scholarly journals Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm

2017 ◽  
Author(s):  
Dessi Kirilova ◽  
Sebastian Karcher
Keyword(s):  
Data Sharing ◽  
Qualitative Data ◽  
Social Science Research ◽  
Science Research ◽  
Data Repository ◽  
Management Options ◽  
Secondary Use ◽  
Access Controls ◽  
Human Participant ◽  
Human Participants

While data sharing is becoming increasingly common in quantitative social inquiry, qualitative data are rarely shared. One factor inhibiting data sharing is a concern about human participant protections and privacy. Protecting the confidentiality and safety of research participants is a concern for both quantitative and qualitative researchers, but it raises specific concerns within the epistemic context of qualitative research. Thus, the applicability of emerging protection models from the quantitative realm must be carefully evaluated for application to the qualitative realm. At the same time, qualitative scholars already employ a variety of strategies for human-participant protection implicitly or informally during the research process.In this practice paper, we assess available strategies for protecting human participants and how they can be deployed. We describe a spectrum of possible data management options, such as anonymization and applying access controls, including some already employed by the Qualitative Data Repository (QDR) in tandem with its pilot depositors. Throughout the discussion, we consider the tension between modifying data or restricting access to them, and retaining their analytic value. We argue that developing explicit guidelines for sharing qualitative data generated through interaction with humans will allow scholars to address privacy concerns and increase the secondary use of their data.

scholarly journals Examining Data Repository Guidelines for Qualitative Data Sharing

2017 ◽  
Vol 13 (1) ◽  
pp. 61-73 ◽  
Author(s):  
Alison L. Antes ◽  
Heidi A. Walsh ◽  
Michelle Strait ◽  
Cynthia R. Hudson-Vitale ◽  
James M. DuBois
Keyword(s):  
Best Practices ◽  
Data Sharing ◽  
Qualitative Data ◽  
The United States ◽  
Future Research ◽  
Data Repository ◽  
Substantial Variation ◽  
Secondary Use ◽  
Rich Information ◽  
Research Questions

Qualitative data provide rich information on research questions in diverse fields. Recent calls for increased transparency and openness in research emphasize data sharing. However, qualitative data sharing has yet to become the norm internationally and is particularly uncommon in the United States. Guidance for archiving and secondary use of qualitative data is required for progress in this regard. In this study, we review the benefits and concerns associated with qualitative data sharing and then describe the results of a content analysis of guidelines from international repositories that archive qualitative data. A minority of repositories provide qualitative data sharing guidelines. Of the guidelines available, there is substantial variation in whether specific topics are addressed. Some topics, such as removing direct identifiers, are consistently addressed, while others, such as providing an anonymization log, are not. We discuss the implications of our study for education, best practices, and future research.

Introduction: Archiving Qualitative Data in Practice: Ethical Feedback

2021 ◽  
Vol 150 (1) ◽  
pp. 7-27
Author(s):  
Pablo Diaz
Keyword(s):  
Data Sharing ◽  
Qualitative Data ◽  
Social Science Research ◽  
Science Research ◽  
Research Data ◽  
Ethical Challenges ◽  
The Past ◽  
Open Research ◽  
Funding Agencies ◽  
The Cost

Over the past twenty years the normative framework that underpins social science research has undergone major shifts. Among the most salient changes is the growing incentive to archive, share and reuse research data. Today, many governments, funding agencies, research infrastructures and editors are pushing what is commonly known as Open Research Data (ORD). By reflecting on concrete experiences of data sharing, the different contributions to this issue point to the ethical challenges posed by this new trend. Through a fine objectivation of the archiving work, they call to take distance from the bureaucratic framework imposed by the new ethics and ORD policies and to think of data sharing as a situated, contextual and dynamic process. The cost of the exercise as well as the sensitivity of certain data and subjects suggest opting for flexible approaches that leave a certain autonomy and freedom of appraisal to researchers.

