Documentation for Sevilleta LTER Datasets
Comprehensive metadata are necessary to support data quality control, efficient dataset archival and retrieval, and functional re-use of the data both by owners and secondary users. The Sevilleta LTER requires complete documentation for long-term maintenance and distribution of study databases. Metadata elements requested in this form are consistent with elements required by the Ecological Metadata Standard (EML), which was adopted by the LTER Coordinating Committee in 2004.
Sevilleta metadata consists of information about:
1) Why the study was done
2) Who is involved with study design, data collection, analysis and data management
3) Detailed study methods, so that a secondary user will be able to understand what was done without contacting the study principal investigator.
4) Where the study was done, with GPS points so the site can be relocated.
5) Detailed variable information
Please fill in this metadata form to the best of your ability. If you have questions, please contact the Sevilleta Information Manager, Kristin Vanderbilt, at 277-2109 or
METADATA TEMPLATE FOR SEVILLETA LTER DATA SETS
1] Data Set Code: {Information manager will enter this field}
2] Data Set Title:
3] Abstract:
4] Keywords: (List for each category. Separate with commas.)
*Location: SEV, Sevilleta National Wildlife Refuge, New Mexico,
*Theme: LTER,
*Habitat:
*Taxonomic:
5] When the samples/data were collected: (Enter date as a single date –and/or- a range of dates in format mm/dd/yyyy. If you only know the year enter the data as 01/01/2008. Copy and paste these elements for multiple date ranges or single dates. It is OK to have a combination of Date Range and Single Date entries.)
Date Range: Begin Date: End Date:
Date Range: Begin Date: End Date:
Or
Single Date:
Single Date:
5a] More information about when the data were collected: {If there was some information in the “When the samples/data were collected:” section that you couldn’t express as single dates, or a range of dates, copy that information into this section. It will be preserved in EML with the formatting that it has}
6] Who is Involved with the Samples/Data: (Enter names as LastName, FirstName; LastName, FirstName. Separate names with a semicolon. (e.g., Gosz, Jim; Parmenter, Robert)
Principle investigator(s):
Field Crew:
Data Manager:
Contact:
Position: Data Manager
Delivery Address: Department of Biology, Castetter Hall 167, University of New Mexico
City: Albuquerque
State: NM
ZipCode: 87131
Email:
Phone: (505) 277-2109
Publisher:
Organization: Sevilleta LTER
Delivery Address: Department of Biology, Castetter Hall 167, University of New Mexico
City: Albuquerque
State: NM
ZipCode: 87131
URL: http://sev.lternet.edu
6a] More information about who is involved with the samples/data: {If there was some information in the “Who is involved with the samples/data:” section that didn’t fit into the form fields in 6], please enter it here. It will be preserved in EML with the formatting that it has.}
7] Where the Data were Collected: {Enter the list of core sites where the data were collected. Separate sites by a comma. Core sites are: Deep Well, Five Points Grass, Five Points Creosote, Blue Grama, Sepultura Canyon, Cerro Montoso, Goat Draw, Black Butte, Sevilleta Field Station, Bronco Well, Rio Salado Grass, Rio Salado Creosote, Red Tank. If your data were not collected at one of the core research sites, enter information about your site in 7a]}
Sites:
7a] Additional Geographic Metadata: {If your data were not collected at one of the Sevilleta Core Sites in the dropdown list in 7], then enter specific data for your sites in the fields below. For studies that contain several sampling locations, give specific site characteristics information about each location. Copy and Paste more Study Area text blocks if you have more than two sites. If you do not know the coordinates of your site, then enter the coordinates for the bounding box of the Sevilleta: North: 34.42, South = 34.19, East = -106.513, West = -107.08. COORDINATES MUST BE ENTERED in DECIMAL DEGREES AND NAD83. There is a degrees/minutes/seconds to decimal converter at http://www.fcc.gov/mb/audio/bickel/DDDMMSS-decimal.html.
Study Area 1:
*Study Area Name:
*Study Area Location:
*Study Area Description:
Elevation:
Landform:
Geology:
Soils:
Hydrology:
Vegetation:
Climate:
Site history:
*GPS coordinates in decimal degrees: Either describe a bounding box, -or- give a point location for the study sites. Use NAD83 and decimal degrees.
Bounding Box:
North Coordinate:
South Coordinate:
East Coordinate:
West Coordinate:
Single Point:
North Coordinate:
West Coordinate:
Study Area 2:
*Study Area Name:
*Study Area Location:
*Study Area Description:
Elevation:
Landform:
Geology:
Soils:
Hydrology:
Vegetation:
Climate:
Site history:
*GPS coordinates in decimal degrees: Either describe a bounding box, -or- give a point location for the study sites. Use NAD83 and decimal degrees.
Bounding Box:
North Coordinate:
South Coordinate:
East Coordinate:
West Coordinate:
Single Point:
North Coordinate:
West Coordinate:
8] How the Data were Collected: {Describe methods and instrumentation used in the study. Press F1 in any field for help. For each bullet under *Methods, enter a *, the title of the method, a colon, and then a description of the method. Copy and paste blocks of instrument fields if you used more than two instruments.
