The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, which increased with the publication date. There was a marginal difference in how assessment categories were weighted by students, with reflections highlighting appreciation for student agency. In course content, students reported the greatest learning gains in describing variables, while collaborative activities (e.g., interacting with peers and instructor) were the most effective support. The most effective tasks to facilitate these learning gains included coding exercises and team-led assignments. Autocoding of student reflections identified 16 themes, and positive sentiments were written nearly 4x more often than negative sentiments. Students positively reflected on their growth in statistical analyses, and negative sentiments focused on how limited prior experience with statistics and coding made them feel nervous. As a group, we encountered several challenges and opportunities in using open science materials. I present key recommendations, based on student experiences, for scientists to consider when publishing open data to provide additional educational benefits to the open science community.

UNASSIGNED: By defining search strategies and related database exports as code/scripts and data, librarians and information professionals can expand the mandate of research data management (RDM) infrastructure to include this work. This new initiative aimed to create a space in McGill University\'s institutional data repository for our librarians to deposit and share their search strategies for knowledge syntheses (KS).
UNASSIGNED: The authors, a health sciences librarian and an RDM specialist, created a repository collection of librarian-authored knowledge synthesis (KS) searches in McGill University\'s Borealis Dataverse collection. We developed and hosted a half-day \"Dataverse-a-thon\" where we worked with a team of health sciences librarians to develop a standardized KS data management plan (DMP), search reporting documentation, Dataverse software training, and howto guidance for the repository.
UNASSIGNED: In addition to better documentation and tracking of KS searches at our institution, the KS Dataverse collection enables sharing of searches among colleagues with discoverable metadata fields for searching within deposited searches. While the initial creation of the DMP and documentation took about six hours, the subsequent deposit of search strategies into the institutional data repository requires minimal effort (e.g., 5-10 minutes on average per deposit). The Dataverse collection also empowers librarians to retain intellectual ownership over search strategies as valuable stand-alone research outputs and raise the visibility of their labor. Overall, institutional data repositories provide specific benefits in facilitating compliance both with PRISMA-S guidance and with RDM best practices.

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.
UNASSIGNED: 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: 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.
RESULTS: 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.

The Rapid Assessment of Avoidable Blindness (RAAB) is a population-based cross-sectional survey methodology used to collect data on the prevalence of vision impairment and its causes and eye care service indicators among the population 50 years and older. RAAB has been used for over 20 years with modifications to the protocol over time reflected in changing version numbers; this paper describes the latest version of the methodology-RAAB7. RAAB7 is a collaborative project between the International Centre for Eye Health and Peek Vision with guidance from a steering group of global eye health stakeholders. We have fully digitised RAAB, allowing for fast, accurate and secure data collection. A bespoke Android mobile application automatically synchronises data to a secure Amazon Web Services virtual private cloud when devices are online so users can monitor data collection in real-time. Vision is screened using Peek Vision\'s digital visual acuity test for mobile devices and uncorrected, corrected and pinhole visual acuity are collected. An optional module on Disability is available. We have rebuilt the RAAB data repository as the end point of RAAB7\'s digital data workflow, including a front-end website to access the past 20 years of RAAB surveys worldwide. This website ( https://www.raab.world) hosts open access RAAB data to support the advocacy and research efforts of the global eye health community. Active research sub-projects are finalising three new components in 2024-2025: 1) Near vision screening to address data gaps on near vision impairment and effective refractive error coverage; 2) an optional Health Economics module to assess the affordability of eye care services and productivity losses associated with vision impairment; 3) an optional Health Systems data collection module to support RAAB\'s primary aim to inform eye health service planning by supporting users to integrate eye care facility data with population data.
In 2020 there were an estimated 1.1 billion people with vision impairment globally. Vision impairment negatively affects people’s quality of life, social inclusion and productivity. The Rapid Assessment of Avoidable Blindness (RAAB) survey tool collects information about the vision and eye health of people aged 50 years and older in a defined population. It has been used worldwide for over 20 years to inform eye health service planning. This paper outlines the current survey methodology and summarises recent and upcoming developments. The RAAB project team has updated the survey to allow users to measure vision and collect other information on mobile devices (telephones or tablets) and send the findings directly to a central computer for automated analysis. The project team has built a new website to store this information and to allow anyone interested to find out more about the surveys done to date. The RAAB project continues to develop new features to make the information collected in surveys more useful for eye health service planning and eye health advocacy.

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.

Angelman syndrome (AS) and duplication 15q (dup15q) syndrome are rare neurogenetic conditions arising from a common locus on the long arm of chromosome 15. Individuals with both conditions share some clinical features (e.g. intellectual disability, epilepsy) and often require lifelong care. Disease-modifying therapies for both conditions are emerging, resulting in a significant need for a better understanding of the natural history of both AS and dup15q. Patient advocacy groups for both conditions recognized a need for a data repository that would link data on individuals from multiple sources to expand research, increase understanding of natural history, and accelerate the development of treatments, resulting in the Linking Angelman and Dup15q Data for Expanded Research (LADDER) Database. This paper describes the development and functionality of the LADDER Database - including challenges, lessons learned, and preliminary feasibility - and how it can be used as a model for other rare conditions.
The LADDER database: a model for advancing research, clinical guidance, and therapeutic development for rare conditions This paper describes the development and functionality of the Linking Angelman and Dup15q Data for Expanded Research (LADDER) Database, which is a data repository for two rare neurogenetic conditions: Angelman syndrome (AS) and duplication 15q (dup15q) syndrome. AS and dup15q syndrome arise from genetic abnormalities on chromosome 15 and share some clinical features (e.g. intellectual disability, epilepsy). LADDER was developed by patient advocacy organizations representing each condition in partnership with RTI International. LADDER links data on individuals from multiple sources to expand research, increase understanding of natural history, and accelerate the development of treatments for both AS and dup15q syndrome. The LADDER Database can be used as a model for expanding research and enhancing clinical trial readiness in other rare conditions.

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it\'s important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

BACKGROUND: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population.
OBJECTIVE: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity.
METHODS: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home).
RESULTS: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy.
CONCLUSIONS: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field\'s move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process.
UNASSIGNED: DERR1-10.2196/53857.

UNASSIGNED: Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure.
UNASSIGNED: We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs.
UNASSIGNED: Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty.
UNASSIGNED: The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.