references.bib

@MISC{Van-Tuyl2023-vp,
  title     = "Hiring, managing, and retaining data scientists and Research
               Software Engineers in academia: A career guidebook from {ADSA}
               and {US}-{RSE}",
  editor    = "Van Tuyl, Steve",
  doi       = {https://doi.org/10.5281/zenodo.8329337},
  url       = {https://zenodo.org/records/8329337},
  abstract  = "The importance of data, software, and computation has long been
               recognized in academia and is reflected in the recent rise of job
               opportunities for data scientists and research software
               engineers. Big data, for example, created a wave of novel job
               descriptions before the term Data Scientist (DS) was widely used.
               And even though software has become a major driver for research
               (Nangia and Katz, 2017), Research Software Engineer (RSE) as a
               formal role has lagged behind in terms of job openings,
               recognition, and prominence within the community. Despite their
               importance in the academic research ecosystem, the value of DS
               and RSE roles is not yet widely understood or appreciated in the
               academic community, and research data, software, and workflows
               are, in many domains, still regarded as by-products of research.
               Data Scientists and Research Software Engineers (DS/RSEs) face
               similar challenges when it comes to career paths in academia -
               both are non-traditional academic professions with few incentives
               and a lack of clear career trajectories. This guidebook presents
               the challenges and suggestions for solutions to improve the
               situation and to reach a wide community of stakeholders needed to
               advance career paths for DS/RSEs.",
  year      =  2023,
  keywords  = "data science; research software engineering; career guidebook"
}


@ARTICLE{Adler-Milstein2017-id,
  title     = "Information blocking: Is it occurring and what policy strategies
               can address it?",
  author    = "Adler-Milstein, Julia and Pfeifer, Eric",
  journal   = "Milbank Q.",
  publisher = "John Wiley \& Sons, Ltd",
  volume    =  95,
  number    =  1,
  pages     = "117--135",
  abstract  = "Policy Points: Congress has expressed concern about electronic
               health record (EHR) vendors and health care providers knowingly
               interfering with the electronic exchange of patient health
               informatio...",
  month     =  mar,
  year      =  2017,
  keywords  = "electronic health records; health policy; incentives;
               interoperability",
  language  = "en"
}

@ARTICLE{Barker2024-ox,
  title     = "A national survey of digital health company experiences with
               electronic health record application programming interfaces",
  author    = "Barker, Wesley and Maisel, Natalya and Strawley, Catherine E and
               Israelit, Grace K and Adler-Milstein, Julia and Rosner, Benjamin",
  journal   = "J. Am. Med. Inform. Assoc.",
  publisher = "Oxford Academic",
  volume    =  31,
  number    =  4,
  pages     = "866--874",
  abstract  = "OBJECTIVES: This study sought to capture current digital health
               company experiences integrating with electronic health records
               (EHRs), given new federally regulated standards-based application
               programming interface (API) policies. MATERIALS AND METHODS: We
               developed and fielded a survey among companies that develop
               solutions enabling human interaction with an EHR API. The survey
               was developed by the University of California San Francisco in
               collaboration with the Office of the National Coordinator for
               Health Information Technology, the California Health Care
               Foundation, and ScaleHealth. The instrument contained questions
               pertaining to experiences with API integrations, barriers faced
               during API integrations, and API-relevant policy efforts.
               RESULTS: About 73\% of companies reported current or previous use
               of a standards-based EHR API in production. About 57\% of
               respondents indicated using both standards-based and proprietary
               APIs to integrate with an EHR, and 24\% worked about equally with
               both APIs. Most companies reported use of the Fast Healthcare
               Interoperability Resources standard. Companies reported that
               standards-based APIs required on average less burden than
               proprietary APIs to establish and maintain. However, companies
               face barriers to adopting standards-based APIs, including high
               fees, lack of realistic clinical testing data, and lack of data
               elements of interest or value. DISCUSSION: The industry is moving
               toward the use of standardized APIs to streamline data exchange,
               with a majority of digital health companies using standards-based
               APIs to integrate with EHRs. However, barriers persist.
               CONCLUSION: A large portion of digital health companies use
               standards-based APIs to interoperate with EHRs. Continuing to
               improve the resources for digital health companies to find, test,
               connect, and use these APIs ``without special effort'' will be
               crucial to ensure future technology robustness and durability.",
  month     =  apr,
  year      =  2024,
  keywords  = "application programming interface; digital health; electronic
               health record; industry",
  language  = "en"
}

