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 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.

scholarly journals BRCA1/2 Variant Data-Sharing Practices

2019 ◽  
Vol 47 (1) ◽  
pp. 88-96 ◽  
Author(s):  
Juli M. Bollinger ◽  
Abhi Sanka ◽  
Lena Dolman ◽  
Rachel G. Liao ◽  
Robert Cook-Deegan
Keyword(s):  
United States ◽  
Data Sharing ◽  
Clinical Management ◽  
Strong Support ◽  
The United States ◽  
Data Sets ◽  
Clinical Laboratories ◽  
The World ◽  
Diverse Data

Accessing BRCA1/2 data facilitates the detection of disease-associated variants, which is critical to informing clinical management of risks. BRCA1/2 data sharing is complex and many practices exist. We describe current BRCA1/2 data-sharing practices, in the United States and globally, and discuss obstacles and incentives to sharing, based on 28 interviews with personnel at U.S. and non-U.S. clinical laboratories and databases. Our examination of the BRCA1/2 data-sharing landscape demonstrates strong support for and robust sharing of BRCA1/2 data around the world, increasing global accesses to diverse data sets.

scholarly journals Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I—conceptual challenges

2020 ◽  
Vol 8 (2) ◽  
pp. e001389 ◽  
Author(s):  
Sergio Rutella ◽  
Michael A Cannarile ◽  
Sacha Gnjatic ◽  
Bruno Gomes ◽  
Justin Guinney ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Sets ◽  
Private Industry ◽  
Stakeholder Groups ◽  
Professional Societies ◽  
Immunotherapy Of Cancer ◽  
Biomarker Data

The sharing of clinical trial data and biomarker data sets among the scientific community, whether the data originates from pharmaceutical companies or academic institutions, is of critical importance to enable the development of new and improved cancer immunotherapy modalities. Through data sharing, a better understanding of current therapies in terms of their efficacy, safety and biomarker data profiles can be achieved. However, the sharing of these data sets involves a number of stakeholder groups including patients, researchers, private industry, scientific journals and professional societies. Each of these stakeholder groups has differing interests in the use and sharing of clinical trial and biomarker data, and the conflicts caused by these differing interests represent significant obstacles to effective, widespread sharing of data. Thus, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to identify the current barriers to biomarker data sharing in immuno-oncology (IO) and to help in establishing professional standards for the responsible sharing of clinical trial data. The conclusions of the committee are described in two position papers: Volume I—conceptual challenges and Volume II—practical challenges, the first of which is presented in this manuscript. Additionally, the committee suggests actions by key stakeholders in the field (including organizations and professional societies) as the best path forward, encouraging the cultural shift needed to ensure responsible data sharing in the IO research setting.

scholarly journals Social science data repositories in data deluge

2017 ◽  
Vol 35 (4) ◽  
pp. 626-649 ◽  
Author(s):  
Wei Jeng ◽  
Daqing He ◽  
Yu Chi
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Qualitative Data ◽  
Data Curation ◽  
Data Repository ◽  
Science Data ◽  
Data Repositories ◽  
Content Type ◽  
Social Science Data ◽  
Current Practices

Purpose Owing 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/approach The 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. Findings The 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/value The 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.

A meta-framework for designing open data studies in psychology: ethical and practical issues of open qualitative data sets

2018 ◽  
Author(s):  
Peter Branney ◽  
Kate Reid ◽  
Nollaig Frost ◽  
Susan Coan ◽  
Amy Mathieson ◽  
...  
Keyword(s):  
Observational Studies ◽  
Quantitative Research ◽  
Qualitative Data ◽  
Open Data ◽  
Open Science ◽  
Data Sets ◽  
Framework Analysis ◽  
Telephone Interviews ◽  
Research Questions

To date, open science, and particularly open data, in Psychology, has focused on quantitative research. This paper aims to explore ethical and practical issues encountered by UK-based psychologists utilising open qualitative datasets. Semi-structured telephone interviews with eight qualitative psychologists were explored using a framework analysis. From the findings, we offer up a context-consent meta-framework as a resource to help in the design of studies sharing their data and/or studies using open data. We recommend ‘secondary’ studies conduct archaeologies of context and consent to examine if the data available is suitable for their research questions. In conclusion, this research is the first we know of in the study of ‘doing’ (or not doing) open science, which could be repeated to develop a longitudinal picture or complemented with additional approaches, such as observational studies of how context and consent are negotiated in pre-registered studies and open data.

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.

scholarly journals Bringing Code to Data: Do Not Forget Governance

10.2196/18087 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e18087
Author(s):  
Christine Suver ◽  
Adrian Thorogood ◽  
Megan Doerr ◽  
John Wilbanks ◽  
Bartha Knoppers
Keyword(s):  
Data Sharing ◽  
Clinical Data ◽  
Local Control ◽  
Data Privacy ◽  
High Performance ◽  
Data Access ◽  
Open Science ◽  
Data Sets ◽  
Regulatory Frameworks ◽  
Legal Constraints

Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

scholarly journals Bringing Code to Data: Do Not Forget Governance (Preprint)

2020 ◽  
Author(s):  
Christine Suver ◽  
Adrian Thorogood ◽  
Megan Doerr ◽  
John Wilbanks ◽  
Bartha Knoppers
Keyword(s):  
Data Sharing ◽  
Clinical Data ◽  
Local Control ◽  
Data Privacy ◽  
High Performance ◽  
Data Access ◽  
Open Science ◽  
Data Sets ◽  
Regulatory Frameworks ◽  
Legal Constraints

UNSTRUCTURED Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

scholarly journals Incentivising research data sharing: a scoping review

2021 ◽  
Vol 6 ◽  
pp. 355
Author(s):  
Helen Buckley Woods ◽  
Stephen Pinfield
Keyword(s):  
Data Sharing ◽  
Cultural Change ◽  
Scoping Review ◽  
Good Practice ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Data Practices

Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.

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.

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