The Inter‐University Consortium For Political And Social Research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87008/1/j.1360-0443.2011.03564.x.pd

A Data-Driven Approach to Appraisal and Selection at a Domain Data Repository

Social scientists are producing an ever-expanding volume of data, leading to questions about appraisal and selection of content given finite resources to process data for reuse. We analyze users’ search activity in an established social science data repository to better understand demand for data and more effectively guide collection development. By applying a data-driven approach, we aim to ensure curation resources are applied to make the most valuable data findable, understandable, accessible, and usable. We analyze data from a domain repository for the social sciences that includes over 500,000 annual searches in 2014 and 2015 to better understand trends in user search behavior. Using a newly created search-to-study ratio technique, we identified gaps in the domain data repository’s holdings and leveraged this analysis to inform our collection and curation practices and policies. The evaluative technique we propose in this paper will serve as a baseline for future studies looking at trends in user demand over time at the domain data repository being studied with broader implications for other data repositories.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145607/1/document.pd

Building on the Rich Metadata from Decades of Health Behavior Studies: The Potential for Common Data Elements (CDEs) to Enhance the Identification of Health Data Across Different Research Projects

Continued analyses of key datasets are extremely important to building understanding of the underlying causes of substance use and addiction, and multiply the benefits of our nation’s investment in this science. ICPSR and the National Addiction and HIV Data Archive Program (NAHDAP) disseminate data from hundreds of NIH-funded research studies, as well as data collected with support from other agencies and foundations, many with questions about health outcomes or status that are not easily discovered with current search protocols which can be either too narrow or too broad. With funding from NIDA, we are working to increase the use of these extant data for health research by making these variables easier to identify. This is of great benefit the research community, providing improved discoverability of relevant health concepts within and, more importantly, across the multiple studies maintained in our repositories.OBSSR/NIHhttps://deepblue.lib.umich.edu/bitstream/2027.42/145467/1/IASSIST2018_CDE_Poster.pdfDescription of IASSIST2018_CDE_Poster.pdf : Poster presentation at IASSIST & CARTO 2018 Annual Meetin

The Enduring Value of Social Science Research: The Use and Reuse of Primary Research Data

This paper was presented at “The Organisation, Economics and Policy of Scientific Research” workshop, Torino, Italy, in April, 2010. See: http://www.carloalberto.org/files/brick_dime_strike_workshopagenda_april2010.pdf.The public-use data analyzed in this paper: Pienta, Amy M., and Jared Lyle. Data Sharing in the Social Sciences, 2009 [United States] Public Use Data. ICPSR29941-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-12-15. https://doi.org/10.3886/ICPSR29941.v1The goal of this paper is to examine the extent to which social science research data are shared and assess whether data sharing affects research productivity tied to the research data themselves. We construct a database from administrative records containing information about thousands of social science studies that have been conducted over the last 40 years. Included in the database are descriptions of social science data collections funded by the National Science Foundation and the National Institutes of Health. A survey of the principal investigators of a subset of these social science awards was also conducted. We report that very few social science data collections are preserved and disseminated by an archive or institutional repository. Informal sharing of data in the social sciences is much more common. The main analysis examines publication metrics that can be tied to the research data collected with NSF and NIH funding – total publications, primary publications (including PI), and secondary publications (non-research team). Multivariate models of count of publications suggest that data sharing, especially sharing data through an archive, leads to many more times the publications than not sharing data. This finding is robust even when the models are adjusted for PI characteristics, grant award features, and institutional characteristics.National Library of Medicine (R01 LM009765). The creation of the LEADS database was also supported by the following research projects at ICPSR: P01 HD045753, U24 HD048404, and P30 AG004590.http://deepblue.lib.umich.edu/bitstream/2027.42/78307/1/pienta_alter_lyle_100331.pdf-

Preserving Quantitative Research-Elicited Data for Longitudinal Analysis. New Developments in Archiving Survey Data in the U.S.

Social science data collected in the United States, both historically and at present, have often not been placed in any public archive -- even when the data collection was supported by government grants. The availability of the data for future use is, therefore, in jeopardy. Enforcing archiving norms may be the only way to increase data preservation and availability in the future.Governmen

How do properties of data, their curation, and their funding relate to reuse?

Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data’s reuse. Using data download logs from the Inter-university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.National Science Foundation grant 1930645 (LH, AP, DA) Institute of Museum and Library Services grant LG-37-19-0134-19 (LH, DA) National Institute of Drug Abuse contract number N01DA-14-5576 (AP)http://deepblue.lib.umich.edu/bitstream/2027.42/168212/5/Hemphill et al Data downloads.pdf4ae71d2a-01c0-4084-84c3-c32ce960e81c5836d8a9-776f-4cd5-ba6e-a0cfd10d555dSEL

Inhibitory effects of megakaryocytic cells in prostate cancer skeletal metastasis

Prostate cancer cells commonly spread through the circulation, but few successfully generate metastatic foci in bone. Osteoclastic cellular activity has been proposed as an initiating event for skeletal metastasis. Megakaryocytes (MKs) inhibit osteoclastogenesis, which could have an impact on tumor establishment in bone. Given the location of mature MKs at vascular sinusoids, they may be the first cells to physically encounter cancer cells as they enter the bone marrow. Identification of the interaction between MKs and prostate cancer cells was the focus of this study. K562 (human MK precursors) and primary MKs derived from mouse bone marrow hematopoietic precursor cells potently suppressed prostate carcinoma PC-3 cells in coculture. The inhibitory effects were specific to prostate carcinoma cells and were enhanced by direct cell-cell contact. Flow cytometry for propidium iodide (PI) and annexin V supported a proapoptotic role for K562 cells in limiting PC-3 cells. Gene expression analysis revealed reduced mRNA levels for cyclin D1, whereas mRNA levels of apoptosis-associated specklike protein containing a CARD (ASC) and death-associated protein kinase 1 (DAPK1) were increased in PC-3 cells after coculture with K562 cells. Recombinant thrombopoietin (TPO) was used to expand MKs in the marrow and resulted in decreased skeletal lesion development after intracardiac tumor inoculation. These novel findings suggest a potent inhibitory role of MKs in prostate carcinoma cell growth in vitro and in vivo. This new finding, of an interaction of metastatic tumors and hematopoietic cells during tumor colonization in bone, ultimately will lead to improved therapeutic interventions for prostate cancer patients. © 2011 American Society for Bone and Mineral Research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78486/1/204_ftp.pd

Using ICPSR Resources to Teach Sociology

The focus on quantitative literacy has been increasingly outside the realm of mathematics. The social sciences are well suited to including quantitative elements throughout the curriculum but doing so can mean challenges in preparation and presentation of material for instructors and increased anxiety for students. This paper describes tools and resources available through the Interuniversity Consortium for Political and Social Research (ICPSR) that will aid students and instructors engaging in quantitative literacy across the curriculum. The Online Learning Center is a source of empirical activities aimed at undergraduates in lower-division substantive courses and Exploring Data through Research Literature presents an alternative to traditional research methods assignments. Searching and browsing tools, archive structures, and extended online-analysis tools make it easier for students in upper-division undergraduate and graduate courses to engage in exercises that increase quantitative literacy, and paper competitions reward them for doing so.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60430/1/Hoelter et al TS 2008 (2).pd

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research

Fostering global data sharing: Highlighting the recommendations of the Research Data Alliance COVID-19 working group

© 2020 Austin CC et al. The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community