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339 results for “Fairness”
Metadata Profile for FAIR Sensor Data based on the SensOr Interfacing Language
<p>Metadata profile to provide FAIR sensor data. The profile is created using SHACL and is based on the SOSA ontology which accurately specifies restrictions on the properties of specific sensors using the QUDT and the SSN ontology. Generic metainformation is modeled using DCTerms. </p>
Awareness of FAIR and FAIR4RS among international research software funders (Dataset)
<p><span>This research employed a mixed methods online survey to investigate research software funders’ perspectives. </span></p> <p><span>All participants gave informed consent at the start of the online survey. The University of Illinois Urbana-Champaign Institutional Review Board (no. 24374) reviewed the study and determined it exempt.</span></p> <p><span>Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds. The data were cleaned and prepared for analysis by removing any identifiable respondent details. </span></p> <h2><span>Survey design</span></h2> <p><span>The survey began by collecting profile information, including institutional affiliation and job title. The survey primarily gathered detailed information about initiatives, policies, or programs to support research software but also included a much smaller set of questions about additional topics, such as strategic funding priorities and awareness of key concepts. The data generated from this survey are too extensive to report in a single manuscript. Here, we focus on the results generated via the set of questions asking about FAIR and FAIR4RS, specifically, the following survey items: </span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Survey item</span></strong></p> </td> <td> <p><strong><span>Response options</span></strong></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR (findable, accessible, interoperable, and reusable) principles for data?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR principles for data?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR4RS principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> </tbody> </table> <p><span> </span></p> <p><span>In addition, an open-ended question asked for further detail about the respondents’ assessments of FAIR4RS’s relevance to their work.</span></p> <h2><span>Sampling</span></h2> <p><span>The survey targeted international research funders, including governmental and non-governmental (e.g., philanthropic) organizations. An initial contact list was created based on participation in the Research Software Association (ReSA) and known responsibilities for research software funding among the authors' networks. This list was refined by removing individuals who had moved to unrelated professional roles or were unavailable long-term due to personal issues.</span></p> <p><span>The final contact list comprised 71 people at 37 funding organizations. After excluding individuals when a member of their organization had already provided a complete response or when the person was no longer working on a relevant topic or was otherwise unavailable (total of n=30), 41 people remained. Of these, five did not complete the survey, while 36 individuals (representing 30 research funding organizations) did, yielding a response rate of 87.8% (and representing 81% of the original organizations). Fully completed survey responses were not required for inclusion in the sample, resulting in varied sample sizes across different survey questions.</span></p> <p><span>The respondents represented governmental (n=26), philanthropic (n=6), and corporate (n=1) research funders.</span></p> <p><span>Respondents’ job titles spanned the following categories: Senior Leadership and Executive (e.g., Vice President of Strategy); Program and Project Management (e.g., Senior Program Manager); Planning and Business Development; and Scientific, Technical, and IT roles (e.g., Scientific Information Lead).</span></p> <p><span>Most respondents, 72.7% (n=24), answered “Yes” to the question, “Has your organization established any policies, initiatives, or programs aimed at supporting research software?” Meanwhile, 18.2% (n=6) said “No,” and 9.1% (n=3) were “Unsure.”</span></p> <p><span>Regarding geographic distribution in the achieved sample, most survey respondents were from North America and Europe, with 15 and 12 participants, respectively. The sample also comprised 4 participants from South America, 3 from Oceania, and 1 from Asia, reflecting a global but uneven representation across continents. Some participating funders covered a broad spectrum of disciplines, while others focused on specific domains such as social sciences, health, environment, physical sciences, or humanities.</span></p>
Dataset: FAIR AMD OCT Datasets Paper
<p>This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this <a href="https://github.com/fairdataihub/FAIR-AMD-OCT-paper-inventory">inventory</a> for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset. </p>
More and Less FAIR Visualizations
<p>Visualizations of what "FAIR" data means-- that creating a FAIR data set does not mean it is something that is "Done" or "Not done", but instead a spectrum of FAIRity.</p>
Network of the European FAIR initiatives: relationships and outputs
<p>This network is an attempt at comprehensively describing the FAIR projects and initiatives in Europe, together with their area of focus, outputs and relationships with one another</p>
Fair RecSys Datasets
