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Earth & Environmental Sciences - Data Management: 5/7/2013 Retreat

This guide includes Data Management materials presented to Earth & Environmental Sciences graduate students on the Rutgers-Newark campus.

Metadata Creation Tools

Additional Learning Resources

These resources are specifically geared towards the Earth & Environmental Sciences.

Goals for EES Graduate Student Retreat - May 2013

For the May 2013 Dept. of Earth & Environmental Sciences Graduate Students Retreat, the focus was on organizing research data so that it would be easier to find in the future. 

  • Learning Objectives include being able to:
  1. connect the importance of proper data mangement with the continuation of the research lifecycle
  2. develop a consistent file naming system that is descriptive, but also succinct
  3. construct a file directory structure to logically organize research-related files
  4. identify elements of metadata and documentation to include with research data files
  • Slides from the session are available on the left.
  • Also along the left side are Additional Learning Resources to help with understanding how to manage research data in Earth & Environmental Sciences.

Data Management Assignment in Preparation for Retreat

1)    Bring a laptop, if you have one.

2)    Bring along your research data files.  We will discuss them during our workshop.

3)    Prepare a 3 minute presentation answering the following:

  • How are you currently dealing with your research data? Describe your workflow[1], including data products created at each step of the way. Consider using a diagram to help your audience visualize it better.
  • What strategies do you use to organize your research data so that you can efficiently find it again later? (e.g., file structure and filing naming)
  • What documentation (e.g., metadata[2], data dictionary[3], information about how the data was processed/analyzed) is included along with your research data? In what format is this information presented? What metadata standards, if any, are being followed?
  • If you create your own analysis code (e.g., MATLAB script), how do you ensure it is something that someone else will understand to the point that s/he will be able to repeat your analysis?

If you have any questions, please feel free to contact Minglu Wang, Data Services Librarian (minglu@rutgers.edu) / Bonnie Fong, Physical Sciences Librarian (bonnie.fong@rutgers.edu).



[1] Workflow refers to the process you follow in collecting, cleaning, analyzing, and visualizing your data. Different types of new data may be created throughout the course of a project – e.g., visualizations, plots, statistical outputs, a new dataset created from the integration of multiple datasets, etc.

[2] Metadata is data that provides descriptive information (e.g., content, context, quality, structure, and accessibility) about a data product. It enables others to search for and use the data product. Metadata format is standardized structure and consistent content for metadata, usually in machine readable extensible markup language (XML) that can be represented in other human readable formats (e.g., HTML, PDF, etc.). Metadata standards are requirements for metadata documentation and are intended ensure the correct use and interpretation of data by its owners and users. Different scientific communities use different sets of metadata standards; common examples are EML (Ecological Metadata language), FGDC (Federal Geographic Data Committee) standard, and ISO 19115 (International Organization for Standardization Geographic information metadata).

[3] A data dictionary provides a detailed description for each element or variable in your dataset and data model. It is used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description. A data dictionary provides a concise guide to understanding and using the data.

Physical Sciences Librarian

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Bonnie Fong
Contact:
John Cotton Dana Library

185 University Ave

Newark, NJ 07102-1814

(973) 353-3811
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