brainhack-report.bib

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@MISC{noauthor_undated-jz,
  title        = "Neuroimaging Data Model Overview ({NIDM-Overview})",
  howpublished = "\url{http://nidm.nidash.org/specs/nidm-overview.html}",
  note         = "Accessed: 2015-11-24",
  keywords     = "Neuroimaging/Standards"
}

@MISC{noauthor_undated-hj,
  title        = "{RDF} - Semantic Web Standards",
  howpublished = "\url{http://www.w3.org/RDF/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{noauthor_undated-fs,
  title        = "Neo4j, the World's Leading Graph Database",
  booktitle    = "Neo4j Graph Database",
  abstract     = "Neo4j, the world's leading graph database, powering numerous
                  organizations worldwide, including more than 50 Global 2000
                  customers.",
  howpublished = "\url{http://neo4j.com/}",
  note         = "Accessed: 2015-11-24"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@MISC{Wikipedia_contributors2015-on,
  title        = "{JSON}",
  booktitle    = "Wikipedia, The Free Encyclopedia",
  author       = "{Wikipedia contributors}",
  abstract     = "JSON, (canonically pronounced /ˈdʒeɪsən/ JAY-sən;[1]
                  sometimes JavaScript Object Notation), is an open standard
                  format that uses human-readable text to transmit data objects
                  consisting of attribute--value pairs. It is the primary data
                  format used for asynchronous browser/server communication
                  (AJAJ), largely replacing XML (used by AJAX).",
  month        =  "23~" # nov,
  year         =  2015,
  howpublished = "\url{https://en.wikipedia.org/w/index.php?title=JSON&oldid=692109528}",
  note         = "Accessed: 2015-11-24"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Bostock2011-ei,
  title       = "D³: {Data-Driven} Documents",
  author      = "Bostock, Michael and Ogievetsky, Vadim and Heer, Jeffrey",
  affiliation = "Computer Science Department of Stanford University, Stanford,
                 CA 94305, USA. mbostock@stanford.edu",
  abstract    = "Data-Driven Documents (D3) is a novel
                 representation-transparent approach to visualization for the
                 web. Rather than hide the underlying scenegraph within a
                 toolkit-specific abstraction, D3 enables direct inspection and
                 manipulation of a native representation: the standard document
                 object model (DOM). With D3, designers selectively bind input
                 data to arbitrary document elements, applying dynamic
                 transforms to both generate and modify content. We show how
                 representational transparency improves expressiveness and
                 better integrates with developer tools than prior approaches,
                 while offering comparable notational efficiency and retaining
                 powerful declarative components. Immediate evaluation of
                 operators further simplifies debugging and allows iterative
                 development. Additionally, we demonstrate how D3 transforms
                 naturally enable animation and interaction with dramatic
                 performance improvements over intermediate representations.",
  journal     = "IEEE Trans. Vis. Comput. Graph.",
  volume      =  17,
  number      =  12,
  pages       = "2301--2309",
  month       =  dec,
  year        =  2011,
    notes = "KA"
}

@MISC{noauthor_undated-ii,
  title        = "{Flask-RESTful} --- {Flask-RESTful} 0.2.1 documentation",
  howpublished = "\url{http://flask-restful-cn.readthedocs.org/en/0.3.4/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{Google_undated-aj,
  title        = "Python Runtime Environment",
  booktitle    = "Google Cloud Platform",
  author       = "{Google}",
  abstract     = "Offers users the ability to build and host web applications
                  on Google's infrastructure.",
  howpublished = "\url{https://cloud.google.com/appengine/docs/python/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{noauthor_undated-jw,
  title        = "{AWS} Python Developer Center",
  howpublished = "\url{https://aws.amazon.com/python/}"
}

@MISC{noauthor_undated-ej,
  title        = "The Web framework for perfectionists with deadlines | Django",
  howpublished = "\url{https://www.djangoproject.com/}",
  note         = "Accessed: 2015-11-4"
}

@MISC{noauthor_undated-ia,
  title        = "Welcome to Flask --- Flask Documentation (0.10)",
  howpublished = "\url{http://flask.pocoo.org/docs/0.10/}",
  note         = "Accessed: 2015-11-3"
}

@MISC{noauthor_undated-if,
  title        = "{NIDM-Results} 1.1.0",
  howpublished = "\url{http://nidm.nidash.org/specs/nidm-results_110.html}",
  note         = "Accessed: 2015-11-24"
}

@ARTICLE{Gorgolewski2015-sf,
  title     = "{NeuroVault.org}: a web-based repository for collecting and
               sharing unthresholded statistical maps of the human brain",
  author    = "Gorgolewski, Krzysztof J and Varoquaux, Gael and Rivera, Gabriel and Schwarz, Yannick and Ghosh, Satrajit S and Maumet, Camille and Sochat, Vanessa V and Nichols, Thomas E and Poldrack, Russell A and Poline, Jean-Baptiste and Yarkoni, Tal and Margulies, Daniel S",
  abstract  = "Here we present NeuroVault --- a web based repository that
               allows researchers to store, share, visualize, and decode
               statistical maps of the human brain. NeuroVault is easy to use
               and employs modern web technologies to provide informative
               visualization of data without the need to install additional
               software. In addition, it leverages the power of the Neurosynth
               database to provide cognitive decoding of deposited maps. The
               data are exposed through a public REST API enabling other
               services and tools to take advantage of it. NeuroVault is a new
               resource for researchers interested in conducting meta- and
               coactivation analyses.",
  journal   = "Front. Neuroinform.",
  publisher = "Frontiers",
  volume    =  9,
  month     =  "21~" # mar,
  year      =  2015,
  keywords  = "data sharing; Statistical parameter mapping (SPM); metaanalysis;
               repository; database;My Papers"
}

