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Dataset results
6 results for “Software fairness”
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>
Awareness of FAIR and FAIR4RS among international research software funders (Dataset)
<p><span>This research employed a mixed methods online survey to investigate research software funders’ perspectives. </span></p> <p><span>All participants gave informed consent at the start of the online survey. The University of Illinois Urbana-Champaign Institutional Review Board (no. 24374) reviewed the study and determined it exempt.</span></p> <p><span>Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds. The data were cleaned and prepared for analysis by removing any identifiable respondent details. </span></p> <h2><span>Survey design</span></h2> <p><span>The survey began by collecting profile information, including institutional affiliation and job title. The survey primarily gathered detailed information about initiatives, policies, or programs to support research software but also included a much smaller set of questions about additional topics, such as strategic funding priorities and awareness of key concepts. The data generated from this survey are too extensive to report in a single manuscript. Here, we focus on the results generated via the set of questions asking about FAIR and FAIR4RS, specifically, the following survey items: </span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Survey item</span></strong></p> </td> <td> <p><strong><span>Response options</span></strong></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR (findable, accessible, interoperable, and reusable) principles for data?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR principles for data?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR4RS principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> </tbody> </table> <p><span> </span></p> <p><span>In addition, an open-ended question asked for further detail about the respondents’ assessments of FAIR4RS’s relevance to their work.</span></p> <h2><span>Sampling</span></h2> <p><span>The survey targeted international research funders, including governmental and non-governmental (e.g., philanthropic) organizations. An initial contact list was created based on participation in the Research Software Association (ReSA) and known responsibilities for research software funding among the authors' networks. This list was refined by removing individuals who had moved to unrelated professional roles or were unavailable long-term due to personal issues.</span></p> <p><span>The final contact list comprised 71 people at 37 funding organizations. After excluding individuals when a member of their organization had already provided a complete response or when the person was no longer working on a relevant topic or was otherwise unavailable (total of n=30), 41 people remained. Of these, five did not complete the survey, while 36 individuals (representing 30 research funding organizations) did, yielding a response rate of 87.8% (and representing 81% of the original organizations). Fully completed survey responses were not required for inclusion in the sample, resulting in varied sample sizes across different survey questions.</span></p> <p><span>The respondents represented governmental (n=26), philanthropic (n=6), and corporate (n=1) research funders.</span></p> <p><span>Respondents’ job titles spanned the following categories: Senior Leadership and Executive (e.g., Vice President of Strategy); Program and Project Management (e.g., Senior Program Manager); Planning and Business Development; and Scientific, Technical, and IT roles (e.g., Scientific Information Lead).</span></p> <p><span>Most respondents, 72.7% (n=24), answered “Yes” to the question, “Has your organization established any policies, initiatives, or programs aimed at supporting research software?” Meanwhile, 18.2% (n=6) said “No,” and 9.1% (n=3) were “Unsure.”</span></p> <p><span>Regarding geographic distribution in the achieved sample, most survey respondents were from North America and Europe, with 15 and 12 participants, respectively. The sample also comprised 4 participants from South America, 3 from Oceania, and 1 from Asia, reflecting a global but uneven representation across continents. Some participating funders covered a broad spectrum of disciplines, while others focused on specific domains such as social sciences, health, environment, physical sciences, or humanities.</span></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>
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>
FAIR Data and Software Carpentries Lesson Mind Map
<p>The FAIR Data and Software Carpentries Concept Map is an instructional planning tool to help in the development of a FAIR Data and Software Carpentries lesson in Library Carpentry.</p>
Dataset: FAIR Biomedical Research Software (FAIR-BioRS) manuscript
<p>Data related to our FAIR-BioRS manuscript. More details are available at the associated GitHub repository: <a href="https://github.com/FAIR-BioRS/Data">https://github.com/FAIR-BioRS/Data</a>. </p>
ScienceDex guides
Understand access before you commit
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