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@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"
}