@MISC{noauthor_undated-dn,
  title        = "{WebFrameworks} - Python Wiki",
  howpublished = "\url{https://wiki.python.org/moin/WebFrameworks/}",
  note         = "Accessed: 2015-11-24"
}

@ARTICLE{Keator2013-rc,
  title       = "Towards structured sharing of raw and derived neuroimaging
                 data across existing resources",
  author      = "Keator, D B and Helmer, K and Steffener, J and Turner, J A and
                 Van Erp, T G M and Gadde, S and Ashish, N and Burns, G A and
                 Nichols, B N",
  affiliation = "Department of Psychiatry and Human Behavior, University of
                 California, Irvine, CA 92617, USA. dbkeator@uci.edu",
  abstract    = "Data sharing efforts increasingly contribute to the
                 acceleration of scientific discovery. Neuroimaging data is
                 accumulating in distributed domain-specific databases and
                 there is currently no integrated access mechanism nor an
                 accepted format for the critically important meta-data that is
                 necessary for making use of the combined, available
                 neuroimaging data. In this manuscript, we present work from
                 the Derived Data Working Group, an open-access group sponsored
                 by the Biomedical Informatics Research Network (BIRN) and the
                 International Neuroimaging Coordinating Facility (INCF)
                 focused on practical tools for distributed access to
                 neuroimaging data. The working group develops models and tools
                 facilitating the structured interchange of neuroimaging
                 meta-data and is making progress towards a unified set of
                 tools for such data and meta-data exchange. We report on the
                 key components required for integrated access to raw and
                 derived neuroimaging data as well as associated meta-data and
                 provenance across neuroimaging resources. The components
                 include (1) a structured terminology that provides semantic
                 context to data, (2) a formal data model for neuroimaging with
                 robust tracking of data provenance, (3) a web service-based
                 application programming interface (API) that provides a
                 consistent mechanism to access and query the data model, and
                 (4) a provenance library that can be used for the extraction
                 of provenance data by image analysts and imaging software
                 developers. We believe that the framework and set of tools
                 outlined in this manuscript have great potential for solving
                 many of the issues the neuroimaging community faces when
                 sharing raw and derived neuroimaging data across the various
                 existing database systems for the purpose of accelerating
                 scientific discovery.",
  journal     = "Neuroimage",
  volume      =  82,
  pages       = "647--661",
  month       =  "15~" # nov,
  year        =  2013,
  keywords    = "Data model; Database; Neuroimaging; Provenance; Web services;
                 XCEDE"
}


@MISC{noauthor_undated-jv,
  title        = "- Neuroimaging Data Model",
  howpublished = "\url{http://nidm.nidash.org/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{noauthor_undated-cp,
  title        = "{SPARQL} Query Language for {RDF}",
  howpublished = "\url{http://www.w3.org/TR/rdf-sparql-query/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{noauthor_undated-pr,
  title        = "{NIDM} Specifications",
  howpublished = "\url{http://nidm.nidash.org/specs/}",
  note         = "Accessed: 2015-11-3"
}

@ARTICLE{Jenkinson2012-pr,
  title       = "{FSL}",
  author      = "Jenkinson, Mark and Beckmann, Christian F and Behrens, Timothy
                 E J and Woolrich, Mark W and Smith, Stephen M",
  affiliation = "FMRIB Centre, Nuffield Department of Clinical Neurosciences,
                 University of Oxford, UK.",
  abstract    = "FSL (the FMRIB Software Library) is a comprehensive library of
                 analysis tools for functional, structural and diffusion MRI
                 brain imaging data, written mainly by members of the Analysis
                 Group, FMRIB, Oxford. For this NeuroImage special issue on
                 ``20 years of fMRI'' we have been asked to write about the
                 history, developments and current status of FSL. We also
                 include some descriptions of parts of FSL that are not well
                 covered in the existing literature. We hope that some of this
                 content might be of interest to users of FSL, and also maybe
                 to new research groups considering creating, releasing and
                 supporting new software packages for brain image analysis.",
  journal     = "Neuroimage",
  volume      =  62,
  number      =  2,
  pages       = "782--790",
  month       =  "15~" # aug,
  year        =  2012,
    notes = "KA"
}

@MISC{noauthor_undated-ca,
  title        = "Research Imaging Institute --- Mango",
  howpublished = "\url{http://ric.uthscsa.edu/mango/index.html}",
  note         = "Accessed: 2015-11-24"
}

@ARTICLE{Yarkoni2011-rg,
  title       = "Large-scale automated synthesis of human functional
                 neuroimaging data",
  author      = "Yarkoni, Tal and Poldrack, Russell A and Nichols, Thomas E and
                 Van Essen, David C and Wager, Tor D",
  affiliation = "Department of Psychology and Neuroscience, University of
                 Colorado at Boulder, Boulder, Colorado, USA.
                 tal.yarkoni@colorado.edu",
  abstract    = "The rapid growth of the literature on neuroimaging in humans
                 has led to major advances in our understanding of human brain
                 function but has also made it increasingly difficult to
                 aggregate and synthesize neuroimaging findings. Here we
                 describe and validate an automated brain-mapping framework
                 that uses text-mining, meta-analysis and machine-learning
                 techniques to generate a large database of mappings between
                 neural and cognitive states. We show that our approach can be
                 used to automatically conduct large-scale, high-quality
                 neuroimaging meta-analyses, address long-standing inferential
                 problems in the neuroimaging literature and support accurate
                 'decoding' of broad cognitive states from brain activity in
                 both entire studies and individual human subjects.
                 Collectively, our results have validated a powerful and
                 generative framework for synthesizing human neuroimaging data
                 on an unprecedented scale.",
  journal     = "Nat. Methods",
  volume      =  8,
  number      =  8,
  pages       = "665--670",
  month       =  aug,
  year        =  2011,
  keywords    = "Neuroimaging/Meta Analysis"
}

