When are researchers willing to share their data? - Impacts of values and uncertainty on open data in academia

Unmasking the Ethical Dimensions of Data-sharing in Health Research: Perspectives from Researchers at a Public University in Uganda

Background

In resource-limited settings like Uganda, ethical sharing of health research data is crucial for advancing scientific knowledge. Despite the growing trend of data sharing in the digital age, its adoption in low-resource contexts is often hampered by complex ethical considerations. This report investigates these ethical concerns using data from researchers at a public university, with the goal of informing the development of practical solutions to promote ethical data-sharing practices in Uganda.

Methods

A qualitative phenomenographic study was conducted with 26 participants at Makerere University College of Health Sciences, including professors, lecturers, research fellows, and PhD students. In-depth interviews were conducted via Zoom or in person, using an interview guide. Data were analyzed thematically using ATLAS.ti (V9), following both deductive and inductive approaches.

Results

The study revealed a complex landscape of data-sharing practices among researchers. Participants had varying understandings of data sharing, with some expressing limited awareness. Incentives were widely recognized as crucial for encouraging data sharing. While acknowledging data sources in publications was appreciated, some researchers advocated for co-authorship for significant contributions. Researchers' autonomy and control over data-sharing practices were influenced by factors such as research concept origination, funding sources, researchers' financial status, and analytical skills. Institutional policies, cultural norms, and customs that promote a 'siloed' research environment also significantly influenced of data-sharing behavior.

Conclusion

This study revealed a complex landscape of data-sharing practices among researchers. The varying interpretations of data sharing highlight the need for enhanced education and awareness regarding its importance. The identified incentives, such as financial rewards and co-authorship, which encourage data sharing, suggest a need to establish a fair data-sharing reward system. Additionally, policies that facilitate researchers' autonomy and data control, while fostering trust, are crucial to address the siloed research culture.

Exploring the understanding of reproducibility among stakeholders within academia and their expectations for a web-based education tool: A qualitative study

Although reproducibility is central to the scientific method, its understanding within the research community remains insufficient. We aimed to explore the perceptions of research reproducibility among stakeholders within academia, learn about possible barriers and facilitators to reproducibility-related practices, and gather their suggestions for the Croatian Reproducibility Network website. We conducted four focus groups with researchers, teachers, editors, research managers, and policymakers from Croatia (n = 23). The participants observed a lack of consensus on the core definitions of reproducibility, both generally and between disciplines. They noted that incentivization and recognition of reproducibility-related practices from publishers and institutions, alongside comprehensive education adapted to the researchers' career stage, could help with implementing reproducibility. Education was considered essential to these efforts, as it could help create a research culture based on good reproducibility-related practices and behavior rather than one driven by mandates or career advancement. This was particularly found to be relevant for growing reproducibility efforts globally. Regarding the Croatian Reproducibility Network website, the participants suggested we adapt the content to users from different disciplines or career stages and offer guidance and tools for reproducibility through which we should present core reproducibility concepts. Our findings could inform other initiatives focused on improving research reproducibility.

German funders' data sharing policies-A qualitative interview study

BACKGROUND

Data sharing is commonly seen as beneficial for science but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders' data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders' data sharing policies and overall efforts to promote data sharing.

METHODS

This study is based on sixteen guided expert interviews with representatives of German funders and German research data experts from stakeholder organisations, who shared their perceptions of German' funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages.

RESULTS

We present our findings in five sections to distinguish our interviewees' perceptions on a) the status quo of German funders' data sharing policies, b) the role of funders in promoting data sharing, c) current and potential measures by funders to promote data sharing, d) general barriers to those measures, and e) the implementation of more binding data sharing requirements.

DISCUSSION AND CONCLUSION

Although funders are perceived to be important promoters and facilitators of data sharing throughout our interviews, only few German funding agencies have data sharing policies in place. Several interviewees stated that funders could do more, for example by providing incentives for data sharing or by introducing more concrete policies. Our interviews suggest the academic freedom of grantees is widely perceived as an obstacle for German funders in introducing mandatory data sharing requirements. However, some interviewees stated that stricter data sharing requirements could be justified if data sharing is a part of good scientific practice.

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.

Longitudinal studies of child mental disorders in the general population: A systematic review of study characteristics

Introduction

Longitudinal studies of child mental disorders in the general population (herein study) investigate trends in prevalence, incidence, risk/protective factors, and sequelae for disorders. They are time and resource intensive but offer life-course perspectives and examination of causal mechanisms. Comprehensive syntheses of the methods of existing studies will provide an understanding of studies conducted to date, inventory studies, and inform the planning of new longitudinal studies.

Methods

A systematic review of the research literature in MEDLINE, EMBASE, and PsycINFO was conducted in December 2022 for longitudinal studies of child mental disorders in the general population. Records were grouped by study and assessed for eligibility. Data were extracted from one of four sources: a record reporting study methodology, a record documenting child mental disorder prevalence, study websites, or user guides. Narrative and tabular syntheses of the scope and design features of studies were generated.

Results

There were 18,133 unique records for 487 studies-159 of these were eligible for inclusion. Studies occurred from 1934 to 2019 worldwide, with data collection across 1 to 68 time points, with 70% of studies ongoing. Baseline sample sizes ranged from n = 151 to 64,136. Studies were most frequently conducted in the United States and at the city/town level. Internalizing disorders and disruptive, impulse control, and conduct disorders were the most frequently assessed mental disorders. Of studies reporting methods of disorder assessment, almost all used measurement scales. Individual, familial and environmental risk and protective factors and sequelae were examined.

