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

Walsh, Heidi A.; Parsons, Meredith; McIntosh, Tristan; Baldwin, Kari; DuBois, James M.

doi:10.29173/iq952

Cited by 29 publications

(55 citation statements)

References 12 publications

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“…The value of research data can be increased by making the data widely accessible to other researchers (i.e., data sharing) (Mozersky et al, 2019;Wang & Lv, 2018). Data sharing may improve the rigor and reproducibility of research, thereby contributing to scientific advancements (Borgman, 2012).…”

Section: Introductionmentioning

confidence: 99%

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Kim

Cho³

et al. 2020

PeerJ

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Background Many scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available. However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown. This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy. Methods From each of the 178 categories of the Web of Science’s 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis. Four of the authors independently reviewed the results of the journal website searches, categorized the journals’ data sharing policies, and extracted the characteristics of individual journals. Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy. Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy. Results Of the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies. Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17–0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05–7.14]). Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98–15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85–4.81]). Conclusions These findings may account for the increase in commercial publishers’ engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals. Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.

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Section: Introductionmentioning

confidence: 99%

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Kim

Cho³

et al. 2020

PeerJ

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“…We report the views of other stakeholder groups-institutional review board (IRB) members, qualitative researchers, and data repository curators-elsewhere. 27 Understanding participants' views about sharing their qualitative data may ensure that consent forms and data-sharing plans are devised in ways that are consistent with participant wishes. This is important for its own sake but also in order to avoid breaching trust in the research enterprise and potentially reducing individuals' willingness to share their data or take part in research.…”

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confidence: 99%

“…The larger project involves engaging several stakeholder groups. We report the views of other stakeholder groups—institutional review board (IRB) members, qualitative researchers, and data repository curators—elsewhere 27 . Understanding participants' views about sharing their qualitative data may ensure that consent forms and data‐sharing plans are devised in ways that are consistent with participant wishes.…”

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confidence: 99%

Research Participant Views regarding Qualitative Data Sharing

Parsons¹,

Walsh²,

Baldwin³

et al. 2020

Ethics & Human Research

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We found no studies in the United States that explored research participants' perspectives about sharing their qualitative data. We present findings from interviews with 30 individuals who participated in sensitive qualitative studies to explore their understanding and concerns regarding qualitative data sharing. The vast majority supported sharing qualitative data so long as their data were deidentified and shared only among researchers. However, they raised concerns about confidentiality if the data were not adequately deidentified and about misuse by secondary users if data were shared beyond the research community. These concerns, though, did not deter them from participating in research. Notably, participants hoped their data would be shared and may have expected or assumed this was already happening. While many could not recollect details about data‐sharing plans for studies in which they participated, they trusted researchers and institutions to appropriately handle data sharing. If individuals view data sharing as an extension or integral part of their participation in qualitative research, then researchers may have a stronger obligation to share qualitative data than previously thought. Guidelines and tools to assist researchers and institutional review board members in ethical and responsible qualitative data sharing are urgently needed.

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“…3 Notably, qualitative healthcare data, including data collected outside of the EHR, are rarely shared. 4 , 5 A major barrier to sharing qualitative data is the challenge of deidentifying unstructured non-numeric text. 5 …”

Section: Introductionmentioning

confidence: 99%

“… 5 The benefits of qualitative data sharing (QDS) include saving research resources, reducing the data collection burden on participants, enabling secondary analyses, facilitating student training, and enhancing transparency, openness, and the ability to verify findings. 4 , 5 In concurrent work, we explored the attitudes of qualitative researchers, data curators, institutional review board (IRB) members, and qualitative research participants regarding the barriers and benefits of QDS. 4 , 13 While attitudes toward QDS vary, there is broad willingness and support of QDS among stakeholder groups.…”

Section: Introductionmentioning

confidence: 99%

Enabling qualitative research data sharing using a natural language processing pipeline for deidentification: moving beyond HIPAA Safe Harbor identifiers

Gupta

Lai

et al. 2021

JAMIA Open

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Objective Sharing health research data is essential for accelerating the translation of research into actionable knowledge that can impact health care services and outcomes. Qualitative health research data are rarely shared due to the challenge of deidentifying text and the potential risks of participant reidentification. Here, we establish and evaluate a framework for deidentifying qualitative research data using automated computational techniques including removal of identifiers that are not considered HIPAA Safe Harbor (HSH) identifiers but are likely to be found in unstructured qualitative data. Materials and Methods We developed and validated a pipeline for deidentifying qualitative research data using automated computational techniques. An in-depth analysis and qualitative review of different types of qualitative health research data were conducted to inform and evaluate the development of a natural language processing (NLP) pipeline using named-entity recognition, pattern matching, dictionary, and regular expression methods to deidentify qualitative texts. Results We collected 2 datasets with 1.2 million words derived from over 400 qualitative research data documents. We created a gold-standard dataset with 280K words (70 files) to evaluate our deidentification pipeline. The majority of identifiers in qualitative data are non-HSH and not captured by existing systems. Our NLP deidentification pipeline had a consistent F1-score of ∼0.90 for both datasets. Conclusion The results of this study demonstrate that NLP methods can be used to identify both HSH identifiers and non-HSH identifiers. Automated tools to assist researchers with the deidentification of qualitative data will be increasingly important given the new National Institutes of Health (NIH) data-sharing mandate.

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

Cited by 29 publications

References 12 publications

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Research Participant Views regarding Qualitative Data Sharing

Enabling qualitative research data sharing using a natural language processing pipeline for deidentification: moving beyond HIPAA Safe Harbor identifiers

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