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Socio-Environmental Knowledge Commons May 19 2023 presentation to NSF-FAIROS RCN.&nbsp

December 5, 2022 presentation on state of "Ethics and Human Rights Frameworks for AI: UNESCO,USA, Rome Call, Israel" delivered by Natalie Meyers, Professor of the Practice University of Notre Dame, USA to the EOSC-Future / RDA Artificial Intelligence & Data Visitation Working Group (AIDV-WG)., {"references":["Tech Ethics Lab University of Notre Dame, \"Looking Back at the Rome Call for AI Ethics: A Global University Summit,\" Tech Ethics Lab, Nov. 10, 2022. [Online]. Available: https://techethicslab.nd.edu/news/looking-back-at-the-rome-call-for-ai-ethics-a-global-university-summit/. [Accessed: Jan. 23, 2023]","Ministry of Innovation, Science and Technology Israel, \"Regulation and Ethics for the Field of Artificial Intelligence in Israel [Draft Policy],\" GOV.IL, Oct. 30, 2022. [Online]. Available: https://www.gov.il/he/departments/news/most-news20223110. [Accessed: Jan. 23, 2023]","White House Office of Science and Technology Policy, \"Blueprint for an AI Bill of Rights | OSTP,\" The White House, Oct. 04, 2022. [Online]. Available: https://www.whitehouse.gov/ostp/ai-bill-of-rights/. [Accessed: Jan. 23, 2023]","UNESCO, \"CAF and UNESCO will create a council to review ethical criteria for artificial intelligence in Latin America and the Caribbean | UNESCO,\" Jun. 23, 2022. [Online]. Available: https://www.unesco.org/en/articles/caf-and-unesco-will-create-council-review-ethical-criteria-artificial-intelligence-latin-america-and. [Accessed: Jan. 23, 2023]","UNESCO World Committee on on the Ethics of Scientific Knowledge and Technology, \"Recommendation on the Ethics of Artificial Intelligence,\" UNESCO, 0000380455, 2021 [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000380455.locale=en. [Accessed: Jan. 23, 2023]","renAIssance Foundation, \"The call | Rome Call,\" Rome Call | What is the Matter with AI Ethics?, Feb. 28, 2020. [Online]. Available: https://www.romecall.org/the-call/. [Accessed: Jan. 23, 2023]"]}

Effective communication is crucial for the success of academic projects, especially within multidisciplinary teams where researchers come from different backgrounds not only on personal and/or cultural level but also from different disciplines. This can lead to misunderstandings which might not be even obvious in meetings and project plans if the same terms might be used for different concepts. Team members implicitly assume that all parties work with the same definition of terms. The project VisDict addresses the communication between workflow providers and domain researchers via the creation of a visual dictionary in a science gateway so that differences in the perception of terms are easily recognized and can be timely resolved. Dictionaries are used as translation tools between natural languages – the approach for translating from computational science to research domains such as physics and biology is novel. In this paper, we go into detail for our approach to build a dictionary in a science gateway, the lessons learned from carefully curating the first entries, and plans for automating its extension to a large set of relevant terms including their illustrations.

Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the FAIR (Findable, Accessible, Interoperable, Reusable) principles and are a driving force for improving the situation for sharing data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. Some may be creating their own gateways outside the community; others may wish to use computational and data infrastructures, but may perceive a lack of support or opportunities. Hypotheses about the reasons that humanities are not well represented as gateways builders and users include lack of funding and support by computer centers. This study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of two interviews as proof of concept for the study. We plan to follow with 12-15 additional interviews for the larger study.

This poster outlines how creators of scientific data can and should work with librarians to make their data FAIR (findable, accessible, interoperable, reusable). The starting point is the observation that the originators of the FAIR concept point out that a 'little FAIR' may go a long way towards achieving FAIR aspirations. Second, the authors point out that value-added FAIR efforts should align with where the data is in the data life cycle. Fully FAIR data is not a binary choice. Third, data has an ongoing presence or life story, that suggests ongoing curation and potential additional FAIR data activities are possible increasing data FAIR-ness. Finally the authors argue that librarians have knowledge, skills, and resources that researchers can leverage in working towards FAIR data.

