Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
96
datasets available to search
ShareScore release 0.7.1
Dataset results
96 results for “FAIR data”
EOSC Nordic WP4 FAIR incentives interview data
<p>Interview data were collected and analysed as part of EOSC Nordic WP4 FAIR. The main goal was to have input about the challenges and incentives of FAIRification of the research data from different stakeholder groups (researchers, data stewards/research support at institutions, representatives of research administration, and funders)</p>
FAIR-IMPACT Synchronisation Force: preliminary data gathered from projects and intiatives
<p>Between the 21st and 24th of November 2022 more than 120 representatives of projects and initiatives funded and/or supported by the European Commission H2020 and Horizon Europe programmes and operating in the EOSC framework joined the FAIR-IMPACT Synchronisation Force collaborative sessions to assess the implementation of the FAIR principles across research projects and initiatives in the EOSC framework.</p> <p>The team focussed on work related to FAIR-IMPACT focus areas, namely Metrics and assessing FAIRness (session 1), Persistent Identifiers (Session 2), Trustworthy and FAIR-enabling repositories (Session 3) and Metadata, semantics and interoperability (Session 4).</p> <p>In order to ensure an interactive meeting, participants were asked to complete a preliminary survey according to the sessions that they would join and complete 4 questions. The inputs received constituted the starting point for the discussions during the Synchronisation Force Workshop and are collected here as pdf files.</p> <p> </p>
Guideline for a FAIR Cultural Studies Research Data Management
<p>Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“.</p> <p>Die <strong>Online-Handreichung</strong> ist verfügbar unter <a href="https://nfdi4culture.de/go/E3625">https://nfdi4culture.de/go/E3625</a>.</p> <p>Als <strong>PDF</strong> ist diese Handreichung verfügbar unter <a href="https://doi.org/10.5281/zenodo.7716941">https://doi.org/10.5281/zenodo.7716941</a>.</p>
[ELMI2023] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p>
Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"
<p>Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""</p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
Persistence and interoperability in FAIR research data management - 1st FAIRsFAIR webinar
<p><strong>Webinar page: </strong>https://zenodo.org/record/3726149#.XnpRJXJ7k1k</p> <p>The main principles of FAIR data (findable, accessible, interoperable and reusable) have received wide acceptance in scientific data management circles. The work of further defining these principles and applying them in day-to-day knowledge sharing is ongoing.</p> <p>The FAIRsFAIR working group "<a href="https://www.fairsfair.eu/fair-practices-semantics-interoperability-and-services">FAIR practices: semantics, interoperability and services</a>" recently published the <a href="http://zenodo.org/record/3557381#.XiVxKSN7nIV">first iteration</a> of three annual reports on the state of FAIR in European scientific data. Based on studies of public information, especially EOSC infrastructure efforts, and on limited surveying and interviews, the report reviews and documents commonalities between infrastructures and obstacles to semantic interoperability - that is the use of metadata and persistent identifiers to enhance dissemination across infrastructures.</p>
Aligning Data Management Plans with Community Standards using FAIR Implementation Profiles
<p>Here you can find the files corresponding to our submission titled 'Aligning DMPs with Community Standards using FIPs'.</p> <p>- VU DMP template and the mapping is included in the folder /VU-DMP-template-and-mapping</p> <p>- All the FAIR Implementation Profiles are included in the folder /FIPs.</p> <p>- The knowledge model we created for the project, and a small demo of the interface are in the folder /KM-and-demo.</p> <p>- The folder /user-study consists of the following:</p> <p> a) The mock DMPs we provided to the participants of this research are in /mock_DMPs.</p> <p> b) We downloaded the resulting DMPs after participants completed their DMPs, they are in the folder /resulting_DMPs.</p> <p> c) Survey results can be found in the folder /survey_results.</p> <p> d) Some Python scripts were used for the analysis of the survey results. They are in the folder /Python_script_for_analysis.</p> <p><br>The project is open source under the license CC-BY 4.0.</p> <p>Contact: Shuai Wang (shuai.wang@vu.nl)</p> <p> </p>
FAIR-IMPACT Synchronisation Force 2023 edition: preliminary data gathered from projects and intiatives
