Pub Date : 2022-10-26DOI: 10.48550/arXiv.2210.14871
Eric Schares
Abstract On August 25, 2022, the White House Office of Science and Technology Policy (OSTP) released a memo regarding public access to scientific research. Signed by Director Alondra Nelson, this updated guidance eliminated the 12-month embargo period on publications arising from U.S. federal funding that had been allowed from a previous 2013 OSTP memo. Although reactions to this updated federal guidance have been plentiful, to date there has not been a detailed analysis of the publications that would fall under this new framework. The OSTP released a companion report along with the memo, but it only provided a broad estimate of total numbers affected per year. Therefore, this study seeks to more deeply investigate the characteristics of U.S. federally funded research over a 5-year period from 2017–2021 to better understand the updated guidance’s impact. It uses a manually created custom filter in the Dimensions database to return only publications that arise from U.S. federal funding. Results show that an average of 265,000 articles were published each year that acknowledge US federal funding agencies, and these research outputs are further examined by publisher, journal title, institutions, and Open Access status. Interactive versions of the graphs are available at https://ostp.lib.iastate.edu/.
{"title":"Impact of the 2022 OSTP memo: A bibliometric analysis of US federally funded publications, 2017–2021","authors":"Eric Schares","doi":"10.48550/arXiv.2210.14871","DOIUrl":"https://doi.org/10.48550/arXiv.2210.14871","url":null,"abstract":"Abstract On August 25, 2022, the White House Office of Science and Technology Policy (OSTP) released a memo regarding public access to scientific research. Signed by Director Alondra Nelson, this updated guidance eliminated the 12-month embargo period on publications arising from U.S. federal funding that had been allowed from a previous 2013 OSTP memo. Although reactions to this updated federal guidance have been plentiful, to date there has not been a detailed analysis of the publications that would fall under this new framework. The OSTP released a companion report along with the memo, but it only provided a broad estimate of total numbers affected per year. Therefore, this study seeks to more deeply investigate the characteristics of U.S. federally funded research over a 5-year period from 2017–2021 to better understand the updated guidance’s impact. It uses a manually created custom filter in the Dimensions database to return only publications that arise from U.S. federal funding. Results show that an average of 265,000 articles were published each year that acknowledge US federal funding agencies, and these research outputs are further examined by publisher, journal title, institutions, and Open Access status. Interactive versions of the graphs are available at https://ostp.lib.iastate.edu/.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"4 1","pages":"1-21"},"PeriodicalIF":6.4,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42327305","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}
Wenceslao Arroyo-Machado, D. Torres-Salinas, R. Costas
Abstract Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.
{"title":"Wikinformetrics: Construction and description of an open Wikipedia knowledge graph data set for informetric purposes","authors":"Wenceslao Arroyo-Machado, D. Torres-Salinas, R. Costas","doi":"10.1162/qss_a_00226","DOIUrl":"https://doi.org/10.1162/qss_a_00226","url":null,"abstract":"Abstract Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"931-952"},"PeriodicalIF":6.4,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43186269","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}
Sirag Erkol, Satyaki Sikdar, F. Radicchi, S. Fortunato
Abstract The exponentially growing number of scientific papers stimulates a discussion on the interplay between quantity and quality in science. In particular, one may wonder which publication strategy may offer more chances of success: publishing lots of papers, producing a few hit papers, or something in between. Here we tackle this question by studying the scientific portfolios of Nobel Prize laureates. A comparative analysis of different citation-based indicators of individual impact suggests that the best path to success may rely on consistently producing high-quality work. Such a pattern is especially rewarded by a new metric, the E-index, which identifies excellence better than state-of-the-art measures.
