The use of informetric methods to study diversity in the scientific workforce: A literature review

1 The use of informetric methods to study diversity in the scientific workforce: A literature review Nicolas Robinson-Garcia1, Carmen Corona-Sobrino2, Zaida Chinchilla-Rodríguez3, Daniel Torres-Salinas1 and Rodrigo Costas4,5 1 Unit for Computational Humanities and Social Sciences (U-CHASS), EC3 Research Group, Information and Communication department, Universidad de Granada, Granada (Spain) 2 Department of Sociology and Social Anthropology, Universitat de València, Valencia (Spain) 3 Instituto de Políticas y Bienes Públicos, Centro Superior de Investigaciones Científicas, Madrid (Spain) 4 Centre for Science and Technology Studies (CWTS), Leiden University, Leiden (Netherlands) 5 Centre for Research on Evaluation, Science and Technology (CREST), Stellenbosch University (South Africa) Abstract This literature review examines the application of informetric methods to assess diversity within the scientific workforce, focusing on recent advances in author name disambiguation, researcher profiling, and the evaluation of individual-level metrics. The study traces the evolution of quantitative approaches, from traditional productivity metrics to modern multidimensional models that incorporate contextual factors such as career trajectory, research practices, and social engagement. Emphasizing methodological innovations, the review explores the potential of advanced algorithms and new data sources (e.g., OpenAlex, ORCID) to offer a nuanced understanding of diversity in science. The review highlights gaps in the current literature, particularly the need to account for diverse individual characteristics, including gender, ethnicity, and team dynamics, and suggests pathways for future research. The findings contribute to ongoing discussions in the field of scientometrics regarding responsible research assessment and the development of equitable evaluation frameworks. U-CHASS White Paper #2 Diversity in the workforce October, 2024 Suggested citation: Robinson-Garcia, N., Corona-Sobrino, C., Chinchilla-Rodríguez, Z., TorresSalinas, D., & Costas, R. (2024) The use of informetric methods to study diversity in the scientific workforce: A literature review. U-CHASS White Papers #2. DOI: 10.5281/zenodo.13880355 This article is a preprint and has not been certified by peer review. 2 Keywords Diversity in scientific workforce, Scientometrics, Informetric methods, Gender and ethnicity in academia, Research evaluation 1 INTRODUCTION Since Lotka’s first study on productivity inequality (Lotka, 1926) and De Solla Price’s law on the exponential growth of science (Price, 1963), interest in quantitatively analyzing researchers’ activity has been constant (Dietz & Bozeman, 2005). This interest was originally rooted in the sociology of science and its attempts at understanding its social stratification (Cole, 1973; Crane, 1972; Merton, 1973). Soon after Garfield launched the Science Citation Index, Merton and Zuckerman saw the value of these data to empirically test many of the theoretical developments in the field (Wouters, 1999, pp. 110–111). But scientometrics shifted towards research assessment in the late 1980s (Abbott et al., 2010; Gingras, 2020). Its robustness for analyzing individual performance was seen as limited and inadequate (Wouters et al., 2013), favoring larger units of analysis such as fields, countries or institutions. Historically, scientometricians have cautioned against using only quantitative metrics to evaluate scholars, as individual characteristics can distort the outcomes (Moed, 2005, p. 54). Evaluative scientometrics has focused on the developing performance indicators, confiding the appropriate interpretation to policy makers and administrators in what is defined as “informed peer review” (Nederhof & Van Raan, 1987). This trust comes at a cost, as users may lack the expertise to understand the caveats of the measures being used (Waltman, 2019), may ignore the policy context in which they are applied (Robinson-Garcia & Ràfols, 2020), or may not make a responsible use of the metrics (Hammarfelt & Rushforth, 2017). Excessive focus on performativity has led academics, practitioners and decision-makers to question efforts to quantify researchers’ activity (Benedictus et al., 2016; Haddow & Hammarfelt, 2019; PardoGuerra, 2022), fueling a renewed debate on responsible research assessment (RRA) that criticizes prioritizing productivity over quality, social engagement, open practices, and the socio-economic impact (Pontika et al., 2022). International initiatives such as the San Francisco Declaration on Research Assessment (DORA) in 2012 or the most recent Coalition for Advancing Research Assessment (CoARA) in 2022 reflect this re-examination. According to Ràfols (2019), one reason for its misuse is the framing “in purely technical terms, paying scant attention to its context and use” (p. 8). The main concern is that indicators based on publications and citations have been inappropriate and counterproductive for the scientific community, masking the potential of scientometric methods. Over the last decades, the refinement in data processing and methodological innovation has offered new opportunities for contextualizing individual performance (Torres-Salinas et al., 2023). Advances such as machine learning algorithms, improved bibliographic metadata, and new data sources (e.g., OpenAlex, Altmetric, ORCID, Overton), have expanded the field’s capabilities. Scientometric methods can now track career trajectories (Jurowetzki et al., 2021; Moed et al., 2013; Robinson-Garcia et al., 2019), study gender bias in science (H. Boekhout et al., 2021; Huang et al., 2020; Larivière et al., 2013) and analyze scholars’ engagement in social media (Costas et al., 2020), among other areas. These advancements bridge between performative and sociological, contextualized approaches. Thus, abandoning a scientometric practice where “superlatives can be used, dichotomous thinking is realistic, with a resultant "zero-sum" mentality, and the "make a hypothesis -find a correlation" method makes sense” (Moravcsik, 1984, p. 75). 