Research Data Management: Best Practices

- 10 Simple Rules for the Care and Feeding of Scientific Data arXiv submission 2014 Jan 9

- Best Practices of Data Management - DataOne

- Manoa Data Talks hosted on Laulima and available to all UH users, a compilation of Best Practices from a range of sources. UH login.

- Best Practices for Preparing Environmental Data Sets to Share and Archive (pdf) by Hook et al, 2010.

- Some Simple Guidelines for Effective Data Management by Elizabeth T. Borer et al., Bulletin of the Ecological Society of America 90(2) 205-214, 

1. use a scripted statistics program such as R or SAS that provides a record of your analyses
2. store files in a non-proprietary format and on a non-proprietary hardware
3. store a copy of your original rough data as a read-only, making copies to use in analysis
4. provide descriptive filenames and designate the first row of tables as a header
5. organize records in rows, using column headings that will allow analysis within columns rather than across columns, example: SITE YEAR RAIN TEMP SPEC_NAME POP
6. set up your tables so that you do not have to add columns when adding data
7. use ASCII characters to minimize translation problems with software programs
8. your data tables should only contain data, comments should be in a read.me text file that accompanies the table

ScholarSpace, the UHM Institutional Repository uses Dublin Core Metadata, a standard that facilitates discoverability of files stored in ScholarSpace.

The DMP Tool site has templates for data management plans based on the specific requirements listed in funder policy documents. The DMP Tool maintains these templates; researchers should consult program officers and policy documents directly for authoritative guidance.

The UK Digital Curation Centre (DCC) also has the Curation Reference Manual, which may be helpful for digital curation techniques and best practices including creating metadata about your data sets for optimal usability and discoverability.

The world of social science data curation has been shaped by the collaborative efforts begun in 1962 of the Inter-university Consortium for Political and Social Research, housed at the University of Michigan. At http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdf there is a 47 page guide to preparing data for archiving. Best practices for dataset preparation are applicable to all disciplines.

The Data Documentation Initiative is another international collaboration that was created to support the sharing of social sciences datasets. See Data Archiving and Networked Services (DANS) for best practices in documenting data collections.

From DataOne: 

Data Management Skillbuilder Hub  - a repository for open educational resources regarding data management 

Data Management Training 

• NCEASʻ (National Center for Ecological Analysis and Synthesis) Learning Hub CoreR Course: Introducing tools like R programming, collaborating with Git and GitHub, and data management best practices.

Data Management Training: a self-paced asynchronous training course created by the UH at Mānoa Library to support Hawaiiʻs research data needs.

Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC) has links to guidelines and introductions to naming and metadata for geosciences and GIS.

Inter-University Consortium for Political and Social Research (ICPSR) has links to information about file naming, metadata, etc. that will be useful to all data managers. The site provides good information on planning and organizing data files before the start of a project.