Beyond the matrix

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 292-302 ◽  
Author(s):  
Sebastian Karcher ◽  
Dessislava Kirilova ◽  
Nicholas Weber
Keyword(s):  
Data Sharing ◽  
Qualitative Data ◽  
Digital Data ◽  
Data Repository ◽  
Data Archiving ◽  
Early Experiences ◽  
Scholarly Publications ◽  
The Matrix ◽  
Novel Method ◽  
Human Participants

The Qualitative Data Repository (QDR) provides infrastructure and guidance for the sharing and reuse of digital data used in qualitative and multi-method social inquiry. In this paper we describe some of the repository’s early experiences providing services developed specifically for the curation of qualitative research data. We focus on QDR’s efforts to address two key challenges for qualitative data sharing. The first challenge concerns constraints on data sharing in order to protect human participants and their identities and to comply with copyright laws. The second set of challenges addresses the unique characteristics of qualitative data and their relationship to the published text. We describe a novel method of annotating scholarly publications, resulting in a “transparency appendix” that allows the sharing of such “granular data” (Moravcsik et al., 2013). We conclude by describing the future directions of QDR’s services for qualitative data archiving, sharing, and reuse.

Something is happening: encountering silence in disability research

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chelsea Jones ◽  
Fiona Cheuk
Keyword(s):  
Design Methodology ◽  
Qualitative Data ◽  
Social Science Research ◽  
Science Research ◽  
Critical Disability Studies ◽  
Content Type ◽  
Research Perspective ◽  
Valid Data ◽  
Collection Practices ◽  
Disability Research

PurposeOften, researchers view silence as antagonistic to equity-aimed projects. Because verbal, written, and textually agentive communications are presumed to be the most valid qualitative-research data, moments of silence are under-analyzed. Yet, we argue that silence holds meaning as data and that it is a valid, rich form of communication.Design/methodology/approachThrough this reflective analysis of silence, we invite readers to reconceptualize silence in research from a critical disability-research perspective with emphasis on crip willfulness. We introduce silence as an interpretive, agentive and relational gesture.FindingsWe attend to silence as necessary in all research because it helps researchers excavate able-bodied expectations about communication in qualitative-data-collection practices.Originality/valueWe demonstrate that silences in research can be an interpretive, relational, and agentive gesture that can teach us about taken-for-granted assumptions about research practices. Revisiting our research encounters with this framing of silence informed by critical disability studies allows us to question how traditional social science research methods value some modalities of expression over others. Rather than viewing silence in research as moments when nothing happens, we show that silence indicates something happening and is valid data.

scholarly journals Are we ready to share qualitative research data? Knowledge and preparedness among qualitative researchers, IRB members, and data repository curators

2020 ◽  
Vol 43 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Jessica Mozersky ◽  
Heidi Walsh ◽  
Meredith Parsons ◽  
Tristan McIntosh ◽  
Kari Baldwin ◽  
...  
Keyword(s):  
Data Sharing ◽  
Qualitative Data ◽  
The United States ◽  
Open Science ◽  
Data Repository ◽  
Data Sets ◽  
Ethical Challenges ◽  
Staff Members ◽  
Stakeholder Groups ◽  
Secondary Analyses

Data sharing maximizes the value of data, which is time and resource intensive to collect. Major funding bodies in the United States (US), like the National Institutes of Health (NIH), require data sharing and researchers frequently share de-identified quantitative data. In contrast, qualitative data are rarely shared in the US but the increasing trend towards data sharing and open science suggest this may be required in future. Qualitative methods are often used to explore sensitive health topics raising unique ethical challenges regarding protecting confidentiality while maintaining enough contextual detail for secondary analyses. Here, we report findings from semi-structured in-depth interviews with 30 data repository curators, 30 qualitative researchers, and 30 IRB staff members to explore their experience and knowledge of QDS. Our findings indicate that all stakeholder groups lack preparedness for QDS. Researchers are the least knowledgeable and are often unfamiliar with the concept of sharing qualitative data in a repository. Curators are highly supportive of QDS, but not all have experienced curating qualitative data sets and indicated they would like guidance and standards specific to QDS. IRB members lack familiarity with QDS although they support it as long as proper legal and regulatory procedures are followed. IRB members and data curators are not prepared to advise researchers on legal and regulatory matters, potentially leaving researchers who have the least knowledge with no guidance. Ethical and productive QDS will require overcoming barriers, creating standards, and changing long held practices among all stakeholder groups.

scholarly journals Pragmatic precision oncology: the secondary uses of clinical tumor molecular profiling

2016 ◽  
Vol 23 (4) ◽  
pp. 773-776 ◽  
Author(s):  
Matthew J Rioth ◽  
Ramya Thota ◽  
David B Staggs ◽  
Douglas B Johnson ◽  
Jeremy L Warner
Keyword(s):  
Science Research ◽  
Molecular Profiling ◽  
Molecular Profile ◽  
Data Repository ◽  
Precision Oncology ◽  
Profile Data ◽  
Secondary Use ◽  
Discovery Science ◽  
Real Time Database ◽  
One Year

Abstract Background Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. Objective To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. Methods A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. Results Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. Conclusions A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology.

