D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions (original) (raw)
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Higher education institutions increasingly rely on machine learning models. However, a growing body of evidence shows that these algorithms may not serve underprivileged communities well and at times discriminate against them. This is all the more concerning in education as negative outcomes have long-term implications. We propose a systematic process for framing, detecting, documenting, and reporting unfairness risks. The systematic approach's outcomes are merged into a framework named FairEd, which would help decision-makers to understand unfairness risks along the environmental and analytical fairness dimension. The tool allows to decide (i) whether the dataset contains risks of unfairness; (ii) how the models could perform along many fairness dimensions; (iii) whether potentially unfair outcomes can be mitigated without degrading performance. The systematic approach is applied to a Chilean University case study, where a predicting student dropout model is aimed to build. First, we capture the nuances of the Chilean context where unfairness emerges along income lines and demographic groups. Second, we highlight the benefit of reporting unfairness risks along a diverse set of metrics to shed light on potential discrimination. Third, we find that measuring the cost of fairness is an important quantity to report on when doing the model selection.
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This document provides an analysis of practices to support FAIR data production within a broad selection of research disciplines and research data repositories. It aims to inform the priorities of stakeholders interested in embedding those practices in research communities. Those stakeholders include policy makers, data librarians and others providing data services to research communities, as well as champions of FAIR principles in those communities. It also identifies priority themes for initial work in FAIRsFAIR to support ESFRI cluster and EOSC projects in FAIR culture change. These include developing a self-assessment framework for research infrastructures and institutions on their progress to support FAIR enabling practices in the communities they serve. This will underpin further work to build capabilities, describe good practice and address the highly uneven awareness of FAIR principles and the lack of information on research community implementation.
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Building upon an analysis of the research data practice landscape in 2019, FAIRsFAIR has prepared a series of recommendations for practical actions to support the realisation of a FAIR ecosystem. These recommendations will be used to inform the development of guidance resources to support further adoption of FAIR data standards and practices by research communities. They are released as a living document that will be refined to reflect the forthcoming work in FAIRsFAIR, other projects funded under the INFRAEOSC-05-2018-2019 call, and other relevant initiatives.
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This report is a milestone of the FAIRsFAIR project. It includes two main results on FAIR assessment at the dataset level: The FAIRsFAIR Data Object Assessment Metrics (v0.3) specification contains 15 metrics proposed by FAIRsFAIR to evaluate the FAIRness of research data objects in Trustworthy Digital Repositories (TDRs). We improved the metrics based on a focus group's feedback and the RDA-endorsed FAIR data maturity model guidelines and specification. A total of 33 FAIR stakeholders, such as research communities, data service providers, standard bodies, and coordination fora participated in the focus group. A preprint of the journal article titled ‘From Conceptualization to Implementation: FAIR Assessment of Research Data Objects’, submitted to CODATA Data Science Journal Special collection on RDA. The article summarizes the metrics development, and its two applications: an awareness-raising self-assessment tool, and a tool for automated assessment of research data FAIRness. ...
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The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable (FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub (DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory framewo...
ENVRI-FAIR D6.2: FAIR training materials catalogue & integration with Common Training Platform
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The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with a wide range of public-private partnership projects, demonstrating and implementing improvements across all aspects of FAIR.