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339 results for “Fairness”
WorldFAIR FAIR Convergence Graph
<p><span>WorldFAIR Project (2024). FAIR Convergence Graphs, v01</span>. </p> <p>Images of graph visualisations of connections among FIPs produced by some WorldFAIR Case Studies and as used in Hodson, S. (2024). WorldFAIR (D2.2) WorldFAIR's Experience with FIPs (second set of FAIR Implementation Profiles for each case study) (Version 1). Zenodo. https://doi.org/10.5281/zenodo.11236094. </p> <p> </p> <p>Visit WorldFAIR online at http://worldfair-project.eu.</p> <p>WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393. </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>
Approximation-guided Fairness Testing through Discriminatory Space Analysis
<h3>Description:</h3> <p>This dataset contains the experimental results from the paper titled "Approximation-guided Fairness Testing through Discriminatory Space Analysis".</p> <p>In this paper, we conducted 24 fairness testing tasks using 7 different fairness testing algorithms: AFT (our proposed algorithm), VBT-X, VBT, THEMIS, ExpGA, SG and LIMI. Each execution was repeated 30 times, with a runtime of 1 hour.</p> <h3>Files Included:</h3> <ol> <li><strong>log.txt</strong>: This file contains the logs of each execution. Each log is labeled with an identifier, such as "'aft-LogReg-Adult-sex-0", which represents the 1st execution of the fairness testing task on (LogReg, Adult, sex) using AFT.</li> <li><strong>discriminatory_instances.zip</strong>: This archive includes all the IDIs (individual discriminatory instances) identified during the experiments.</li> <li><strong>res.txt</strong>: This file contains the averaged results of the 30 repetitions. It includes four metrics: #IDIs/sec (the number of indentified IDIs per second), #Tests/sec (the number of generated test cases per second), SuccessRatio (the success ratio of test cases), Diversity (the diversity of IDIs) and Naturalness (the naturalness of IDIs).</li> </ol>
(Un)Fair Process Mining Event Logs (Converted to OCEL)
<p><strong>Converted to OCEL 1.0 JSONOCEL and OCEL 2.0 XML from traditional event logs available at: </strong><a href="https://zenodo.org/records/8059489" target="_new" rel="noopener">Zenodo - Record 8059489</a>.</p> <p><strong>Object Types:</strong> <code>{ Person }</code></p> <p><strong>Person-level Attributes:</strong></p> <ul> <li><strong>(int) overallProtected:</strong> An attribute (0/1) indicating whether the person has experienced discrimination. <em>(Note: If you're developing a fairness assessment algorithm, only use this attribute in the testing phase!)</em></li> <li><strong>(int) sumBoolDiscrFactors:</strong> Counts the number of possible discrimination factors that apply to the person.</li> <li><strong>(int) reworkedActivities:</strong> The total amount of rework involved in the person’s processing.</li> <li><strong>(float) throughputTime:</strong> The total processing time for a person.</li> <li><strong>(int) numOcc_ACTIVITY:</strong> Counts the number of times an activity occurs in the person’s lifecycle.</li> </ul> <p><strong>Event-level Attributes:</strong></p> <ul> <li><strong>resource:</strong> The resource involved in processing a given person.</li> </ul> <p> </p> <p><strong>* Hiring</strong></p> <p>The data describes a multifaceted recruitment process with diverse application pathways ranging from minimal processing to extensive multi-step procedures. The variability of these routes, largely dependent on numerous determinants, yields a spectrum of outcomes from instant rejection to successful job offers.</p> <p>The logs include attributes such as age, citizenship, German proficiency, gender, religion, and years of education. While these attributes may inform candidate profiles, their misuse could engender discrimination. Variables like age and education may signify experience and skills, citizenship and German language may address job logistics, but these should not unjustly eliminate applicants. Gender and religion, unrelated to job performance, must not sway hiring. Therefore, the use of these attributes must uphold fairness, avoiding any potential bias.</p> <p><strong>* Hospital</strong></p> <p>The data depicts a hospital treatment process that commences with registration at an Emergency Room or Family Department and advances through stages of examination, diagnosis, and treatment. Notably, unsuccessful treatments often entail repetitive diagnostic and treatment cycles, underscoring the iterative nature of healthcare provision.</p> <p>The logs incorporate patient attributes such as age, underlying condition, citizenship, German language proficiency, gender, and private insurance. These attributes, influencing the treatment process, may unveil potential discrimination. Factors like age and condition might affect case complexity and treatment path, while citizenship may highlight healthcare access disparities. German proficiency can impact provider-patient communication, thus affecting care quality. Gender could spotlight potential health disparities, while insurance status might indicate socio-economic influences on care quality or timeliness. Therefore, a comprehensive examination of these attributes vis-a-vis the treatment process could shed light on potential biases or disparities, fostering fairness in healthcare delivery.</p> <p><strong>* Lending</strong></p> <p>This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.</p> <p>The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes.</p> <p><strong>* Renting</strong></p> <p>The data represents a rental process. It begins with a prospective tenant applying to view a property. Subsequent steps include an initial screening phase, viewing, decision-making, and a potential extensive screening. The process ends with the acceptance or rejection of the prospective tenant. In some cases, a tenant may apply for viewing but be rejected without the viewing occurring.</p> <p>The logs contain attributes that can shed light on potential biases in the process. 'Age', 'citizen', 'German speaking', 'gender', 'religious affiliation', and 'yearsOfEducation' might influence the rental process, leading to potential discrimination. While some attributes may provide useful insights into a potential tenant's reliability, misuse could result in discrimination. Thus, fairness must be observed in utilizing these attributes to avoid potential biases and ensure equitable treatment.</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>