Ethics, Epistemology, and Openness in Research with Human Participants

2021 ◽  
pp. 1-17
Author(s):  
Diana Kapiszewski ◽  
Elisabeth Jean Wood
Keyword(s):  
Social Science ◽  
Political Science ◽  
Ways Of Knowing ◽  
Science Research ◽  
Ethical Principles ◽  
The Political ◽  
Multiple Features ◽  
Human Participant ◽  
Research Transparency ◽  
Human Participants

The political science discipline has recently engaged in contentious debate about the value of “research transparency,” particularly for research with human participants. The discipline is also holding vital conversations about research ethics and is rekindling dialogue about different ways of knowing. We offer an integrated account of how the actions that scholars who conduct human participant research take to respect ethical principles (which vary by research substance and settings), and their epistemological commitments (which vary across researchers), influence openness, a broader concept than “transparency.” These principles and commitments shape scholars’ openness practices simultaneously—both independently and in concert—serving as a prism through which multiple features of a research project are refracted, and resulting in a scholar’s inclination and ability to pursue openness in different ways and to different degrees with the audiences of her work. We also show how ethical principles and epistemological commitments can not only constrain and prevent openness, but also animate and require it. We suggest that scholars pursuing openness ethically, and in ways that honor their epistemological commitments, represents good social science, and we offer strategies for doing so. To develop our argument, we focus primarily on two research methods, ethnography and interviews, and on openness toward two audiences, human participants and research communities. Our account illuminates how the heterogeneity of human participant research makes it inappropriate, indeed impossible, to develop blanket rules for pursuing openness. Throughout, we highlight the importance of reflexivity for the ethical conduct of, and for being ethically open about, political science research.

scholarly journals Transfer of Data using Raspberry PI

2021 ◽  
Vol 9 (VI) ◽  
pp. 2298-2302
Author(s):  
Prof. Martand Jha
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Digital Literacy ◽  
Data Exchange ◽  
Low Cost ◽  
Social Science Research ◽  
Science Research ◽  
Literacy Skills ◽  
Raspberry Pi ◽  
Science Literature

Data sharing is not a new thing. Individuals, have been sharing the data between organizations and governments even before computers and networks were invented. However, advancements in digital literacy, skills, technology, and the adaptation of regulatory systems to the digital space over the last decade have allowed data to be exchanged more quickly and on a larger scale than ever before. We've started gathering examples of data sharing practice. The process of making research data accessible to other researchers or organizations for the purposes of social science research is known as data sharing. Informal data exchange among researchers and formal data exchange through data archives and repositories are both viable options for data sharing. Data exchange was first discussed in the social science literature. The advancement of computational technology for handling machine readable data, as well as the increased use of sample surveys as a primary mode of data collection, shaped the literature in the early 1960s. The Raspberry Pi is a simple embedded device with a small footprint and low cost that is used to minimize system complexity in terms of speed and area in real time applications.

scholarly journals On the Same Page: A Formal Process for Training Multiple Interviewers

2018 ◽  
Author(s):  
Carolyn Sattin-Bajaj
Keyword(s):  
Data Collection ◽  
Training Program ◽  
Qualitative Data ◽  
Social Science Research ◽  
Science Research ◽  
Training Models ◽  
Multi Stage ◽  
Data Collection And Analysis ◽  
Qualitative Methodologies ◽  
Research Assistants

The increased utilization of qualitative methodologies as part of mixed-method health and social science research has highlighted the need for training procedures for every stage of qualitative data collection and analysis. Yet, few group training models exist for collecting reliable, valid qualitative interview data. This article presents a multi-stage, collaborative interview training process for a large team of research assistants. The training program combines insights and techniques used in both structured and semi-structured interviewing. It also includes ongoing instruction and feedback prior to and during data collection in an effort to ensure consistency and reliability. In the article, I describe each stage of the training program in detail, review some of the challenges encountered during implementation, and conclude with a discussion of how researchers and course instructors might adapt the methods to fit their particular needs.

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