37 research outputs found
The OpenCitations Data Model
A variety of schemas and ontologies are currently used for the
machine-readable description of bibliographic entities and citations. This
diversity, and the reuse of the same ontology terms with different nuances,
generates inconsistencies in data. Adoption of a single data model would
facilitate data integration tasks regardless of the data supplier or context
application. In this paper we present the OpenCitations Data Model (OCDM), a
generic data model for describing bibliographic entities and citations,
developed using Semantic Web technologies. We also evaluate the effective
reusability of OCDM according to ontology evaluation practices, mention
existing users of OCDM, and discuss the use and impact of OCDM in the wider
open science community.Comment: ISWC 2020 Conference proceeding
OpenCitations: an Open e-Infrastructure to Foster Maximum Reuse of Citation Data
OpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations collaborates with projects that are part of the Open Science ecosystem and complies with the UNESCO founding principles of Open Science, the I4OC recommendations, and the FAIR data principles that data should be Findable, Accessible, Interoperable and Reusable. Since its data satisfies all the Reuse guidelines provided by FAIR in terms of richness, provenance, usage licenses and domain-relevant community standards, OpenCitations provides an example of a successful open e-infrastructure in which the reusability of data is integral to its mission
Retractions in Arts and Humanities: an Analysis of the Retraction Notices
The aim of this work is to understand the retraction phenomenon in the arts and humanities domain through an analysis of the retraction notices â formal documents stating and describing the retraction of a particular publication. The retractions and the corresponding notices are identified using the data provided by Retraction Watch. Our methodology for the analysis
combines a metadata analysis and a content analysis (mainly performed using a topic modeling process) of the retraction notices. Considering 343 cases of retraction, we found that many retraction notices are neither identifiable nor findable. In addition, these were not always separated from the original papers, introducing ambiguity in understanding how these notices were perceived by the community (i.e., cited). Also, we noticed that there is no systematic way to write a retraction notice. Indeed, some retraction notices presented a complete discussion of the reasons for retraction, while others tended to be more direct and succinct. We have also reported many notices having similar text while addressing different retractions. We think a further study with a larger collection should be done using the same methodology to confirm and investigate our findings further
Do open citations inform the qualitative peer-review evaluation in research assessments? An analysis of the Italian National Scientific Qualification
In the past, several works have investigated ways for combining quantitative
and qualitative methods in research assessment exercises. Indeed, the Italian
National Scientific Qualification (NSQ), i.e. the national assessment exercise
which aims at deciding whether a scholar can apply to professorial academic
positions as Associate Professor and Full Professor, adopts a quantitative and
qualitative evaluation process: it makes use of bibliometrics followed by a
peer-review process of candidates' CVs. The NSQ divides academic disciplines
into two categories, i.e. citation-based disciplines (CDs) and
non-citation-based disciplines (NDs), a division that affects the metrics used
for assessing the candidates of that discipline in the first part of the
process, which is based on bibliometrics. In this work, we aim at exploring
whether citation-based metrics, calculated only considering open bibliographic
and citation data, can support the human peer-review of NDs and yield insights
on how it is conducted. To understand if and what citation-based (and,
possibly, other) metrics provide relevant information, we created a series of
machine learning models to replicate the decisions of the NSQ committees. As
one of the main outcomes of our study, we noticed that the strength of the
citational relationship between the candidate and the commission in charge of
assessing his/her CV seems to play a role in the peer-review phase of the NSQ
of NDs
Evaluating the availability of open citation data
Citation data of scientific publications are essential for different purposes, such as evaluating research and building digital library collections. In this paper, we analyze to which extent citation data of publications are openly available, using the intersection of the Crossref metadata and unpaywall snapshot as publication dataset and the COCI dataset as open citation data. We reveal that for 24.2% of the publications, the citation data is openly available, while for 16.6%, the citation data is closed. We find that the percentage of publications with open citation data has increased over the years. We observe that whether publications are published with open access has no influence on whether their citations are openly available. However, publications published in journals from the Directory of Open Access Journals (DOAJ) tend to have more citation data openly available than publications from other journals