DATA MANAGEMENT
Supplement to the Clean Water Act Section 106 Tribal Guidance
Track 2 CAFO: Chemical Assessment FY 2006
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Table of Contents
Introduction 1
What is the purpose of this tribal data management document? 1
Data Management 3
I. What Is Data Management? 3
II. Data Management Procedures 4
III. Data Types 6
IV. Data Management Format s 9
STORET-Compatible Data Submissions 12
I. Getting Your Data Into a STORET-Compatible Format 12
II. Ways to Submit Data 16
III. Retrieving Data from the STORET Warehouse 19
IV. Ongoing Support 20
ATTACHMENT A: Examples of Tribal Data Management 21
ATTACHMENT B: Glossary 26
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Data Management
Supplement to the Clean Water Act Section 106 Tribal Guidance
Introduction
What is the purpose of this tribal data management document?
In October 2006, the United States Environmental Protection Agency (EPA) issued the Final
Guidance on Awards of Grants to Indian Tribes under Section 106 of the Clean Water Act
(Tribal 106 Guidance). The Tribal 106 Guidance provides a flexible framework for tribes and
EPA regions to ensure that tribes' water quality program goals are met while being consistent
with Section 106 of the Clean Water Act (CWA), the Governmental Performance and Results
Act (GPRA), and other federal requirements. The Tribal 106 Guidance outlines new water
quality reporting requirements and data expectations for all tribal programs receiving 106 funds.
These requirements will help tribes collect and manage critical data and information for effective
management of water quality programs, to measure environmental results, and report them to
EPA.
As discussed in Chapter 8 and Appendix A of the Tribal 106 Guidance, EPA requires that tribes
submit to EPA a tribal Assessment Report that contains information about water quality on tribal
land and demonstrates national results for the Section 106 Tribal program. The Assessment
Report consists of: (1) a description of the tribe's monitoring strategy; (2) a water quality
assessment; and (3) surface water quality data submitted electronically in a STORET-
compatible format that includes metadata for at least the nine basic parameters that are being
monitored (dissolved oxygen, pH, water temperature, turbidity, total phosphorus, total nitrogen,
macroinvertebrates, E. coli or enterococci, basic habitat information), as required considering the
Tribal 106 Guidance and a tribal water quality program's level of sophistication.
This document is intended to supplement the Tribal 106 Guidance by providing useful
suggestions and tips to tribes about how to establish a data management system that reflects
tribal water quality goals and objectives, and manage the system so that the data is accessible and
reliable. As a result of good data management, tribes are empowered to use the data to
determine the physical, chemical, and biological conditions of a water body so water quality
trends can be identified and measured to ensure that water quality can be maintained and/or
improved. An organized and thorough data management program provides the foundation for
measuring and achieving water quality environmental results while helping tribes meet the EPA
data reporting requirements. Attachment A provides examples of existing tribal management
programs. Attachment B provides a glossary of terms used throughout this document.
This document provides basic data management principles that will be helpful to all tribes and all
water quality programs, whether their programs are at the fundamental, intermediate, or mature
level as defined by the Tribal 106 Guidance.
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Two additional templates have been created for assisting tribes in developing monitoring
strategies and performing data analysis and assessment1. These resources are available to assist
tribal programs in meeting the tribal Assessment Report requirements. The data management
principles presented in this document are meant to assist tribes with getting data into a usable
format in order to begin putting together data assessments (statistics, data summaries, etc). This
document is not intended as a tutorial for data submission to EPA, however information about
data submission to EPA and support for submission is included in the document.
This document has been reviewed and approved for publication by EPA. Mention of trade
names or commercial products or services does not convey, and should not be interpreted as
conveying, official EPA approval, endorsement, or recommendation. Any use of words such as
"should" or "may" are not intended to be mandatory language, rather, these are statements of
suggestion that a tribal program may or may not adopt depending upon available resources.
1 EPA Tribal Monitoring Strategy and Assessment Workgroup. 2008. Developing a Tribal Water Monitoring Strategy:
Supplement to the Clean Water Act Section 106 Tribal Guidance.
EPA Tribal Monitoring Strategy and Assessment Workgroup. 2008. Data Assessment and Reporting: Supplement to the Clean
Water Act Section 106 Tribal Guidance.
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Data Management
I. What Is Data Management?
Data management is the process of reporting and storing the information you collect as part of
your water quality monitoring program. It helps you store information in a format that allows
you to manipulate, summarize, and analyze your results to make decisions. Managing data
properly does take time, but it will save you time in the end and help you maintain and present
accurate information about water quality in your area. Data management is a crucial part of a
monitoring strategy2 and can be a way to integrate your thinking about many aspects of your
work.
Figure 1 shows that the ultimate goal of monitoring water quality is to protect traditional tribal
lifeways. It also shows that there are many necessary components that are part of accomplishing
that goal - of which good data management is one. Without the components in the highlighted
box (data management) the steps could not be followed. In this process, tribal data can be shared
with others but that does not need to dictate the process of management.
Figure 1. Components Contributing to Protection of Traditional Tribal Lifeways
" EPA Tribal Monitoring Strategy and Assessment Workgroup. 2008. Developing a Tribal Water Monitoring Strategy:
Supplement to the Clean Water Act Section 106 Tribal Guidance.. (Please refer to Appendix A of the Tribal 106 Guidance as
well.)
Page 3 of 28
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Water quality monitoring data generally consist of the following information:
1. Where a sample or measurement was collected
2. When it was collected
3. What was collected
4. How it was collected
5. Why it was collected
6. Who collected it
Data management ensures that this information is accessible and understandable. The purpose of
data management is to convert your monitoring results into a useful set of data to use for making
decisions about your water quality monitoring program and meet tribal water quality goals.
II. Data Management Procedures
Data management procedures are processes for storing and sharing your water quality
monitoring data in a controlled and standard way. Take a step back to consider the goals of your
water quality monitoring program. Any decisions you can make in advance of managing your
data can help you develop standard procedures and save time. For example, you may want to
have a meeting with others in your group who collect water quality monitoring data, your lab, or
your manager to discuss ways to report the data so that information can be documented in similar
ways over time. This will avoid confusion when looking at past information, and prevent the
need to review procedures to make them consistent. Coordinating with your laboratory will allow
it to report data in formats that are helpful and consistent for you. These decisions and meetings
will help develop effective data management to help you understand your data over time and will
help any new people that are hired in your organization to understand the data.