<p>Building on the successful Synchronisation Force <a href="https://fair-impact.eu/events/synchronisation-force-events/synchronisation-force-1st-workshop-november-2022">workshop 2022</a>, the 2023 edition of the Synchronisation Force workshop aimed to discuss common challenges and priorities related to turning the FAIR principles into practice. The workshop took place as a series of seven virtual sessions between 2 November 2023 and 8 February 2024, with the five core sessions between 27 November and 7 December 2023.</p> <p>Invited to this workshop were selected, key FAIR representatives of projects and initiatives in the EOSC framework.</p> <p>In this record we saved the preliminary information gathered from the workshop participants collected via a survey. The information were used as preliminary discussion points for each of the five sessions organised, and were considered useful top ensure an interactive discussion.</p>
Submission and acceptance data for journals involved in the Taylor & Francis FAIR data pilot
<p>Submission, acceptance and peer review data for journals involved in the Taylor & Francis FAIR data pilot. Data is anonymised. Includes journal article submissions, acceptances and peer review for the years 2018, 2019 and 2020.</p>
Terms4FAIRskills - an overview for the Research Data Alliance 'Birds of a Feather' session, Skills and training curriculums to support FAIR for Research Software
<p>Short talk (7m) on the terms4FAIRskills initiative for the Research Data Alliance 'Birds of a Feather' session, Skills and training curriculums to support FAIR for Research Software, at RDA plenary 18 on 3 Nov 2021.</p> <p>Please see https://www.rd-alliance.org/skills-and-training-curriculums-support-fair-research-software for more information on the BoF session. Please see https://terms4fairskills.github.io/ for more information on the terms4FAIRskills initiative.</p>
Forging the Path to FAIR Data Through Data Repository and Journal Publication Community Partnerships
Open the record for dataset details and reuse information.
FAIRmat Tutorial 4: NOMAD Oasis and FAIR data collaboration and sharing
<p>FAIRmat further develops NOMAD from a central publishing service to a federated data management platform. The NOMAD Oasis is part of this. Institutes, universities, and research groups use NOMAD Oasis as a local repository to manage their research data. Each method is different and requires ways of data acquisition, different data formats, different analysis tools, but FAIR-ness requires that all data is well described with rich specific metadata.</p> <p>In this tutorial, we focus on how to get started with NOMAD Oasis and adapt it to your research. One the first day, two talks will introduce you the general FAIRmat strategy and its "bottom-up" approach to manage heterogenous but FAIR data. On the second day, we will give the practical, step-by-step guides to get started with an Oasis: How you can install NOMAD Oasis, create example data, add schemas, and create ELNs.</p> <p> </p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p> <p><strong> </strong></p>
FAIRmat Tutorial 8: Using NOMAD as an Electronic lab notebook (ELN) for FAIR data
<p>Approaching the era of big data-driven materials science, one crucial step to collecting, describing, and sharing experimental data is the adoption of electronic laboratory notebooks (ELN). At present, most synthesis data are not structured comprehensively or not even stored digitally but in handwritten lab books. The <a href="https://www.fairmat-nfdi.eu/fairmat" target="_blank" rel="noopener">FAIRmat project</a> is offering a solution by developing and operating the open-source software <a href="https://nomad-lab.eu/" target="_blank" rel="noopener">NOMAD</a>. NOMAD provides ELN functionalities that aim for a secure environment to protect the integrity of both data and metadata, whilst also affording the flexibility to adopt new synthetic processes or changes to existing ones without recourse to further software development.</p> <p>In this FAIRmat tutorial, we focus on the usage of NOMAD as an ELN which enables the users to generate data following the FAIR principles. We will show how we adopted NOMAD to capture data from synthesis and experiment and make use of an automated data workflow. The key point here is writing a data schema and its implementation in NOMAD. After defining the used terms, we will start explaining this process by writing a simple schema and then go on to more advanced usage of NOMAD, e.g. using the build in csv/xlsx-file parser, automatized data visualization, adding extra functionality by usage of base classes, referencing to other data entries in NOMAD, and searching your ELN data. The tutorial is aimed at both scientists new to NOMAD and structured data as well as data stewards. Each lecture of the tutorial will be followed directly by a Q&A session and a hands-on tutorial.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 10: FAIR electronic-structure data in NOMAD