3 This review examines the recent development of author-level metrics focused on exploring the conditions under which science is produced. Table 1 summarizes the focus areas of a selection of reviews which partly touch upon the use of scientometric methods applied to the scientific workforce. These reviews have focused on author-level metrics and their performative roles in measuring research productivity and impact (Alonso et al., 2009; Mering, 2017; Waltman, 2016). De Rijcke et al., 2016) and Gauffriau (2021), also consider team dynamics and collaboration patterns, although the former from a policy perspective, while the latter from a methodological viewpoint. Context is only considered by de Rijcke et al. (2016) and (Martín-Martín et al., 2018), although again from different perspectives, with the latter looking more into behavioral aspects influenced by social media. However, there remains a significant gap in addressing the role of disambiguation algorithms and the recent methodological advances for studying individual characteristics like gender and nationality, as well as contextual factors such as career trajectory and research practices. This review addresses multiple facets of the scientific workforce which can serve understand and inform on the activities, roles and conditions which shape scientists’ performance, key to understanding how scientometrics can contribute to our knowledge of diversity within the scientific workforce. This review aims to fill these gaps, offering a more nuanced understanding of the diversity within the scientific workforce and the tools available to study it. Table 1. Comparison between bibliometric reviews related with individual-level metrics and diversity Reference Author Focus Performativity Context Individual characteristics Team Dynamics Author algorithms Alonso et al., 2009 ✔ ✔ de Rijcke et al., 2016 ✔ ✔ ✔ ✔ Gauffriau, 2022 ✔ ✔ ✔ Martín-Martín et al., 2018 ✔ ✔ ✔ Mering, 2017 ✔ ✔ Waltman, 2016 ✔ ✔ ✔ Wildgaard et al., 2014 ✔ ✔ ✔ Wildgaard, 2019 ✔ ✔ The review is divided into four sections. Section 2 examines changes related to data sources, which have been key for the development of the field and the exploration of new methods and venues. It focuses on two specific aspects: 1) improvements in author name disambiguation algorithms and 2) the expansion of author profiles. The next three sections look into different components which affect 4 the conditions under which research takes place. Section 3 examines methods developed to study individual characteristics of the scientific workforce such as gender, career length or nationality. Section 4 focuses on measuring contextual variables related to individuals such as career trajectory, research practices, funding or social outreach. Section 5 examines methods to study team dynamics and the roles researchers adopt, studied scientometrically through authorship, contribution statements or collaboration patterns. We conclude by discussing the implications and opportunities these new scientometric approaches bring to the fields of sociology of science, research policy and science of science, as well as pointing to potential gaps in the literature and future lines of inquiry. 2 DATA SOURCES The development and implementation of individual level-metrics is linked to that of data sources. Their inclusion of metrics, the launch of author profiles and the improvement of the bibliographic metadata related to the authors of the publications have fostered the popularity of certain metrics and the possibilities for quantifying individuals’ academic activities. Research on author name disambiguation has a long tradition within and beyond the field of scientometrics (e.g., Rodrigues et al., 2024). But due to the economic cost, infrastructure and expertise needed to develop them, their use has not expanded until data providers incorporated their own disambiguation algorithms and made public automatically populated researcher profiles. The first milestone on the provision of author profiles and particularly author-level metrics was the introduction of author identifiers by the main bibliometric databases. New data providers such as Microsoft Academic 1 , Dimensions (Visser et al., 2021) or OpenAlex 2 have also introduced their own unique author identifiers. Author identifiers and profiles have expanded the analytical potential of scientometric studies, as well as becoming a fundamental search tool for database users, evaluators, researcher managers and scientists. Here we review the main milestones on how data providers have contributed to expand the use (and misuse) of individual level metrics (Haddow & Hammarfelt, 2019), while at the same time increasing the opportunities for more detailed and fine-grained analyses. We observe three major developments: the introduction of author name disambiguation algorithms, the creation and expansion of author profiles and registries, and the introduction of researcher-level features. 2.1 Author name disambiguation Author name disambiguation is a task that has historically been conducted by librarians through authority control for developing catalogs and creating author headings (Tillett, 2004). For instance, OCLC introduced in 2003 its Virtual International Authority File (VIAF) which aims at standardizing and linking author names across library catalogs and databases. This issue soon became a major concern in the field of scientometrics (Smalheiser & Torvik, 2009) due to the exponential increase of research publications and authors (Bornmann et al., 2021; Milojević et al., 2018). This situation required automating the disambiguation process of author names. In their seminal review, Smalheiser and Torvik (2009) already acknowledge the opportunities that a proper disambiguation approach could bring to the field, when indicating that “attaching a person to a set of documents is a key step towards a major breakthrough in information science” (p. 6-34). However, at the time the 1 Now to be replaced by OpenAlex (http://openalex.org). 2 More in the OpenAlex online documentation at https://docs.openalex.org/api-entities/authors/authordisambiguation 5 approaches proposed did not meet the optimal quality to fulfill such expectations. The main challenges can be summarized as follows: ● Lack of incentives to create a global author registry. Especially in the case of occasional and low productive authors (De Solla Price, 1980), who represent a large portion of the scientific workforce (Ioannidis et al., 2014). ● Technical and feasibility issues to manually curate author data in large bibliographic collections or databases. ● Challenges for assessing and validating unsupervised machine learning approaches. ● Limitations of the bibliographic metadata for allowing a comprehensive selection of features that could be used in the disambiguation process (e.g. classifications, author-affiliation linkages, author e-mails, etc.). Issues such as linking affiliations with authors in bibliographic records, full indexing of references or the use of external information such as author registries can improve machine learning models when dealing with ambiguous author names. Author name disambiguation methods can be grouped into two categories (Ferreira et al., 2012): author grouping methods and author assignment methods. In most cases, these are all unsupervised methods. In author grouping (or clustering) methods, a given similarity function is calculated to a matrix of publications based on their bibliographic metadata (e.g., author names, affiliations, references, e-mails, etc.), which are then clustered together. Each cluster of publications will represent a disambiguated ‘author’. This is the approach followed at the Centre for Science and Technology Studies (CWTS) at Leiden University (Caron & van Eck, 2014). It uses a rule-scoring approach in which publications sharing a high number of bibliographic elements are given a greater chance of belonging to the same individual. It groups metadata information into four groups: author data, article data, source data and citation data. Elements from each group receive different scores and then pairs of papers with high scores are matched together. Next, a clustering approach is followed