To determine what data management procedures may be right for you, ask yourself the following
questions:
1. What data are currently collected?
- Some examples may be:
¦ Field measurements for temperature, pH, and turbidity on field sheets
¦ Continuous data from YSI or other data loggers from lakes and streams
monitoring locations
¦ Water chemistry sample results from a lab
¦ Benthic macro invertebrates counts or fish tissue sample results
2. What data are being recorded?
- Some examples may be:
¦ Results for the core and supplemental water quality indicators (see page 4-10 of
the Tribal 106 Guidance) being used to meet monitoring strategy goals and
objectives
¦ Field sheet result values
¦ Metadata for your results, such as the methods you used in collected or analyzing
a sample
Page 4 of 28
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¦ Locational information for
sampling stations
3. How will field and laboratory data be
handled?
- Some examples may be:
¦ Field data are entered on a field
sheet at the station, then these data
are entered in a spreadsheet and the hard copy field sheet is filed away
¦ Field data are entered using a portable digital data entry form and then directly
uploaded to your database
¦ Results from a lab may be sent back to you in a standard formatted spreadsheet
- These processes may already be documented in your Quality Assurance Project Plan
(QAPP) or QAPP Standard Operating Procedures (SOPs)
4. How will data be entered and validated?
- Some examples may be:
¦ Field data are entered in a
standard data spreadsheet that has
consistent headings and naming conventions
¦ A staff member enters the field results data from each station visited on a certain
day into the program database, and the field results are linked via a unique
identifier assigned to them to the sample results that will come back from the lab
¦ A second staff member produces a report from the program database and cross
checks the data values with those received from the lab
- These processes may be documented in your QAPP or QAPP SOPs
5. How will data be managed to make them useful for their intended purpose?
- Some examples may be:
¦ Data are within a database that multiple staff members can access from their
computers using a user name and password
¦ Data that are of questionable quality is flagged within a comment field in the
spreadsheet so that when data analysis is done, these data can be carefully
considered
6. How many people are performing data entry or data management?
- Some examples may be:
¦ All staff members need access to a single database that includes monitoring data
for all types of monitoring sites
¦ Certain staff handle biological and habitat data while others handle lab data so
separate spreadsheets are kept
¦ A spreadsheet tracks who has entered the data and at what time, so that others
know if an entry has been changed.
7. Who needs to know information about the data and how do they want to see the
information?
- Some examples may be:
Metadata are data that are supplemental
to your data, or "data about data For
example, the result values receivedfrom
your lab are data, while information like
the time the sample was taken and the
sample collection procedure are both
metadata.
Note: An EPA-approved QAPP must be in
place before a tribe can take
environmental measurements using EPA
project funds.
Page 5 of 28
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¦ Tribal council members want a report and a graph showing how water
temperature and flow have been changing over the past 2 years
¦ Staff putting together the tribal Assessment Report need raw continuous pH data
to determine if levels have spiked
¦ Reports to EPA that require certain information in a particular format.
Working through the above questions will help you decide which procedures you will need to
create and which tools you may need in order to manage your data. Once you have answered
these questions, you may want to develop written procedures for data handling, records
management, and documentation. For example, when you consider how field and lab data will be
handled, you may decide that you will enter data collected in the field into spreadsheets and keep
hard copies of field sheets in a filing cabinet. You may decide that the lab data will be analyzed
from an outside lab and that they will send you electronic copies of results that you will copy
into spreadsheets. In any case, you could set up a few procedures about how your water quality
monitoring program will operate and how you will record and track information. You could
come up with a standard format for spreadsheets so that the items are reported in a specific order
and names of what you are recording stay the same.
As far as records management, a good practice is to keep hard copies of all lab reports and field
sheets in a filing cabinet, and electronic copies of all files on a specific computer with backup
files on a CD, DVD, or external drive.
Once you have decided on what your data management
procedures are, one way to approach documentation is to
write out all the steps of your data management process
as if you were explaining them to a new person. You can
save and date the document and update it anytime your
procedures change. This will help you remember what
the procedures are and communicate them to others. For
example, if you collect your field data in the spring and
summer and produce a report to your tribal council each September, you can describe that
process in your documentation, and include a copy of the report you use each year.
III. Data Types
Data types describe the way organizations, such as tribes, generally categorize water quality
monitoring information. Your organization may not have these exact names for the same types of
data, but these are some examples of the concepts that you are probably already capturing or
should capture. These types of data influence the way you might organize your data within a
data management system. Here are some examples of the types of data related to water quality.
Some of these can be considered metadata, which contextualize monitoring data results, such as
who, why, where, and how.
1. Who you are
- Examples:
- An organization that collects water quality monitoring information
Note: It is considered a good
practice to keep archived backup
files in a physically different
building than your main hardcopy
or electronic files. This protects
your files against an unforeseen
disaster situation (e.g., fire, flood,
tornado, etc.)
Page 6 of 28
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- Name of staff member who was doing field measurements and taking water
samples
- Name of lab staff person who was analyzing a sample
2. Why you are collecting the data - An organization may have one or more projects to
collect data.
- Examples:
¦ Lakes profile study
¦ Wadeable stream condition study
¦ Little Northeastern Creek condition study
3. Where you are collecting data - Stations are places where monitoring activities occur.
- Examples:
¦ Atlantic Beach Near Gordan's Pond
¦ Collets Run, above confluence with Entry Run
¦ Chatfield In-Reservoir Near Dam
¦ Alluvial Well - Flying C Ranch
- Stations or monitoring locations have metadata associated with them. This may
include using a GPS device to record the latitude/longitude of the station, recording
directions to the station, or other overall observations of the station that may impact
sampling. Metadata are an important component of your data, as they provides
context for your monitoring activities and results.
4. How you collect data - Monitoring activities are conducted by an organization's staff.
- Examples:
¦ Field measurement (parameters measured in field using instruments, e.g.,
temperature or Secchi disk depth)
¦ Field observation (parameters observed in the field using the eye or ear, e.g.,
stream bank erosion)
¦ Routine sample (media collected in the field for analysis by the lab, such as a
water sample)
¦ Quality control (QC) sample (samples used to ensure that no contamination is
introduced during collection, transport or analysis of routine samples, - e.g., field
and lab blanks - or samples used to assess the variability in collection or analysis
methods, e.g., duplicates and splits)
¦ Integrated vertical profile sample (discrete sample derived from a continuous
vertical segment at your station, e.g., temperature measured at intervals from
surface to bottom of waterbody)
- Activities will have metadata associated with them. For example, the sampling
collection method used may be an important piece of information. Your QAPP or
SOPs will often contain this information.
