Adopting open data practices can improve collaboration, safeguard data, and help researchers get ahead of data sharing requirements from funders and publishers. Data sharing and transparency can benefit science and increase researcher impact. But what does it take to make data genuinely open? This presentation will provide strategies for meaningfully open data, offer choices in data sharing, describe some limitations of openness, and help researchers get a jump start preparing data for openness.

This presentation is part of the Open Science Webinar Series from Michigan State University’s Center for Statistical Training and Consulting (CSTAT).
Open Science is the transparent and accessible knowledge that is shared and developed through collaborative networks. Open Science practices encompass open source, open access, open data, open code, open peer review, and pre-registration. Advantages are to improve reproducibility of research, better reporting, and the potential of answering more complex questions. Disadvantages are publication costs and lower quality articles. Learning how to prepare open data, to document open material, to pre-register studies, to follow reporting guidelines can be time consuming, but it is necessary to develop such Open Science skills for high impact research. Free registration: https://cstat.msu.edu/events