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Calls to support co-designed and usable science to inform management of natural resources are growing. Making the shift will require diverse collaborations between those who hold, share, and use knowledge., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ripple et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study investigates how scientific knowledge is politicized on Twitter. Identifying discursive modes of online politicization and analyzing how they relate to different online issue publics allows us to weigh in on the scholarly debate about when the politicization of science on social media becomes problematic in a democratic context. This is a complicated question in "knowledge societies" where increasing science-politics confluence means that some degree of politicization is necessary for science-informed policymaking and (online) public debate. We look at how pandemic science was politicized through becoming discursively linked with an already highly politicized science issue on Twitter, namely, climate change. Our mixed-methods analysis demonstrates that some politicizations of science seek to contest science-informed policy while others are better characterized as ideological science rejection. We argue for the advantages of this approach of identifying science rejection over approaches that seek to distinguish information from dis-/misinformation.

The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place-notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Addressing the climate crisis requires radical and urgent action at all levels of society. Universities are ideally positioned to lead such action but are largely failing to do so. At the same time, many academic scientists find their work impeded by bureaucracy, excessive competitiveness, and a loss of academic freedom. Here, drawing on the framework of "Doughnut Economics," developed by Kate Raworth, we suggest seven new principles for rethinking the norms of scientific practice. Based on these, we propose a call to action, and encourage academics to take concrete steps towards the creation of a flourishing scientific enterprise that is fit for the challenges of the 21 st century., Competing Interests: AU, CK No competing interests declared, (© 2023, Urai and Kelly.)

Using visualization techniques based on social networks, this study aims to analyze stem cell research in Spain, as reflected in the Science Citation Index (SCI) database between 1997 and 2010 , divided into three sub-periods. The selected unit of analysis was the KeyWords Plus descriptors (KW+), the unit of measurement was their co-occurrence , and the Pajek and VOSviewer tools were used to generate and display the social networks. The results show two complementary images of research: the static structure, distinguishing between clinical and basic research, and the evolutionary dynamic, analysing both the most established and the emerging lines. The main contribution of this work is to present a methodology for the visualization and detection of the main research lines over time, demonstrating its applicability and its predictability in scientific and geographic domains. [ABSTRACT FROM AUTHOR]