5. What data you are collecting - Results are the data collected or produced from monitoring
activities.
- Examples:
¦ pH
¦ Total nitrogen
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¦ Fecal coliform
¦ Count of mayflies
¦ Undercut bank presence (% Rch)
- Results will have metadata associated with them. For example, the analytical method
that was used to attain a result value is an important piece of information that should
accompany results. Your QAPP will often contain this information.
6. What field data you are collecting - Field data are results that come from assessing onsite
water quality characteristics
- Examples:
¦ Water temperature
¦ pH
¦ Existing streambank erosion
7. What lab data you are collecting - Laboratory data are results that must be analyzed by a
lab.
Examples:
¦ Total phosphorous
¦ Total nitrogen
¦ E. Coli
8. How you check the data you are collecting - quality assurance (QA) or QC results are
another type of result data that are very important to capture, not only for overall data
quality, but for later data analysis. You may decide to track these types of results
separately from the regular results, while linking them via a common monitoring location
(see page 4-20 of the Tribal 106 Guidance for additional information on the QA/QC
process and tribal responsibilities).
9. What additional data do you collect - Assessment data, geographic information systems
(GIS) data, wildlife surveys, etc.
These data types may already drive how you organize and manage data overall. For example,
from year to year, your organization name will likely stay the same, although staff members may
change. Similarly, the project will likely stay the same from year to year. Many tribes organize
their information under one project. However, some tribes want to set up separate projects to
track information related to specific studies or funding sources. Stations or monitoring locations
often stay the same over time, but sometimes additional stations and water quality indicators will
be added as a tribal water quality environmental program becomes more sophisticated. The
monitoring activities and results are the data types that will change each time water quality data
are collected.
As you begin or continue to implement data management for these data types within
spreadsheets or a database, a few principles are important to keep in mind:
1. Required or optional data - within databases and spreadsheets, it is important to agree to
what types of data you will track and how much of these data will be required. For
example, you might always require the entry of the unit of measure for any result value.
Page 8 of 28
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Or, you might say that whenever a result value is not detected, a reason for the non-detect
must be given in a separate field. In a database, enforcing these rules is easy once you
have set them up. Spreadsheets may be documented so that all users can adhere to the
rules that have been defined by your program.
2. Common identifying conventions - it is an important practice to begin to use common
naming conventions, especially in tracking what types of water quality indicators or
parameters you are sampling. For example, if you are tracking dissolved oxygen by a
consistent name, it will be much easier to find, manipulate, and analyze all the
measurement values associated with this parameter. Another example may be making
sure a consistent naming scheme is in place for identifying all of your monitoring
stations. In a database, drop down lists based on valid values can make it very easy to
implement common naming conventions. You can also set up common values lists
within spreadsheets.
3. Standard data formats - similar to common identifying conventions, having standard
formats for certain data elements will also make it easier to manipulate data once you
have begun to analyze it. For example specifying that all dates need to have a format of
YYYY-MM-DD or that all latitude and longitude coordinates should be in decimal
degrees will make the data useful for all staff members. Be sure to know the exact
capabilities of the software you are using
before assuming that the data format you
define is absolute.
You may also be managing assessment data or GIS
data. Regardless of the types of data you currently
collect or plan to collect, you should list the data
types and determine how you will manage
information for each data type. The same data
management principles apply whether you are
managing water quality monitoring data,
assessment data, or GIS data.
IV. Data Management Formats
Figure 2 is a road map for you to determine your current level of data management and what
recommendations you can consider in fully implementing data management as you move
forward (e.g., Recommendation A, etc). There are many ways to manage your data, so you
should choose the one that works best for your organization now and in the long term. Each
format has advantages and disadvantages. But don't underestimate the potential for what seems
like less work now making far more work for you in the future as your program develops.
Some formats you may be currently using include:
1. Paper reports
2. Electronic documents, such as word processing or scanned documents
Water quality monitoring data consists
of field data or lab results associated
with sampling events at a monitoring
location.
Assessment data are decisions about
water quality that are made based on
comparisons of water quality monitoring
data against water quality standards.
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Recommendation A. Some tribes keep track of information in paper reports that are filed in
filing cabinets. Keeping papers can be relatively easy, especially if that is what you have been
doing previously and/or if labs mail you paper reports. It is also smart to keep paper copies of
important data and reports as a back-up system to data stored electronically. However, keeping
track of all of your data on paper makes it very difficult to analyze the data, make environmental
Page 10 of 28
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decisions, and share the information with your community. You also run a greater risk of losing
the data over time. You could also contact your lab and request that they provide data to you in
an electronic format specified by your environmental program. Once you get data in an
electronic format, you can expand your data management capabilities and use the data collected
to meet the water quality goals and objective of your environmental program.
Recommendation B. Other tribes keep track of information in electronic documents such as
Microsoft (MS) Word, Word Perfect, or PDF documents. The advantage of having electronic
documents is that they are easy to print and revise. Although electronic documents are helpful,
they are not very effective methods for managing data. It is difficult to find, manipulate, sort,
and analyze data when it is in a document format. If you maintain electronic documents with
your data, you may consider pasting or entering the data into a spreadsheet or database. Please
note that if you do transfer data from electronic documents to spreadsheets or a database, it is
important to do a QC check to ensure that the data were transferred correctly.
Recommendation C. Spreadsheets organize data in tables (rows and columns) that you
manually construct and can directly rearrange, sort, search, and use to perform many calculations
and statistical and graphical analyses. Several spreadsheet programs are available, such as MS
Excel. If you have all of your data in spreadsheets, your options begin to open up a little bit
more. You can easily copy and paste data, rearrange data for specific reports, and even create
charts to present the data. If you are using a spreadsheet format, it is helpful to define a
consistent standard format for your spreadsheets (i.e., same columns, same column order). You
may also develop a standard way of reporting certain items, like the parameter names and units
of measurement. For example, if you sometimes record "temp" and other times record
"temperature," or sometimes record "degrees" and other times "degC," you should choose the
name and units you want to use consistently. Unlike hard copy or electronic word processing
documents, more formal business rules (e.g., required or conditionally required fields) can be
incorporated into a spreadsheet. Because of the inherent lack of a centralized location, it is
frequently found that the process of manually rearranging data is quite time consuming and can
generate many files and versions. Consequently, it becomes even more important to institute a
good file management plan. You can develop a storage plan for how to name and track your
spreadsheets, and to ensure that you keep one master version of the spreadsheet to avoid
confusion about what is the most up to date, correct spreadsheet. It is very important to keep
separate read-only copies of spreadsheets somewhere in case you make a mistake as you change
a spreadsheet, and to prevent others from inadvertently changing data in them.
