Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
36
datasets available to search
ShareScore release 0.7.1
Dataset results
36 results for “scientometrics”
Scientometric Dataset for Benford's law
<p>This dataset proposes datasets built around Benford's law in the field of Scientometrics.</p> <p>This new version adds data on the ratio</p> <p>All first digit data are available</p>
Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p>
Bibliographic dataset based on Scientometrics, including provenance information compliant with the OpenCitations Data Model
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals, bibliographic resources, and authors always appear unambiguously, without duplicates. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p> <p>The dataset is distributed as two journal files, one for the data and one for the provenance, readable via the triplestore Blazegraph. There are 4,960,087 data triples and 19,348,027 provenance triples, which corresponds to 1,134,545 entities and 2,696,689 snapshots. Therefore, on average, each entity has two snapshots. Among the data, there are 231,217 agent roles, 221,602 responsible agents, 206,003 bibliographic resources, 142,472 citations, 141,555 bibliographical references, 108,112 identifiers, and 83,584 resource embodiments.</p> <p>The code to generate and modify such collections is available at <a href="https://doi.org/10.5281/zenodo.5579754">https://doi.org/10.5281/zenodo.5579754</a>. </p>
Corresponding spreadsheet to the Paper 'An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council' submitted to the Scientometric journal by Roberta Ruggieri, Fabrizio Pecoraro and Daniela Luzi from National Research Council, Italy.
<p>Gender equality and Open Access (OA) are priorities within the European Research Area (ERA) and cross-cutting issues in European research program H2020. Gender and openness are also key elements of Responsible Research and Innovation (RRI). However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent, unrelated topics.<br> This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016-2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support.<br> Our results indicate that gender disparities in scientific production still persist in particularly in STEM disciplines (Science, Technology, Engineering and Mathematics), while in medical and agricultural sciences the gender gap is the closest to parity. A positive dynamic toward OA publishing and women scientific production is shown when open disciplines with well-established practices are related to articles supported by funds. A slightly higher women propensity toward OA is shown when considering Gold OA,OA or authorships with women in the first and last article by-line position. Moreover, the prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the week enforcement of the Italian OA policies.</p>
Scientometric Indexes of Scientific Journals
<p>Scientometric data from 62,856 journals consolidated from Scopus, SJC, Diamond Journals, ISSN database, Google Scholar and Brazilian QUALIS (2022). Its purpose is to support researchers who want to analyze and compare various scientometric attributes and also the economic model used by scientific journals around the world.</p><p>CSV UTF-8 File, separated by semicolon.</p><p><strong>Metadata:</strong></p><p>ISSN-L: Journal Identified used by ISSN</p><p>ISSN: Secondary Journal identifier (used for Print or other medium versions);</p><p>Name is in the journal origin language;</p><p>"ASJC" is the Knowledge Area Code based on All Science Journal Category used by Citescore/Scopus;</p><p>"Citescore" is based on 2022 Scopus;</p><p>"Google H5" are based on 2020 database;</p><p>"Qualis" is the Brazilian score calculated for the 2017-2021 quadrienal;</p><p>Country Name (País) is in English merged from all databases;</p><p>"Modelo" can have 3 values: D-Diamond Journals (No money involved), T-Closed (You have to pay to access), A-APC (You have to pay to publish);</p><p>This is a working in progress and we intend to add more atributes and fill some gaps on knowledge areas field (ASJC) and other empty attributes.</p>
OC-782K: Knowledge Graph of "Scientometrics" modelled according to the OpenCitations Data Model
<p>This dataset is a knowledge graph extracted from a <a href="https://static.aminer.cn/misc/na-data-kdd18.zip">t</a>riplestore covering information about the journal <em>Scientometrics</em> and modelled according to the OpenCitations Data Model. The original triplestore is available <a href="https://doi.org/10.5281/zenodo.5151264">here</a>. This KG was extracted for a research project on knowledge graph embeddings (KGEs) for author disambiguation. Structural triples of the knowledge graph are split into training, testing and validation for applying representation learning methods. Textual literals and numeric literals were stored separately in order to implement multimodal approaches for KGEs (see <a href="https://arxiv.org/abs/1802.00934">arXiv:1802.00934</a>). For the same reason, textual literals and numeric literals are already stored into sentence embeddings and a numeric matrix respectively in the files <em>textual_literals.npy </em>and <em>numeric_literals.npy</em>. The file <em>and_eval</em><em>.json </em>contains the evaluation dataset used for evaluating our AND architecture. For the script used to gather this dataset see the GitHub repository: <a href="https://github.com/sntcristian/and-kge/tree/main/aminer">https://github.com/sntcristian/and-kge/tree/main/open-citations</a>.</p>
Figure 5 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 5 Number of Plecoptera species recorded in each Brazilian political region and state: a) Gripopterygidae; b) Perlidae; c) total recorded species per state, including endemic species, along with their respective percentages. Note: The data excludes the Onychoplax genus and other species lacking information regarding their records' locality in Brazil. Brazil state codes are derived from ISO 3166-2: BR (ISO, 2023).