@MISC{noauthor_undated-hq,
  title        = "{NIDM} {API} --- nidm 1.0 documentation",
  howpublished = "\url{http://nidm-api.readthedocs.org/en/latest/}",
  note         = "Accessed: 2015-11-24"
}

@MISC{noauthor_undated-tz,
  title        = "{GitHut} - Programming Languages and {GitHub}",
  abstract     = "A small place to discover more about the usage of programming
                  languages in GitHub.",
  howpublished = "\url{http://githut.info/}",
  note         = "Accessed: 2015-11-24"
}


@ARTICLE{Wang2016-nd,
  title       = "{SchizConnect}: Mediating neuroimaging databases on
                 schizophrenia and related disorders for large-scale
                 integration",
  author      = "Wang, Lei and Alpert, Kathryn I and Calhoun, Vince D and
                 Cobia, Derin J and Keator, David B and King, Margaret D and
                 Kogan, Alexandr and Landis, Drew and Tallis, Marcelo and
                 Turner, Matthew D and Potkin, Steven G and Turner, Jessica A
                 and Ambite, Jose Luis",
  affiliation = "Department of Psychiatry and Behavioral Sciences, Northwestern
                 University Feinberg School of Medicine, Chicago, IL, USA;
                 Department of Radiology, Northwestern University Feinberg
                 School of Medicine, Chicago, IL, USA. Electronic address:
                 leiwang1@northwestern.edu. Department of Psychiatry and
                 Behavioral Sciences, Northwestern University Feinberg School
                 of Medicine, Chicago, IL, USA. The Mind Research Network,
                 Albuquerque, NM, USA; University of New Mexico Health Sciences
                 Center, Albuquerque, NM, USA; Department of Electrical and
                 Computer Engineering, University of New Mexico, Albuquerque,
                 NM, USA; Department of Psychiatry, University of New Mexico,
                 Albuquerque, NM, USA; Department of Psychiatry, School of
                 Medicine, Yale University, New Haven, CT, USA. Department of
                 Psychiatry and Behavioral Sciences, Northwestern University
                 Feinberg School of Medicine, Chicago, IL, USA. Brain Imaging
                 Center, University of California, Irvine, CA, USA. The Mind
                 Research Network, Albuquerque, NM, USA. Department of
                 Psychiatry and Behavioral Sciences, Northwestern University
                 Feinberg School of Medicine, Chicago, IL, USA. The Mind
                 Research Network, Albuquerque, NM, USA. Information Sciences
                 Institute, University of Southern California, Marina del Rey,
                 CA, USA. Department of Computer Science, Georgia State
                 University, Atlanta, GA, USA; Neuroscience Institute, Georgia
                 State University, Atlanta, GA, USA. Brain Imaging Center,
                 University of California, Irvine, CA, USA; Department of
                 Psychiatry \& Human Behavior, University of California,
                 Irvine, School of Medicine, Irvine, CA, USA. The Mind Research
                 Network, Albuquerque, NM, USA; Department of Psychology,
                 Georgia State University, Atlanta, GA, USA; Neuroscience
                 Institute, Georgia State University, Atlanta, GA, USA.
                 Information Sciences Institute, University of Southern
                 California, Marina del Rey, CA, USA; Digital Government
                 Research Center, University of Southern California, Los
                 Angeles, CA, USA; Department of Computer Science, University
                 of Southern California, Los Angeles, CA, USA.",
  abstract    = "SchizConnect (www.schizconnect.org) is built to address the
                 issues of multiple data repositories in schizophrenia
                 neuroimaging studies. It includes a level of
                 mediation-translating across data sources-so that the user can
                 place one query, e.g. for diffusion images from male
                 individuals with schizophrenia, and find out from across
                 participating data sources how many datasets there are, as
                 well as downloading the imaging and related data. The current
                 version handles the Data Usage Agreements across different
                 studies, as well as interpreting database-specific
                 terminologies into a common framework. New data repositories
                 can also be mediated to bring immediate access to existing
                 datasets. Compared with centralized, upload data sharing
                 models, SchizConnect is a unique, virtual database with a
                 focus on schizophrenia and related disorders that can mediate
                 live data as information is being updated at each data source.
                 It is our hope that SchizConnect can facilitate testing new
                 hypotheses through aggregated datasets, promoting discovery
                 related to the mechanisms underlying schizophrenic
                 dysfunction.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1155--1167",
  month       =  "1~" # jan,
  year        =  2016,
  keywords    = "Data mediation and integration; Mega analysis;
                 Neuroinformatics; Schizophrenia databases"
}

