Inter-university Consortium For Political And Social Research Research Articles

A dataset for measuring the impact of research data and their curation

Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

Exploratory and directed search strategies at a social science data archive

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

Developing systems to encourage FAIR and secure research data

Welcome to first issue of IASSIST Quarterly for 2024; this is Volume 48 of the journal (IQ 48(1) 2024). The three papers in this issue represent different aspects of researchers’ use of data archives. Experts from three major data archives (ICPSR, IPUMS, and GESIS) share their experiences developing systems to make research resources in their archives more findable, accessible, and usable. Lafia, Million, and Hemphill in their article “Exploratory and directed search strategies at a social science data archive” investigate search strategies of users of a large social science data archive. In an effort to understand how users search for curated research data, the authors analyze data queries issued through the Inter-University Consortium for Political and Social Research (ICPSR) website’s search box. The study is meant to inform archives and repositories of their users’ experience and help develop systems that encourage dataset findability, accessibility and reuse. The authors identify two types of data searches, exploratory and directed searches. They find that while users that issued exploratory queries were able to navigate the ICPSR website successfully and refine their searches, they may benefit from more explicit support for query reformation. The authors suggest ways development of tools and training could improve users’ experience. In the article “Stewarding our resources: Building a sustainable IPUMS archival document access system” Daina Magnuson describes IPUMS experience building a web interface to support exploration and dissemination of archival materials associated with IPUMS International (IPUMS-I). Ancillary materials, including thousands of unique pieces of census and survey documentation were obtained by IPUMS-I during data acquisition efforts. This rich source material was curated and preserved by archival staff. Magnuson describes in details the development of a system that would make these resources findable, searchable and downloadable to internal users as well as IPUMS researchers. Strategies describe may inform other data archives considering developing similar tools. Deborah Wiltshire in her article “Developing canonical ‘Safe Researcher’ training materials for Trusted Research Environment” describes the development of training materials for researchers applying to access restricted data through Trusted Research Environment (TRE). The increased use of a virtual desktop environment, as opposed to physical safe rooms, offers researchers easier and more flexible access to restricted data; however, it offers fewer physical safeguards. Attending a mandatory ‘Safe Researcher’ training is one of the principles of the Five Safe Framework[i] developed by the UK’s Office for National Statistics to bridge this gap and assure the safe use of sensitive data. The process described in the article could be easily adapted by other TREs according to their needs and settings (e.g., in-person vs. online training). In the spirit of making information findable, accessible and reusable, we’re happy to share that the Open Journal System platform from the University of Alberta has enabled author linking to ORCID. This means that IQ authors are now able to connect their ORCID iDs to their IQ publications, so that their ORCID profiles will automatically update with those citations as their articles are published. For existing IQ account holders, log into your account at the upper right of the IQ page, and go to Profile > Public. Then click “Create or Connect your ORCID iD” (just below the Homepage URL field). For details, see this video walkthrough of profile authentication. New IQ users will automatically be prompted to link (or create) their ORCID iDs during IQ account creation. Authors are also encouraged to note their ORCID iD link with their affiliation and contact information in an endnote when submitting a manuscript for publication. We hope to see many of you at the 49th Annual IASSIST Conference in Halifax, Nova Scotia, Canada, May 28-31, 2024! Ofira Schwartz and Michele Hayslett, March 2024

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

ABSTRACTData users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter‐university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine‐readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot‐based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.

<em>How and Why Do Researchers Reference Data</em>? A Study of Rhetorical Features and Functions of Data References in Academic Articles

Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly impact and give credit to the original data producers. Furthermore, it can be challenging to understand researchers’ motivations for referencing data. Like references to academic literature, data references perform various rhetorical functions, such as paying homage, signaling disagreement, or drawing comparisons. This paper studies how and why researchers reference social science data in their academic writing. We develop a typology to model relationships between the entities that anchor data references, along with their features (access, actions, locations, styles, types) and functions (critique, describe, illustrate, interact, legitimize). We illustrate the use of the typology by coding multidisciplinary research articles (n = 30) referencing social science data archived at the Inter-university Consortium for Political and Social Research (ICPSR). We show how our typology captures researchers’ interactions with data and purposes for referencing data. Our typology provides a systematic way to document and analyze researchers’ narratives about data use, extending our ability to give credit to data that support research.

