Data Infrastructure and Local Stakeholder Engagement with Biodiversity Conservation Research

Biodiversity research that informs conservation action is increasingly data intensive. Cutting-edge projects at large institutions use massive aggregated datasets to build dynamic models and conduct novel analyses of natural systems. Most of these research institutions are geographically distant from the highest-priority conservation areas, which are found in South America, Africa, and Southeast Asia. There, data is typically collected by or with the help of local residents hired as field assistants. These field assistants have few meaningful opportunities to participate in biodiversity research and conservation beyond data logging. The literature indicates the data revolution has increased demand for impersonal and integrated large-scale systems that aggregate biodiversity data across sources with minimal friction. In this study, interviews were conducted with six active conservation workers to identify elements of these data systems that create barriers to field assistants’ engagement with the projects they make possible. As both creators and consumers of data, all six relayed frustration with various aspects of their data workflows. Regarding field assistant interaction with digital data systems, they observed that their field assistants engaged only at the initial point of data entry or not at all. Some suggested mobile apps as a good solution for field data collection. However, some also expressed doubt that their local assistants had the necessary knowledge background to navigate digital systems or understand scientific methodologies. These results suggest that trying to mold field assistants to fit existing data infrastructure and adapting purpose-built data systems to nontechnical users are both sub-optimal solutions. A human-mediated capacity building paradigm, which requires embedding people who are both culturally literate and data literate alongside field assistants, is explored as an alternative path to making data meaningful. Improving the accessibility of data this way can empower local communities to share ownership in biodiversity conservation. The substance of this article is based upon a panel presentation at RDAP Summit 2019

Supporting the Proliferation of Data-Sharing Scholars in the Research Ecosystem

Librarians champion the value of openness in scholarship and have been powerful advocates for the sharing of research data. College and university administrators have recently joined in the push for data sharing due to funding mandates. However, the researchers who create and control the data usually determine whether and how data is shared, so it is worthwhile to look at what they are incentivized to do. The current scholarly publishing landscape plus the promotion and tenure process create a “prisoner’s dilemma” for researchers as they decide whether or not to share data, consistent with the observation that researchers in general are eager for others to share data but reluctant to do so themselves. If librarians encourage researchers to share data and promote openness without simultaneously addressing the academic incentive structure, those who are intrinsically motivated to share data will be selected against via the promotion and tenure process. This will cause those who are hostile to sharing to be disproportionately recruited into the senior ranks of academia. To mitigate the risk of this unintended consequence, librarians must advocate for a change in incentives alongside the call for greater openness. Highly-cited datasets must be given similar weight to highly-cited articles in promotion and tenure decisions in order for researchers to reap the rewards of their sharing. Librarians can help by facilitating data citation to track the impact of datasets and working to persuade higher administration of the value of rewarding data sharing in tenure and promotion

Yes, use AI, but not like that! Helping student researchers navigate conflicting messages about generative AI

University students are being encouraged by their peers, or even their institutions, to use generative AI tools to make the research process more efficient and less stressful. However, they are also cautioned against inappropriate use of generative AI by course instructors, research supervisors, and those same institutions. Many librarians are reluctant to enter the discussion and provide instruction in the appropriate use of generative AI, perhaps to avoid the appearance of endorsement. On the other hand, the implications of student use of AI for information literacy cannot be ignored. One possible strategy is for librarians to explore the use of generative AI for meeting information needs in partnership with students. Engaging in frank discussion about its advantages and limitations provides an opportunity for information literacy instruction while building student trust that librarians’ knowledge and skills are still relevant in the new information landscape. Recently, the presenters offered a one-shot workshop, “Responsible Use of AI Research Tools”, for the first time to graduate students in various fields. In this workshop, instructors demonstrated some applications of Microsoft Copilot before allowing students to experiment with the tool themselves. The ensuing discussion focused on how well the AI identified sources to support its responses, which sources were likely to come up, and what sources would necessarily be missed. Post-workshop student feedback forms indicated a positive response to the the session and continued interest in this topic. This presentation will examine lessons learned from this workshop, plans for future workshops on AI in academic research, and suggestions for librarians interested in teaching similar instructional sessions