to add new papers to the original pair, constituting an author’s oeuvre. The algorithm favors precision upon recall. That is, when uncertainty exists, it will not group publications and assign publications to different ‘authors’, meaning that while there are no gaps in terms of coverage, author splits are quite common especially among East-Asian names and disciplines with hyper-authorship (Robinson-Garcia et al., 2019; Sugimoto et al., 2017, app. Supplementary Information). Tekles & Bornmann (2020) attempted to assess the performance of the CWTS disambiguation in comparison with three other approaches, concluding that it was the most effective of the four. In a different context, (Abramo & D’Angelo, 2023) discussed the differences between using external data to disambiguate names versus the algorithm when analyzing individuals’ productivity at the institutional level. Another example of author grouping methods is that developed for the Microsoft Academic Graph (MAG) (Sinha et al., 2015) and now continued in OpenAlex (Priem et al., 2022). In this case, they adopt a graph-based approach in which they examine the relationships between an array of features including author name, affiliated institutions, concepts tagged in their works, citations, coauthors, and third-party identifiers. As a validation method, they rely on the ORCID author registry (discussed below) as a golden set, helping to establish initial clusters and resolve ambiguities. The system continuously updates cluster information, giving preference to author clusters with higher matching scores. This dynamic process includes a safeguard against ORCID clashes within a cluster, ensuring 6 consistency in names. According to the documentation provided by OpenAlex 3 , up to July 2023, there are around 92 million author profiles currently within the OpenAlex database. Author assignment methods use a probabilistic approach to determine authorship employing bibliographic metadata (e.g., likelihood of publishing in a topic, with certain co-author, in a journal, etc.). It feeds from the library tradition, as observed from the approach followed for the VIAF, which uses a collaborative approach to author name disambiguation, integrating data from various national libraries and authority files. Although there is evidence that later they are also using group methods to better disambiguate authors (Hickey & Toves, 2014). The PubMed ID also applies an author assignment method by combining bibliographic metadata and heuristic techniques to uniquely identify authors within the PubMed database. The algorithm begins with the collection and preprocessing of detailed bibliographic data, including author names, titles, abstracts, co-authors, and affiliations. It then extracts features such as shared title words, journal names, medical subject headings, publication language, affiliations, and specific name attributes like middle initials and suffixes. Using these features, the algorithm applies heuristic rules to evaluate the likelihood that similar or identical names represent the same individual, considering contextual details from coauthors and affiliations (Liu et al., 2017). Table 2. Types of author name disambiguation methods. AUTHOR GROUPING METHODS These methods group various records believed to belong to the same author based on similarities in metadata such as name, affiliation, co-authors, and publication venues. Algorithms Definition Examples (Reference) Rule Scoring algorithm Groups author records by evaluating the similarity of various attributes using scoring rules. CWTS Author ID (Caron & van Eck, 2014) Graph-based algorithm Uses a network of interconnected entities to disambiguate authors by analyzing relationships within the graph. MAG Author ID (Sinha et al., 2015) OpenAlex ID (Priem et al., 2022) AUTHOR ASSIGNMENT METHODS This method assigns records to specific author profiles using rules or algorithms that consider individual attributes and relationships among records to ensure accurate author identification. 3 The information related to the author name disambiguation method deployed by OpenAlex was retrieved from https://github.com/ourresearch/openalex-name-disambiguation/tree/main/V3 7 Algorithms Definition Examples (Reference) Collaborative algorithm Integrates data from multiple authority files to standardize and disambiguate author names. VIAF ID (https://viaf.org/) Heuristic-based algorithm Applies predefined rules and heuristics to match and differentiate authors. PubMed ID (W. Liu et al., 2014) 2.2 Author profiles Author name disambiguation algorithms have facilitated the creation and population of researcher profiles. These profiles display authors’ outputs along with additional individual features and metrics. The evaluative culture around metrics and performativity has spurred their use and popularity (Hammarfelt & Rushforth, 2017; Martín-Martín et al., 2018). ORCID and Google Scholar Citations have been two major players in promoting and expanding these tools, albeit for different reasons. ORCID, due to its open nature and ability to integrate with other tools, ensures data quality and serves as a golden set to test and refine algorithms used by other platforms. Google Scholar Citations has gained immense popularity among researchers, particularly through the introduction of the hindex and other author-level metrics. A list of the main researcher profiles currently available and their characteristics are included in Table 3. Scopus was the first database to introduce its Author ID in 2006 (Boudry & Durand-Barthez, 2020). This identifier aims to provide individual-level metrics and display researchers’ academic work. Scopus employs an automatic name disambiguation algorithm which authors can curate upon request. The Author ID algorithm uses bibliographic metadata (i.e., affiliation, subject area, geographic location, co-authors, email address) to ensure accurate disambiguation of authors with similar names. This algorithm favors precision over recall (Moed et al., 2013), complemented by manual curation and tested a golden set of around 12,000 curated author profiles (Baas et al., 2020). Profiles are enriched with ORCID data, with studies reporting a 97%-98% recall rate and 99%-100% precision (Aman, 2018b; Kawashima & Tomizawa, 2015). In 2008, Web of Science launched its ResearcherID. Initially, it invited authors to create their own profiles and suggested publication lists for manual curation (Bornmann & Williams, 2017). This approach was later combined with algorithmically generated records. In 2012, ORCID integration further enhanced ResearcherID, facilitating interoperability between platforms (Aman, 2018b). In 2017, ResearcherID merged with Publons after its acquisition (Teixeira da Silva & Al-Khatib, 2019). This integration allowed for verified peer review records and enhanced researcher profiles. Authors can manually update their profiles and correct any data, ensuring accurate representation. The introduction of Google Scholar Citations in 2011 marked a significant milestone. This free service requires researchers to sign up to create their profiles, which they can either curate themselves or populate automatically based on Google Scholar’s algorithm. While initially met with skepticism, particularly from older scientists (Ortega, 2015), Google Scholar Citations has become one of the most popular academic platforms alongside ResearchGate and Academia.edu (Martín-Martín et al., 2018). Its popularity has been somewhat controversial due to its informal misuse in research assessment (Bohannon, 2014) being a tool that lacks of quality control and hence is subject to manipulation and gaming (Delgado López-Cózar et al., 2014). 