The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-intensive science because it makes complex data pipelines possible, studies reproducible, and data reusable. Yet the complexities of the hands-on, technical, and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of data curators invisible but also hides the impact that curators' work has on the later usability, reliability, and reproducibility of data. To better understand the work and impact of data curation, we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). We asked: What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulated accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. While we identified numerous curatorial actions performed by ICPSR curators, we also found that curators rely on a number of craft practices to perform their jobs. The reality of their work practices defies the rote sequence of events implied by many life cycle or workflow models. Further, we show that craft practices are needed to enact data curation best practices and standards. The craft that goes into data curation is often invisible to end users, but it is well recognized by ICPSR curators and their supervisors. Explicitly acknowledging and supporting data curators as craftspeople is important in creating sustainable and successful curatorial infrastructures.

Subdivisions and crossroads: Identifying hidden community structures in a data archive’s citation network

Abstract Data archives are an important source of high-quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities—those that use the same scientific data sets—are organized. This paper analyzes the community structure of an authoritative network of data sets cited in academic publications, which have been collected by a large, social science data archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through network analysis, we identified communities of social science data sets and fields of research connected through shared data use. We argue that communities of exclusive data reuse form “subdivisions” that contain valuable disciplinary resources, while data sets at a “crossroads” broadly connect research communities. Our research reveals the hidden structure of data reuse and demonstrates how interdisciplinary research communities organize around data sets as shared scientific inputs. These findings contribute new ways of describing scientific communities to understand the impacts of research data reuse.

Learning While Black: A Qualitative Analysis of the Impact of Race in a U.S. High School

One of the major contributing factors to the Black-White achievement gap is school context. While many factors can affect academic achievement amongst Black youth, school climate and inter-racial relations are among the root causes which impact Black students’ development. In this qualitative study, we conducted secondary analysis of existing data consisting of interview transcripts and focus group discussions held in 2013 with 21 Black students who attended a public charter high school in Michigan. Open access data were downloaded from the University of Michigan Inter-University Consortium for Political and Social Research (ICPSR) database. We analyzed qualitative data for themes and constructs related to school context and such influences on Black youth development. The following three constructs were identified: racial identity of Black youth, the impact of teachers on Black students’ racial identity and development, and lastly, exploring the dominant culture of the U.S. education system. For researchers, this data highlights a need for more studies on the intersection of race, school environment, and youth development. For administrators and policy makers, the results emphasize the need for investment and engagement in the daily experiences of Black students, reducing racism, and increasing diversity of curriculum. Teachers and school administrators may benefit from specialized training to better address cultural, educational, and developmental needs of Black youth in the context of their role in school.

“It’s Like the Elephant in the Room” A Qualitative Analysis of Racism in a U.S. High School

Identifying and addressing systemic racial oppression in the education system is a key component in confronting pervasive health and economic disparities for Black students. In this qualitative study, we conducted secondary analysis of existing data. Transcripts of interviews and focus group discussions belonged to 21 Black students in a charter school in Michigan in the year 2013. Open access data were downloaded from University of Michigan’s Inter-university Consortium for Political and Social Research (ICPSR) database in 2022. We used an inductive analytic approach to analyze the qualitative data for constructs related to experiences of structural racism. Three constructs that characterized students’ experiences of structural racism in the U.S. education system were found: (a) Lack of Color-conscious Curriculum; (b) Selective Cultural Erasure; and (c) The Demonstration of Racialized Power. For researchers, these data highlight a need for more studies on the effects of structural racism in the U.S. education system on educational, economic, and health outcomes. For administrative and policy makers, the results emphasize the need for educational initiatives that address deeply-rooted structural inequalities in the U.S. education system. This may include adoption of color-conscious curriculum and culturally responsive pedagogy, the utilization of restorative justice practices, and reparations for Black Americans.

