Are Public Repository Requirements Exacerbating Lack of Diversity?

Although public repository requirements are aimed at researchers and designed to ensure that the utility of the limited data we have is optimized, these policies also have ramifications for research participants. In this opinion article, I discuss how the nature of such repositories can subject participants whose data are 'banked' to unwitting participation in scientific projects they might find objectionable. In addition, concerns about the privacy of banked genomic data are exacerbated by recent projects that demonstrate the ability to re-identify genomic data, raising the specter of discriminatory or oppressive use of this information. These concerns are most likely to discourage participation in research that requires data sharing among those who have experienced these phenomena and are less likely to discount their likelihood.

Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations

The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner.

Humanizing Big Data: Recognizing the Human Aspect of Big Data

The term “big data” refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as “recreational” genetic testing such as ancestry testing, as well as research studies, provide one type of “big data.” Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this “humanizing” process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

Embracing data preservation, sharing, and re-use in traumatic stress research

This editorial argues that it is time for the traumatic stress field to join the growing international movement towards Findable, Accessible, Interoperable, and Re-usable (FAIR) research data, and that we are well-positioned to do so. The field has a huge, largely untapped resource in the enormous number of rich potentially re-usable datasets that are not currently shared or preserved. We have several promising shared data resources created via international collaborative efforts by traumatic stress researchers, but we do not yet have common standards for data description, sharing, or preservation. And, despite the promise of novel findings from data sharing and re-use, there are a number of barriers to researchers' adoption of FAIR data practices. We present a vision for the future of FAIR traumatic stress data, and a call to action for the traumatic stress research community and individual researchers and research teams to help achieve this vision.

What motivates the sharing of consumer-generated genomic information?

Genomic medicine is an emerging practice that followed the completion of the Human Genome Project and that considers genomic information about an individual in the provision of their clinical care. Large and start-up direct-to-consumer genetic testing companies like Ancestry, 23andMe, Luna DNA, and Nebula Genomics have capitalized on findings from the Human Genome Project by offering genetic health testing services to consumers without a clinical intermediary. Genomic medicine is thus further propelled by unprecedented supply and demand market forces driven by direct-to-consumer genetic testing companies. As government entities like the National Human Genome Research Institute question how genomics can be implemented into routine medical practice to prevent disease and improve the health of all members of a diverse community, we believe that stakeholders must first examine how and scenarios in which stakeholders can become motivated to share or receive genomic information. In this commentary, we discuss consumers three scenarios: satisfying personal curiosity, providing a social good, and receiving a financial return. We examine these motivations based on recent events and current avenues through which have engaged or can engage in genomic data sharing via private, secure (e.g. centralized genomic databases and de-centralized platforms like blockchain) and public, unsecure platforms (e.g. open platforms that are publicly available online). By examining these scenarios, we can likely determine how various stakeholders, such as consumers, might prefer to extract value from genomic information and how privacy preferences among those stakeholders might vary depending on how they seek to use or share genomic information. From there, one can recommend best practices to promote transparency and uphold privacy standards and expectations among stakeholders engaged in genomic medicine.

International Brain Initiative: An Innovative Framework for Coordinated Global Brain Research Efforts

The International Brain Initiative (IBI) has been established to coordinate efforts across existing and emerging national and regional brain initiatives. This NeuroView describes how to be involved and the new opportunities for global collaboration that are emerging between scientists, scientific societies, funders, industry, government, and society.

Insurance Customers' Expectations for Sharing Health Data: Qualitative Survey Study

Background

Insurance organizations are essential stakeholders in health care ecosystems. For addressing future health care needs, insurance companies require access to health data to deliver preventative and proactive digital health services to customers. However, extant research is limited in examining the conditions that incentivize health data sharing.

Objective

This study aimed to (1) identify the expectations of insurance customers when sharing health data, (2) determine the perceived intrinsic value of health data, and (3) explore the conditions that aid in incentivizing health data sharing in the relationship between an insurance organization and its customer.

Methods

A Web-based survey was distributed to randomly selected customers from a Finnish insurance organization through email. A single open-text answer was used for a qualitative data analysis through inductive coding, followed by a thematic analysis. Furthermore, the 4 constructs of commitment, power, reciprocity, and trust from the social exchange theory (SET) were applied as a framework.

