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339 results for “Fairness”
Fair-weather friends: Priority determines disease outcomes in an agonistic multi-pathogen crop pathosystem
Open the record for dataset details and reuse information.
The FAIR is in Town: figshare, The Turing Way, and Open Science Quest at the OSFAIR2019
<p><strong>Episode Summary:</strong> </p> <p>In this episode we are highlighting some of the tools on show at the Open Science FAIR 2019 in Porto, Portugal. </p> <p><strong>Links: </strong></p> <ul> <li><a href="https://figshare.com/">figshare</a> <ul> <li><a href="https://figshare.com/authors/Alan_Hyndman/580840">Alan Hyndman</a></li> </ul> </li> <li><a href="https://the-turing-way.netlify.com/introduction/introduction">The Turing Way Book</a></li> <li><a href="https://github.com/alan-turing-institute/the-turing-way">The Turing Way GitHub </a> <ul> <li><a href="https://www.research.manchester.ac.uk/portal/rachael.ainsworth.html">Rachael Ainsworth</a></li> </ul> </li> <li><a href="https://zenodo.org/record/2646121#.XYJVASgzZPY">Open Science Quest</a> <ul> <li><a href="https://twitter.com/jonatortue">Jonathan England</a></li> </ul> </li> </ul> <p><strong>Quotes:</strong></p> <p>'So two reasons: because you have to and just because it is for the good of society'</p> <p>'Make reproducible research too easy not to do'</p> <p>'I wanted to change the way people became aware of Open Science best practices'</p>
Open Data: FAIR, foul, and meta
<p><strong>Episode Summary: </strong></p> <p>In this episode we are discussing what metadata is, the obstacles to sharing research data, and how Open Science recommendations are being transformed into actions. Our interview guest will be Professor Eva Mendez from Universidad Carlos III in Madrid who is the Chair of the European Open Science Policy Platform.</p> <p><strong>Links:</strong></p> <p><a href="https://www.orion-openscience.eu/publications/training-materials/201808/factsheets">Factsheets (Open Data and Data Management)</a></p> <p><a href="https://uc3m.academia.edu/EvaM%C3%A9ndez/CurriculumVitae">Professor Eva Mendez</a></p> <p><a href="https://ec.europa.eu/research/openscience/index.cfm?pg=open-science-policy-platform">Open Science Policy Platform</a></p> <p><strong>Quotes:</strong></p> <p>'Open doesn't necessarily mean FAIR'</p>
OpenPREDICT: Open and FAIR implementation of the PREDICT method for drug repurposing
<p>This dataset is used for Open and FAIR implementation of the PREDICT method. It is described in the paper titled "PREDICT: a method for inferring novel drug indications with application to personalized medicine.", Gottlieb A, Stein GY, Ruppin E, Sharan R. Mol Syst Biol. 2011;7:496. Published 2011 Jun 7. doi:10.1038/msb.2011.26</p>
Templates for FAIRness evaluation criteria - RDA-SHARC ig
<p>The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This document is a reusable template that aims to support FAIRification assessment with human understandable criteria. The level of compliance for each criterion can be used to prioritise the most appropriate and sufficient training, support and actions.</p>
Licence to share: how Creative Commons brought clarity to the digital rights and enabled fair content reuse across the web.
<p>CC 0, CC BY or CC BY-NC-SA? We talked to Cable Green, the interim CEO and Director of Open Education at Creative Commons. Cable explained the rationale behind the licenses on digital work and provided us with a practical guide to how to use these. Further on, we ventured into the world of Open Education and how it is empowered by Creative Commons licensing. And yes - we did also talk about Covid-19 and how it is changing the way we think about open access and collaboration.</p> <p>Episode links:</p> <p><a href="https://creativecommons.org/">https://creativecommons.org/</a></p>
fair-data/fairdatapoint: 0.7.2
Changed <ul> <li>Changed the url of Swagger UI from <code>/ui</code> to the base url <code>/</code></li> </ul>
Data from: The ontogeny of fairness in seven societies
A sense of fairness plays a critical role in supporting human cooperation. Adult norms of fair resource sharing vary widely across societies, suggesting that culture shapes the acquisition of fairness behaviour during childhood. Here we examine how fairness behaviour develops in children from seven diverse societies, testing children from 4 to 15 years of age (n = 866 pairs) in a standardized resource decision task. We measured two key aspects of fairness decisions: disadvantageous inequity aversion (peer receives more than self) and advantageous inequity aversion (self receives more than a peer). We show that disadvantageous inequity aversion emerged across all populations by middle childhood. By contrast, advantageous inequity aversion was more variable, emerging in three populations and only later in development. We discuss these findings in relation to questions about the universality and cultural specificity of human fairness.
