Journal:Ten simple rules to enable multi-site collaborations through data sharing

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Full article title Ten simple rules to enable multi-site collaborations through data sharing
Journal PLOS Computational Biology
Author(s) Boland, Mary Regina; Karczewski, Konrad J.; Tatonetti, Nicholas P.
Author affiliation(s) Columbia University (NY), Broad Institute of MIT and Harvard, Massachusetts General Hospital
Primary contact Email: mary dot boland @ columbia dot edu
Year published 2017
Volume and issue 13(1)
Page(s) e1005278
DOI 10.1371/journal.pcbi.1005278
ISSN 1553-7358
Distribution license Creative Commons Attribution 4.0 International
Website http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005278
Download http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005278&type=printable (PDF)

Open access, open data, and software are critical for advancing science and enabling collaboration across multiple institutions and throughout the world. Despite near universal recognition of its importance, major barriers still exist to sharing raw data, software, and research products throughout the scientific community. Many of these barriers vary by specialty[1], increasing the difficulties for interdisciplinary and/or translational researchers to engage in collaborative research. Multi-site collaborations are vital for increasing both the impact and the generalizability of research results. However, they often present unique data sharing challenges. We discuss enabling multi-site collaborations through enhanced data sharing in this set of Ten Simple Rules.

Collaboration is an essential component of research[2] that takes many forms, including internal (across departments within a single institution) and external collaborations (across institutions). However, multi-site collaborations with more than two institutions encounter more complex challenges because of institutional-specific restrictions and guidelines.[3] Vicens and Bourne focus on collaborators working together on a shared research grant.[4] They do not discuss the specific complexities of multi-site collaborations and the vital need for enhanced data sharing in the multi-site and large-scale collaboration context, in which participants may or may not have the same funding source and/or research grant.

While challenging, multi-site collaborations are equally rewarding and result in increased research productivity.[5][6] One highly successful multi-site and translational collaboration is the Electronic Medical Records and Genomics (eMERGE) network (URL: https://emerge.mc.vanderbilt.edu/) initiated in 2007.[7] The eMERGE network links biorepository data with clinical information from electronic health records (EHRs). They were able to find novel associations and replicate many known associations between genetic variants and clinical phenotypes that would have been more difficult without the collaboration.[8] eMERGE members also collaborated with other consortiums and networks, including the Alzheimer’s Disease Genetics Consortium[9] 


and the NINDS Stroke Genetics Network [10], to name a few. Other successful collaborations include OHDSI: Observational Health Data Sciences and Informatics (http://www.ohdsi.org/), which builds off of the methodology from the Observational Medical Outcomes Partnership (OMOP) [11], and CIRCLE: Clinical Informatics Research Collaborative (http://circleinformatics.org/). In genetics, there are many consortiums, including ExAC: The Exome Aggregation Consortium (http://exac.broadinstitute.org/), the 1000 Genomes Project Consortium (http://www.1000genomes.org/), the Australian BioGRID (https://www.biogrid.org.au/), The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/), Genotype-Tissue Expression Portal (GTEx: http://www.gtexportal.org/home/), and Encyclopedia of DNA Elements at UCSC (ENCODE: https://genome.ucsc.edu/ENCODE/) among others.

Funding

MRB was supported by NLM T15 LM00707 from Jul 2014–Jun 2016 and by the NCATS, NIH, through TL1 TR000082, formerly the NCRR, TL1 RR024158 from Jul 2016–Jun 2017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests

The authors have declared that no competing interests exist.

References

  1. ↑ Reichman, O.J.; Jones, M.B.; Schildhauer, M.P. (2011). "Challenges and opportunities of open data in ecology". Science 331 (6018): 703–5. doi:10.1126/science.1197962. PMID 21311007. 
  2. ↑ Bozeman, B.; Fay, D.; Slade, C.P. (2013). "Research collaboration in universities and academic entrepreneurship: the-state-of-the-art". The Journal of Technology Transfer 38 (1): 1–67. doi:10.1007/s10961-012-9281-8. 
  3. ↑ Brown, P.; Morello-Frosch, R.; Brody, J.G. (2008). "IRB Challenges in Multi-Partner Community-Based Participatory Research". Proceedings of The American Sociological Association Annual Meeting 2008: 1-31. https://www.brown.edu/research/research-ethics/irb-challenges-multi-partner-community-based-participatory-research. 
  4. ↑ Vicens, Q.; Bourne, P.E. (2007). "Ten simple rules for a successful collaboration". PLOS Computational Biology 3 (3): e44. doi:10.1371/journal.pcbi.0030044. PMC PMC1847992. PMID 17397252. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1847992. 
  5. ↑ Jones, B.F.; Wuchty, S.; Uzzi, B. (2008). "Multi-university research teams: shifting impact, geography, and stratification in science". Science 322 (5905): 1259-62. doi:10.1126/science.1158357. PMID 18845711. 
  6. ↑ Börner, K.; Contractor, N.; Falk-Krzesinski, H.J. et al. (2010). "A multi-level systems perspective for the science of team science". Science Translational Medicine 2 (49): 49cm24. doi:10.1126/scitranslmed.3001399. PMC PMC3527819. PMID 20844283. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527819. 
  7. ↑ Gottesman, O.; Kuivaniemi, H.; Tromp, G. et al. (2013). "The Electronic Medical Records and Genomics (eMERGE) Network: Past, present, and future". Genetics in Medicine 15 (10): 761-71. doi:10.1038/gim.2013.72. PMC PMC3795928. PMID 23743551. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3795928. 
  8. ↑ Feng, Q.; Wei, W.Q.; Chung, C.P. et al. (2016). "The effect of genetic variation in PCSK9 on the LDL-cholesterol response to statin therapy". The Pharmacogenomics Journal. doi:10.1038/tpj.2016.3. PMC PMC4995153. PMID 26902539. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995153. 
  9. ↑ Karch, C.M.; Ezerskiy, L.A.; Bertelsen, S. et al. (2016). "Alzheimer's Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci". PLOS One 11 (2): e0148717. doi:10.1371/journal.pone.0148717. PMC PMC4769299. PMID 26919393. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769299. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.

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