The financial maintenance of social science data archives: Four case studies of long‐term infrastructure work

AbstractContributing to the literature on knowledge infrastructure maintenance, this article describes a historical longitudinal analysis of revenue streams employed by four social science data organizations: the Roper Center for Public Opinion, the Inter‐university Consortium for Political and Social Research (ICPSR), the UK Data Archive (UKDA), and the LIS Cross‐National Data Center in Luxembourg (LIS). Drawing on archival documentation and interviews, we describe founders' assumptions about revenue, changes to revenue streams over the long term, practices for developing and maintaining revenue streams, the importance of financial support from host organizations, and how the context of each data organization shaped revenue possibilities. We extend conversations about knowledge infrastructure revenue streams by showing the types of change that have occurred over time and how it occurs. We provide examples of the types of flexibility needed for data organizations to remain sustainable over 40–60 years of revenue changes. We distinguish between Type A flexibilities, or development of new products and services, and Type B flexibilities, or continuous smaller adjustments to existing revenue streams. We argue that Type B flexibilities are as important as Type A, although they are easily overlooked. Our results are relevant to knowledge infrastructure managers and stakeholders facing similar revenue challenges.

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.

An Insider’s Take on Data Curation: Context, Quality, and Efficiency

This commentary describes how context, quality, and efficiency guide data curation at the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR). These three principals manifest from necessity. A primary purpose of this work is to facilitate secondary data analysis but in order to so, the context of data must be documented. Since a mistake in this work would render any results published from the data inaccurate, quality is paramount. However, optimizing data quality can be time consuming, so automative curation practices are necessary for efficiency. The implementation of these principles (context, quality, and efficiency) is demonstrated by a recent case study with a high-profile dataset. As the nature of data work changes, these principles will continue to guide the practice of curation and establish valuable skills for future curators to cultivate.

Study protocol for TILDA COVID-19 survey. Altered lives in a time of crisis: preparing for recovery from the impact of the COVID-19 pandemic on the lives of older adults

Background: Older adults are the most at-risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and among the most affected by measures put in place to prevent the spread of the virus. While the full effect of the public health measures, such as social distancing and wearing masks in public spaces, implemented since March 2020 are not yet known, it is expected that they will have a severely damaging effect on physical and psychological wellbeing. The Irish Longitudinal Study on Ageing (TILDA) has been researching the lives of older adults in Ireland since 2008 with data collection conducted at two-year intervals. With an established research infrastructure, TILDA was ideally placed to examine the effect of the coronavirus disease 2019 (COVID-19) pandemic on older adults. The aim of this study is to document the lives of older adults during the COVID-19 pandemic to understand the effect of the pandemic and public health responses on their wellbeing. Methods: Data was collected from TILDA participants via self-completion-questionnaire (SCQ). The SCQ contains ten sections that capture information on many aspects of people’s lives during the pandemic including, changes in behaviour and social interactions, physical and psychological wellbeing indicators, healthcare utilisation, and exposure to SARS-CoV-2. Ethical approval was granted by the National Research Ethics Committee (NREC). Conclusions: Research findings will be shared in a variety of formats including research reports and briefs, presentations, and academic papers. Data will be archived in the Irish Social Science Data Archive (ISSDA) and the Inter-university Consortium for Political and Social Research (ICPSR). As well as documenting the impact of the COVID-19 pandemic on older adults, findings from this study will provide important information to policy-makers as we respond to the damage caused by the COVID-19 pandemic.

Advocating for reproducibility

As guest editors, we are excited to publish this special double issue of IASSIST Quarterly. The topics of reproducibility, replicability, and transparency have been addressed in past issues of IASSIST Quarterly and at the IASSIST conference, but this double issue is entirely focused on these issues.
 In recent years, efforts “to improve the credibility of science by advancing transparency, reproducibility, rigor, and ethics in research” have gained momentum in the social sciences (Center for Effective Global Action, 2020). While few question the spirit of the reproducibility and research transparency movement, it faces significant challenges because it goes against the grain of established practice.
 We believe the data services community is in a unique position to help advance this movement given our data and technical expertise, training and consulting work, international scope, and established role in data management and preservation, and more. As evidence of the movement, several initiatives exist to support research reproducibility infrastructure and data preservation efforts:
 
 Center for Open Science (COS) / Open Science Framework (OSF)[i]
 Berkeley Initiative for Transparency in the Social Sciences (BITSS)[ii]
 CUrating for REproducibility (CURE)[iii]
 Project Tier[iv]
 Data Curation Network[v]
 UK Reproducibility Network[vi]
 