@ARTICLE{Kini2016-sg,
  title       = "Data integration: Combined imaging and electrophysiology data
                 in the cloud",
  author      = "Kini, Lohith G and Davis, Kathryn A and Wagenaar, Joost B",
  affiliation = "Department of Bioengineering, University of Pennsylvania, 240
                 Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA
                 19104-6321, USA. Electronic address: lkini@mail.med.upenn.edu.
                 Department of Neurology, Hospital of the University of
                 Pennsylvania, 3400 Spruce Street, 3 West Gates Bldg,
                 Philadelphia PA 19104, USA. Electronic address:
                 Kathryn.Davis@uphs.upenn.edu. Department of Neurology,
                 Hospital of the University of Pennsylvania, 3400 Spruce
                 Street, 3 West Gates Bldg, Philadelphia PA 19104, USA.
                 Electronic address: joostw@seas.upenn.edu.",
  abstract    = "There has been an increasing effort to correlate
                 electrophysiology data with imaging in patients with
                 refractory epilepsy over recent years. IEEG.org provides a
                 free-access, rapidly growing archive of imaging data combined
                 with electrophysiology data and patient metadata. It currently
                 contains over 1200 human and animal datasets, with multiple
                 data modalities associated with each dataset (neuroimaging,
                 EEG, EKG, de-identified clinical and experimental data, etc.).
                 The platform is developed around the concept that scientific
                 data sharing requires a flexible platform that allows sharing
                 of data from multiple file formats. IEEG.org provides high-
                 and low-level access to the data in addition to providing an
                 environment in which domain experts can find, visualize, and
                 analyze data in an intuitive manner. Here, we present a
                 summary of the current infrastructure of the platform,
                 available datasets and goals for the near future.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1175--1181",
  month       =  "1~" # jan,
  year        =  2016,
    notes = "KA"
}

@ARTICLE{Jernigan2016-dz,
  title       = "The Pediatric Imaging, Neurocognition, and Genetics ({PING})
                 Data Repository",
  author      = "Jernigan, Terry L and Brown, Timothy T and Hagler, Jr, Donald
                 J and Akshoomoff, Natacha and Bartsch, Hauke and Newman, Erik
                 and Thompson, Wesley K and Bloss, Cinnamon S and Murray, Sarah
                 S and Schork, Nicholas and Kennedy, David N and Kuperman,
                 Joshua M and McCabe, Connor and Chung, Yoonho and Libiger,
                 Ondrej and Maddox, Melanie and Casey, B J and Chang, Linda and
                 Ernst, Thomas M and Frazier, Jean A and Gruen, Jeffrey R and
                 Sowell, Elizabeth R and Kenet, Tal and Kaufmann, Walter E and
                 Mostofsky, Stewart and Amaral, David G and Dale, Anders M and
                 {Pediatric Imaging, Neurocognition and Genetics Study}",
  affiliation = "Center for Human Development, University of California, San
                 Diego, La Jolla, CA, USA; Department of Cognitive Science,
                 University of California, San Diego, La Jolla, CA, USA;
                 Department of Psychiatry, University of California, San Diego,
                 La Jolla, CA, USA. Electronic address: tjernigan@ucsd.edu.
                 Multimodal Imaging Laboratory, University of California, San
                 Diego, La Jolla, CA, USA; Department of Neurosciences,
                 University of California, San Diego, La Jolla, CA, USA.
                 Multimodal Imaging Laboratory, University of California, San
                 Diego, La Jolla, CA, USA; Department of Radiology, University
                 of California, San Diego, La Jolla, CA, USA. Center for Human
                 Development, University of California, San Diego, La Jolla,
                 CA, USA; Department of Psychiatry, University of California,
                 San Diego, La Jolla, CA, USA. Multimodal Imaging Laboratory,
                 University of California, San Diego, La Jolla, CA, USA. Center
                 for Human Development, University of California, San Diego, La
                 Jolla, CA, USA; Department of Psychiatry, University of
                 California, San Diego, La Jolla, CA, USA. Department of
                 Psychiatry, University of California, San Diego, La Jolla, CA,
                 USA; Stein Institute for Research on Aging, University of
                 California, San Diego, La Jolla, CA, USA. The Qualcomm
                 Institute, University of California, San Diego, La Jolla, CA,
                 USA. Department of Pathology, University of California, San
                 Diego, La Jolla, CA, USA. Human Biology, J. Craig Venter
                 Institute, USA. Department of Psychiatry, University of
                 Massachusetts Medical School, Boston, MA, USA. Multimodal
                 Imaging Laboratory, University of California, San Diego, La
                 Jolla, CA, USA; Department of Radiology, University of
                 California, San Diego, La Jolla, CA, USA. Department of
                 Psychology, University of Washington, Seattle, WA, USA.
                 Department of Psychology, Yale University, New Haven, CT, USA.
                 The Qualcomm Institute, University of California, San Diego,
                 La Jolla, CA, USA. Center for Human Development, University of
                 California, San Diego, La Jolla, CA, USA. Sackler Institute
                 for Developmental Psychobiology, Weil Cornell Medical College,
                 New York, NY, USA. Department of Medicine, University of
                 Hawaii, Queen's Medical Center, Honolulu, HI, USA. Department
                 of Medicine, University of Hawaii, Queen's Medical Center,
                 Honolulu, HI, USA. Department of Psychiatry, University of
                 Massachusetts Medical School, Boston, MA, USA. Departments of
                 Pediatrics and Genetics, Yale University, School of Medicine,
                 New Haven, CT, USA. Department of Pediatrics, University of
                 Southern California, Children's Hospital Los Angeles, Los
                 Angeles, CA, USA. Department of Neurology, Athinoula A.
                 Martinos Center for Biomedical Imaging, Massachusetts General
                 Hospital, Charlestown, MA, USA. Boston Children's Hospital,
                 Boston, MA, USA. Kennedy Krieger Institute, Johns Hopkins
                 University School of Medicine, Baltimore, MD, USA. Department
                 of Psychiatry and Behavioral Sciences, University of
                 California-Davis, Davis, CA, USA. Department of Cognitive
                 Science, University of California, San Diego, La Jolla, CA,
                 USA; Multimodal Imaging Laboratory, University of California,
                 San Diego, La Jolla, CA, USA; Department of Neurosciences,
                 University of California, San Diego, La Jolla, CA, USA;
                 Department of Radiology, University of California, San Diego,
                 La Jolla, CA, USA.",
  abstract    = "The main objective of the multi-site Pediatric Imaging,
                 Neurocognition, and Genetics (PING) study was to create a
                 large repository of standardized measurements of behavioral
                 and imaging phenotypes accompanied by whole genome genotyping
                 acquired from typically-developing children varying widely in
                 age (3 to 20years). This cross-sectional study produced
                 sharable data from 1493 children, and these data have been
                 described in several publications focusing on brain and
                 cognitive development. Researchers may gain access to these
                 data by applying for an account on the PING portal and filing
                 a data use agreement. Here we describe the recruiting and
                 screening of the children and give a brief overview of the
                 assessments performed, the imaging methods applied, the
                 genetic data produced, and the numbers of cases for whom
                 different data types are available. We also cite sources of
                 more detailed information about the methods and data. Finally
                 we describe the procedures for accessing the data and for
                 using the PING data exploration portal.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1149--1154",
  month       =  "1~" # jan,
  year        =  2016,
    notes = "KA"
}