Simulated (fair and unfair) coin tosses
<p>In this student project it was to be experimentally figured out how<br> fair a fair coin behaves.</p> <p>To achieve this, fair coin tosses with a different number of maximum coin tosses<br> were simulated and it was analyzed how often chains of given lengths of<br> ones/zeros (assumed to be heads/tails) appear and how much more often<br> zeros appear per experiment compared to ones.</p> <p>This maximum number was<br> - 10 tosses per experiment<br> - 100 tosses per experiment<br> - 1.000 tosses per experiment<br> - 10.000 tosses per experiment<br> - 100.000 tosses per experiment<br> - 1.000.000 tosses per experiment<br> - 10.000.000 tosses per experiment<br> - 100.000.000 tosses per experiment<br> - 1.000.000.000 tosses per experiment<br> - 10.000.000.000 tosses per experiment</p> <p>Except for the last three, all experiments were repeated 1.000 times.<br> The 100.000.000 tosses per experiment was repeated 100 times, the 1.000.000.000<br> tosses per experiment was repeated 10 times and the 10.000.000.000 tosses per<br> experiment was NOT repeated.</p> <p>For these one/zero chain distributions the fitting parameters were determined,<br> assuming a geometrical distribution.</p> <p><br> The experiments with up to 100.000.000 tosses per experiment were repeated<br> with an UNFAIR coin, using a Maxwell Boltzmann statistic for the distribution<br> of coin tosses.</p> <p>--------------------------------------------------</p> <p>In the data-folder these experiments are divided into the normal distributed<br> and Maxwell-Boltzmann distributed coin toss experiments.</p> <p>These two folders contain the folders with the data for the different<br> maximum number of coin tosses per experiment.</p> <p>Each of these folders contains two subfolders which contain the distributions<br> of the respective chains of ones or zeros (heads or tails).</p> <p>In this folder (NOT the subfolders!) in addition can be found:<br> - a file with the fitting parameters for each heads/tails-chain-distribution,<br> - a file with an analysis of these fitting parameters,<br> - a file in which it is analyzed how much more often zero was the result of<br> the toss (for one experiment)<br> - a file with an analysis of that,<br> - and two files in which the chain-distributions are summed up.</p> <p>The result for each coin toss in each experiment were NOT saved.</p> <p><br> For the Maxwell Boltzmann distributed coin tosses also the distribution<br> itself was saved for each experiment and can be found in the respective<br> subfolder.</p> <p>--------------------------------------------------</p> <p>In the scripts folder, all the programs can be found that were created<br> to create and analyze all the data.</p> <p>All programs are published as free software under the<br> GNU General Public License version 3 as published by the<br> Free Software Foundation</p> <p>--------------------------------------------------</p> <p>ATTENTION:<br> The .tar.gz-file contains more than 35000 files.<br> It may take a while to extract all the files.</p>
Modern Marvels of the 1939/1940 New York World's Fair
<p>The 1939/1940 New York World's Fair's impressive display of modern marvels suggests that historians and economists should rethink their standard explanations for the extended economic conditions of the Great Depression.</p>
FAIR Implementation Profiles for Social Science
<p>The repository consists of several folders.</p> <p><code>/FICs</code> consists of files for FAIR Implementation Communities. <code>/FIPs</code> consists of files for FAIR Implementation Profiles of these communities. <code>/FIP_reports</code> consists of reports for FAIR Implementation Profiles of these communities.</p> <p>In the file <code>Comparison_all_FIPs.xlsx</code> you can find detailed comparison of the FIPs. The file <code>Convergece_matrix.xlsx</code> gives you details of the convergence analysis.</p> <p>Please contact the authors for the latest version of the FIPs since FIPs are living documents.</p> <p>Please report errors to Shuai Wang at <a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>. Thank you!</p>
ITINERIS FAIR convergence matrix review
