@MISC{NIDM_Working_Group_undated-eb,
  title        = "{NIDM}",
  author       = "{NIDM Working Group}",
  abstract     = "The Neuroimaging Data Model (NIDM) is a collection of
                  specification documents that define extensions the W3C PROV
                  standard for the domain of human brain mapping. NIDM uses
                  provenance information as means to link components from
                  different stages of the scientific research process from
                  dataset descriptors and computational workflow, to derived …",
  year	       = "",
  howpublished = "\url{https://urldefense.com/v3/__http://nidm.nidash.org/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwANNyTlZEQ$ }",
  note         = "Accessed: 2021-7-8"
}

@MISC{noauthor_undated-cd,
  title        = "Semantic Web - {W3C}",
  year	       = "",
  howpublished = "\url{https://urldefense.com/v3/__https://www.w3.org/standards/semanticweb/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOYswolwA$ }",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Maumet2016-ab,
  title    = "Sharing brain mapping statistical results with the neuroimaging
              data model",
  author   = "Maumet, Camille and Auer, Tibor and Bowring, Alexander and Chen,
              Gang and Das, Samir and Flandin, Guillaume and Ghosh, Satrajit
              and Glatard, Tristan and Gorgolewski, Krzysztof J and Helmer,
              Karl G and Jenkinson, Mark and Keator, David B and Nichols, B
              Nolan and Poline, Jean-Baptiste and Reynolds, Richard and Sochat,
              Vanessa and Turner, Jessica and Nichols, Thomas E",
  abstract = "Only a tiny fraction of the data and metadata produced by an fMRI
              study is finally conveyed to the community. This lack of
              transparency not only hinders the reproducibility of neuroimaging
              results but also impairs future meta-analyses. In this work we
              introduce NIDM-Results, a format specification providing a
              machine-readable description of neuroimaging statistical results
              along with key image data summarising the experiment.
              NIDM-Results provides a unified representation of mass univariate
              analyses including a level of detail consistent with available
              best practices. This standardized representation allows authors
              to relay methods and results in a platform-independent
              regularized format that is not tied to a particular neuroimaging
              software package. Tools are available to export NIDM-Result
              graphs and associated files from the widely used SPM and FSL
              software packages, and the NeuroVault repository can import
              NIDM-Results archives. The specification is publicly available
              at: https://urldefense.com/v3/__http://nidm.nidash.org/specs/nidm-results.html__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwANOQtW7Tw$ .",
  journal  = "Sci Data",
  volume   =  3,
  pages    = "160102",
  doi	   = "10.1038/sdata.2016.102",
  month    =  dec,
  year     =  2016,
  language = "en"
}

@MISC{Keator_undated-eb,
  title       = "{simple2\_NIDM\_examples}: {ReproNim} simple2 {NIDM} files for
                 {ABIDE/ADHD200} available from datalad",
  author      = "Keator, David",
  year	       = "",
  abstract    = "ReproNim simple2 NIDM files for ABIDE/ADHD200 available from
                 datalad - dbkeator/simple2\_NIDM\_examples: ReproNim simple2
                 NIDM files for ABIDE/ADHD200 available from datalad",
  institution = "Github"
}

@ARTICLE{Moreau2008-bu,
  title     = "Special issue: The first provenance challenge",
  author    = "Moreau, Luc and Lud{\"a}scher, Bertram and Altintas, Ilkay and
               Barga, Roger S and Bowers, Shawn and Callahan, Steven and Chin,
               George and Clifford, Ben and Cohen, Shirley and Cohen-Boulakia,
               Sarah and Davidson, Susan and Deelman, Ewa and Digiampietri,
               Luciano and Foster, Ian and Freire, Juliana and Frew, James and
               Futrelle, Joe and Gibson, Tara and Gil, Yolanda and Goble,
               Carole and Golbeck, Jennifer and Groth, Paul and Holland, David
               A and Jiang, Sheng and Kim, Jihie and Koop, David and Krenek,
               Ales and McPhillips, Timothy and Mehta, Gaurang and Miles, Simon
               and Metzger, Dominic and Munroe, Steve and Myers, Jim and Plale,
               Beth and Podhorszki, Norbert and Ratnakar, Varun and Santos,
               Emanuele and Scheidegger, Carlos and Schuchardt, Karen and
               Seltzer, Margo and Simmhan, Yogesh L and Silva, Claudio and
               Slaughter, Peter and Stephan, Eric and Stevens, Robert and Turi,
               Daniele and Vo, Huy and Wilde, Mike and Zhao, Jun and Zhao, Yong",
  abstract  = "Abstract The first Provenance Challenge was set up in order to
               provide a forum for the community to understand the capabilities
               of different provenance systems and the expressiveness of their
               provenance representations. To this end, a functional magnetic
               resonance imaging workflow was defined, which participants had
               to either simulate or run in order to produce some provenance
               representation, from which a set of identified queries had to be
               implemented and executed. Sixteen teams responded to the
               challenge, and submitted their inputs. In this paper, we present
               the challenge workflow and queries, and summarize the
               participants' contributions. Copyright ? 2007 John Wiley \&
               Sons, Ltd.",
  journal   = "Concurr. Comput.",
  publisher = "Wiley",
  volume    =  20,
  number    =  5,
  pages     = "409--418",
  month     =  apr,
  year      =  2008,
  language  = "en"
}

