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96 results for “FAIR Data”
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
Towards FAIR phytolith data: first steps down a long and winding road.
<p>This is a video recording of a presentation given at the International Meeting of Phytolith Research on 7th September 2021. It introduces the FAIR phytoliths project that strives to improve the FAIRness of phytolith data for the phytolith community. It starts by explaining what FAIR is, what constraints there currently are on data sharing and what the benefits of implementing FAIR for phytolith data would be. It then goes on to explain the project - what has been done so far, our plan over the next year and also how the phytolith community can get involved. </p> <p>A pdf and powerpoint with a script of the talk can be found here: <a href="http://zenodo.org/record/5336872#.YTZUWp30lPY">10.5281/zenodo.5336872</a></p>
Data of FAIR Assessment in Research Objects with FAIROs
<p>This data has been used to test the FAIRness of Research Objects of the platform ROHub (https://reliance.rohub.org).</p> <p>It is composed by two sets:</p> <p>* Folder research objects: It contains the collection of Research Objects used.</p> <p>* Folder assessment: It contains the assessment of each Research Object using FAIROs.</p>
Comparison tables for evaluating FAIR Digital Object and Linked Data
<p>RO-Crate and tables from the paper "<em>Evaluating FAIR Digital Object and Linked Data as distributed object systems</em>" <a href="https://doi.org/10.48550/arXiv.2306.07436">https://doi.org/10.48550/arXiv.2306.07436</a></p> <p>We systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself.</p> <ul> <li>table1.html<br> Considering FDO and Web according to the quality levels of the Interoperability Framework for Fast Data (Delgado 2016)</li> <li> <p>table2.html<br> Mapping the Metamodel concepts from the Interoperability Framework for Fast Data (Delgado 2016) to equivalent concepts for FDO and Web.</p> </li> <li> <p>table3.html<br> Checking FDO guidelines (Bonino et al. 2019; Anders et al. 2023) against its current implementations as DOIP (DONA 2018) and Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022), with suggestions for required additions</p> </li> <li> <p>table4.html<br> Comparing FAIR Digital Object (with the DOIP 2.0 protocol (DONA 2018)) and Web technologies (using Linked Data) as middleware infrastructures (Zarras 2004)</p> </li> <li> <p>table5.html</p> <p>Assessing RDA’s FAIR Data Maturity Model (FAIR Data Maturity Model Working Group 2020; Bahim et al. 2020) (first 2 columns) against the FDO guidelines (Bonino et al. 2019), FDO implemented with the protocol DOIPv2 (DONA 2018), Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022) and examples from Linked Data practices in general. (— indicates Unspecified, may be possible with additional conventions)</p> </li> </ul> <p>A web rendering of this RO-Crate is available at <a href="https://w3id.org/ro/doi/10.5281/zenodo.8075229">https://w3id.org/ro/doi/10.5281/zenodo.8075229</a></p>
Welcome-Introduction to the Workshop " A first approach to an ELSA Curriculum for Data Scientists The FAIR Data Spaces Project as a Use Case
<p>Welcome-Introduction to the Workshop " A first approach to an ELSA Curriculum for Data Scientists The FAIR Data Spaces Project as a Use Case", contains a brieff description of the FAIR Data Spaces project</p>
Open Data: FAIR, foul, and meta
<p><strong>Episode Summary: </strong></p> <p>In this episode we are discussing what metadata is, the obstacles to sharing research data, and how Open Science recommendations are being transformed into actions. Our interview guest will be Professor Eva Mendez from Universidad Carlos III in Madrid who is the Chair of the European Open Science Policy Platform.</p> <p><strong>Links:</strong></p> <p><a href="https://www.orion-openscience.eu/publications/training-materials/201808/factsheets">Factsheets (Open Data and Data Management)</a></p> <p><a href="https://uc3m.academia.edu/EvaM%C3%A9ndez/CurriculumVitae">Professor Eva Mendez</a></p> <p><a href="https://ec.europa.eu/research/openscience/index.cfm?pg=open-science-policy-platform">Open Science Policy Platform</a></p> <p><strong>Quotes:</strong></p> <p>'Open doesn't necessarily mean FAIR'</p>
fair-data/fairdatapoint: 0.7.2
Changed <ul> <li>Changed the url of Swagger UI from <code>/ui</code> to the base url <code>/</code></li> </ul>
Data from: The ontogeny of fairness in seven societies
A sense of fairness plays a critical role in supporting human cooperation. Adult norms of fair resource sharing vary widely across societies, suggesting that culture shapes the acquisition of fairness behaviour during childhood. Here we examine how fairness behaviour develops in children from seven diverse societies, testing children from 4 to 15 years of age (n = 866 pairs) in a standardized resource decision task. We measured two key aspects of fairness decisions: disadvantageous inequity aversion (peer receives more than self) and advantageous inequity aversion (self receives more than a peer). We show that disadvantageous inequity aversion emerged across all populations by middle childhood. By contrast, advantageous inequity aversion was more variable, emerging in three populations and only later in development. We discuss these findings in relation to questions about the universality and cultural specificity of human fairness.
