Abstract This paper presents an analysis of the Overton policy document database, describing the makeup of materials indexed and the nature in which they cite academic literature. We report on various aspects of the data, including growth, geographic spread, language representation, the range of policy source types included, and the availability of citation links in documents. Longitudinal analysis over established journal category schemes is used to reveal the scale and disciplinary focus of citations and determine the feasibility of developing field-normalized citation indicators. To corroborate the data indexed, we also examine how well self-reported funding outcomes collected by UK funders correspond to data indexed in the Overton database. Finally, to test the data in an experimental setting, we assess whether peer-review assessment of impact as measured by the UK Research Excellence Framework (REF) 2014 correlates with derived policy citation metrics. Our findings show that for some research topics, such as health, economics, social care, and the environment, Overton contains a core set of policy documents with sufficient citation linkage to academic literature to support various citation analyses that may be informative in research evaluation, impact assessment, and policy review.
{"title":"Overton: A bibliometric database of policy document citations","authors":"M. Szomszor, E. Adie","doi":"10.1162/qss_a_00204","DOIUrl":"https://doi.org/10.1162/qss_a_00204","url":null,"abstract":"Abstract This paper presents an analysis of the Overton policy document database, describing the makeup of materials indexed and the nature in which they cite academic literature. We report on various aspects of the data, including growth, geographic spread, language representation, the range of policy source types included, and the availability of citation links in documents. Longitudinal analysis over established journal category schemes is used to reveal the scale and disciplinary focus of citations and determine the feasibility of developing field-normalized citation indicators. To corroborate the data indexed, we also examine how well self-reported funding outcomes collected by UK funders correspond to data indexed in the Overton database. Finally, to test the data in an experimental setting, we assess whether peer-review assessment of impact as measured by the UK Research Excellence Framework (REF) 2014 correlates with derived policy citation metrics. Our findings show that for some research topics, such as health, economics, social care, and the environment, Overton contains a core set of policy documents with sufficient citation linkage to academic literature to support various citation analyses that may be informative in research evaluation, impact assessment, and policy review.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"624-650"},"PeriodicalIF":6.4,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43471463","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 As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13—Climate Action (SDG 13). The four methods (Elsevier, STRINGS, SIRIS, and Dimensions) have each developed search strings with the help of subject matter experts, which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualized some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalizing search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest data set. Meanwhile, data providers might customize their methods for varying interpretations of the SDGs.
{"title":"A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case study of SDG 13—Climate Action","authors":"P. Purnell","doi":"10.1162/qss_a_00215","DOIUrl":"https://doi.org/10.1162/qss_a_00215","url":null,"abstract":"Abstract As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13—Climate Action (SDG 13). The four methods (Elsevier, STRINGS, SIRIS, and Dimensions) have each developed search strings with the help of subject matter experts, which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualized some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalizing search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest data set. Meanwhile, data providers might customize their methods for varying interpretations of the SDGs.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"976-1002"},"PeriodicalIF":6.4,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46402790","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 Two partly conflicting academic pressures from the seriousness of the Covid-19 pandemic are the need for faster peer review of Covid-19 health-related research and greater scrutiny of its findings. This paper investigates whether decreases in peer review durations for Covid-19 articles were universal across 97 major medical journals, as well as Nature, Science, and Cell. The results suggest that on average, Covid-19 articles submitted during 2020 were reviewed 1.7–2.1 times faster than non-Covid-19 articles submitted during 2017–2020. Nevertheless, while the review speed of Covid-19 research was particularly fast during the first 5 months (1.9–3.4 times faster) of the pandemic (January–May 2020), this speed advantage was no longer evident for articles submitted in November–December 2020. Faster peer review was also associated with higher citation impact for Covid-19 articles in the same journals, suggesting it did not usually compromise the scholarly impact of important Covid-19 research. Overall, then, it seems that core medical and general journals responded quickly but carefully to the pandemic, although the situation returned closer to normal within a year.