Results

From the 5000 customers invited to participate, we received 452 surveys (response rate: 9.0%). Customer characteristics were found to reflect customer demographics. Of the 452 surveys, 48 (10.6%) open-text responses were skipped by the customer, 57 (12.6%) customers had no expectations from sharing health data, and 44 (9.7%) customers preferred to abstain from a data sharing relationship. Using the SET framework, we found that customers expected different conditions to be fulfilled by their insurance provider based on the commitment, power, reciprocity, and trust constructs. Of the 452 customers who completed the surveys, 64 (14.2%) customers required that the insurance organization meets their data treatment expectations (commitment). Overall, 4.9% (22/452) of customers were concerned about their health data being used against them to profile their health, to increase insurance prices, or to deny health insurance claims (power). A total of 28.5% (129/452) of customers expected some form of benefit, such as personalized digital health services, and 29.9% (135/452) of customers expected finance-related compensation (reciprocity). Furthermore, 7.5% (34/452) of customers expected some form of empathy from the insurance organization through enhanced transparency or an emotional connection (trust).

Conclusions

To aid in the design and development of digital health services, insurance organizations need to address the customers’ expectations when sharing their health data. We established the expectations of customers in the social exchange of health data and explored the perceived values of data as intangible goods. Actions by the insurance organization should aim to increase trust through a culture of transparency, commitment to treat health data in a prescribed manner, provide reciprocal benefits through digital health services that customers deem valuable, and assuage fears of health data being used to prevent providing insurance coverage or increase costs.

Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study

Background

Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy.

Objective

The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer.

Methods

We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17).

Results

Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data.

Conclusions

Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research

Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

Protecting patient privacy in survival analyses

OBJECTIVE

Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis.

MATERIALS AND METHODS

We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model.

RESULTS

We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves.

DISCUSSION

The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications.

CONCLUSION

The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.

Did awarding badges increase data sharing in BMJ Open? A randomized controlled trial

Sharing data and code are important components of reproducible research. Data sharing in research is widely discussed in the literature; however, there are no well-established evidence-based incentives that reward data sharing, nor randomized studies that demonstrate the effectiveness of data sharing policies at increasing data sharing. A simple incentive, such as an Open Data Badge, might provide the change needed to increase data sharing in health and medical research. This study was a parallel group randomized controlled trial (protocol registration: doi:10.17605/OSF.IO/PXWZQ) with two groups, control and intervention, with 80 research articles published in BMJ Open per group, with a total of 160 research articles. The intervention group received an email offer for an Open Data Badge if they shared their data along with their final publication and the control group received an email with no offer of a badge if they shared their data with their final publication. The primary outcome was the data sharing rate. Badges did not noticeably motivate researchers who published in BMJ Open to share their data; the odds of awarding badges were nearly equal in the intervention and control groups (odds ratio = 0.9, 95% CI [0.1, 9.0]). Data sharing rates were low in both groups, with just two datasets shared in each of the intervention and control groups. The global movement towards open science has made significant gains with the development of numerous data sharing policies and tools. What remains to be established is an effective incentive that motivates researchers to take up such tools to share their data.

Informed consent in genomic research and biobanking: taking feedback of findings seriously

Genomic research and biobanking present several ethical, social and cultural challenges, particularly when conducted in settings with limited scientific research capacity. One of these challenges is determining the model of consent that should support the sharing of human biological samples and data in the context of international collaborative research. In this paper, we report on the views of key research stakeholders in Ghana on what should count as good ethical practice when seeking consent for genomic research and biobanking in Africa. This study was part of a multi-country qualitative case study conducted in three African countries: Ghana, Uganda and Zambia under the auspices of the Human Heredity and Health in Africa initiative (H3Africa). Our study suggests that while participants are willing to give consent for their samples and associated data to be used for future research purposes, they expect to receive feedback about the progress of the research and about the kinds of research being undertaken on their samples and data. These expectations need to be anticipated and discussed during the consent process which should be seen as part of an ongoing communication process throughout the research process.