<p>Four multimedia recommender systems datasets to study popularity bias and fairness:</p> <ol> <li>Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)</li> <li>MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)</li> <li>BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/)</li> <li>MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)</li> </ol> <p>Each dataset contains of user interactions (user_events.txt) and three user groups that differ in their inclination to popular/mainstream items: LowPop (low_main_users.txt), MedPop (med_main_users.txt), and HighPop (high_main_users.txt).</p> <p>The format of the three user files are "user,mainstreaminess"</p> <p>The format of the user-events files are "user,item,preference"</p> <p>Example Python-code for analyzing the datasets as well as more information on the user groups can be found on Github (https://github.com/domkowald/FairRecSys) and on Arxiv (https://arxiv.org/abs/2203.00376)</p> <p> </p> <p> </p>
Generating FAIR Research Data in Experimental Tribology
<p><strong>Stream video at:</strong> <a href="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a></p> <p>To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology.</p> <p><a href="http://www.nature.com/articles/s41597-022-01429-9">https://www.nature.com/articles/s41597-022-01429-9</a> - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al.</p> <p>Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability and reusability of experimental tribological data beyond typical publication practices.</p> <p><a href="https://doi.org/10.5281/zenodo.5720626">https://doi.org/10.5281/zenodo.5720626</a> - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment</p> <p><a href="https://doi.org/10.5281/zenodo.5720198">https://doi.org/10.5281/zenodo.5720198</a> or <a href="https://github.com/nick-garabedian/TriboDataFAIR-Ontology">https://github.com/nick-garabedian/TriboDataFAIR-Ontology</a> or <a href="https://fairsharing.org/3597">https://fairsharing.org/3597</a> - TriboDataFAIR Ontology</p> <p><a href="https://doi.org/10.5281/zenodo.5720218">https://doi.org/10.5281/zenodo.5720218</a> or <a href="https://github.com/nick-garabedian/SurfTheOWL">https://github.com/nick-garabedian/SurfTheOWL</a> - SurfTheOWL</p> <p><a href="https://kadi4mat.iam-cms.kit.edu/">https://kadi4mat.iam-cms.kit.edu/</a> - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook </p>
CompBMus_FAIR
<p>Making Byzantine Computational Musicology FAIR - a working example</p> <p>This is the repository for the <strong>Making Byzantine Computational Musicology FAIR - a working example</strong> project.</p> <p>We have submitted a short paper for <a href="https://dlfm.web.ox.ac.uk/">this</a>.</p> <p>In this repository, there is a folder with the dataset, the data package schemas in YAML file, as well as the python code (main.py) for describing and validating the data. </p> <p>Upon acceptance of the paper, the accepted draft will also be uploaded.</p>
FaIR v1.6.2 calibrated and constrained parameter set
<p>These .json files provides the 2237 ensemble members that are used to run the FaIR simple climate model in the IPCC's Sixth Assessment Report contributions to Working Group 1 and Working Group 3.</p> <p>v1.1 of this dataset includes two versions of the files:</p> <ul> <li>the original full parameter set `fair-1.6.2-wg3-params.json` as in v1.0</li> <li>reduced file size versions `fair-1.6.2-wg3-params-slim.json` and `fair-1.6.2-wg3-params-common.json` where only the parameters that vary between ensemble members are included in the former and everything else is in the latter. This is optimised for running the Working Group 3 climate assessment.</li> </ul> <p>Drawn from an initial prior ensemble of 1 million, the following constraints are placed upon the results:</p> <ul> <li>representation of observed warming from 1850-2019, within observational uncertainty</li> <li>representation of observed ocean heat content change 1971-2018, within observational uncertainty</li> <li>reproduction of near-present day atmospheric concentrations of CO2 from the carbon cycle</li> <li>airborne fraction of CO2 with a distribution similar to the assessed range of Chapter 5, IPCC Working Group 1 AR6</li> <li>equilibrium climate sensitivity with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>transient climate response with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>projected future warming that is of a similar distribution to the SSP scenarios of Chapter 4, IPCC Working Group 1, AR6, when run with prescribed concentrations</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>v1.1: inclusion of lightweight parameter sets.</li> <li>v1.0: original upload full parameter sets.</li> </ul>
Forschungsdatenpublikationen in den Geowissenschaften - Fragen zu Datenqualität und FAIR-Prinzipien (Daten aus Befragung)
<p>Die vorliegenden Daten sind zugehörig zu einer Befragung im Rahmen einer Masterarbeit im gemeinsamen Studiengang "Digitales Datenmanagement" der Humboldt-Universität zu Berlin und der Fachhochschule Potsdam. Die Befragung fand im Zeitrum vom 27.06.2022 bis 15.07.2022 statt. Befragt wurde der E-Mailverteiler der teilnehmenden Einrichtungen des NFDI4Earth-Konsortiums. In die Auswertung fand nur jener Anteil der Fragebögen Eingang, der vollständig komplettiert wurde (25 von 47). Dementsprechend enthält dieser Datensatz nur jenen Teil an Informationen. Der komplette Datensatz, inkl. nicht komplettierter Bögen, kann bei berechtigtem Interesse ggf. zur Verfügung gestellt werden. Kontakt: marcelmeistring01@gmail.com</p>
FAIR Metrics SKOS Mapping
<p>SKOS mapping between four FAIR metrics/indicators, based on "Best Effort".</p> <p>Created within the EOSC Synergy project task 3.3.</p> <ul> <li>RDA FAIR data maturity model indicators</li> <li>FAIRsFAIR Data Object Assessment Metrics</li> <li>FAIR Maturity Indicators from FAIRsharing</li> <li>FAIR Enough data maturity indicators</li> </ul>
FAIR Data sharing à la FBM : garantie d'une recherche plus transparente et reproductible
<p>Nouveaux services développés à la FBM UNIL/CHUV pour répondre aux principes FAIR/ Open Research Data</p>
