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Cover -- Copyright -- Title Page -- Table of Contents -- Acknowledgements -- The Everyday World of Affect and Mothering -- Sisterly Conversation -- Becoming Mother, Performing Mother -- milk -- Navigating the Waters of Early Motherhood -- A Poetics of Maternal Failure -- Objects of a Maternal Haunting -- Empty Maternal -- The "Dark Side" of Mothering -- That Baby Will Cost You -- Blood, Mud, Poop, and Vomit -- Unforgivable or Outlaw Emotions? -- fail -- Blurring Boundaries, Manoeuvring Motherhood -- Anishinaabe Fasting -- "Mothering the Mother" -- Instinct, Expertise, Connection -- Families We Don't Choose -- Sisterly Conversation -- About the Contributors.

This article provides an overview of the elements necessary to build a sustainable research data infrastructure. I argue that it needs the financial and intellectual engagement of a community of practice. Most attention has been paid to researchers and policy-makers, but a third group—government programmatic agencies—must be a focal point since they act as both data producers and as policy implementers. I also discuss possible business models that are both consistent with serving the needs of multiple stakeholders and that are not completely dependent on the largesse of the public purse.

Why America's data system is broken, and how to fix it.Why, with data increasingly important, available, valuable and cheap, are the data produced by the American government getting worse and costing more' State and local governments rely on population data from the US Census Bureau; prospective college students and their parents can check data from the National Center for Education Statistics; small businesses can draw on data about employment and wages from the Bureau of Labor Statistics. But often the information they get is out of date or irrelevant, based on surveys--a form of information gathering notorious for low response rates. In A Data Manifesto, Julia Lane argues that bad data is bad for democracy. Her book is a wake-up call to America to fix its broken public data system.

In common with many countries, the substantial United States investment in R&D is characterized by limited documentation of the nature and results of those investments (MacIlwain 2010, Marburger 2005). Despite the increased calls for reporting by key stakeholders, current data systems cannot meet the new requirements; indeed, the conclusion of the Science of Science Policy interagency group's Federal Research Roadmap (National Science and Technology Council 2008) was that the science policy data infrastructure was inadequate for decision-making. In response to this need, a new data system is being built (STAR METRICS) drawing from administrative records; this paper describes the initial results of that effort - focusing on documenting the scientific workforce supported by expenditures during the 2011 Federal fiscal year from awards made by the National Science Foundation. The contribution of the paper is threefold. First it describes in a non-technical fashion how these new data can contribute to our understanding of the initial results of science investments. Second, it shows how new computational technologies can be used to go beyond the traditional methods of manual reporting and administrative program coding to capture information at the most granular units of analysis possible. Finally, it discusses the lessons learned for the collection and analysis of data. The most important is leveraging existing data, not relying on surveys and manual reporting; the deficiencies of each have been well documented (Lane 2010).