Developing and using the Neuroimaging and Data Sharing Data Model… › fac › sci › statistics...
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NIDASH DM working group has developed terminologies for DICOM [6,7] and neuroimaging terms [2],and the Neuroimaging Data Model (NI-DM) [2,5]. NI-DM is a neuroimaging-specific extension of the PROV Data Model [11] to facilitate sharing of semantically meaningful neuroimaging provenance and derived data. Using these tools, we have developed applications to federate data across relational databases and Excel spreadsheets [4], visualizing FreeSurfer segmentations across a large cohort [3], and modeling SPM generated statistical results [8]. We have begun development of detailed specifications of the core NI- DM standard, an extension of the W3C PROV model, and "object models" specifying the recommended set of entities, agents, and activities to describe a workflow and/or derived data product: http://nidm.nidash.org/specs/ We have also developed and deployed a website for sharing raw statistical maps (NeuroVault.org) which will use NI-DM. The INCF-TF meetings have encouraged adoption of these resources. We are linking this work with projects that are providing data, hosting data, developing lexicons or ontologies, and generating derived data. The group includes developers and remains in close contact with projects that plan to use the developed resources, or may do so in the future when articles will be linked to actual data analysis repository (e.g., Neurosynth, Neurovault, Brainspell), as well as integration platforms such as NeuroDebian. Developing and using the Neuroimaging and Data Sharing Data Model: the NIDASH Working Group David Keator 1 , Satrajit Ghosh 2 , Camille Maumet 3 , Guillaume Flandin 4 , Nolan Nichols 5 , Thomas Nichols 6 , Gully Burns 7 , Rüdiger Brüehl 8 , Cameron Craddock 9 , Blaise Frederick 10 , Krzysztof Gorgolewski 11 , Daniel Marcus 12 , Michael Hanke 13 , Christian Haselgrove 14 , Karl Helmer 15 , Arno Klein 16 , Michael Milham 17 , Russell Poldrack 18 , Franck Michel 19 , Jason Steffener 20 , Yannick Schwartz 21 , Rich Stoner 22 , Jessica Turner 23 , David Kennedy 24 , Jean-Baptiste Poline 25 1 University of California Irvine, Irvine, CA, 2 MIT, Cambridge, MA, 3 University of Warwick, Coventry, United Kingdom, 4 UCL Institute of Neurology, London, United Kingdom, 5 University of Washington, Seattle, WA, 6 University of Warwick, Dept. of Statistics, Coventry, United Kingdom, 7 University of Southern California, Los Angeles, CA, 8 Medical Metrology, Physikalisch- Technische Bundesanstalt, Berlin, Germany, 9 Child Mind Institute, New York, NY, 10 McLean Hospital, Harvard Medical School, Belmont, MA, 11 Max Planck Institute for Human Brain and Cognitive Sciences, Leipzig, Germany, 12 Neuroimaging Informatics and Analysis Center at Washington University, St. Louis, LA, 13 Otto-von-Guericke Universität, Magdeburg, Germany, 14 UMass Medical School, Worcester, MA, 15 Massachusetts General Hospital, Charlestown, MA, 16 columbia university, New York, United States, 17 Nathan Kline Institute for Psychiatric Research, New York, NY, 18 UT Austin, Austin, United States, 19 Centre national de la recherche scientifique, Paris, France, 20 Columbia University, New York, United States, 21 CEA, France, 22 University of California San Diego, San Diego, CA, 23 Georgia State University, Atlanta, United States, 24 University of Massachusetts Medical School, Worcester, MA, 25 Helen Wills Neuroscience Institute, UC Berkeley, CA, USA In neuroimaging, data sharing remains an exception [1]. While other disciplines (e.g. bioinformatics) require data be made available to publish a paper, in neuroimaging, data sharing is mainly confined to sharing the paper itself. The Biomedical Informatics Research Network (BIRN) Derived Data Working Group (DDWG) [10] recently joined forces with the Neuroimaging Data Sharing Task Force (NIDASH-TF) formed by the International Neuroinformatics Coordinating Facility’s (INCF) Program on Standards for Data Sharing [9] supporting widespread publication and use of provenance, derived data, and resources in neuroimaging. The NIDASH-TF is working to facilitate sharing of neuroimaging data in a variety of ways, whether raw or derived data and its provenance, lexical information, or exchange formats, while taking into account legal and ethical considerations. Introduction Methods The NIDASH Data Model (DM) working group holds weekly calls with participating members from the international