Conclusions

These results summarize characteristics of existing longitudinal studies of child mental disorders in the general population, provide an understanding of studies conducted to date, encourage comprehensive and consistent reporting of study methodology to facilitate meta-analytic syntheses of longitudinal evidence, and offer recommendations and suggestions for the design of future studies. Registration DOI: 10.17605/OSF.IO/73HSW.

Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health"

Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices.

Perspectives on scientific error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings.

Estimating social bias in data sharing behaviours: an open science experiment

Open data sharing is critical for scientific progress. Yet, many authors refrain from sharing scientific data, even when they have promised to do so. Through a preregistered, randomized audit experiment (N = 1,634), we tested possible ethnic, gender and status-related bias in scientists' data-sharing willingness. 814 (54%) authors of papers where data were indicated to be 'available upon request' responded to our data requests, and 226 (14%) either shared or indicated willingness to share all or some data. While our preregistered hypotheses regarding bias in data-sharing willingness were not confirmed, we observed systematically lower response rates for data requests made by putatively Chinese treatments compared to putatively Anglo-Saxon treatments. Further analysis indicated a theoretically plausible heterogeneity in the causal effect of ethnicity on data-sharing. In interaction analyses, we found indications of lower responsiveness and data-sharing willingness towards male but not female data requestors with Chinese names. These disparities, which likely arise from stereotypic beliefs about male Chinese requestors' trustworthiness and deservingness, impede scientific progress by preventing the free circulation of knowledge.

A Survey of Research Participants’ Privacy-Related Experiences and Willingness to Share Real-World Data with Researchers

Background: Real-world data (RWD) privacy is an increasingly complex topic within the scope of personalized medicine, as it implicates several sources of data. Objective: To assess how privacy-related experiences, when adjusted for age and education level, may shape adult research participants’ willingness to share various sources of real-world data with researchers. Methods: An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch, a national health research registry. Descriptive analyses were conducted to assess survey participant demographics. Logistic regression was conducted to assess the association between participants’ five distinct privacy-related experiences and their willingness to share each of the 19 data sources with researchers, adjusting for education level and age range. Results: A total of 598 ResearchMatch adults were contacted and 402 completed the survey. Most respondents were over the age of 51 years (49% total) and held a master’s or bachelor’s degree (63% total). Over half of participants (54%) had their account accessed by someone without their permission. Almost half of participants (49%) reported the privacy of their personal information being violated. Analyses showed that, when adjusted for age range and education level, participants whose reputations were negatively affected as a result of information posted online were more likely to share electronic medical record data (OR = 2.074, 95% CI: 0.986–4.364) and genetic data (OR = 2.302, 95% CI: 0.894–5.93) versus those without this experience. Among participants who had an unpleasant experience as a result of giving out information online, those with some college/associates/trade school compared to those with a doctoral or other terminal degree were significantly more willing to share genetic data (OR = 1.064, 95% CI: 0.396–2.857). Across all privacy-related experiences, participants aged 18 to 30 were significantly more likely than those over 60 years to share music streaming data, ridesharing history data, and voting history data. Additionally, across all privacy-related experiences, those with a high school education were significantly more likely than those with a doctorate or other terminal degree to share credit card statement data. Conclusions: This study offers the first insights into how privacy-related experiences, adjusted for age range and education level, may shape ResearchMatch participants’ willingness to share several sources of real-world data sources with precision medicine researchers. Future work should further explore these insights.

Data rescue: saving environmental data from extinction

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.

Scientific Cooperation: Supporting Circumpolar Permafrost Monitoring and Data Sharing

While the world continues to work toward an understanding and projections of climate change impacts, the Arctic increasingly becomes a critical component as a bellwether region. Scientific cooperation is a well-supported narrative and theme in general, but in reality, presents many challenges and counter-productive difficulties. Moreover, data sharing specifically represents one of the more critical cooperation requirements, as part of the “scientific method [which] allows for verification of results and extending research from prior results”. One of the important pieces of the climate change puzzle is permafrost. In general, observational data on permafrost characteristics are limited. Currently, most permafrost data remain fragmented and restricted to national authorities, including scientific institutes. The preponderance of permafrost data is not available openly—important datasets reside in various government or university labs, where they remain largely unknown or where access restrictions prevent effective use. Although highly authoritative, separate data efforts involving creation and management result in a very incomplete picture of the state of permafrost as well as what to possibly anticipate. While nations maintain excellent individual permafrost research programs, a lack of shared research—especially data—significantly reduces effectiveness of understanding permafrost overall. Different nations resource and employ various approaches to studying permafrost, including the growing complexity of scientific modeling. Some are more effective than others and some achieve different purposes than others. Whereas it is not possible for a nation to effectively conduct the variety of modeling and research needed to comprehensively understand impacts to permafrost, a global community can. In some ways, separate scientific communities are not necessarily concerned about sharing data—their work is secured. However, decision and policy makers, especially on the international stage, struggle to understand how best to anticipate and prepare for changes, and thus support for scientific recommendations during policy development. To date, there is a lack of research exploring the need to share circumpolar permafrost data. This article will explore the global data systems on permafrost, which remain sporadic, rarely updated, and with almost nothing about the subsea permafrost publicly available. The authors suggest that the global permafrost monitoring system should be real time (within technical and reasonable possibility), often updated and with open access to the data (general way of representing data required). Additionally, it will require robust co-ordination in terms of accessibility, funding, and protocols to avoid either duplication and/or information sharing. Following a brief background, this article will offer three supporting themes, (1) the current state of permafrost data, (2) rationale and methods to share data, and (3) implications for global and national interests.