<p>The Italian Integrated Environmental Research Infrastructures System (ITINERIS) Project commenced in November 2022, aiming to establish the Italian Hub for Research Infrastructures (RIs) in the environmental scientific domain. Its mission involves providing access to data and services, contributing to addressing current and anticipated environmental challenges. ITINERIS oversees a network of national nodes encompassing 22 RIs—18 from the environmental domain, 2 from agri-food with strong ties to the environment, and 2 from the Physical Sciences and Engineering domain, offering support services for the marine domain. The participating RIs include ESFRI Landmarks (Italian nodes) like ACTRIS, EMSO, Euro-Argo, ICOS, and LifeWatch in the environmental domain, as well as ANAEE in the Health&Food domain. ESFRI projects (Italian nodes) such as DANUBIUS-RI, DiSSCo, and e-LTER operate in the environmental domain, while EMPHASIS and EUIBISBA function in the Health&Food domain. Additionally, EU RIs like ECORD, EUFAR, Eurofleets, JERICO, and SIOS operate in the environmental domain, and national RIs like ATLAS, CeTRA, Laura Bassi, and SMINO function in the environmental domain, with Geosciences and LNS serving as support services for the Physical Sciences and Engineering domain, especially for the marine domain. Deliverable 2.8 aims to provide a concise review of the existing FAIR Implementation Profiles (FIPs) relevant to the ITINERIS project using the FIP Wizard. The matrix was constructed by downloading all FIPs from the GitHub SPARQL query (https://github.com/peta-pico/dsw-nanopub-api/blob/main/tables/new_matrix.csv). The table was refined to include only RIs participating in ITINERIS and SeaDataNet FIPs (SeaDataNet-CDI and SeaDataNet-Sextant). SeaDataNet was included in the analysis due to its relevance to marine subdomain RIs within ITINERIS. The most updated version of each FIP was considered, and only FERs declared as "available" and "in use" by each community were selected. The resulting matrix illustrates the number of common FERs between each pair of RIs, serving as an indicator of convergence. In this analysis, SeaDataNet FIPs and the "LifeWatch marine" community were excluded. RIs represented by more than one community were merged in the cross table to acknowledge the collaborative nature of contributions.</p>
[Supplementary material] AI-Driven Fairness Testing of Large Language Models: A Preliminary Study
<div>This is the supplementary material of the paper entitled <em>AI-Driven Fairness Testing of Large Language Models: A Preliminary Study</em>.</div> <div> </div> <div>The material is organized into two main folders:</div> <div> <ul> <li><strong>evaluation_data/</strong>: This folder contains the results of the fairness evaluations performed on three different language models: Gemma, Llama3, and Mistral. Each subfolder corresponds to a specific model and includes detailed <em>.csv</em> files documenting evaluation results across the 9 metamorphic relations (MRs) evaluated. Each <em>.csv</em> file contains the following columns: <ul> <li><em>test_id</em>: ID of the test.</li> <li><em>role</em>: Role, if applicable, involved in the prompts associated with the test.</li> <li><em>bias_type</em>: Type of bias being studied with the test.</li> <li><em>prompt_1</em>: Source test case executed on the model under test.</li> <li><em>response_1</em>: Response of the model to the source test case.</li> <li><em>prompt_2</em>: Follow-up test case executed on the model under test.</li> <li><em>response_2</em>: Response of the model to the follow-up test case.</li> <li><em>verdict</em>: Classification made by the judge model, which can take the following values: <ul> <li>'BIASED': If bias is detected.</li> <li>'UNBIASED': If no bias is detected.</li> <li>'INVALID': If the model under test failed to respond to either of the test cases (source or follow-up).</li> </ul> </li> <li><em>severity</em>: Classification of the bias severity made by the judge model, which can take the following values: <ul> <li>'LOW', 'MODERATE', or 'HIGH' (if the test is biased).</li> <li>Assigns 'N/A' if the test is not biased.</li> </ul> </li> <li><em>generation_explanation</em>: Explanation provided by the model generator, detailing how the base prompts were constructed.</li> <li><em>evaluation_explanation</em>: Explanation provided by the judge model, detailing the rationale behind the evaluation and justifying the assigned <em>verdict </em>for the test.</li> <li><em>manual_revision</em>: This field was completed based on the consensus of two authors to validate the <em>verdict</em>. It can take one of the following values: <ul> <li>'TP': The test was classified as biased, and it is indeed biased.</li> <li>'FP': The test was classified as biased, but it is not biased. </li> <li>'TN': The test was classified as unbiased, and it is indeed unbiased.</li> <li>'FN': The test was classified as unbiased, but it is actually biased.</li> <li>'INVALID': The model under test failed to respond to at least one of the prompts.</li> </ul> </li> </ul> </li> <li><strong>prompts/</strong>: This folder provides example prompts used during the generation and evaluation: <ul> <li><em>generation.txt</em>: Includes the prompt tied to the relation <em>MR1: Comparison - Single attribute</em>.</li> <li><em>evaluation.txt</em>: Includes the prompt used to evaluate <em>comparison</em> MRs, specifically for those involving demographic attributes.</li> </ul> </li> </ul> </div>
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear. in Tragulidae
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear.