community as well as several INCF hosted meetings per year. The INCF wiki is the primary resource for disseminating information and contains weekly minutes, publications, and links to products. http://wiki.incf.org/mediawiki/ index.php/Neuroimaging_Task_Force NIDASH code is made available through the “NI-” GitHub organisation. https://github.com/ni- NI-DM Data Model Site provides specification documents and examples for NI-DM end-users. http://nidm.nidash.org The NIDASH-TF meets several times a year to review progress on various projects that will make data sharing easier and more fruitful for the scientific community. Results [1] Poline J.B., Breeze J., Ghosh S., et al. Data sharing in neuroimaging research. Frontiers in Neuroinformatics. 2012; 6:9. [2] Keator D.B., Helmer K., Steffener J., et al. Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage. 2013 Nov 15;82:647-61 [3] Nichols B.N., Stoner R., Keator D.B., et al. There’s an app for that: a semantic data provenance framework for reproducible brain imaging. Human Brain Mapping, Seattle, WA. 2013. [4] Nichols B.N., Steffener J., Haselgrove C., et al. Mapping Neuroimaging Resources into the NIDASH Data Model for Federated Information Retrieval. Neuroinformatics 2013, Stockholm, Sweden. 2013. [5] Ghosh S., Nichols B. N., Gadde S., et al. XCEDE-DM: A neuroimaging extension to the W3C provenance data model. Poster presentation at Neuro-Informatics Congress.Munich,Germany 2012. [6] K.G. Helmer, S. Ghosh, B.N. Nichols, et al. INCF Neuroscience2012, Munich, Germany, 2012. [7] K.G. Helmer, S. Ghosh, D. Keator, et al. The Addition of Neuroimaging Acquisition, Processing and Analysis Terms to Neurolex. Human Brain Mapping, Hamburg, Germany. 2014. [8] C. Maumet, T. Nichols, B.N. Nichols, et al. Extending NI-DM to share the results and provenance of a neuroimaging study: an example with SPM. Human Brain Mapping, Hamburg, Germany. 2014. [9] http://www.incf.org/core/programs/datasharing [10] https://wiki.birncommunity.org The future short-term goals of the NIDASH DM working group are to: 1. Refine existing terminologies and object models. 2. Work with existing software packages to incorporate the data model (e.g. SPM) 3. Create similar models for related tools (e.g., FSL, AFNI) so that common aspects across software packages can be identified 4. Facilitate broad and expanded use of the NI-DM standard for data querying and data exchange, fostering applications such as meta analyses. Conclusions Acknowledgments: We would like to acknowledge the work of all the INCF task force members as well as of many other colleagues who have helped the task force. We are particularly indebted to Mathew Abrams, Linda Lanyon, Roman Valls Guimera and Sean Hill for their support at the INCF. Further we acknowledge the long-standing support of Derived Data Working Group activities by the BIRN coordinating center (NIH 1 U24 RR025736-01), and the Wellcome Trust for support of CM & TEN. References
Transcript of Developing and using the Neuroimaging and Data Sharing Data Model… › fac › sci › statistics...
NIDASH DM working group has developed terminologies for DICOM [6,7] and neuroimaging terms [2],and the Neuroimaging Data Model (NI-DM) [2,5]. NI-DM is a neuroimaging-specific extension of the PROV Data Model [11] to facilitate sharing of semantically meaningful neuroimaging provenance and derived data.
Using these tools, we have developed applications to federate data across relational databases and Excel spreadsheets [4], visualizing FreeSurfer segmentations across a large cohort [3], and modeling SPM generated statistical results [8].
We have begun development of detailed specifications of the core NI-DM standard, an extension of the W3C PROV model, and "object models" specifying the recommended set of entities, agents, and activities to describe a workflow and/or derived data product: http://nidm.nidash.org/specs/
We have also developed and deployed a website for sharing raw statistical maps (NeuroVault.org) which will use NI-DM. The INCF-TF meetings have encouraged adoption of these resources. We are linking this work with projects that are providing data, hosting data, developing lexicons or ontologies, and generating derived data. The group includes developers and remains in close contact with projects that plan to use the developed resources, or may do so in the future when articles will be linked to actual data analysis repository (e.g., Neurosynth, Neurovault, Brainspell), as well as integration platforms such as NeuroDebian.
