data sources could allow us to
expand the analysis and examine other critical topics, like parenthood policies.
Investigating the citation performance of outward and returnee researchers
could provide us with additional insights into the individual-level consequences
of scholars’ migration decisions. In addition, the observed association between
return migration and personal and professional factors, including disciplines
and collaborative ties, can be further investigated with the aim of finding the
mechanisms involved, such as the emergence of discipline-specific centers that
are particularly attractive for migrant researchers.
Declarations
Availability of data and material. The bibliometric data used in this
study is proprietary and cannot be released. Scopus data is owned and main-
tained by Elsevier. Our inferred migration events dataset is publicly available
in a FigShare data repository (Zhao et al,2022).
Competing interests. The authors have no competing interests to declare
that are relevant to the content of this article.
Funding. This study has been funded by the German Academic Exchange
Service (DAAD) with funds from the Federal Ministry of Education and
Research (BMBF). This study has received access to the bibliometric data
through the project “Kompetenzzentrum Bibliometrie,” and the authors
acknowledge their funder BMBF (funding identification number 01PQ17001).
Acknowledgments. This article is a substantially extended version of the
paper Zhao et al (2021) presented at the 18th International Conference on
Scientometrics and Informetrics (ISSI 2021). The authors highly appreciate
the comments from anonymous reviewers, the suggestions from Rezvaneh
Rezapour, and the technical support from Tom Theile.
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Appendix: Mapping topics to disciplines
Table 1provides the intermediate results from the topic model (30 topics) and
how the topics are mapped into 17 disciplines (inspired by the All Science
Journal Classification) based on their similarities.
Return migration in academia 27
Table 1: Details of mapping 30 topics to 17 disciplines
Topic Most frequent keywords Discipline result
01 space, earth, wave, model, surface, field, datum, rock,
seismic, structure
Earth and Planetary Sciences
02 material, property, surface, apply, structure, film,
growth, metal, thin film, magnetic
Material Science
03 disease, cardiovascular, patient, clinical, function, heart,
medicine, cardiac, lung
Medicine
04 infection, disease, clinical, immunology, virus, patient,
vaccine, microbiology, transplantation, blood
Immunology and Microbiol-
ogy
05 ecology, population, evolution, forest, diversity, specie,
genetic, biology, animal, conservation
Agricultural, Biological and
Environmental Sciences
06 protein, molecular, gene, genetic, biology, cell, biologi-
cal, expression, nature, mutation
Biochemistry, Genetics and
Molecular Biology
07 model, simulation, engineering, flow, dynamic, design,
numerical, structure, modeling, experimental
Engineering
08 network, note computer, subserie note artificial intel-
ligence, note bioinformatic, ieee, information, model,
datum, design, engineering
Computer Science
09 patient, surgery, clinical, treatment, cancer, outcome,
disease, therapy, pediatric
Medicine
10 plant, cell, metabolism, physiology, metabolic, stress,
biology, response, enzyme, arabidopsis
Agricultural, Biological and
Environmental Sciences
11 brain, neuroscience, rat, mouse, alzheimer disease, neu-
rology, parkinson disease, disease, model, receptor
Neuroscience
12 laser, optical, engineering, spie society optical, optic,
measurement, spectroscopy, fiber, pulse, apply
Physics and Astronomy
13 management, health, economic, education, social, ger-
many, development, policy, review
Economics and Social Science
14 cell, cancer, expression, tumor, gene, molecular, stem,
receptor, mouse, apoptosis
Biochemistry, Genetics and
Molecular Biology
15 polymer, material, comp osite, engineering, property,
surface, fiber, coating, application, technology
Material Sciences
16 nanoparticle, chemistry, surface, cell, membrane, chem-
ical, physical, spectroscopy, microscopy, electrochemical
Chemistry and Chemical
Engineering
17 chemistry, synthesis, chemical, structure, complex, reac-
tion, organic, crystal, molecular,
Chemistry and Chemical
Engineering
18 cancer, patient, therapy, treatment, clinical, breast can-
cer, carcinoma, radiotherapy, tumor, oncology
Medicine
19 galaxy, star, astrophysical, cluster, society, xray,
monthly notice royal astronomical, formation, general,
evolution
Earth and Planetary Sciences
20 power, ieee, electronic, communication, technology, sen-
sor, ieee transaction, measurement, device, application
Engineering
21 energy, process, technology, production, engineering,
gas, atmospheric, environmental, water, development
Energy
22 drug, clinical, skin, exposure, risk, allergy, medicine,
test, assessment,
Pharmacology, Toxicology
and Pharmaceutics
23 physics, physical review, letter, physical, energy, mea-
surement, letter section, high, nuclear, elementary par-
ticle, high energy
Physics and Astronomy
24 water, marine, environmental, new, sediment, climate,
carbon, change, soil, ocean
Earth and Planetary Sciences
25 patient, child, disorder, cognitive, treatment, therapy,
rehabilitation, physical, depression, pain
Psychology
26 model, theory, datum, mathematical, function, applica-
tion, problem, dynamic, stochastic, time
Mathematics
27 surgery, bone, injury, treatment, fracture, tissue,
trauma, surgical, clinical, implant
Medicine
28 soil, foo d, plant, apply, microbial, nutrition, activity,
production, quality, microbiology
Agricultural, Biological and
Environmental Sciences
29 imaging, image, magnetic resonance, mri, medicine,
stroke, brain, radiology, ultrasound, compute tomogra-
phy
Health Professions
30 plasma, physics, solar, nuclear, fusion, beam, ion, elec-
tron, radiation, nuclear instrument
Physics and Astronomy
