Open AccessJournal Article
Data sharing
206
About: This article is published in Storage Management Solutions archive. The article was published on 01 May 1998. and is currently open access. The article focuses on the topics: Data sharing.
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Citations
Is data sharing the future of clinical trials
TL;DR: It is proposed that, in the near future, publishers will mandate that researchers share de-identified patient level data (IPD) for all clinical trials published in their journals and clinical researchers would have no recourse against such a policy.
5
Data credit distribution: A new method to estimate databases impact
Dennis Dosso,Gianmaria Silvello +1 more
TL;DR: A data credit distribution strategy (CDS) based on data provenance is proposed and a system that uses the information provided by data citations to distribute the credit in a relational database accordingly is implemented.
5
Assessment of the impact of shared data on the scientific literature
Michael P. Milham,R. Cameron Craddock,Michael Fleischmann,Jake Son,Jon Clucas,Helen Y. Xu,Bonhwang Koo,Anirudh Krishnakumar,Bharat B. Biswal,Francisco X. Castellanos,Stanley J. Colcombe,Adriana Di Martino,Xi-Nian Zuo,Arno Klein +13 more
TL;DR: A brain imaging case study is presented that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017), and it is demonstrated that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines.
Resident-to-resident elder mistreatment (R-REM) intervention for direct care staff in assisted living residences: study protocol for a cluster randomized controlled trial.
Jeanne A. Teresi,Jeanne A. Teresi,Stephanie Silver,Mildred Ramirez,Jian Kong,Joseph P. Eimicke,Gabriel Boratgis,Rhoda Meador,Leslie Schultz,Mark S. Lachs,Karl Pillemer +10 more
TL;DR: The authors conducted the first systematic, prospective study of resident-to-resident elder mistreatment in nursing homes and developed an intervention for direct care staff to enhance knowledge of R-REM and increase reporting and resident safety by reducing falls and associated injuries.
What is "data sharing" and why should biomedical researchers embrace it?
TL;DR: The advent of the internet and sophisticated bioinformatics has made it feasible to share primary data in a way that would have been impracticable some while ago, and these combine to give momentum to the idea of data sharing.
5
References
Data, disease and diplomacy: GISAID's innovative contribution to global health
Stefan Elbe,Gemma Buckland-Merrett +1 more
- 10 Jan 2017
TL;DR: The article finds that the Global Initiative on Sharing All Influenza Data contributes to global health in at least five ways: collating the most complete repository of high‐quality influenza data in the world; facilitating the rapid sharing of potentially pandemic virus information during recent outbreaks; supporting the World Health Organization's biannual seasonal flu vaccine strain selection process; developing informal mechanisms for conflict resolution around the sharing of virus data.
2.1K
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
2K
Meta-analysis and the science of research synthesis
TL;DR: The opportunity provided by the recent fortieth anniversary of meta-analysis is taken to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.
1.3K
Machine Learning for Precision Psychiatry: Opportunities and Challenges.
TL;DR: This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.
650
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.
TL;DR: A computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers, is developed, which can be considered a first step toward defining general microbial dysbiosis.
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