Legal regulations, ethical guidelines and recent policies to increase transparency of clinical trials

Timely and accurate dissemination of outcomes is essential to accomplish main benefits of scientific research including clinical trials. Clinical trial results can be disseminated in two main ways: by publication in a peer-reviewed journal and by posting on a publicly available clinical trial register. The credibility of the literature on clinical trials is significantly diminished because a high percentage of trials is not published. While current legal regulations both in the European Union (EU) and the USA impose a duty to submit summary results of clinical trials to a respective register (EU Clinical Trial Register and ClinicalTrials.gov, respectively), the compliance with this requirement has been generally inadequate. Trial outcomes can be also made accessible by data sharing. However, in spite of the wide promotion of this idea, the access of investigators to participant-level datasets remains limited. The main objective of this review is to discuss current legal regulations, international standards, ethical guidelines and recent policies pertaining to dissemination of clinical trial results.

Reproducible research practices, openness and transparency in health economic evaluations: study protocol for a cross-sectional comparative analysis

INTRODUCTION

There has been a growing awareness of the need for rigorously and transparent reported health research, to ensure the reproducibility of studies by future researchers. Health economic evaluations, the comparative analysis of alternative interventions in terms of their costs and consequences, have been promoted as an important tool to inform decision-making. The objective of this study will be to investigate the extent to which articles of economic evaluations of healthcare interventions indexed in MEDLINE incorporate research practices that promote transparency, openness and reproducibility.

METHODS AND ANALYSIS

This is the study protocol for a cross-sectional comparative analysis. We registered the study protocol within the Open Science Framework (osf.io/gzaxr). We will evaluate a random sample of 600 cost-effectiveness analysis publications, a specific form of health economic evaluations, indexed in MEDLINE during 2012 (n=200), 2019 (n=200) and 2022 (n=200). We will include published papers written in English reporting an incremental cost-effectiveness ratio in terms of costs per life years gained, quality-adjusted life years and/or disability-adjusted life years. Screening and selection of articles will be conducted by at least two researchers. Reproducible research practices, openness and transparency in each article will be extracted using a standardised data extraction form by multiple researchers, with a 33% random sample (n=200) extracted in duplicate. Information on general, methodological and reproducibility items will be reported, stratified by year, citation of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement and journal. Risk ratios with 95% CIs will be calculated to represent changes in reporting between 2012-2019 and 2019-2022.

ETHICS AND DISSEMINATION

Due to the nature of the proposed study, no ethical approval will be required. All data will be deposited in a cross-disciplinary public repository. It is anticipated the study findings could be relevant to a variety of audiences. Study findings will be disseminated at scientific conferences and published in peer-reviewed journals.

Sharing of Clinical Trial Data and Samples: The Cancer Patient Perspective

Introduction: Today, many initiatives and papers are devoted to clinical trial data (and to a lesser extent sample) sharing. Journal editors, pharmaceutical companies, funding agencies, governmental organizations, regulators, and clinical investigators have been debating the legal, ethical, and social implications of clinical data and sample sharing for several years. However, only little research has been conducted to unveil the patient perspective. Aim: To substantiate the current debate, we aimed to explore the attitudes of patients toward the re-use of clinical trial samples and data and to determine how they would prefer to be involved in this process. Materials and Methods: Sixteen in-depth interviews were conducted with cancer patients currently participating in a clinical trial. Results: This study indicates a general willingness of cancer patients participating in a clinical trial to allow re-use of their clinical trial data and/or samples by the original research team, and a generally open approach to share data and/or samples with other research teams, but some would like to be informed in this case. Despite divergent opinions about how patients prefer to be engaged, ranging from passive donors up to those explicitly wanting more control, participants expressed positive opinions toward technical solutions that allow indicating their preferences. Conclusion: Patients were open to sharing and re-use of data and samples to advance medical research but opinions varied on the level of patient involvement and the need for re-consent. A stratified approach for consent that allows individualization of data and sample sharing preferences may be useful, yet the implementation of such an approach warrants further research.

Measuring the Effects of Sharing Mobile Health Data During Diabetes Consultations: Protocol for a Mixed Method Study

BACKGROUND

There is rising demand for health care's limited resources. Mobile health (mHealth) could be a solution, especially for those with chronic illnesses such as diabetes. mHealth can increases patients' options to self-manage their health, improving their health knowledge, engagement, and capacity to contribute to their own care decisions. However, there are few solutions for sharing and presenting patients' mHealth data with health care providers (HCPs) in a mutually understandable way, which limits the potential of shared decision making.