Figure 6 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 6 Number of Plecoptera species and endemic species recorded in each terrestrial biome (a) and Hydrographic Region (b) of Brazil. Note: the number in parentheses represents the percentage of endemic species.
Figure 7 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 7 Cumulative number of papers describing Brazilian Plecoptera published in journals over 10-year intervals from 1969 to 2023.
Figure 2 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 2 The numbers of Brazilian Plecoptera.a) cumulative count of reported species and the rate of species descriptions over 10-years from 1839 to 2023; b) yearly distribution of reported species.
Figure 3 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 3 Number of Plecoptera species described for Brazil by author contributions. Cumulative percentage of descriptions represented by the Pareto line (in orange).
Figure 1 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 1 Plecoptera fauna of Brazil: selected species from the Perlidae family - a) Anacroneuria sp., b) Kempnyia neotropica, and Gripopterygidae family - c) Gripopteryx pilosa, d) Gripopteryx cancellata, e) Gripopteryx sp., f) Tupiperla tessellata, g) Guaranyperla sp. Photographs by ©Frederico F. Salles.
V 1.0 Dataset for "Emergence and Evolution of Big Data Research: A 30-year (1993-2022) Scientometric Analysis of The Knowledge Field"
<p>This dataset includes the bibliometric data used in the scientometric analysis of the field of big data research over a 30-year period (1993-2022). The data was collected from the Scopus database, and contains information on 70,163 articles and 315,235 author keywords. The dataset is structured by 17 interrelated data categories that trace the conceptual emergence and evolution of the big data field, focusing on keyword co-occurrences, disciplinary distributions, and the temporal growth of publications. This dataset supports the analyses presented in the related manuscript.</p>
The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics (Dataset)
<p>This dataset contains the data used to completed the article under peer review:</p> <p>References:<br> Cabezas, A.; Milanés, Y.;Alba, R.; Delgado, A.M. (2023). The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics. (Article under peer review)</p> <p>Institutions: Spain (Universidad Internacional de La Rioja, Universidad Pablo de Olavide, Hospital Universitario Virgen de las Nieves)</p>
Raw Bibliobmetric Data for the article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis"
<p>Bibliometric data for CiteSpace related to the scientific article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis".</p>
Basic scientometric characteristics of ICCE
<p>EXLS-file with basic scientometric characteristics of the<br> International Commission for Continental Erosion (ICCE)</p>
Figure 4 in A Scientometric Approach to the Taxonomy of Brazilian Plecoptera: An Overview of Data
Figure 4 Percentage of Brazilian type-specimens of Plecoptera housed in institutions worldwide.
Figure 3 in 30 years of research on insect galls in Brazil: a scientometric review
Figure 3. Number of publications about insect galls in Brazil (from CAPES database): (a) by topics (knowledge areas); and (b) by subtopics of ecology.
Figure 5 in 30 years of research on insect galls in Brazil: a scientometric review
Figure 5. Geographic distribution of insect gall inventories among Brazilian states (from CAPES and additional databases): (a) the distribution of study area locations; and (b) distribution of the state of origin of the first author.
Source data for the scientometric analysis of citizen science research publications
<p>Source data for the scientometric comparison of citizen science research (n=5749 documents) and a semi-random sample of publications (n=5734) retrieved from the Web of Science Core Collection and published between 1997-2021. The data include information on: author(s) full name(s) ("AF"); year of publication ("PY"); title ("TI"); Digital Object Identifier ("DI"); abstract ("AB"); open access indicator ("OA"); author(s) affiliation(s) ("C1"); document type ("DT"); funding entity ("FU"); funding text ("FX"); total number of citations ("TC"); and identifier of the collection ("group"): CS for citizen science and SRS for the semi-random sample collection respectively.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.