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339 results for “Fairness”
Piège astucieux pour détecter les pontes de la mouche du chou, Delia radicum, qui peut faire des ravages sur les navets, radis et choux. (Photos A. Freitag) in Société Vaudoise D'Entomologie (Sve)
Piège astucieux pour détecter les pontes de la mouche du chou, Delia radicum, qui peut faire des ravages sur les navets, radis et choux. (Photos A. Freitag)
[ELMI2023] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p>
Challenges and Limitations in the Design and Implementation of Fair and Equitable Machine Learning Algorithms in Healthcare
<p>We provide the programs in Python, a CSV file with references, images used in the article and a text corpus generated with Python.</p>
(Un)Fair Process Mining Event Logs
<p><strong>License: </strong>CC-BY-4.0</p> <p><strong>Event Logs:</strong></p> <p>We introduce a set of 12 distinct event logs, three for each of the four domains: hiring, healthcare, lending, and renting. These event logs have been carefully curated and simulated, each containing 10,000 cases, thereby providing an extensive resource for researchers focusing on fairness in process mining.</p> <p>In each of these domains, the three event logs represent varying degrees of discrimination, offering researchers an opportunity to explore the nuances and complexities that arise in diverse real-world scenarios. By presenting each log with a thorough description of the inherent processes and their respective attributes, we aim to provide a robust groundwork for understanding the potential sources of discrimination and addressing fairness in process mining.</p> <p>We have ensured that all the event logs are provided in the eXtensible Event Stream (XES) standard format. This adherence to a recognized standard not only ensures broad compatibility but also facilitates interoperability across a variety of process mining tools. By choosing this common format, we aim to encourage and simplify the utilization of these logs for researchers across different platforms.</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>
Video Tutorial for the FAIR Game
<p>A video tutorial about the FAIR Game</p>
Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"
<p>Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""</p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
FAIR assessment practices: Experiences from KonsortSWD and BERD@NFDI [Dataset 2 KonsortSWD]
<p>The dataset refers to the poster, which presents FAIR assessment experiences in the context of the two NFDI consortia KonsortSWD and BERD@NFDI, employing the established Research Data Alliance's FAIR Data Maturity Model (RDA-FDMM) and the F-UJI Tool, an automated solution. RDA-FDMM, a manual technique, is more comprehensive, while the automated F-UJI tool effectively detects areas of improvement in metadata presentation that automated means can address. Our experiences highlight the need to examine both machine-readable as well as non-machine-readable elements and acknowledge automated tools' limitations, while valuing their insights. As the research ecosystem advances, metadata representation should be made increasingly machine-readable. We recommend a "FAIR by design" approach from the beginning to ensure alignment with FAIR principles in project outcomes. Continuous assessments during a project’s lifetime promote ongoing research data infrastructure improvements within the NFDI consortia context, contributing to NFDI infrastructure innovation and optimization.</p>
FAIR assessment practices: Experiences from KonsortSWD and BERD@NFDI [Dataset 1 BERD@NFDI]
<p>This dataset evidences the BERD@NFDI results of a poster, which is documented as a related work. The poster presents FAIR assessment experiences in the context of the two NFDI consortia KonsortSWD and BERD@NFDI, employing the established Research Data Alliance's FAIR Data Maturity Model (RDA-FDMM) and the F-UJI Tool, an automated solution. RDA-FDMM, a manual technique, is more comprehensive, while the automated F-UJI tool effectively detects areas of improvement in metadata presentation that automated means can address. Our experiences highlight the need to examine both machine-readable as well as non-machine-readable elements and acknowledge automated tools' limitations, while valuing their insights. As the research ecosystem advances, metadata representation should be made increasingly machine-readable. We recommend a "FAIR by design" approach from the beginning to ensure alignment with FAIR principles in project outcomes. Continuous assessments during a project's lifetime promote ongoing research data infrastructure improvements within the NFDI consortia context, contributing to NFDI infrastructure innovation and optimization.</p>
First trial fairly dataset v.1.1.
A dataset from the Fairly Toolset workshop
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Persistence and interoperability in FAIR research data management - 1st FAIRsFAIR webinar
<p><strong>Webinar page: </strong>https://zenodo.org/record/3726149#.XnpRJXJ7k1k</p> <p>The main principles of FAIR data (findable, accessible, interoperable and reusable) have received wide acceptance in scientific data management circles. The work of further defining these principles and applying them in day-to-day knowledge sharing is ongoing.</p> <p>The FAIRsFAIR working group "<a href="https://www.fairsfair.eu/fair-practices-semantics-interoperability-and-services">FAIR practices: semantics, interoperability and services</a>" recently published the <a href="http://zenodo.org/record/3557381#.XiVxKSN7nIV">first iteration</a> of three annual reports on the state of FAIR in European scientific data. Based on studies of public information, especially EOSC infrastructure efforts, and on limited surveying and interviews, the report reviews and documents commonalities between infrastructures and obstacles to semantic interoperability - that is the use of metadata and persistent identifiers to enhance dissemination across infrastructures.</p>
Simulation Results Regarding Fairness, Dissatisfaction, Relative Time Loss and Inefficiency on 2+1 Roadways.