@ARTICLE{Hodge2016-ht,
  title       = "{ConnectomeDB-Sharing} human brain connectivity data",
  author      = "Hodge, Michael R and Horton, William and Brown, Timothy and
                 Herrick, Rick and Olsen, Timothy and Hileman, Michael E and
                 McKay, Michael and Archie, Kevin A and Cler, Eileen and Harms,
                 Michael P and Burgess, Gregory C and Glasser, Matthew F and
                 Elam, Jennifer S and Curtiss, Sandra W and Barch, Deanna M and
                 Oostenveld, Robert and Larson-Prior, Linda J and Ugurbil,
                 Kamil and Van Essen, David C and Marcus, Daniel S",
  affiliation = "Department of Radiology, Washington University School of
                 Medicine, St. Louis, MO, USA. Electronic address:
                 hodgem@mir.wustl.edu. Department of Radiology, Washington
                 University School of Medicine, St. Louis, MO, USA. Department
                 of Radiology, Washington University School of Medicine, St.
                 Louis, MO, USA. Department of Radiology, Washington University
                 School of Medicine, St. Louis, MO, USA. Deck5 Consulting,
                 Normal, IL, USA. Department of Radiology, Washington
                 University School of Medicine, St. Louis, MO, USA. Department
                 of Radiology, Washington University School of Medicine, St.
                 Louis, MO, USA. Department of Radiology, Washington University
                 School of Medicine, St. Louis, MO, USA. Department of
                 Radiology, Washington University School of Medicine, St.
                 Louis, MO, USA. Department of Psychiatry, Washington
                 University School of Medicine, St. Louis, MO, USA. Department
                 of Psychiatry, Washington University School of Medicine, St.
                 Louis, MO, USA. Department of Anatomy and Neurobiology,
                 Washington University School of Medicine, St. Louis, MO, USA.
                 Department of Anatomy and Neurobiology, Washington University
                 School of Medicine, St. Louis, MO, USA. Department of Anatomy
                 and Neurobiology, Washington University School of Medicine,
                 St. Louis, MO, USA. Department of Psychiatry, Washington
                 University School of Medicine, St. Louis, MO, USA. Radboud
                 University Nijmegen, Donders Institute for Brain, Cognition
                 and Behaviour, Nijmegen, The Netherlands. Department of
                 Radiology, Washington University School of Medicine, St.
                 Louis, MO, USA; Department of Neurology, Washington University
                 School of Medicine, St. Louis, MO, USA. Center for Magnetic
                 Resonance Imaging, University of Minnesota, Minneapolis, MN,
                 USA. Department of Anatomy and Neurobiology, Washington
                 University School of Medicine, St. Louis, MO, USA. Department
                 of Radiology, Washington University School of Medicine, St.
                 Louis, MO, USA.",
  abstract    = "ConnectomeDB is a database for housing and disseminating data
                 about human brain structure, function, and connectivity, along
                 with associated behavioral and demographic data. It is the
                 main archive and dissemination platform for data collected
                 under the WU-Minn consortium Human Connectome Project.
                 Additional connectome-style study data is and will be made
                 available in the database under current and future projects,
                 including the Connectome Coordination Facility. The database
                 currently includes multiple modalities of magnetic resonance
                 imaging (MRI) and magnetoencephalograpy (MEG) data along with
                 associated behavioral data. MRI modalities include structural,
                 task, resting state and diffusion. MEG modalities include
                 resting state and task. Imaging data includes unprocessed,
                 minimally preprocessed and analysis data. Imaging data and
                 much of the behavioral data are publicly available, subject to
                 acceptance of data use terms, while access to some sensitive
                 behavioral data is restricted to qualified investigators under
                 a more stringent set of terms. ConnectomeDB is the public side
                 of the WU-Minn HCP database platform. As such, it is geared
                 towards public distribution, with a web-based user interface
                 designed to guide users to the optimal set of data for their
                 needs and a robust backend mechanism based on the commercial
                 Aspera fasp service to enable high speed downloads. HCP data
                 is also available via direct shipment of hard drives and
                 Amazon S3.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1102--1107",
  month       =  "1~" # jan,
  year        =  2016,
  keywords    = "Connectome Coordination Facility; Connectomics; Data sharing;
                 Human Connectome Project; Neuroinformatics databases; Open
                 access; XNAT"
}

