Quantitative Science Studies最新文献_第10页

Overton: A bibliometric database of policy document citations Overton：政策文件引用文献计量数据库

IF 6.4

Quantitative Science Studies Pub Date : 2022-01-19 DOI: 10.1162/qss_a_00204

M. Szomszor, E. Adie

{"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}

引用次数: 19

A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case study of SDG 13—Climate Action 识别与联合国可持续发展目标相关出版物的不同方法的比较:可持续发展目标13 -气候行动的案例研究

IF 6.4

Quantitative Science Studies Pub Date : 2022-01-06 DOI: 10.1162/qss_a_00215

P. Purnell

{"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}

引用次数: 9

Covid-19 refereeing duration and impact in major medical journals Covid-19在主要医学期刊的评审时间和影响

IF 6.4

Quantitative Science Studies Pub Date : 2021-12-23 DOI: 10.1162/qss_a_00176

K. Kousha, M. Thelwall

引用次数: 3

Scopus 1900–2020: Growth in articles, abstracts, countries, fields, and journals Scopus 1900-2020:文章，摘要，国家，领域和期刊的增长

IF 6.4

Quantitative Science Studies Pub Date : 2021-12-12 DOI: 10.1162/qss_a_00177

M. Thelwall, Pardeep Sud

{"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}

引用次数: 22

A quantitative and qualitative open citation analysis of retracted articles in the humanities 人文学科撤稿文章的定量和定性开放引文分析

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-09 DOI: 10.1162/qss_a_00222

Ivan Heibi, S. Peroni

{"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}

引用次数: 5

The data set knowledge graph: Creating a linked open data source for data sets 数据集知识图：为数据集创建链接的开放数据源

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-05 DOI: 10.1162/qss_a_00161

Michael Färber, David Lamprecht

{"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}

引用次数: 14

A framework for creating knowledge graphs of scientific software metadata 创建科学软件元数据知识图谱的框架

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-05 DOI: 10.1162/qss_a_00167

Aidan Kelley, D. Garijo

{"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}

引用次数: 13

AIDA: A knowledge graph about research dynamics in academia and industry AIDA:关于学术界和工业界研究动态的知识图谱

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-05 DOI: 10.1162/qss_a_00162

Simone Angioni, Angelo Salatino, Francesco Osborne, Diego Reforgiato, Recupero, E. Motta

{"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}

引用次数: 22

Data citation and the citation graph 《我是特朗普政府内部抵抗力量的一部分》，《纽约时报》，2018年9月5日。

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-05 DOI: 10.1162/qss_a_00166

P. Buneman, Dennis Dosso, Matteo Lissandrini, G. Silvello

{"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}

引用次数: 10

New trends in scientific knowledge graphs and research impact assessment 科学知识图谱和研究影响评估的新趋势

IF 6.4

Quantitative Science Studies Pub Date : 2021-11-05 DOI: 10.1162/qss_e_00160

P. Manghi, A. Mannocci, Francesco Osborne, Dimitris Sacharidis, Angelo Salatino, Thanasis Vergoulis

引用次数: 10