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Jeanson F, Gibson SJ, Alper P, Bernier A, Woolley JP, Mietchen D, Strug A, Becker R, Kamerling P, Sanchez Gonzalez MDC, Mah N, Novakowski A, Wilkinson MD, Benhamed OM, Landi A, Krog GP, Müller H, Riaz U, Veal C, Holub P, van Enckevort E, Brookes AJ. Getting your DUCs in a row - standardising the representation of Digital Use Conditions. Sci Data 2024; 11:464. [PMID: 38719839 PMCID: PMC11078994 DOI: 10.1038/s41597-024-03280-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use.
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Grants
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- Algerian Ministry of Higher Education and Scientific Research
- The European Reference Network on Rare Multisystemic Vascular Diseases (VASCERN), which is partly co-funded by the European Union within the framework of the EU4Health programme – “VASCERN Action Grant".
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Affiliation(s)
- Francis Jeanson
- Centre for Analytics, Ontario Brain Institute, Toronto, Canada.
| | - Spencer J Gibson
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Pinar Alper
- Luxembourg National Data Service, Esch-sur-Alzette, Luxembourg
| | - Alexander Bernier
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - J Patrick Woolley
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Andrzej Strug
- Medical Laboratory Diagnostics Department, Medical University of Gdańsk, Gdańsk, Poland
| | - Regina Becker
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Pim Kamerling
- Center for Radiology and Nuclear Medicine, VASCERN ERN /Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Nancy Mah
- Biomedical Data & Bioethics Group, Fraunhofer Institute for Biomedical Engineering, Sulzbach/Saar, Germany
| | | | - Mark D Wilkinson
- Departamento de Biotecnología-Biología Vegetal, ETSI Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid, Madrid, Spain
| | - Oussama Mohammed Benhamed
- Departamento de Biotecnología-Biología Vegetal, ETSI Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid, Madrid, Spain
| | - Annalisa Landi
- Research, Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, Bari, Italy
| | | | | | - Umar Riaz
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Colin Veal
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Petr Holub
- BBMRI-ERIC, Graz, Austria
- Institute of Computer Science, Masaryk University, Brno, Czechia
| | - Esther van Enckevort
- University of Groningen, Groningen, Netherlands
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
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2
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Martone ME. The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR. Front Neuroinform 2024; 17:1276407. [PMID: 38250019 PMCID: PMC10796549 DOI: 10.3389/fninf.2023.1276407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 01/23/2024] Open
Abstract
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.
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Affiliation(s)
- Maryann E. Martone
- Department of Neurosciences, University of California, San Diego, CA, United States
- San Francisco Veterans Administration Hospital, San Francisco, CA, United States
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3
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Behan B, Jeanson F, Cheema H, Eng D, Khimji F, Vaccarino AL, Gee T, Evans SG, MacPhee FC, Dong F, Shahnazari S, Sparks A, Martens E, Lasalandra B, Arnott SR, Strother SC, Javadi M, Dharsee M, Evans KR, Nylen K, Mikkelsen T. FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research. Front Neuroinform 2023; 17:1158378. [PMID: 37274750 PMCID: PMC10233014 DOI: 10.3389/fninf.2023.1158378] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/10/2023] [Indexed: 06/06/2023] Open
Abstract
The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability. Since the inception of the Brain-CODE platform and services in 2012, the Ontario Brain Institute (OBI) has pioneered data sharing activities aligned with FAIR principles in neuroscience. Here, we describe how Brain-CODE has operationalized data sharing according to the FAIR principles. Findable-Brain-CODE offers an interactive and itemized approach for requesters to generate data cuts of interest that align with their research questions. Accessible-Brain-CODE offers multiple data access mechanisms. These mechanisms-that distinguish between metadata access, data access within a secure computing environment on Brain-CODE and data access via export will be discussed. Interoperable-Standardization happens at the data capture level and the data release stage to allow integration with similar data elements. Reusable - Brain-CODE implements several quality assurances measures and controls to maximize data value for reusability. We will highlight the successes and challenges of a FAIR-focused neuroinformatics platform that facilitates the widespread collection and sharing of neuroscience research data for learning health systems.
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Affiliation(s)
| | | | | | - Derek Eng
- Ontario Brain Institute, Toronto, ON, Canada
| | | | | | - Tom Gee
- Indoc Research, Toronto, ON, Canada
| | | | | | - Fan Dong
- Indoc Research, Toronto, ON, Canada
| | | | | | | | | | | | | | | | | | | | - Kirk Nylen
- Ontario Brain Institute, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
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4
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Yang W, Guo Z, Shi W, Qin L, Xia X, Hao B, Liao S. Security and Sharing of NIPT Data Are the Basis of Ethical Decision-Making Related to Non-Medical Traits. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:29-31. [PMID: 36919545 DOI: 10.1080/15265161.2023.2169392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Affiliation(s)
- Wenke Yang
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Zhenglong Guo
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Weili Shi
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Litao Qin
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Xiaoliang Xia
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Bingtao Hao
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
| | - Shixiu Liao
- Henan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University; and National Health Commission Key Laboratory of Birth Defects Prevention, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics
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5
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Leipzig J, Nüst D, Hoyt CT, Ram K, Greenberg J. The role of metadata in reproducible computational research. PATTERNS (NEW YORK, N.Y.) 2021; 2:100322. [PMID: 34553169 PMCID: PMC8441584 DOI: 10.1016/j.patter.2021.100322] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Affiliation(s)
- Jeremy Leipzig
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
| | - Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Karthik Ram
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Jane Greenberg
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
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Sharma A, Nilsen TB, Czerwinska KP, Onitiu D, Brenna L, Johansen D, Johansen HD. Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies. Front Big Data 2021; 4:624424. [PMID: 34056584 PMCID: PMC8155614 DOI: 10.3389/fdata.2021.624424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 04/01/2021] [Indexed: 12/03/2022] Open
Abstract
Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using gossips. We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance.
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Affiliation(s)
- Aakash Sharma
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Thomas Bye Nilsen
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Katja Pauline Czerwinska
- Faculty of Design, Computer Science, Media, RheinMain University of Applied Sciences, Wiesbaden, Germany
| | - Daria Onitiu
- Northumbria Law School, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Lars Brenna
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Dag Johansen
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Håvard D Johansen
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
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Gu W, Hasan S, Rocca-Serra P, Satagopam VP. Road to effective data curation for translational research. Drug Discov Today 2021; 26:626-630. [PMID: 33338655 DOI: 10.1016/j.drudis.2020.12.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/02/2020] [Accepted: 12/09/2020] [Indexed: 10/22/2022]
Abstract
Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the European Translational Research Infrastructure & Knowledge management Services (eTRIKS) program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry.
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Affiliation(s)
- Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg; ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | | | - Venkata P Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg; ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg.
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