{"title":"Consistency pays off in science","authors":"Sirag Erkol, Satyaki Sikdar, F. Radicchi, S. Fortunato","doi":"10.1162/qss_a_00252","DOIUrl":"https://doi.org/10.1162/qss_a_00252","url":null,"abstract":"Abstract The exponentially growing number of scientific papers stimulates a discussion on the interplay between quantity and quality in science. In particular, one may wonder which publication strategy may offer more chances of success: publishing lots of papers, producing a few hit papers, or something in between. Here we tackle this question by studying the scientific portfolios of Nobel Prize laureates. A comparative analysis of different citation-based indicators of individual impact suggests that the best path to success may rely on consistently producing high-quality work. Such a pattern is especially rewarded by a new metric, the E-index, which identifies excellence better than state-of-the-art measures.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"4 1","pages":"491-500"},"PeriodicalIF":6.4,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42370557","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 aim of this study was to investigate whether the presence of an author photograph and biography in scientific articles could have an impact on article citations. The impact of a photograph and biography, in combination with certain author characteristics (i.e., gender, affiliation country (measured as whether the author was affiliated with a high-income country or not), and scientific impact (measured as whether the author was a high-impact author or not)), was also examined, while controlling for several covariates. This study focused on a sample of articles published in the time span of 2016–2018 in chemistry and chemical engineering journals by Elsevier. The articles were downloaded from Scopus. The analysis was done using random effects within-between model analyses. Within authors, the results showed no significant impact of author photograph and biography on citations. Different patterns were found for visibility of articles when the presence of an author photograph and biography was combined with author characteristics. While being affiliated to a high-income country and being a high-impact author had a positive impact on citations, gender (female) had a negative impact. For gender, there was a small citation disadvantage of 5% for female authors when they provided a photograph and biography.
{"title":"Can the presence of an author photograph and biography have an impact on article citations? The case of chemistry and chemical engineering","authors":"T. Dehdarirad","doi":"10.1162/qss_a_00219","DOIUrl":"https://doi.org/10.1162/qss_a_00219","url":null,"abstract":"Abstract The aim of this study was to investigate whether the presence of an author photograph and biography in scientific articles could have an impact on article citations. The impact of a photograph and biography, in combination with certain author characteristics (i.e., gender, affiliation country (measured as whether the author was affiliated with a high-income country or not), and scientific impact (measured as whether the author was a high-impact author or not)), was also examined, while controlling for several covariates. This study focused on a sample of articles published in the time span of 2016–2018 in chemistry and chemical engineering journals by Elsevier. The articles were downloaded from Scopus. The analysis was done using random effects within-between model analyses. Within authors, the results showed no significant impact of author photograph and biography on citations. Different patterns were found for visibility of articles when the presence of an author photograph and biography was combined with author characteristics. While being affiliated to a high-income country and being a high-impact author had a positive impact on citations, gender (female) had a negative impact. For gender, there was a small citation disadvantage of 5% for female authors when they provided a photograph and biography.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1024-1039"},"PeriodicalIF":6.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44068143","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 Overlay maps of science are global base maps over which subsets of publications can be projected. Such maps can be used to monitor, explore, and study research through its publication output. Most maps of science, including overlay maps, are flat in the sense that they visualize research fields at one single level. Such maps generally fail to provide both overview and detail about the research being analyzed. The aim of this study is to improve overlay maps of science to provide both features in a single visualization. I created a map based on a hierarchical classification of publications, including broad disciplines for overview and more granular levels to incorporate detailed information. The classification was obtained by clustering articles in a citation network of about 17 million publication records in PubMed from 1995 onwards. The map emphasizes the hierarchical structure of the classification by visualizing both disciplines and the underlying specialties. To show how the visualization methodology can help getting both an overview of research and detailed information about its topical structure, I studied two cases: coronavirus/Covid-19 research and the university alliance called Stockholm Trio.
