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Short abstract:

New laws passed say that scientists have to included data management plans in funding applications to the National Science Foundation in the United States. This paper uncovers the reasons for this law and how data management plans can be interpreted by science and technology studies scholars.

Long abstract:

The United States’ science data policy has landed in a weird place. Data management plans are now required to ensure “research reproducibility and replicability” on National Science Foundation grants according to new legislative code. There are several reasons given in various administrative rules for more access to and transparency around research data including equity, research replicability, projecting the superiority of American science, ensuring the return on investment to taxpayers, incentivizing better research data management, legitimizing a future for data science and data-driven research, and enhancing the quality of science. But there is little evidence that data management plans achieve these ends. This paper asks, how did the United States end up with “data management plans” as a requirement? And, what do data management plans tell us about the future of science data? We examine the history of data management plans. Then, we draw from our corpus of nearly 1000 data management plans from projects funded by the National Science Foundation-funded and delineate different approaches to reading data management plans: as scientific furniture, as instruments for accessing funding, as evidence of the neoliberalization of science, as a process document for scientific knowledge production and institutional coordination, as a fantasy document, as an indicator of the future, aligning temporalities, and as part of the institutionalization of data-oriented science. By sharing findings about the purposes and premises of planning for data management we argue they are an important vehicle for understanding the political project of open science in the United States.