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339 results for “fairness”
Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues
<p>Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues.</p> <p>The method is explained in the <a href="https://doi.org/10.5281/zenodo.4146819">report on status, gap analysis and roadmap towards harmonised and federated metadata catalogues for EU national Photon and Neutron RIs</a>.</p>
Copper Tribology FAIR Data Experiments - Sapphire Counterbody
<p><strong>Abstract: </strong>This digital artefact documents the results of experiments in metals tribology. It is constructed strictly observing the FAIR data principles. The experiments test the reciprocation sliding of a 10-mm single-crystal sapphire sphere against a polycrystal (average size ~45 µm) copper base body. The range of normal loads is 0–4.5 N, the sliding velocity is always 0.5 mm/s, and the experiments are performed in ambient 50% RH atmosphere.</p> <p><strong>How to work with this data: </strong>The files in this Zenodo record carry the metadata and descriptions associated with the experiments. They are serialized in RDF. All raw and processed data is collected into RO-Crates (<a href="https://doi.org/10.3233/DS-210053">10.3233/DS-210053</a>), and are stored at institutional servers with the following addresses (these are also semantically linked using Zenodo's schema on the right):</p> <ol> <li><a href="https://dx.doi.org/10.35097/1028">https://dx.doi.org/10.35097/1028</a></li> <li><a href="https://dx.doi.org/10.35097/1029">https://dx.doi.org/10.35097/1029</a></li> <li><a href="https://dx.doi.org/10.35097/1030">https://dx.doi.org/10.35097/1030</a></li> <li><a href="https://dx.doi.org/10.35097/1036">https://dx.doi.org/10.35097/1036</a></li> <li><a href="https://dx.doi.org/10.35097/1037">https://dx.doi.org/10.35097/1037</a></li> <li><a href="https://dx.doi.org/10.35097/1038">https://dx.doi.org/10.35097/1038</a></li> <li><a href="https://dx.doi.org/10.35097/1039">https://dx.doi.org/10.35097/1039</a></li> <li><a href="https://dx.doi.org/10.35097/1041">https://dx.doi.org/10.35097/1041</a></li> <li><a href="https://dx.doi.org/10.35097/1043">https://dx.doi.org/10.35097/1043</a></li> <li><a href="https://dx.doi.org/10.35097/1045">https://dx.doi.org/10.35097/1045</a></li> <li><a href="https://dx.doi.org/10.35097/1047">https://dx.doi.org/10.35097/1047</a></li> <li><a href="https://dx.doi.org/10.35097/1049">https://dx.doi.org/10.35097/1049</a></li> <li><a href="https://dx.doi.org/10.35097/1051">https://dx.doi.org/10.35097/1051</a></li> <li><a href="https://dx.doi.org/10.35097/1052">https://dx.doi.org/10.35097/1052</a></li> <li><a href="https://dx.doi.org/10.35097/1053">https://dx.doi.org/10.35097/1053</a></li> <li><a href="https://dx.doi.org/10.35097/1054">https://dx.doi.org/10.35097/1054</a></li> <li><a href="https://dx.doi.org/10.35097/1057">https://dx.doi.org/10.35097/1057</a></li> <li><a href="https://dx.doi.org/10.35097/1058">https://dx.doi.org/10.35097/1058</a></li> <li><a href="https://dx.doi.org/10.35097/1059">https://dx.doi.org/10.35097/1059</a></li> <li><a href="https://dx.doi.org/10.35097/1060">https://dx.doi.org/10.35097/1060</a></li> <li><a href="https://dx.doi.org/10.35097/1061">https://dx.doi.org/10.35097/1061</a></li> <li><a href="https://dx.doi.org/10.35097/1063">https://dx.doi.org/10.35097/1063</a></li> <li><a href="https://dx.doi.org/10.35097/1065">https://dx.doi.org/10.35097/1065</a></li> <li><a href="https://dx.doi.org/10.35097/1066">https://dx.doi.org/10.35097/1066</a></li> <li><a href="https://dx.doi.org/10.35097/1067">https://dx.doi.org/10.35097/1067</a></li> <li><a href="https://dx.doi.org/10.35097/1070">https://dx.doi.org/10.35097/1070</a></li> <li><a href="https://dx.doi.org/10.35097/1071">https://dx.doi.org/10.35097/1071</a></li> <li><a