OBJECTIVE

Through a six-month mixed method feasibility study in Norway, we aim to explore the impacts that a system for sharing patient-gathered data from mHealth devices has on patients and HCPs during diabetes consultations.

METHODS

Patients with diabetes will be recruited through their HCPs. Participants will use the Diabetes Diary mobile phone app to register and review diabetes self-management data and share these data during diabetes consultations using the FullFlow data-sharing system. The primary outcome is the feasibility of the system, which includes HCP impressions and expectations (prestudy survey), usability (System Usability Scale), functionalities used and data shared during consultations, and study-end focus group meetings. Secondary outcomes include a change in the therapeutic relationship, patient empowerment and wellness, health parameters (HbA1c and blood pressure), and the patients' own app-registered health measures (blood glucose, medication, physical activity, diet, and weight). We will compare measures taken at baseline and at six months, as well as data continuously gathered from the app. Analysis will aim to explain which measures have changed and how and why they have changed during the intervention.

RESULTS

The Full Flow project is funded for 2016 to 2020 by the Research Council of Norway (number 247974/O70). We approached 14 general practitioner clinics (expecting to recruit 1-2 general practitioners per clinic) and two hospitals (expecting to recruit 2-3 nurses per hospital). By recruiting through the HCPs, we expect to recruit 74 patients with type 2 and 33 patients with type 1 diabetes. Between November 2018 and July 2019, we recruited eight patients and 15 HCPs. During 2020, we aim to analyze and publish the results of the collected data from our patient and HCP participants.

CONCLUSIONS

We expect to better understand what is needed to be able to share data. This includes potential benefits that sharing patient-gathered data during consultations will have on patients and HCPs, both individually and together. By measuring these impacts, we will be able to present the possibilities and challenges related to a system for sharing mHealth data for future interventions and practice. Results will also demonstrate what needs to be done to make this collaboration between HCPs and patients successful and subsequently further improve patients' health and engagement in their care.

User-focused data sharing agreements: a foundation for the genomic future

Data sharing agreements that clearly describe what individuals are agreeing to and what responsibilities data stewards will undertake are crucial for the establishment, maintenance, and flourishing of genomic datasets. To optimize genomic data resources, researchers, care professionals, and informaticians must regard system design, user objectives, and environmental considerations through users' eyes, identifying fundamental values on which to build and potential barriers to success that must be avoided. Design of agreements that promote desired data sharing and protect valuable data resources as necessary begins with a review of user interests and concerns. Nontraditional approaches for informed consent (eg, abbreviated informed consent, electronic informed consent, and dynamic consent) can facilitate achievement of data donors' privacy-related goals while making data available to researchers. Transparency in individual-researcher interactions, recognition and accommodation of cultural differences, and identification of shared needs and goals create a foundation for data sharing agreements that work over short and long terms.

Evaluation of Privacy Risks of Patients' Data in China: Case Study

BACKGROUND

Patient privacy is a ubiquitous problem around the world. Many existing studies have demonstrated the potential privacy risks associated with sharing of biomedical data. Owing to the increasing need for data sharing and analysis, health care data privacy is drawing more attention. However, to better protect biomedical data privacy, it is essential to assess the privacy risk in the first place.

OBJECTIVE

In China, there is no clear regulation for health systems to deidentify data. It is also not known whether a mechanism such as the Health Insurance Portability and Accountability Act (HIPAA) safe harbor policy will achieve sufficient protection. This study aimed to conduct a pilot study using patient data from Chinese hospitals to understand and quantify the privacy risks of Chinese patients.

METHODS

We used g-distinct analysis to evaluate the reidentification risks with regard to the HIPAA safe harbor approach when applied to Chinese patients' data. More specifically, we estimated the risks based on the HIPAA safe harbor and limited dataset policies by assuming an attacker has background knowledge of the patient from the public domain.

