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Cooperation of GlycoPOST and UniCarb-DR towards a comprehensive glycomics data repository workflow

ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2025年第5期417卷 1015-1023页

作者： Takahashi, Yushi Karlsson, Niclas G. Okuda, Shujiro Aoki-Kinoshita, Kiyoko F. Niigata Univ Sch Med Med AI Ctr Niigata Japan Oslo Metropolitan Univ Fac Hlth Sci Dept Life Sci & Hlth Div Pharm Oslo Norway Soka Univ Fac Sci & Engn Glycan & Life Syst Integrat Ctr Tokyo Japan

In glycomics, two data repositories, GlycoPOST and UniCarb-DR, have been developed to accumulate experimental data generated by glycomics and glycoproteomics mass spectrometry experiments. In order to enhance the interrelation between these two data repositories, we have upgraded the framework for both of them;we have unified their respective data submission systems and constructed a mechanism that can automatically cross-reference corresponding entries. In addition to this integration, the metadata registration system was also extended so that liquid chromatography experiments can be reported according to standard reporting guidelines specified by MIRAGE (Minimum Information Required for A Glycomics Experiment). Furthermore, by augmenting the visualization software used in UniCarb-DR, we have been able to introduce new functionality into GlycoPOST to enable the visualization of unpublished experimental identification result files during an embargo period defined by the data provider. As a result, this work introduces a new framework by which glycomics researchers can take advantage of GlycoPOST and UniCarb-DR in an integrated manner.

关键词： Glycomics Glycoinformatics Mass spectrometry Liquid chromatography Metadata data repository

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Aotearoa genomic data repository: An ahuru mowai for taonga species sequencing data

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MOLECULAR ECOLOGY RESOURCES 2025年第2期25卷 e13866页

作者： Te Aika, Ben Liggins, Libby Rye, Claire Perkins, E. Owen Huh, Jun Brauning, Rudiger Godfery, Tracey Black, Michael A. Univ Otago Res & Enterprise Off Dunedin New Zealand Massey Univ Sch Nat Sci Auckland New Zealand Genom Aotearoa Auckland New Zealand Univ Auckland New Zealand eSci Infrastruct Auckland New Zealand AgResearch Ltd Invermay Agr Ctr Mosgiel New Zealand Univ Otago Dept Biochem Dunedin New Zealand

The Aotearoa Genomic data repository (AGDR) is an initiative to provide a secure within-nation option for the storage, management and sharing of non-human genomic data generated from biological and environmental samples originating in Aotearoa New Zealand. This resource has been developed to follow the principles of Maori data Sovereignty, and to enable the right of kaitiakitanga (guardianship), so that iwi, hapu and whanau (tribes, kinship groups and families) can effectively exercise their responsibilities as guardians over biological entities that they regard as taonga (precious or treasured). While the repository is designed to facilitate the sharing of data-making it findable by researchers and interoperable with data held in other genomic repositories-the decision-making process regarding who can access the data is entirely in the hands of those holding kaitiakitanga over each data set. No data are made available to the requesting researcher until the request has been approved, and the conditions for access (which can vary by data set) have been agreed to. Here we describe the development of the AGDR, from both a cultural perspective, and a technical one, and outline the processes that underpin its operation.

关键词： benefit-sharing CARE principles data repository indigenous data sovereignty Maori

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Introducing the suicide prevention trials database: A publicly available data repository of suicide prevention studies

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SUICIDE AND LIFE-THREATENING BEHAVIOR 2025年第1期55卷 e13152页

作者： O'Neil, Maya E. Veazie, Stephanie Krushnic, Danielle Hannon, Sara Baker-Robinson, William Adams, Joren Clauss, Kate Constans, Joseph Hamblen, Jessica L. Somohano, Vanessa C. Denneson, Lauren M. VA Portland Hlth Care Syst HSR Ctr Improve Vet Involvement Care CIVIC Portland OR USA Oregon Hlth & Sci Univ Dept Psychiat Portland OR USA Oregon Hlth & Sci Univ Dept Med Informat & Clin Epidemiol Portland OR USA VA Portland Healthcare Syst Northwest Mental Illness Res Educ & Clin Ctr Portland OR 98661 USA Univ Calif Berkeley Sch Publ Hlth Div Epidemiol Berkeley CA USA Oregon Hlth & Sci Univ Sch Med Portland OR USA Vet Hlth Adm Off Res & Dev Washington DC USA Tulane Univ Sch Publ Hlth & Trop Med Dept Social Behav & Populat Sci New Orleans LA USA Natl Ctr Posttraumat Stress Disorder Dept Vet Affairs White River Jct VT USA Geisel Sch Med Dartmouth Dept Psychiat Hanover NH USA VA Portland Hlth Care Syst R&D 663710 SW US Vet Hosp Rd Portland OR 97239 USA

