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

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-

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

Classifying the evolutionary and ecological features of neoplasms

The consensus conference was supported by Wellcome Genome Campus Advanced Courses and Scientific Conferences. C.C.M. is supported in part by US NIH grants P01 CA91955, R01 CA149566, R01 CA170595, R01 CA185138 and R01 CA140657 as well as CDMRP Breast Cancer Research Program Award BC132057. M.J. is supported by NIH grant K99CA201606. K.S.A. is supported by NCI 5R21 CA196460. K. Polyak is supported by R35 CA197623, U01 CA195469, U54 CA193461, and the Breast Cancer Research Foundation. K.J.P. is supported by NIH grants CA143803, CA163124, CA093900 and CA143055. D.P. is supported by the European Research Council (ERC-617457- PHYLOCANCER), the Spanish Ministry of Economy and Competitiveness (BFU2015-63774-P) and the Education, Culture and University Development Department of the Galician Government. K.S.A. is supported in part by the Breast Cancer Research Foundation and NCI R21CA196460. C.S. is supported by the Royal Society, Cancer Research UK (FC001169), the UK Medical Research Council (FC001169), and the Wellcome Trust (FC001169), NovoNordisk Foundation (ID 16584), the Breast Cancer Research Foundation (BCRF), the European Research Council (THESEUS) and Marie Curie Network PloidyNet. T.A.G. is a Cancer Research UK fellow and a Wellcome Trust funded Investigator. E.S.H. is supported by R01 CA185138-01 and W81XWH-14-1-0473. M.Gerlinger is supported by Cancer Research UK and The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. M.Ge., M.Gr., Y.Y., and A.So. were also supported in part by the Wellcome Trust [105104/Z/14/Z]. J.D.S. holds the Edward B. Clark, MD Chair in Pediatric Research, and is supported by the Primary Children's Hospital (PCH) Pediatric Cancer Research Program, funded by the Intermountain Healthcare Foundation and the PCH Foundation. A.S. is supported by the Chris Rokos Fellowship in Evolution and Cancer. Y.Y. is a Cancer Research UK fellow and supported by The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. E.S.H. was supported in part by PCORI grants 1505–30497 and 1503–29572, NIH grants R01 CA185138, T32 CA093245, and U10 CA180857, CDMRP Breast Cancer Research Program Award BC132057, a CRUK Grand Challenge grant, and the Breast Cancer Research Foundation. A.R.A.A. was funded in part by NIH grant U01CA151924. A.R.A.A., R.G. and J.S.B. were funded in part by NIH grant U54CA193489

CXCL12γ Promotes Development of Metastatic Castration Resistant Prostate Cancer by Induction of Cancer Stem Cell and Neuroendocrine Phenotypes

There is evidence that cancer stem-like cells (CSC) and neuroendocrine behavior play critical roles in the pathogenesis and clinical course of metastatic castration-resistant prostate cancer (m-CRPC). However, there is limited mechanistic understanding of how CSC and neuroendocrine phenotypes impact the development of m-CRPC. In this study, we explored the role of the intracellular chemokine CXCL12γ in CSC induction and neuroendocrine differentiation and its impact on m-CRPC. CXCL12γ expression was detected in small-cell carcinoma of metastatic tissues and circulating tumor cells from m-CRPC patients and in prostate cancer cells displaying an neuroendocrine phenotype. Mechanistic investigations demonstrated that overexpression of CXCL12γ induced CSC and neuroendocrine phenotypes in prostate cancer cells through CXCR4-mediated PKCα/NFκB signaling, which promoted prostate tumor outgrowth, metastasis, and chemoresistance in vivo Together, our results establish a significant function for CXCL12γ in m-CRPC development and suggest it as a candidate therapeutic target to control aggressive disease.Significance: Expression of CXCL12γ induces the expression of a cancer stem cell and neuroendocrine phenotypes, resulting in the development of aggressive m-CRPC. Cancer Res; 78(8); 2026-39. ©2018 AACR