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1. Translation Tutorial: Toward a Theory of Race for Fairness in Machine Learning

   Moss, Emanuel (January 2019, Proceedings of the Conference on Fairness, Accountability, and Transparency)

   While computer scientists working on questions of fairness have diligently produced algorithmic approaches that seek to minimize disparate impacts across racial categories, the concept of race itself remains either unexamined, or constrained by definitions arising in legal and policy domains. While this may be appropriate for some applications, it is not altogether obvious that the FAT community benefits from refraining from developing a theory of race to guide its own practices. This tutorial will translate concepts from critical race theory and social scientific discourses into concepts legible to a community of machine learning practitioners through a dis- cussion of these theories and small-group activities that illustrate the salience of these theories for problems of fairness in machine learning. 

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2. Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group

   https://doi.org/10.12688/wellcomeopenres.16378.1  

   Austin, Claire C.; Bernier, Alexander; Bezuidenhout, Louise; Bicarregui, J uan; Biro, Timea; Cambon-Thomsen, Anne; Carroll, Stephanie Russo; Cournia, Zoe; Dabrowski, Piotr Wojciech; Dialio, Gayo; et al (November 2020, Wellcome open research)

   null (Ed.)

   The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community. 

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3. A Project-Based Collaboration between Software Engineering and Criminology Students: Building Applications to Understand Racial Injustice in the Criminal Justice System.

   Pulimood, S. Monisha; Leigey, Margaret; Bates, Diane C.; Pearson, Kim; Mishra, Supriya (January 2022, Proceedings of the 44th International Conference on Software Engineering: Joint Track on Software Engineering Education and Training (ICSE-JSEET '22))

   Undergraduate software engineering courses typically require students to work on team-based projects that reinforce disciplinary content and soft skills. At the same time, these students, particularly those at liberal arts institutions, are required to take courses that focus on civic issues, including on racial and ethnic inequality. Often, students perceive these courses to be outside their disciplinary areas, and may not comprehend how these topics are applicable to computer science and software engineering. This paper reports on the experience of Pulimood and Leigey as they and their students grappled with issues of racial injustice in the criminal justice system, and drew upon their own disciplinary backgrounds to apply computational thinking and software engineering principles to help the community better understand these issues and advocate for reform. The paper also describes the experience of teaching courses from different disciplines in a collaborative model, working closely with a local community partner to support its work on an identified social issue, and the learning outcomes, as well as the benefits and challenges of this approach. Recommendations and future directions are also discussed. 

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4. MaRDA FAIR materials microscopy and LIMS data working groups’ community recommendations

   https://doi.org/10.1557/s43577-025-00882-2  

   Taillon, Joshua_A; Barnard, Edward_S; Bartolo, Laura_M; Chan, Maria_K_Y; Stach, Eric_A; Taheri, Mitra_L; Brinson, L_Catherine; Voorhees, Peter_W (April 2025, MRS Bulletin)

   AbstractManaging, processing, and sharing research data and experimental context produced on modern scientific instrumentation all present challenges to the materials research community. To address these issues, two MaRDA Working Groups on FAIR Data in Materials Microscopy Metadata and Materials Laboratory Information Management Systems (LIMS) convened and generated recommended best practices regarding data handling in the materials research community. Overall, the Microscopy Metadata Group recommends (1) instruments should capture comprehensive metadata about operators, specimens/samples, instrument conditions, and data formation; and (2) microscopy data and metadata should use standardized vocabularies and community standard identifiers. The LIMS Group produced the following guides and recommendations: (1) a cost and benefit comparison when implementing LIMS; (2) summaries of prerequisite requirements, capabilities, and roles of LIMS stakeholders; and (3) a review of metadata schemas and information-storage best practices in LIMS. Together, the groups hope these recommendations will accelerate breakthrough scientific discoveries via FAIR data. Impact statementWith the deluge of data produced in today’s materials research laboratories, it is critical that researchers stay abreast of developments in modern research data management, particularly as it relates to the international effort to make data more FAIR – findable, accessible, interoperable, and reusable. Most crucially, being able to responsibly share research data is a foundational means to increase progress on the materials research problems of high importance to science and society. Operational data management and accessibility are pivotal in accelerating innovation in materials science and engineering and to address mounting challenges facing our world, but the materials research community generally lags behind its cognate disciplines in these areas. To address this issue, the Materials Research Coordination Network (MaRCN) convened two working groups comprised of experts from across the materials data landscape in order to make recommendations to the community related to improvements in materials microscopy metadata standards and the use of Laboratory Information Management Systems (LIMS) in materials research. This manuscript contains a set of recommendations from the working groups and reflects the culmination of their 18-month efforts, with the hope of promoting discussion and reflection within the broader materials research community in these areas. Graphical abstract 

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5. Toward a new conceptualisation of language revitalisation

   https://doi.org/10.1080/01434632.2020.1827645  

   Grenoble, Lenore A.; Whaley, Lindsay J. (October 2020, Journal of Multilingual and Multicultural Development)

   null (Ed.)

   This paper outlines a new model of language revitalisation that understands language to be a characteristic of a nexus of social activities rather than an independent object. Language use is one of an overall set of factors contributing to the wellbeing of a particular community. Our model treats language as one node (or a cluster of nodes) in a complex system of interacting behaviours. Changes to another node or in the language node(s) itself can impact overall social wellbeing, something often ignored by linguists (but not by other social scientists working in Indigenous communities). Disruption to an existing network occurs within a time frame; the longer the disruption, the more likely that the network redefines the group. Variables that define the language ecology operate on multiple levels. For the group and for individuals within the group, there can be considerable variation in usage and proficiency over time. Sustainability cannot be reduced to simple cause-and-effect relationships between sociocultural variables. The next phase of language revitalisation projects should be built around the concept of language activity as part of promoting community wellbeing. The use of complex networks that have been applied to human wellbeing in other contexts support our argument. 

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