Ten Simple Rules for Digital Data Storage

PLoS Computational Biology

Volumn 12, Issue 10, 2016, Pages

  Hart, Edmund M ^a   Barmby, Pauline ^b   LeBauer, David ^c   Michonneau, François ^d,e   Mount, Sarah ^f   Mulrooney, Patrick ^g   Poisot, Timothée ^h   Woo, Kara H ⁱ   Zimmerman, Naupaka B ^j   Hollister, Jeffrey W ^k  

^a UNIVERSITY OF VERMONT   (United States)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d UNIVERSITY OF FLORIDA   (United States)

^e UNIVERSITY OF FLORIDA   (United States)

^f KING'S COLLEGE LONDON   (United Kingdom)

^g UNIVERSITY OF CALIFORNIA   (United States)

^h UNIVERSITÉ DE MONTRÉAL   (Canada)

ⁱ WASHINGTON STATE UNIVERSITY   (United States)

^j UNIVERSITY OF ARIZONA   (United States)

^k U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

SIMPLE++;

DIGITAL STORAGE;

ACCESS TO INFORMATION; APACHE SPARK DATABASE; ARCHIVAL RESOURCE KEY PROGRAM; COMPUTER INTERFACE; COST; DECISION MAKING; DIGITAL OBJECT IDENTIFIER PROGRAM; EDITORIAL; FACTUAL DATABASE; HADOOP DISTRIBUTED FILE SYSTEM; INFORMATION PROCESSING; MAPREDUCE PROGRAM; ONLINE SYSTEM; PERSISTENT UNIFORM RESOURCE LOCATOR PROGRAM; PRACTICE GUIDELINE; PRIVACY; REPRODUCIBILITY; SECURE HASH ALGORITHM 2; SOFTWARE; STRUCTURED QUERY LANGUAGE DATABASE; SYSTEMATIC ERROR; UNIFORM RESOURCE LOCATOR PROGRAM; VALIDATION PROCESS; WEB APPLICATION PROGRAMMING INTERFACE; HUMAN; INFORMATION RETRIEVAL; STANDARDS; TRENDS;

DATABASES, FACTUAL; HUMANS; INFORMATION STORAGE AND RETRIEVAL;

EID: 84994082460     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005097     Document Type: Editorial

References (37)