Supplementary Figure 1: The road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands
<p><em>Supplementary Figure 1: a flow chart conceptualizing the gap analysis. A generic NGS process diagram was created, based on a commonly used care workflow (Step 1). Next, a questionnaire about the inventory of (meta)data standards and retrieval of gaps was drafted (Step 2), which together with the process diagram was used as a basis for the subsequent interviews (Step 3). In parallel with the first three steps, a short literature review was performed (Step 4). The interviews were processed and current gaps were identified, anonymized and classified (Step 5). Finally, the results are shared with the community through presentations, publications and suggestions for next steps for addressing the identified gaps.</em></p>
BigWig files for FAIRE-seq data
<p><span>Butterfly wings exhibit a diversity of patterns which can vary between forewings and hindwings and spatially across the same wing. Regulation of morphological variation involves changes in how genes are expressed across different spatial scales which is driven by chromatin dynamics during development. How patterns of chromatin dynamics correspond to morphological variation remains unclear. Here we compared the chromatin landscape between forewings and hindwings and also across the proximal and distal regions of the hindwings in two butterfly species, <i>Bicyclus anynana </i>and<i> Danaus plexippus</i>. We found that the chromatin profile varied significantly between the different wing regions, however, there was no clear correspondence between the chromatin profile and the wing patterns. In some cases, wing regions with different phenotypes shared the same chromatin profile whereas those with a similar phenotype had a different profile. We also found that in the forewing, open chromatin regions were AT rich whereas those in the hindwing were GC rich. GC content also varied between the proximal and hindwing regions. These differences in GC content were also reflected in the transcription factor binding motifs that were differentially enriched between the wings and wing regions. Our results suggest that the chromatin landscape varies between different wing tissues and even spatially within the same tissue with no clear correspondence to phenotype. These findings may be explained by differences in how Hox genes cooperate with other transcription factors that show preferences for specific GC content and function either as activators or repressors in different wings or wing regions.</span></p>
Raw Data for Fairly flexible: Brown-tufted capuchins and a squirrel monkey adjust their motor responses in a foraging task
<p>Raw Data for Manuscript entitled: </p> <p>Fairly flexible: Brown-tufted capuchins and a squirrel monkey adjust their motor responses in a foraging task</p>
FAIRness of Repositories & Their Data: A Report from LIBER's Research Data Management Working Group
<p>Data repositories play a crucial role in the evolution of Open Science. The FAIR Data Principles establish how to make data Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). The FAIR principles are as follows: </p> <p><strong>To Be Findable</strong></p> <ul> <li>F1. (meta)data are assigned a globally unique and eternally persistent identifier.</li> <li>F2. data are described with rich metadata.</li> <li>F3. (meta)data are registered or indexed in a searchable resource.</li> <li>F4. metadata specify the data identifier.</li> </ul> <p><strong>To Be Accessible:</strong></p> <ul> <li>A1 (meta)data are retrievable by their identifier using a standardized communications protocol.</li> <li>A1.1 the protocol is open, free, and universally implementable.</li> <li>A1.2 the protocol allows for an authentication and authorization procedure, where necessary.</li> <li>A2 metadata are accessible, even when the data are no longer available.</li> </ul> <p><strong>To Be Interoperable</strong></p> <ul> <li>I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.</li> <li>I2. (meta)data use vocabularies that follow FAIR principles.</li> <li>I3. (meta)data include qualified references to other (meta)data.</li> </ul> <p><strong>To Be Reusable</strong></p> <ul> <li>R1. meta(data) have a plurality of accurate and relevant attributes.</li> <li>R1.1. (meta)data are released with a clear and accessible data usage license.</li> <li>R1.2. (meta)data are associated with their provenance.