href="https://dx.doi.org/10.35097/1072">https://dx.doi.org/10.35097/1072</a></li> <li><a href="https://dx.doi.org/10.35097/1073">https://dx.doi.org/10.35097/1073</a></li> <li><a href="https://dx.doi.org/10.35097/1074">https://dx.doi.org/10.35097/1074</a></li> <li><a href="https://dx.doi.org/10.35097/1075">https://dx.doi.org/10.35097/1075</a></li> <li><a href="https://dx.doi.org/10.35097/1076">https://dx.doi.org/10.35097/1076</a></li> <li><a href="https://dx.doi.org/10.35097/1077">https://dx.doi.org/10.35097/1077</a></li> <li><a href="https://dx.doi.org/10.35097/1078">https://dx.doi.org/10.35097/1078</a></li> <li><a href="https://dx.doi.org/10.35097/1079">https://dx.doi.org/10.35097/1079</a></li> <li><a href="https://dx.doi.org/10.35097/1080">https://dx.doi.org/10.35097/1080</a></li> <li><a href="https://dx.doi.org/10.35097/1082">https://dx.doi.org/10.35097/1082</a></li> <li><a href="https://dx.doi.org/10.35097/1083">https://dx.doi.org/10.35097/1083</a></li> <li><a href="https://dx.doi.org/10.35097/1085">https://dx.doi.org/10.35097/1085</a></li> <li><a href="https://dx.doi.org/10.35097/1086">https://dx.doi.org/10.35097/1086</a></li> <li><a href="https://dx.doi.org/10.35097/1087">https://dx.doi.org/10.35097/1087</a></li> <li><a href="https://dx.doi.org/10.35097/1088">https://dx.doi.org/10.35097/1088</a></li> <li><a href="https://dx.doi.org/10.35097/1089">https://dx.doi.org/10.35097/1089</a></li> <li><a href="https://dx.doi.org/10.35097/1090">https://dx.doi.org/10.35097/1090</a></li> <li><a href="https://dx.doi.org/10.35097/1091">https://dx.doi.org/10.35097/1091</a></li> <li><a href="https://dx.doi.org/10.35097/1092">https://dx.doi.org/10.35097/1092</a></li> <li><a href="https://dx.doi.org/10.35097/1093">https://dx.doi.org/10.35097/1093</a></li> <li><a href="https://dx.doi.org/10.35097/1094">https://dx.doi.org/10.35097/1094</a></li> <li><a href="https://dx.doi.org/10.35097/1095">https://dx.doi.org/10.35097/1095</a></li> <li><a href="https://dx.doi.org/10.35097/1096">https://dx.doi.org/10.35097/1096</a></li> <li><a href="https://dx.doi.org/10.35097/1097">https://dx.doi.org/10.35097/1097</a></li> <li><a href="https://dx.doi.org/10.35097/1098">https://dx.doi.org/10.35097/1098</a></li> <li><a href="https://dx.doi.org/10.35097/1099">https://dx.doi.org/10.35097/1099</a></li> <li><a href="https://dx.doi.org/10.35097/1100">https://dx.doi.org/10.35097/1100</a></li> </ol> <p>Records have been anonymized prior to publishing.</p> <p><strong>Statistics about the data:</strong></p> <ul> <li>151,045 RDF triples</li> <li>51 Experimental Series, 542 Individual Events</li> <li>89 Lab Equipment Descriptions</li> <li>108 Experimental Object Descriptions</li> <li>412.1 GB in Total Size</li> </ul> <p><strong>Types of procedures and equipment involved (and count):</strong></p> <ul> <li>Data Processing: 293</li> <li>Block Specimen: 89</li> <li>Light Microscopy: 76</li> <li>Data Publication: 54</li> <li>Tribological Experiment: 50</li> <li>Optical Surface Profilometry: 33</li> <li>Metal Sawing: 30</li> <li>Polishing: 27</li> <li>Electron Microscopy: 26</li> <li>Grinding: 25</li> <li>Electropolishing: 18</li> <li>Heat Treatment: 8</li> <li>Software: 6</li> <li>Tribometer: 6</li> <li>Ultrasonic Cleaner: 3</li> <li>Cup Grinding Machine: 2</li> <li>Electropolishing Machine: 2</li> <li>Furnace: 2</li> <li>Grinding Machine: 2</li> <li>Optical Surface Profilometer: 2</li> <li>Band Saw: 1</li> <li>Demagnetizing Plate: 1</li> <li>Electrolyte: 1</li> <li>Hardness Tester: 1</li> <li>Light Microscope: 1</li> <li>Scanning Electron Microscope: 1</li> <li>Tactile Surface Profilometer: 1</li> <li>Wire Saw: 1</li> </ul> <p><strong>Vocabulary Schema Used:</strong> Schema.org, <a href="https://doi.org/10.5281/zenodo.7709546">Vocabulary of Tribological Experiments</a></p> <p><strong>Versions:</strong></p> <ul> <li>0.2.0 Added reverse links into metadata to published DOIs; other minor fixes</li> <li>0.1.0 First Publication</li> </ul> <p><strong>Related Documents:</strong></p> <ul> <li>Bachelor's and Master's theses (to be published)</li> </ul> <p> </p> <p><strong>For any questions, suggestions, or anything else:</strong> <a href="mailto:nikolay.garabedian@kit.edu">nikolay.garabedian@kit.edu</a> or <a href="https://www.linkedin.com/in/nick-garabedian/">linkedin.com/in/nick-garabedian/</a></p> <p><strong>More information will be continuously updated.