2017 Joint NACP / Ameriflux Meeting, Bethesda, MD
March 26, 2017
Abstract
Scientists spend considerable time conducting field studies and experiments, analyzing the data collected, and writing research papers, but an often overlooked activity is effectively managing the resulting data. The purpose of this workshop is to provide guidance on fundamental data management practices that investigators should perform during the course of data collection to improve the usability of their data sets. The target audience is early career scientists (graduate students, post-docs), but is open to any researchers who would benefit from developing better data management skills. Faculty members who would like to include exercises on best practices for preparing data as part of their curricula are encouraged to attend. Topics covered will include data structure, quality control, and data documentation. We will also discuss the elements of an effective data management plan for use in grant proposals and project planning. Workshop participants should bring their own laptop to participate in hands-on activities and are encouraged to bring their own data sets, which instructors will assist in organizing. 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 questions that they might help address.
Workshop Agenda
| 1. Introduction & Ten principles of data management | Alison Boyer, ORNL | 1:00 |
| 2. How to write a data management plan | Debjani Deb, ORNL | 2:00 |
| Break | 2:30 - 2:45 | |
| 3. Standards and tools to access and visualize data | Yaxing Wei, ORNL | 2:45 |
| 4. Archiving data with ORNL DAAC | Debjani Deb, ORNL | 3:30 |
| 5. Writing a readme file / Open Q&A | Workshop team | 4:00 |
| Closing | Alison Boyer, ORNL | 4:45 |
Example readme files:
HU_README_insitu_ch4_co2_co_2013.txt
readme_Happy_Valley.txt
readme_peatland_type_fort_mcmurray_east.txt
Other Materials:
DAAC_csv_standard_20170222.pdf
CF-compliance_guidelines.pdf
Outline_for_Data_Management_Plan.pdf
Example Jupyter notebooks with R and Python:
opendap_demo_r.pdf
opendap_demo_r.ipynb
opendap_demo_python.pdf
opendap_demo_python.ipynb
Past Workshops: http://daac.ornl.gov/resources/tutorials/workshops.shtml