Ingest Process: Submission and ‘Pre-Ingest’ Activities

Becoming a trustworthy repository is an ongoing process of refinement and revision. Trust is based on transparent adherence to and certification against community standards. Archival trust is initiated with the ingest process, particularly the portion of ingest dealing with acquisition of content. Examples are provided of submission and ‘pre-ingest’ activities implemented at the Inter-university Consortium for Political and Social Research (ICPSR), a data repository of social and behavioral science research. Possible future refinements are also discussed.http://deepblue.lib.umich.edu/bitstream/2027.42/122875/1/20160111_Ingest Process- Submission and ‘Pre-Ingest’ Activities_final.pdfDescription of 20160111_Ingest Process- Submission and ‘Pre-Ingest’ Activities_final.pdf : Articl

ICPSR and the Data Seal of Approval: Accreditation Experiences and Opportunities

This presentation, given at the International Forum on 'Polar Data Activities in Global Data Systems' on October 16, 2013 in Tokyo, Japan, discussed ICPSR's experiences applying for and acquiring the Data Seal of Approval (DSA) in 2010. The Data Seal of Approval provided an inexpensive, relatively quick, and straightforward accreditation process. The results of the DSA process helped ICPSR to continue to refine processes and procedures. The DSA provides a low barrier of entry for repositories to certify that they are trustworthy, while helping them to improve their own systems. The Seal carries meaning that is easily recognized, especially as more repositories complete the assessment and as more producers and consumers recognize the value added.https://deepblue.lib.umich.edu/bitstream/2027.42/145468/1/ICPSR_polar_forum_20131016 (1).pptxhttps://deepblue.lib.umich.edu/bitstream/2027.42/145468/3/S5_01_Lyle.pdfDescription of ICPSR_polar_forum_20131016 (1).pptx : PresentationDescription of S5_01_Lyle.pdf : Repor

Sharing and citing research data: A repository's perspective

Formal data citation is a key element of the growing data-sharing infrastructure, not only facilitating sharing, discovery, and proper use, but also enabling data impact tracking that allows researchers to receive credit for their contributions. Specialized data repositories, such as ICPSR, integrate data citations within study metadata to enhance access and encourage data sharing. National and international efforts are underway to encourage adoption of these types of practices. The eventual result should be that more data creators will benefit from citations by receiving credit for their work. More researchers will benefit by readily finding reproducible research. And more funding agencies will benefit by tracking supported projects’ usage and gauging impact beyond the initial funding. ICPSR and its topical archives, like NACJD, provide an example of how data citation can encourage data archiving and secondary use. They support the growth of the ICPSR Bibliography of Data-related Literature and see the collection as evidence of new scientific findings for consideration in shaping public policy. The Bibliography’s two-way linkages between data and data-associated publications have improved the discovery and the chances of good secondary use of ICPSR data. Due to inconsistent and inadequate data-citing practices in the scholarly literature, tracking data reuse is costly and labor-intensive. Despite this, ICPSR continues to value and invest in the collection of data-related publications, while promoting the creation and use of standards for citing and sharing research data according to best practices.http://deepblue.lib.umich.edu/bitstream/2027.42/115490/1/012_Ch04_Sharing_and_Citing_Resear_final.pdfDescription of 012_Ch04_Sharing_and_Citing_Resear_final.pdf : Book chapte

Opaque data citation: Actual citation practice and its implication for tracking data use

In the social and behavioral science literature, much data attribution is incomplete and does not include persistent identifiers. Instead, authors mention data opaquely. Without explicit data citation, a publication cannot automatically or definitively link to a data source. The human effort required to find, interpret, and link opaque citations is costly and inefficient, especially at scale. So data use often goes untracked, and data creators go uncredited. Before it can change, current practice must first be understood. This poster categorizes actual ways in which authors refer to data. ICPSR is a social and behavioral science data repository in the United States, which curates and disseminates over 10,000 data collections and attempts to track their reuse. The examples in this poster are taken from the ICPSR Bibliography of Data-related Literature, a searchable database that provides curated two-way links between data archived at ICPSR and over 75,000 scholarly publications.https://deepblue.lib.umich.edu/bitstream/2027.42/142393/1/Opaque Data Citation poster-DeepBlue.pdfDescription of Opaque Data Citation poster-DeepBlue.pdf : Poste

American Economic Association (AEA) Data & Code Repository at openICPSR

In 2019, the American Economic Association (AEA) adopted a Data and Code Availability Policy “to improve the reproducibility and transparency of materials supporting research published in the AEA journals by providing improved guidance on the types of materials required, increased quality control, and more review earlier in the publication process.” The AEA initiative is one of the most comprehensive reproducibility and data/code sharing initiatives in the social sciences. In this presentation, we review the AEA workflow, including how the AEA assesses compliance with the policy and the accuracy of the information by running code to reproduce the reported results. We also demonstrate the newly established AEA Data and Code Repository at the Inter-university Consortium for Political and Social Research (ICPSR), which facilitates the AEA's workflow and review. Each data collection in the repository receives a persistent digital identifier (DOI), as well as descriptive metadata to increase findability, including JEL codes and subject terms. Data collections are also linked back to the journal article. Additionally, the AEA migrated their entire back archive of more than 3,000 data and code supplements to the AEA Data and Code Repository at ICPSR. This represents almost two decades of required data sharing associated with AEA journal publications.http://deepblue.lib.umich.edu/bitstream/2027.42/156061/1/Lyle ICPSR MIDAS Reproducibility Challenge 2020.pdfDescription of Lyle ICPSR MIDAS Reproducibility Challenge 2020.pdf : PresentationSEL

Thoracic stent graft placement for repair of iatrogenic aortic injury secondary to sheath placement during pacemaker insertion

We describe the inadvertent cannulation of the proximal descending thoracic aortic stent with a five French sheath during attempted pacemaker placement in an 88- year-old male. The injury was managed successfully by the percutaneous placement of a thoracic aortic stent graft with good outcome. Our case highlights the feasibility of managing this uncommon injury with this technique

Transaortic gunshot wound through perivisceral segment successfully managed by placement of thoracic stent graft

We describe a 36-year-old woman who presented to our facility after sustaining a gunshot wound to the epigastric region. The gunshot resulted in injury to the left lobe of the liver and the twelfth thoracic vertebral body as well as in a through- and-through injury to the abdominal aorta at the level of the celiac axis. The vascular injury was managed successfully by placement of a thoracic stent graft with coverage of the celiac axis. This case demonstrates the feasibility of managing this uncommon injury with endovascular techniques. (J Vasc Surg Cases and Innovative Techniques 2018;4:24-6.

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-

Capturing Data Provenance from Statistical Software

We have created tools that automate one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software.  Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis.  The C2Metadata ("Continuous Capture of Metadata for Statistical Data") Project creates a metadata workflow paralleling the data management process by deriving provenance information from scripts used to manage and transform data.  C2Metadata differs from most previous data provenance initiatives by documenting transformations at the variable level rather than describing a sequence of opaque programs.  Command scripts for statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations.   SDTL can be used to add variable-level provenance to data catalogues and codebooks and to create "variable lineages" for auditing software operations.   Better data documentation makes research more transparent and expands the discovery and re-use of research data