{"title":"Letter","authors":"Stephanie L. L. Pfirman, M. Laubichler","doi":"10.1162/qss_c_00269","DOIUrl":"https://doi.org/10.1162/qss_c_00269","url":null,"abstract":"","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139314645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent decades have witnessed a dramatic shift in the cross-border collaboration mode of researchers, with countries increasingly cooperating and competing with one another. It is crucial for leaders in academia and policy to understand the full extent of international research collaboration, their country's position within it, and its evolution over time. However, evidence for such world-scale dynamism is still scarce. This paper provides unique evidence of how international collaboration clusters have formed and evolved over the past 50 years across various scientific publications, using data from OpenAlex, a large-scale Open Bibliometrics platform launched in 2022. We first examine how the global presence of top-tier countries has changed in 15 natural science disciplines over time, as measured by publication volumes and international collaboration rates. Notably, we observe that the US and China have been rapidly moving closer together for decades but began moving apart after 2019. We then perform a hierarchical clustering to analyse and visualise the international collaboration clusters for each discipline and period. Finally, we provide quantitative evidence of a `Shrinking World' of research collaboration at a global scale over the past half-century. Our results provide valuable insights into the big picture of past, present and future international collaboration.
{"title":"A half-century of global collaboration in science and the ‘Shrinking World’","authors":"Okamura, Keisuke","doi":"10.1162/qss_a_00268","DOIUrl":"https://doi.org/10.1162/qss_a_00268","url":null,"abstract":"Recent decades have witnessed a dramatic shift in the cross-border collaboration mode of researchers, with countries increasingly cooperating and competing with one another. It is crucial for leaders in academia and policy to understand the full extent of international research collaboration, their country's position within it, and its evolution over time. However, evidence for such world-scale dynamism is still scarce. This paper provides unique evidence of how international collaboration clusters have formed and evolved over the past 50 years across various scientific publications, using data from OpenAlex, a large-scale Open Bibliometrics platform launched in 2022. We first examine how the global presence of top-tier countries has changed in 15 natural science disciplines over time, as measured by publication volumes and international collaboration rates. Notably, we observe that the US and China have been rapidly moving closer together for decades but began moving apart after 2019. We then perform a hierarchical clustering to analyse and visualise the international collaboration clusters for each discipline and period. Finally, we provide quantitative evidence of a `Shrinking World' of research collaboration at a global scale over the past half-century. Our results provide valuable insights into the big picture of past, present and future international collaboration.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Floriana Gargiulo, Sylvain Fontaine, Michel Dubois, Paola Tubaro
ABSTRACT Recently, the set of knowledge referred to as “artificial intelligence” (AI) has become a mainstay of scientific research. AI techniques have not only greatly developed within their native areas of development but have also spread in terms of their application to multiple areas of science and technology. We conduct a large-scale analysis of AI in science. The first question we address is the composition of what is commonly labeled AI, and how the various sub-fields within this domain are linked together. We reconstruct the internal structure of the AI ecosystem through the co-occurrence of AI terms in publications, and we distinguish between 15 different specialties of AI. Further, we investigate the spreading of AI outside its native disciplines. We bring to light the dynamics of the diffusion of AI in the scientific ecosystem and we describe the disciplinary landscape of AI applications. Finally we analyze the role of collaborations for the interdisciplinary spreading of AI. While the study of science frequently emphasizes the openness of scientific communities, we show that collaborations between those scholars who primarily develop AI and those who apply it are quite rare. Only a small group of researchers can gradually establish bridges between these communities.
{"title":"A meso-scale cartography of the AI ecosystem","authors":"Floriana Gargiulo, Sylvain Fontaine, Michel Dubois, Paola Tubaro","doi":"10.1162/qss_a_00267","DOIUrl":"https://doi.org/10.1162/qss_a_00267","url":null,"abstract":"ABSTRACT Recently, the set of knowledge referred to as “artificial intelligence” (AI) has become a mainstay of scientific research. AI techniques have not only greatly developed within their native areas of development but have also spread in terms of their application to multiple areas of science and technology. We conduct a large-scale analysis of AI in science. The first question we address is the composition of what is commonly labeled AI, and how the various sub-fields within this domain are linked together. We reconstruct the internal structure of the AI ecosystem through the co-occurrence of AI terms in publications, and we distinguish between 15 different specialties of AI. Further, we investigate the spreading of AI outside its native disciplines. We bring to light the dynamics of the diffusion of AI in the scientific ecosystem and we describe the disciplinary landscape of AI applications. Finally we analyze the role of collaborations for the interdisciplinary spreading of AI. While the study of science frequently emphasizes the openness of scientific communities, we show that collaborations between those scholars who primarily develop AI and those who apply it are quite rare. Only a small group of researchers can gradually establish bridges between these communities.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"125 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Verena Weimer, Tamara Heck, Thed van Leeuwen, Marc Rittberger
Abstract This mapping review addresses scientometric indicators that quantify open scholarship. The goal is to determine what open scholarship metrics are currently being applied and which are discussed (e.g., in policy papers). The paper contributes to a better understanding of how open scholarship is quantitatively recorded in research assessment and where gaps can be identified. The review is based on a search in four databases, each with 22 queries. Out of 3,385 hits, we coded 248 documents chosen according to the research questions. The review discusses the open scholarship metrics of the documents as well as the topics addressed in the publications, the disciplines the publications come from, and the journals in which they were published. The results indicate that research and teaching practices are unequally represented regarding open scholarship metrics. Open research material is a central and exhausted topic in publications. Open teaching practices, on the other hand, play a role in the discussion and strategy papers of the review, but open teaching material is not recorded using concrete scientometric indicators. Here, we see a research gap and discuss the potential for further research and investigation.