8 Table 3. Overview of the main researcher profiles available 1 Researcher Profile Year Structure Update method Coverage Owner Individual-level features Interoperability ORCID ID 2012 Alphanumeric Manually curated Comprehensive ORCID. Bio, work history, grants, no metrics Integrates with CrossRef, DataCite, other academic databases, export options Scopus Author ID 2006 Numeric Algorithmically updated Indexed in Scopus Elsevier Basic bio, affiliations, h-index, citations Limited integration with ORCID, export options Web of Science Researcher ID 2008 Alphanumeric Supervised automated Indexed in Web of Science Clarivate Bio, work history, publications, hindex, citations Integrates with Publons, export options Dimensions Researcher ID 2018 Alphanumeric Algorithmically updated Indexed in Dimensions Digital Science Basic bio, affiliations, citations, altmetrics Integrates with ORCID, export options Google Scholar Profile 2011 Alphanumeric Supervised automated Comprehensive Google Bio, work history, research interests, h-index, citations Limited integration, export options ResearchGate ID 2008 Alphanumeric Supervised automated Comprehensive ResearchGate GmbH Bio, work history, publications, research interests, RG Score, citations Limited integration, export options Academia.edu ID 2008 Alphanumeric Manually curated Comprehensive Academia.edu Bio, work history, publications, research interests, no metrics Limited integration, export options CRIS Profiles (e.g., Converis, Pure) -- Alphanumeric Institutional Manually curated and supervised Institutional databases Various (e.g., Clarivate, Elsevier) Comprehensive bio, work history, funding, publications 2 9 ORCID ID, launched in 2012, stands out for its universal approach to researcher identification. Providing unique identifiers, ORCID integrates with multiple databases, enhancing interoperability and ensuring consistency across platforms. Researchers can manually curate their ORCID profiles, which include comprehensive information such as bio, work history, and grants. ORCID’s integration with databases like CrossRef and DataCite underscores its role in the broader academic ecosystem, facilitating seamless data exchange and verification. Despite becoming pivotal as a benchmarking tool for the rest of the profiles, it is an underutilized data source in scientometric studies (Costas et al., 2024). Dimensions Researcher ID, introduced in 2018, follows a two-step algorithm similar to the CWTS algorithm, using affiliation data, co-authorship, citation patterns, and subject area traits to cluster publications belonging to individuals (Hook et al., 2018). These clusters are connected using ORCID and DOIs, resulting in a unique researcher ID assigned to 20 million researchers. This system has successfully assigned researcher IDs to about 87% of publicationauthor combinations. CRIS (Current Research Information System) author profiles, such as those from Converis and Pure, provide another approach to managing researchers’ information. These systems are often used by institutions to integrate internal personnel databases with institutional repositories and other external databases (Rybinski et al., 2017). CRIS profiles typically include comprehensive information about researchers’ outputs, funding, affiliations, and other activities, offering a rich dataset for analysis and reporting. For instance, the Flemish Research Information System connects around 95% of its researchers with their ORCID IDs to facilitate interoperability between systems, enhancing the accuracy and comprehensiveness of the data collected (van Leeuwen et al., 2016). CRIS profiles overcome some limitations of author name disambiguation algorithms by allowing researchers to manually curate and verify their information, although they may still contain partial or incomplete data if not fully maintained by the researchers themselves also making it a costly system. The rise of academic social networks like ResearchGate and Academia.edu has further expanded the landscape of researcher profiles. These platforms offer venues for researchers to display their work, engage with peers, and enhance their visibility. However, they also present challenges regarding data reliability and privacy (Gumpenberger et al., 2016; Thelwall & Kousha, 2015). Each of these profiles—Scopus Author ID, Web of Science Researcher ID, Google Scholar Citations, ORCID ID, Dimensions Researcher ID, ResearchGate, and Academia.edu—has unique features and limitations. Scopus and Web of Science provide robust, proprietary systems with varying degrees of researcher control. Google Scholar and ORCID offer open, researchermanaged profiles with broad adoption and interoperability. Dimensions combines automated updates with comprehensive metrics, while academic social networks like ResearchGate and Academia.edu focus on community engagement and visibility. Together, these profiles enhance the evaluation of scientific performance by providing diverse tools and platforms that cater to diverse needs and preferences within the academic community. 16 Figure 1. Contextual factors affecting individuals’ performance Contextual factors influencing the production of knowledge can be key for understanding academic success as well as providing the optimal conditions for academic development. These have been explored by large in fields closely related to scientometrics such as sociology of science, science policy or management (D’Este & Robinson-García, 2023; El-Ouahi et al., 2021; Franzoni et al., 2018; Latour & Woolgar, 1979; Robinson-Garcia et al., 2018). In this stream of literature we find studies on the effect on productivity of intersectoral job changes (Dietz & Bozeman, 2005), contextual factors leading to international mobility and performance (Franzoni et al., 2018), institutional factors and logics affecting performance (Sauermann & Stephan, 2012) or the relation between interdisciplinary research and societal relevance (D’Este & Robinson-García, 2023) among others. Figure 1 resumes the four types of contextual factors that can affect individual performance. These are related to past trajectory, practices, funding conditions and societal relevance of research outcomes. Scientometric measures at the individual level have been developed unevenly for each of them, being past trajectory the one that has received most of the attention. 