BackgroundHealthcare, research, policy, and legislative stakeholders need timely, accurate, and detailed information on the effectiveness and potential harms of suicide prevention approaches. We created the Suicide Prevention Trials database (SPTD) to provide a centralized, publicly accessible, detailed database of harmonized study-level suicide prevention clinical trial *** searched for randomized controlled trials (RCTs) of suicide prevention published from 1980 to 2023. Over 300 data variables were extracted from each *** identified a total of 140 unique RCTs in 180 articles. Most of the included RCTs compared two treatment arms (92%), and the remainder compared three arms (88%). Nearly half of the RCTs reported on Behavioral Interventions (49%), followed by Care Management, Follow-up, or Monitoring (16%). Typically, the comparator condition was Treatment as Usual (53%). Interventions were most often delivered in person (61%) in an individual format (79%).ConclusionsThe SPTD provides efficient, accurate, up-to-date access to a comprehensive suicide prevention trials database, which can be utilized by a range of stakeholders. It can reduce the time required for high-quality systematic reviews and provides researchers, administrators, and funders with current data on the state of the literature.

关键词： clinical trials data repository evidence synthesis meta-analysis quantitative methods suicide suicide prevention

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Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository

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Neuroscience Applied 2025年 4卷

作者： Khuntia, Adyasha Buciuman, Madalina-Octavia Fanning, John Stolicyn, Aleks Vetter, Clara Armio, Reetta-Liina From, Tiina Goffi, Federica Hahn, Lisa Kaufmann, Tobias Laurikainen, Heikki Maggioni, Eleonora Martinez-Zalacain, Ignacio Ruef, Anne Dong, Mark Sen Schwarz, Emanuel Squarcina, Letizia Andreassen, Ole Bellani, Marcella Brambilla, Paolo Haren, Neeltje van Hietala, Jarmo Lawrie, Stephen M. Soriano-Mas, Carles Whalley, Heather Taquet, Maxime Meisenzahl, Eva Falkai, Peter Wiegand, Ariane Koutsouleris, Nikolaos Department of Psychiatry and Psychotherapy University Hospital Ludwig-Maximilian University Munich Germany International Max-Planck School for Translational Psychiatry Germany Max-Planck Institute of Psychiatry Munich Munich Germany Max Planck Institute of Psychiatry Munich Germany German Center for Mental Health Partner Site Munich Augsburg Germany Division of Psychiatry Centre for Clinical Brain Sciences University of Edinburgh Scotland Edinburgh United Kingdom Munich Center of Machine Learning Munich Germany Helmholtz Association - Munich School for Data Science Munich Germany Department of Psychiatry University of Turku Turku Finland Turku PET Centre University of Turku Turku Finland Turku University Hospital Turku Finland Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Neurosciences and Mental Health Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico University of Milan Milan Italy NORMENT Centre Division of Mental Health and Addiction Oslo University Hospital Oslo Norway Department of Psychiatry and Psychotherapy University of Tübingen Germany German Center for Mental Health Partner Site Tübingen Germany Bellvitge Biomedical Research Institute-IDIBELL Psychiatry Department Bellvitge University Hospital CIBERSAM and Department of Psychobiology and Methodology of Health Sciences Universitat Autònoma de Barcelona Spain Department of Radiology Bellvitge University Hospital Barcelona Spain Hector Institute for Artificial Intelligence in Psychiatry Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany Department of Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany German Center for Mental Health Partner Site Mannheim-Heidelberg-Ulm Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Section of Psychiatry Dept of Neuroscience

The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible data repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R²) = 0.77, P < .001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R² = 0.74,P < .001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R² = 0.79, P < .001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, mu

关键词： data repository Machine learning Multi-site data Neuroimaging Psychiatry Structural MRI Transdiagnostic

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Development and implementation of a public data repository for photovoltaic systems: Case study malta's living laboratories

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Solar Energy Advances 2025年 5卷