@MISC{noauthor_undated-ex,
  title        = "{OpenNeuro}",
  abstract     = "A free and open platform for sharing MRI, MEG, EEG, iEEG,
                  ECoG, ASL, and PET data",
  howpublished = "\url{https://urldefense.com/v3/__https://openneuro.org/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOqFeu97g$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@MISC{Nagpal2017-qd,
  title        = "{L1} and {L2} Regularization Methods",
  booktitle    = "Towards Data Science",
  author       = "Nagpal, Anuja",
  abstract     = "In my last post, I covered the introduction to Regularization
                  in supervised learning models. In this post, let's go over
                  some of the regularization techniques widely used and the key
                  difference…",
  month        =  oct,
  year         =  2017,
  howpublished = "\url{https://urldefense.com/v3/__https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAPIl2bL8g$ }",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Varoquaux2015-mv,
  title     = "Scikit-learn",
  author    = "Varoquaux, G and Buitinck, L and Louppe, G and Grisel, O and
               Pedregosa, F and Mueller, A",
  abstract  = "Machine learning is a pervasive development at the intersection
               of statistics and computer science. While it can benefit many
               data-related applications, the technical nature of the research
               literature and the corresponding algorithms slows down its
               adoption. Scikit-learn is an open-source software project that
               aims at making machine learning accessible to all, whether it be
               in academia or in industry. It benefits from the general-purpose
               Python language, which is both broadly adopted in the scientific
               world, and supported by a thriving ecosystem of contributors.
               Here we give a quick introduction to scikit-learn as well as to
               machine-learning basics.",
  journal   = "GetMob. Mob. Comput. Commun.",
  publisher = "Association for Computing Machinery (ACM)",
  volume    =  19,
  number    =  1,
  pages     = "29--33",
  month     =  jun,
  year      =  2015,
  copyright = "https://urldefense.com/v3/__http://www.acm.org/publications/policies/copyright*5C_policy*5C*Background__;JSUj!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAO2o_wDcQ$ ",
  language  = "en"
}

@MISC{noauthor_undated-gx,
  title        = "{SPARQL} Query Language for {RDF}",
  howpublished = "\url{https://urldefense.com/v3/__https://www.w3.org/TR/rdf-sparql-query/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOrwchC-w$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Keator2013-fs,
  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",
  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",
  doi	   = "10.1016/j.neuroimage.2013.05.094",
  month    =  nov,
  year     =  2013,
  keywords = "Data model; Database; Neuroimaging; Provenance; Web services;
              XCEDE",
  language = "en"
}