Recommendation D. Databases (such as MS Access, Oracle, or MS SQL databases) organize
data in tables, but you typically interact with them through data entry screens. Although
databases take a considerable amount of preparation time, once they are completed, it is easy to
enter, store, retrieve, perform relational queries, and report the data. Database systems can vary
in complexity, from simple "Out of the Box" programs like MS Access, to more complex,
customized systems, like Oracle or SQL Server. Databases are the best way to ensure data
consistency and that the most correct version of the data is maintained. They can, however,
represent a fairly significant technical hurdle to those who have not worked with them.
Therefore, it is recommended that you identify a qualified person who has primary responsibility
for database management and maintenance. If you have a database, you are most likely already
meeting your need for managing data.
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Recommendation E. If you have multiple databases to manage your field and lab water quality
data, it is recommended that you combine your data into one database. Part of the power of
having a database is that you can store multiple types of monitoring data together, such as lab
and field data, in an easily accessible format. A database will allow these data to be related to
each other for a given station, on a specific date. A database also eliminates the need to store
data more than once, such as station information. Raw monitoring data should be stored
separately from assessment data because this allows for future assessments to be made based on
the same data. For example, the result for 8° C for water temperature would be kept in a separate
database from the assessment conclusion that the stream is determined to be meeting the Tribe's
threshold for supporting a cold water fish habitat.
GIS software gives you another way to store and display your data that combines simple
database and graphing functions with the power of putting data directly onto a map of the
watershed. GIS software allows you to visually analyze, manipulate and report on your data
based on the location of your sampling point in the river or watershed. Most GIS software also
easily imports other types of data formats into a table to which the spatial information can be
linked.
STORET-Compatible Data Submissions
I. Getting Your Data Into a STORET-Compatible Format
The STORET (short for STOrage and RETrieval) Data Warehouse (or STORET Warehouse) is a
repository for water quality, biological, and physical data and is used by tribal environmental
organizations, state environmental agencies, EPA, other federal agencies, universities, private
citizens, and many others. Formatting your monitoring data to be STORET-compatible, not only
benefits the EPA, it also benefits tribes and other agencies. By putting data in a STORET-
compatible format, you are formatting the data you are managing (with Excel or Access, etc.)
into a standard set of data elements, formal business rules and allowable values that comply with
the STORET Warehouse, allowing the data to be placed in the STORET Warehouse. Once data
are in the STORET Warehouse, they can be retrieved for use in a variety of studies and analyses.
For instance, your tribe can acquire your data and data from surrounding jurisdictions to identify
upstream issues that might be of concern to your reservation. The STORET Warehouse can also
be useful in the event of an internal data loss.
As part of the required tribal Assessment Report, tribes are required to submit monitoring data to
EPA electronically in a STORET-compatible format for each assessed surface water body. This
section describes what it means to put data into a STORET-compatible format.
There are different ways to submit data to the STORET Warehouse, which will be discussed in
the next section. It is important to note that the Water Quality Exchange (WQX) is the new way
to place data into the STORET Warehouse and will be discussed in the next section. The
STORET-compatible format as outlined below is very similar to the WQX format, and any
efforts in putting data in a STORET-compatible format are well worth it for any type of data
submission.
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For each data type, STORET requires that certain information be reported in a very specific way.
These data types answer the questions defined in Section III: Who, Why, Where, How, and
What. Tables 1 through 3 show information needed to submit data to STORET Warehouse and
where to get the information.
¦ Field Name is the data parameter that can be sent to STORET. You will likely have
similar names in your own data sets.
¦ Description explains what the field name means and other helpful hints.
¦ Source provides information on where to get the data from.
Please note Tables 1 through 3 come from the STORET Data Template, an MS Excel file that is
available from the EPA STORET Web site (http://www.epa.gov/storet/national websim.html).
The Template also contains other worksheets that can be very helpful to understanding STORET.
The template was created as a result of consultation with tribes throughout the country. It can be
used to actually manage data or simply as a guide to see how data can be organized for easy
import into the STORET Warehouse.
Table 1. Project Information in STORET
Field Name
Description
Source
Project ID
A unique ID for the Project to which data will be assigned
User Created
Name
A unique Name for the Project
User Created
Start Date
Date on which the Project started (MM/DD/YYYY format)
Date which monitoring work began (may
cover multiple years)
Duration
Planned duration of the Project
Either planned end date of monitoring for
this purpose or "Ongoing"
Purpose
Project goals, expectations, or why these data were
collected
User Created
Table 2. Station Information in STORET
Field Name
Description
Source
Station ID
Unique identifier for the Station
User Created
Station Name
A geographically descriptive Station name
User Created
Primary Type
Station's primary type
Listed in template allowable values
Latitude
Latitude. See available format options. (DD.DDDDDDD
recommended)
GPS reading, Map Interpolation, etc. -
based on methods listed in template
Longitude
Longitude. See available format options. (DDD-DDDDDDD
recommended)
GPS reading, Map Interpolation, etc. -
based on methods listed in template
Geopositioning
Method
Method used to determine Lat/Long coordinates
Listed in template allowable values
Geopositioning
Datum
Datum used to determine Lat/Long
Listed in template allowable values
State
Postal abbreviation for Station's primary state, not required
for Great Lake and Ocean Stations
The state containing the Station
County
Name or FIPS code for Station's primary county
The county containing the Station
Station
Station Description
User Created
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Field Name
Description
Source
Description
Table 3. Results Information in STORET
Field Name
Description
Source
Activity ID
A unique identifier for each activity (an activity can be a
unique lab ID, a suite of on-site field measurements, an
individual sample, etc)
User-created
Medium
Medium in which this activity occurs
Listed in template allowable values
Activity Type
Type of activity
Listed in template allowable values
Activity
Category
Category of activity
Listed in template allowable values
Activity Start
Date
Date activity began (MM/DD/YYYY)
Date samples or measurements taken
Sample
Collection
Procedure ID
Valid STORET sample collection procedure, required if
activity type is sample
User-created based on QAPP
Characteristic
Name
Valid STORET characteristic name
Listed in template allowable values
Result Value
Measured value of characteristic
From lab results or field sheet
Result Value
Units
Units for characteristic Result
From lab Results or field sheet
Sample
Fraction
Fraction for characteristic, required for certain characteristics
(see Non-Taxa Characteristics for details)
Lab Results
Field/Lab
Procedure
The field or lab procedure used to analyze the sample,
required for certain characteristics (see Non-Taxa
Characteristics for details)
Listed in template allowable values or
created by organization staff based on
QAPP
Field/Lab
Procedure
Source
Source acronym for field/lab procedure, required if field/lab
procedure entered
Listed in template allowable values or use
organization ID
To manage and report data in STORET-compatible format, you will need to provide at least the
data fields listed in Tables 1 through 3. The STORET template provides detailed guidance on the
requirements for each field that you may need to provide. For example, you will need to make
sure that:
- Field length of data in a data field is not longer than what STORET will accept
- You provide information in the defined formats or select the appropriate allowable
value from the STORET lists, as defined in the template
- You provide the unique identifier that STORET requires for all projects, stations, and
activities within your organization.
