Search for: All records

Institutions have made significant investments to support public access to research data requirements; yet have little comparative data about these services, infrastructure, and costs. To address this need, the research team undertook a mixed-methods approach to understand the institution-wide expenses for research data management and sharing and began to draft an expense model for data management and sharing. This model is further useful for institutions that provide research data management and sharing. 

This dataset is the result of studies conducted during phase one (NSF-funded) of the Realities of Academic Data Sharing (RADS) Initiative, based out of the Association of Research Libraries. Studies were conducted with federally-funded researchers and institutional administrators who support data sharing practices within their department or unit at the following institutions: Cornell University, Duke University, University of Michigan, University of Minnesota, Virginia Tech, and Washington University in St. Louis. The 2022 RADS studies were retrospective, investigating data sharing and management activities and support services from 2013 to 2022. Two surveys were utilized to collect data, the Institutional Infrastructure Survey for administrators and the Researcher Survey for federally-funded researchers. This dataset presents data from both of these surveys. Project website: https://www.arl.org/realities-of-academic-data-sharing-rads-initiative/ 

This is a story about the challenges and opportunities that surfaced while answering a deceptively complex question - where's the data? As faculty and researchers publish articles, datasets, and other research outputs to meet promotion and tenure requirements, address federal funding policies, and institutional open access and data sharing policies, many online locations for publishing these materials have developed over time. How can we capture where all of the research generated on an academic campus is shared and preserved? This presentation will discuss how our multi-institution collaboration, the Reality of Academic Data Sharing (RADS) Initiative, sought to answer this question. We programmatically pulled DOIs from DataCite and CrossRef, making the naive assumption that these platforms, the two predominant DOI registration agencies for US data, would present us with a neutral and unbiased view of where data from our affiliated researchers were shared. However, as we dug into the data, we found inconsistencies in the use and completeness of the necessary metadata fields for our questions, as well as differences in how DOIs were assigned across repositories. Additionally, we recognized the systematic and privileged bias introduced by our choice of data sources. Specifically, while DataCite and CrossRef provide easy discovery of research outputs because they aggregate DOIs, they are also costly commercial services. Many repositories that cannot afford such services or lack local staffing and knowledge required to use these services are left out of the technology that has recently been labeled “global research infrastructure”. Our presentation will identify the challenges we encountered in conducting this research specifically around finding the data, and cleaning and interpreting the data. We will further engage the audience in a discussion around increasing representation in the global research infrastructure to discover and account for more research outputs. 

As requirements for swift and sustainable data sharing are growing, questions of where and how researchers are sharing data are becoming increasingly important for institutions to answer. One of the goals of the Reality of Academic Data Sharing (RADS) Initiative, comprised of six academic institutions from the Data Curation Network (DCN), was to answer this question. This presentation will discuss the process of how RADS determined where data from our researchers are shared. To do this, we programmatically pulled DOIs from DataCite, making the naive assumption that the information we were collecting, the metadata fields we were utilizing, and the platforms we were using would present us with a neutral and unbiased view of where data from our affiliated researchers were shared. However, as we dug into the data, we found inconsistencies in the use and completeness of the necessary metadata fields for our questions, as well as differences in how DOIs were assigned across repositories. While we expected some differences, we did not anticipate these subtle differences would dramatically affect how we interpret the answer to the question of where data are shared. Our presentation will highlight examples in our work that show how these subtleties in the data are systematic and challenge our assumptions of neutrality of not just the data, but of our platforms and practices as well. By examining these biases, we are forced to reexamine the decisions behind how we practice and, as we move forward as information and repository managers, how to reduce bias or assumption of neutrality. As a community, we often rely on data-driven decisions and decision makers need to be aware of these biases, especially as we are likely to see increased investments due to the evolving data policies and practices.