</strong></p> <p> </p> <p><strong>Metadata Preview:</strong></p> <pre><code>@prefix k4m784: <https://kadi4mat.iam-cms.kit.edu/records/784#> . @prefix ns1: <https://purls.helmholtz-metadaten.de/vp/kitmtxx-41732562-vp/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix schema: <https://schema.org/> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . <https://kadi4mat.iam-cms.kit.edu/records/784> a schema:Dataset ; rdfs:isDefinedBy [ ns1:gen_inf_equ-de13e7d124b44501a2f75fe330ac1704-vp [ ns1:com_orx_ven-d0357c2850b345cbb8e0185605de3faa-vp "Centre Suisse d'Electronique et de Microtechnique" ; ns1:equ_idx_xxx-55f9296489064da2b4839d6c2d27b3e3-vp "10-157" ; ns1:equ_orx_pro-bb4767e222854d6dae67280595736f6a-vp "Pin-on-Disc Machine" ; 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Box" ; ns1:pri_ofx_loa-000c058b7b324521a13bc44f8cdbb210-vp "Dead Weight(s)" ; ns1:sam_sub_met-d0762a0ba7964091b0e9da84932345a2-vp "Rim around Disc" ; ns1:vib_mit_met-2cc6a1c2302b4ceda57a78c553f8867d-vp "Lab Bench" ] ] ] ; schema:additionalType k4m784:lab%20equipment ; schema:author <https://kadi4mat.iam-cms.kit.edu/users/65> ; schema:dateCreated "2021-02-17T15:23:52.448311+00:00"^^xsd:dateTime ; schema:dateModified "2023-04-29T14:17:13.990908+00:00"^^xsd:dateTime ; schema:identifier "7addb4cda29d4f80b3e63df5ac674e7c-vp-aaazd-kitmt" ; schema:keywords "001066-79dfbfc9fd3882a1c2d10053800c6306-vp", "09aa26cf985681760020f32222a06c25-vp", "7addb4cda29d4f80b3e63df5ac674e7c-vp", "csem", "d879a97efd724dedbc16e6b223b8e961-vp", "kitmtxx-41732562-vp", "tri_xxx_xxx-c98170df4aa14cd38e9ba0d1b94ebdf0-vp", "tribometer" ; schema:license <https://creativecommons.org/licenses/by/4.0/> ; schema:name "Tribometer CSEM" ; schema:text """*Info:* A unidirectional pin-on-disc tribometer that can be used to conduct tribological experiments. It allows monitoring and recording of friction force and linear wear (via capacitive distance sensors)""".</code></pre> <p> </p>
How to Ensure Researchers Share Their FAIR Data: Practical Tips and Tools [Online Workshop, Recording]
<p>The online hands-on workshop was aimed at trainers and support staff covering critical elements of data sharing and available tools and resources for supporting Open Science including:<br> • Open Science resources and Data Management Planning<br> • Consent and Ethical considerations<br> • Legislation and Licence frameworks<br> The objectives of the workshop were i) to raise awareness of key tools and resources available for Open Science training ii) to enable a platform to exchange ideas regarding key training topics and iii)n to provide training materials and worksheets for future reuse.<br> The workshop consisted of presentations, demos, a roundtable discussion on ethical considerations, a showcase of licence frameworks at different European archives and an exercise with all participants fostering an exchange of experiences focused on learnt lessons.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=uztTCRFRZHg"> the CESSDA Training YouTube channel</a>.</p>
Orphanet catalog FAIR Data Point
<pre>This is POC FAIR Data Point to describe biobank and patient registry. The content of this FAIR Data Point is extracted via EJPRD LDP</pre>
Data and code for EDI overview paper, data collection characteristics, FAIR evaluation, downloads, and citations
The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.