 While many new initiatives have launched in recent years, prior to the now commonly used phrase “reproducibility crisis” and Ioannidis publishing the essay, “Why Most Published Research Findings are False,” we know that the data services community was supporting reproducibility in a variety of ways (e.g., data management, data preservation, metadata standards) in wellestablished consortiums such as Inter-university Consortium for Political and Social Research (ICPSR) (Ioannidis, 2005).
 The articles in this issue comprise several very important aspects of reproducible research:
 
 Identification of barriers to reproducibility and solutions to such barriers
 Evidence synthesis as related to transparent reporting and reproducibility
 Reflection on how information professionals, researchers, and librarians perceive the reproducibility crisis and how they can partner to help solve it.
 
 The issue begins with “Reproducibility literature analysis” which looks at existing resources and literature to identify barriers to reproducibility and potential solutions. The authors have compiled a comprehensive list of resources with annotations that include definitions of key concepts pertinent to the reproducibility crisis.
 The next article addresses data reuse from the perspective of a large research university. The authors examine instances of both successful and failed data reuse instances and identify best practices for librarians interested in conducting research involving the common forms of data collected in an academic library.
 Systematic reviews are a research approach that involves the quantitative and/or qualitative synthesis of data collected through a comprehensive literature review. “Methods reporting that supports reader confidence for systematic reviews in psychology” looks at the reproducibility of electronic literature searches reported in psychology systematic reviews.
 A fundamental challenge in reproducing or replicating computational results is the need for researchers to make available the code used in producing these results. But sharing code and having it to run correctly for another user can present significant technical challenges. In “Reproducibility, preservation, and access to research with Reprozip, Reproserver” the authors describe open source software that they are developing to address these challenges. 
 Taking a published article and attempting to reproduce the results, is an exercise that is sometimes used in academic courses to highlight the inherent difficulty of the process. The final article in this issue, “ReprohackNL 2019: How libraries can promote research reproducibility through community engagement” describes an innovative library-based variation to this exercise.
 
 Harrison Dekker, Data Librarian, University of Rhode Island
 Amy Riegelman, Social Sciences Librarian, University of Minnesota
 
 References 
 Center for Effective Global Action (2020), About the Berkeley Initiative for Transparency in the Social Sciences. Available at: https://www.bitss.org/about (accessed 23 June 2020).
 Ioannidis, J.P. (2005) ‘Why most published research findings are false’, PLoS Medicine, 2(8), p. e124. doi: https://doi.org/10.1371/journal.pmed.0020124
 
 [i] https://osf.io
 [ii] https://www.bitss.org/
 [iii] http://cure.web.unc.edu
 [iv] https://www.projecttier.org/
 [v] https://datacurationnetwork.org/
 [vi] https://ukrn.org

Why they shared: recovering early arguments for sharing social scientific data.

Most social scientists today think of data sharing as an ethical imperative essential to making social science more transparent, verifiable, and replicable. But what moved the architects of some of the U.S.'s first university-based social scientific research institutions, the University of Michigan's Institute for Social Research (ISR), and its spin-off, the Inter-university Consortium for Political and Social Research (ICPSR), to share their data? Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical imperative intrinsic to social science but as a useful means to the diverse ends of financial stability, scholarly and institutional autonomy, and epistemological reproduction. I conclude that data sharing must be evaluated not only on the basis of the scientific ideals its supporters affirm, but also on the professional objectives it serves.

Finding, Accessing, and Using Secondary Data for Research on Gifted Education and Advanced Academics

Secondary data analysis can benefit researchers of advanced academics by providing large sample sizes and a variety of data on multiple topics. However, using secondary data comes with unique challenges. This article will outline how gifted education researchers can find, access, and use secondary data. Data are available on children from birth to adulthood and are typically accessed through the Inter-university Consortium for Political and Social Research (ICPSR) or the National Center for Education Statistics (NCES). The majority of data sources have public-use files available, but some sensitive data may require special permissions. This article includes examples of advanced academic research that used popular databases along with software options for utilizing available data. We conclude with considerations researchers should take into account when considering using secondary data analysis, such as computer memory and technical skills.

