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96 results for “FAIR data”
EOSC Task Force on FAIR Metrics and Data Quality: FAIR Evaluation community survey 2023
<p>The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the <a href="https://ec.europa.eu/eusurvey/">EUsurvey tool</a> between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.</p>
Dataset Dental research data availability and quality according to FAIR principles
<p>This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached. </p>
FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment
<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>Quick start with the dataset in README.txt (<em>included in the newest version of the dataset</em>)</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="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a> - Generating FAIR Research Data in Experimental Tribology - Get Scientific Results Ready for ML</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>
Four Essential Components for FAIR Data: Capability & Category-Specific Requirements
<p>Adapted from Bailo (2019) and Peng (2023), this diagram illustrates FAIR requirements specific to data, metadata, and infrastructure, aligned with the definitions of individual FAIR principles. It highlights the critical role of enterprise capabilities—including processes, systems, standards, tools, and skills—in supporting FAIR data. These four components are essential for systematically enhancing the overall FAIRness of an organization's scientific data collection</p> <p> </p>
[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing
<p>This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).</p><p>The supplementary information consists of three files:</p><p><strong>SI 1</strong> - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.</p><p><strong>SI 2</strong> - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.</p>
Data from: A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics
<p>Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).</p> <p>Fish-AIR:<br> This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:</p> <p>extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, <br> uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.</p> <p>The outputs from the Minnow_Segmented_Traits workflow are:</p> <p>sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al. </p> <p>presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.</p> <p>heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.</p> <p>minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).</p> <p>burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.</p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans
<p>This dataset is published in 2017 by the OpenAIRE project and the FAIR Data Expert Group.</p> <p>It contains two survey data files, two pdf-files summarising the results in a report and an infographic, and a Readme.txt file.</p> <p>The OpenAIRE project supports the open science ambitions of the European Commission. The project and in particular the Research Data Management team provide support, training and information on the Open Research Data Pilot. In this context, a survey was carried out to collect feedback on the Horizon 2020 template for Data Management Plans (DMPs). The team collaborated with the FAIR data expert group, which is providing recommendations to the European Commission on turning FAIR data into reality. One of the specific tasks of the Expert Group is contributing to an evaluation of the Horizon 2020 approach to DMPs, including future revisions of the template and the development of additional sector/ discipline-specific guidance. The aim of the survey was to collect experiences of researchers and DMP reviewers with the DMP template and guidelines on FAIR data management in Horizon 2020. The survey assesses the usefulness of the guidelines and any aspects that are confusing and unclear to determine what improvements can be made.</p> <p>Feedback was sought from both researchers and research support staff. The survey was initially scheduled to run from 22 May to 21 June 2017. Several organisations were asked to help announce the survey, including OpenAIRE’s National Open Access Desks, the FAIR data expert group, FOSTER, LIBER, and the RDA Interest Group on Active DMPs. When the first survey responses showed only a small share of researchers, more stakeholders were contacted to specifically target this community. The European Research Area was approached, whose project officers circulated the survey call among award holders of EC projects. Early-career researchers were also informed through the YEAR network and EURODOC. This resulted in an extension of the survey to 21 July 2017.</p> <p>At the close of the survey on 21 July 2017, a total number of 289 responses were reached. 50% of the respondents indicated that they were researchers, and 60% that they were (also) research support staff. OpenAIRE and the FAIR data expert group are very pleased with this balanced outcome and would like to thank all colleagues and organisations who promoted the survey, as well as everyone who took part in it.</p> <p> </p>