<p>The FAIRmat consortium aims to extend the current NOMAD-Lab (meta)data structure to a large variety of materials-science data. Given our strong foundation in computational data, especially DFT, we are now extending our scope. In this tutorial, we will explain the (meta)data structure for <em>ab initio</em> calculations, with an emphasis on precision and on going beyond the accuracy limits of DFT.</p> <p>This tutorial is suitable for new and experienced researchers who want to learn about the latest features in treating DFT and beyond DFT methodologies. We will give a brief introduction to the NOMAD Lab and the FAIRmat consortium, followed by a guided tutorial where we will:</p> <ol> <li>Show you how you can upload, publish, and explore <em>ab initio</em> computational data.</li> <li>Show you how to define your own complex workflows, linking between DFT and beyond DFT calculations.</li> <li>Give you examples of the post-processing capabilities of the NOMAD Lab.</li> </ol> <p>In more detail: Precision settings are now searchable, allowing for “data-quality” filtering over the NOMAD data. Using simple queries, we will show how to generate a sampling that extrapolates towards the basis set limit. For those already familiar with their code of choice, there is also the native tier quick filter that matches recommended developer settings. Moreover, for ease in navigating the density-functional space, we will be presenting a new, knowledge-based categorization system that is more refined and semantically richer than Jacob’s ladder. Finally, we will show the latest developed schemas which try to cover computational techniques that go beyond DFT and which are useful to treat excited-state and advanced many-body properties: the <em>GW</em> approximation, Bethe-Salpeter equation (BSE) solutions, tight-binding-based modeling (using Wannier projections or Slater-Koster fittings), and Dynamical Mean-Field Theory (DMFT). </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Third FAIRmat users meeting Nov2023 - FAIR Data Principles in Perovskite Solar Cell Research using NOMAD - Daniel Baumann
<p>Daniel Baumann (doctoral research project at KIT in the group of Ulrich Paetzold) highlights how NOMAD Oasis supports experimental materials researchers in the field of perovskite photovoltaics. A live demonstration of experimental planning, documentation, as well as automated data evaluation is presented. Particularly, in conjunction with a highly repeatable robotic spin-coating setup, the realization of this vast potential may be closer than anticipated. </p>
FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations
<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD’s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows. <br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported? <strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIR data literacy project
<p>Data obtained from FAIR data study</p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
Benchmark datasets to study fairness in synthetic data generation
<p>The traveltime dataset is based on the Folktables project covering US census data. The target is a binary variable encoding whether or not the individual needs to travel more than 20 minutes for work; here, having a shorter travel time is the desirable outcome. We use a subset of data from the states of California, Florida, Maine, New York, Utah, and Wyoming states in 2018. Although the folktables dataset does not have any missing values, there are some values recorded as NaN due to the Bureau's data collection methodology. We remove the "esp" column, which encodes the employment status of parents, and has 99.55% missing values. We encode the missing values in the povpip, income to poverty ratio (0.85%), to -1 in accordance to the methodology in Ding et al.. See https://arxiv.org/pdf/2108.04884 for metadata.</p> <p>The cardio (a) dataset contains patient data recorded during medical examination, including 3 binary features supplied by the patient. The target class denotes the presence of cardiovascular disease. This dataset represents predictive tasks that allocate access to priority medical care for patients, and has been used for fairness evaluations in the domain.</p> <p>The credit dataset contains historical financial data of borrowers, including past non-serious delinquencies. Here, a serious delinquency is considered to be 90 days past due, and this is the target variable.</p> <p>The German Credit dataset (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) contains financial and personal information regarding loan-seeking applicants.</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.