@ARTICLE{Herrick2016-bw,
  title       = "{XNAT} Central: Open sourcing imaging research data",
  author      = "Herrick, Rick and Horton, William and Olsen, Timothy and
                 McKay, Michael and Archie, Kevin A and Marcus, Daniel S",
  affiliation = "Department of Radiology, Washington University School of
                 Medicine, St. Louis, MO, USA. Electronic address:
                 rick.herrick@wustl.edu. Department of Radiology, Washington
                 University School of Medicine, St. Louis, MO, USA. Deck5
                 Consulting, Normal, IL, USA. Department of Radiology,
                 Washington University School of Medicine, St. Louis, MO, USA.
                 Department of Radiology, Washington University School of
                 Medicine, St. Louis, MO, USA. Department of Radiology,
                 Washington University School of Medicine, St. Louis, MO, USA.",
  abstract    = "XNAT Central is a publicly accessible medical imaging data
                 repository based on the XNAT open-source imaging informatics
                 platform. It hosts a wide variety of research imaging data
                 sets. The primary motivation for creating XNAT Central was to
                 provide a central repository to host and provide access to a
                 wide variety of neuroimaging data. In this capacity, XNAT
                 Central hosts a number of data sets from research labs and
                 investigative efforts from around the world, including the
                 OASIS Brains imaging studies, the NUSDAST study of
                 schizophrenia, and more. Over time, XNAT Central has expanded
                 to include imaging data from many different fields of
                 research, including oncology, orthopedics, cardiology, and
                 animal studies, but continues to emphasize neuroimaging data.
                 Through the use of XNAT's DICOM metadata extraction
                 capabilities, XNAT Central provides a searchable repository of
                 imaging data that can be referenced by groups, labs, or
                 individuals working in many different areas of research. The
                 future development of XNAT Central will be geared towards
                 greater ease of use as a reference library of heterogeneous
                 neuroimaging data and associated synthetic data. It will also
                 become a tool for making data available supporting published
                 research and academic articles.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1093--1096",
  month       =  "1~" # jan,
  year        =  2016,
  keywords    = "Data sharing; Neuroinformatics databases; Open access; Open
                 source; XNAT; XNAT Central"
}

@ARTICLE{Book2016-ro,
  title       = "Neuroimaging data sharing on the neuroinformatics database
                 platform",
  author      = "Book, Gregory A and Stevens, Michael C and Assaf, Michal and
                 Glahn, David C and Pearlson, Godfrey D",
  affiliation = "Olin Neuropsychiatry Research Center, Hartford Hospital,
                 Hartford CT, USA. Electronic address:
                 gregory.a.book@gmail.com. Olin Neuropsychiatry Research
                 Center, Hartford Hospital, Hartford CT, USA. Olin
                 Neuropsychiatry Research Center, Hartford Hospital, Hartford
                 CT, USA; Yale University, Department of Psychiatry, New Haven,
                 CT, USA. Olin Neuropsychiatry Research Center, Hartford
                 Hospital, Hartford CT, USA. Olin Neuropsychiatry Research
                 Center, Hartford Hospital, Hartford CT, USA; Yale University,
                 Department of Psychiatry, New Haven, CT, USA.",
  abstract    = "We describe the Neuroinformatics Database (NiDB), an
                 open-source database platform for archiving, analysis, and
                 sharing of neuroimaging data. Data from the multi-site
                 projects Autism Brain Imaging Data Exchange (ABIDE),
                 Bipolar-Schizophrenia Network on Intermediate Phenotypes parts
                 one and two (B-SNIP1, B-SNIP2), and Monetary Incentive Delay
                 task (MID) are available for download from the public instance
                 of NiDB, with more projects sharing data as it becomes
                 available. As demonstrated by making several large datasets
                 available, NiDB is an extensible platform appropriately suited
                 to archive and distribute shared neuroimaging data.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1089--1092",
  month       =  "1~" # jan,
  year        =  2016,
  notes = "KA"
}

@ARTICLE{Landis2016-wo,
  title       = "{COINS} Data Exchange: An open platform for compiling,
                 curating, and disseminating neuroimaging data",
  author      = "Landis, Drew and Courtney, William and Dieringer, Christopher
                 and Kelly, Ross and King, Margaret and Miller, Brittny and
                 Wang, Runtang and Wood, Dylan and Turner, Jessica A and
                 Calhoun, Vince D",
  affiliation = "The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM,
                 USA. Electronic address: dlandis@mrn.org. The Mind Research
                 Network, 1101 Yale Blvd NE, Albuquerque, NM, USA. The Mind
                 Research Network, 1101 Yale Blvd NE, Albuquerque, NM, USA. The
                 Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM,
                 USA. The Mind Research Network, 1101 Yale Blvd NE,
                 Albuquerque, NM, USA. The Mind Research Network, 1101 Yale
                 Blvd NE, Albuquerque, NM, USA. The Mind Research Network, 1101
                 Yale Blvd NE, Albuquerque, NM, USA. The Mind Research Network,
                 1101 Yale Blvd NE, Albuquerque, NM, USA. The Mind Research
                 Network, 1101 Yale Blvd NE, Albuquerque, NM, USA; Department
                 of Psychology, Georgia State University, Atlanta, GA, USA. The
                 Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM,
                 USA; Department of Electrical and Computer Engineering,
                 University of New Mexico, Albuquerque, NM, USA.",
  abstract    = "Neuroimaging data collection is inherently expensive.
                 Maximizing the return on investment in neuroimaging studies
                 requires that neuroimaging data be re-used whenever possible.
                 In an effort to further scientific knowledge, the COINS Data
                 Exchange (DX) (http://coins.mrn.org/dx) aims to make data
                 sharing seamless and commonplace. DX takes a three-pronged
                 approach towards improving the overall state of data sharing
                 within the neuroscience community. The first prong is
                 compiling data into one location that has been collected from
                 all over the world in many different formats. The second prong
                 is curating the data so that it can be stored in one
                 consistent format and so that data QA/QC measures can be
                 assured. The third prong is disseminating the data so that it
                 is easy to consume and straightforward to interpret. This
                 paper explains the concepts behind each prong and describes
                 some challenges and successes that the Data Exchange has
                 experienced.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1084--1088",
  month       =  "1~" # jan,
  year        =  2016,
  keywords    = "COINS; DX; Data Exchange; Data sharing; Neuroinformatics"
}

