Daily life in the Open Biologist's second job, as a Data Curator.

Background: Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications. Methods: Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity. Conclusion: We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

Researchers should openly share data associated with their publications unless there is a valid reason not to. Additionally, datasets have to be described with enough detail to ensure that they are reproducible and reusable by others. Since most research institutions offer limited professional support in this area, the responsibility for data sharing largely falls to researchers themselves. However, many research groups still struggle to follow data reusability principles in practice. In this work, we describe our data curation (data organization and management) efforts working directly with the researchers who create the data. We show the steps we took to organize, standardize, and share several datasets in biological sciences, pointing out the main challenges we faced. Finally, we suggest simple and practical data management actions, as well as tools that experimentalists can integrate into their daily work, to make sharing data easier and more effective.

Current state of data stewardship tools in life science.

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

A secure healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain.

Enhancing data privacy security in medical data sharing is crucial for the informatization development in the healthcare sector. This paper proposes a healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain (2DCM-DS). Specifically, a new two-dimensional chaotic mapping is proposed, which demonstrates superior chaotic performance. Then, by incorporating biometric audio information as an identity credential and integrating it with the proposed two-dimensional chaotic mapping, we design a data encryption method that establishes a strongly coupled and bi-directionally verifiable data ownership relationship in healthcare data sharing. Finally, we employ blockchain as the underlying network and design corresponding smart contracts to support 2DCM-DS. This approach addresses potential issues of unauthorized access, malicious tampering, and single points of failure in centralized data sharing. Experimental results demonstrate that 2DCM-DS effectively protects data security under the specified attack models. The results validate the security and efficiency of the 2DCM-DS, proving its application potential in healthcare insurance data sharing scenarios.

The GENESIS database and tools: A decade of discovery in Mendelian genomics.

In the past decade, human genetics research saw an acceleration of disease gene discovery and further dissection of the genetic architectures of many disorders. Much of this progress was enabled via data aggregation projects, collaborative data sharing among researchers, and the adoption of sophisticated and standardized bioinformatics analyses pipelines. In 2012, we launched the GENESIS platform, formerly known as GEM.app, with the aims to 1) empower clinical and basic researchers without bioinformatics expertise to analyze and explore genome level data and 2) facilitate the detection of novel pathogenic variation and novel disease genes by leveraging data aggregation and genetic matchmaking. The GENESIS database has grown to over 20,000 datasets from rare disease patients, which were provided by multiple academic research consortia and many individual investigators. Some of the largest global collections of genome-level data are available for Charcot-Marie-Tooth disease, hereditary spastic paraplegia, and cerebellar ataxia. A number of rare disease consortia and networks are archiving their data in this database. Over the past decade, more than 1500 scientists have registered and used this resource and published over 200 papers on gene and variant identifications, which garnered >6000 citations. GENESIS has supported >100 gene discoveries and contributed to approximately half of all gene identifications in the fields of inherited peripheral neuropathies and spastic paraplegia in this time frame. Many diagnostic odysseys of rare disease patients have been resolved. The concept of genomes-to-therapy has borne out for a number of such discoveries that let to rapid clinical trials and expedited natural history studies. This marks GENESIS as one of the most impactful data aggregation initiatives in rare monogenic diseases.

The origins of ADNI.

A brief history of events surrounding the conceptualization and original implementation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a public-private partnership (PPP) is provided from the perspective of three individuals directly involved from the outset. Potential barriers and how they were addressed are summarized, especially the decision to make all data freely accessible in real-time. Decisions made at the beginning of ADNI are revisited in light of what has been learned over the past 20 years, especially the importance of the investment in cerebrospinal fluid (CSF) and blood measures and the commitment to data sharing. The key elements of ADNI's success from the authors' perspective are also summarized. HIGHLIGHTS: Informal interactions among colleagues were the beginning of something big. An NIH Director's personal decision on open data sharing has had perhaps the greatest impact of any single decision in the past several decades in terms of advancing clinical biomarker research. After 20 years, blood-based biomarkers of brain disease may soon take the place of brain imaging for purposes of diagnosis and drug development.

Privacy Risk Assessment for Synthetic Longitudinal Health Data.

INTRODUCTION: A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. METHODS: Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. RESULTS: The resulting privacy scores are discussed, which vary greatly between the different types of attacks. CONCLUSION: Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem.

The new EU-US data protection framework's implications for healthcare.

In July 2023, the United States and the European Union introduced the Data Privacy Framework (DPF), introducing the third generation of cross-border data transfer agreements constituting adequacy with respect to personal data transfers under the General Data Protection Regulation (GDPR) between the European Union (EU) and the US. This framework may be used in cross-border healthcare and research relationships, which are highly desirable and increasingly essential to innovative health technology development and health services deployment. A reliable model meeting EU adequacy requirements could enhance the transfer of patient and research participant data. While the DPF might present a familiar terrain for US organizations, it also brings unique challenges. A notable concern is the ability of individual EU Member States to establish individual and additional requirements for health data that are more restrictive than GDPR requirements, which are not anticipated by the DPF. This article highlights the DPF's potential impact on the healthcare and research sectors, finding that the DPF may not provide the degree of lawful health data transfer desirable for healthcare entities. We examine the DPF against a background of existing Health Insurance Portability and Accountability Act obligations and other GDPR transfer tools to offer alternatives that can improve the likelihood of reliable, lawful health data transfer between the US and EU.