FAIR Data: just data done right
<p>An aphorism about FAIR Data, in graphical form, inspired by "Sticker open science: just science done right": Melanie Imming, & Jon Tennant. (2018). Sticker open science: just science done right (ENG). Zenodo. https://doi.org/10.5281/zenodo.1285575 <br> <br> . </p> <p> </p>
Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"
<p>The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a></p>
Data: Algorithms for new types of fair stable matchings
<p>This data corresponds to the data and experiments described in Section 5 of<br> the following paper:</p> <p>Algorithms for new types of fair stable matchings<br> Authors: Frances Cooper and David Manlove</p> <ul> <li>The paper is located at: <a href="https://arxiv.org/abs/2001.10875">https://arxiv.org/abs/2001.10875</a></li> <li>The software is located at: <a href="https://zenodo.org/record/3630383">https://zenodo.org/record/3630383</a></li> <li>The data is located at: <a href="https://zenodo.org/record/3630349">https://zenodo.org/record/3630349</a></li> </ul> <p>See the README for more information.</p>
Analysed data from interviews on FAIR-enabling services
<p>Within FAIRsFAIR task 2.4, we carried out five semi-structured interviews with data service owners to understand how services currently support the FAIR principles, what are transferable insights and recommendations, and what are common challenges and pitfalls. This document collects the insights captured from the interviews. This work has been used as input for the basic framework on FAIRness of services developed by FAIRsFAIR task 2.4 (see https://doi.org/10.5281/zenodo.4292599).</p> <p> </p>
Supplementary data files for manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research"
<div>This data repository contains all the necessary supplementary files for the manuscript titled "<strong>From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research.</strong>"</div> <div> </div> <div>The repository is a copy of the <a href="https://github.com/IMI-COMBINE/template2graphs">GitHub page</a> with the source code used to generate the graph and additional files required for the Lab Data Template.</div> <div> </div> <div>Below we provide a brief overview of the data files in the `additional folder` and their underlying purpose:</div> <div> <ul> <li>The <strong>Data Survey</strong> collects relevant project and data set information to set up a Data Management Plan. It can serve as an input for Lab Data Template development.</li> <li>The <strong>Lab Data Templates</strong> facilitate the collection of AMR research data (in vivo and in vitro) in several sub-tables. The Excel format is compatible with upload procedures into the data repository 'grit' and serves as input for a knowledge graph workflow.</li> <li>The <strong>Data dictionary</strong> is connected to the Lab Data Templates and ensures harmonized data entries. In addition, the dictionaries collect metadata beyond the content of the Lab Data Template (e.g. bacterial strain information or compound information) and link to ontologies where possible.</li> <li>The <strong>FAIR assessments</strong> have been used as a primer for improving the template. This report is generated using the FAIR-DSM model.</li> </ul> </div> <div>The templates have been used during the IMI2 GNA NOW project to collect information and have been improved according to FAIR standards in collaboration with the IMI FAIRplus project ("post FAIRification").</div>
ICOPS Workshop Series - FAIR data
<p><strong>This is the sixth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p> <p>In this workshop we had multiple speakers:</p> <ul> <li> <p>Introduction to FAIR and FAIR Phytoliths project results - Emma Karoune - slides in the main presentation</p> </li> <li> <p>FAIR Data principles and Imaging Resources - Jean-Marie Burel - slides attached as pdf.</p> </li> <li> <p>Sobre la nomenclatura fitolitica en Argentina y el uso del ICPN 2.0 - Maria-Gabriela Musaubach - slides in the main presentation</p> </li> </ul> <p>Youtube video of the workshop:</p> <p>Emma Karoune <a href="https://www.youtube.com/watch?v=VsphfgppcWg&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=2&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_1.Emma.Introduction to FAIR and FAIR Phytoliths Project results - YouTube</a></p> <p>Jean-Marie Burel <a href="https://www.youtube.com/watch?v=yNcLApDHxFY&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=1&ab_channel=ICOPSopenphytoliths">FAIR Data workshop_2.Jean Marie. FAIR Principles and imaging resources - YouTube</a></p> <p>Maria-Gabriela Musaubach <a href="https://www.youtube.com/watch?v=UZizjXCvnbM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=4&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_3.Gabi. Sobre la nomenclatura fitolítica en Argentina, el uso del ICPN 2.0. - YouTube</a></p> <p>Round table <a href="https://www.youtube.com/watch?v=--OO8upPhhM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=3&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_4.Round table on the FAIR Data session - YouTube</a></p>