{"title":"The quantification of Open Scholarship – a mapping review","authors":"Verena Weimer, Tamara Heck, Thed van Leeuwen, Marc Rittberger","doi":"10.1162/qss_a_00266","DOIUrl":"https://doi.org/10.1162/qss_a_00266","url":null,"abstract":"Abstract This mapping review addresses scientometric indicators that quantify open scholarship. The goal is to determine what open scholarship metrics are currently being applied and which are discussed (e.g., in policy papers). The paper contributes to a better understanding of how open scholarship is quantitatively recorded in research assessment and where gaps can be identified. The review is based on a search in four databases, each with 22 queries. Out of 3,385 hits, we coded 248 documents chosen according to the research questions. The review discusses the open scholarship metrics of the documents as well as the topics addressed in the publications, the disciplines the publications come from, and the journals in which they were published. The results indicate that research and teaching practices are unequally represented regarding open scholarship metrics. Open research material is a central and exhausted topic in publications. Open teaching practices, on the other hand, play a role in the discussion and strategy papers of the review, but open teaching material is not recorded using concrete scientometric indicators. Here, we see a research gap and discuss the potential for further research and investigation.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135923273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kathleen Gregory, Anton Ninkov, Chantal Ripp, Emma Roblin, Isabella Peters, Stefanie Haustein
Abstract Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing—or not citing—data in their academic work. Here, we present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.
摘要数据引用，或参考文献列表中对数据的引用，越来越被视为跟踪数据重用和激励数据共享的重要手段。虽然数据引用实践的学科差异已经通过科学计量学方法得到了很好的记录，但我们还不知道这些实践在学科内的代表性如何。我们也不知道研究人员在学术工作中引用或不引用数据的动机。在这里，我们展示了已知最大的调查结果(n = 2,492)，以明确调查数据引用实践，偏好和动机，使用学科的学术作者的代表性样本，如Web of Science (WoS)所示。我们提出了研究人员目前重复使用和引用数据的做法和动机，并研究了他们希望自己的数据如何被引用的偏好。最后，我们讨论了两大集群中的学科模式，重点关注社会科学和人文科学的模式，并考虑了我们的结果对跟踪和奖励数据共享和重用的影响。
{"title":"Tracing data: A survey investigating disciplinary differences in data citation","authors":"Kathleen Gregory, Anton Ninkov, Chantal Ripp, Emma Roblin, Isabella Peters, Stefanie Haustein","doi":"10.1162/qss_a_00264","DOIUrl":"https://doi.org/10.1162/qss_a_00264","url":null,"abstract":"Abstract Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing—or not citing—data in their academic work. Here, we present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136080081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Gender differences in research funding exist but bias evidence is elusive and findings are contradictory. Bias has multiple dimensions, but in evaluation processes bias would be the outcome of the reviewers' assessment. Evidence in observational approaches is often based either on outcome distributions or on modeling bias as the residual. Causal claims are usually mixed with simple statistical associations. In this paper we use an experimental design to measure the effects of a cause: the effect of the gender of the principal investigator (PI) on the score of a research funding application (treatment). We embedded a hypothetical research application description in a field experiment. The subjects were the reviewers selected by a funding agency and the experiment was implemented simultaneously with the funding call's peer review assessment. We manipulated the application item that described the gender of the PI, with two designations: female PI and male PI. Treatment was randomly allocated with block assignment and the response rate was 100% of the population, avoiding problems of biased estimates in pooled data. Contrary to some previous research, we find no evidence that male or female PIs received significantly different scores, nor any evidence of same-gender preferences of reviewers regarding the applicants' gender.