If you are implementing this within your own data management system, meeting these STORET
requirements will not be difficult. This helps to make the use of these data easier for everyone
involved by aiding data standardization, which is essential for data sharing at the national level.
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Metadata, or data about your data, are very important for telling potential data users how data
were gathered and why certain data may be unreliable. For this reason, it is encouraged that all
relevant QC data be entered into STORET, as well as specific sample collection procedures and
field/lab procedures.
Data standards are consistent ways to report data in formats so they are easy to share. When
reporting Results, you will need to use the appropriate allowable value for characteristic names.
The STORET characteristic names are based on an EPA data standard. Table 4 shows the list of
the parameters defined in the Tribal 106 Guidance and the corresponding STORET
characteristics. While you are only required to manage and report data on particular
characteristics listed in the Tribal 106 Guidance, many tribes have decided that it is easier to
manage and report data on all the characteristics they monitor. One of the first activities you will
want to do if you plan to submit characteristics to STORET that are not listed in Table 4 is to
match the characteristic names you use to those in STORET. You can look up a long list of
allowable values for characteristics on the STORET template.
Table 4. Water Quality Parameters (allowable values in STORET)
Parameter as Defined in the Tribal
106 Guidance (page A-4)
Corresponding STORET Characteristic
Dissolved Oxygen
Dissolved oxygen (DO)
Water Temperature
Temperature, water
Turbidity
Turbidity
PH
PH
Phosphorous
Phosphorous3
Total Nitrogen
Nitrogen
E. Coli
Escherichia coli
Enterococci
Enterococcus Group Bacteria
aTotal phosphorous is calculated as the sum of both orthophosphate and phosphorous,
depending on your methodology, you may need to include both of these characteristics to
accurately describe total phosphorous. Alternatively, you can use the characteristic of
'Phosphorous' combined with the sample fraction of 'Total' to describe total phosphorous.
Because biological and habitat data are handled differently than physical/chemical
characteristics, you should contact your region or the STORET Helpdesk at 1-800-424-9067 or
at storet@epa.gov to discuss this electronic format.
There are also standard ways to report the procedures you use when collecting data in the field
and that the laboratory uses to evaluate samples. For example, when you report a result to
STORET you will need to select a Field/Laboratory Procedure source, such as the National
Institute for Occupational Safety and Health or the EPA. You will also select the Procedure ID,
such as 150.1 for pH or 150.2 for pH by continuous monitoring. All of these allowable values are
listed on the STORET template. If you use procedures that are unique, you can ask that they be
added to STORET by contacting the STORET Helpdesk. One of the first activities you will
want to do if you plan to submit data to STORET is to identify the sources and IDs for the field
and laboratory procedures you use for each characteristic. You can work with your laboratory to
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find out its procedures for analyzing certain parameters. Tribal QAPPs, SOPs, and even data
logger manuals are also good resources for procedural IDs.
Overall, the following steps will help you get your data into a STORET-compatible format. At
any point in this process, contact the Tribal STORET support line at 1-800-844-0638 or
owsupport@rti.org if you have any questions about how to organize your data, what are the
allowable values, or other topics. For these steps below, the STORET template available on the
EPA Web site will show specifics such as allowable and required values:
http://www.epa.gov/storet/national websim.html.
1. Ensure that you are collecting all the required data fields listed in Tables 1 to 3, above,
and as shown in the template available on the EPA Web site.
2. Figure out what values STORET will allow for your parameters and field and laboratory
procedures. Refer to the STORET template for help.
3. Get your data formatted according to STORET's specific required fields, field length, and
field type. You might want to use the STORET template, which lays out these items.
4. Before you can send your data to STORET, you need to request an Organization ID by
contacting the STORET Helpdesk at 1-800-424-9067 or at storet@epa.gov.
There are other applications that have been created by members of the user community that may
be used to help you put your data into a STORET-compatible format. You may find some of
these tools on the STORET Web site (http://www.epa.gov/STORET/otherapps.htmn.
II. Ways to Submit Data
As you may know, the STORET Warehouse contains raw surface and ground water biological,
chemical, and physical data (from 1999 and beyond) collected by federal, state and local
agencies, Indian Tribes, volunteer groups, academics, and others. The STORET Warehouse will
continue to be the repository for all STORET data.
Section 5 talked about how to put your data into a format that is accessible by the STORET
Warehouse. Once your data are in this format, the next step is to submit the data electronically
to the STORET Warehouse using the different tools and mechanisms available. Figure 3 shows
the different mechanisms for submitting data to the STORET Warehouse.
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Figure 3. Mechanisms for Submitting Data
EPA expects that once data are in a STORET-conipatible format, they will be submitted to the
S TO RET Warehouse. EPA recognizes that due to resource limitations, certain tribes need EPA
support in completing the data submission. In these cases, submitting the data electronically to
your project officer (via e-mail, CD, DVD, etc.) may be sufficient for meeting the data
submission requirement. This option is not discussed below, and should be discussed further
between you and your project officer.
To submit your data electronically into the STORET Warehouse, determine what mechanism is
best for your tribe. Each approach has advantages and disadvantages, but all are acceptable
means for meeting the data submission requirements set forth in the Tribal 106 Guidance. Here
are some options to consider (described in more detail below):
1. Submit data through the Web STORET Import Module (National WebSIM)
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2. Submit data through WQX using an Exchange Network Node or Node Client
3. Submit data through the WQX Web Submission Tool (WQXWeb)
This document does not include detailed instructions on how to submit data using these tools.
Please see the resources for support at the end of this document for more information about the
steps to be taken to utilize these tools.
