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339 results for “Fairness”
Curso Gestión de datos de investigación en la práctica científica (VIDEO 3 de 3): Datos FAIR
<p>Tercer video del curso de gestión de datos de investigación en el marco de la colaboración entre los países de <a href="https://www.lareferencia.info/es/">LA Referencia</a> con el fin de fortalecer las capacidades regionales relacionadas a la administración de datos de investigación científica. </p> <p>En este video se abordan los principios FAIR: Findability, Accessibility, Interoperability, and Reusability.</p> <p>La realización de los videos han sido gestionados por la Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT) de la República de Panamá.</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>
Fairlex: A multilingual benchmark for evaluating fairness in legal text processing
<p>We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.</p>
FAIR Island Community Zenodo Repository Metadata Assessment
<p>A FAIR assessment for the FAIR Island Community Zenodo Repository. </p>
FAIR and Open multilingual clinical trials in Wikidata and Wikipedia
<p>"FAIR and Open multilingual clinical trials in Wikidata and Wikipedia" was a December 2021 presentation by Lane Rasberry and Cherrie Kwok. It was made at the conference "Understanding Wikipedia’s Dark Matter - Translation and Multilingual Practice in the World'’s Largest Online Encyclopaedia" hosted by the Centre for Translation and the Department of Translation, Interpreting and Intercultural Studies, both at Hong Kong Baptist University.</p> <ul> <li>conference page <a href="https://ctn.hkbu.edu.hk/wikiconf2021/">https://ctn.hkbu.edu.hk/wikiconf2021/</a></li> <li>watch video <a href="https://www.youtube.com/watch?v=5yRhCENeezQ">https://www.youtube.com/watch?v=5yRhCENeezQ</a></li> <li>slides archive <a href="https://commons.wikimedia.org/wiki/File:FAIR_and_Open_multilingual_clinical_trials_in_Wikidata_and_Wikipedia.pdf">https://commons.wikimedia.org/wiki/File:FAIR_and_Open_multilingual_clinical_trials_in_Wikidata_and_Wikipedia.pdf</a></li> </ul> <p> </p> <p> </p> <p> </p>
NASA TOPS FAIR Pinwheel
<p>NASA Pinwheel based on the FAIR (findable, accessible, inclusive, and reproducible)</p>
New York State Fair Sand Sculpture 2022
Scanned using Polycam on a Pixel 6 Pro, cleanup in blender, no remesh. Source: Objaverse 1.0 / Sketchfab
Exemplar Output to demonstrate FAIR Projection from a CSV file
<p>This is 1000 lines of sample data from UniProt that I will use to demonstrate the ability of FAIR Projection to dynamically project it out as RDF triples</p>
ULF wave events during THEMIS-CMO-FAIR magnetic conjunctions
<p>This dataset corresponds to an hdf5 file with stored information on Ultra Low Frequency (ULF) wave events during magnetic conjunctions between (1) NASA's Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellites, (2) USGS College Alaska (CMO) magnetic observatory, and (3) the FAIR GNSS receiver near Fairbanks, Alaska. Magnetic field measurements are used for THEMIS (~3s sampling interval) and CMO (1s sampling interval), while 1s Total Electron Content (TEC) measurements are used from high-rate RINEX data for FAIR obtained from the NASA Crustal Dynamics Data Information System (CDDIS) archive of space geodesy data. The file also contains solar wind measurements and geomagnetic activity indices from NASA's OMNIWeb (https://omniweb.gsfc.nasa.gov/) database for the same magnetic conjunction intervals. </p> <p>ULF wave events are identified in different components of the magnetic field measured at THEMIS and/or CMO. They are also identified Total Electron Content (TEC) measured between FAIR and various GNSS satellites. Several properties of the waves are recorded, including wave power (nT^2 or TECU^2, see below for conversion factor to TECU^2/Hz or nT^2/Hz) and frequency, as well as supporting information. </p> <p> </p> <p><strong>The HDF5 file contains the following components:</strong></p> <p>- Groups: Logical containers that hold datasets and other groups.