{"title":"Gender bias in funding evaluation: A randomized experiment","authors":"Laura Cruz-Castro, Luis Sanz-Menéndez","doi":"10.1162/qss_a_00263","DOIUrl":"https://doi.org/10.1162/qss_a_00263","url":null,"abstract":"Abstract Gender differences in research funding exist but bias evidence is elusive and findings are contradictory. Bias has multiple dimensions, but in evaluation processes bias would be the outcome of the reviewers' assessment. Evidence in observational approaches is often based either on outcome distributions or on modeling bias as the residual. Causal claims are usually mixed with simple statistical associations. In this paper we use an experimental design to measure the effects of a cause: the effect of the gender of the principal investigator (PI) on the score of a research funding application (treatment). We embedded a hypothetical research application description in a field experiment. The subjects were the reviewers selected by a funding agency and the experiment was implemented simultaneously with the funding call's peer review assessment. We manipulated the application item that described the gender of the PI, with two designations: female PI and male PI. Treatment was randomly allocated with block assignment and the response rate was 100% of the population, avoiding problems of biased estimates in pooled data. Contrary to some previous research, we find no evidence that male or female PIs received significantly different scores, nor any evidence of same-gender preferences of reviewers regarding the applicants' gender.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Interdisciplinarity is a fundamental asset in today's research landscape, but its rules and habits vary from those of disciplinary approaches. This article aims to evaluate the impact of researchers' participation in interdisciplinary projects on their scientific careers. To do so, we conducted a survey of researchers working at the Centre National de la Recherche Scientifique (CNRS), the largest public multidisciplinary research institution in France. The survey is based on a sample of 970 respondents, representative of scientists from all disciplines and involved to varying degrees in interdisciplinarity. The main results indicate that involvement in interdisciplinary projects often starts very early (PhD, postdoctoral), and that interdisciplinarity is not slowing down career development. Interdisciplinarity has, however, certain specificities, such as the longer duration of projects and the absence of adequate scientific journals. In terms of valorization of scientific results, differences in disciplinary uses are found. Assessment criteria for interdisciplinary projects or careers do not take sufficient account of these specificities; they are considered inadequate to the challenges of interaction between disciplines and should be rethought. We make four proposals, which we believe essential to better recognize interdisciplinary scientific engagement.
{"title":"Interdisciplinary research: motivations and challenges for researcher careers","authors":"Marylin Vantard, Claire Galland, Martina Knoop","doi":"10.1162/qss_a_00265","DOIUrl":"https://doi.org/10.1162/qss_a_00265","url":null,"abstract":"Abstract Interdisciplinarity is a fundamental asset in today's research landscape, but its rules and habits vary from those of disciplinary approaches. This article aims to evaluate the impact of researchers' participation in interdisciplinary projects on their scientific careers. To do so, we conducted a survey of researchers working at the Centre National de la Recherche Scientifique (CNRS), the largest public multidisciplinary research institution in France. The survey is based on a sample of 970 respondents, representative of scientists from all disciplines and involved to varying degrees in interdisciplinarity. The main results indicate that involvement in interdisciplinary projects often starts very early (PhD, postdoctoral), and that interdisciplinarity is not slowing down career development. Interdisciplinarity has, however, certain specificities, such as the longer duration of projects and the absence of adequate scientific journals. In terms of valorization of scientific results, differences in disciplinary uses are found. Assessment criteria for interdisciplinary projects or careers do not take sufficient account of these specificities; they are considered inadequate to the challenges of interaction between disciplines and should be rethought. We make four proposals, which we believe essential to better recognize interdisciplinary scientific engagement.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136139313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The Arabic Citation Index (ARCI) was launched in 2020. This article provides an overview of the scientific literature contained in this new database and explores its possible usage in research evaluation. As of May 2022, ARCI had indexed 138,283 scientific publications published between 2015 and 2020. ARCI’s coverage is characterized by using the metadata available in scientific publications. First, I investigate the distributions of the indexed literature at various levels (research domains, countries, languages, open access). Articles make up nearly all the documents indexed with a share of 99% of ARCI. The Arts & Humanities and Social Sciences fields have the highest concentration of publications. Most indexed journals are published in Egypt, Algeria, Iraq, Jordan, and Saudi Arabia. About 8% of publications in ARCI are published in languages other than Arabic. Second, I use an unsupervised machine learning model, Latent Dirichlet Allocation, and the text mining algorithm of VOSviewer to uncover the main topics in ARCI. These methods provide a better understanding of ARCI’s thematic structure. Next, I discuss how ARCI can complement global standards in the context of a more inclusive research evaluation. Finally, I suggest a few research opportunities after discussing the findings of this study.
