
Sharing and reusing data can effectively reduce redundant efforts in data collection. It also enhances the efficiency of scientific research investments by preventing the reinvention of the wheel. Building a sustainable data-reuse process and culture requires frameworks that include policies, standards, roles, and responsibilities, all of which must address the diverse needs of data providers, curators, and (re)users alike. A critical step in the data sharing and (re)use cycle involves researchers depositing their research data into data-sharing infrastructures, keeping their data in a sharable and usable form, making other researchers aware of their data, and identifying data that are (re)usable for their needs. In this session, I aim to introduce some ideas and techniques for preparing research data for sharing. I will also introduce 2 to 3 data repositories ideal for researchers depositing their data and seeking (re)usable data for their studies. Additionally, I will offer several tips and strategies for identifying and utilizing (re)usable data effectively. The session is structured to last approximately 50 minutes, comprising a 40-minute presentation followed by a 10-minute question-and-answer segment. This presentation was part of the UC Love Data Week 2025 program (https://uc-love-data-week.github.io).
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