4.1 Trajectory and career Career trajectories have been studied in scientometric by profiling scholars’ experience throughout their career length by looking into affiliation changes. This allows tracking and studying career changes. Career trajectory has also been studied to analyze academic success or identify systemic constraints affecting individual career prospects. An example is the effect that author position, ─ a longstanding proxy for leadership in evaluation processes (ChinchillaRodríguez et al., 2019), ─ has on individuals’ chances to have a long academic career (Milojević et al., 2018), or how task specialization may affect such career length (Robinson-Garcia et al., 2020). Laudel (2003) was among the first to suggest using bibliographic data to reconstruct career trajectories. She saw the potential of scientometrics, not only to look into institutional mobility, 17 but also to reconstruct ‘cognitive careers’ or ‘research trails’, i.e., “successive stages of knowledge production building on each other” (Gläser & Laudel, 2015, p. 301). However, it is the introduction of author profiles and author name disambiguation algorithms that made this type of approach finally possible at a large scale. The first large-scale scientometric studies analyzing career trajectories focused on geographical mobility (Moed et al., 2013; Moed & Halevi, 2014; Robinson-Garcia et al., 2016; Sugimoto, Robinson-Garcia, et al., 2017). Since then, studies on the international movement of scholars using scientometric methods have increased. Here the range of studies goes from methodological approaches (Aman, 2018a; Robinson-Garcia et al., 2019) to investigating international trends (Chinchilla-Rodríguez et al., 2018; Murray et al., 2023; Sanliturk et al., 2023) or focusing on specific regional dynamics (El-Ouahi et al., 2021; Miranda-González et al., 2020; Subbotin & Aref, 2021; J. Wang et al., 2019). We also find studies analyzing the interplay between specific individual traits or team dynamics and mobility. These studies have serve to confirm gender differences in terms of international mobility (Momeni et al., 2022; Zhao et al., 2023), analyze the role of mobility on collaboration patterns (Wang et al., 2019) as a reinforcing mechanism (Boekhout et al., 2021), or understand how institutional and geographic constraints affect career trajectories (Vaccario et al., 2020). Also looking into mobility, but in this case, inter-sectoral mobility, Yegros-Yegros et al. (2021) explored the role researchers holding multiple affiliations play in bridging between institutions. They reported that researchers holding multiple affiliations within a country bridge between sectors, while those with international multiple affiliations will bridge between universities. Using a similar approach, Jurowetzki et al., (2021) looked into the phenomenon of scientific brain drain from academia to the public sector in the field of AI. 4.2 Research dissemination practices Building upon previous research (Robinson-Garcia et al., 2023), we define research dissemination practices activities, workflows and routines which may or may not be influenced by cultural, disciplinary or institutional logics and which define ways of operating external to the quality or productivity of scholars (i.e., researchers conducting fieldwork may publish less than those focused on meta-analyses, and their number of papers may not necessarily reflect their productivity as scientists). These practices include but can go beyond open scholarship or responsible practices (Moher et al., 2020). Scientometric attempts have focused on the characterization and profiling of researchers based on their research practices. In some cases, this is done by combining scientometric data with other data sources such as survey data (e.g., D’Este & Robinson-García, 2023; Ramos-Vielba et al., 2022). For instance, we observe author-level analyses on publication patterns which aim at identifying distinct profiles of researchers by their use of different communication channels (ArroyoMachado & Robinson-Garcia, 2023; Verleysen & Ossenblok, 2017). Other attempts relate to capturing specific activities which are not present in bibliographic databases. An example can be found in the work by Mongeon et al. (2017) who link authorships of datasets with publications in order to explore data sharing practices among scholars. With regard to data sharing, we find several attempts at developing author level metrics, due to the interest in promoting transparency (Bierer et al., 2017). Sixto-Costoya et al. (2021) investigated ORCID as a potential tool to investigate data sharing practices at the author level. While Hood & Sutherland (2021) suggested different metrics to encourage data sharing and data citation. 18 4.3 Funding The role funding plays has also been studied through scientometric means, in this case looking into questions such as its effect on career advancement (Bol et al., 2018) or the pertinence of funding schemes (Fedderke & Goldschmidt, 2015). However, these studies tend to combine scientometric data with other sources, as linking authorship with funding sources is not always feasible. This explains the lack of research in this area. The indexation of funding acknowledgements by bibliographic databases (Costas & van Leeuwen, 2012) has opened the possibility to explore the role of funding in science. Still, technical issues linking authors to funding schemes , along with the omission of funding bodies in many publications impede their use at the individual level (Álvarez-Bornstein & Montesi, 2021). In this regard, author name disambiguation algorithms may not be the answer to solve this issue, but the expansion in the use and integration of author registries like ORCID or other CV-based applications, in which these other activities can be included and easily linked to other activities and outputs of individual researchers (Costas et al., 2024). 4.4 Social media outreach The attention gathered around the irruption of social media (Sugimoto, Work, et al., 2017) has also included the study of individual’s behavior within altmetric studies. In this regard efforts have been directed in four directions. First, trying to identify and describe researchers’ presence on different social media platforms (Martín-Martín et al., 2018; Torres-Salinas & Milanés-Guisado, 2014). These studies tend to depart from a list of researchers for which then an online presence in different platforms is tracked in order to estimate the usage scientists make of these tools. Then, they follow up by looking at their levels of activity and interaction through the formulation of various metrics. Second, we find studies characterizing and profiling researchers who actively use social media tools to promote their research or interact with other audiences (Bruns et al., 2014; Díaz-Faes et al., 2019; Holmberg et al., 2014; Robinson-Garcia et al., 2018). Their aim is to understand how they use these tools rather than to observe if they do use them. In this regard, they focus on interactions with other users or self-depictions on these platforms. The third stream of literature relates to the development of methodological solutions to monitor researchers’ activities on social media platforms. The goal is to find ways in which social media accounts can be linked to researcher profiles or at least tagged as academic accounts. To do so, two approaches have been observed. The first one relies solely on the information depicted by the descriptions included in social media lists (Ke et al., 2017). The second approach consists on matching social media information with publication data via author profiles in order to identify those researchers who also have an online presence (Costas et al., 2020; Mongeon et al., 2023). The last group of studies relates to performativity and social visibility. Here, altmetric indicators are grouped at the author level by using author identifiers and then scholars are profiled or compared based on the social media activity surrounding their publications. For example, Ramos-Vielba et al. (2022), combine scientometric and survey data to propose a value creation model of science-society interactions. Within the same project and using the same dataset, D’Este & Robinson-García (2023) explore the relation between interdisciplinary research and societal visibility. Finally, Arroyo-Machado & Torres-Salinas (2023) propose developing altmetric profiles at the author level focusing on different aspects or dimensions of societal visibility. 