@ARTICLE{Gillon2024-vu,
  title         = "{ODIN}: Open Data In Neurophysiology: Advancements, Solutions
                   \& Challenges",
  author        = "Gillon, Colleen J and Baker, Cody and Ly, Ryan and Balzani,
                   Edoardo and Brunton, Bingni W and Schottdorf, Manuel and
                   Ghosh, Satrajit and Dehghani, Nima",
  journal       = "arXiv [q-bio.NC]",
  abstract      = "Across the life sciences, an ongoing effort over the last 50
                   years has made data and methods more reproducible and
                   transparent. This openness has led to transformative insights
                   and vastly accelerated scientific progress. For example,
                   structural biology and genomics have undertaken systematic
                   collection and publication of protein sequences and
                   structures over the past half-century, and these data have
                   led to scientific breakthroughs that were unthinkable when
                   data collection first began. We believe that neuroscience is
                   poised to follow the same path, and that principles of open
                   data and open science will transform our understanding of the
                   nervous system in ways that are impossible to predict at the
                   moment. To this end, new social structures along with active
                   and open scientific communities are essential to facilitate
                   and expand the still limited adoption of open science
                   practices in our field. Unified by shared values of openness,
                   we set out to organize a symposium for Open Data in
                   Neuroscience (ODIN) to strengthen our community and
                   facilitate transformative neuroscience research at large. In
                   this report, we share what we learned during this first ODIN
                   event. We also lay out plans for how to grow this movement,
                   document emerging conversations, and propose a path toward a
                   better and more transparent science of tomorrow.",
  month         =  jul,
  year          =  2024,
  archivePrefix = "arXiv",
  primaryClass  = "q-bio.NC"
}

@INCOLLECTION{Hermes2023-aw,
  title     = "How can intracranial {EEG} data be published in a standardized
               format?",
  author    = "Hermes, Dora and Cimbalnek, Jan",
  booktitle = "Studies in Neuroscience, Psychology and Behavioral Economics",
  publisher = "Springer International Publishing",
  address   = "Cham",
  pages     = "595--604",
  abstract  = "Sharing data or code with publications is not something new and
               licenses for public sharing have existed since the late 20s
               century. More recent worldwide efforts have led to an increase in
               the amount of data shared: funding agencies require that data are
               shared, journals request that data are made available, and some
               journals publish papers describing data resources. For
               intracranial EEG (iEEG) data, considering how and when to share
               data does not happen only at the stage of publication. Human
               subjects’ rights demand that data sharing is something that
               should be considered when writing an ethical protocol and
               designing a study before data are collected. At that moment, it
               should already be considered what levels of data will be
               collected and potentially shared. This includes levels of data
               directly from the amplifier, reformatted or processed data,
               clinical information and imaging data. In this chapter we will
               describe considerations and scholarship behind sharing iEEG data,
               to make it easier for the iEEG community to share data for
               reproducibility, teaching, advancing computational efforts,
               integrating iEEG data with other modalities and allow others to
               build on previous work.",
  year      =  2023,
  language  = "en"
}