Developing and using the Neuroimaging and Data Sharing Data Model: the NIDASH Working Group
David Keator1, Satrajit Ghosh2, Camille Maumet3, Guillaume Flandin4, Nolan Nichols5, Thomas Nichols6, Gully Burns7, Rüdiger Brüehl8, Cameron Craddock9, Blaise Frederick10, Krzysztof Gorgolewski11, Daniel Marcus12, Michael Hanke13, Christian Haselgrove14, Karl Helmer15, Arno Klein16, Michael Milham17, Russell Poldrack18, Franck Michel19, Jason Steffener20, Yannick Schwartz21, Rich Stoner22, Jessica Turner23, David Kennedy24, Jean-Baptiste Poline25
1University of California Irvine, Irvine, CA, 2MIT, Cambridge, MA, 3University of Warwick, Coventry, United Kingdom, 4UCL Institute of Neurology, London, United Kingdom, 5University of Washington, Seattle, WA, 6University of Warwick, Dept. of Statistics, Coventry, United Kingdom, 7University of Southern California, Los Angeles, CA, 8Medical Metrology, Physikalisch-Technische Bundesanstalt, Berlin, Germany, 9Child Mind Institute, New York, NY, 10McLean Hospital, Harvard Medical School, Belmont, MA, 11Max Planck Institute for Human Brain and Cognitive Sciences, Leipzig, Germany, 12Neuroimaging Informatics and Analysis Center at Washington University, St. Louis, LA, 13Otto-von-Guericke Universität, Magdeburg, Germany, 14UMass Medical School, Worcester, MA, 15Massachusetts General Hospital, Charlestown, MA, 16columbia university, New York, United States, 17Nathan Kline Institute for Psychiatric Research, New York, NY, 18UT Austin, Austin, United States, 19Centre national de la recherche scientifique, Paris, France, 20Columbia University, New York, United States, 21CEA, France, 22University of California San Diego, San Diego, CA, 23Georgia State University, Atlanta, United States, 24University of Massachusetts Medical School, Worcester, MA, 25Helen Wills Neuroscience Institute, UC Berkeley, CA, USA
In neuroimaging, data sharing remains an exception [1]. While other disciplines (e.g. bioinformatics) require data be made available to publish a paper, in neuroimaging, data sharing is mainly confined to sharing the paper itself. The Biomedical Informatics Research Network (BIRN) Derived Data Working Group (DDWG) [10] recently joined forces with the Neuroimaging Data Sharing Task Force (NIDASH-TF) formed by the International Neuroinformatics Coordinating Facility’s (INCF) Program on Standards for Data Sharing [9] supporting widespread publication and use of provenance, derived data, and resources in neuroimaging. The NIDASH-TF is working to facilitate sharing of neuroimaging data in a variety of ways, whether raw or derived data and its provenance, lexical information, or exchange formats, while taking into account legal and ethical considerations.
Introduction
Methods The NIDASH Data Model (DM) working group holds weekly calls with participating members from the international community as well as several INCF hosted meetings per year.
The INCF wiki is the primary resource for disseminating information and contains weekly minutes, publications, and links to products. http://wiki.incf.org/mediawiki/index.php/Neuroimaging_Task_Force NIDASH code is made available through the “NI-” GitHub organisation. https://github.com/ni-
NI-DM Data Model Site provides specification documents and examples for NI-DM end-users. http://nidm.nidash.org
The NIDASH-TF meets several times a year to review progress on various projects that will make data sharing easier and more fruitful for the scientific community.
Results
[1] Poline J.B., Breeze J., Ghosh S., et al. Data sharing in neuroimaging research. Frontiers in Neuroinformatics. 2012; 6:9. [2] Keator D.B., Helmer K., Steffener J., et al. Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage. 2013 Nov 15;82:647-61 [3] Nichols B.N., Stoner R., Keator D.B., et al. There’s an app for that: a semantic data provenance framework for reproducible brain imaging. Human Brain Mapping, Seattle, WA. 2013. [4] Nichols B.N., Steffener J., Haselgrove C., et al. Mapping Neuroimaging Resources into the NIDASH Data Model for Federated Information Retrieval. Neuroinformatics 2013, Stockholm, Sweden. 2013. [5] Ghosh S., Nichols B. N., Gadde S., et al. XCEDE-DM: A neuroimaging extension to the W3C provenance data model. Poster presentation at Neuro-Informatics Congress.Munich,Germany 2012. [6] K.G. Helmer, S. Ghosh, B.N. Nichols, et al. INCF Neuroscience2012, Munich, Germany, 2012. [7] K.G. Helmer, S. Ghosh, D. Keator, et al. The Addition of Neuroimaging Acquisition, Processing and Analysis Terms to Neurolex. Human Brain Mapping, Hamburg, Germany. 2014. [8] C. Maumet, T. Nichols, B.N. Nichols, et al. Extending NI-DM to share the results and provenance of a neuroimaging study: an example with SPM. Human Brain Mapping, Hamburg, Germany. 2014. [9] http://www.incf.org/core/programs/datasharing [10] https://wiki.birncommunity.org
The future short-term goals of the NIDASH DM working group are to: 1. Refine existing terminologies and object models. 2. Work with existing software packages to incorporate the data model (e.g. SPM) 3. Create similar models for related tools (e.g., FSL, AFNI) so that common aspects across software packages can be identified 4. Facilitate broad and expanded use of the NI-DM standard for data querying and data exchange, fostering applications such as meta analyses.
Conclusions
Acknowledgments: We would like to acknowledge the work of all the INCF task force members as well as of many other colleagues who have helped the task force. We are particularly indebted to Mathew Abrams, Linda Lanyon, Roman Valls Guimera and Sean Hill for their support at the INCF. Further we acknowledge the long-standing support of Derived Data Working Group activities by the BIRN coordinating center (NIH 1 U24 RR025736-01), and the Wellcome Trust for support of CM & TEN.
References