1. Reid JG, Carroll A, Veeraraghavan N, Dahdouli M, Sundquist A, English A, et al. Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline. BMC bioinformatics. 2014;15: 30. doi: 10.1186/1471-2105-15-3024475911
2. Hampton SE, Strasser C, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL, et al. Big data and the future of ecology. Frontiers in Ecology and the Environment. 2013; 130312142848005. doi: 10.1890/120103
3. Eisenstein DJ, SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way, and Extra-Solar Planetary Systems. The Astronomical Journal. 2011;142: 72. doi: 10.1088/0004-6256/142/3/72
4. Adams J, Collaborations: The rise of research networks. Nature. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. 2012;490: 335–6.
5. Fraser LH, Henry HA, Carlyle CN, White SR, Beierkuhnlein C, Cahill JF, et al. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Frontiers in Ecology and the Environment. Ecological Society of America; 2013;11: 147–155. doi: 10.1890/110279
6. Robertson MAG Tim, Döring The GBIF integrated publishing toolkit: Facilitating the efficient publishing of biodiversity data on the internet. PLoS ONE. Public Library of Science; 2014;9: e102623. doi: 10.1371/journal.pone.010262325099149
7. Wolkovich EM, Regetz J, O’Connor MI, Advances in global change research require open science by individual researchers. Global Change Biology. 2012;18: 2102–2110. doi: 10.1111/j.1365-2486.2012.02693.x
8. Roche DG, Lanfear R, Binning SA, Haff TM, Schwanz LE, Cain KE, et al. Troubleshooting public data archiving: suggestions to increase participation. PLoS biology. 2014;12: e1001779. doi: 10.1371/journal.pbio.100177924492920
9. White E, Baldridge E, Brym Z, Locey K, McGlinn D, Supp S, Nine simple ways to make it easier to (re)use your data. Ideas in Ecology and Evolution. 2013;6: 1–10. doi: 10.4033/iee.2013.6b.6.f
10. Goodman A, Pepe A, Blocker AW, Borgman CL, Cranmer K, Crosas M, et al. Ten simple rules for the care and feeding of scientific data. PLoS computational biology. 2014;10: e1003542. doi: 10.1371/journal.pcbi.100354224763340
11. Pepe A, Goodman A, Muench A, Crosas M, Erdmann C, Golden AA-J, How Do Astronomers Share Data? Reliability and Persistence of Datasets Linked in AAS Publications and a Qualitative Study of Data Practices among US Astronomers. PLoS ONE. 2014;9: e104798. doi: 10.1371/journal.pone.010479825165807
12. Vines TH, Albert AYK, Andrew RL, Débarre F, Bock DG, Franklin MT, et al. The availability of research data declines rapidly with article age. Current biology: CB. Elsevier; 2014;24: 94–7. doi: 10.1016/j.cub.2013.11.01424361065
13. Michener WK, Jones MB, Ecoinformatics: supporting ecology as a data-intensive science. Trends in ecology & evolution. 2012;27: 85–93. doi: 10.1016/j.tree.2011.11.01622240191
14. Michener WK, Brunt JW, Helly JJ, Kirchner TB, Stafford SG, Nongeospatial metadata for the ecological sciences. Ecological Applications. Eco Soc America; 1997;7: 330–342. doi: 10.1890/1051-0761(1997)007%5B0330:NMFTES%5D2.0.CO;2
15. Marcial LH, Hemminger BM, Scientific data repositories on the Web: An initial survey. Journal of the American Society for Information Science and Technology. 2010;61: 2029–2048. doi: 10.1002/asi.21339
16. Michener WK, Ten simple rules for creating a good data management plan. PLoS computational biology. Public Library of Science; 2015;11: e1004525. doi: 10.1371/journal.pcbi.100452526492633
17. Wilson G, Software Carpentry: lessons learned. F1000Research. 2014;3: 62. doi: 10.12688/f1000research.3-62.v124715981
18. Teal TK, Cranston Ka, Lapp H, White E, Wilson G, Ram K, et al. Data Carpentry: Workshops to Increase Data Literacy for Researchers. International Journal of Digital Curation. 2015;10: 135–143. doi: 10.2218/ijdc.v10i1.351
19. Roche DG, Kruuk LEB, Lanfear R, Binning SA, Public Data Archiving in Ecology and Evolution: How Well Are We Doing?PLoS Biology. 2015;13: e1002295. doi: 10.1371/journal.pbio.100229526556502
20. Higgins D, Berkley C, Jones M, Managing heterogeneous ecological data using morpho [Internet]. IEEE Comput. Soc; 2002. pp. 69–76. doi: 10.1109/SSDM.2002.1029707
21. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology. Am Soc Microbiol; 2009;75: 7537–7541. doi: 10.1128/AEM.01541-0919801464
22. Koziol Q, Matzke R, Hdf5: A new generation of hdf: Reference manual and user guide. National Center for Supercomputing Applications, Champaign, Illinois, USA, http://hdf.ncsa.uiuc.edu/nra/HDF5. 1998;
23. Rew R, Davis G, NetCDF: An interface for scientific data access. Computer Graphics and Applications, IEEE. IEEE; 1990;10: 76–82. doi: 10.1109/38.56302
24. Wickham H, Tidy Data. Journal of Statistical Software. 2014;59: 1–23. http://www.jstatsoft.org/v59/i10doi: 10.18637/jss.v059.i10
25. Buffalo V, Bioinformatics data skills: Reproducible and robust research with open source tools. O’Reilly Media, Inc. 2015.
26. NCBI. NCBI is phasing out sequence gIs—use accession.Version instead! http://www.ncbi.nlm.nih.gov/news/03-02-2016-phase-out-of-GI-numbers/;
27. Preston-Werner T. Semantic Versioning 2.0.0. http://semver.org; 2014.
28. Johnson GT, Goodsell DS, Autin L, Forli S, Sanner MF, Olson AJ, 3D molecular models of whole HIV-1 virions generated with cellPACK. Faraday Discuss. The Royal Society of Chemistry; 2014;169: 23–44. doi: 10.1039/C4FD00017J25253262
29. Strasser C, Cook R, Michener W, Budden A, Primer on Data Management: What you always wanted to know [Internet]. California Digital Libraries; 2012. doi: 10.5060/D2251G48
30. Goodin D. Poorly anonymized logs reveal NYC cab drivers’ detailed whereabouts [Internet]. 2015. http://arstechnica.com/tech-policy/2014/06/poorly-anonymized-logs-reveal-nyc-cab-drivers-detailed-whereabouts/
31. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS genetics. Public Library of Science; 2008;4: e1000167. doi: 10.1371/journal.pgen.100016718769715
32. Kahn SD, On the future of genomic data. Science (New York, NY). 2011;331: 728–9. doi: 10.1126/science.119789121311016
33. Wandelt S, Bux M, Leser U, Trends in genome compression. Current Bioinformatics. Bentham Science Publishers; 2014;9: 315–326. doi: 10.2174/1574893609666140516010143
34. Gaye A, Marcon Y, Isaeva J, LaFlamme P, Turner A, Jones EM, et al. DataSHIELD: taking the analysis to the data, not the data to the analysis. International journal of epidemiology. 2014;43: 1929–44. doi: 10.1093/ije/dyu18825261970
35. Briney K. Rule of 3. DATA AB INITIO. http://dataabinitio.com/?p=320; 2013.
36. Henkel H, Hutchison V, Strasser C, Rebich Hespanha S, Vanderbilt K, Wayne L, et al. DataONE Education Modules. https://www.dataone.org/education-modules; 2012.
37. Witt M, Carlson J, Brandt S, Cragin M, Constructing Data Curation Profiles. International Journal of Digital Curation. 2009;4: 93–103. doi: 10.2218/ijdc.v4i3.117