<p>Simulation results obtained by the Cooperative Lane Management and Traffic flow Optimisation (CoLMTO) platform (see https://doi.org/10.5281/zenodo.801531) regarding fairness, dissatisfaction, relative time loss and inefficiency on 2+1 roadways.</p>
Lego Ship + Maker's Faire San Jose California
Legos allow for many possibilities with many possible arrangements. These were on show at the Maker's Fare in San Jose California. Created in RealityCapture by Capturing Reality from 92 images in 00h:45m:29s. Source: Objaverse 1.0 / Sketchfab
Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module
<p><strong>Objective</strong> <br>To assess the use of Health Level Seven Fast Healthcare Interoperability Resources (FHIR<sup>®</sup>) for implementing the Findable, Accessible, Interoperable, and Reusable guiding principles for scientific data (FAIR). Additionally, present a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. <br><br><strong>Material and Methods</strong> <br>A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department dataset (MIMIC-ED), a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. <br><br><strong>Results</strong> <br>The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. <br><br><strong>Discussion</strong> <br>Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. <br><br><strong>Conclusion</strong> <br>To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures. Keywords: FAIR Guiding Principles, HL7 FHIR, Reusable Data, MIMIC-IV</p>
Wedding Fair, Zurich, 8 January 2023 (Wedding fair_02_CH_080123)
<p>Wedding fair in Zurich</p> <p>8 January 2022</p> <p>Videos and photos</p>
Aligning Data Management Plans with Community Standards using FAIR Implementation Profiles
<p>Here you can find the files corresponding to our submission titled 'Aligning DMPs with Community Standards using FIPs'.</p> <p>- VU DMP template and the mapping is included in the folder /VU-DMP-template-and-mapping</p> <p>- All the FAIR Implementation Profiles are included in the folder /FIPs.</p> <p>- The knowledge model we created for the project, and a small demo of the interface are in the folder /KM-and-demo.</p> <p>- The folder /user-study consists of the following:</p> <p> a) The mock DMPs we provided to the participants of this research are in /mock_DMPs.</p> <p> b) We downloaded the resulting DMPs after participants completed their DMPs, they are in the folder /resulting_DMPs.</p> <p> c) Survey results can be found in the folder /survey_results.</p> <p> d) Some Python scripts were used for the analysis of the survey results. They are in the folder /Python_script_for_analysis.</p> <p><br>The project is open source under the license CC-BY 4.0.</p> <p>Contact: Shuai Wang (shuai.wang@vu.nl)</p> <p> </p>
FAIR-IMPACT Synchronisation Force 2023 edition: preliminary data gathered from projects and intiatives
<p>Building on the successful Synchronisation Force <a href="https://fair-impact.eu/events/synchronisation-force-events/synchronisation-force-1st-workshop-november-2022">workshop 2022</a>, the 2023 edition of the Synchronisation Force workshop aimed to discuss common challenges and priorities related to turning the FAIR principles into practice. The workshop took place as a series of seven virtual sessions between 2 November 2023 and 8 February 2024, with the five core sessions between 27 November and 7 December 2023.</p> <p>Invited to this workshop were selected, key FAIR representatives of projects and initiatives in the EOSC framework.</p> <p>In this record we saved the preliminary information gathered from the workshop participants collected via a survey. The information were used as preliminary discussion points for each of the five sessions organised, and were considered useful top ensure an interactive discussion.</p>
Research Space and DMPTool - Streamlining the research lifecycle and enhancing FAIR principles with a series of interoperable tools
<p>A video demonstration of the integration between the Electronic Lab Notebook, RSpace, and the DMPTool. Presented at the FORCE11 annual conference on December 7, 2021.</p>
Submission and acceptance data for journals involved in the Taylor & Francis FAIR data pilot
<p>Submission, acceptance and peer review data for journals involved in the Taylor & Francis FAIR data pilot. Data is anonymised. Includes journal article submissions, acceptances and peer review for the years 2018, 2019 and 2020.</p>
ScienceDex guides
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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.