</li> <li>R1.3. (meta)data meet domain-relevant community standards. </li> </ul> <p><strong>Methodology</strong></p> <p>Based on the FAIR Data Principles, two questionnaires were created. The first (hereafter #Q1 - see Appendix #1) targeted repository managers and/or librarians and consisted of 40 questions. The second (hereafter #Q2 - see Appendix #2) targeted technical staff responsible for repository development and maintenance and consisted of 25 questions. </p> <p>Members of LIBER’s <a href="https://libereurope.eu/strategy/research-infrastructures/rdm/">Research Data Management (RDM) Working Group</a> circulated the questionnaires between December 2018 and February 2019. Responses were collected from managers and/or librarians of 29 repositories for the first (#Q1) questionnaire. </p> <p>In addition, technical staff responsible for the development and maintenance of 14 repositories (Table 1) responded to the second (#Q2) questionnaire. In 11 cases, repositories filled out both #Q1 and #Q2. </p> <p>In this report, the responses for both questionnaires have been merged and analyzed to gain a comprehensive picture about FAIRness at the level of repositories and their data.<br> </p>
Lezione su FAIR data
<p>Registrazione video della lezione tenuta il 19 settembre - no editing [potrebbero esserci pause]</p>
Quantifying representativeness in RCTs using ML fairness metrics - Data and codes
<p>The "Quantifying representativeness in RCTs using ML fairness metrics - Data and codes" is used to quantify representativeness in randomized clinical trials (RCTs) and provide insights to improve the clinical trial equity and health equity. We developed RCT representativeness metrics based on Machine Learning (ML) Fairness Research. Visualizations and statistical tests based on proposed metrics enable researchers and physicians to rapidly visualize and assess subgroup representation in RCTs. The approach enables users to determine underrepresentation, absence, or other misrepresentation of subgroups indicating potential limitations of RCTs. The method could help support generalizability evaluation of existing RCT cohorts, enrollment target decisions for new RCTs (if eligibility criteria are included), and monitoring of RCT enrollment, ultimately contributing to more equitable public health outcomes. We apply the proposed RCT representativeness metrics to three landmark clinical trials released in the last decade: Action to Control Cardiovascular Risk in Diabetes (ACCOD), Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), and Systolic Blood Pressure Intervention Trial (SPRINT). This dataset contains the processed data and results for the experiments and visualization codes in the paper titled "Quantifying representativeness in randomized clinical trials using machine learning fairness metrics."</p>
Trusted World of Corona (TWOC) - WHO COVID-19 Case Report Form (CRF) Synthetic Data - FAIR
<p>The synthetic dataset that models COVID-19 real world observations from WHO COVID-19 RAPID Version CRFs of hospitalized patients for the hypothesis under study, originally created by the TWOC project.</p>
fair calibration data
<p><strong>Note: please be careful selecting calibrations in your own work; the newest isn't always the "best", or the right one for your needs.</strong></p> <p><strong>If you're unsure, please contact me.</strong></p> <p>If you use fair calibrations in your own work, please cite:</p> <p>Smith, C., Cummins, D. P., Fredriksen, H.-B., Nicholls, Z., Meinshausen, M., Allen, M., Jenkins, S., Leach, N., Mathison, C., and Partanen, A.-I.: fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections, Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, 2024.</p> <p> </p> <p>This dataset contains the full data, input scripts and produced output data for the <strong>updated-2023 calibration</strong><strong> </strong>of <strong>fair v2.2.2</strong>.</p> <p>The zipfile contains everything, allowing you perform your own analysis. The <a href="https://github.com/chrisroadmap/fair-calibrate/tree/v1.4.3">GitHub version</a> contains enough for "bare bones" reproducibility. In addition, I have tried to provide all of the individual files that would be needed to run a historical run of fair.</p> <p><strong>fair v2.2.2</strong></p> <p>Obtainable from https://pypi.org/project/fair/</p> <p>From the command line:</p> <p><code>pip install fair==2.2.2</code></p> <p>or</p> <p><code>conda install -c conda-forge fair==2.2.2</code></p> <p><strong>Calibration v1.5</strong></p> <p>A 1.6 million member prior and 841 member posterior are implemented.</p> <ul> <li>1.6 million prior ensemble</li> <li>Climate response calibrated on 49 abrupt-4xCO2 experiments from CMIP6 and sampled using correlated kernel density estimates</li> <li>Methane lifetime calibrated on 4 AerChemMIP experiments for 1850 and 2014 (Thornhill et al. 2021a, 2021b). Unlike other variables which are sampled around some prior uncertainty, only the best estimate historical calibration is used. The base (1750) lifetime has been fixed and consistently used across projections</li> <li>Carbon cycle uses the parameters from Leach et al. 2021 calibrated for FaIR 2.0.0 using 11 C4MIP models.