{"title":"Covid-19 refereeing duration and impact in major medical journals","authors":"K. Kousha, M. Thelwall","doi":"10.1162/qss_a_00176","DOIUrl":"https://doi.org/10.1162/qss_a_00176","url":null,"abstract":"Abstract Two partly conflicting academic pressures from the seriousness of the Covid-19 pandemic are the need for faster peer review of Covid-19 health-related research and greater scrutiny of its findings. This paper investigates whether decreases in peer review durations for Covid-19 articles were universal across 97 major medical journals, as well as Nature, Science, and Cell. The results suggest that on average, Covid-19 articles submitted during 2020 were reviewed 1.7–2.1 times faster than non-Covid-19 articles submitted during 2017–2020. Nevertheless, while the review speed of Covid-19 research was particularly fast during the first 5 months (1.9–3.4 times faster) of the pandemic (January–May 2020), this speed advantage was no longer evident for articles submitted in November–December 2020. Faster peer review was also associated with higher citation impact for Covid-19 articles in the same journals, suggesting it did not usually compromise the scholarly impact of important Covid-19 research. Overall, then, it seems that core medical and general journals responded quickly but carefully to the pandemic, although the situation returned closer to normal within a year.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1-17"},"PeriodicalIF":6.4,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47938218","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 Scientometric research often relies on large-scale bibliometric databases of academic journal articles. Long-term and longitudinal research can be affected if the composition of a database varies over time, and text processing research can be affected if the percentage of articles with abstracts changes. This article therefore assesses changes in the magnitude of the coverage of a major citation index, Scopus, over 121 years from 1900. The results show sustained exponential growth from 1900, except for dips during both world wars, and with increased growth after 2004. Over the same period, the percentage of articles with 500+ character abstracts increased from 1% to 95%. The number of different journals in Scopus also increased exponentially, but slowing down from 2010, with the number of articles per journal being approximately constant until 1980, then tripling due to megajournals and online-only publishing. The breadth of Scopus, in terms of the number of narrow fields with substantial numbers of articles, simultaneously increased from one field having 1,000 articles in 1945 to 308 fields in 2020. Scopus’s international character also radically changed from 68% of first authors from Germany and the United States in 1900 to just 17% in 2020, with China dominating (25%).
{"title":"Scopus 1900–2020: Growth in articles, abstracts, countries, fields, and journals","authors":"M. Thelwall, Pardeep Sud","doi":"10.1162/qss_a_00177","DOIUrl":"https://doi.org/10.1162/qss_a_00177","url":null,"abstract":"Abstract Scientometric research often relies on large-scale bibliometric databases of academic journal articles. Long-term and longitudinal research can be affected if the composition of a database varies over time, and text processing research can be affected if the percentage of articles with abstracts changes. This article therefore assesses changes in the magnitude of the coverage of a major citation index, Scopus, over 121 years from 1900. The results show sustained exponential growth from 1900, except for dips during both world wars, and with increased growth after 2004. Over the same period, the percentage of articles with 500+ character abstracts increased from 1% to 95%. The number of different journals in Scopus also increased exponentially, but slowing down from 2010, with the number of articles per journal being approximately constant until 1980, then tripling due to megajournals and online-only publishing. The breadth of Scopus, in terms of the number of narrow fields with substantial numbers of articles, simultaneously increased from one field having 1,000 articles in 1945 to 308 fields in 2020. Scopus’s international character also radically changed from 68% of first authors from Germany and the United States in 1900 to just 17% in 2020, with China dominating (25%).","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"37-50"},"PeriodicalIF":6.4,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48757692","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 In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations of retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gathered the citing entities and annotated their basic metadata (e.g., title, venue, subject) and the characteristics of their in-text citations (e.g., intent, sentiment). Using these data, we performed a quantitative and qualitative study of retractions in the humanities, presenting descriptive statistics and a topic modeling analysis of the citing entities’ abstracts and the in-text citation contexts. As part of our main findings, we noticed that there was no drop in the overall number of citations after the year of retraction, with few entities that have either mentioned the retraction or expressed a negative sentiment toward the cited publication. In addition, on several occasions, we noticed a higher concern/awareness by citing entities belonging to the health sciences domain about citing a retracted publication, compared with the humanities and social science domains. Philosophy, arts, and history are the humanities areas that showed higher concern toward the retraction.
{"title":"A quantitative and qualitative open citation analysis of retracted articles in the humanities","authors":"Ivan Heibi, S. Peroni","doi":"10.1162/qss_a_00222","DOIUrl":"https://doi.org/10.1162/qss_a_00222","url":null,"abstract":"Abstract In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations of retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gathered the citing entities and annotated their basic metadata (e.g., title, venue, subject) and the characteristics of their in-text citations (e.g., intent, sentiment). Using these data, we performed a quantitative and qualitative study of retractions in the humanities, presenting descriptive statistics and a topic modeling analysis of the citing entities’ abstracts and the in-text citation contexts. As part of our main findings, we noticed that there was no drop in the overall number of citations after the year of retraction, with few entities that have either mentioned the retraction or expressed a negative sentiment toward the cited publication. In addition, on several occasions, we noticed a higher concern/awareness by citing entities belonging to the health sciences domain about citing a retracted publication, compared with the humanities and social science domains. Philosophy, arts, and history are the humanities areas that showed higher concern toward the retraction.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"953-975"},"PeriodicalIF":6.4,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48528848","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 Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.
{"title":"The data set knowledge graph: Creating a linked open data source for data sets","authors":"Michael Färber, David Lamprecht","doi":"10.1162/qss_a_00161","DOIUrl":"https://doi.org/10.1162/qss_a_00161","url":null,"abstract":"Abstract Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"2 1","pages":"1324-1355"},"PeriodicalIF":6.4,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47852306","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 An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over 10,000 scientific software entries from public code repositories.