Questionnaire used in FAIR Workbench user evaluation
<p>A pdf copy of the Questionnaire filled out by users at the end of the FAIR Workbench user evaluation study. The questions regarded the evaluation notebook, published here: http://doi.org/10.5281/zenodo.5045448</p>
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>
Prosocial Behavior and the Individual Normative Standard of Fairness within a Dynamic Context: Experimental Evidence
<p>The data is of the economic experiment conducted in Georgia in summer 2022. The experiment was conducted online via LIONESS web-based software. The experiment was conducted in Georgian Language. The instructions and PC screen information translated from Georgian language into English language. The participants were mostly students of different major, average age of 20, about 16% were non-students. 63% was female. The data consist of two treatments: (1) Control treatment - two round standard double blind dictator game, dictators' roles were kept constant in both rounds (2) Treatment T1 - consisted from three parts, in part 1 subjects play standard double blind Dictator Game, in part 2 subjects play tend rounds of Public Goods Game with peer punishment, and in part 3 subjects play same standard double blind Dictator Game; dictators' stay in same role in part 1 and part 3. In part 2 , in Public Goods Game was partner treatment with constant group composition. </p>
Innspelsmøte om prosjektet Kuratornettverk for FAIR forskingsdata, 9. november 2022
<p>Opptak av innspelsmøte på prosjektet Kuratornettverk for FAIR forskingsdata, 9. november 2022</p>
Artifact for "MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software"
<p>This artifact is for the paper entitled “MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software”, which is accepted by ESEC/FSE 2022. MAAT is a novel ensemble approach to improving the fairness-performance trade-off for ML software. It outperforms state-of-the-art bias mitigation methods. The artifact has also been placed on GitHub (https://github.com/chenzhenpeng18/FSE22-MAAT) under the Apache License, publicly accessible to other researchers. In this artifact, we provide the source code of MAAT and other existing bias mitigation methods that we use in our study, as well as the intermediate results, the installation instructions, and a replication guideline (included in the README). The replication guideline provides detailed steps to replicate all the results for all the research questions.</p>
INGRIDKG: A FAIR Knowledge Graph of Graffiti
<p>Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no<br> suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project<br> (INGRID) closes this gap by dealing with graffiti image collections that have been made available to the project for public<br> use. Within INGRID, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid<br> access to a comprehensive data source on INGRID targeted especially by researchers. In particular, we present INGRIDKG, an<br> RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update INGRIDKG<br> by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion,<br> link discovery and data fusion approaches to the original data. The current version of INGRIDKG contains 460,640,154 triples<br> and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of<br> our knowledge graph for different applications. INGRIDKG is publicly available under the Creative Commons Attribution 4.0<br> International license.</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>
RFF-SP scenarios with FaIR v2.1
<p>This dataset contains output from the 10,000 <a href="https://zenodo.org/record/6016583">Resources for the Future Socioeconomic Projections</a>, run with the <a href="https://github.com/OMS-NetZero/FAIR">FaIR reduced complexity climate model (v2.1.0)</a> using an IPCC Sixth Assessment Report consistent <a href="https://zenodo.org/record/7545157">calibration of 1,001 probabilistic ensemble members</a> (v1.0). A total of 10,010,000 climate projections are produced. Climate projections are produced for 1750 to 2301. FaIR is run using stochastically generated internal variability.</p> <p>The RFF-SPs contain CO2, CH4 and N2O emissions. The scenarios have been infilled using the <a href="https://github.com/GranthamImperial/silicone">Silicone</a> package (<a href="https://gmd.copernicus.org/articles/13/5259/2020/">Lamboll et al. 2020</a>) to decompose the total CO2 into fossil and land-use components, and to infill emissions of other greenhouse gases and short-lived climate forcers, following the same strategy used to infill scenarios in the IPCC Sixth Assessment Report Working Group 3 from a <a href="https://data.ene.iiasa.ac.at/ar6">large database of integrated assessment model pathways</a> (see <a href="https://gmd.copernicus.org/articles/15/9075/2022/">Kikstra et al. 2022</a>). To extend scenarios beyond 2100 - the time horizon of IAM pathways - the approach consistent with extending the SSPs for CMIP6 is used (<a href="https://gmd.copernicus.org/articles/13/3571/2020/">Meinshausen et al. 2020</a>, sec. 2.3).