These resources are specifically geared towards the Earth & Environmental Sciences.
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.
- connect the importance of proper data mangement with the continuation of the research lifecycle
- develop a consistent file naming system that is descriptive, but also succinct
- construct a file directory structure to logically organize research-related files
- identify elements of metadata and documentation to include with research data files
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:
 Workflow refers to the process you follow in collecting, cleaning, analyzing, and visualizingyour 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.
 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).
 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.
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