{"title":"A framework for creating knowledge graphs of scientific software metadata","authors":"Aidan Kelley, D. Garijo","doi":"10.1162/qss_a_00167","DOIUrl":"https://doi.org/10.1162/qss_a_00167","url":null,"abstract":"Abstract An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over 10,000 scientific software entries from public code repositories.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"2 1","pages":"1423-1446"},"PeriodicalIF":6.4,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46123915","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}
Simone Angioni, Angelo Salatino, Francesco Osborne, Diego Reforgiato, Recupero, E. Motta
Abstract Academia and industry share a complex, multifaceted, and symbiotic relationship. Analyzing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonize their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current data sets of scholarly data cannot be used for such a purpose because they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21 million publications and 8 million patents according to the research topics drawn from the Computer Science Ontology. 5.1 million publications and 5.6 million patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.
学术界和产业界有着复杂的、多方面的、共生的关系。分析他们之间的知识流动，了解哪个方向具有最大的潜力，并发现协调他们努力的最佳策略是几个利益相关者的关键任务。研究出版物和专利是分析这一领域的理想媒介，但目前的学术数据集不能用于这一目的，因为它们缺乏对相关研究主题和工业部门的高质量描述。本文引入了学术界/行业动态(AIDA)知识图谱，该图谱根据从计算机科学本体中提取的研究主题描述了2100万份出版物和800万项专利，并根据作者所属单位的类型和提出的工业部门本体(INDUSO)中的66个工业部门进一步描述了510万份出版物和560万项专利。AIDA是由一个自动管道生成的，该管道集成了来自微软学术图、维度、DBpedia、计算机科学本体和全球研究标识数据库的数据。它在CC BY 4.0下公开提供，可以作为转储文件下载或通过triplestore查询。我们在手工制作的黄金标准上评估了生成管道的不同部分，产生了具有竞争力的结果。
{"title":"AIDA: A knowledge graph about research dynamics in academia and industry","authors":"Simone Angioni, Angelo Salatino, Francesco Osborne, Diego Reforgiato, Recupero, E. Motta","doi":"10.1162/qss_a_00162","DOIUrl":"https://doi.org/10.1162/qss_a_00162","url":null,"abstract":"Abstract Academia and industry share a complex, multifaceted, and symbiotic relationship. Analyzing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonize their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current data sets of scholarly data cannot be used for such a purpose because they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21 million publications and 8 million patents according to the research topics drawn from the Computer Science Ontology. 5.1 million publications and 5.6 million patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"2 1","pages":"1356-1398"},"PeriodicalIF":6.4,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48808221","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}
P. Buneman, Dennis Dosso, Matteo Lissandrini, G. Silvello
Abstract The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data.
{"title":"Data citation and the citation graph","authors":"P. Buneman, Dennis Dosso, Matteo Lissandrini, G. Silvello","doi":"10.1162/qss_a_00166","DOIUrl":"https://doi.org/10.1162/qss_a_00166","url":null,"abstract":"Abstract The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"2 1","pages":"1399-1422"},"PeriodicalIF":6.4,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49277776","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}
P. Manghi, A. Mannocci, Francesco Osborne, Dimitris Sacharidis, Angelo Salatino, Thanasis Vergoulis
In recent decades, we have experienced a continuously increasing publication rate of scientific articles and related research objects (e.g., data sets, software packages). As this trend keeps growing, practitioners in the field of scholarly knowledge are confronted with several challenges. In this special issue, we focus on two major categories of such challenges: (a) those related to the organization of scholarly data to achieve a flexible, context-sensitive, finegrained, and machine-actionable representation of scholarly knowledge that at the same time is structured, interlinked, and semantically rich, and (b) those related to the design of novel, reliable, and comprehensive metrics to assess scientific impact.
{"title":"New trends in scientific knowledge graphs and research impact assessment","authors":"P. Manghi, A. Mannocci, Francesco Osborne, Dimitris Sacharidis, Angelo Salatino, Thanasis Vergoulis","doi":"10.1162/qss_e_00160","DOIUrl":"https://doi.org/10.1162/qss_e_00160","url":null,"abstract":"In recent decades, we have experienced a continuously increasing publication rate of scientific articles and related research objects (e.g., data sets, software packages). As this trend keeps growing, practitioners in the field of scholarly knowledge are confronted with several challenges. In this special issue, we focus on two major categories of such challenges: (a) those related to the organization of scholarly data to achieve a flexible, context-sensitive, finegrained, and machine-actionable representation of scholarly knowledge that at the same time is structured, interlinked, and semantically rich, and (b) those related to the design of novel, reliable, and comprehensive metrics to assess scientific impact.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"2 1","pages":"1296-1300"},"PeriodicalIF":6.4,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48447399","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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