TY - JOUR
T1 - Coins
T2 - An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets
AU - Scott, Adam
AU - Courtney, Will
AU - Wood, Dylan
AU - de la Garza, Raul
AU - Lane, Susan
AU - King, Margaret
AU - Wang, Runtang
AU - Roberts, Jody
AU - Turner, Jessica A.
AU - Calhoun, Vince D.
N1 - Funding Information:
This research was supported in part by the National Institutes of Health (NIH), under grants 1 R01 EB 000840, 1 R01 EB 005846, and 1 R01 EB 006841.
Funding Information:
One challenge lies in measuring the value of data sharing efforts. Several grant agencies such as the NIH (2003) and the NSF (2011) already recognize the value, and many institutions have implemented data sharing. The centralization and standardization of data has been shown to be both economically more efficient as well as facilitating sharing (Walden et al., 2011). Sharing study data may also increase researchers’ citation rate (Piwowar et al., 2007). Consider HeLa cells, a cell line available centrally for use by the scientific community in general. They can be propagated indefinitely and have been used in more than 60,000 scientific articles (Skloot, 2011). The acceleration of discovery through reuse via data sharing may not match the extreme success of HeLa cells in biology, but the copying fidelity is higher and the HeLa cells serve as exemplar to the concept of research recycling. Tracking simple metrics such as data reuse count and the number of publications may be a first step to provide metrics for how much data sharing actually occurs.
Publisher Copyright:
� 2011 Scott, Courtney, Wood, de la Garza, Lane, King, Wang, Roberts, Turner and Calhoun.
PY - 2011/12/23
Y1 - 2011/12/23
N2 - The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.
AB - The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.
KW - Brain imaging
KW - Database
KW - Neuroinformatics
UR - http://www.scopus.com/inward/record.url?scp=84993945123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84993945123&partnerID=8YFLogxK
U2 - 10.3389/fninf.2011.00033
DO - 10.3389/fninf.2011.00033
M3 - Article
C2 - 22275896
AN - SCOPUS:84993945123
SN - 1662-5196
VL - 5
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 33
ER -