19 5 TEAM DYNAMICS A weakness in research evaluation is its difficulty in reconciling the notion of individual evaluation in the context of collaboration and teamwork (Walsh et al., 2019). In a setting in which teams in science become the norm (Mongeon, Smith, et al., 2017; Wuchty et al., 2007), many studies on research careers point towards an excessive focus on promoting scientific leadership to the detriment of authors who are team members (Chinchilla-Rodríguez et al., 2024; Milojević et al., 2018; Robinson-Garcia et al., 2020). The rise of teams leads to further diversity in the way researchers collaborate, distribute work and specialize, always mediated by disciplinary differences and characteristics. The literature on research collaboration and team dynamics is vast, both in sociology of science as well as in scientometrics. However, there are important differences in the approaches and findings reported in each field. Sociologists of science have focused on the internal mechanics of scientific teams such as power dynamics, social interactions, cultural norms or internal structure (Knorr-Cetina, 1982; Latour & Woolgar, 1979; Walsh & Lee, 2015; Whitley, 2000). Scientometricians and scientists of science, on the other hand, have looked into quantifiable aspects of scientific collaboration, such as coauthorship networks, large-scale analyses on scientific communities and network structure (Börner et al., 2010; Calero et al., 2006; Newman, 2004; Raan, 2008). Furthermore, their operational definition of teams also varies with research teams being defined through coauthorship (e.g., Xu et al., 2022), departmental units (e.g., Engels et al., 2013) or project-based teams (e.g., Bone et al., 2020). At the individual level, we can group scientometric studies on team dynamics into three groups: 1) studies analyzing order and hierarchy in the author byline of publications, 2) studies profiling researchers based on their co-authorship patterns and networks, and 3) more recently, studies looking into contribution statements and specialization. 5.1 Author order Authorship plays an essential role in academic career progression, as it is used as a source of recognition or credit, being the entry point into what is known as the ‘reward system’ of science (Biagioli, 2003; Merton, 1968). However, collaborative research challenges how credit should be distributed. For instance, in a multi-authored paper, the more prestigious authors will gather more recognition than those less known (Merton, 1968). Different fields have responded differently to such challenges. While in some cases, authors are listed alphabetically, in most, credit is distributed unequally on the author byline of papers. That is, adhering different levels of prestige, depending on authors’ position (Frandsen & Nicolaisen, 2010; Marušić et al., 2011). Some disciplines order authors by decreasing order of contribution (Bu et al., 2020; Grando & Bernhard, 2003), whereas most lab-based disciplines exhibit an inverted U-shape, with first authors and last authors having performed the most contributions (Larivière et al., 2021). There are exceptions to those dominant trends—such as economics, mathematics and business, management and accounting—where researchers show a strong trend to sign in alphabetical order (Fernandes & Cortez, 2020; Waltman, 2012; Wohlrabe & Bornmann, 2022). When comparing author position with contribution statements, we observe that first authors will be the most invested researchers in a given study, last position will be hold by those responsible of coordinating, supervising or acquiring funding, while middle positions will be reserved to less involved authors, normally those contributing with technical or field work 20 (Escabias & Robinson-Garcia, 2022; Sauermann & Haeussler, 2017). However, this alignment might change based on disciplinary differences, institutional policies or misconduct, conflict or fraud in authorship (Biagioli, 2003; Frandsen & Nicolaisen, 2010; Walsh et al., 2019). As authorship plays an important role in career progression from undergraduate to professorship, the position of authors in byline publications is usually used in the assessment of researcher’ scientific contributions (Bhandari et al., 2004; Hess et al., 2015; Perneger et al., 2017). This naturally leads to a relation between authorship order and career length, especially when looking at first and last author positions (Escabias & Robinson-Garcia, 2022; RobinsonGarcia et al., 2020). But also leads to unequal power dynamics (Xu et al., 2024), which in the context of evaluation, can lead to malpractices such as the inclusion of non-contributing authors (known as “honorary” or “guest” authors) or the exclusion of qualifying authors (known as “ghost” authors), gaming and limiting the potential of scientometric methods to capture credit through authorship in an ever more team-based science (Greenland & Fontanarosa, 2012; Jabbehdari & Walsh, 2017). The scientometric community has devised a variety of counting methods to distribute credit among members of multi-authored papers (Gauffriau, 2021). These range from full counting, – that is, giving full credit to all authors of a given publication –, to fractional counting, – distributing credit equally by the number of authors of a publication. In between there is a large range of different weighting systems which aim at distributing credit unequally among authors on the basis that position order reflects their level of involvement in a given publication. Some examples are harmonic (Hagen, 2008) or geometric (Liu & Fang, 2023) allocation of credit among others. However, all these methods are somewhat arbitrary and unsubstantiated, as the true contribution of each author can vary significantly and is not always accurately reflected by their position in the author list. This inherent arbitrariness raises questions about the fairness and accuracy of these credit distribution methods (Kim & Kim, 2015). 