This report summarises the work of the FAIR for Research Software Subgroup 4, which was tasked with reviewing the Towards FAIR Principles for Research Software paper and identifying new research that had been published after that paper was written. A survey was circulated to the subgroup, as well as further afield, that asked some general questions around FAIR for research software along with specific questions about the suitability of the principles presented in the Towards FAIR Principles for Research Software. This report summarises and analyses the results of the survey., {"references":["FAIR4RS WG. (2021). FAIR4RS Subgroup 4 - reading list of new research (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4555865"]}

Presentations given in the session on Establishing a Community of Practice (CoP) for Infectious Diseases Data at theResearch Data Alliance Virtual Plenary 17 (Edinburgh, Scotland, 19-23 April 2021).&nbsp

This kinship diagram is a lighthearted visualization inspired by the CJAL Special Issue: Refusing Crisis Narratives . We hope it spurs discussion, maybe laughter, and another way of positioning the ways librarians can say and hear "NO" as a complete sentence. We know this genealogy is, by virtue of its motivation, bound to be: arbitrary, incomplete, inaccurate, and always a work in progress, open to argument, revision, and criticism. We are okay with that. This version is static. To view the most recent version, please visit:bit.ly/refusal-kinship-diagram

Die FAIR-Prinzipien werden seit ihrer Veröffentlichung im Jahr 2016 von einem breiten Spektrum an Stakeholdern zitiert, übernommen und angewendet. Ziel der 15 FAIR-Leitprinzipien ist es nicht, konkrete technische Implementierungen vorzuschreiben, sondern Leitlinien zur Verbesserung der Auffindbarkeit, Zugänglichkeit, Interoperabilität und Nachnutzbarkeit digitaler Ressourcen an die Hand zu geben. Dies hat vermutlich zur breiten Anwendung der FAIR-Prinzipien beigetragen, da einzelne Stakeholder-Communitys ihre eigenen Lösungen umsetzen können. Allerdings gingen damit auch uneinheitliche Interpretationen einher, die das Risiko inkompatibler Umsetzungen zur Folge haben. Wenngleich die FAIR-Prinzipien auf einer übergeordneten Ebene formuliert sind und sich in vielfältiger Weise interpretieren und umsetzen lassen, bedarf es für eine wirkliche Interoperabilität somit einer stärkeren Konvergenz weithin zugänglicher und (wieder)verwendbarer Implementierungsentscheidungen. Mit den Überlegungen zur Umsetzung der FAIR-Prinzipien unterstützen wir eine schnellere globale Mitwirkung und Konvergenz hin zu zugänglichen, soliden, auf breiter Ebene angewandten und einheitlichen FAIR-Implementierungen. Jede selbst identifizierte Stakeholder-Community kann sich entweder dafür entscheiden, Lösungen aus vorhandenen Implementierungen wiederzuverwenden, oder bei Bedarf die Herausforderung annehmen, die benötigte Lösung, die in Zukunft idealerweise wiederum von anderen Communitys angewandt werden kann, selbst zu entwickeln. Nachfolgend wollen wir für jedes der FAIR-Prinzipien Interpretationen und Überlegungen zu seiner Implementierung (Entscheidungen und Herausforderungen) erörtern. Die vorliegende deutsche Übersetzung des Dokuments entstand im Rahmen des Verbundprojektes EcoDM mit der Förderung des Bundesministeriums für Bildung und Forschung (BMBF) unter dem Förderkennzeichen 16DWWQP.

This meeting focuses on researcher’s tools for knowledge preservation, filling a gap in the RDA discussion thus far, which is how to make it easy for researchers to preserve their research results in a way that is beneficial to sharing with others and for further research. This meeting represents the fifth in a series, with a narrowing of scope of the Preservation Tools Techniques and PoliciesIG to focus on tools for researchers.

FAIRsFAIR invited providers of data services – and all other interested stakeholders - across the full range of scientific disciplines to participate in a workshop to validate and further develop the FAIR assessment framework for data services. The proposed framework is described in the recently published FAIRsFAIRReport on Basic Framework on FAIRness of Servicesand was introduced to the research community at adedicated webinar. This is the second workshop, after a first workshop in February. The purpose of the workshop, which will be hosted by the authors of the report, is to discuss the six key aspects of the framework with potential users to identify gaps and formulate improvements. Feedback received will be incorporated into a further iteration of the report to be published later in 2021. Why a FAIR Assessment Framework for Data Services? The FAIR assessment framework for data services aims to assist service providers to ensure their data services are ‘FAIR enabling’ and optimally support the FAIRification process. This is important because realizing the full potential of a FAIR ecosystem - in which research outputs can be easily shared and optimally reused - requires looking beyond the FAIRification of data and other digital objects to also consider the infrastructure and services that act on those objects. During the workshop the 3rd iteration of the Assessment Framework was reviewed, it is available here: https://doi.org/10.5281/zenodo.4771937. The recording of the workshop is available onYouTube.