@ARTICLE{Hanisch2015-cu,
  title     = "The Virtual Astronomical Observatory: Re-engineering access to
               astronomical data",
  author    = "Hanisch, R J and Berriman, G B and Lazio, T J W and Emery Bunn, S
               and Evans, J and McGlynn, T A and Plante, R",
  journal   = "Astron. Comput.",
  publisher = "Elsevier BV",
  volume    =  11,
  pages     = "190--209",
  abstract  = "The US Virtual Astronomical Observatory was a software
               infrastructure and development project designed both to begin the
               establishment of an operational Virtual Observatory (VO) and to
               provide the US coordination with the international VO effort. The
               concept of the VO is to provide the means by which an astronomer
               is able to discover, access, and process data seamlessly,
               regardless of its physical location. This paper describes the
               origins of the VAO, including the predecessor efforts within the
               US National Virtual Observatory, and summarizes its main
               accomplishments. These accomplishments include the development of
               both scripting toolkits that allow scientists to incorporate VO
               data directly into their reduction and analysis environments and
               high-level science applications for data discovery, integration,
               analysis, and catalog cross-comparison. Working with the
               international community, and based on the experience from the
               software development, the VAO was a major contributor to
               international standards within the International Virtual
               Observatory Alliance. The VAO also demonstrated how an
               operational virtual observatory could be deployed, providing a
               robust operational environment in which VO services worldwide
               were routinely checked for aliveness and compliance with
               international standards. Finally, the VAO engaged in community
               outreach, developing a comprehensive web site with on-line
               tutorials, announcements, links to both US and internationally
               developed tools and services, and exhibits and hands-on training
               at annual meetings of the American Astronomical Society and
               through summer schools and community days. All digital products
               of the VAO Project, including software, documentation, and
               tutorials, are stored in a repository for community access. The
               enduring legacy of the VAO is an increasing expectation that new
               telescopes and facilities incorporate VO capabilities during the
               design of their data management systems.",
  month     =  jun,
  year      =  2015,
  language  = "en"
}

@ARTICLE{Larobina2023-vq,
  title     = "Thirty years of the {DICOM} standard",
  author    = "Larobina, Michele",
  journal   = "Tomography",
  publisher = "mdpi.com",
  volume    =  9,
  number    =  5,
  pages     = "1829--1838",
  abstract  = "Digital Imaging and Communications in Medicine (DICOM) is an
               international standard that defines a format for storing medical
               images and a protocol to enable and facilitate data communication
               among medical imaging systems. The DICOM standard has been
               instrumental in transforming the medical imaging world over the
               last three decades. Its adoption has been a significant
               experience for manufacturers, healthcare users, and research
               scientists. In this review, thirty years after introducing the
               standard, we discuss the innovation, advantages, and limitations
               of adopting the DICOM and its possible future directions.",
  month     =  oct,
  year      =  2023,
  keywords  = "DICOM; communication protocols; file formats; metadata;
               quantitative imaging",
  language  = "en"
}

@INPROCEEDINGS{Mustra2008-xk,
  title     = "Overview of the {DICOM} standard",
  author    = "Mustra, Mario and Delac, Kresimir and Grgic, Mislav",
  booktitle = "2008 50th International Symposium ELMAR",
  publisher = "IEEE",
  volume    =  1,
  pages     = "39--44",
  abstract  = "Digital technology has in the last few decades entered almost
               every aspect of medicine. There has been a huge development in
               noninvasive medical imaging equipment. Because there are many
               medical equipment manufacturers, a standard for storage and
               exchange of medical images needed to be developed. DICOM (Digital
               Imaging and Communication in Medicine) makes medical image
               exchange more easy and independent of the imaging equipment
               manufacturer. Besides the image data, DICOM file format supports
               other information useful to describe the image. This makes DICOM
               easy to use and the data exchange fast and safe while avoiding
               possible confusion caused by multiple files for the same study.",
  month     =  sep,
  year      =  2008
}


@ARTICLE{Scroggins2020-ut,
  title     = "Once {FITS}, Always {FITS}? Astronomical Infrastructure in
               Transition",
  author    = "Scroggins, Michael and Boscoe, Bernadette M",
  journal   = "IEEE Ann. Hist. Comput.",
  publisher = "IEEE",
  volume    =  42,
  number    =  2,
  pages     = "42--54",
  abstract  = "The flexible interchange transport system (FITS) file format has
               become the de facto standard for sharing, analyzing, and
               archiving astronomical data over the last four decades. FITS was
               adopted by astronomers in the early 1980s to overcome
               incompatibilities between operating systems. On the back of FITS’
               success, astronomical data became both backward compatible and
               easily shareable. However, new advances in the astronomical
               instrumentation, computational technologies, and analytic
               techniques have resulted in new data that do not work well within
               the traditional FITS format. Tensions have arisen between the
               desire to update the format to meet new analytic challenges and
               adherence to the original edict for the FITS file format to be
               backward compatible. We examine three inflection points in the
               governance of FITS: first, initial development and success,
               second, widespread acceptance and governance by the working
               group, and third, the challenges to FITS in a new era of
               increasing data and computational complexity within astronomy.",
  year      =  2020
}