{"title":"Improving overlay maps of science: Combining overview and detail","authors":"Peter Sjögårde","doi":"10.1162/qss_a_00216","DOIUrl":"https://doi.org/10.1162/qss_a_00216","url":null,"abstract":"Abstract Overlay maps of science are global base maps over which subsets of publications can be projected. Such maps can be used to monitor, explore, and study research through its publication output. Most maps of science, including overlay maps, are flat in the sense that they visualize research fields at one single level. Such maps generally fail to provide both overview and detail about the research being analyzed. The aim of this study is to improve overlay maps of science to provide both features in a single visualization. I created a map based on a hierarchical classification of publications, including broad disciplines for overview and more granular levels to incorporate detailed information. The classification was obtained by clustering articles in a citation network of about 17 million publication records in PubMed from 1995 onwards. The map emphasizes the hierarchical structure of the classification by visualizing both disciplines and the underlying specialties. To show how the visualization methodology can help getting both an overview of research and detailed information about its topical structure, I studied two cases: coronavirus/Covid-19 research and the university alliance called Stockholm Trio.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1097-1118"},"PeriodicalIF":6.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42402120","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 To reconstruct topics in bibliometric networks, one must use algorithms. Specifically, researchers often apply algorithms from the class of network community detection algorithms (such as the Louvain algorithm) that are general-purpose algorithms not intentionally programmed for a bibliometric task. Each algorithm has specific properties “inscribed,” which distinguish it from the others. It can thus be assumed that different algorithms are more or less suitable for a given bibliometric task. However, the suitability of a specific algorithm when it is applied for topic reconstruction is rarely reflected upon. Why choose this algorithm and not another? In this study, I assess the suitability of four community detection algorithms for topic reconstruction, by first deriving the properties of the phenomenon to be reconstructed—topics—and comparing if these match with the properties of the algorithms. The results suggest that the previous use of these algorithms for bibliometric purposes cannot be justified by their specific suitability for this task.
{"title":"Know thy tools! Limits of popular algorithms used for topic reconstruction","authors":"Matthias Held","doi":"10.1162/qss_a_00217","DOIUrl":"https://doi.org/10.1162/qss_a_00217","url":null,"abstract":"Abstract To reconstruct topics in bibliometric networks, one must use algorithms. Specifically, researchers often apply algorithms from the class of network community detection algorithms (such as the Louvain algorithm) that are general-purpose algorithms not intentionally programmed for a bibliometric task. Each algorithm has specific properties “inscribed,” which distinguish it from the others. It can thus be assumed that different algorithms are more or less suitable for a given bibliometric task. However, the suitability of a specific algorithm when it is applied for topic reconstruction is rarely reflected upon. Why choose this algorithm and not another? In this study, I assess the suitability of four community detection algorithms for topic reconstruction, by first deriving the properties of the phenomenon to be reconstructed—topics—and comparing if these match with the properties of the algorithms. The results suggest that the previous use of these algorithms for bibliometric purposes cannot be justified by their specific suitability for this task.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1054-1078"},"PeriodicalIF":6.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42924484","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 This study examines the extent to which women are represented among the world’s highly cited researchers (HCRs) and explores their representation over time and across fields, regions, and countries. The study identifies 11,842 HCRs in all fields and uses Gender-API, Genderize.Io, Namsor, and the web to identify their gender. Women’s share of HCRs grew from 13.1% in 2014 to 14.0% in 2021; however, the increase is slower than that of women’s representation among the general population of authors. The data show that women’s share of HCRs would need to increase by 100% in health and social sciences, 200% in agriculture, biology, earth, and environmental sciences, 300% in mathematics and physics, and 500% in chemistry, computer science, and engineering to close the gap with men. Women’s representation among all HCRs in North America, Europe, and Oceania ranges from 15% to 18%, compared to a world average of 13.7%. Among countries with the highest number of HCRs, the gender gap is least evident in Switzerland, Brazil, Norway, the United Kingdom, and the United States and most noticeable in Asian countries. The study reviews factors that can be seen to influence the gender gap among HCRs and makes recommendations for improvement.
