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We investigate in this article how repository platforms change the sharing and preservation of digital objects in academic libraries. We use evidence drawn from semi-structured interviews with 31 data repository managers working at 21 universities using the product Figshare for institutions. We first show that repository managers use this platform to bring together actors, technologies, and processes usually scattered across the library to assign to them the tasks that they value less—such as data preparation or IT maintenance—and spend more time engaging in activities they appreciate—such as raising awareness of data sharing. While this platformization of data management improves their job satisfaction, we reveal how it simultaneously accentuates the outsourcing of libraries' core mission to private actors. We eventually discuss how this platformization can deskill librarians and perpetuate precarity politics in university libraries. [ABSTRACT FROM AUTHOR]

Biological sources are significant contributors to aquatic soundscapes. Soniferous fish can dominate the soundscape in certain locations, at specific times and frequencies, particularly during the production of choruses. Passive acoustic monitoring of fish choruses can provide important ecological information about soniferous fish populations. This study presents the Australian Fish Chorus Catalogue, an inventory of fish choruses detected from 83 locations in Australian estuarine and marine waters. The Australian Fish Chorus Catalogue contains data on fish chorus occurrence and the spectral and temporal measurements, spectrographic images, and audio examples of 301 fish choruses. This catalogue has been developed to establish the foundations of an ongoing effort to document, quantify, compare, and track Australian fish choruses. We hope this open-access data depository will be used as a reference for future research and will facilitate an increase in understanding of fish choruses, which can then be applied to the monitoring and management of fish populations and their respective ecosystems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Study Objectives: The Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System contains individual-patient-level traumatic brain injury (TBI) data, which when combined, allows for the examination of rates and outcomes for key subpopulations at risk for developing sleep disturbance. Methods: This proof-of-concept study creates a model system for harmonizing data (i.e., combining and standardizing data) across FITBIR studies for participants with and without a history of TBI to estimate rates of sleep disturbance and identify risk factors. Results: Three studies were eligible for harmonization (N = 1753). Sleep disturbance was common among those with a history of mild TBI (63%). Individuals with mild TBI were two to four times more likely to have sleep disturbance compared to those with no history of TBI. Conclusions: This study established methods, harmonization code, and meta-databases that are publicly available on the FITBIR website. We demonstrated how the harmonization of FITBIR studies can answer TBI research questions, showing that associations between TBI and sleep disturbance may be influenced by demographic factors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal's data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities. [ABSTRACT FROM AUTHOR]

Data sharing is a major tenet in the global challenge to improve the reproducibility of scientific findings. Current researcher attitudes toward data sharing and Open Science in general are still far from optimal. The practice of data sharing and how it should be managed remain unclear and inconsistent, with many researchers keen to receive from, but not give back to the community. The lack of a data sharing culture, systemic resistance, misconceptions on data ownership and the unjustified fear of being "scooped", all concur to create an enormous barrier to the promotion of scientific research based on increased information quality, transparency and openness, and replicability of results. These factors are also compounded by the erroneous perception that the sharing of data compromises competitiveness. Here, we present a rigorous observational study based on 198 researchers in the biomedical areas to evaluate factors affecting perception and natural attitude to data sharing in the biomedical sciences. [ABSTRACT FROM AUTHOR]

The SINFONIA project’s main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the environment during the management of patients suspected or diagnosed with lymphoma, brain tumors, and breast cancers. The project plan defines a series of key objectives to be achieved on the way to the main objective. One of these objectives is to develop and operate a repository to collect, pool, and share data from imaging and non-imaging examinations and radiation therapy sessions, histological results, and demographic information related to individual patients with lymphoma, brain tumors, and breast cancers. This paper presents the final version of that repository, a cloud-based platform for imaging and non-imaging data. It results from the implementation and integration of several software tools and programming frameworks under an evolutive architecture according to the project partners’ needs and the constraints of the General Data Protection Regulation. It provides, among other services, data uploading and downloading, data sharing, file decompression, data searching, DICOM previsualization, and an infrastructure for submitting and running Artificial Intelligence models.

Abstract 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.

From July 2022 to December 2022, the World Data System (WDS) International Technology (ITO) and International Program (IPO) Offices conducted a review of strategic plans and technical roadmaps of all current WDS members and the set of Canadian repositories that participated in the Digital Research Alliance of Canada's CoreTrustSeal Certification Support and Funding Pilot (Digital Research Alliance of Canada, 2022). In this paper, we describe how a new organizational assessment method was designed and utilized to identify the needs and challenges faced by the WDS and Canadian CTS Pilot members. Our method relied on reviewing public-facing documentation provided by the repositories, with a priority on strategic plans and technical road maps. In total, we reviewed 95 sources of information, including 33 strategic plans and 3 technical roadmaps describing a total of 95 out of the original 147 target organizations. In this paper, we also describe our assessment tool and the overarching challenges and goals we identified through the usage of this tool. Finally, we will describe the limitations of our methodology and provide recommendations from the World Data System on how best to assist the WDS members and the cohort of Canadian data repositories based on our findings.