@ARTICLE{Crawford2016-zl,
  title       = "The Image and Data Archive at the Laboratory of Neuro Imaging",
  author      = "Crawford, Karen L and Neu, Scott C and Toga, Arthur W",
  affiliation = "Laboratory of Neuro Imaging, USC Mark and Mary Stevens
                 Neuroimaging and Informatics Institute, University of Southern
                 California, Los Angeles, CA 90095, USA. Laboratory of Neuro
                 Imaging, USC Mark and Mary Stevens Neuroimaging and
                 Informatics Institute, University of Southern California, Los
                 Angeles, CA 90095, USA. Laboratory of Neuro Imaging, USC Mark
                 and Mary Stevens Neuroimaging and Informatics Institute,
                 University of Southern California, Los Angeles, CA 90095, USA.
                 Electronic address: toga@loni.usc.edu.",
  abstract    = "The LONI Image and Data Archive (IDA)(1) is a repository for
                 sharing and long-term preservation of neuroimaging and
                 biomedical research data. Originally designed to archive
                 strictly medical image files, the IDA has evolved over the
                 last ten years and now encompasses the storage and
                 dissemination of neuroimaging, clinical, biospecimen, and
                 genetic data. In this article, we report upon the genesis of
                 the IDA and how it currently securely manages data and
                 protects data ownership.",
  journal     = "Neuroimage",
  volume      =  124,
  number      = "Pt B",
  pages       = "1080--1083",
  month       =  "1~" # jan,
  year        =  2016,
  keywords    = "Data repository; Data sharing; IDA"
}


@ARTICLE{Reid2015-gt,
  title       = "{ANIMA}: A data-sharing initiative for neuroimaging
                 meta-analyses",
  author      = "Reid, Andrew T and Bzdok, Danilo and Genon, Sarah and Langner,
                 Robert and M{\"{u}}ller, Veronika I and Eickhoff, Claudia R
                 and Hoffstaedter, Felix and Cieslik, Edna-Clarisse and Fox,
                 Peter T and Laird, Angela R and Amunts, Katrin and Eickhoff,
                 Simon B",
  affiliation = "Institute of Neuroscience and Medicine 1, Research Centre
                 J{\"{u}}lich, J{\"{u}}, lich, Germany. Electronic address:
                 a.reid@fz-juelich.de. Institute of Neuroscience and Medicine
                 1, Research Centre J{\"{u}}lich, J{\"{u}}, lich, Germany;
                 Institute of Clinical Neuroscience and Medical Psychology,
                 Heinrich Heine University, D{\"{u}}sseldorf, Germany; Parietal
                 team, INRIA, Neurospin, bat 145, CEA Saclay, 91191
                 Gif-sur-Yvette, France. Institute of Neuroscience and Medicine
                 1, Research Centre J{\"{u}}lich, J{\"{u}}, lich, Germany;
                 Institute of Clinical Neuroscience and Medical Psychology,
                 Heinrich Heine University, D{\"{u}}sseldorf, Germany.
                 Institute of Neuroscience and Medicine 1, Research Centre
                 J{\"{u}}lich, J{\"{u}}, lich, Germany; Institute of Clinical
                 Neuroscience and Medical Psychology, Heinrich Heine
                 University, D{\"{u}}sseldorf, Germany. Institute of
                 Neuroscience and Medicine 1, Research Centre J{\"{u}}lich,
                 J{\"{u}}, lich, Germany; Institute of Clinical Neuroscience
                 and Medical Psychology, Heinrich Heine University,
                 D{\"{u}}sseldorf, Germany. Institute of Neuroscience and
                 Medicine 1, Research Centre J{\"{u}}lich, J{\"{u}}, lich,
                 Germany; Department of Psychiatry, Psychotherapy and
                 Psychosomatics, University Hospital Aachen, Aachen, Germany.
                 Institute of Neuroscience and Medicine 1, Research Centre
                 J{\"{u}}lich, J{\"{u}}, lich, Germany; Institute of Clinical
                 Neuroscience and Medical Psychology, Heinrich Heine
                 University, D{\"{u}}sseldorf, Germany. Institute of
                 Neuroscience and Medicine 1, Research Centre J{\"{u}}lich,
                 J{\"{u}}, lich, Germany; Institute of Clinical Neuroscience
                 and Medical Psychology, Heinrich Heine University,
                 D{\"{u}}sseldorf, Germany. University of Texas Health Sciences
                 Center at San Antonio, San Antonio, TX. Florida International
                 University, Miami, FL. Institute of Neuroscience and Medicine
                 1, Research Centre J{\"{u}}lich, J{\"{u}}, lich, Germany; C.
                 \& O. Vogt Institute for Brain Research, Heinrich Heine
                 University, D{\"{u}}sseldorf, Germany. Institute of
                 Neuroscience and Medicine 1, Research Centre J{\"{u}}lich,
                 J{\"{u}}, lich, Germany; Institute of Clinical Neuroscience
                 and Medical Psychology, Heinrich Heine University,
                 D{\"{u}}sseldorf, Germany.",
  abstract    = "Meta-analytic techniques allow cognitive neuroscientists to
                 pool large amounts of data across many individual task-based
                 functional neuroimaging experiments. These methods have been
                 aided by the introduction of online databases such as
                 Brainmap.org or Neurosynth.org, which collate peak activation
                 coordinates obtained from thousands of published studies.
                 Findings from meta-analytic studies typically include brain
                 regions which are consistently activated across studies for
                 specific contrasts, investigating cognitive or clinical
                 hypotheses. These regions can be subsequently used as the
                 basis for seed-based connectivity analysis, or formally
                 compared to neuroimaging data in order to help interpret new
                 findings. To facilitate such approaches, we have developed a
                 new online repository of meta-analytic neuroimaging results,
                 named the Archive of Neuroimaging Meta-analyses (ANIMA). The
                 ANIMA platform consists of an intuitive online interface for
                 querying, downloading, and contributing data from published
                 meta-analytic studies. Additionally, to aid the process of
                 organizing, visualizing, and working with these data, we
                 present an open-source desktop application called Volume
                 Viewer. Volume Viewer allows users to easily arrange imaging
                 data into composite stacks, and save these sessions as
                 individual files, which can also be uploaded to the ANIMA
                 database. The application also allows users to perform basic
                 functions, such as computing conjunctions between images, or
                 extracting regions-of-interest or peak coordinates for further
                 analysis. The introduction of this new resource will enhance
                 the ability of researchers to both share their findings and
                 incorporate existing meta-analytic results into their own
                 research.",
  journal     = "Neuroimage",
  month       =  "28~" # jul,
  year        =  2015,
  notes = "bluch"
}