Are the effects of parental control/support and peer delinquency on future offending cumulative or interactive? A multiple group analysis of 10 longitudinal studies

PurposeThis study assessed whether the combined effect of parental control/support and peer delinquency on future participant offending was cumulative, interactive, or redundant. MethodsA review of database studies available through the Inter-university Consortium for Political and Social Research (ICPSR) website identified 10 longitudinal studies with prospective self-report data on parental control/support, peer delinquency or deviance, and future offending in participants 18 years of age and younger. ResultsA multiple group analysis revealed that while there was mild to moderate evidence of measurement invariance or consistency of results across gender and race, there was no evidence of consistency of substantive relationships across the 10 studies. A review of findings from each individual study revealed that while the parent and peer main effects were each significant in 9 out of the 10 studies, the parent x peer interaction was significant in only 1 out of 10 studies. ConclusionsThe fact that the parent and peer main effects achieved combined significance in 8 out of 10 studies, while their interaction achieved significance in only one study, suggests that the relationship between parenting and peers is more cumulative than interactive. These results have important implications for theory, research, and practice.

Establishing an International Data Linkage Repository Workgroup Toward a Benchmarking Repository

IntroductionAccess to real data with diverse attributes is critical for effective development of any data analytic algorithm. Benchmarking data repositories have all been vital to the development of research communities focused on algorithm development. This work reports on the development of such a data repository for record linkage.
 Objectives and ApproachEstablishing a common benchmarking repository of real data can propel a field to the next level of rigor by facilitating comparison of different algorithms, understanding what type of algorithms work best under certain real data conditions and problem domains, promoting transparency and replicability of research, and creating incentives for proper citations for contributions. In addition, benchmarking repositories can bring together the diverse stakeholders (e.g., computer scientists, statisticians, data custodians, data users including social, behaviour, economic, and health (SBEH) scientists) that can advance the field more effectively than could researchers from any single discipline.
 ResultsIn Fall 2016, international leaders in record linkage formed a Data Linkage Repository workgroup (DLRep) to establish a benchmarking data repository for record linkage. The workgroup is working in collaboration with The Inter-university Consortium for Political and Social Research (ICPSR) to host the site data repository planned for release in Summer 2018. The repository for record linkage research will house various types of real data that require linking with metadata, unique handles for citations, proposed algorithms for evaluation criteria, and a platform for posting, sharing, and comparing results as well as citations of relevant papers. Some datasets will have the gold standard published that researchers can evaluate their results against. Other datasets will gather results to build the gold standard as a community.
 Conclusion/ImplicationsRecord linkage methodology is important to domains where data needs to be integrated from multiple sources, including diverse disciplines. Establishing an international interdisciplinary research community around a benchmark data linkage repository to validate and compare linkage algorithms is crucial to fully realizing the social benefits of data about people.

Social science data repositories in data deluge

PurposeOwing to the recent surge of interest in the age of the data deluge, the importance of researching data infrastructures is increasing. The open archival information system (OAIS) model has been widely adopted as a framework for creating and maintaining digital repositories. Considering that OAIS is a reference model that requires customization for actual practice, this paper aims to examine how the current practices in a data repository map to the OAIS environment and functional components.Design/methodology/approachThe authors conducted two focus-group sessions and one individual interview with eight employees at the world’s largest social science data repository, the Interuniversity Consortium for Political and Social Research (ICPSR). By examining their current actions (activities regarding their work responsibilities) and IT practices, they studied the barriers and challenges of archiving and curating qualitative data at ICPSR.FindingsThe authors observed that the OAIS model is robust and reliable in actual service processes for data curation and data archives. In addition, a data repository’s workflow resembles digital archives or even digital libraries. On the other hand, they find that the cost of preventing disclosure risk and a lack of agreement on the standards of text data files are the most apparent obstacles for data curation professionals to handle qualitative data; the maturation of data metrics seems to be a promising solution to several challenges in social science data sharing.Originality/valueThe authors evaluated the gap between a research data repository’s current practices and the adoption of the OAIS model. They also identified answers to questions such as how current technological infrastructure in a leading data repository such as ICPSR supports their daily operations, what the ideal technologies in those data repositories would be and the associated challenges that accompany these ideal technologies. Most importantly, they helped to prioritize challenges and barriers from the data curator’s perspective and to contribute implications of data sharing and reuse in social sciences.

Inter-University Consortium for Political and Social Research (ICPSR)

Not applicable