Distal prong of MA fairly broad and curved, not protruding beyond cymbium in ventral view; indentation between distal and proximal prong round- ed (3b) in A revision of the African wolf spider genus Amblyothele Simon
Distal prong of MA fairly broad and curved, not protruding beyond cymbium in ventral view; indentation between distal and proximal prong round- ed (3b)
Distal prong of MA short, hardly longer than proximal one, straight (6a); TA fairly long and spiniform (6b) in A revision of the African wolf spider genus Amblyothele Simon
Distal prong of MA short, hardly longer than proximal one, straight (6a); TA fairly long and spiniform (6b)
How repositories can contribute their FAIR share
<p>Findable, accessible, interoperable and reusable (FAIR) data are an increasingly important aspect of open scholarship. Increasing the production and use of FAIR data requires a wide range of stakeholders across the research ecosystem to actively play their parts. FAIRsFAIR – Fostering Fair Data Practices in Europe – aims to supply practical solutions for applying the FAIR data principles throughout the research data life cycle, which can be included in the strategies that research organisations and implemented to enable a FAIR data culture.</p> <p>This 90 minute virtual workshop focuses on the work FAIRsFAIR carries out in collaboration with repositories to enable them to play their role in helping to make and keep data FAIR over time. It covers an early draft of a transition support programme for repositories wishing to improve their capacity to support FAIR data production and use. Following an overview of the draft programme, attendees review and discuss the draft support programme, consider how it might be applied within their own repositories, and how they can support and promote relevant aspects of the programme within their institutions and the wider community.</p> <p>This record is a virtual workshop recording.</p> <p>Slides: Herterich, Patricia, & Davidson, Joy. (2020, June). How repositories can contribute their FAIR share. Zenodo. <a href="http://doi.org/10.5281/zenodo.3871523">http://doi.org/10.5281/zenodo.3871523</a></p>
SUNRISE : Faire de la photosynthèse artificielle une réalité
<p>French version of the SUNRISE presentation video.</p> <p>Mieux que n'importe quelle page «qui sommes nous», cette vidéo vous donne une vue d'ensemble de cette initiative de recherche internationale visant à faire de la photosynthèse artificielle une réalité pour un avenir durable.</p>
Data for D7.1 FAIR in European Higher Education
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022.</p> <p>FAIRsFAIR Work Package 7 “FAIR Data Science and Professionalisation” aims to develop resources and build communities that support the uptake of RDM and FAIR practice within higher education curricula.</p> <p>The data published here stems from a both a web-based questionnaire with 90 responses conducted within FAIRsFAIR WP7 between 19 September and 15 November 2019.</p> <p>The questionnaire covered several dimensions of research data management at HEIs relevant for the implementation of FAIRsFAIR WP7, as well as WP3 “FAIR Data Policy Practice” and WP6 “FAIR Competence Centre”. These dimensions included:</p> <ul> <li>Institutional research data management policies </li> <li>Support services for research data management</li> <li>Competence development of students and graduates</li> <li>Universities and EOSC</li> <li>FAIRsFAIR support for universities</li> </ul> <p>The data resulting from the survey has been used as the basis for <a href="http://doi.org/10.5281/zenodo.3629683">D7.1 FAIR in European Higher Education</a>.</p> <p>The following files are available:</p> <ul> <li>Codebook including original questionnaire</li> <li>Dataset</li> </ul>
FAIR in practice reference list
<p>This is a collection of information curated by the FAIR Practice Task Force of the EOSC FAIR Working Group.</p> <p>It aims to provide a reading list of published information on efforts to apply the FAIR principles.</p> <p>Please read the README tab in the spreadsheet for instructions on how to use this resource.</p>
The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge
