TitleBodyTechnical Expertise RequiredCostAdditional Information
Assign descriptive file names

File names should reflect the contents of the file and include enough information to uniquely identify the data file. File names may contain information such as project acronym, study title, location, investigator, year(s) of study, data type, version number, and file type.

When choosing a file name, check for any database management limitations on file name length and use of special characters. Also, in general, lower-case names are less software and platform dependent. Avoid using spaces and special characters in file names, directory paths and field names. Automated processing, URLs and other systems often use spaces and special characters for parsing text string. Instead, consider using underscore ( _ ) or dashes ( - ) to separate meaningful parts of file names. Avoid $ % ^ & # | : and similar.

If versioning is desired a date string within the file name is recommended to indicate the version.

Avoid using file names such as mydata.dat or 1998.dat.

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.

Describe format for spatial location

Spatial coordinates should be reported in decimal degrees format to at least 4 (preferably 5 or 6) significant digits past the decimal point. An accuracy of 1.11 meters at the equator is represented by +/- 0.00001. This does not include uncertainty introduced by a GPS instrument.

Provide latitude and longitude with south latitude and west longitude recorded as negative values, e.g., 80 30' 00" W longitude is -80.5000.

Make sure that all location information in a file uses the same coordinate system, including coordinate type, datum, and spheroid. Document all three of these characteristics (e.g., Lat/Long decimal degrees, NAD83 (North American Datum of 1983), WGRS84 (World Geographic Reference System of 1984)). Mixing coordinate systems [e.g., NAD83 and NAD27 (North American Datum of 1927)] will cause errors in any geographic analysis of the data.

If locating field sites is more convenient using the Universal Transverse Mercator (UTM) coordinate system, be sure to record the datum and UTM zone (e.g., NAD83 and Zone 15N), and the easting and northing coordinate pair in meters, to ensure that UTM coordinates can be converted to latitude and longitude.

To assure the quality of the geospatial data, plot the locations on a map and visually check the location.

Describe formats for date and time

For date, always include four digit year and use numbers for months. For example, the date format yyyy-mm-dd would appear as 2011-03-15 (March 15, 2011).

If Julian day is used, make sure the year field is also supplied. For example, mmm.yyyy would appear as 122.2011, where mmm is the Julian day.

If the date is not completely known (e.g. day not known) separate the columns into parts that do exist (e.g. separate column for year and month). Don't introduce a day because the database date format requires it.

For time, use 24-hour notation (13:30 hrs instead of 1:30 p.m. and 04:30 instead of 4:30 a.m.). Report in both local time and Coordinated Universal Time (UTC). Include local time zone in a separate field. As appropriate, both the begin time and end time should be reported in both local and UTC time. Because UTC and local time may be on different days, we suggest that dates be given for each time reported.

Be consistent in date and time formats within one data set.

Describe method to create derived data products

When describing the process for creating derived data products, the following information should be included in the data documentation or the companion metadata file:

  • Description of primary input data and derived data
  • Why processing is required
  • Data processing steps and assumptions
    • Assumptions about primary input data
    • Additional input data requirements
    • Processing algorithm (e.g., volts to mol fraction, averaging)
    • Assumptions and limitations of algorithm
    • Describe how algorithm is applied (e.g., manually, using R, IDL)
  • How outcome of processing is evaluated
    • How problems are identified and rectified
    • Tools used to assess outcome
    • Conditions under which reprocessing is required
  • How uncertainty in processing is assessed
    • Provide a numeric estimate of uncertainty
  • How processing technique changes over time, if applicable
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
Describe the research project

The research project description should contain the following information:

  • Who: project personnel (principal investigator, researchers, technicians, others)
  • Where: location and description of study site or sites
  • When: range of dates for the project
  • Why: rational for the project (abstract)
  • How: description of project methods

Other useful information might include the project title, the overarching project (if any), institution(s) involved, and source of funding.

Describe the units of measurement for each observation

The units of reported parameters need to be explicitly stated in the data file and in the documentation. We recommend SI units (The International System of Units) but recognize that each discipline has its own commonly used units of measure. The critical aspect here is that the units be defined so that others understand what is reported.

Do not use abbreviations when describing the units. For example the units for respiration are moles of carbon dioxide per meter squared per year.

Document steps used in data processing

Different types of new data may be created in the course of a project, for instance visualizations, plots, statistical outputs, a new dataset created by integrating multiple datasets, etc. Whenever possible, document your workflow (the process used to clean, analyze and visualize data) noting what data products are created at each step. Depending on the nature of the project, this might be as a computer script, or it may be notes in a text file documenting the process you used (i.e. process metadata). If workflows are preserved along with data products, they can be executed and enable the data product to be reproduced.

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/)