OpenCitations, an infrastructure organization for open scholarship

Peroni, Silvio; Shotton, David M.

doi:10.1162/qss_a_00023

Cited by 110 publications

(91 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…COCI has detected over 624 million citation relationships involving over 53 million documents (Peroni and Shotton 2020 ). The citations recorded in this source are only a fraction of the citations that have actually occurred among the documents covered by CrossRef, because some publishers that deposit lists of references or CrossRef have not agreed to make them available, and other publishers and preprint servers do not deposit any references in CrossRef or do it only for some document types (Shotton 2018 ; van Eck et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations

et al. 2020

View full text Add to dashboard Cite

New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science Core Collection (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89–94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of WoS citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. After Scopus, Dimensions is fourth largest (54% of all citations), including 84% of Scopus citations and 88% of WoS citations. It found more citations than Scopus in 36 categories, more than WoS in 185, and displays some coverage gaps, especially in the Humanities. Following WoS, COCI is the smallest, with 28% of all citations. Google Scholar is still the most comprehensive source. In many subject categories Microsoft Academic and Dimensions are good alternatives to Scopus and WoS in terms of coverage.

show abstract

Section: Introductionmentioning

confidence: 99%

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Extraction of industrial category -hasIndustrialSector In this step, we characterised documents from industry according to the Industrial Sectors Ontology (INDUSO) 10 , an ontology that we designed for this specific task. We designed INDUSO by merging and arranging in a taxonomy a large set of industrial sectors that we extracted from the affiliations of the paper authors and the patent assignees.…”

Section: Extraction Of Affiliation Types -Hasaffiliationtype Hasassimentioning

confidence: 99%

“…Today, we have several large-scale knowledge graphs which describe these documents. Some examples include Microsoft Academic Graph 3 , Open Research Corpus [1], the OpenCitations Corpus [10], Scopus 4 , AMiner Graph [17], the Open Academic Graph (OAG) 5 , Core [7], Dimensions Corpus 6 , and the United States Patent and Trademark Office Corpus 7 . However, these resources are unfit to support large-scale analysis about the knowledge flow since they suffer from three main limitations: 1) they do not directly classify a document according to its provenance (e.g., academia, industry), 2) they offer only coarse-grained characterizations of research topics, and 3) they do not characterize companies according to their sectors (e.g., automotive, financial, energy, electronics).…”

Section: Introductionmentioning

confidence: 99%

Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Angioni

Salatino

Osborne

et al. 2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

View full text Add to dashboard Cite

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author's affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.

show abstract

“…Typically, the two sectors are either analysed separately [15,[17][18][19][20] or together on a small scale [10,11], using a limited sample of papers and patents. Most of these analyses rely on knowledge graphs describing research publications, such as Microsoft Academic Graph [21], Scopus 2 , Semantic Scholar 3 , Aminer [22], Core [23], OpenCitations [24], and others. Other resources, such as Dimensions 4 , the United States Patent and Trademark Office corpus 5 , the PatentScope corpus 6 and the European Patent Office dataset 7 , offer a similar description of patents.…”

Section: Literature Reviewmentioning

confidence: 99%

ResearchFlow: Understanding the Knowledge Flow Between Academia and Industry

Salatino

Osborne

Motta

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding bodies, researchers, investors, and companies. To this purpose, we introduce ResearchFlow, an approach that integrates semantic technologies and machine learning to quantifying the diachronic behaviour of research topics across academia and industry. ResearchFlow exploits the novel Academia/Industry DynAmics (AIDA) Knowledge Graph in order to characterize each topic according to the frequency in time of the related i) publications from academia, ii) publications from industry, iii) patents from academia, and iv) patents from industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry. We applied ResearchFlow to a dataset of 3.5M papers and 2M patents in Computer Science and highlighted several interesting patterns. We found that 89.8% of the topics first emerge in academic publications, which typically precede industrial publications by about 5.6 years and industrial patents by about 6.6 years. However this does not mean that academia always dictates the research agenda. In fact, our analysis also shows that industrial trends tend to influence academia more than academic trends affect industry. We evaluated ResearchFlow on the task of forecasting the impact of research topics on the industrial sector and found that its granular characterization of topics improves significantly the performance with respect to alternative solutions.

show abstract

OpenCitations, an infrastructure organization for open scholarship

Cited by 110 publications

References 15 publications

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations

Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

ResearchFlow: Understanding the Knowledge Flow Between Academia and Industry

Contact Info

Product

Resources

About