<p><strong>Episode Summary</strong></p> <p>In this episode we talked about 'almighty' algorithms with Carlos Castillo, Lorenzo Porcaro, Marzieh Karimihaghighi, David Solans, and Francesco Fabbri from the Web Science & Social Computing Research Group, and the department of Engineering in Information & Communication Technologies, in Universitat Pompeu Fabra in Barcelona. We discussed how bias can enter into algorithm systems, how bias is measured, and what systems are impacted by it. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.upf.edu/web/wssc/">Web Science and Social Computing Research Group</a></p> <ul> <li><a href="https://www.upf.edu/web/etic/entry/-/-/24095/adscripcion/carlos-alberto-alejandro-castillo">Carlos Castillo</a></li> <li><a href="https://www.linkedin.com/in/marzieh-karimihaghighi-706b5554/?originalSubdomain=ir">Marzieh Karimihaghighi</a></li> <li><a href="https://www.linkedin.com/in/david-solans-noguero-48269b85/?originalSubdomain=es">David Solans</a></li> <li><a href="https://www.linkedin.com/in/francesco-fabbri/?originalSubdomain=it">Francesco Fabbri</a></li> <li><a href="https://www.linkedin.com/in/lorenzo-porcaro-7a8792b1/?originalSubdomain=es">Lorenzo Porcaro</a></li> </ul>
Unequal - but fair? Weights in the serial integration of haptic texture information
<p>The sense of touch is characterized by its sequential nature. In texture perception, enhanced spatio-temporal extension of exploration leads to better discrimination performance due to combination of repetitive information. We have previously shown that the gains from additional exploration are smaller than the Maximum Likelihood Estimation (MLE) model of an ideal observer would assume. Here we test if this suboptimal integration can be explained by unequal weighting of information. Participants stroke 2 to 5 times across a virtual grating and judged the ridge period in a 2IFC task. We presented slightly discrepant period information in one of the strokes in the standard grating. Results show linearly decreasing weights of this information with spatio-temporal distance (number of intervening strokes) to the comparison grating. For each exploration extension (number of strokes) the stroke with the highest number of intervening strokes to the comparison was completely disregarded. The results are consistent with the notion that memory limitations are responsible for the unequal weights. This study raises the question if models of optimal integration should include memory decay as an additional source of variance and thus not expect equal weights.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2014). Unequal - but fair? Weights in the serial integration of haptic texture information. <em>Haptics: Neuroscience, Devices, Modeling, and Applications</em> (pp. 386-392). Springer: Heidelberg.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate file.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept - Supplementary Material
<h3>Abstract</h3> <p>Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.</p> <h3>Description of the data and file structure</h3> <p>This repository complements the publication <a href="https://www.biorxiv.org/content/10.1101/2024.03.22.586299v2" target="_blank" rel="noopener">"The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept".</a> It contains the result data from the proof of concept field test of V1.0 of the FAIR-Device, conducted between July and August 2021 at the Thünen Institute of Agricultural Technology in Braunschweig. The repository comprises three compressed files (.zip):</p> <ul> <li><strong>FAIR-D_captures.zip</strong>: <ul> <li>Video captures from the field tests organized by recording day.</li> <li>Filenames indicating recording time (hh-mm-ss).</li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_Tables&Code.zip</strong>: <ul> <li>Processed results from the obtained image captures, organized into: <ul> <li><strong>Monitoring2021_TotalPeriod.xlsx: </strong>Result table with taxonomic classifications, data analysis, and charts.</li> <li><strong>iNat_observations.xlsx: </strong>iNaturalist reviews analysis table.</li> <li><strong>R: </strong>code for generating article graphics.</li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_V1.0_3D_Models.zip</strong>: <ul> <li>Complete 3D design of the FAIR-D V1.0 in .stl format, organized into: <ul> <li><strong>3D_print_parts</strong>: 3D-printable parts with spatial coordinates for correct positioning in CAD software.</li> <li><strong>other_3Dparts</strong>: Non-printable parts, also in .stl format with spatial coordinates.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>NOTE: For visualizing the videos, we recommend <strong>VLC media player</strong> - <a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a> </p>
FAIR Data Point