作者： Bartolo, Brian Azzopardi, Brian Scerri, Kenneth The Foundation for Innovation and Research – Malta (FiR.mt) Birkirkara Malta The University of Malta (UM) Department of Systems and Control Engineering Faculty of Engineering Msida Malta Malta College of Arts Science and Technology (MCAST) Paola Malta Azzopardi & Associates Birkirkara Malta

The transition to renewable energy, particularly Photovoltaic systems (PVs), is pivotal in achieving carbon neutrality goals. However, effective performance monitoring through data sharing remains challenging, especially in regions with unique environmental conditions. This study addresses these gaps by introducing Malta's first public data repository for PVs under the PROMISE project. The initiative integrates high-precision, laboratory-grade sensors compliant with IEC standards to ensure robust data acquisition and processing. The methodology builds on international best practices, employing advanced monitoring frameworks to capture meteorological and system parameters with high temporal resolution. Using three of the ten PV Living Laboratories in Malta, the system reflects typical installations in a Mediterranean Island Territory climate and the Maltese context. data collection and processing adhere to European standards and established practices from existing public data repositories, ensuring compatibility and interoperability. By leveraging open-source platforms, the repository enables real-time visualization and the provision of structured historical datasets. Performance metrics, such as the DC performance ratio, are further employed to highlight the value of data sharing and collaboration in optimizing system performance and advancing predictive maintenance strategies. This work establishes a scalable framework for local PV monitoring and data sharing, advancing research and innovation in renewable energy and promoting Malta's participation in foreign programs. Future expansions aim to include additional laboratories, automate data uploads, and integrate Artificial intelligence (AI) driven fault detection tools, enhancing the system's utility and reliability. © 2025 The Author(s)

关键词： data repository data sharing framework Performance Ratio (PR) Photovoltaic systems (PVS) Real-time Monitoring Resolution

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Freeze casting - A review of processing, microstructure and properties via the open data repository, ***

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PROGRESS IN MATERIALS SCIENCE 2018年 94卷 243-305页

作者： Scotti, Kristen L. Dunand, David C. Northwestern Univ Dept Mat Sci & Engn Evanston IL 60208 USA

Freeze-casting produces materials with complex, three-dimensional pore structures which may be tuned during the solidification process. The range of potential applications of freeze-cast materials is vast, and includes: structural materials, biomaterials, filtration membranes, pharmaceuticals, and foodstuffs. Fabrication of materials with application specific microstructures is possible via freeze casting, however, the templating process is highly complex and the underlying principles are only partially understood. Here, we report the creation of a freeze-casting experimental data repository, which contains data extracted from similar to 800 different freeze-casting papers (as of August 2017). These data pertain to variables that link processing conditions to microstructural characteristics, and finally, mechanical properties. The aim of this work is to facilitate broad dissemination of relevant data to freeze-casting researchers, promote better informed experimental design, and encourage modeling efforts that relate processing conditions to microstructure formation and material properties. An initial, systematic analysis of these data is provided and key processing-structure-property relationships posited in the freeze-casting literature are discussed and tested against the database. Tools for data visualization and exploration available through the web interface are also provided. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Ice-templating Solidification Porous materials Mechanical properties data repository

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Characterizing Pharmacogenomic-Guided Medication Use With a Clinical data repository

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CLINICAL PHARMACOLOGY & THERAPEUTICS 2017年第2期102卷 340-348页

作者： Mathias, P. C. Hendrix, N. Wang, W-J Keyloun, K. Khelifi, M. Tarczy-Hornoch, P. Devine, B. Univ Washington Dept Lab Med Seattle WA 98195 USA Univ Washington Pharmaceut Outcomes Res & Policy Program Seattle WA 98195 USA Univ Washington Dept Biomed Informat & Med Educ Seattle WA 98195 USA Univ Washington Dept Pediat Div Neonatol Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Washington Dept Hlth Serv Seattle WA 98195 USA

The extent to which pharmacogenomic-guided medication use has been adopted in various health systems is unclear. To assess the uptake of pharmacogenomic-guided medication use, we determined its frequency across our health system, which does not have a structured testing program. Using a multisite clinical data repository, we identified adult patients' first prescribed medications between January 2011 and December 2013 and investigated the frequency of germline and somatic pharmacogenomic testing, by the Pharmacogenomics Knowledgebase level of the US Food and Drug Administration label information. There were 268,262 medication orders for drugs with germline pharmacogenomic testing information in their drug labels. Pharmacogenomic testing was detected for 1.5% (129/8,718) of medication orders with recommended or required testing. Of the 3,817 medication orders associated with somatic pharmacogenomic testing information in their drug labels, 20% (372/1,819) of required tests were detected. The low rates of detectable pharmacogenomic testing suggest that structured testing programs are required to achieve the success of precision medicine.