Data access arrangements in genomic research consortia.

One of the most common terms that is used to describe entities responsible for sharing genomic data for research purposes is 'genomic research consortium'. However, there is a lack of clarity around the language used by consortia to describe their data sharing arrangements. Calls have been made for more uniform terminology. This article reports on a review of the genomic research consortium literature illustrating a wide diversity in the language that has been used over time to describe the access arrangements of these entities. The second component of this research involved an examination of publicly available information from a dataset of 98 consortia. This analysis further illustrates the wide diversity in the access arrangements adopted by genomic research consortia. A total of 12 different access arrangements were identified, including four simple forms (open, consortium, managed and registered access) and eight more complex tiered forms (for example, a combination of consortium, managed and open access). The majority of consortia utilised some form of tiered access, often following the policy requirements of funders like the US National Institutes of Health and the UK Wellcome Trust. It was not always easy to precisely identify the access arrangements of individual consortia. Greater consistency, clarity and transparency is likely to be of benefit to donors, depositors and accessors alike. More work needs to be done to achieve this end.

From Fragmentation to Open Access: Building an Open Data Portal for Health Data Dissemination.

Open data is defined as data that can be used and redistributed by anyone with minimal or no restrictions. A design science research methodology was applied to the development of an open data portal for theMinistry of Health Sri Lanka (MoH) to share national datasets. Following requirement gathering and literature review, the open data portal was developed using open-source software and implemented at the MoH Sri Lanka. Fifty datasets obtained from the MoH were categorised and published in the open data portal. However, several barriers cast doubt on the long-term feasibility of the open data portal project.

Designing for data sharing: Considerations for advancing health equity in data management and dissemination.

Data sharing, the act of making scientific research data available to others, can accelerate innovation and discoveries, and ultimately enhance public health. The National Cancer Institute Implementation Science Centers in Cancer Control convened a diverse group of research scientists, practitioners, and community partners in three interactive workshops (May-June 2022) to identify and discuss factors that must be considered when designing research for equitable data sharing with a specific emphasis on implementation science and social, behavioral, and population health research. This group identified and operationalized a set of seven key considerations for equitable data sharing-conceptualized as an inclusive process that fairly includes the perspectives and priorities of all partners involved in and impacted by data sharing, with consideration of ethics, history, and benefits-that were integrated into a framework. Key data-sharing components particularly important for health equity included: elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and adverse impact of data sharing on diverse beneficiaries of health data and research.

Data sharing is a key strategy for advancing our understanding of human health and healthcare. Three interactive workshops that included researcher scientists, physicians, and community members were held by the National Cancer Institute Implementation Science Centers in Cancer Control. The group discussed ways to incorporate health equity and equitable data sharing into implementation science and social, behavioral, and population health research. Equitable data sharing is an inclusive process that considers the points of view and priorities of all partners involved with data sharing and considers ethics, history, and benefits. Seven key components emerged from these discussions and were included in a framework. The components included elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and negative impacts of data sharing on diverse beneficiaries of health data and research.

Data Management in Biobanking: Strategies, Challenges, and Future Directions.

Biobanking plays a pivotal role in biomedical research by providing standardized processing, precise storing, and management of biological sample collections along with the associated data. Effective data management is a prerequisite to ensure the integrity, quality, and accessibility of these resources. This review provides a current landscape of data management in biobanking, discussing key challenges, existing strategies, and potential future directions. We explore multiple aspects of data management, including data collection, storage, curation, sharing, and ethical considerations. By examining the evolving technologies and methodologies in biobanking, we aim to provide insights into addressing the complexities and maximizing the utility of biobank data for research and clinical applications.

Evaluating the discoverability of supporting research materials in ClinicalTrials.gov for US federally funded COVID-19 clinical studies.

Objective: The objective of this study was to evaluate the discoverability of supporting research materials, including supporting documents, individual participant data (IPD), and associated publications, in US federally funded COVID-19 clinical study records in ClinicalTrials.gov (CTG). Methods: Study registration records were evaluated for (1) links to supporting documents, including protocols, informed consent forms, and statistical analysis plans; (2) information on how unaffiliated researchers may access IPD and, when applicable, the linking of the IPD record back to the CTG record; and (3) links to associated publications and, when applicable, the linking of the publication record back to the CTG record. Results: 206 CTG study records were included in the analysis. Few records shared supporting documents, with only 4% of records sharing all 3 document types. 27% of records indicated they intended to share IPD, with 45% of these providing sufficient information to request access to the IPD. Only 1 dataset record was located, which linked back to its corresponding CTG record. The majority of CTG records did not have links to publications (61%), and only 21% linked out to at least 1 results publication. All publication records linked back to their corresponding CTG records. Conclusion: With only 4% of records sharing all supporting document types, 12% sufficient information to access IPD, and 21% results publications, improvements can be made to the discoverability of research materials in federally funded, COVID-19 CTG records. Sharing these materials on CTG can increase their discoverability, therefore increasing the validity, transparency, and reusability of clinical research.