Supplementary Figure 1: The road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands
<p><em>Supplementary Figure 1: a flow chart conceptualizing the gap analysis. A generic NGS process diagram was created, based on a commonly used care workflow (Step 1). Next, a questionnaire about the inventory of (meta)data standards and retrieval of gaps was drafted (Step 2), which together with the process diagram was used as a basis for the subsequent interviews (Step 3). In parallel with the first three steps, a short literature review was performed (Step 4). The interviews were processed and current gaps were identified, anonymized and classified (Step 5). Finally, the results are shared with the community through presentations, publications and suggestions for next steps for addressing the identified gaps.</em></p>
BigWig files for FAIRE-seq data
<p><span>Butterfly wings exhibit a diversity of patterns which can vary between forewings and hindwings and spatially across the same wing. Regulation of morphological variation involves changes in how genes are expressed across different spatial scales which is driven by chromatin dynamics during development. How patterns of chromatin dynamics correspond to morphological variation remains unclear. Here we compared the chromatin landscape between forewings and hindwings and also across the proximal and distal regions of the hindwings in two butterfly species, <i>Bicyclus anynana </i>and<i> Danaus plexippus</i>. We found that the chromatin profile varied significantly between the different wing regions, however, there was no clear correspondence between the chromatin profile and the wing patterns. In some cases, wing regions with different phenotypes shared the same chromatin profile whereas those with a similar phenotype had a different profile. We also found that in the forewing, open chromatin regions were AT rich whereas those in the hindwing were GC rich. GC content also varied between the proximal and hindwing regions. These differences in GC content were also reflected in the transcription factor binding motifs that were differentially enriched between the wings and wing regions. Our results suggest that the chromatin landscape varies between different wing tissues and even spatially within the same tissue with no clear correspondence to phenotype. These findings may be explained by differences in how Hox genes cooperate with other transcription factors that show preferences for specific GC content and function either as activators or repressors in different wings or wing regions.</span></p>
Distribution. Fairly widespread in Zimbabwe, extending marginally into S Zambia, E Malawi, and W Mozambique, with isolated populations occurring in Eritrea, Ethiopia, South Sudan, E Uganda, W & SE Kenya, NE Tanzania, and N Zambia. in Molossidae
Distribution. Fairly widespread in Zimbabwe, extending marginally into S Zambia, E Malawi, and W Mozambique, with isolated populations occurring in Eritrea, Ethiopia, South Sudan, E Uganda, W & SE Kenya, NE Tanzania, and N Zambia.
Raw Data for Fairly flexible: Brown-tufted capuchins and a squirrel monkey adjust their motor responses in a foraging task
<p>Raw Data for Manuscript entitled: </p> <p>Fairly flexible: Brown-tufted capuchins and a squirrel monkey adjust their motor responses in a foraging task</p>
Distribution. NE & E Madagascar from the Mananara River in the N to the Mangoro and Onive rivers in the S; although the precise distributional limits are unknown, the Diademed Sifaka is thought to be the most widely distributed of the sifakas, occurring throughout Madagascar's E rainforests. Historically, its distribution extended farther N to just S of the Antainambalana River, but it has not been found in recent years during fairly extensive studies of that region. in Indriidae
Distribution. NE & E Madagascar from the Mananara River in the N to the Mangoro and Onive rivers in the S; although the precise distributional limits are unknown, the Diademed Sifaka is thought to be the most widely distributed of the sifakas, occurring throughout Madagascar's E rainforests. Historically, its distribution extended farther N to just S of the Antainambalana River, but it has not been found in recent years during fairly extensive studies of that region.
Fair For Fusion Demonstrator II
<p>Demonstration video of the Fair for Fusion Demonstrator II.</p>
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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.