关键词： health care organization Pharmaceutical Preparations drug use data repository Drug Labeling germ line Precision Medicine program testing

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The INDEPTH data repository: An International Resource for Longitudinal Population and Health data From Health and Demographic Surveillance Systems

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JOURNAL OF EMPIRICAL RESEARCH ON HUMAN RESEARCH ETHICS 2015年第3期10卷 324-333页

作者： Herbst, Kobus Juvekar, Sanjay Bhattacharjee, Tathagata Bangha, Martin Patharia, Nidhi Tei, Titus Gilbert, Brendan Sankoh, Osman INDEPTH Network Accra Ghana UKZN Africa Ctr Hlth & Populat Studies Durban South Africa KEM Hosp Res Ctr Vadu HDSS VRHP Pune Maharashtra India KEM Hosp Res Ctr Pune Maharashtra India Univ Witwatersrand Johannesburg South Africa Hanoi Med Univ Hanoi Vietnam

The International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) is a global network of research centers that conduct longitudinal health and demographic evaluation of populations in low- and middle-income countries (LMICs) currently in 52 health and demographic surveillance system (HDSS) field sites situated in sub-Saharan Africa (14 countries), Asia (India, Bangladesh, Thailand, Vietnam, and Indonesia), and Oceania (Papua New Guinea). Through this network of HDSS field sites, INDEPTH is capable of producing reliable longitudinal data about the lives of people in the research communities as well as how development policies and programs affect those lives. The aim of the INDEPTH data repository is to enable INDEPTH member centers and associated researchers to contribute and share fully documented, high-quality datasets with the scientific community and health policy makers.

关键词： data sharing health and demographic surveillance data repository research data management metadata

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A study of the roles of metadata standard and data repository in science, technology, engineering and mathematics researchers' data reuse

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ONLINE INFORMATION REVIEW 2021年第7期45卷 1306-1321页

作者： Kim, Youngseek Sungkyunkwan Univ Dept Lib & Informat Sci Seoul South Korea

Purpose This research investigates how the availabilities of both metadata standards and data repositories influence researchers' data reuse intentions either directly or indirectly as mediated by the norms of data reuse and their attitudes toward data reuse. Design/methodology/approach The theory of planned behavior (TPB) was employed to develop the research model of researchers' data reuse intentions, focusing on the roles of metadata standards, data repositories and norms of data reuse. The proposed research model was evaluated using the structural equation modeling (SEM) method based on the survey responses received from 811 STEM (science, technology, engineering and mathematics) researchers in the United States. Findings This research found that the availabilities of both metadata standards and data repositories significantly affect STEM researchers' norm of data reuse, which influences their data reuse intentions as mediated by their attitudes toward data reuse. This research also found that both the availability of data repositories and the norm of data reuse have a direct influence on data reuse intentions and that norm of data reuse significantly increases the effect of attitude toward data reuse on data reuse intention as a moderator. Research limitations/implications The modified model of TPB provides a new perspective in apprehending the roles of resource facilitating conditions such as the availabilities of metadata standards and data repositories in an individual's attitude, norm and their behavioral intention to conduct a certain behavior. Practical implications This study suggests that scientific communities need to develop more supportive metadata standards and data repositories by considering their roles in enhancing the community norm of data reuse, which eventually lead to data reuse behaviors. Originality/value This study sheds light on the mechanism of metadata standard and data repository in researchers' data reuse behaviors through their comm

关键词： data reuse Metadata standard data repository Norm of data reuse Theory of planned behavior

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High-throughput whole-slide scanning to enable large-scale data repository building

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JOURNAL OF PATHOLOGY 2022年第4期257卷 383-390页

作者： Zarella, Mark D. Alvarez, Keysabelis Rivera Johns Hopkins Univ Dept Pathol Baltimore MD 21287 USA Johns Hopkins Univ Dept Biomed Engn Koch Canc Res BldgRoom 1551550 Orleans St Baltimore MD 21287 USA

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high-throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher-quality imaging data using fewer personnel. Here we review several practical considerations for deploying high-throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high-throughput scanning realizable to laboratories with limited resources. (c) 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

关键词： digital pathology virtual slide whole-slide imaging whole-slide scanning artificial intelligence machine learning computational pathology data repository

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