Facilitating remote and virtual access provision by European research infrastructures - requirements, issues, and recommendations.

Research Infrastructures (RIs) are strategic assets facilitating innovation and knowledge advancement across all scientific disciplines. They provide researchers with advanced tools and resources that go beyond individual or institutional capacities and promote collaboration, community-building and the application of scientific standards. Remote and virtual access to RIs enables scientists to use these essential resources without the necessity of being physically present. The COVID-19 pandemic restrictions where a catalyst for the expansion and further development of remote and virtual access models, particularly in fields where physical access had been the predominant model. The eRImote project explores pathways for digital and remote RI access through targeted surveys, stakeholder workshops, expert groups discussions, and the analysis of specific use cases. This paper provides a definition of remote and virtual access and remote training and explores their implementation across various RIs, highlighting the implications for their operational processes and the dynamics of interaction between RIs and their user communities. It presents the identified advantages, obstacles, and best-practices, alongside strategies and recommendations to navigate and mitigate challenges effectively. Key issues and recommendations are summed up separately for remote access, virtual access, and remote training, complemented by general recommendations for facilitating remote and virtual access to RIs. These relate to budgeting and funding, the balancing of RI access models, the need for regulatory frameworks for sample shipments, collaboration among RIs, impact assessment of remote and virtual access on user interactions, operational efficiency and the environment footprint of RIs, and the adaption of data sharing policies. Stakeholders are broadly invited to give their feedback on the paper's findings and conclusions, which will be integrated into improved versions of this paper.

Australian Attitudes Towards Waivers of Consent Within the Context of Genomic Data Sharing.

This research identifies the circumstances in which Human Research Ethics Committees (HRECs) are trusted by Australians to approve the use of genomic data - without express consent - and considers the impact of genomic data sharing settings, and respondent attributes, on public trust. Survey results (N = 3013) show some circumstances are more conducive to public trust than others, with waivers endorsed when future research is beneficial and when privacy is protected, but receiving less support in other instances. Still, results imply attitudes are influenced by more than these specific circumstances, with different data sharing settings, and participant attributes, affecting views. Ultimately, this research raises questions and concerns in relation to the criteria HRECs use when authorising waivers of consent in Australia.

An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.de/), which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation.

Data sharing considerations to maximize the use of pathogen biological and genomics resources data for public health.

Public sector data associated with health are a highly valuable resource with multiple potential end-users, from health practitioners, researchers, public bodies, policy makers, and industry. Data for infectious disease agents are used for epidemiological investigations, disease tracking and assessing emerging biological threats. Yet, there are challenges in collating and re-using it. Data may be derived from multiple sources, generated and collected for different purposes. While public sector data should be open access, providers from public health settings or from agriculture, food, or environment sources have sensitivity criteria to meet with ethical restrictions in how the data can be reused. Yet, sharable datasets need to describe the pathogens with sufficient contextual metadata for maximal utility, e.g. associated disease or disease potential and the pathogen source. As data comprise the physical resources of pathogen collections and potentially associated sequences, there is an added emerging technical issue of integration of omics 'big data'. Thus, there is a need to identify suitable means to integrate and safely access diverse data for pathogens. Established genomics alliances and platforms interpret and meet the challenges in different ways depending on their own context. Nonetheless, their templates and frameworks provide a solution for adaption to pathogen datasets.

Putting patients at the center of health information exchange design: An exploration of patient preferences for data sharing.

BACKGROUND: Despite the many benefits of Health Information Exchange (HIE), Studies reported patients concerns about the privacy and security of sharing their health information. To address these concerns, it is important to understand their needs, preferences, and priorities in the design and implementing HIE systems. OBJECTIVE: The aim of this study is to investigate patients' preferences for HIE consent option and examine the extent to which they are comfortable sharing the different parts of their medical records. METHOD: A self-administered survey was conducted. The survey was administrated online and the total number of respondents was 660 participants. RESULTS: The most popular option selected by participants for sharing HIE information was to share information with their permission once when they register (33.3%) followed by the option to share their information temporarily on demand during their clinical visit (23.8%). The types of information which participants were willing to share the most were general data such as age, weight, height, and gender, followed closely by data needed for medical emergency. In contrast, the information which participants were less likely to share were data related to financial status or income, followed by data related to sexual disease, and mental illnesses.