@MISC{noauthor_undated-os,
  title        = "{ADHD200}",
  howpublished = "\url{https://urldefense.com/v3/__http://fcon_1000.projects.nitrc.org/indi/adhd200/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOrUiGhpg$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@MISC{Ravan2020-il,
  title        = "What is {REST}",
  author       = "{Ravan} and {Jay} and {Person} and Young, Dave and Packer,
                  Jason and Choudhary, Chandrajeet and {Luis} and {Tiamo} and
                  {Alexander} and {Anto} and {应琪瑜} and {Sunny} and {Gaurav} and
                  Talada, Rob and Metkar, Piyush and {Iris} and Masne, Snehal",
  abstract     = "REST is an architecture style for designing networked
                  applications. REST is a lightweight alternative to mechanisms
                  like RPC (Remote Procedure Calls) and Web Services (SOAP)
                  etc.",
  month        =  may,
  year         =  2020,
  howpublished = "\url{https://urldefense.com/v3/__https://restfulapi.net/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwANWVMCgmg$ }",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Di_Martino2014-af,
  title    = "The autism brain imaging data exchange: towards a large-scale
              evaluation of the intrinsic brain architecture in autism",
  author   = "Di Martino, A and Yan, C-G and Li, Q and Denio, E and
              Castellanos, F X and Alaerts, K and Anderson, J S and Assaf, M
              and Bookheimer, S Y and Dapretto, M and Deen, B and Delmonte, S
              and Dinstein, I and Ertl-Wagner, B and Fair, D A and Gallagher, L
              and Kennedy, D P and Keown, C L and Keysers, C and Lainhart, J E
              and Lord, C and Luna, B and Menon, V and Minshew, N J and Monk, C
              S and Mueller, S and M{\"u}ller, R-A and Nebel, M B and Nigg, J T
              and O'Hearn, K and Pelphrey, K A and Peltier, S J and Rudie, J D
              and Sunaert, S and Thioux, M and Tyszka, J M and Uddin, L Q and
              Verhoeven, J S and Wenderoth, N and Wiggins, J L and Mostofsky, S
              H and Milham, M P",
  abstract = "Autism spectrum disorders (ASDs) represent a formidable challenge
              for psychiatry and neuroscience because of their high prevalence,
              lifelong nature, complexity and substantial heterogeneity. Facing
              these obstacles requires large-scale multidisciplinary efforts.
              Although the field of genetics has pioneered data sharing for
              these reasons, neuroimaging had not kept pace. In response, we
              introduce the Autism Brain Imaging Data Exchange (ABIDE)-a
              grassroots consortium aggregating and openly sharing 1112
              existing resting-state functional magnetic resonance imaging
              (R-fMRI) data sets with corresponding structural MRI and
              phenotypic information from 539 individuals with ASDs and 573
              age-matched typical controls (TCs; 7-64 years)
              (https://urldefense.com/v3/__http://fcon*5C_1000.projects.nitrc.org/indi/abide/__;JQ!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAO7YmE_HA$ ). Here, we
              present this resource and demonstrate its suitability for
              advancing knowledge of ASD neurobiology based on analyses of 360
              male subjects with ASDs and 403 male age-matched TCs. We focused
              on whole-brain intrinsic functional connectivity and also survey
              a range of voxel-wise measures of intrinsic functional brain
              architecture. Whole-brain analyses reconciled seemingly disparate
              themes of both hypo- and hyperconnectivity in the ASD literature;
              both were detected, although hypoconnectivity dominated,
              particularly for corticocortical and interhemispheric functional
              connectivity. Exploratory analyses using an array of regional
              metrics of intrinsic brain function converged on common loci of
              dysfunction in ASDs (mid- and posterior insula and posterior
              cingulate cortex), and highlighted less commonly explored regions
              such as the thalamus. The survey of the ABIDE R-fMRI data sets
              provides unprecedented demonstrations of both replication and
              novel discovery. By pooling multiple international data sets,
              ABIDE is expected to accelerate the pace of discovery setting the
              stage for the next generation of ASD studies.",
  journal  = "Mol. Psychiatry",
  volume   =  19,
  number   =  6,
  pages    = "659--667",
  month    =  jun,
  year     =  2014,
  language = "en"
}

@ARTICLE{Ripley2001-iu,
  title     = "The {R} project in statistical computing",
  author    = "Ripley, Brian D",
  journal   = "MSOR connect.",
  publisher = "Educational Development Unit, University of Greenwich",
  volume    =  1,
  number    =  1,
  pages     = "23--25",
  month     =  feb,
  year      =  2001
}