@ARTICLE{Musen2022metadata,
  title     = "Without appropriate metadata, data-sharing mandates are
               pointless",
  author    = "Musen, Mark A",
  abstract  = "Funders and investigators must demand appropriate metadata
               standards to take data from foul to FAIR. Funders and
               investigators must demand appropriate metadata standards to take
               data from foul to FAIR.",
  journal   = "Nature",
  publisher = "Springer Science and Business Media LLC",
  volume    =  609,
  number    =  7926,
  pages     = "222",
  month     =  sep,
  year      =  2022,
  keywords  = "Research data; Research management",
  language  = "en"
}


@software{zarr,
  author       = {Alistair Miles and
                  jakirkham and
                  M Bussonnier and
                  Josh Moore and
                  Dimitri Papadopoulos Orfanos and
                  Davis Bennett and
                  David Stansby and
                  Joe Hamman and
                  James Bourbeau and
                  Andrew Fulton and
                  Gregory Lee and
                  Ryan Abernathey and
                  Norman Rzepka and
                  Zain Patel and
                  Mads R. B. Kristensen and
                  Sanket Verma and
                  Saransh Chopra and
                  Matthew Rocklin and
                  AWA BRANDON AWA and
                  Max Jones and
                  Martin Durant and
                  Elliott Sales de Andrade and
                  Vincent Schut and
                  raphael dussin and
                  Shivank Chaudhary and
                  Chris Barnes and
                  Juan Nunez-Iglesias and
                  shikharsg},
  title        = {zarr-developers/zarr-python: v3.0.0-alpha},
  month        = jun,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v3.0.0-alpha},
  doi          = {10.5281/zenodo.11592827},
  url          = {https://doi.org/10.5281/zenodo.11592827}
}

@inproceedings{Norman2021CloudBank,
author = {Norman, Michael and Kellen, Vince and Smallen, Shava and DeMeulle, Brian and Strande, Shawn and Lazowska, Ed and Alterman, Naomi and Fatland, Rob and Stone, Sarah and Tan, Amanda and Yelick, Katherine and Van Dusen, Eric and Mitchell, James},
title = {{CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education}},
year = {2021},
isbn = {9781450382922},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3437359.3465586},
doi = {10.1145/3437359.3465586},
abstract = {CloudBank is a cloud access entity founded to enable the computer science research and education communities to harness the profound computational potential of public clouds. By delivering a set of managed services designed to alleviate common points of friction associated with cloud adoption, Cloudbank serves as an integrated service provider to the research and education community. These services include front-line help desk support, cloud solution consulting, training, account management, cost monitoring and optimization support, and automated billing. CloudBank has a multi-cloud pay-per-use billing model and aims to serve the spectrum of cloud users from novice to advanced.},
booktitle = {Practice and Experience in Advanced Research Computing},
articleno = {45},
numpages = {4},
keywords = {Cloud Computing},
location = {Boston, MA, USA},
series = {PEARC '21}
}

@article{Connolly2023Software,
	author = {Connolly, Andrew and Hellerstein, Joseph and Alterman, Naomi and Beck, David and Fatland, Rob and Lazowska, Ed and Mandava, Vani and Stone, Sarah},
	journal = {Harvard Data Science Review},
	number = {2},
	year = {2023},
	month = {apr 27},
	note = {https://hdsr.mitpress.mit.edu/pub/f0f7h5cu},
	publisher = {},
	title = {

{Software} {Engineering} {Practices} in {Academia}: Promoting the 3Rs---{Readability}, {Resilience}, and {Reuse}},
	volume = {5},
}

@article{pestilli2021community,
  title={A community-driven development of the Brain Imaging Data Standard (BIDS) to describe macroscopic brain connections},
  author={Pestilli, Franco and Poldrack, Russ and Rokem, Ariel and Satterthwaite, Theodore and Feingold, Franklin and Duff, Eugene and Pernet, Cyril and Smith, Robert and Esteban, Oscar and Cieslak, Matt},
  journal={OSF},
  year={2021}
}