Fairly
<h2>Changes to source code layout</h2> <pre><code>- Move cli module under fairly module - Add unit test for CLI - No new features</code></pre> <p>#68</p>
De Jong Gierveld Loneliness Scale: validity, reliability and fairness in Peruvian adults
<p><span><strong><span>Abstract:</span></strong></span></p> <p><strong><span>Background: </span></strong><span>Loneliness, transient or long-lasting, constitutes one of the mental health problems of great public impact with peculiar characteristics. It can be defined as a subjective and unpleasant experience, in turn associated with other serious psychological symptoms. This variable in question has not been addressed in a timely manner, among other reasons due to the scarcity of instruments for specific populations. Although the De Jong Gierveld Loneliness Scale (</span><span>DJGLS), <span>based on Weiss' multidimensional model, has been adapted and validated in different contexts, it is still insufficient in Peru. Precisely, the objective was to determine the psychometric properties of the DJGLS, </span></span><span>its internal structure and factorial invariance.</span></p> <p><strong><span>Methods:</span></strong><span> An online survey of 1248 Peruvians between 18 and 70 years of age (M= 27.37, SD= 11.29) from all regions was used. The validation of the DJGLS was analyzed with Exploratory Factor Analysis </span><span>(EFA), <span>Confirmatory Factor </span>Analysis (CFA), <span>convergent validity, measurement invariance and internal consistency reliability.</span></span></p> <p><strong><span>Results: </span></strong><span>Psychometric properties were found with adequate values in its internal structure by means of the CFA, where it was found that the components of the scale are interrelated and the data matrix is factorizable. Here we present a model of two specific factors and one general factor, which is consistent with theory and has practical utility, revealing acceptable reliability values and invariance between sexes.</span></p> <p><strong><span>Conclusions:</span></strong><span> Adequate psychometric properties, which allow for a better data collection process in further related research, are revealed.</span></p> <p><span> </span></p> <p><strong><span>Keywords</span></strong><span>: loneliness; validity; reliability; fairness; adults.</span></p>
FAIRness Assessment of Biomedical Data Using Automated Tools (Dataset)
<p>The data were collected as part of a Master's thesis project aimed at evaluating various automated FAIR assessment tools, applying them to biomedical data. The data sets identifiers were gathered as part of the Open Data LoM and IoM incentivization at Charité Universitätsmedizin Berlin, available at <a title="Dataset of the results of data validation for articles from 2021" href="https://doi.org/10.5281/zenodo.8249758">https://doi.org/10.5281/zenodo.8249758</a>, and reused in this project.</p> <p>The data represents cleaned, aggregated, and transformed results obtained from the API services of the following FAIR assessment tools: F-UJI, FAIR Enough, FAIR-Checker, and FAIR EVA.</p> <p>The raw data in .Rdata format will be shared on GitHub repository at <a title="FAIR Tools Analysis" href="https://github.com/anastasiabright/fair-tools-analysis">https://github.com/anastasiabright/fair-tools-analysis</a>.</p>
Assessment of Fair Trade education programs in France: data 2022 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
Assessment of Fair Trade education programs in France: high school data (2019-2022)
<p>see the technical report (period 2019-2021) on researchgate:</p> <p><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></p> <p> </p>
Assessment of Fair Trade education programs in France: data 2021 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
Assessment of Fair Trade education programs in France: control groups for universities (2019-2022)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
DIETxPOSOME: a FAIR database detailing food contaminants occurrence in selected foods
<p>The DIETxPOSOME FAIR database provides detailed quantification of 73 contaminants, including 4 heavy metals, 18 polycyclic aromatic hydrocarbons (PAHs), 10 pesticides, 29 mycotoxins, and 12 heterocyclic aromatic amines (HAAs), in 16 food items across various food groups (cereals, vegetables, fruits, starchy roots, dairy products, nuts, meat, eggs, fish, legumes and vegetable oils). Data was obtained through a combination of literature extraction protocols and machine learning.</p> <p>A list of pertinent research articles on food contaminants in globally significant food items was sourced from PubMed. Machine learning enabled the automatic filtering of these papers (<a href="../records/7826130">https://zenodo.org/records/7826130</a>), which were subjected to a manual evaluation. Subsequently, specific foods (wheat, maize, beef, cheese, pasta, potatoes, carrots, rice, bread, chicken eggs, peanuts, beans, cabbage, apple, olive oil, and salmon) were selected based on their comprehensive contaminant profiles. Information was extracted from 145 relevant articles of the 151 research articles pertaining to the selected food items. Following extraction, the data was confirmed and evaluated by three reviewers, adhering to stringent criteria. </p> <p>The food items in the DIETxPOSOME FAIR database can be integrated into complex dietary patterns in an experimental context, for example, aligned with the EAT-Lancet Commission Reference Diet for Healthy and Sustainable Food Systems. This framework allows the simulation of real-world dietary exposure or worst-case scenarios, facilitating comprehensive risk assessment of unavoidable food contaminants.</p> <p> </p>
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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.