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84 results for “Social Sciences”
Aisa 1st annual conference "Nostra res agitur: open science as a social question" [section 2]
<p>Video degli interventi del pomeriggio del 22 ottobre 2015, secondo l'ordine seguente: saluti delle autorità (0:0:00-0:03:06); saluti del Presidente (0:03:07-0:12:52); J-C. Guédon (0:12.53-1:14:22); G. Destro Bisol (1:14:23-1:45:10); dibattito (1:45:11-1:56:04). La registrazione è stata curata dal Team Elearning ed Eventi del Centro Serra dell'università di Pisa.</p>
The European Language Social Science Thesaurus (ELSST)
<p>The European Language Social Science Thesaurus (ELSST) is a broad-based, multilingual thesaurus for the social sciences. It is owned and published by the Consortium of European Social Science Data Archives (CESSDA) and its national Service Providers. The thesaurus consists of over 3,400 concepts and covers the core social science disciplines: politics, sociology, economics, education, law, crime, demography, health, employment, information and communication technology, and environmental science.</p> <p>ELSST is used for data discovery within CESSDA and facilitates access to data resources across Europe, independent of domain, resource, language or vocabulary.</p> <p><strong><em>Recommended Citation</em></strong>: CESSDA and Service Providers (2024) The European Language Social Science Thesaurus (ELSST) (Version 5), <a href="https://elsst.cessda.eu">https://elsst.cessda.eu</a>. DOI: 10.5281/zenodo.13843400</p>
Alternative metrics and social impact of research about Social Sciences in Cuba
<p>The evaluation of social impact of research is a subject demanded by the scientific and social community. The present research is developed with the objective of describing the social impact of the results of scientific research in the field of Social Sciences in Cuba. 5 dimensions of analysis and 16 alternative indicators were used, through the use of altmetric tools and data sources. The sample collection for the study was carried out through the Scopus database and the altmetric data provider PlumX Metrics. For the analysis, statistical techniques of trend and correlation between indicators, data visualization and scientific information were used. The results show that the indicators with the greatest presence were citations in Scopus and CrossRef, Views count, Full Text Views, Abstract Views, Readers in Mendeley captures and the social network metrics Facebook and Twitter. The research results with the greatest social impact are related to climate change and environmental policy, scientific production about COVID-19, higher education, sustainable development, gender studies, legislation, and tourism.</p>
Dataset for proceedings paper: How international is co-authorship outside of Web of Science? The case of social sciences and humanities in Flanders, Belgium
<p>This is an anonymized dataset containing data on international collaboration in the social sciences and humanities (SSH) in Flanders, Belgium. It is based on all peer-reviewed publications in <a href="https://www.ecoom.be/en/data-collections/vabb-shw">VABB-SHW</a>, regardless of their indexation in WoS or other databases.</p> <p>The dataset accompanies the conference paper 'How international is co-authorship outside of Web of Science? The case of social sciences and humanities in Flanders, Belgium,' which was submitted to <a href="https://cns-iu.github.io/workshops/2023-07-02_issi/index.html">ISSI 2023</a>.</p>
Le Journal of Open Humanities Data (JOHD) : enjeux et défis dans la publication de data papers pour les sciences humaines et sociales (SHS)
<p>This repository hosts the pre-print version of the paper and the data used for the following publication:</p> <p>Marongiu, Paola; Pedrazzini, Nilo; Ribary, Marton; McGillivray, Barbara (forthcoming). "<strong>Le <em>Journal of Open Humanities Data</em> (<em>JOHD</em>) : enjeux et défis dans la publication de <em>data papers</em> pour les sciences humaines et sociales (SHS)</strong>". In Schopfel, Joachim and Kosmopoulos, Christine (eds.) Publier, partager, réutiliser les données de la recherche: les data papers et leurs enjeux. Lille: Presses Universitaires du Septentrion.</p> <p>The file "johd_publications.xlsx" contains the data used for the section "Publier des<em> data papers</em> pour les SHS : l’expérience du<em> JOHD</em>". This includes all relevant information about the papers published by the Journal of Open Humanities data. The title of the published paper; the DOI; the date of publication; the special collection in which the paper was published (if any); the keywords associated with the paper; the names of the authors.</p> <p>The zip folder "datapapers_catalyseurs.zip" contains the data used for the section "Les <em>data papers</em> : des catalyseurs de la réutilisation des données ?". This includes information about the papers published by the Journal of Open Humanities data and their corresponding datasets (downloads, views, tweets, citations and Altmetric).</p>
SciStarter: exploring project connections across the citizen science landscape: a social network analysis of shared volunteers
Open the record for dataset details and reuse information.