</p> <p>- Datasets: Multi-dimensional arrays holding the actual data.</p> <p>- Attributes: Metadata attached to datasets or groups providing additional information. </p> <p> </p> <p><strong>The top-level groups</strong> contain the names of each conjunction event including the conjunction date (YYYY-MM-DD) and middle time (HHMM), THEMIS satellite probe name (tha, thd, or the), ground magnetometer name (cmo), and GPS receiver name (fair).</p> <p> </p> <p><strong>The second-level groups</strong><strong> </strong>contain group names of individual GPS satellite (GPS**_psd_peak_info), three components of ground magnetic field perturbations (dgB*_psd_peak_info), three components of satellite magnetic field perturbations (dsB*_psd_peak_info), solar wind and geomagnetic indices (omni_data), and THEMIS electron flux (thm_eflux_data).</p> <p> </p> <p><strong>Under each GPS satellite group (GPS**_psd_peak_info), relevant datasets are: </strong></p> <p>common_peak_arr: this is a 1 by 6 array contains information on if a common peak to the TEC PSD is identified in any magnetic field perturbations from the THEMIS satellite or ground magnetometer. It is organized by [dgBx, dgBy, dgBz, dsBx, dsBy, dsBz]. [0,0,1,0,0,0] means a common peak with TEC PSD is identified in the ground magnetometer perturbation in the Bz component. Note that TEC data from a GPS satellite may not have any common peaks with magnetic field perturbations from the THEMIS satellite or ground magnetometer, in this case, this dataset (common_peak_arr) will be missing in this GPS satellite group.</p> <p>common_peak_freq [HZ]: the frequencies of all the common peaks identified.</p> <p>end_lat [degree]: latitude of IPP at 450 km at the end of the conjunction time.</p> <p>end_lon [degree]: longitude of IPP at 450 km at the end of the conjunction time.</p> <p>mad_dVTEC [TECU]: the median absolute deviation of the detrended VTEC time series during the conjunction interval.</p> <p>max_Elev [degree]: maximum elevation angle of the GPS satellite during the conjunction interval.</p> <p>max_VTEC [TECU]: maximum value of the VTEC time series during the conjunction interval.</p> <p>max_dVTEC [TECU]: maximum value of the detrended VTEC time series during the conjunction interval.</p> <p>mean_Elev [degree]: mean elevation angle of the GPS satellite during the conjunction interval.</p> <p>mean_lat [degree]: mean latitude of IPP at 450 km during the conjunction interval.</p> <p>mean_lon [degree]: mean longitude of IPP at 450 km during the conjunction interval.</p> <p>mean_VTEC [TECU]: mean value of VTEC time series during the conjunction interval.</p> <p>mid_lat [degree]: latitude of IPP at 450 km at the middle of the conjunction time.</p> <p>mid_lon [degree]: longitude of IPP at 450 km at the middle of the conjunction time.</p> <p>min_Elev [degree]: minimum elevation angle of the GPS satellite during the conjunction interval.</p> <p>min_VTEC [TECU]: minimum value of the VTEC time series during the conjunction interval.</p> <p>min_dVTEC [TECU]: minimum value of the detrended VTEC time series during the conjunction interval.</p> <p>peak_freq [HZ]: the frequencies of all peaks identified in the dVTEC PSD.</p> <p>peak_power [TECU^2]: the power of all peaks identified in the dVTEC PSD. To convert to TECU^2/Hz, multiply by 0.000868 .</p> <p>peak_prominence: the prominences of all peaks identified in the dVTEC PSD</p> <p>peak_width: FWHM of all peaks identified in the dVTEC PSD</p> <p>start_lat [degree]: latitude of IPP at 450 km at the beginning of the conjunction time.</p> <p>start_lon [degree]: longitude of IPP at 450 km at the beginning of the conjunction time.</p> <p> </p> <p><strong>Under each magnetic field perturbation group (d*B*_psd_peak_info), relevant datasets are: </strong></p> <p>common_peak_freq [HZ]: the frequencies of all the common peaks identified. This will be missing if no common peak is identified.