{"title":"Gender gap among highly cited researchers, 2014–2021","authors":"Lokman I. Meho","doi":"10.1162/qss_a_00218","DOIUrl":"https://doi.org/10.1162/qss_a_00218","url":null,"abstract":"Abstract This study examines the extent to which women are represented among the world’s highly cited researchers (HCRs) and explores their representation over time and across fields, regions, and countries. The study identifies 11,842 HCRs in all fields and uses Gender-API, Genderize.Io, Namsor, and the web to identify their gender. Women’s share of HCRs grew from 13.1% in 2014 to 14.0% in 2021; however, the increase is slower than that of women’s representation among the general population of authors. The data show that women’s share of HCRs would need to increase by 100% in health and social sciences, 200% in agriculture, biology, earth, and environmental sciences, 300% in mathematics and physics, and 500% in chemistry, computer science, and engineering to close the gap with men. Women’s representation among all HCRs in North America, Europe, and Oceania ranges from 15% to 18%, compared to a world average of 13.7%. Among countries with the highest number of HCRs, the gender gap is least evident in Switzerland, Brazil, Norway, the United Kingdom, and the United States and most noticeable in Asian countries. The study reviews factors that can be seen to influence the gender gap among HCRs and makes recommendations for improvement.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1003-1023"},"PeriodicalIF":6.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45910563","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 Data is one of the most used terms in scientific vocabulary. This article focuses on the relationship between data and research by analyzing the contexts of occurrence of the word data in a corpus of 72,471 research articles (1980–2012) from two distinct fields (Social sciences, Physical sciences). The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process, and how they gain value for researchers who hold them. Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word data mostly occurs at the beginning and end of research articles. Adjectives and verbs accompanying the noun data turn out to be even more important than data itself in specifying data. The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data-handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.
{"title":"Researchers and their data: A study based on the use of the word data in scholarly articles","authors":"Frédérique Bordignon, M. Maisonobe","doi":"10.1162/qss_a_00220","DOIUrl":"https://doi.org/10.1162/qss_a_00220","url":null,"abstract":"Abstract Data is one of the most used terms in scientific vocabulary. This article focuses on the relationship between data and research by analyzing the contexts of occurrence of the word data in a corpus of 72,471 research articles (1980–2012) from two distinct fields (Social sciences, Physical sciences). The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process, and how they gain value for researchers who hold them. Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word data mostly occurs at the beginning and end of research articles. Adjectives and verbs accompanying the noun data turn out to be even more important than data itself in specifying data. The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data-handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1156-1178"},"PeriodicalIF":6.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41655642","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}
B. Nguyen, Markus Luczak-Rösch, J. Dinneen, V. Larivière
Abstract Measuring international research collaboration (IRC) is essential to various research assessment tasks but the effect of various measurement decisions, including which data sources to use, has not been thoroughly studied. To better understand the effect of data source choice on IRC measurement, we design and implement a data quality assessment framework specifically for bibliographic data by reviewing and selecting available dimensions and designing appropriate computable metrics, and then validate the framework by applying it to four popular sources of bibliographic data: Microsoft Academic Graph, Web of Science (WoS), Dimensions, and the ACM Digital Library. Successful validation of the framework suggests it is consistent with the popular conceptual framework of information quality proposed by Wang and Strong (1996) and adequately identifies the differences in quality in the sources examined. Application of the framework reveals that WoS has the highest overall quality among the sets considered; and that the differences in quality can be explained primarily by how the data sources are organized. Our study comprises a methodological contribution that enables researchers to apply this IRC measurement tool in their studies and makes an empirical contribution by further characterizing four popular sources of bibliographic data and their impact on IRC measurement.
