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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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2
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Nadtochiy A, Luu P, Fraser SE, Truong TV. VoDEx: a Python library for time annotation and management of volumetric functional imaging data. Bioinformatics 2023; 39:btad568. [PMID: 37699009 PMCID: PMC10562951 DOI: 10.1093/bioinformatics/btad568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023] Open
Abstract
SUMMARY In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (e.g. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. AVAILABILITY AND IMPLEMENTATION VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub (https://github.com/LemonJust/vodex). A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub (https://github.com/LemonJust/napari-vodex). The software version at the time of submission is archived at Zenodo (version v1.0.18, https://zenodo.org/record/8061531).
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Affiliation(s)
- Anna Nadtochiy
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, United States
- Translational Imaging Center, University of Southern California, Los Angeles, CA 90089, United States
| | - Peter Luu
- Translational Imaging Center, University of Southern California, Los Angeles, CA 90089, United States
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States
| | - Scott E Fraser
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, United States
- Translational Imaging Center, University of Southern California, Los Angeles, CA 90089, United States
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States
| | - Thai V Truong
- Translational Imaging Center, University of Southern California, Los Angeles, CA 90089, United States
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States
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3
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Huerta EA, Blaiszik B, Brinson LC, Bouchard KE, Diaz D, Doglioni C, Duarte JM, Emani M, Foster I, Fox G, Harris P, Heinrich L, Jha S, Katz DS, Kindratenko V, Kirkpatrick CR, Lassila-Perini K, Madduri RK, Neubauer MS, Psomopoulos FE, Roy A, Rübel O, Zhao Z, Zhu R. FAIR for AI: An interdisciplinary and international community building perspective. Sci Data 2023; 10:487. [PMID: 37495591 PMCID: PMC10372139 DOI: 10.1038/s41597-023-02298-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/09/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- E A Huerta
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, Illinois, 60637, USA.
| | - Ben Blaiszik
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
- Globus, University of Chicago, Chicago, Illinois, 60637, USA
| | - L Catherine Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, 27708, USA
| | - Kristofer E Bouchard
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Biological Systems & Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, 94720, USA
| | - Daniel Diaz
- Department of Physics, University of California San Diego, La Jolla, California, 92093, USA
| | - Caterina Doglioni
- Lund University, Department of Physics, Box 118, 221 00, Lund, Sweden
- School of Physics & Astronomy, The University of Manchester, Manchester, M13 9PL, UK
| | - Javier M Duarte
- Department of Physics, University of California San Diego, La Jolla, California, 92093, USA
| | - Murali Emani
- Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois, 60439, USA
| | - Ian Foster
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
- Department of Computer Science, University of Chicago, Chicago, Illinois, 60637, USA
| | - Geoffrey Fox
- Biocomplexity Institute and Department of Computer Science, University of Virginia, Charlottesville, Virginia, 22904, USA
| | - Philip Harris
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Lukas Heinrich
- Technical University Munich, Arcisstraβe 21, 80333, München, Germany
| | - Shantenu Jha
- Computational Science Initiative Brookhaven National Laboratory Upton, New York, 11973, USA
- Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Daniel S Katz
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Volodymyr Kindratenko
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Christine R Kirkpatrick
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California, 92093, USA
| | - Kati Lassila-Perini
- Helsinki Institute of Physics, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland
| | - Ravi K Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
| | - Mark S Neubauer
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Fotis E Psomopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, 57001, Greece
| | - Avik Roy
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Oliver Rübel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Zhizhen Zhao
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Ruike Zhu
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
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4
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Nadtochiy A, Luu P, Fraser SE, Truong TV. VoDEx: a Python library for time annotation and management of volumetric functional imaging data. ARXIV 2023:arXiv:2305.07438v1. [PMID: 37214133 PMCID: PMC10197724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (eg. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. Availability and Implementation: VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub https://github.com/LemonJust/vodex. A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub https://github.com/LemonJust/napari-vodex.
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Affiliation(s)
- Anna Nadtochiy
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, CA, 90089, USA
| | - Peter Luu
- Department of Biological Sciences, Division of Molecular and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, CA, 90089, USA
| | - Scott E. Fraser
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biological Sciences, Division of Molecular and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, CA, 90089, USA
| | - Thai V. Truong
- Department of Biological Sciences, Division of Molecular and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, CA, 90089, USA
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5
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Kelliher JM, Rudolph M, Vangay P, Abbas A, Borton MA, Davenport ER, Davenport KW, Erazo NG, Herman C, Karstens L, Kocurek B, Lutz HL, Myers KS, Ockert I, Rodriguez FE, Santistevan C, Saunders JK, Smith ML, Vogtmann E, Windsor A, Wood-Charlson EM, Woodley L, Eloe-Fadrosh EA. Cohort-based learning for microbiome research community standards. Nat Microbiol 2023; 8:751-753. [PMID: 37069400 DOI: 10.1038/s41564-023-01361-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Affiliation(s)
| | - Marisa Rudolph
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pajau Vangay
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Arwa Abbas
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | | | - Natalia G Erazo
- Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA
| | | | - Lisa Karstens
- Oregon Health and Science University, Portland, OR, USA
| | - Brandon Kocurek
- Center for Veterinary Medicine, U.S. Food and Drug Administration, Laurel, MD, USA
| | | | - Kevin S Myers
- Wisconsin Energy Institute and Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Ingrid Ockert
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Camille Santistevan
- Center for Scientific Collaboration and Community Engagement, Oakland, CA, USA
| | - Jaclyn K Saunders
- Woods Hole Oceanographic Institution, Falmouth, MA, USA
- University of Georgia, Athens, GA, USA
| | | | | | - Amanda Windsor
- Center for Food Safety and Applied Nutrition, Office of Regulatory Science, U.S. Food and Drug Administration, College Park, MD, USA
| | | | - Lou Woodley
- Center for Scientific Collaboration and Community Engagement, Oakland, CA, USA
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