Data from: Problems with combining modelling and social science approaches to understand artisanal fisheries bycatch
Open the record for dataset details and reuse information.
FIGURE 1 in Ontogeny of an arlequin: morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816) (Decapoda: Palaemonidae), traced through citizen science and social media data mining
FIGURE 1. Morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816). A–C. Specimens from Capo Noli (Italy, Mediterranean Sea) (~44.199232N, 8.420455E), 15–16 m, on anthropogenic debris laying on a detritic bottom, 5–13.IX.2020. A. Photo by Walter Bassi. B–C. Photos by Alessandro Raho. D. Specimen from La Laja beach, Gran Canaria (Spain, Atlantic Ocean) (~28.060335N, -15.418428E), 1 m, amidst algae in a tide pool, 29.VIII.2017. Photo by Alberto Navarro. E. Specimen from Bat Galim reef, Haifa (Israel, Mediterranean Sea) (~32.833317N, 34.97431E), 2 m, under a rock on a rocky bottom, ~2017. Photo by Sarah Ohayon. F. Specimen from Capo Caccia, Sardinia (Italy, Mediterranean Sea) (~40.565506N, 8.165579E), 5 m, detritic bottom with rocks, 28.VIII.2015. Photo by Marco Colombo.
FAIR Implementation Profiles for Social Science
<p>The repository consists of several folders.</p> <p><code>/FICs</code> consists of files for FAIR Implementation Communities. <code>/FIPs</code> consists of files for FAIR Implementation Profiles of these communities. <code>/FIP_reports</code> consists of reports for FAIR Implementation Profiles of these communities.</p> <p>In the file <code>Comparison_all_FIPs.xlsx</code> you can find detailed comparison of the FIPs. The file <code>Convergece_matrix.xlsx</code> gives you details of the convergence analysis.</p> <p>Please contact the authors for the latest version of the FIPs since FIPs are living documents.</p> <p>Please report errors to Shuai Wang at <a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>. Thank you!</p>
D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE
<p>This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis</p>
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species. </span></span></span></span></span></span></span></span></span></span></span></p>
Supplementary files for "Building bridges between natural and social science disciplines: A standardised methodology to combine data on ecosystem quality trends"
<p>Data sources and mapping methodology</p>
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
<div> <div> <div> <div> <p>Gorgonian forests are among the most complex of subtidal habitats in the Mediterranean Sea, supporting high biodiversity and providing diverse ecosystem services. Despite their iconic status, the geographical distribution and condition of gorgonian species is poorly known. Using multiple online data sources, our primary aims were to compile, map and analyse observations of gorgonian forests in Italian coastal waters to assess the biological complexity of gorgonian forests; evaluate impacts and vulnerable species, and identify areas of special interest inside and outside of existing MPAs to help prioritise conservation strategies and actions.</p> </div> </div> </div> </div>
Replication data for: A global perspective on social stratification in science
<p><strong>Replication materials for "A global perspective on social stratification in science"</strong></p> <p>This data comprises the number of Scopus authors by OECD macro field of science, country, authors' inferred gender, author's academic age, and bibliometric class. The sum of the column counts is: 8,225,368. </p> <p>**Please note**: there are 554 rows in the table indicated with "NA" where some of these variables are missing.</p> <p><strong>Paper metadata</strong><br>**Title**: A global perspective on social stratification in science</p> <p>**Authors**: Aliakbar Akbaritabara (Max Planck Institute for Demographic Research, Rostock, Germany), Andres F. Castro Torres (Max Planck Institute for Demographic Research, Rostock, Germany; Center for Demographic Studies, Barcelona, Spain), Vincent Larivière (Université de Montréal, Montréal, Canada)</p> <p>**Corresponding author and email**: Aliakbar Akbaritabar, akbaritabar@demogr.mpg.de</p> <p>**Abstract**: <br>To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.</p> <p>**Keywords**:<br>bibliometric data; research productivity; scientific collaboration; scientific mobility; scholarly impact and citations<br><br></p> <p><strong>Description of the data</strong><br>The dataset contains SIX columns as follows:</p> <p>**base_oecd**: Six OECD macro fields of science such as "Agricultural_Sciences", "Engineering_and_Technology", "Humanities", "Medical_and_Health_Sciences", "Natural_Sciences", "Social_Sciences", and "NA" (for missing) </p> <p>**base_countr**: Country name (see details in the paper)</p> <p>**gender**: Inferred gender based on the name of the author; it includes "fem" for "female", "mal" for "male", and "unkn" for "uknown" gender who are the authors whose gender could not be determined. Please see methods section for the details.</p> <p>**basc_acadag**: Academic age grouped into age groups such as "10-14", "15-20", "2-5", "21-25", "6-9", "One", and "NA" (for missing age group)</p> <p>**cluster6**: Bibliometric class assigned to authors aggregated at the country level as reported in the paper such as "BT", "ML", "LW", "MH", "TP", "HG", and "NA" (for missing). Please see methods section in the paper for the details.</p> <p>**count**: Number of unique authors</p> <p>For queries, contact: akbaritabar@demogr.mpg.de</p>