Example:
*Methods:
*Experimental design: Randomized complete block design.
*Setting up plots: Plots were selected by …..
*Sample Plant Cover: A 100cm X 100 cm quadrat was placed ….}
* Methods:
* Instrument Name:
* Manufacturer:
* Model Number:
* Instrument Name:
* Manufacturer:
* Model Number:
9] Variable Descriptions: Each variable description should include the following information:
Variable X:
* Name: The label on the column in the data set (e.g. NO3_Conc)
* Label: Enter a more descriptive label (e.g. NO3 Concentration)
* Definition: A description of what the variable is: (e.g. Nitrate concentration in water sample.)
* Data Type: Nominal is used to represent named categories, a list of coded values, or plain text descriptions; Ratio measurements are numerical (e.g. degrees, concentration, meter); Datetime should be used for variables that are dates, days, months, years, etc.
* Units of Measure: Leave blank if the variable is nominal (categorical); otherwise enter the units of measure (e.g. ppm, g/ml, m, mm/dd/yyyy)
* Precision of Measurements: Example: 0.001 means precision to the thousandths. Leave blank if the variable is nominal. Other possible entries could be “1” for one stem, or “1” for one year
* Range or List of Values: List the codes and their meanings (e.g. CM = Cerro Montosa, DW = Deep Well) for categorical variables. Separate codes by commas. If the variable is nominal, but the description is something like plots number 1 – 100 (and you don’t want to enter 1 = plot 1, 2 = plot 2) then leave this blank. Example: The variable is Plot_Number, and you have 500 plots. Fill in the variable information as follows:
Variable X
* Name: Plot_Number
* Label: Plot Number Associated with Data
* Definition: Plot identification numbers ranging from 1 to 500
* Data Type: Nominal
* Units of Measure:
* Precision of Measurements:
* Range or List of Values:
* Missing Data Code:
* Computational Method for Derived Data:
*Minimum Value: For nominal or datetime variables, enter the minimum value.
*Maximum Value: For nominal or datetime variables, enter the maximum value.
* Missing Data Code: Enter the code, followed by an =, followed by the code definition.
Example for numeric data: -888 = data was not collected during this sampling interval, -999 = data was not collected due to human error; Other symbols such as . (a period) can be used for missing categorical data. Leave blank if there are no missing data for this variable. If there is more than one code used, separate them with commas.
* Computational Method for Derived Data: Example: Plant volume = cover * height. Leave blank if there was no computational method.
Variable 1:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 2:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 3:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 4:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 5:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 6:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 7:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 8:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 9:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 10:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 11:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 12:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 13:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 14:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 15:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 16:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 17:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 18:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 19:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
Variable 20:
*Name:
*Label:
*Definition:
*Data Type:
*Units of Measure:
*Precision of Measurements:
*Range or List of Values:
*Minimum Value:
*Maximum Value:
*Missing Data Code:
*Computational Method for Derived Data:
10] QA/QC Procedures? {Describe how the data were checked for accuracy}
11] Additional metadata:
12] Distribution: {Information Manager will fill in}
13] Intellectual Rights:
Any Sevilleta LTER data set and accompanying metadata can be used for academic, research, and other professional purposes. Permission to use the data is granted to the Data User subject to the following terms: Data User will: 1) notify the designated contact (e.g., Principle Investigator or Data Set Contact) when any derivative work based on or derived from the data and documentation is distributed; 2) notify users that such derivative work is a modified version and not the original data and documentation distributed by the Sevilleta LTER; 3) not redistribute original data and documentation; 4) acknowledgethe support of the Sevilleta LTER and appropriate NSF Grant numbers in any publications using these data and documentation. (e.g. Data sets were provided by the Sevilleta LTER Data Bank. Funding for these data was provided by the National Science Foundation Long-Term Ecological Research program (NSF Grant numbers BSR 88-11906, DEB9411976, DEB0080529, DEB0217774); and 5) send two reprints of any publications resulting from use of the data and documentation to the following address: Sevilleta LTER Program Attn: Information Manager, Department of Biology, MSC03 2020, University of New Mexico, Albuquerque, NM 87131
14] LTER Project Information:
Title: Sevilleta Long Term Ecological Research (LTER) Project
Personnel:
GivenName: Will
SurName: Pockman
Delivery Address: Department of Biology, University of New Mexico
City: Albuquerque
State: NM
ZipCode: 87131
Email:
Phone: (505) 277-6303
Role: Principal Investigator
Abstract: The overarching goal of the Sevilleta LTER is to understand how abiotic pulses and constraints affect dynamics and stability in an arid landscape. Key landscape components of the Sevilleta LTER include desert grassland and shrubland, piñon-juniper woodlands and the Middle Rio Grande riparian corridor.
Distribution: http://sev.lternet.edu
Funding: NSF grants BSR 88-11906, DEB 9411976,DEB 0080529 and DEB 0217774
15] Maintenance: Note any changes to the data file here that result in a new version of this metadata document being generated, such as the addition of another year of data. Please enter the date of your update, your name, and then describe the changes to the file.