{"title":"Assessing the quality of bibliographic data sources for measuring international research collaboration","authors":"B. Nguyen, Markus Luczak-Rösch, J. Dinneen, V. Larivière","doi":"10.1162/qss_a_00211","DOIUrl":"https://doi.org/10.1162/qss_a_00211","url":null,"abstract":"Abstract Measuring international research collaboration (IRC) is essential to various research assessment tasks but the effect of various measurement decisions, including which data sources to use, has not been thoroughly studied. To better understand the effect of data source choice on IRC measurement, we design and implement a data quality assessment framework specifically for bibliographic data by reviewing and selecting available dimensions and designing appropriate computable metrics, and then validate the framework by applying it to four popular sources of bibliographic data: Microsoft Academic Graph, Web of Science (WoS), Dimensions, and the ACM Digital Library. Successful validation of the framework suggests it is consistent with the popular conceptual framework of information quality proposed by Wang and Strong (1996) and adequately identifies the differences in quality in the sources examined. Application of the framework reveals that WoS has the highest overall quality among the sets considered; and that the differences in quality can be explained primarily by how the data sources are organized. Our study comprises a methodological contribution that enables researchers to apply this IRC measurement tool in their studies and makes an empirical contribution by further characterizing four popular sources of bibliographic data and their impact on IRC measurement.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"529-559"},"PeriodicalIF":6.4,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48703990","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 Research funders spend considerable efforts collecting information on the outcomes of the research they fund. To help funders track publication output associated with their funding, Crossref initiated FundRef in 2013, enabling publishers to register funding information using persistent identifiers. However, it is hard to assess the coverage of funder metadata because it is unknown how many articles are the result of funded research and should therefore include funder metadata. In this paper we looked at 5,004 publications reported by researchers to be the result of funding by a specific funding agency: the Dutch Research Council NWO. Only 67% of these articles contain funding information in Crossref, with a subset acknowledging NWO as funder name and/or Funder IDs linked to NWO (53% and 45%, respectively). Web of Science (WoS), Scopus, and Dimensions are all able to infer additional funding information from funding statements in the full text of the articles. Funding information in Lens largely corresponds to that in Crossref, with some additional funding information likely taken from PubMed. We observe interesting differences between publishers in the coverage and completeness of funding metadata in Crossref compared to proprietary databases, highlighting the potential to increase the quality of open metadata on funding.
研究资助者花费大量精力收集他们资助的研究成果的信息。为了帮助资助者跟踪与其资助相关的出版物产出，Crossref于2013年发起了FundRef，使出版商能够使用持久标识符注册资助信息。然而，很难评估资助者元数据的覆盖范围，因为不知道有多少文章是资助研究的结果，因此应该包括资助者元数据。在这篇论文中，我们研究了5004篇由研究人员报告的论文，这些论文都是由一个特定的资助机构资助的:荷兰研究委员会NWO。这些文章中只有67%在Crossref中包含资助信息，其中一部分承认NWO是资助者姓名和/或与NWO相关的资助者id(分别为53%和45%)。Web of Science (WoS)、Scopus和Dimensions都能够从文章全文中的资助声明中推断出额外的资助信息。Lens中的资金信息与Crossref中的基本一致，还有一些额外的资金信息可能来自PubMed。我们观察到，与专有数据库相比，出版商在Crossref中资助元数据的覆盖范围和完整性方面存在有趣的差异，这突出了提高开放资助元数据质量的潜力。
{"title":"The availability and completeness of open funder metadata: Case study for publications funded by the Dutch Research Council","authors":"Bianca Kramer, H. D. Jonge","doi":"10.1162/qss_a_00210","DOIUrl":"https://doi.org/10.1162/qss_a_00210","url":null,"abstract":"Abstract Research funders spend considerable efforts collecting information on the outcomes of the research they fund. To help funders track publication output associated with their funding, Crossref initiated FundRef in 2013, enabling publishers to register funding information using persistent identifiers. However, it is hard to assess the coverage of funder metadata because it is unknown how many articles are the result of funded research and should therefore include funder metadata. In this paper we looked at 5,004 publications reported by researchers to be the result of funding by a specific funding agency: the Dutch Research Council NWO. Only 67% of these articles contain funding information in Crossref, with a subset acknowledging NWO as funder name and/or Funder IDs linked to NWO (53% and 45%, respectively). Web of Science (WoS), Scopus, and Dimensions are all able to infer additional funding information from funding statements in the full text of the articles. Funding information in Lens largely corresponds to that in Crossref, with some additional funding information likely taken from PubMed. We observe interesting differences between publishers in the coverage and completeness of funding metadata in Crossref compared to proprietary databases, highlighting the potential to increase the quality of open metadata on funding.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"583-599"},"PeriodicalIF":6.4,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45861616","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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