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Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery

Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omi...

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Autores principales: Almeida, Carlos A., Torres-Espin, Abel, Huie, J. Russell, Sun, Dongming, Noble-Haeusslein, Linda J., Young, Wise, Beattie, Michael S., Bresnahan, Jacqueline C., Nielson, Jessica L., Ferguson, Adam R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015816/
https://www.ncbi.nlm.nih.gov/pubmed/33651310
http://dx.doi.org/10.1007/s12021-021-09512-z
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author Almeida, Carlos A.
Torres-Espin, Abel
Huie, J. Russell
Sun, Dongming
Noble-Haeusslein, Linda J.
Young, Wise
Beattie, Michael S.
Bresnahan, Jacqueline C.
Nielson, Jessica L.
Ferguson, Adam R.
author_facet Almeida, Carlos A.
Torres-Espin, Abel
Huie, J. Russell
Sun, Dongming
Noble-Haeusslein, Linda J.
Young, Wise
Beattie, Michael S.
Bresnahan, Jacqueline C.
Nielson, Jessica L.
Ferguson, Adam R.
author_sort Almeida, Carlos A.
collection PubMed
description Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12021-021-09512-z.
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spelling pubmed-90158162022-09-02 Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery Almeida, Carlos A. Torres-Espin, Abel Huie, J. Russell Sun, Dongming Noble-Haeusslein, Linda J. Young, Wise Beattie, Michael S. Bresnahan, Jacqueline C. Nielson, Jessica L. Ferguson, Adam R. Neuroinformatics Original Article Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12021-021-09512-z. Springer US 2021-03-02 2022 /pmc/articles/PMC9015816/ /pubmed/33651310 http://dx.doi.org/10.1007/s12021-021-09512-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Almeida, Carlos A.
Torres-Espin, Abel
Huie, J. Russell
Sun, Dongming
Noble-Haeusslein, Linda J.
Young, Wise
Beattie, Michael S.
Bresnahan, Jacqueline C.
Nielson, Jessica L.
Ferguson, Adam R.
Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title_full Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title_fullStr Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title_full_unstemmed Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title_short Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery
title_sort excavating fair data: the case of the multicenter animal spinal cord injury study (mascis), blood pressure, and neuro-recovery
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015816/
https://www.ncbi.nlm.nih.gov/pubmed/33651310
http://dx.doi.org/10.1007/s12021-021-09512-z
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