1. Submit data through the Web STORET Import Module (National WebSIM)
National WebSIM is a Web-based user interface for uploading text files of Project, Station, and
Results data. More information about this option, including a data management template and
National WebSIM tutorials can be found at http://www.epa.gov/storet/national websim.html. In
addition, the National WebSIM tutorials on the Web site help you understand how it works and
how to submit electronic files to STORET.
Please note that National WebSIM will be phased out as it is replaced with WQXWeb.
WQXWeb will be similar to National WebSIM, so any effort made with National WebSIM will
not be wasted. EPA plans to continue to support National WebSIM as WQXWeb is released so
that tribes can transition to the tool in a complete and comfortable fashion. National WebSIM
will remain available until September 2009. Any progress that you make now towards data
management, including submitting data to STORET through National WebSIM, will help you
organize your data and prepare for the future.
2. Submit data through WQX
WQX is another way to submit your data into the
STORET Data Warehouse. WQX is a framework
that makes it easier for tribes, states, and others to
submit and share water quality monitoring data over
the Internet. WQX uses EPA's Exchange Network
(http://www.exchangenetwork.net/exchanges/water/
wqx.htm) to flow water quality monitoring data to
STORET from a tribe's data system via Exchange
Network nodes or node client. Tribes that have been
awarded Exchange Network grants use WQX to
submit files. Most tribes submitting data through
WQX directly have database systems for managing
data. Some tribes have begun working together
toward utilizing WQX for submitting data. If you are considering this route, you should stay
aware of activities going on in your region through your Exchange Network grant representative,
106 grant representative, and monitoring coordinator. More information about the WQX can be
found at http://www.epa.gov/storet/wqx. html.
3. Submit data through WQXWeb
EPA's Exchange Network is a way to
exchange environmental information
securely over the internet, using an
approach based on standard
technology and protocols.
Exchange Network Grants are
awarded to states, territories, and
federally recognized tribes to assist in
the development and implementation of
Exchange Network Nodes, exchanges,
tools, and related backend systems.
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WQXWeb is a tool that will eventually replace the National WebSIM, but it will work in much
the same way. Like WebSIM, WQXWeb allows the uploading of text files, but also allows the
creation of a WQX file and the submittal of this file to the STORET Warehouse via WQX.
Standardized templates, as well as tutorials, will continue to be available to assist tribes in
formatting data in the correct format for data submission. The tool also allows tribes to correct
data errors upon submission. Tribes will be able to easily transition from the National WebSIM
to the new WQX web submission tool because they will be familiar with formatting and
preparing data for submission using a Web-based tool.
Once data is submitted to the STORET Warehouse, it is available for use by your tribe, EPA, and
the general public who have an interest in the water quality data in your area. As a part of a
tribal Assessment Report, the data submitted to EPA will enable water quality managers to have
a better picture of how water quality is improving in Indian Country. If you have privacy
concerns about the availability of monitoring data through the STORET Warehouse, please see
Appendix C (Letter to Tribal Leaders on Information Access) of the Tribal 106 Guidance, and
consult with your project officer.
Some tribes may find that they have errors in
data that they have submitted to the STORET
Warehouse. All the tools for submitting data
described above allow a tribe to change any
submitted data. For example, if a set of results
taken during a certain sampling season was
taken using equipment that was later found to
be defective, the tribe can use any of the
submission tools to delete the erroneous data
and resubmit the new data.
III. Retrieving Data from the STORET
Warehouse
You may wish to perform analyses on data that has been loaded to the STORET Warehouse by
you or other organizations having jurisdiction in your area. In order to do this, you will need to
retrieve the necessary data from the STORET Warehouse. You may download data from the
STORET Data Warehouse by going to the STORET Web site
(http://www.epa.gov/storet/index.html) and clicking on the "Download Data" link. You will
have the choice between accessing Legacy STORET data or Modernized STORET data. All
data that was loaded during or after 1999 will be found in the Modernized STORET data. The
STORET Warehouse offers several query options. Once you have selected a query option, a
form will help you filter your data down to only the data that are needed. Once you have
requested the data, you will need to select what data fields that you want and provide your e-mail
address so that you can receive a web-link telling you where to download your data set. Detailed
instructions for downloading data from the STORET Data Warehouse are available from
http://www.epa.gov/storet/Downloading STORET Data.pdf.
A second option for retrieving data from the STORET Data Warehouse is through Web Services.
Web Services offer a way to allow computers to retrieve data directly from the STORET Data
The STORET Warehouse does not provide
QA/QC services (i.e., does not check data
for "bad" values), but rather, trusts data
providers to perform QA/QC before the
data is submitted. It is generally
unnecessary to report questionable data to
the EPA, unless the data are used as part of
your water quality assessment. Data with
questionable data quality can be flagged
within STORET if shared.
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Warehouse in predefined formats using a computer to computer protocol for directly sharing
information. You may find more information on STORET Web Services at:
http://www.epa.gov/STORET/web_services.html.
IV. Ongoing Support
If you have questions about getting your data into a STORET-compatible format or how to
submit data to STORET, there are ways to seek help. Table 5 lists some resources that may be
helpful to you.
Table 5. STORET Resources for Tribes
Item
Location
STORET Web site
http://www.epa.aov/storet/index.html
Information on National WebSIM
http://www.epa.aov/storet/national websim.html
Information on WQX
http://www.epa.aov/storet/wax.html
STORET Tribal data management template
http://www.epa.aov/storet/national websim.html
National WebSIM Tutorials and Example
Files
http://www.epa.aov/storet/national websim.html
Data Management Applications
http://www.epa.aov/STORET/otherapps.html
Information on Exchange Network
http://www.exchanaenetwork.net/
STORET Helpdesk
1-800-424-9067 or storet(®epa.aov
Tribal STORET/WQX Support Line
1-800-844-0638 or at owsupport(®rti.ora
The STORET helpdesk and tribal STORET/WQX support line are useful resources to assist you
with any issues that arise with STORET/WQX. EPA's STORET helpdesk can set up
Organization IDs and enter field/lab procedures into the system. The tribal support line can
answer questions about data management and reporting, or help you troubleshoot problems with
STORET/WQX.
As part of submitting data, you should determine how you plan to submit data and how often you
should submit it to EPA. That way, you can reserve the staff hours necessary to submit data on a
routine basis. The more often you submit data, the more familiar you will become with the
process. It will get easier every time. Once you have decided on a plan for submitting data, you
should document it and revise the plan as necessary.
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ATTACHMENT A: Examples of Tribal Data Management
This section provides examples of tribal data management systems that were provided by tribes.