@MISC{Nosek2019CultureChange,
  title        = "Strategy for Culture Change",
  author       = "Nosek, Brian",
  abstract     = "Strategy for Culture Change",
  howpublished = "\url{https://www.cos.io/blog/strategy-for-culture-change}",
  note         = "Accessed: 2024-6-19",
  language     = "en"
}



@ARTICLE{Poldrack2024BIDS,
  title    = "The Past, Present, and Future of the Brain Imaging Data Structure
              ({BIDS})",
  author   = "Poldrack, Russell A and Markiewicz, Christopher J and Appelhoff,
              Stefan and Ashar, Yoni K and Auer, Tibor and Baillet, Sylvain and
              Bansal, Shashank and Beltrachini, Leandro and Benar, Christian G
              and Bertazzoli, Giacomo and Bhogawar, Suyash and Blair, Ross W
              and Bortoletto, Marta and Boudreau, Mathieu and Brooks, Teon L
              and Calhoun, Vince D and Castelli, Filippo Maria and Clement,
              Patricia and Cohen, Alexander L and Cohen-Adad, Julien and
              D'Ambrosio, Sasha and de Hollander, Gilles and de la
              Iglesia-Vay{\'a}, Mar{\'\i}a and de la Vega, Alejandro and
              Delorme, Arnaud and Devinsky, Orrin and Draschkow, Dejan and
              Duff, Eugene Paul and DuPre, Elizabeth and Earl, Eric and
              Esteban, Oscar and Feingold, Franklin W and Flandin, Guillaume
              and Galassi, Anthony and Gallitto, Giuseppe and Ganz, Melanie and
              Gau, R{\'e}mi and Gholam, James and Ghosh, Satrajit S and
              Giacomel, Alessio and Gillman, Ashley G and Gleeson, Padraig and
              Gramfort, Alexandre and Guay, Samuel and Guidali, Giacomo and
              Halchenko, Yaroslav O and Handwerker, Daniel A and Hardcastle,
              Nell and Herholz, Peer and Hermes, Dora and Honey, Christopher J
              and Innis, Robert B and Ioanas, Horea-Ioan and Jahn, Andrew and
              Karakuzu, Agah and Keator, David B and Kiar, Gregory and Kincses,
              Balint and Laird, Angela R and Lau, Jonathan C and Lazari,
              Alberto and Legarreta, Jon Haitz and Li, Adam and Li, Xiangrui
              and Love, Bradley C and Lu, Hanzhang and Marcantoni, Eleonora and
              Maumet, Camille and Mazzamuto, Giacomo and Meisler, Steven L and
              Mikkelsen, Mark and Mutsaerts, Henk and Nichols, Thomas E and
              Nikolaidis, Aki and Nilsonne, Gustav and Niso, Guiomar and
              Norgaard, Martin and Okell, Thomas W and Oostenveld, Robert and
              Ort, Eduard and Park, Patrick J and Pawlik, Mateusz and Pernet,
              Cyril R and Pestilli, Franco and Petr, Jan and Phillips,
              Christophe and Poline, Jean-Baptiste and Pollonini, Luca and
              Raamana, Pradeep Reddy and Ritter, Petra and Rizzo, Gaia and
              Robbins, Kay A and Rockhill, Alexander P and Rogers, Christine
              and Rokem, Ariel and Rorden, Chris and Routier, Alexandre and
              Saborit-Torres, Jose Manuel and Salo, Taylor and Schirner,
              Michael and Smith, Robert E and Spisak, Tamas and Sprenger, Julia
              and Swann, Nicole C and Szinte, Martin and Takerkart, Sylvain and
              Thirion, Bertrand and Thomas, Adam G and Torabian, Sajjad and
              Varoquaux, Gael and Voytek, Bradley and Welzel, Julius and
              Wilson, Martin and Yarkoni, Tal and Gorgolewski, Krzysztof J",
  abstract = "The Brain Imaging Data Structure (BIDS) is a community-driven
              standard for the organization of data and metadata from a growing
              range of neuroscience modalities. This paper is meant as a
              history of how the standard has developed and grown over time. We
              outline the principles behind the project, the mechanisms by
              which it has been extended, and some of the challenges being
              addressed as it evolves. We also discuss the lessons learned
              through the project, with the aim of enabling researchers in
              other domains to learn from the success of BIDS.",
  journal  = "ArXiv",
  month    =  jan,
  year     =  2024,
  language = "en"
}