</li> <li>Aerosol cloud interactions depend on SO2, BC and OC, using new calibrations from 13 RFMIP and AerChemMIP models, with the APRP code fixed by Mark Zelinka (Zelinka et al. 2023). Prior is a trapezoidal distribution with vertices at (-2.2, -1.6, -0.4, +0.2) W/m2.</li> <li>Aerosol radiation interactions use prior values from AR6 Ch6, with best estimates and uncertainties scaled to create a prior in the range of -0.6 to 0.0 W/m2.</li> <li>Ozone uses the same methodology as AR6 (Smith et al. 2021).</li> <li>Effective radiative forcing uncertainty follows the distributions in AR6, with asymmetric distributions switched to skew-normal.</li> <li>Contrails are excluded from the calibration</li> <li>fair version bumped to v2.2.2</li> <li>many more individual files included in the calibration output which should make plugging and playing easier</li> </ul> <p><strong>Constraint sets</strong></p> <p>AR6_updated_no-contrails (v1.5.1)</p> <p>As v1.5.0, except</p> <ul> <li>Emissions and concentrations from CMIP6 historical</li> <li>Volcanic forcing time series from CMIP6.</li> <li>Solar forcing time series from CMIP6.</li> <li>Warming 1995-2014 relative to 1850-1900 range from AR6.</li> <li>CO2 concentrations constrained to AR6 estimate for 2014.</li> <li>Ocean heat content from IGCC (1971-2018), linear, from AR6.</li> <li>Aerosol ERF, ERFari and ERFaci as in the fair calibration from AR6 WG1 Chapter 7 (e.g. -1.68, -1.15, -0.60)</li> </ul> <p><strong>updated_2023 (v1.5.0)</strong></p> <ul> <li>841-member posterior (deliberately chosen).</li> <li>Emissions and concentrations from CMIP7 historical (Zeb Nicholls, Jarmo Kikstra et al.)</li> <li>Volcanic forcing time series updated to use CMIP7 (from Thomas Aubry and collaborators).</li> <li>Solar forcing time series updated to use CMIP7 (Funke et al. 2024).</li> <li>Temperature from IGCC 2024 (Forster et al. 2025) (1850-2024, mean of 4 datasets).</li> <li>Warming 2004-2023 relative to 1850-1900 range from IGCC 2023 (Forster et al. 2024).</li> <li>CO2 concentrations constrained to IGCC estimate for 2023.</li> <li>Ocean heat content from IGCC (1971-2020), linear, from IGCC.</li> <li>two step constraining procedure used: first RMSE of less than 0.19K, then 8-variable distribution fitting.</li> <li>Aerosol ERF, ERFari and ERFaci as in AR6 WG1</li> <li>No future warming constraints</li> </ul> <p><strong>Performance relative to AR6 assessed ranges</strong></p> <p>On request, as Zenodo seems to have removed the ability to format tables in the dataset description.</p> <p><strong>References</strong></p> <ul> <li>Forster et al. 2024: <a href="https://doi.org/10.5194/essd-16-2625-2024" rel="nofollow">https://doi.org/10.5194/essd-16-2625-2024</a></li> <li>Forster et al. 2025: <a href="https://doi.org/10.5194/essd-17-2641-2025" rel="nofollow">https://doi.org/10.5194/essd-17-2641-2025</a></li> <li>Funke et al. 2024: <a href="https://doi.org/10.5194/gmd-17-1217-2024" rel="nofollow">https://doi.org/10.5194/gmd-17-1217-2024</a></li> <li>Leach et al. 2021: <a href="https://doi.org/10.5194/gmd-14-3007-2021" rel="nofollow">https://doi.org/10.5194/gmd-14-3007-2021</a></li> <li>Smith et al. 2021: <a href="https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FGD_Chapter07_SM.pdf" rel="nofollow">https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FGD_Chapter07_SM.pdf</a></li> <li>Thornhill et al. 2021a: <a href="https://doi.org/10.5194/acp-21-853-2021" rel="nofollow">https://doi.org/10.5194/acp-21-853-2021</a></li> <li>Thornhill et al. 2021b: <a href="https://doi.org/10.5194/acp-21-1105-2021" rel="nofollow">https://doi.org/10.5194/acp-21-1105-2021</a></li> <li>Zelinka et al. 2023: <a href="https://doi.org/10.5194/acp-23-8879-2023" rel="nofollow">https://doi.org/10.5194/acp-23-8879-2023</a></li> </ul>
FAIR Data and Software Carpentries Lesson Mind Map
<p>The FAIR Data and Software Carpentries Concept Map is an instructional planning tool to help in the development of a FAIR Data and Software Carpentries lesson in Library Carpentry.</p>
UnFAIR dataset for hands-on FAIR data sharing course
<p>UnFAIR dataset for hands-on FAIR data sharing course</p>
Do Fair Comparisons or Harms Data Increase Responsiveness to Feedback About Antibiotic Prescribing: 2x2 Factorial Trial
ClinicalTrials.gov study NCT04594200. IPD Sharing: NO. Countries: 1. Publications: 4.
Quantifying representativeness in RCTs using ML fairness metrics - Data and codes
Open the record for dataset details and reuse information.
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.