Barriers and Opportunities for Tribal Access to Public Health Data to Advance Health Equity.

Public health authorities (PHAs), including Tribal nations, have the right and responsibility to protect and promote the health of their citizens. Although Tribal nations have the same need and legal authority to access public health data as any other PHA, significant legal challenges continue to impede Tribal data access.

Are Open Science instructions targeted to ecologists and evolutionary biologists sufficient? A literature review of guidelines and journal data policies.

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field-specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long-term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non-existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.

European survey: citizens' attitudes on personalized medicine, genetic testing and health data sharing - design and delivery.

In the transformative landscape of healthcare, personalized medicine emerges as a pivotal shift, harnessing genetic, environmental and lifestyle data to tailor medical treatments for enhanced outcomes and cost efficiency. Central to its success is public engagement and consent to share health data amidst rising data privacy concerns. To investigate European public opinion on this paradigm, we executed a comprehensive cross-sectional survey to capture the general public's views on personalized medicine and data-sharing modalities, including digital tools and electronic records. The survey was distributed in eight major European Union countries and the results aim at guiding future policymaking and trust-building measures for secure health data exchange. This article delineates our methodological approach, whereby survey findings will be expounded in subsequent publications.

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Moving Genomics into the Clinic: Platforms for Implementing Clinical Genomic Data-Sharing in Ways That Address Ethical, Legal and Social Implications.

This section explores the challenges involved in translating genomic research into genomic medicine. A number of priorities have been identified in the Australian National Health Genomics Framework for addressing these challenges. Responsible collection, storage, use and management of genomic data is one of these priorities, and is the primary theme of this section. The recent release of Genomical, an Australian data-sharing platform, is used as a case study to illustrate the type of assistance that can be provided to the health care sector in addressing this priority. The section first describes the National Framework and other drivers involved in the move towards genomic medicine. The section then examines key ethical, legal and social factors at play in genomics, with particular focus on privacy and consent. Finally, the section examines how Genomical is being used to help ensure that the move towards genomic medicine is ethically, legally and socially sound and that it optimises advances in both genomic and information technology.

Dataset on a reliability generalization meta-analysis of the Oxford COVID-19 vaccine hesitancy scale.

The Oxford COVID-19 Vaccine Hesitancy Scale is a 7-item psychometric scale developed by Freeman and colleagues a year after detecting the first case of the disease in 2019. The scale assesses people's thoughts, feelings, and behavior toward COVID-19 vaccines. A comprehensive search of major electronic databases, including Scopus, Clarivate Analytics, and PubMed, was conducted to extract eligible articles for inclusion in this meta-analysis. This paper reports information on data collected for a reliability generalization meta-analysis of the Oxford COVID-19 Vaccine Hesitancy Scale. The dataset incorporates information on the average reliability of the scale as measured with Cronbach's alpha in 20 studies included in the meta-analysis. Several benefits can be derived from the dataset. In particular, the research community would find this dataset beneficial as it can enhance their understanding of the health challenges of COVID-19, helping them come up with better solutions to eradicate the disease.

Community-Engaged Data Science (CEDS): A Case Study of Working with Communities to Use Data to Inform Change.

Data-informed decision making is a critical goal for many community-based public health research initiatives. However, community partners often encounter challenges when interacting with data. The Community-Engaged Data Science (CEDS) model offers a goal-oriented, iterative guide for communities to collaborate with research data scientists through data ambassadors. This study presents a case study of CEDS applied to research on the opioid epidemic in 18 counties in Ohio as part of the HEALing Communities Study (HCS). Data ambassadors provided a pivotal role in empowering community coalitions to translate data into action using key steps of CEDS which included: data landscapes identifying available data in the community; data action plans from logic models based on community data needs and gaps of data; data collection/sharing agreements; and data systems including portals and dashboards. Throughout the CEDS process, data ambassadors emphasized sustainable data workflows, supporting continued data engagement beyond the HCS. The implementation of CEDS in Ohio underscored the importance of relationship building, timing of implementation, understanding communities' data preferences, and flexibility when working with communities. Researchers should consider implementing CEDS and integrating a data ambassador in community-based research to enhance community data engagement and drive data-informed interventions to improve public health outcomes.

'I just LOVE data': perceptions and practices of data sharing and privacy among users of the Lioness.

High profile data breaches and the proliferation of self-tracking technologies generating bio-feedback data have raised concerns about data privacy and data sharing practices among users of these devices. However, our understanding of how self-trackers in sexual health populations, where the data may be sensitive, personal, and stigmatising, perceive data privacy and sharing is limited. This study combined industry consultation with a survey of users of the world's first biofeedback smart vibrator, the Lioness, that enables users to monitor and analyse their sexual response intensity and orgasm duration over time. We found users of the Lioness are motivated to self-track by both individual and altruistic goals: to learn more about their bodies, and to contribute to research that leads to better sexual health outcomes. Perceptions of data privacy and data sharing were shaped by an eagerness to collaborate with sexual health researchers to challenge traditional male-centric perspectives in biomedical research on women's sexual health, where gender plays a crucial role in defining healthcare systems and outcomes. This study extends our understanding of the non-digital aspects of self-tracking by emphasising the role of gender and inclusive healthcare advocacy in shaping perceptions of data privacy and sharing within sexual health populations.

