Understanding scientific data sharing outside of the academy

Sharing and reuse of scientific data, which can enhance the transparency and reproducibility of research and lead to the creation of new knowledge from existing data, is both a growing scholarly communication practice and an expanding area of interest in information science. However, much of the literature to date has focused on the data practices of scientists working in academic environments, with less research done on understanding the practices of scientists working in other types of environments, such as government or industry. This poster presents the results of a study in which data from a worldwide survey of scientists were analyzed to determine if differences in data practices, perceptions, and access to resources for data sharing existed between scientists who reported their primary work sector as academic and those who reported a non‐academic primary work sector. Researchers\u27 perceptions of data sharing and reuse were generally positive and did not differ significantly by work sector. However, differences were found in actual reported data sharing practices, even when controlling for researchers\u27 age, geographic location, and subject discipline. Researchers outside of academia had lesser odds of reporting sharing all their data. Differences were also found in reported barriers to data sharing, as well as in reported access to and use of data sharing resources, suggesting that data sharing challenges faced by scientists working outside of academia may differ from those faced by their academic peers. Implications for the adoption of data sharing practices and technologies, as well as for knowledge sharing and creation across work sectors, are discussed, and suggestions are offered for further research

Towards a metadata model for research information management systems

This research is supported by an OCLC/ALISE Library and Information Research Grant for 2016 and a National Leadership Grant from the Institute of Museum and Library Services (IMLS) of the U.S. Government (grant # LG-73-16-0006-16). This article reflects the findings and conclusions of the authors, and does not necessarily reflect the views of IMLS, OCLC, and ALISE

Exploring data practices of the earthquake engineering community

There is a need to compare and contrast data practices of different disciplines and groups. This study explores data practices in earthquake engineering (EE), an interdisciplinary field with a variety of research activities and dynamic data types and forms. Findings identify the activities of typical EE research projects, the types and forms of data produced and used in those activities, the project roles played by EE researchers in connection with data practices, the tools used to manage data in those activities, the types and sources of data quality problems in EE, and the perceptions of data quality in EE. A strong relation exists among these factors, with a stronger role for test specimens and high quality documentation and more blurring of project roles than in other fields. Suggestions are provided for resolving contradictions impeding EE researchers’ curation and archiving activities and for future research on data practices

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146402/1/asi24039_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146402/2/asi24039.pd