Please note that the Fond du Lac, Penobscot Indian Nation, and Yurok Tribe Environmental
Program are considered to have mature data management programs because they use databases
to manage their water quality environmental data and provide data to EPA in a STORET-
compatible format.
Fond du Lac
Fond du Lac's water quality monitoring program includes physical, chemical, and biological data
from reservation lakes and streams, going back to 1998. In 2008, we will begin collecting similar
data from a targeted group of wetlands. Physical field data include a Hydrolab profile in lakes
(temperature, dissolved oxygen, pH, specific conductance, turbidity and depth), Secchi
transparency; and in streams, continuous temp loggers, flow/discharge measurements, and
habitat assessments. We have begun to use BioSonics integrated hydroacoustic/GPS equipment
to collect data that will generate accurate bathymetric maps, including substrate type and
vegetation mapping. Chemistries include nutrients (TKN, nitrite-nitrate, ammonia, total
phosphorus, ortho P), total suspended solids, total hardness, alkalinity, color (correlation verified
one season with dissolved organic carbon), chloride and metals (arsenic, cadmium, chromium,
copper, lead, nickel, selenium, zinc), chlorophyll a (plus ash free dry mass for stream
periphyton). Biological data include algal community and zooplankton from lakes (will add fish
this year), and benthic macroinvertebrates and fish in streams. Additionally, we have sampled
fish tissue for mercury and other contaminants of concern, and have done two sediment studies
on reservation lakes and the St. Louis River, looking at physical characteristics (grain size,
moisture, organic content), mercury, lead, PCBs, and toxicity tests.
Our water quality data is integrated into an Access database, which was organized and designed
to facilitate STORET transfer. We record field data and lab data in separate, hard copy
notebooks, which are kept as a permanent record after the information is entered into the
database. Contract labs submit both electronic files (generally Excel spreadsheets) and hard
copies to us; our database has some forms designed specifically for ease of import of these files
(including our Hydrolab output). Our sediment data is in a separate Access database, designed to
be similar to the National Sediment Quality Database.
The contract lab that analyzes most of our chemistry data formats the electronic files so that they
can be easily imported to our Access database. Since the database was designed to be similar to
STORET, our intent was to make data migration as simple as possible.
We have used National WebSIM to migrate our water quality data to STORET. After the
training we received, we successfully uploaded all of our water chemistry data from 1998-2006
into STORET. Since we have had staff turnover in the interim, we have not yet entered the 2007
data; will be training new staff to carry on this duty.
Our Access database has provided us with a comprehensive electronic "parking lot" for all of our
water quality monitoring data. Because of the way it is designed, the data is in a secure place
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with only two key staff able to enter or edit the data, and it is backed up nightly. We have been
able to extract data—either specific parameters or specific waterbodies—to be able to conduct
analyses, do assessment and reporting, as well as being able to easily share data with other
resource management agencies in a format they can use.
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Penobscot Indian Nation
Penobscot Indian Nation (PIN) collects general water quality monitoring data from late May to
early October. Table 1 describes the different locations and types of monitoring we do.
Table A-l. PIN Water Monitoring Program
Location or
Type
Number of
Stations
Characteristics monitored
Penobscot
River
84
temperature, pHa, dissolved oxygen, conductivity3, BODa, E. coli bacteria3, color3,
total suspended solids3, turbidity3, secchi disc visibility, foam (index).
Additional parameters monitored less frequently include: phosphorous, nitrogen,
chlorine, closed cell pH, alkalinity, tannin and lignin, chlorophyll a, organic carbon
Penobscot
River
Tributaries
30
temperature, dissolved oxygen, conductivity, E. coli bacteria, color, total suspended
solids, and turbidity
Trust Land
Lakes and
Ponds
temperature, pH3, dissolved oxygen, conductivity3, E. coli bacteria3, color3, total
suspended solids3, turbidity3, secchi disc visibility, foam (index), alkalinity, develop
trophic status index
Biomonitoring
4-6
This method involves examining communities of aquatic insects to determine water
quality conditions
Ambient
Toxics
Monitoring
Contaminant monitoring primarily focuses on aquatic species that are used by Tribal
members or that have ecological importance. Species examined include:
Smallmouth bass, white suckers, muskrat, snapping turtles, freshwater mussels, and
fiddlehead ferns.
Toxics examined include: dioxins, furans, PCBs, chlorophenols, and trace elements
(including Mercury).
Regularly collect Penobscot River fish samples for ME DEP programs including
Dioxin Monitoring Program and Surface Waters Ambient Toxics Program. Provide
assistance to University of Maine researchers (i.e. bioassay development,
histeopathology, endocrine disruption)
Other Water
Quality
Projects
13 ambient
sites
4 point
source
sites
Monitoring Project to document and determine the cause of algae blooms during
summer months on the Penobscot River. Sampling within the watershed for
temperature, nitrogen, phosphorous, chlorophyll a, total suspended solids, dissolved
organic carbon, algal taxonomy, presence of toxins and physical/visual
characteristics.
Preliminary study to determine presence and characteristics of sediments in the
Penobscot River. Assist USGS with occurrence & distribution study of contaminated
sediments.
Continuous temperature monitoring Project to examine daily water temperature
patterns at numerous sites within the mainstem and tributaries of the Penobscot
River. Assessment & management plan of non-point source pollution on Trust Lands
and Indian Island
Spring thaw sampling on Lakes/Ponds to determine effects of acidity on fish
Cooperative waste load allocation study in 1997, 1998 and 2001 with Maine
Department of Environmental Protection, EPA, and several dischargers to collect
data for dissolved oxygen modeling of the Penobscot and Piscataquis Rivers.
a Because the Penobscot Nation Water Resources Program has a Water Quality Monitoring Cooperative Agreement
with ME DEP, collected data are made available for use in Maine's 305 (b) Water Quality Assessment report to
Congress. The classifications of over 400 miles of Penobscot River watershed were upgraded in the last
reclassification effort by DEP as the result of Penobscot Indian Nation water quality data.
We manage our data in a MS Access database called Penobscot Indian Nation Environmental
(PINE). Field data on physical/chemical parameters are collected on mobile digital assistants
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using forms created in Visual CE (software from Syware) based on tables in the PINE database.
This data is synchronized with a field database file. The field database has forms for making
final edits, checking in the samples and documenting bacteria sample preparation. The relevant
field information is moved to a laboratory and archive database file. The laboratory database file
has forms for data entry on all in-house analyses done by PIN including: bacteria, total
suspended solids, biological oxygen demand, alkalinity, conductivity, turbidity, color, and pH.