{"title":"The Arabic Citation Index: Toward a better understanding of Arab scientific literature","authors":"Jamal El-Ouahi","doi":"10.1162/qss_a_00261","DOIUrl":"https://doi.org/10.1162/qss_a_00261","url":null,"abstract":"Abstract The Arabic Citation Index (ARCI) was launched in 2020. This article provides an overview of the scientific literature contained in this new database and explores its possible usage in research evaluation. As of May 2022, ARCI had indexed 138,283 scientific publications published between 2015 and 2020. ARCI’s coverage is characterized by using the metadata available in scientific publications. First, I investigate the distributions of the indexed literature at various levels (research domains, countries, languages, open access). Articles make up nearly all the documents indexed with a share of 99% of ARCI. The Arts & Humanities and Social Sciences fields have the highest concentration of publications. Most indexed journals are published in Egypt, Algeria, Iraq, Jordan, and Saudi Arabia. About 8% of publications in ARCI are published in languages other than Arabic. Second, I use an unsupervised machine learning model, Latent Dirichlet Allocation, and the text mining algorithm of VOSviewer to uncover the main topics in ARCI. These methods provide a better understanding of ARCI’s thematic structure. Next, I discuss how ARCI can complement global standards in the context of a more inclusive research evaluation. Finally, I suggest a few research opportunities after discussing the findings of this study.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135353410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ranking and categorizations of academic articles of a dataset have traditionally been based on the distribution of their total citations. This ranking formed the basis for the definition of the h-index. As an alternative methodology, the ranking of articles of a dataset can be performed according to the distribution of the average citations of the articles. Applying this same principle to the h-index itself leads to an average h-index, the ha-index: the largest number of papers ha published by a researcher that has obtained at least ha citations per year on average. The new ha-index offers more consistency, increased selectivity, and fairer treatment of younger scholars compared to the classic h-index. With its normalized time aspect, the method leads to better acknowledgment of progress. The evolution of the h-indexes over time shows how the ha-index reaches its full potential earlier and offers more stability over time. The average citation ha-index partly solves the problem of the temporality of the h-index. The ha-index can also be applied to academic journals. In particular, the application of the ha-index to journals leads to more stability as they reach their limit sooner. The ha-index brings a response to the inflation of h-index levels. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00259
{"title":"The ha-index, the average citation h-index","authors":"Y. Fassin","doi":"10.1162/qss_a_00259","DOIUrl":"https://doi.org/10.1162/qss_a_00259","url":null,"abstract":"\u0000 The ranking and categorizations of academic articles of a dataset have traditionally been based on the distribution of their total citations. This ranking formed the basis for the definition of the h-index. As an alternative methodology, the ranking of articles of a dataset can be performed according to the distribution of the average citations of the articles. Applying this same principle to the h-index itself leads to an average h-index, the ha-index: the largest number of papers ha published by a researcher that has obtained at least ha citations per year on average. The new ha-index offers more consistency, increased selectivity, and fairer treatment of younger scholars compared to the classic h-index. With its normalized time aspect, the method leads to better acknowledgment of progress. The evolution of the h-indexes over time shows how the ha-index reaches its full potential earlier and offers more stability over time. The average citation ha-index partly solves the problem of the temporality of the h-index. The ha-index can also be applied to academic journals. In particular, the application of the ha-index to journals leads to more stability as they reach their limit sooner. The ha-index brings a response to the inflation of h-index levels.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00259\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47655510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Science of science (SciSci) is a growing field encompassing diverse interdisciplinary research programs that study the processes underlying science. The field has benefited greatly from access to massive digital databases containing the products of scientific discourse—including publications, journals, patents, books, conference proceedings, and grants. The subsequent proliferation of mathematical models and computational techniques for quantifying the dynamics of innovation and success in science has made it difficult to disentangle universal scientific processes from those dependent on specific databases, data-processing decisions, field practices, etc. Here we present pySciSci, a freely available and easily adaptable package for the analysis of large-scale bibliometric data. The pySciSci package standardizes access to many of the most common datasets in SciSci and provides efficient implementations of common and advanced analytical techniques. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00260
{"title":"Reproducible Science of Science at scale: pySciSci","authors":"Alexander J. Gates, A. Barabási","doi":"10.1162/qss_a_00260","DOIUrl":"https://doi.org/10.1162/qss_a_00260","url":null,"abstract":"\u0000 Science of science (SciSci) is a growing field encompassing diverse interdisciplinary research programs that study the processes underlying science. The field has benefited greatly from access to massive digital databases containing the products of scientific discourse—including publications, journals, patents, books, conference proceedings, and grants. The subsequent proliferation of mathematical models and computational techniques for quantifying the dynamics of innovation and success in science has made it difficult to disentangle universal scientific processes from those dependent on specific databases, data-processing decisions, field practices, etc. Here we present pySciSci, a freely available and easily adaptable package for the analysis of large-scale bibliometric data. The pySciSci package standardizes access to many of the most common datasets in SciSci and provides efficient implementations of common and advanced analytical techniques.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00260\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46819991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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