FAIR Charging Station data package (Normalised)
<p>FAIR and normalised dataset based on the BNetzA charging station data.</p> <p>Original source: <a href="https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/E-Mobilitaet/Ladesaeulenkarte/start.html">BNetzA Ladesaeulenregister (from 01.12.2024)</a></p> <p>Cleaning and annotation scripts: <a href="https://doi.org/10.5281/zenodo.10201060">FAIR Charging station data</a></p> <p>Metadata key reference:<a href="https://github.com/OpenEnergyPlatform/oemetadata/blob/develop/metadata/latest/metadata_key_description.md"> OEMETADATA Key description</a></p> <p>The data can be loaded individually from the csv files or as a whole using <a href="https://github.com/frictionlessdata/frictionless-py">frictionless.py</a>, for example, unzipping and calling:</p> <p> </p> <blockquote> <p>import frictionless as fl</p> </blockquote> <blockquote> <p>package = fl.Package('bnetza_charging_stations_normalised_01_12_2024.json')</p> </blockquote>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Testing of AgReFed FAIR data Minimum Thresholds and Stretch Targets
<p>This dataset is a testing of the FAIR thresholds for participation in The Australian Research Federation (AgReFed). The participants in the project assessed their data products before and after project works to improve the maturity of their datasets. The technology and information employed to progress the FAIR maturity of the data was recorded here.</p> <p>This data was used in the testing of the Minimum Thresholds and Stretch Targets developed by Box et al. (2019). Box, Paul, Levett, Kerry, Simons, Bruce, & Wong, Megan. (2019). Guidelines for the development of a Data Stewardship and Governance.</p>
FAIR raw data and heat maps of ARAP deposition modeling
<p>FAIR Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431</a>, DOI: <a href="https://doi.org/10.3390/jpm11050431">https://doi.org/10.3390/jpm11050431</a></p> <p>1. pdf/A of deposition heat maps (incl probability values) for 5 different ARAP modes</p> <p>2. xls-formatted file of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>3.-7. rpt-formatted MPPD v3.04 files of deposition raw data sets for 5 different ARAP modes</p> <p>8.-12. csv-formatted files of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>13. pdf/A of deposition heat maps (incl probability values) for 5 different ERAP modes (upon rehydration of ARAPs)</p> <p>14. txt-formatted README file for Hofer et al 2021</p>
Fair Data Awareness Survey - Australia - 2017
<p>This record describes a survey around the awareness of the FAIR data principles, undertaken in Australia in 2017 by ANDS, Nectar and RDS. ANDS (Australian National Data Service), Nectar (National eResearch Collaboration Tools and Resources), and RDS (Research Data Services) are NCRIS facilities. NCRIS is an Australian Federal Government investment in research infrastructure. ANDS(ands.org.au), Nectar(nectar.org.au) and RDS(rds.edu.au) have integrated their work in line with proposals laid out in the NCRIS Roadmap (https://docs.education.gov.au/node/43736), early in 2017.</p> <p>The survey was conducted as a Google Form, and analysed in a 12 page report (see Summary of Full Results - attached). Results of the demographics and quantitative responses are shared attached to this record. The qualitative responses are not shared, for reasons of confidentiality.</p> <p><strong>Background (from Summary Report)</strong></p> <p>ANDS/RDS/Nectar undertook a baseline survey to assess level of awareness around FAIR in the research community at eResearch Australasia conference (Oct 2017) and through an online survey. The online survey was closed a few weeks later on 16.11.17. A list of questions is provided (see Are you FAIR aware? Google Form.pdf). There were 249 responses.</p>
FAIR Data Practices in Europe infographic (European Research Data Landscape study)
<p>Infographic of the findings on on FAIR data practices in Europe, part of the European Research Data Landscape study.</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.