<h2>What's Changed</h2> <ul> <li>Set permit ACCEPTED for existing index entries by @dennisvang in https://github.com/FAIRDataTeam/FAIRDataPoint/pull/774</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/FAIRDataTeam/FAIRDataPoint/compare/v1.18.0...v1.18.1</p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Diversity-aware Fairness Testing of Machine Learning Classifiers through Hashing-based Sampling
<p>The experimental results of the evaluation of VBT-X.</p> <h2>Abstract</h2> <div> <h3>Context:</h3> <p>There are growing concerns about algorithmic fairness, as some machine learning (ML)-based algorithms have been found to exhibit biases against protected attributes such as gender, race, age and so on. Individual fairness requires an ML classifier to produce similar outputs for similar individuals. Verification Based Testing (<span>Vbt</span>) is a state-of-the-art black-box testing algorithm for individual fairness that leverages constraint solving to generate test cases.</p> </div> <div> <h3>Objective:</h3> <p>Generating diverse test cases is expected to facilitate efficient detection of diverse discriminatory data instances (i. e., cases that violate individual fairness). Hashing-based sampling techniques draw a sample approximately uniformly at random from the set of solutions of given Boolean constraints. We propose <span>Vbt</span>-X, which improves <span>Vbt</span> with hashing-based sampling, aiming to improve its testing performance.</p> </div> <div> <h3>Method:</h3> <p>We realize hashing-based sampling for <span>Vbt</span>. The challenge is that the off-the-shelf hashing-based sampling techniques cannot be integrated in a straightforward manner because the constraints in <span>Vbt</span> are generally not Boolean. Moreover, we propose several enhancement techniques to make <span>Vbt</span>-X more efficient.</p> </div> <div> <h3>Results:</h3> <p>To evaluate our method, we conduct experiments, where <span>Vbt</span>-X is compared to <span>Vbt</span>, <span>Sg</span> and ExpGA (other well-known fairness testing algorithms) over a set of configurations consisting of several datasets, protected attributes, and ML classifiers. The results show that, with each configuration, <span>Vbt</span>-X detects more discriminatory data instances with higher diversity than <span>Vbt</span> and <span>Sg</span>. <span>Vbt</span>-X detects discriminatory data instances with higher diversity than ExpGA, though the number of discriminatory data instances detected by <span>Vbt</span>-X is lesser than ExpGA.</p> </div> <div> <h3>Conclusion:</h3> <p>Our proposed method performs better than other state-of-the-art black-box fairness testing algorithms, particularly in terms of diversity. Our method can serve to efficiently identify flaws in ML classifiers with respect to individual fairness for subsequent improvements of an ML classifier. On the other hand, although our method is specific to individual fairness, it could work for testing other aspects of a software system such as security and counterfactual explanations with some technical adaptations, which remains for future work.</p> </div> <p> </p> <div> <h2>Acknowledgments</h2> <p>This paper is partly based on results obtained from a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO). This paper is supported by JST SPRING, Grant Number JPMJSP2131.</p> </div>
How Fair Is Bioarchaeological Data
<p>Data collected for conference proceeding accessible here :<br>Lien-Talks, A. (2024). How FAIR is Bioarchaeological Data: with a particular emphasis on making archaeological science data Reusable. https://doi.org/10.5281/zenodo.10935099</p>
Dataset shown on the HMC FAIR Data Dashboard as of June 2023
<p>This dataset contains literature metadata harvested for 15 research centers of the Helmholtz Association of German research centers (KIT, FZJ, DESY, AWI, DLR, GFZ, HZB, GEOMAR, MDC, UFZ, HZDR, DZNE, GSI, CISPA, HMGU) during the first two quarters of 2023 as well as metadata of linked data publications, as extracted from the ScholExplorer API. F-UJI scores for these data publications are based on F-UJI version 1.4.7 using the FAIRsFAIR metrics 0.4. The status of the data represents the data shown on the HMC Dashboard on Open and FAIR Data in Helmholtz (<a title="https://fairdashboard.helmholtz-metadaten.de/" href="https://fairdashboard.helmholtz-metadaten.de/">https://fairdashboard.helmholtz-metadaten.de/</a>) as of June 2023.</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.