Lessons learned: A neuroimaging research center's transition to open and reproducible science
Human functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2022.988084/full |
| _version_ | 1817996149003386880 |
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| author | Keith A. Bush Maegan L. Calvert Clinton D. Kilts |
| author_facet | Keith A. Bush Maegan L. Calvert Clinton D. Kilts |
| author_sort | Keith A. Bush |
| collection | DOAJ |
| description | Human functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and tools to both manage this complexity as well as to enhance the rigor and reproducibility of neuroimaging science. We group these best practices under four categories: experiment pre-registration, FAIR data principles, reproducible neuroimaging analyses, and open science. While there is growing recognition of the need to implement these best practices there exists little practical guidance of how to accomplish this goal. In this work, we describe lessons learned from efforts to adopt these best practices within the Brain Imaging Research Center at the University of Arkansas for Medical Sciences over 4 years (July 2018–May 2022). We provide a brief summary of the four categories of best practices. We then describe our center's scientific workflow (from hypothesis formulation to result reporting) and detail how each element of this workflow maps onto these four categories. We also provide specific examples of practices or tools that support this mapping process. Finally, we offer a roadmap for the stepwise adoption of these practices, providing recommendations of why and what to do as well as a summary of cost-benefit tradeoffs for each step of the transition. |
| first_indexed | 2024-04-14T02:17:56Z |
| format | Article |
| id | doaj.art-a2c3bd2cf2a746e4a6e2101eb929c955 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-04-14T02:17:56Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-a2c3bd2cf2a746e4a6e2101eb929c9552022-12-22T02:18:07ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2022-08-01510.3389/fdata.2022.988084988084Lessons learned: A neuroimaging research center's transition to open and reproducible scienceKeith A. BushMaegan L. CalvertClinton D. KiltsHuman functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and tools to both manage this complexity as well as to enhance the rigor and reproducibility of neuroimaging science. We group these best practices under four categories: experiment pre-registration, FAIR data principles, reproducible neuroimaging analyses, and open science. While there is growing recognition of the need to implement these best practices there exists little practical guidance of how to accomplish this goal. In this work, we describe lessons learned from efforts to adopt these best practices within the Brain Imaging Research Center at the University of Arkansas for Medical Sciences over 4 years (July 2018–May 2022). We provide a brief summary of the four categories of best practices. We then describe our center's scientific workflow (from hypothesis formulation to result reporting) and detail how each element of this workflow maps onto these four categories. We also provide specific examples of practices or tools that support this mapping process. Finally, we offer a roadmap for the stepwise adoption of these practices, providing recommendations of why and what to do as well as a summary of cost-benefit tradeoffs for each step of the transition.https://www.frontiersin.org/articles/10.3389/fdata.2022.988084/fullopen sciencereproducible neuroimagingFAIRpreregistrationtransitionneuroimaging |
| spellingShingle | Keith A. Bush Maegan L. Calvert Clinton D. Kilts Lessons learned: A neuroimaging research center's transition to open and reproducible science Frontiers in Big Data open science reproducible neuroimaging FAIR preregistration transition neuroimaging |
| title | Lessons learned: A neuroimaging research center's transition to open and reproducible science |
| title_full | Lessons learned: A neuroimaging research center's transition to open and reproducible science |
| title_fullStr | Lessons learned: A neuroimaging research center's transition to open and reproducible science |
| title_full_unstemmed | Lessons learned: A neuroimaging research center's transition to open and reproducible science |
| title_short | Lessons learned: A neuroimaging research center's transition to open and reproducible science |
| title_sort | lessons learned a neuroimaging research center s transition to open and reproducible science |
| topic | open science reproducible neuroimaging FAIR preregistration transition neuroimaging |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2022.988084/full |
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