Objective: The rapidly increasing prevalence and application of machine learning (ML) across disciplines creates a pressing need to establish guidance for data curation professionals. However, we must first understand the characteristics of ML-related objects shared in generalist and specialist repositories and the extent to which repository metadata fields enable findability and reuse of ML objects. Methods: We used a combination of API queries and web scraping to retrieve metadata for ML objects in eight commonly used generalist and ML-specific data repositories. We assessed both metadata schema and characteristics of deposited ML objects, within the context of the widely adopted FAIR Principles. We also calculated summary statistics for properties of objects, including number of objects per year, dataset size, domains represented, and availability of related resources. Results: Generalist repositories excelled at providing provenance metadata, specifically unique identifiers, unambiguous citations, clear licenses, and related resources, while specialist repositories emphasized ML-specific descriptive metadata, such as number of attributes and instances and task type. In terms of object content, we noted a wide range of file formats, as well as licenses, all of which impact reusability. Conclusions: Generalist repositories will benefit from some of the practices adopted by specialists, and specialist repositories will benefit from adopting proven data curation practices of generalist repositories. A step forward for repositories will be to invest more into use of labels and persistent identifiers to improve workflow documentation, provenance, and related resource linking of ML objects, which will increase their findability, interoperability, and reusability.

Daniel Hsiang-Te Tsai,1,2 J Simon Bell,3 Shahab Abtahi,4 Brenda N Baak,5 Marloes T Bazelier,4 Ruth Brauer,6 Adrienne YL Chan,6– 9 Esther W Chan,9– 12 Haoqian Chen,13 Celine SL Chui,9,14,15 Sharon Cook,16 Stephen Crystal,16 Poonam Gandhi,13 Sirpa Hartikainen,17 Frederick K Ho,18 Shao-Ti Hsu,1 Jenni Ilomäki,3 Ju Hwan Kim,19 Olaf H Klungel,4 Marjaana Koponen,17 Wallis CY Lau,6,8,9 Kui Kai Lau,20,21 Terry YS Lum,22 Hao Luo,22 Kenneth KC Man,6,8,9 Jill P Pell,18 Soko Setoguchi,13,23 Shih-Chieh Shao,1,24 Chin-Yao Shen,1 Ju-Young Shin,19,25,26 Patrick C Souverein,4 Anna-Maija Tolppanen,17 Li Wei,6,9 Ian CK Wong,6,8,9,27 Edward Chia-Cheng Lai1 1School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 2Centre for Neonatal and Paediatric Infection, St George’s University of London, London, UK; 3Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia; 4Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; 5PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands; 6Research Department of Practice and Policy, UCL School of Pharmacy, London, UK; 7Groningen Research Institute of Pharmacy, Unit of Pharmacotherapy, ‐Epidemiology and ‐Economics, University of Groningen, Groningen, the Netherlands; 8Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 9Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China; 10Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 11Department of Pharmacy, the University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China; 12The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, People’s Republic of China; 13Center for Pharmacoepidemiology and Treatment Science (PETS), Institute for Health, Rutgers University, New Brunswick, NJ, USA; 14School of Nursing, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 15School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 16Center for Health Services Research, Rutgers University, New Brunswick, NJ, USA; 17Kuopio Research Centre of Geriatric Care and School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 18School of Health and Wellbeing, University of Glasgow, Glasgow, UK; 19School of Pharmacy, Sungkyunkwan University, Suwon, South Korea; 20Division of Neurology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 21State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 22Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 23Department of Medicine, Rutgers Robert Wood Johnson Medical School and Pharmacoepidemiology and Treatments Science, Institute for Health, Rutgers University, New Brunswick, NJ, USA; 24Department of Pharmacy, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; 25Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea; 26Department of Biohealth Regulatory Science, Sungkyunkwan University, Seoul, South Korea; 27Aston Pharmacy School, Aston University, Birmingham, UKCorrespondence: Edward Chia-Cheng Lai, School of Pharmacy and Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Email edward_lai@mail.ncku.edu.twPurpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN).Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives.Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK.Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.Keywords: meta-data, data repository, Neurological and Mental Health Global Epidemiology Network, NeuroGEN

Study Objectives: The Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System contains individual-patient-level traumatic brain injury (TBI) data, which when combined, allows for the examination of rates and outcomes for key subpopulations at risk for developing sleep disturbance. Methods: This proof-of-concept study creates a model system for harmonizing data (i.e., combining and standardizing data) across FITBIR studies for participants with and without a history of TBI to estimate rates of sleep disturbance and identify risk factors. Results: Three studies were eligible for harmonization (N = 1753). Sleep disturbance was common among those with a history of mild TBI (63%). Individuals with mild TBI were two to four times more likely to have sleep disturbance compared to those with no history of TBI. Conclusions: This study established methods, harmonization code, and meta-databases that are publicly available on the FITBIR website. We demonstrated how the harmonization of FITBIR studies can answer TBI research questions, showing that associations between TBI and sleep disturbance may be influenced by demographic factors.

Background: 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). Case Presentation: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]