Laboratory data also get sent to the common archive database file. An uploading tool copies
both field and laboratory data into the main PINE database file.
The process for real-time electronic entry of field and laboratory data has been amended to
accommodate entering data from paper reports from previous years. Current efforts include
making data processing more efficient, incorporating more quality-assurance checks, and
producing a good data entry form for information from external laboratories. In addition, we are
working to add continuous data from data loggers/data sondes into the database.
PIN is currently matching the PINE database fields to the required WQX elements. We plan to
develop a data entry process for the external labs to use so that they can easily import the data
when it is received. Once the matching is done, we will run a series of database queries and
exporting the Results to an XML file for submission to EPA.
At this stage of developing the PINE database, we are just beginning to be able to look at all of
the data for one sampling season at the end of it. Our first Assessment Report to EPA included a
summary of the average, minimum and maximum of each characteristic they analyzed in the PIN
lab for each site they visited. We also plan to calculate a trophic state index for each of the lakes
they monitor. As we begin entering more past data, we plan to look at trends over time.
Benefits of data management for our program include:
• Improved ability to protect traditional tribal lifeways
• Integration of all aspects of monitoring so each team member has a better understanding
of how their contribution fits into the effort as a whole
• Improved quality of data entry
• More timely assessment of problems with water bodies, quality-control sampling/analysis
efforts, and the ability to produce reports on problems and analysis
• Better and more timely information to decision-makers
• Time savings in the long run
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Yurok Tribe Environmental Program
The Yurok Tribe Environment Program (YTEP) collects continuous water quality data
(temperature, pH, conductivity, Dissolved Oxygen), bacteria, nutrients, and chemicals. We also
measure stage height and flow via continuous monitors. In order to manage our data, we built an
SQL-based data warehouse. The database includes an XML translator to generate files and
submit them to WQX. Water quality monitoring data is pushed from the database into WQX on a
regular basis, as data packages are entered, validated, quality assured (QA'd), and approved.
YTEP used Exchange Network funding to enhance their data management system. (For more
information on these grants, see the Exchange Network Grants web site at:
http://www.exchangenetwork.net/grants/index.htm)
YTEP prepares an annual report summarizing water quality data collected. We also prepare a
report summarizing the hydrological data collected and presenting the rating curves generated by
staff based on data. We do not do much analysis beyond minimum/maximum/average charting,
seven-day moving averages, percent exceedance for temperature, and identification of
measurements which violate water quality standards.
Benefits of data management for the YTEP include enhanced data quality, improved quality
assurance/quality control (QA/QC), streamlined data handling, and increase in efficiency
(reduced staff time demand for data handling).
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ATTACHMENT B: Glossary
Allowable Values - Acceptable data values that can be input for a given data element. These
values provide information on the exact spelling and capitalization needed for acceptance into
the STORET Warehouse.
Assessment Data - Decisions about water quality that are made based on comparisons of water
quality monitoring data against water quality standards.
Characteristic Names - Standard names used to describe measured or analyzed parameters.
Common Values Lists -List of acceptable data values that are allowed to be input into the
STORET Warehouse. Common values ensure consistency between data entered by different
organizations.
Conditionally Required Fields - Data fields in STORET/WQX that are required only when
certain conditions are met in other data fields. For example, a time zone must be entered if you
enter the time of an Activity, but is otherwise not required.
Data Elements - A basic unit of data within a data flow or transfer. It is often called a data field
in a database.
Data Field - A column in a database that can hold one specific data parameter. For example, the
station ID for all stations would be contained within one data field.
Data Standards - Consistent ways to report data so that they are easily understood and shared
between data users.
Distributed STORET - Desktop software that allows users to enter and upload data directly to
the STORET Warehouse. Distributed STORET will not be supported after September 2009.
Exchange Network Node - Software that facilitates the transfer of data between an
organization's database and other databases on EPA's Exchange Network. Nodes use standards
based web services and extensible Markup Language (XML) to allow for the seamless exchange
of data between organizations regardless of hardware, operating system, or programming
environment.
Extensible Markup Language (XML) - An open standard language that describes data using
rigid syntax rules. XML allows data to be transferred between different data systems, regardless
of computer system or platform. XML overcomes system incompatibility by translating
information into a common data structure and format.
Field length - The maximum number of characters or numbers that can be placed in a data field.
Field name - The name of a data field, which provides a common reference for a data field.
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Field type - Term referring to whether a data field can contain freely formatted text data, data of
a defined format, or must conform to a list of allowable values.
Formal Business Rules - Rules that ensure completeness and consistent structure of data input
into the STORET Warehouse.
GIS Data - Data of a spatial nature, such as a map associated with information about your
waterbodies, produced using a Geographic Information System.
Metadata - Data that are supplemental to and add context to your data, or "data about data."
For example, the Result values received from your lab are data, while information like the time
the sample was taken and the sample collection procedure are both metadata.
National WebSIM - Web-based tool used to upload water quality monitoring data to STORET
Data Warehouse from spreadsheets and databases. Will be replaced by WQX Web Submission
Tool (late 2008 or 2009).
Node Client - Software that can be downloaded on to individuals' computers and can initiate
Exchange Network requests for data or a submission of data to other nodes. Unlike nodes, node
clients can not respond to requests for data.
Quality Assurance (QA)/Quality Control (QC) - Processes, samples, or results that help
validate and check the quality of monitoring data and the processes used to acquire results from
monitoring activities.
Relational Queries - Queries used to access data in a database that relate data in one table to
data in another table by the use of matching values in key fields.
Required Fields - Data fields in STORET/WQX that must be included in a data submission
under all circumstances.
Spatial information - Information that allows data to be associated with a location on a map.
STORET Data Template - Spreadsheet tool used to facilitate the entry of water quality data for
eventual upload to the STORET Warehouse. Will be called STORET/WQX data template in the
future.
STORET Data Warehouse - The repository for water quality data, a warehouse for both
STOrage and RETrieval of water quality data from across the U.S.
Water Quality Exchange (WQX) - The new data transmission standard for upload to the
STORET Warehouse. WQX uses the XML programming language to format data.
Water Quality Monitoring Data - Field data or lab results associated with sampling events at
a monitoring location.
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Wildlife Surveys - Studies designed to census and measure the number of species in order to
assess the ecological health of an area.
WQX Web Submission Tool (WQXWeb) - The replacement for WebSIM that will convert
data (from spreadsheets and other flat file formats) to the XML format and migrate the data to
the STORET Warehouse. This tool will fully replace the current function of WebSIM.
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