RESULTS

The experiments were conducted on 0.83 million patients (with data field of date of birth, gender, and surrogate ZIP codes generated based on home address) across 33 provincial-level administrative divisions in China. Under the Limited Dataset policy, 19.58% (163,262/833,235) of the population could be uniquely identifiable under the g-distinct metric (ie, 1-distinct). In contrast, the Safe Harbor policy is able to significantly reduce privacy risk, where only 0.072% (601/833,235) of individuals are uniquely identifiable, and the majority of the population is 3000 indistinguishable (ie the population is expected to share common attributes with 3000 or less people).

CONCLUSIONS

Through the experiments based on real-world patient data, this work illustrates that the results of g-distinct analysis about Chinese patient privacy risk are similar to those from a previous US study, in which data from different organizations/regions might be vulnerable to different reidentification risks under different policies. This work provides reference to Chinese health care entities for estimating patients' privacy risk during data sharing, which laid the foundation of privacy risk study about Chinese patients' data in the future.

Data Sharing: Partnering With Others to Advance Student Health

This interview provides a practical example of how a school district appropriately shares data with outside partners. It is a practical example of how to apply the principles found in the article on data sharing, which is part of the "data and school nursing" articles series being published in NASN School Nurse during the 2018-2019 school year.

Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

Vehicular sensor networks (VSNs) have emerged as a paradigm for improving traffic safety in urban cities. However, there are still several issues with VSNs. Vehicles equipped with sensing devices usually upload large amounts of data reports to a remote cloud center for processing and analyzing, causing heavy computation and communication costs. Additionally, to choose an optimal route, it is required for vehicles to query the remote cloud center to obtain road conditions of the potential moving route, leading to an increased communication delay and leakage of location privacy. To solve these problems, this paper proposes an efficient privacy-preserving data sharing (EP 2 DS) scheme for fog-assisted vehicular sensor networks. Specifically, the proposed scheme utilizes fog computing to provide local data sharing with low latency; furthermore, it exploits a super-increasing sequence to format the sensing data of different road segments into one report, thus saving on the resources of communication and computation. In addition, using the modified oblivious transfer technology, the proposed scheme can query the road conditions of the potential moving route without disclosing the query location. Finally, an analysis of security suggests that the proposed scheme can satisfy all the requirements for security and privacy, with the evaluation results indicating that the proposed scheme leads to low costs in computation and communication.

Harmonised collection of data in youth mental health: Towards large datasets

OBJECTIVE

The current international trend is to create large datasets with existing data and/or deposit newly collected data into repositories accessible to the scientific community. These practices lead to more efficient data sharing, better detection of small effects, modelling of confounders, establishment of sample generalizability and identification of differences between any given disorders. In Australia, to facilitate such data-sharing and collaborative opportunities, the Neurobiology in Youth Mental Health Partnership was created. This initiative brings together specialised researchers from around Australia to work towards a better understanding of the cross-diagnostic neurobiology of youth mental health and the translation of this knowledge into clinical practice. One of the mandates of the partnership was to develop a protocol for harmonised prospective collection of data across research centres in the field of youth mental health in order to create large datasets.

METHODS

Four key research modalities were identified: clinical assessments, brain imaging, neurocognitive assessment and collection of blood samples. This paper presents the consensus set of assessments/data collection that has been selected by experts in each domain.

CONCLUSION

The use of this core set of data will facilitate the pooling of psychopathological and neurobiological data into large datasets allowing researchers to tackle important questions requiring very large numbers. The aspiration of this transdiagnostic approach is a better understanding of the mechanisms underlying mental illnesses.

Modeling consent in the time of COVID-19

Effective responses to the COVID-19 pandemic require novel solutions for research and responsible data sharing. Biobanking presents itself as a key priority in furthering our understanding of COVID-19. In this article, we propose a tripartite approach to consent to create resources for research relating to COVID-19. The approach aims to link three levels of participation: COVID-19 patients, respiratory/infectious disease patients, and longitudinal study participants. We explore the potential approaches that can be taken to consent processes with these three participant groups. We furthermore describe an access model for both single-site and multi-site data and sample storage. Through dealing with these topics at a high level, the model may be adapted to local legal and ethical requirements while still pursuing its ultimate goal: the creation of a research infrastructure that supports transparent, strong, and open science.