@book{Mons2018DataStewardshipBook,
	address = {Milton},
	author = {Mons, Barend},
	date-added = {2024-06-17 11:30:13 -0700},
	date-modified = {2024-06-17 11:30:13 -0700},
	doi = {10.1201/9781315380711},
	edition = {1},
	id = {cdi{\_}askewsholts{\_}vlebooks{\_}9781315351148},
	isbn = {9780815348184},
	keywords = {big data ; Bioinformatics ; Business enterprises ; COMPUTERSCIENCEnetBASE ; data curation ; data formatting ; data integration ; Data Preparation \& Mining ; Data protection ; data publishing ; Database management ; FAIR data ; Information resources management ; Information technology ; INFORMATIONSCIENCEnetBASE ; SCI-TECHnetBASE ; Statistical Computing ; STATSnetBASE ; STMnetBASE},
	n2 = {Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard .},
	publisher = {CRC Press},
	title = {Data Stewardship for Open Science: Implementing FAIR Principles},
	volume = {1},
	year = {2018},
	bdsk-url-1 = {https://doi.org/10.1201/9781315380711}}


@MISC{Koch2012-ve,
  title        = "Observatories of the mind",
  booktitle    = "Nature Publishing Group {UK}",
  author       = "Koch, Christof and Clay Reid, R",
  abstract     = "An ambitious project to map the mouse brain at the Allen
                  Institute for Brain Science is a huge undertaking that may
                  unify neuroscience, argue Christof Koch and R. Clay Reid.",
  month        =  mar,
  year         =  2012,
  howpublished = "\url{http://dx.doi.org/10.1038/483397a}",
  note         = "Accessed: 2024-6-17",
  language     = "en"
}



@ARTICLE{Basaglia2023-dq,
  title    = "Data preservation in high energy physics",
  author   = "Basaglia, T and Bellis, M and Blomer, J and Boyd, J and Bozzi, C
              and Britzger, D and Campana, S and Cartaro, C and Chen, G and
              Couturier, B and David, G and Diaconu, C and Dobrin, A and
              Duellmann, D and Ebert, M and Elmer, P and Fernandes, J and
              Fields, L and Fokianos, P and Ganis, G and Geiser, A and Gheata,
              M and Lopez, J B Gonzalez and Hara, T and Heinrich, L and
              Hildreth, M and Herner, K and Jayatilaka, B and Kado, M and
              Keeble, O and Kohls, A and Naim, K and Lange, C and
              Lassila-Perini, K and Levonian, S and Maggi, M and Marshall, Z
              and Vila, P Mato and Me{\v c}ionis, A and Morris, A and Piano, S
              and Potekhin, M and Schr{\"o}der, M and Schwickerath, U and
              Sexton-Kennedy, E and {\v S}imko, T and Smith, T and South, D and
              Verbytskyi, A and Vidal, M and Vivace, A and Wang, L and Watt, G
              and Wenaus, T and {DPHEP Collaboration}",
  abstract = "Data preservation is a mandatory specification for any present
              and future experimental facility and it is a cost-effective way
              of doing fundamental research by exploiting unique data sets in
              the light of the continuously increasing theoretical
              understanding. This document summarizes the status of data
              preservation in high energy physics. The paradigms and the
              methodological advances are discussed from a perspective of more
              than ten years of experience with a structured effort at
              international level. The status and the scientific return related
              to the preservation of data accumulated at large collider
              experiments are presented, together with an account of ongoing
              efforts to ensure long-term analysis capabilities for ongoing and
              future experiments. Transverse projects aimed at generic
              solutions, most of which are specifically inspired by open
              science and FAIR principles, are presented as well. A prospective
              and an action plan are also indicated.",
  journal  = "The European Physical Journal C",
  volume   =  83,
  number   =  9,
  pages    = "795",
  month    =  sep,
  year     =  2023
}




@inproceedings{wells1979fits,
  title={FITS-a flexible image transport system},
  author={Wells, Donald Carson and Greisen, Eric W},
  booktitle={Image processing in astronomy},
  pages={445},
  year={1979}
}