</p> <p>Code and instructions to reproduce the results is available at <a href="https://github.com/chrisroadmap/rff-fair2.1">https://github.com/chrisroadmap/rff-fair2.1</a>.</p> <p>Dataset contents:</p> <ul> <li><strong>output[0-9].zip</strong>: RFF-SP projections, in batches of 1,000 individual netCDF files (data_output/stochastic/run?????.nc) where ????? is in the range 00001 to 10000). Each file contains: <ul> <li>Global mean near-surface air temperature, rebased to 1850-1900 mean</li> <li>Ocean heat content change since 1750</li> <li>Effective radiative forcing with respect to pre-industrial (IPCC Sixth Assessment Report Working Group 1 convention of anthropogenic components using a 1750 baseline and natural components using a long pre-1750 mean)</li> <li>CO2 concentrations (ppm)</li> <li>CH4 concentrations (ppb)</li> <li>N2O concentrations (ppb)</li> </ul> </li> <li><strong>infilled_extended.zip</strong>: These are the infilled emissions used to run FaIR, containing 53 emissions species (data_processed/infilled_extended/emissions?????.csv). Time period covered is 2015 to 2300. Pre-2015 emissions can be obtained from the <a href="https://zenodo.org/record/4589756">Reduced Complexity Model Intercomparison Project</a>, using the CMIP6 historical emissions from 1750 to 2014. Bridging from 2015 to 2020 was performed using SSP2-4.5, as in the original RFF-SPs for CO2, CH4 and N2O.</li> <li><strong>ssp.zip</strong>: For completeness and comparison, the eight main SSP scenarios (ssp119, ssp126, ssp245, ssp370, ssp434, ssp460, ssp534-over, ssp585) are run with the same emissions scenarios and calibration dataset.</li> <li><strong>rcp.zip</strong>: For extra completeness, the four RCP scenarios (rcp26, rcp45, rcp60 and rcp85) have been provided. These are run with the same calibration dataset and <em><strong>therefore are not entirely consistent with IPCC AR6 assessed ranges of climate change</strong></em>. They are provided for comparison only.</li> </ul> <p>Notes about the data</p> <ul> <li>All variables in each file have dimension (timebounds, ensemble member) except for N2O which is unaffected by climate in this calibration of FaIR, having dimensions of (timebounds).</li> <li>Scenario 00001 runs from 1750 to 2301. Other scenarios run from 2020 to 2301, as the historical period is the same for every scenario. This approximately halves the size of the data output. RCPs run from 1765 to 2301.</li> <li>Note that all variables in the output files are on <em>timebounds</em>. 2020 corresponds to 2020-01-01, 2021 to 2021-01-01, and so on. To approximate midyear values, take the mean of consecutive years. Infilled emissions are on <em>timepoints</em>, i.e. are representative of midyear values. <a href="https://docs.fairmodel.net/en/latest/intro.html#time">See here for an explanation</a>.</li> </ul>
Characterization of the flow through innovative permeable fairings at the VKI WAABLIEF facility
<p>Aircraft-noise mitigation is a significant issue in the aviation industry due to the operation and expansion of airports and the growth of residential areas surrounding them. Take-off and landing are considered critical phases of a flight path regarding the noise impact. During a typical approach, the airframe noise has become a dominant noise source as the engine noise has been reduced after intensive studies performed for decades. Landing gears (LGs) are among the main contributors to the generation of this noise source, and one of the promising noise-reduction technologies (NRTs) that have been proposed consists of placing flow-permeable fairings in front of them. However, the development of these NRTs is limited due to the strong integration constraints imposed by the other disciplines and the lack of knowledge of the complex flow physics involved in the problem.</p> <p>The project INVENTOR, funded by the European Union in the framework of the H2020 MG-1-5-2019 call ”Advancements in aerodynamics and innovative propulsion systems for quieter and greener aircrafts” (Grant Agreement number: 860538), aims to fill this scientific gap and understand the noise generated by LGs integrated with porous fairings through numerical methods. Nevertheless, these flow-permeable materials must be modeled and validated through measurements to obtain high-accuracy numerical predictions. Therefore, the objective of the present submission is to provide an extensive experimental dataset of the characterization of the flow through fairings fitted with innovative porous materials. The measurement campaign has been carried out at the WAABLIEF facility of the von Karman Institute for Fluid Dynamics (VKI) and contains particle-image-velocimetry, hot-wire-anemometry, and total-pressure measurements at different flow speeds and with several materials. The document attached to the dataset includes all the information required to numerically reproduce the VKI experimental setup.</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>
Replication package of: 'Perceived Fairness and Consequences of Affirmative Action Policies'
<p>Replication package for: Schildberg-Hörisch, H., Schwarz, M., Trieu, C., and Willrodt, J. (2023). “Perceived Fairness and Consequences of Affirmative Action Policies”, Economic Journal, conditionally accepted.</p>
First trial fairly dataset v.1.1.
A dataset from the Fairly Toolset workshop
Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module
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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.