@article{201cbe7e711e420f969f79270a8a3ad1,
title = "New linked data on research investments: Scientific workforce, productivity, and public value",
abstract = "Longitudinal micro-data derived from transaction level information about wage and vendor payments made by Federal grants on multiple US campuses are being developed in a partnership involving researchers, university administrators, representatives of Federal agencies, and others. This paper describes the UMETRICS data initiative that has been implemented under the auspices of the Committee on Institutional Cooperation. The resulting data set reflects an emerging conceptual framework for analyzing the process, products, and impact of research. It grows from and engages the work of a diverse and vibrant community. This paper situates the UMETRICS effort in the context of research evaluation and ongoing data infrastructure efforts in order to highlight its novel and valuable features. Refocusing data construction in this field around individuals, networks, and teams offers dramatic possibilities for data linkage, the evaluation of research investments, and the development of rigorous conceptual and empirical models. Two preliminary analyses of the scientific workforce and network approaches to characterizing scientific teams ground a discussion of future directions and a call for increased community engagement.",
keywords = "IRIS, Linked data, Occupation classification, STAR METRICS, Science of science policy, Scientific networks, Scientific workforce, UMETRICS",
author = "Lane, {Julia I.} and Jason Owen-Smith and Rosen, {Rebecca F.} and Weinberg, {Bruce A.}",
note = "Funding Information: The framework we propose stands in sharp contrast to that commonly used by science funders, who – consistent with their mandate to manage research investments rather than document their returns – emphasize individual grants to the virtual exclusion of people, teams, and the later use of scientific products. Hence their primary unit of analysis is the grant, and research administrators spend much time and energy trying to link research grants to research outputs by requiring scientists to acknowledge specific grants and report results on a grant-by-grant basis. The science of science policy framework recognizes that the social organization and work practices of cutting edge science do not fall cleanly within individual projects bounded by particular goals and clear starting or ending dates. Most of the work of discovery and training takes place in collaborative groups that encompass multiple overlapping projects. In practice, the work of individuals and teams is supported by and integrates a pastiche of grants that serve multiple purposes and often span several funding agencies. Even though the primary lever for policy makers to influence the character, goals or uses of science is funding individual projects, the implications and effects of new funding arrangements or incentives can only be fully understood in the context of the individual and collective careers that are the cornerstone of contemporary science and training. Misunderstanding this basic view will lead to misspecification of any analysis. Funding Information: Funding agencies such NIH and NSF currently struggle to respond to questions about the STEM workforce ( Marburger, 2011 ). They are largely unable to systematically evaluate the effectiveness of their STEM training and research grant policies ( National Science and Technology Council, 2008 ). Indeed, when the NIH Director convened a Biomedical Research Workforce Working Group tasked with identifying the optimal workforce composition and training levels necessary to support a maximally productive biomedical research ecosystem, “The working group was frustrated and sometimes stymied throughout its study by the lack of comprehensive data regarding biomedical researchers.” The working group pointed out that much of what is known about the STEM workforce comes from small-scale and/or cross-sectional surveys such as the Survey of Graduate Students and Postdocs, Survey of Earned Doctorates (which is population level, but cross sectional) and Survey of Doctorate Recipients. 11 11 They identified three key problems with using these existing datasets in STEM workforces policy analyses: (1) the data are manually collected and/or are small-scale and/or cross-sectional rather than longitudinal, (2) data is not captured for trainees on research teams funded by standard research award mechanisms and is incomplete for foreign trained researchers, and (3) the data collection systems do not enable the large-scale long-term tracking of trainees once they enter the workforce ( National Academies, 2005 ). In what follows, we sketch two initial, complementary approaches to addressing questions about the composition of the research workforce and the conditions of STEM research production that highlight the potential value the kind of linked micro-data being developed through UMETRICS. Publisher Copyright: {\textcopyright} 2015 Elsevier B.V. All rights reserved.",
year = "2015",
doi = "10.1016/j.respol.2014.12.013",
language = "English (US)",
volume = "44",
pages = "1659--1671",
journal = "Research Policy",
issn = "0048-7333",
publisher = "Elsevier",
number = "9",
}