5.2 Contribution statements To reduce the arbitrariness of credit distribution methods based on author counting, the introduction of contribution statements in publication records has greatly improved opportunities to scientometrically study author roles in multi-authored publications (Allen et al., 2014). Contributorship statements emerged in the late 1990s, primarily in biomedical journals, to address these challenges (Rennie et al., 1997; Smith, 1997). Contribution disclosures show that first authors are more likely to have conceived research, analyzed data, and written the paper, as well as performed research and analyzed the data than middle and last authors. Last authors are more likely to have conceived research and written the paper than first or middle authors (Larivière et al., 2016; Perneger et al., 2017). Also, the number of contributions conducted by each author seems to be informed by their author byline. Lu et al. (2020) identified three types of authors based on the distribution of tasks among co-authors. They differentiated between those who contribute to a task by themselves (specialists), those who share the burden on most tasks (team players), and those who combine both roles (versatile). These types seemed to be associated with specific groupings of tasks. Team players would be involved equally in the most common tasks across studies (i.e., data analysis, writing the draft, conceiving and designing the study, performing experiments). Versatile authors have a similar profile, although more focused on performing experiments. But specialists would tend to contribute to lesser common tasks such as 21 contributing with tools. These roles were found across fields and were normally associated with author position, although they were less clearly identifiable in large teams (Lu et al., 2022). More specifically, Robinson-Garcia et al. (2020), identified three types of researchers based on their different forms of contributorships. The methodological approach to define these typologies is different from that followed by Lu et al. (2019). They apply an archetypal analysis to identify extreme combinations of contributions at the author level. However, there are similarities between the findings of both studies. Robinson-Garcia et al. (2020) refers to leaders as those “characterized by high coefficient values for all contributions” (p. 9), specialists as those “characterized by high coefficient values for PE and AD” (p., 9), - where PE refers to performing experiments and AD to analyzing data-, and supporting authors as those “characterized by generally low values for all contributorships” (p. 9). These types hold a great resemblance with what Lu et al. (2019) refer to as team players, versatile and specialists, accordingly. A study by Zhao et al. (2024) further explores this by examining the impact of the number of thought leaders (as defined by those contributing through conceptualization tasks) on team performance. They reported that teams with more thought leaders tend to produce more cited outputs but were less disruptive. This nuanced understanding helps refine how credit is attributed and how team dynamics affect research outcomes. Inferring contributions based solely on author order can be highly problematic, especially in an evaluative context, as these are major trends rather than common and cross-disciplinary practice (Sauermann & Haeussler, 2017). Still, understanding team dynamics and distribution of labor can help understand and improve biases when attributing credit and designing science policies promoting a healthy and sustainable scientific ecosystem (Brand et al., 2015; Milojević et al., 2018). The Contributor Roles Taxonomy or CRediT aims at providing a universal contribution taxonomy that allows cross-comparison between journals, fields and organization, thus contributing to such understanding (Allen et al., 2014; Larivière et al., 2021). Beyond understanding team dynamics, this information can also help understand biases in science such as gender biases (Larivière et al., 2021; Robinson-Garcia et al., 2020) or geographical biases (van Schalkwyk, 2023). 5.3 Collaboration patterns Scientific collaboration is a critical dimension in analyzing researchers’ activities and performance (Bozeman & Corley, 2004; Perkmann et al., 2013). It also plays a vital role in promoting diversity within the scientific workforce by fostering inclusive research environments (Freeman & Huang, 2014). Beyond author order, there is a vast number of studies examining co-authorship patterns, types of collaboration and size of collaborating networks (Guimerà et al., 2005; Heinze & Bauer, 2007; Newman, 2001). These studies tend to characterize collaboration patterns and tie them with performance or outcomes. Over 50 years ago, De Solla Price & Beaver (1966) highlighted that differences in productivity among authors are linked to collaboration. Since then, we have learned that collaboration is structured in small world (Newman, 2001) and self-organized networks in which co-authorships are determined through preferential attachment and Matthew Effect (Wagner & Leydesdorff, 2005). These networks often facilitate diverse collaborations, which can bring together a variety of perspectives and expertise. Furthermore, external factors affect the dynamics of collaboration, including structural, climate-related, and institutional aspects (Adams et al., 22 2014; Chinchilla-Rodríguez et al., 2018; Luukkonen et al., 1993). Citation impact increases exponentially with the number of collaborating authors from the same institution and linearly with the number of domestic and foreign institutions (Gazni et al., 2012; Katz & Hicks, 1997). In terms of methodological approaches, Social Network Analysis techniques are dominant within these studies, applied to scientometric data (Otte & Rousseau, 2002). These techniques can also be used to analyze the diversity of collaboration networks and their impact on research outcomes. Survey or CV data are used in many cases either substituting or complementing bibliographic data (Bozeman & Gaughan, 2011; Gaughan & Bozeman, 2002). The distinction between collaboration types is essential in order to identify and understand the type and impact of outcomes produced. Bozeman & Boardman (2014) suggest distinguishing between “knowledge-based collaborations” which enhance research productivity and “property-based collaborations” which affect economic development and wealth. This can be operationalized by looking into inter-sectoral co-authorship (Yegros-Yegros et al., 2016) as a proxy to identify entrepreneurial authors. But not only industry collaboration can be identified through co-authorship, but through a combination of altmetric (Robinson-Garcia et al., 2018), scientometric and survey methods (D’Este & Robinson-García, 2023), it is possible to profile researchers based on their societal engagement, linking these profiles with research outputs, interdisciplinary metrics and citation impact measures. The combination of scientometric approaches with other data collection methods seems to be desirable, given that coauthorship, as collaboration is normally operationalized, tends to make visible only certain types of collaborations (Laudel, 2002). 