It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
The Pepperdine University Libraries are here to support your efforts in data management by providing the resources to help preserve, document, manage, and publish your data as you travel through the research life-cycle.
What is Data?
In this context, data is described as units of information observed, collected, or created during the course of research. Data typically falls into one of four categories;
Observational - Captured in real-time and usually irreplaceable. Examples include: sensor readings, telemetry, survey results, and images.
Experimental - Data from lab equipment, often reproducible, but can be expensive. Examples include: gene sequences, chromatograms, and magnetic field readings.
Simulation - Data generated from test models. Examples include: climate models and economic models.
Derived or Compiled - Reproducible (but very expensive). Examples include: text and data mining, compiled databases, and 3D models.
Lifecycle of Data Management
Source: Data Documentation Initiative (DDI) Technical Specification, Part I: Overview, Version 3.0, April 2008, http://www.ddialliance.org.
Why Do I Need A Data Management Plan?
Planning for data management early on will make curation activities much easier throughout the data lifecycle.
To comply with the federal agencies proposal requirements.
To describe how data will be maintained and what resources will be needed to preserve it.
To have a well described and organized data when posting a supplemental data set with your publication.
To prepare data for review before and/or after the article is accepted for publication, as required by some publishers.
To facilitate the re-use of data sets, open access, and data sharing.
Data Can Take on Many Formats:
This is not limited to scientific data; it includes social science statistical and ethnographic data, humanities texts, or any other data used or produced in the course of academic research, whether it takes the form of text, numbers, image, audio, video, models, analytic code, etc.
Text (flat text files, Word, PDF)
Numerical (SPSS, STATA, Excel, Acess, MySQL)
Multimedia (jpeg, tiff, dicom, mpeg, quicktime)
Models (3D, statistical)
Software (Java, C)
Domain-specific (FITS in astronomy, CIF in chemistry)
Instrument-specific (Olympus Confocal Microscope Data Format)