Data Management Practices for Early Career Scientists: Closing Robert Cook Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge, TN February 3, 2013

NACP Best Data Management Practices, February 3, 2013 Plan for archiving data “Begin with the end in mind” Identified the Data Center Collaborated with data center during project Communicated: Volume Number of Files Special needs Delivery dates 2

NACP Best Data Management Practices, February 3, 2013 Followed Fundamental Data Practices Define the contents of your data files Use consistent data organization Use stable file formats Assign descriptive file names Preserve information Perform basic quality assurance Provide documentation Protect your data 3

NACP Best Data Management Practices, February 3, 2013 What to submit to the archive? Well-structured data files, with variables, units, and fill values well defined Metadata files (optional) Document that describes the data set Companion files that describe project, protocols, or field sites (photographs) –Material from Project Web site or Wiki 4

5  Exploration and Distribution –provide tools to explore, access, and extract data  Post-Project Data Support –provide long-term secure archiving –serve as a buffer between end users and PIs –provide usage statistics  Stewardship –security, disaster recovery –migration to new computer systems Data Center: Stewardship and Archive Functions  Ingest –perform QA checks –compile project-provided metadata –generate additional metadata –convert to archival file formats  Metadata / Documentation –prepare final metadata record and documentation  Archive / Publish − generate citation and DOI (digital object identifier)

NACP Best Data Management Practices, February 3, 2013 Workshop Goal Provide fundamental data management practices that investigators should perform during the course of data collection. 6 To improve the usability of data sets You Collaborators People outside your project By following the practices taught in this workshop, your data will be less prone to error, more efficiently structured for analysis, and more readily understandable for any future research.

7 Workshop Sponsors