The reproducibility of research results is the basic requirement for the reliability of scientific discovery, yet it is hard to achieve. Whereas funding agencies, scientific journals, and professional societies are developing guidelines, requirements, and incentives, and researchers are developing tools and processes, the role of a university in promoting and enabling reproducible research has been unclear. In this report, we describe the Reproducibility Challenge that we organized at the University of Michigan to promote reproducible research in data science and Artificial Intelligence (AI). We observed that most researchers focused on nuts-and-bolts reproducibility issues relevant to their own research. Many teams were building their own reproducibility protocols and software for lack of any available options off the shelf. If we could help them with this, they would have preferred to adopt rather than build anew. We argue that universities—their data science centers and research support units—have a critical role to play in promoting "actionable reproducibility" (Goeva et al., 2020) through creating and validating tools and processes, and subsequently enabling and encouraging their adoption.

"Open access" has become a central theme of journal reform inacademic publishing. In this article, Iexamine the consequences of an important technological loophole in which publishers can claim to be adhering to the principles of open access by releasing articles in proprietary or “locked” formats that cannot be processed by automated tools, whereby even simple copy and pasting of text is disabled. These restrictions will prevent the development of an important infrastructural element of a modern research enterprise, namely,scientific data science, or the use of data analytic techniques to conduct meta-analyses and investigations into the scientific corpus. I give a brief history of the open access movement, discuss novel journalistic practices, and an overview of data-driven investigation of the scientific corpus. I arguethat particularly in an era where the veracity of many research studies has been called into question, scientific data science should be oneof the key motivations for open access publishing. The enormous benefits of unrestricted access to the research literature should prompt scholars from all disciplines to reject publishing models whereby articles are released in proprietary formats or are otherwise restricted from being processed by automated tools as part of a data science pipeline.

In collaboration with the University of Washington (UW) and Berkeley, and under the sponsorship of the Moore and Sloan foundations, NYU is working on a new initiative to 'harness the potential of data scientists and big data'. As part of this initiative, we aim to increase awareness of sharing, preservation, provenance, and reproducibility best practices across UW, NYU, Berkeley campuses and encourage their adoption.