@ARTICLE{Rubel2022NWB,
  title    = "The Neurodata Without Borders ecosystem for neurophysiological
              data science",
  author   = "R{\"u}bel, Oliver and Tritt, Andrew and Ly, Ryan and Dichter,
              Benjamin K and Ghosh, Satrajit and Niu, Lawrence and Baker,
              Pamela and Soltesz, Ivan and Ng, Lydia and Svoboda, Karel and
              Frank, Loren and Bouchard, Kristofer E",
  abstract = "The neurophysiology of cells and tissues are monitored
              electrophysiologically and optically in diverse experiments and
              species, ranging from flies to humans. Understanding the brain
              requires integration of data across this diversity, and thus
              these data must be findable, accessible, interoperable, and
              reusable (FAIR). This requires a standard language for data and
              metadata that can coevolve with neuroscience. We describe design
              and implementation principles for a language for neurophysiology
              data. Our open-source software (Neurodata Without Borders, NWB)
              defines and modularizes the interdependent, yet separable,
              components of a data language. We demonstrate NWB's impact
              through unified description of neurophysiology data across
              diverse modalities and species. NWB exists in an ecosystem, which
              includes data management, analysis, visualization, and archive
              tools. Thus, the NWB data language enables reproduction,
              interchange, and reuse of diverse neurophysiology data. More
              broadly, the design principles of NWB are generally applicable to
              enhance discovery across biology through data FAIRness.",
  journal  = "Elife",
  volume   =  11,
  month    =  oct,
  year     =  2022,
  keywords = "FAIR data; Neurophysiology; archive; data ecosystem; data
              language; data standard; human; mouse; neuroscience; rat",
  language = "en"
}


@ARTICLE{Gorgolewski2016BIDS,
  title    = "The {Brain} {Imaging} {Data} {Structure}, a format for organizing and
              describing outputs of neuroimaging experiments",
  author   = "Gorgolewski, Krzysztof J and Auer, Tibor and Calhoun, Vince D and
              Craddock, R Cameron and Das, Samir and Duff, Eugene P and
              Flandin, Guillaume and Ghosh, Satrajit S and Glatard, Tristan and
              Halchenko, Yaroslav O and Handwerker, Daniel A and Hanke, Michael
              and Keator, David and Li, Xiangrui and Michael, Zachary and
              Maumet, Camille and Nichols, B Nolan and Nichols, Thomas E and
              Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and
              Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and
              Turner, Jessica A and Varoquaux, Ga{\"e}l and Poldrack, Russell A",
  abstract = "The development of magnetic resonance imaging (MRI) techniques
              has defined modern neuroimaging. Since its inception, tens of
              thousands of studies using techniques such as functional MRI and
              diffusion weighted imaging have allowed for the non-invasive
              study of the brain. Despite the fact that MRI is routinely used
              to obtain data for neuroscience research, there has been no
              widely adopted standard for organizing and describing the data
              collected in an imaging experiment. This renders sharing and
              reusing data (within or between labs) difficult if not impossible
              and unnecessarily complicates the application of automatic
              pipelines and quality assurance protocols. To solve this problem,
              we have developed the Brain Imaging Data Structure (BIDS), a
              standard for organizing and describing MRI datasets. The BIDS
              standard uses file formats compatible with existing software,
              unifies the majority of practices already common in the field,
              and captures the metadata necessary for most common data
              processing operations.",
  journal  = "Sci Data",
  volume   =  3,
  pages    = "160044",
  month    =  jun,
  year     =  2016,
  language = "en",
  url = {https://www.nature.com/articles/sdata201644}

