TitleBodyTechnical Expertise RequiredCostAdditional Information
Create a data dictionary

A data dictionary provides a detailed description for each element or variable in your dataset and data model. Data dictionaries are 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.

Create, manage, and document your data storage system

Data files should be managed to avoid disorder. To facilitate access to files, all storage devices, locations and access accounts should be documented and accessible to team members. Use appropriate tools, such as version control tools, to keep track of the history of the data files. This will help with maintaining files in different locations, such as at multiple off-site backup locations or servers.

Data sets that result in many files structured in a file directory can be difficult to decipher. Organize files logically to represent the structure of the research/data. Include human readable "readme" files at critical levels of the directory tree. A "readme" file might include such things as explanations of naming conventions and how the structure of the directory relates to the structure of the data.

Describe the contents of data files

A description of the contents of the data file should contain the following:

  • Define the parameters and the units on the parameter
  • Explain the formats for dates, time, geographic coordinates, and other parameters
  • Define any coded values
  • Describe quality flags or qualifying values
  • Define missing values
Document taxonomic information

Identification of any species represented in the data set should be as complete as possible.

  • Use a standard taxonomy whenever possible
  • Full taxonomic tree to most specific level available
  • Source of taxonomy should accompany taxonomic tree (if available)
  • References used for taxonomic identification should be provided, if appropriate (e.g. technical document, journal article, book, database, person, etc.)

Examples of standardized identification systems:

  • Integrated Taxonomic Information System (http://www.itis.gov/)
  • Species 2000 (http://www.sp2000.org/)
  • USDA Plants (http://plants.usda.gov/index.html)
  • Global Biodiversity Information Facility (http://www.gbif.org/informatics/name-services/using-names-data/)
Maintain consistent data typing

Choose the right data type and precision for data in each column. As examples: (1) use date fields for dates; and (2) use numerical fields with decimal places precision. Comments and explanations should not be included in a column that is meant to include numeric values only. Comments should be included in a separate column that is designed for text. This allows users to take advantage of specialized search and computing functionality and improves data quality. If a particular spreadsheet or software system does not support data typing, it is still recommended that one keep the data type consistent within a column and not mix numbers, dates and text.

Separate data values from annotations

A separate column should be used for data qualifiers, descriptions, and flags, otherwise there is the potential for problems to develop during analyses. Potential entries in the descriptor column:

  • Potential sources of error
  • Missing value justification (e.g. sensor off line, human error, data rejected outside of range, data not recorded
  • Flags for values outside of expected range, questionable etc.
Understand the geospatial parameters of multiple data sources

Understand the input geospatial data parameters, including scale, map projection, geographic datum, and resolution, when integrating data from multiple sources. Care should be taken to ensure that the geospatial parameters of the source datasets can be legitimately combined. If working with raster data, consider the data type of the raster cell values as well as if the raster data represent discrete or continuous values. If working with vector data, consider feature representation (e.g., points, polygons, lines). It may be necessary to re-project your source data into one common projection appropriate to your intended analysis. Data product quality degradation or loss of data product utility can result when combining geospatial data that contain incompatible geospatial parameters. Spatial analysis of a dataset created from combining data having considerably different scales or map projections may result in erroneous results.

Document the geospatial parameters of any output dataset derived from combining multiple data products. Include this information in the final data product's metadata as part of the product's provenance or origin.