1. Graduate
Office
Student
Success
Series
GOOD DATA
PRACTICES FOR
RESEARCH
January 12, 2012
Heather Coates, MLS, MS | Digital Scholarship & Data Management Librarian

2. CONTEXT: DATA LIFECYCLE
Source: DDI Structural Reform Group. “DDI Version 3.0 Conceptual Model." DDI Alliance. 2004. Accessed on 11 August 2008.
<http://www.icpsr.umich.edu/DDI/committee-info/Concept-Model-WD.pdf>.

3. OVERVIEW
Planning
Describing the data
Handling data files
Storage & backup

4. PLAN AHEAD: BEYOND THE PROTOCOL
Plan early, before data collection
 Identify ethical and legal issues
 Define the data model
 Think about a data organization strategy
 Identify the most appropriate tools: instruments & software

5. ETHICAL & LEGAL ISSUES
 Privacy
 Are there people (human subjects) involved in your project? Animals?
 Does the study involve personal or health information? Can it be used to
identify an individual?
 Copyright
 Are you using copyrighted data?
 Have you sought permission?
 Intellectual Property
 You should cite any product that you use for your project: data,
publications, software, etc.

6. DESCRIBING YOUR DATA
 Describe the research project
 Describe overall organization of your dataset
 Describe your data files
 Describe the methods used to create your data
 Describe measurement techniques (protocols, instruments)
 Data processing – why, how, assumptions
 Sensor network, taxonomic information, spatial location
 Choose & use standard terminology (concepts, methods, tools)
 Identify and use relevant metadata standards
 Data citation
 Describe the timeframe

7. HANDLING DATA FILES
 Create, manage, and document your data storage system
 Use descriptive file names
 Define
 Formats for date and time
 Units of measurement
 Parameters
 Missing code values
 Values that are estimated
 Use consistent codes
 Use appropriate field delimiters
 Store data values separately from data annotations or notes
 Store data at the right level of precision
 Quality assurance & data integrity
 Version control & authenticity

8. STORAGE & BACKUP
 Backup your data: regular intervals, 3 copies
 Local
 Semi-local
 Remote
 Document your backup strategy
 Make sure backup locations are secure and accessible
 Use standard file formats
 Non-proprietary, open format
 Commonly used in your community
 Unencrypted*
 Uncompressed*

9. PROCESSING & ANALYSIS
 Defining your research questions and documenting your data are
iterative processes
 Inform each other
 Are never done, until the project is complete
 Developing good documentation will make analysis easier and more
efficient
 Having good documentation will make writing your
paper/thesis/dissertation much easier
 Use your readme or codebook files as source documents for your
methods sections
 Having good documentation will identify problems sooner, when
it may be possible to resolve them or minimize the damage to
your data

10. RESOURCES
 @IU
 IUWare
 IUanyWARE
 StatMath
 ITTraining
 RFS & SDA
 Open access/public use data sets
 DataCite
 ICPSR
 Data.gov
 Subject liaison librarians can assist in locating data on your topic

11. THANK YOU
Find us at http://ulib.iupui.edu/digitalscholarship
Heather Coates, MLS, MS
Digital Scholarship & Data Management Librarian
hcoates@iupui.edu
317-278-7125