}

@ARTICLE{Wilkinson2016FAIR,
  title    = "The {FAIR} Guiding Principles for scientific data management and
              stewardship",
  author   = "Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, I
              Jsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak,
              Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva
              Santos, Luiz Bonino and Bourne, Philip E and Bouwman, Jildau and
              Brookes, Anthony J and Clark, Tim and Crosas, Merc{\`e} and
              Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo,
              Chris T and Finkers, Richard and Gonzalez-Beltran, Alejandra and
              Gray, Alasdair J G and Groth, Paul and Goble, Carole and Grethe,
              Jeffrey S and Heringa, Jaap and 't Hoen, Peter A C and Hooft, Rob
              and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott
              J and Martone, Maryann E and Mons, Albert and Packer, Abel L and
              Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van
              Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and
              Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz,
              Morris A and Thompson, Mark and van der Lei, Johan and van
              Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and
              Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and
              Mons, Barend",
  abstract = "There is an urgent need to improve the infrastructure supporting
              the reuse of scholarly data. A diverse set of
              stakeholders-representing academia, industry, funding agencies,
              and scholarly publishers-have come together to design and jointly
              endorse a concise and measureable set of principles that we refer
              to as the FAIR Data Principles. The intent is that these may act
              as a guideline for those wishing to enhance the reusability of
              their data holdings. Distinct from peer initiatives that focus on
              the human scholar, the FAIR Principles put specific emphasis on
              enhancing the ability of machines to automatically find and use
              the data, in addition to supporting its reuse by individuals.
              This Comment is the first formal publication of the FAIR
              Principles, and includes the rationale behind them, and some
              exemplar implementations in the community.",
  journal  = "Sci Data",
  volume   =  3,
  pages    = "160018",
  month    =  mar,
  year     =  2016,
  language = "en"
}

@article{nstc2022desirable,
  title={Desirable Characteristics of Data Repositories for Federally Funded Research},
  author={{The National Science and Technology Council}},
  journal={Executive Office of the President of the United States, Tech. Rep},
  year={2022}
}

@TECHREPORT{NIST2019,
  title  = "{U.S}. {LEADERSHIP} {IN} {AI}: A Plan for Federal Engagement in
            Developing Technical Standards and Related Tools",
    year = 2019,
    author = "{{National Institute of Standards and Technology}}"
}

@MISC{Van_Rossum2008BDFL,
  title        = "Origin of {BDFL}",
  year = 2008,
  author       = "van Rossum, Guido",
  howpublished = "\url{https://www.artima.com/weblogs/viewpost.jsp?thread=235725}",
  note         = "Accessed: 2023-6-19"
}


@ARTICLE{Baumgartner2023TeamScience,
  title    = "How to build up big team science: a practical guide for
              large-scale collaborations",
  author   = "Baumgartner, Heidi A and Alessandroni, Nicol{\'a}s and
              Byers-Heinlein, Krista and Frank, Michael C and Hamlin, J Kiley
              and Soderstrom, Melanie and Voelkel, Jan G and Willer, Robb and
              Yuen, Francis and Coles, Nicholas A",
  abstract = "The past decade has witnessed a proliferation of big team science
              (BTS), endeavours where a comparatively large number of
              researchers pool their intellectual and/or material resources in
              pursuit of a common goal. Despite this burgeoning interest, there
              exists little guidance on how to create, manage and participate
              in these collaborations. In this paper, we integrate insights
              from a multi-disciplinary set of BTS initiatives to provide a
              how-to guide for BTS. We first discuss initial considerations for
              launching a BTS project, such as building the team, identifying
              leadership, governance, tools and open science approaches. We
              then turn to issues related to running and completing a BTS
              project, such as study design, ethical approvals and issues
              related to data collection, management and analysis. Finally, we
              address topics that present special challenges for BTS, including
              authorship decisions, collaborative writing and team
              decision-making.",
  journal  = "R Soc Open Sci",
  volume   =  10,
  number   =  6,
  pages    = "230235",
  month    =  jun,
  year     =  2023,
  keywords = "big team science; collaboration; meta-science; science of team
              science",
  language = "en"
}

@ARTICLE{Koch2016TeamScience,
  title    = "Big Science, Team Science, and Open Science for Neuroscience",
  author   = "Koch, Christof and Jones, Allan",
  abstract = "The Allen Institute for Brain Science is a non-profit private
              institution dedicated to basic brain science with an internal
              organization more commonly found in large physics projects-large
              teams generating complete, accurate and permanent resources for
              the mouse and human brain. It can also be viewed as an experiment
              in the sociology of neuroscience. We here describe some of the
              singular differences to more academic, PI-focused institutions.",
  journal  = "Neuron",
  volume   =  92,
  number   =  3,
  pages    = "612--616",
  month    =  nov,
  year     =  2016,
  language = "en"
}