@MISC{noauthor_undated-sw,
  title        = "{RDF} 1.1 Turtle",
  howpublished = "\url{https://urldefense.com/v3/__https://www.w3.org/TR/turtle/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOrt1eyHA$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Zuo2014-tw,
  title    = "An open science resource for establishing reliability and
              reproducibility in functional connectomics",
  author   = "Zuo, Xi-Nian and Anderson, Jeffrey S and Bellec, Pierre and Birn,
              Rasmus M and Biswal, Bharat B and Blautzik, Janusch and Breitner,
              John C S and Buckner, Randy L and Calhoun, Vince D and
              Castellanos, F Xavier and Chen, Antao and Chen, Bing and Chen,
              Jiangtao and Chen, Xu and Colcombe, Stanley J and Courtney,
              William and Craddock, R Cameron and Di Martino, Adriana and Dong,
              Hao-Ming and Fu, Xiaolan and Gong, Qiyong and Gorgolewski,
              Krzysztof J and Han, Ying and He, Ye and He, Yong and Ho, Erica
              and Holmes, Avram and Hou, Xiao-Hui and Huckins, Jeremy and
              Jiang, Tianzi and Jiang, Yi and Kelley, William and Kelly, Clare
              and King, Margaret and LaConte, Stephen M and Lainhart, Janet E
              and Lei, Xu and Li, Hui-Jie and Li, Kaiming and Li, Kuncheng and
              Lin, Qixiang and Liu, Dongqiang and Liu, Jia and Liu, Xun and
              Liu, Yijun and Lu, Guangming and Lu, Jie and Luna, Beatriz and
              Luo, Jing and Lurie, Daniel and Mao, Ying and Margulies, Daniel S
              and Mayer, Andrew R and Meindl, Thomas and Meyerand, Mary E and
              Nan, Weizhi and Nielsen, Jared A and O'Connor, David and Paulsen,
              David and Prabhakaran, Vivek and Qi, Zhigang and Qiu, Jiang and
              Shao, Chunhong and Shehzad, Zarrar and Tang, Weijun and
              Villringer, Arno and Wang, Huiling and Wang, Kai and Wei, Dongtao
              and Wei, Gao-Xia and Weng, Xu-Chu and Wu, Xuehai and Xu, Ting and
              Yang, Ning and Yang, Zhi and Zang, Yu-Feng and Zhang, Lei and
              Zhang, Qinglin and Zhang, Zhe and Zhang, Zhiqiang and Zhao, Ke
              and Zhen, Zonglei and Zhou, Yuan and Zhu, Xing-Ting and Milham,
              Michael P",
  abstract = "Efforts to identify meaningful functional imaging-based
              biomarkers are limited by the ability to reliably characterize
              inter-individual differences in human brain function. Although a
              growing number of connectomics-based measures are reported to
              have moderate to high test-retest reliability, the variability in
              data acquisition, experimental designs, and analytic methods
              precludes the ability to generalize results. The Consortium for
              Reliability and Reproducibility (CoRR) is working to address this
              challenge and establish test-retest reliability as a minimum
              standard for methods development in functional connectomics.
              Specifically, CoRR has aggregated 1,629 typical individuals'
              resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18
              international sites, and is openly sharing them via the
              International Data-sharing Neuroimaging Initiative (INDI). To
              allow researchers to generate various estimates of reliability
              and reproducibility, a variety of data acquisition procedures and
              experimental designs are included. Similarly, to enable users to
              assess the impact of commonly encountered artifacts (for example,
              motion) on characterizations of inter-individual variation,
              datasets of varying quality are included.",
  journal  = "Sci Data",
  volume   =  1,
  pages    = "140049",
  month    =  dec,
  year     =  2014,
  language = "en"
}

@MISC{Fischl2012-cq,
  title   = "{FreeSurfer}",
  author  = "Fischl, Bruce",
  journal = "NeuroImage",
  volume  =  62,
  number  =  2,
  pages   = "774--781",
  year    =  2012
}

@MISC{noauthor_undated-az,
  title        = "Welcome to click --- click documentation (8.0.X)",
  howpublished = "\url{https://urldefense.com/v3/__https://click.palletsprojects.com/en/8.0.x/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAO7b5JSlQ$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@ARTICLE{Brooke1923-dx,
  title    = "Patsy",
  author   = "Brooke, Margaret C",
  journal  = "Psychol Clin",
  volume   =  15,
  number   = "1-2",
  pages    = "41--43",
  month    =  mar,
  year     =  1923,
  language = "en"
}

@MISC{noauthor_undated-on,
  title       = "{PyNIDM}",
  abstract    = "Contribute to incf-nidash/PyNIDM development by creating an
                 account on GitHub.",
  year	       = "",
  institution = "Github"
}

@MISC{noauthor_undated-he,
  title        = "{PROV-Overview}",
  howpublished = "\url{https://urldefense.com/v3/__https://www.w3.org/TR/prov-overview/__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAOC-PZm6Q$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}

@MISC{noauthor_undated-eh,
  title        = "Apache License, Version 2.0",
  abstract     = "Home page of The Apache Software Foundation",
  howpublished = "\url{https://urldefense.com/v3/__https://www.apache.org/licenses/LICENSE-2.0.html__;!!OLgoXmg!DBnZP1sUXq9i6aeL_u17nlbXYqcFpnmGxSssHbXH-p6gBHMjrSfQVqMUwAO5QGW0Vw$ }",
  year	       = "",
  note         = "Accessed: 2021-7-8"
}