@ARTICLE{Gao2015-pz,
  title     = "Pycortex: an interactive surface visualizer for {fMRI}",
  author    = "Gao, James S and Huth, Alexander G and Lescroart, Mark D and
               Gallant, Jack L",
  abstract  = "Surface visualizations of fMRI provide a comprehensive view of
               cortical activity. However, surface visualizations are difficult
               to generate and most common visualization techniques rely on
               unnecessary interpolation which limits the fidelity of the
               resulting maps. Furthermore, it is difficult to understand the
               relationship between flattened cortical surfaces and the
               underlying 3D anatomy using tools available currently. To
               address these problems we have developed pycortex, a Python
               toolbox for interactive surface mapping and visualization.
               Pycortex exploits the power of modern graphics cards to sample
               volumetric data on a per-pixel basis, allowing dense and
               accurate mapping of the voxel grid across the surface.
               Anatomical, functional and fiduciary information can be
               projected onto the cortical surface. The surface can be inflated
               and flattened interactively, aiding interpretation of the
               correspondence between the anatomical surface and the flattened
               cortical sheet. The output of pycortex can be viewed using
               WebGL, a technology compatible with modern web browsers. This
               allows complex fMRI surface maps to be distributed broadly
               online without requiring installation of complex software.",
  journal   = "Front. Neuroinform.",
  publisher = "Frontiers",
  volume    =  9,
  month     =  "7~" # sep,
  year      =  2015,
  keywords  = "fMRI; visualization; python; WebGL; data sharing"
}

@ARTICLE{Gutman2014-hz,
  title       = "Web based tools for visualizing imaging data and development
                 of {XNATView}, a zero footprint image viewer",
  author      = "Gutman, David A and Dunn, Jr, William D and Cobb, Jake and
                 Stoner, Richard M and Kalpathy-Cramer, Jayashree and Erickson,
                 Bradley",
  affiliation = "Department of Biomedical Informatics, Emory University
                 Atlanta, GA, USA. Department of Biomedical Informatics, Emory
                 University Atlanta, GA, USA. Georgia Institute of Technology,
                 College of Computing Atlanta, GA, USA. Department of
                 Neurosciences, University of California San Diego School of
                 Medicine La Jolla, CA, USA. Harvard-MIT Division of Health
                 Sciences and Technology, Martinos Center for Biomedical
                 Imaging Charlestown, MA, USA. Department of Radiology, Mayo
                 Clinic Rochester, MN, USA.",
  abstract    = "Advances in web technologies now allow direct visualization of
                 imaging data sets without necessitating the download of large
                 file sets or the installation of software. This allows
                 centralization of file storage and facilitates image review
                 and analysis. XNATView is a light framework recently developed
                 in our lab to visualize DICOM images stored in The Extensible
                 Neuroimaging Archive Toolkit (XNAT). It consists of a
                 PyXNAT-based framework to wrap around the REST application
                 programming interface (API) and query the data in XNAT.
                 XNATView was developed to simplify quality assurance, help
                 organize imaging data, and facilitate data sharing for intra-
                 and inter-laboratory collaborations. Its zero-footprint design
                 allows the user to connect to XNAT from a web browser,
                 navigate through projects, experiments, and subjects, and view
                 DICOM images with accompanying metadata all within a single
                 viewing instance.",
  journal     = "Front. Neuroinform.",
  volume      =  8,
  pages       = "53",
  month       =  "27~" # may,
  year        =  2014,
  keywords    = "DICOM-viewer; MRI; PyXNAT; XNAT; biomedical imaging;
                 radiology; web-based image viewer"
}