Edmund M. Hart

PDFR32 | Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of various data formats, dataset sizes, data complexity, data use cases, and data-sharing practices. Improvements in high-throughput DNA sequencing, sustained institutional support for large sensor networks [1,2], and sky surveys with large-format digital cameras [3] have created massive quantities of data. At the same time, the combination of increasingly diverse research teams [4] and data aggregation in portals (e.g., for biodiversity data, GBIF.org, or iDigBio) necessitates increased coordination among data collectors and institutions [5,6]. Consequently, “data” can now mean anything from petabytes of information stored in professionally maintained databases to spreadsheets on a single computer to handwritten tables in lab notebooks on shelves. All remain important, but data curation practices continue to keep pace with the changes brought about by new forms of data and new data collection and storage practices | This article describes ten simple rules for digital data storage that grew out of a long discussion among instructors for the Software and Data Carpentry initiatives [17,18]. Software and Data Carpentry instructors are scientists from diverse backgrounds who have encountered a variety of data storage challenges and are active in teaching other scientists best practices for scientific computing and data management. Thus, this paper represents a distillation of collective experience and hopefully will be useful to scientists facing a variety of data storage challenges. We additionally provide a glossary of common vocabulary for readers who may not be familiar with particular terms |

Contents
1. Introduction pg. 1
2. Rule 1: Anticipate How Your Data Will Be Used pg. 2
3. Rule 2: Know Your Use Case pg. 2
4. Rule 3: Keep Raw Data Raw pg. 3
5. Rule 4: Store Data in Open Formats pg. 3
6. Rule 5: Data Should Be Structured for Analysis pg. 4
7. Table A&B Fig. 1: Untidy Data Example & Tidy Data Sample pg. 5
8 Rule 6: Data Should Be Uniquely Identifiable pg. 5
9. Rule 7: Link Relevant Metadata pg. 6
10. Rule 8: Adopt the Proper Privacy Protocols
11. Rule 9: Have a Systematic Backup Scheme pg. 7
12. Rule 10: The Location and Method of Data Storage Depend on How Much Data You Have
13. Further Reading and Resources pg. 8
14. Glossary: pg. 8
15. Acknowledgments pg. 10
16. References pg. 11

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Repositories

• Global Biodiversity Information Facility (GBIF, http://www.gbif.org) provides an international
open data infrastructure to publish and disseminate biodiversity information.

• Integrated Digitized Biocollections (iDigBio, https://www.idigbio.org) is a project funded
by the National Science Foundation facilitates the digitization of natural history collections
and provides data and images for biological specimens.

• Integrated Taxonomic Information System (ITIS, http://www.itis.gov) is an international
partnership of governmental organizations that aims at providing authoritative taxonomic
information for plants, animals, fungi, and microbes.

PLOS Computational Biology | DOI:10.1371/journal.pcbi.1005097 October 20, 2016… (meer)