-
Notifications
You must be signed in to change notification settings - Fork 2
/
references.bib
157 lines (149 loc) · 8.23 KB
/
references.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
@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"
}