6 DISCUSSION The scientometric toolbox has greatly expanded in the last decades, allowing for contextualized approaches at the individual level which do not only benchmark but also explain and describe its diversity. These developments expand the opportunities to learn and explain scientific performance, productivity and impact beyond outdated and simplistic notions of meritocracy and excellence (Moed & Halevi, 2015; Moravcsik, 1984). The integration of machine learning algorithms, the improved quality of bibliographic metadata, and new data sources such as OpenAlex and ORCID, have allowed for more nuanced and detailed analyses. These advancements enable researchers to track career trajectories, study gender bias, and examine scholars’ engagement in social media, bridging the gap between performative and sociological approaches. Still, it is important to note that there are many other types of diversity at the individual level that cannot be currently accounted for using scientometrics, such as cognitive and physical diversity, socioeconomic diversity, sexual orientation diversity or neurodiversity among others. By incorporating a variety of metrics beyond simple publication and citation counts, such as funding acknowledgments, author contributions, and social media activity, scientometric methods can provide a more holistic understanding of researchers' activities and their contexts. This approach helps to illuminate the diverse conditions under which science is produced, offering valuable insights into how individual characteristics, team dynamics, and contextual factors shape scientific performance. Table 4 summarizes the main opportunities and gaps identified in this review in order to inspire and set future research goals and agendas. Table 4. Summary of the main opportunities and limitations identified in the literature about the use of scientometric methods to study diversity within the scientific workforce 23 Section Subsection Opportunities Limitations Data sources Author name disambiguation Improved accuracy with machine learning algorithms High economic cost and infrastructure requirements Author profiles Enhanced researcher profiles with ORCID and other tools Variability in data quality and coverage Individual characteristics Career length Tracking career trajectories using bibliographic metadata Methodological differences and lack of consideration for career breaks Gender Improving our understanding on gender inequity in academia Reliance on binary gender models and potential biases National and/or ethnic background Combination of surname data with affiliation for ethnic classification Limitations in data sources and potential misclassification Context Trajectory and career Reconstructing career paths using author profiles Difficulty in capturing full career trajectories accurately Dissemination practices Profiling researchers based on publication patterns and other activities Limited by data availability Funding Linking research investment and policies to outputs Challenges in accurately matching authorship with funding sources Social outreach Studying researchers’ engagement beyond academia Limited to social media and data reliability issues Team dynamics Author order Credit distribution in multiauthored papers Potential reinforcement of existing inequalities through author order Contribution statements Detailed insights into individual contributions to research Variability in reporting practices and contribution accuracy Collaboration patterns Characterizing collaboration networks and their impact Limited visibility of certain types of collaborations 24 Despite these advancements, significant challenges remain. Scientometric methods have historically faced criticism for their over-reliance and presumptions added to quantitative metrics based on secondary (i.e., publications) data, which can sometimes obscure the qualitative aspects of research performance. Traditional metrics such as publication and citation counts may not adequately capture the diverse contributions of researchers, particularly those from underrepresented groups. The inherent biases in these metrics can reinforce existing inequalities and fail to account for the varied roles and responsibilities within research teams. Furthermore, methodological challenges in accurately identifying and analyzing individual characteristics such as gender and ethnicity persist. The assignment of gender based on author names and the classification of ethnic origin using surnames, while common, can introduce errors and biases. These methods often rely on binary gender models and may not accurately reflect the complex, socially constructed nature of race and ethnicity. Additionally, the lack of comprehensive and reliable data on these characteristics can hinder efforts to study diversity at a large scale. The context in which research is conducted also plays a crucial role in shaping individuals’ scientific performance. Factors such as career trajectory, research practices, funding conditions, and social outreach influence the production and visibility of scientific knowledge. Scientometric indicators have traditionally been used without adequate consideration of these contextual factors, limiting their ability to provide a complete picture of researchers' activities and achievements. Recent efforts to contextualize these indicators, by incorporating information from a broad range of sources and reporting metrics in ways that are comprehensible to non-experts, represent a step forward. However, more work is needed to develop robust frameworks that can account for the diverse contexts in which science is produced. The study of diversity within the scientific workforce using scientometric methods offers both opportunities and challenges. Advances in data processing and methodological innovations have expanded the analytical potential of these methods, allowing for more detailed and nuanced analyses of researchers' activities and performance. However, significant challenges remain, particularly related to the inherent biases in traditional metrics and the methodological difficulties in accurately identifying and analyzing individual characteristics and contextual factors. Addressing these challenges requires a critical and nuanced approach to the use of scientometric methods. By integrating diverse data sources, developing more inclusive metrics, and considering the context in which research is conducted, future research can provide a more comprehensive and equitable understanding of diversity in the scientific workforce. This will not only enhance our understanding of how science is produced but also inform policies and practices aimed at promoting diversity and inclusion in academia. FUNDING INFORMATION This work is part of the COMPARE project (REF: PID2020-117007RA-I00) funded by the Spanish Ministry of Science (Ref: MCIN/AEI /10.13039/501100011033 FSE invierte en tu futuro. Nicolas Robinson-Garcia is funded by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (REF: RYC2019-027886-I). 25 REFERENCES Abbott, A., Cyranoski, D., Jones, N., Maher, B., Schiermeier, Q., & Van Noorden, R. (2010). Metrics: Do metrics matter? Nature News, 465(7300), 860–862. https://doi.org/10.1038/465860a Abramo, G., D’Angelo, C. A., & Murgia, G. (2016). 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