</p> <p>peak_freq [HZ]: the frequencies of all peaks identified in the PSD of specific magnetic field component perturbation.</p> <p>peak_power [nT^2]: the power of all peaks identified in the PSD of specific magnetic field component perturbation. To convert to nT^2/Hz, multiply by 0.000868 .</p> <p>peak_prominence: the prominences of all identified in the PSD of specific magnetic field component perturbation.</p> <p>peak_width: FWHM of all peaks identified in the PSD of specific magnetic field component perturbation.</p> <p> </p> <p><strong>Under the omni_data group, relevant datasets are: </strong></p> <p>AE_INDEX [nT]: mean AE index during the conjunction interval.</p> <p>AL_ INDEX [nT]: mean AL index during the conjunction interval.</p> <p>ASY_D [nT]: mean ASY_D index during the conjunction interval.</p> <p>ASY_H [nT]: mean ASY_H index during the conjunction interval.</p> <p>AU_INDEX [nT]: mean AU index during the conjunction interval.</p> <p>BX_GSE [nT]: mean IMF Bx in the GSE coordinates during the conjunction interval.</p> <p>BY_GSE [nT]: mean IMF By in the GSE coordinates during the conjunction interval.</p> <p>BZ_GSE [nT]: mean IMF Bz in the GSE coordinates during the conjunction interval.</p> <p>DST [nT]: mean Dst index during the conjunction interval.</p> <p>F10_INDEX: mean F10 index during the conjunction interval.</p> <p>KP: mean Kp index during the conjunction interval.</p> <p>Pressure [nPa]: mean solar wind dynamic pressure during the conjunction interval.</p> <p>SYM_D [nT]: mean SYM_D index during the conjunction interval.</p> <p>SYM_H [nT]: mean SYM_H index during the conjunction interval.</p> <p>Vx [km/s]: mean solar wind speed in the X direction during the conjunction interval.</p> <p>Vy [km/s]: mean solar wind speed in the Y direction during the conjunction interval.</p> <p>Vz [km/s]: mean solar wind speed in the Z direction during the conjunction interval.</p> <p>proton_density [cc]: mean solar wind proton density during the conjunction interval.</p> <p> </p> <p><strong>Under the thm_eflux_data group, the relevant dataset is:</strong></p> <p>thm_eflux [ergs/cm^2/s]: mean THEMIS electron flux during the conjunction interval.</p> <p> </p>
FAIR Metadata Concepts in DataCite Metadata Schema
<p>Documentation Concepts that support the FAIR Principles are mapped to the DataCite Metadata Schema using json Paths.</p>
Forging the Path to FAIR Data Through Data Repository and Journal Publication Community Partnerships
Open the record for dataset details and reuse information.
Figure 2 in Detection of enteropathogens and research of pesticide residues in Lactuca sativa from traditional and agroecological fairs
Figure 2. Difenoconazole calibration curve.
Preliminary findings on the bioaccumulation and marine trophic transfer of the antifouling biocide DCOIT in soluble and nanostructured forms (FAIR dataset)
<p>The present dataset includes the raw data regarding the bioaccumulation, trophic transfer, and biomagnification of DCOIT, in the soluble and nanostructured forms, in the marine bivalve <em>Mytillus galloprovincialis</em>, using three different uptake routes. This data supports the main findings of the study that both forms of the biocide can be trophically transferred as well as suggests that encapsulation is a good strategy to minimize biocidal bioaccumulation and thus protect marine ecosystems. </p>
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>
Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi
<p>This is the dataset and companion website to the paper <a href="https://www.usenix.org/conference/atc17/program/presentation/hoilan-jorgesen">Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi</a> which was published at USENIX ATC 17.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/airtime-fairness/">https://www.cs.kau.se/tohojo/airtime-fairness/</a></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.