Crossref as a source of scientometric data for social & human sciences [dataset]
<p>This is a dataset used in and produced by research described in the paper titled "Crossref as a source of scientometric data for social & human sciences".</p>
International Journal of Applied Research in Social Sciences
<p>International Journal of Applied Research in Social Sciences</p>
International Journal of Applied Research in Social Sciences
<p>International Journal of Applied Research in Social Sciences</p>
*Metrics Survey on Usage of Social Media Services in Science
<p>Response data from the first online survey of the *metrics-project. Aim of the survey was to determine the status quo of online platform usage by researchers, with a strong focus lying on researchers from social sciences and economics. </p> <p>The questionnaire had originally been implemented in LimeSurvey. Dissemination started on 31 March 2017; the survey was finally taken offline on 13 February 2019. The call for participation was disseminated via a combination of direct personalized mails (containing recipients' first and surnames), direct non-personalized mails and mailing lists (see "Are There Different Types of Online Research Impact?" by Lemke, Mehrazar, Mazarakis, & Peters [2018] for further details). A total of ~54,000 recipients has been targeted this way. </p>
Google Scholar as a Data Source for Research Assessment in the Social Sciences
<p>Column 1</p> <p>Source</p> <p>Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.</p> <p>Column 2</p> <p>Authors</p> <p>The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.</p> <p>Column 3</p> <p>Title</p> <p>Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.</p> <p>Column 4</p> <p>Title translated with Google Translate</p> <p>In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.</p> <p>Column 5</p> <p>Language</p> <p>Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.</p> <p>Column 6</p> <p>Document type</p> <p>Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.</p> <p>Column 7</p> <p>Full-text available</p> <p>Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.</p> <p>Column 8</p> <p>On research evaluation</p> <p>Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.</p> <p>Column 9</p> <p>Publication year</p> <p>The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].</p> <p>Column 10</p> <p>English abstract</p> <p>Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.</p>
Portuguese citizen science and social media images of "Cortaderia Selloana"
<p>In the first dataset (Citizen_science_dataset), we compiled 2,273 images with <em>Cortaderia selloana</em> from the main Portuguese citizen science platform on invasive alien plants (<a href="https://invasoras.pt/">https://invasoras.pt/</a>) and iNaturalist (<a href="https://www.inaturalist.org/">https://www.inaturalist.org/</a>), from which only those containing the species with plumes (1,323) were retained. This set of 1,323 images was manually checked, and low resolution and blurred images were removed (resulting in 1,237 images). A similar number (1,237) of outdoor images without <em>Cortaderia selloana</em> (in similar context to the ones with <em>Cortaderia selloana</em>) was additionally collected from Flickr, resulting in a total of 2,474 images. </p> <p>The second dataset (Social_media_dataset) consists of publicly available images taken in Portugal from four social media platforms: Facebook (<a href="https://www.facebook.com/">https://www.facebook.com/</a>), Flickr (<a href="https://www.flickr.com/">https://www.flickr.com/</a>), Instagram (<a href="https://www.instagram.com/">https://www.instagram.com/</a>) and Twitter (<a href="https://twitter.com/">https://twitter.com/</a>). Image collection was done by searching for common names of <em>C. selloana</em> in Portuguese (e.g., “Erva das pampas”, “Capim das pampas”), as well as the scientific name of the species (i.e., “<em>Cortaderia selloana</em>”), resulting in a total of 400 images. We also collected the same number of outdoor images without the <em>C. selloana</em> from the same social media platforms). The final size of the transferability set was 800 images.</p>
ScienceDex guides
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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.