Analytical code sharing practices in biomedical research.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

Accelerating the continuous community sharing of digital neuromorphology data.

The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 h to 2 min. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.

Data Availability Statements and Data Sharing in Urology: A False Promise?

BACKGROUND AND OBJECTIVE: It is considered standard for authors of scientific papers to provide access to their raw data. The purpose of this study was to investigate data availability statements (DAS) and the actual availability of data in urology. METHODS: The DAS policies of the top ten urology journals were retrieved. Then 190 selected papers were classified according to their DAS status. Finally, we contacted the corresponding authors of papers that stated that data were available on request to enquire about this possibility. KEY FINDINGS AND LIMITATIONS: All journals either required or highly recommended a DAS. Among the selected articles, 52% (99/190) included a DAS stating data availability, most often on reasonable request to the corresponding author. A formal DAS was lacking in 29.5% (56/190) of the articles, with an additional 18.3% (35/190) citing various reasons for data unavailability. On contact, 23.4% (15/64) of corresponding authors indicated a willingness to share their data. Overall, data were unavailable in 73.7% (140/190) of cases. There was no difference between papers dealing with malignant and benign diseases. CONCLUSIONS AND CLINICAL IMPLICATIONS: There is a gap between the intention to share data and actual practice in major urological journals. As data sharing plays a critical role in safeguarding the reliability of published results and in the potential for reanalysis and merging of datasets, there is a clear need for improvement. Easier access to data repositories and stronger enforcement of existing journal policies are essential. PATIENT SUMMARY: To ensure the reliability of data and allow further analyses, major urology journals require authors to make their data available to other researchers when possible. However, in practice we found that data were only accessible for about a quarter of published scientific papers.

Whole genome sequencing in (recurrent) glioblastoma: challenges related to informed consent procedures and data sharing.

Increased use of whole genome sequencing (WGS) in neuro-oncology for diagnostics and research purposes necessitates a renewed conversation about informed consent procedures and governance structures for sharing personal health data. There is currently no consensus on how to obtain informed consent for WGS in this population. In this narrative review, we analyze the formats and contents of frameworks suggested in literature for WGS in oncology and assess their benefits and limitations. We discuss applicability, specific challenges, and legal context for patients with (recurrent) glioblastoma. This population is characterized by the rarity of the disease, extremely limited prognosis, and the correlation of the stage of the disease with cognitive abilities. Since this has implications for the informed consent procedure for WGS, we suggest that the content of informed consent should be tailor-made for (recurrent) glioblastoma patients.

Normative challenges in data governance: insights from global health research.

Many important questions in health professions education require datasets that are built from several sources, in some cases using data collected for a different purpose. In building and maintaining these datasets, project leaders will need to make decisions about the data. While such decisions are often construed as technical, there are several normative concerns, such as who should have access, how the data will be used, how products resulting from the data will be shared, and how to ensure privacy of the individuals the data is about is respected, etc. Establishing a framework for data governance can help project leaders in avoiding problems, related to such matters, that could limit what can be learned from the data or that might put the project (or future projects) at risk. In this paper, we highlight several normative challenges to be addressed when determining a data governance framework. Drawing from lessons in global health, we illustrate three kinds of normative challenges for projects that rely on data from multiple sources or involved partnerships across institutions or jurisdictions: (1) legal and regulatory requirements, (2) consent, and (3) equitable sharing and fair distribution.

Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community.

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Lessons Learned Establishing the Palliative Care Research Cooperative's Qualitative Data Repository.

Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.

BACKGROUND: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. RESULTS: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. CONCLUSIONS: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care.

Introduction: Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. Methods: We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. Results: We have developed the pre-built packages "ResearchData-to-FHIR," "FHIR-to-OMOP," and "Addons," which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Conclusion: Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

PHA4GE quality control contextual data tags: standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training.

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

Journal requirement for data sharing statements in clinical trials: a cross-sectional study.

OBJECTIVES: Data sharing statements are considered routine in clinical trial reporting and represent a step toward data transparency. The International Committee of Medical Journal Editors (ICMJE) required clinical trials to publish data sharing statements. We aimed to assess the requirement for data sharing statements of individual participant data by biomedical journals and explore associations between journal characteristics and journal requirements for data sharing statements. STUDY DESIGN AND SETTING: In this cross-sectional study, we included all biomedical journals that published clinical trials from January 1, 2019, to December 31, 2022, and that were indexed by the Journal Citation Reports. The study outcome was the journal requirement for data sharing statements. Multivariable logistic regression analysis was used to assess the relationship between journal characteristics and requirement for data sharing statements. RESULTS: Of the 3229 biomedical journals included in the analysis, 2345 (72.6%) required authors to include data sharing statements. Journals published in the UK (OR, 3.19 [95% CI, 2.43-4.22]) and endorsing the Consolidated Standards of Reporting Trials (OR, 3.30 [95% CI, 2.78-3.92]) had greater odds of requiring data sharing statements. Journals that were open access, non-English language, in the Journal Citation Reports group of clinical medicine, and on the ICMJE list had lower odds of requiring data sharing statements, with ORs ranging from 0.18 to 0.81. CONCLUSION: Despite ICMJE recommendations, more than 27% of the biomedical journals that published clinical trials did not require clinical trials to include data sharing statements, highlighting room for improved transparency.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.