Tips and Tricks
                            for Setting Up
                            STORET Data
U.S. Environmental Protection Agency
March 2004

           Tips and Tricks  for Setting up STORET Data


One of the most important features of STORET as a data management system is its ability to
maintain data of documented quality.  Properly documenting your data by providing rich
metadata (i.e., data about data) can ensure that monitoring results will be used appropriately. In
STORET, much of this metadata needs to be set up in the system up front, before you begin to
enter monitoring results.  STORET was designed to be very flexible so that varied ways of
documenting and describing data can be accommodated.

Because of the many ways STORET can be set up, the task can seem daunting to first-time users.
To provide useful insight to new users, we conducted a survey of current users asking how they
implemented their initial setup and entered metadata. Thirteen users from different types of
monitoring organizations responded to the survey.  The purpose of this document is to provide
helpful ideas for setting up various aspects of STORET. This information was compiled from the
survey responses and is designed to help you customize STORET for efficient data import,
retrieval, and display.

We asked the respondents to comment on various types of STORET data, including

    •  Water chemistry,
    •  Sediment chemistry,
    •  Tissue chemistry,
    •  Physical measurements,
    •  Habitat,
    •  Biological information (both raw and metrics data), and
    •  Data loggers.

Although the survey respondents enter data in all of these categories, most submitted input on
water chemistry, physical measurements, and raw biological data.

Before you enter information into STORET, it is important to take the  time to analyze your data
and decide how you want to enter it. You will also want to consider what data you want to get
out of STORET. Determining how to organize and enter data before you start may save you
many hours of reformatting and processing the data. While it is useful to determine how to
organize your data before entering them into STORET, it is also important to be flexible, since
you may have different types of data to put in and get out of STORET.

In general, it is helpful to structure the data you plan to enter into STORET in a fashion similar
to your current data analysis system—this minimizes time to rearrange or process data. You can
use the information in this document to help you find a data situation similar to your own and
determine how someone else approached the problems and set up their data for STORET. It is up
to you to define a system that is useful and efficient for your situation.

This document is divided into sections according to the main information categories in STORET:
projects, stations, station visits, trips, activities, characteristic groups, lab/field analytical
Tips and Tricks for Setting Up STORET Data

procedures and equipment, and sample collection procedures Each of these information
categories provide important metadata for results you will enter into STORET. The next sections
cover each of these categories, describe options for defining them in STORET based on the
experiences of the survey respondents, and list advantages and disadvantages of the various
approaches. For more details, see Appendix A, which contains the complete text of all responses
to the survey.
Tips and Tricks for Setting Up STORET Data


Project descriptions contain essential information about purpose, procedures, standards, and
methods related to the project. The advantage of using the projects in STORET is that they allow
you to retrieve data  sets that are specific to a project. For example, by associating sampling
stations with a lake monitoring project, a staff member can go into the database and retrieve all
sampling data associated with the specific project for analysis.

A project in STORET must include the following elements at a minimum (note that other
metadata can be stored as well):

   •   Unique Identifier (up to 8 characters long),
   •   Start Date,
   •   Duration (enter "ongoing" for continuous monitoring),
   •   Name (e.g., "Alameda Creek Volunteer Monitoring"), and
   •   Purpose (e.g., "Monitor effects of urban development on water quality of Alameda

There are many ways to set up projects in STORET, each with advantages and disadvantages.
The survey respondents reported setting up anywhere  from 2 to 1,000 projects, based on how
their projects are defined. Table 1 shows the different  ways the survey respondents have set up
projects for STORET.

                    Table 1. Options for Defining Projects in STORET
Options for Defining Projects
Program Name or
Program Purpose5'8'10
Program Name and Year6
Hydrologic unit code (HUC) or
Collection Entity -
Location and Date -1 1
Easy to retrieve data from STORET-1' 13
Less initial formatting 2
Data owners can define data organization4
Quick to retrieve project-specific data4
Matches USGS data "water year" books3
Easy to retrieve data from STORET3
Can track separately 5
Easy to retrieve data from STORET6
Easy to see any changes to stations 6
Forces a QC check of HUCs7
Works well when multiple entities are involved
in one project9
Tracks environmental problems by location
and time-11
Data can be sorted different ways -1 1
May limit retrieval options9
Difficult to deal with multiple years3
Difficult to categorize data that fulfill
more than one purpose5

Does not take advantage of STORET
metadata capabilities7
Requires more cooperation between

 Numbers in the tables correspond to the number assigned to each survey respondent and identify the respondent(s)
associated with an option or response. The full text of the specific comments can be found in related sections of
Appendix A.
Tips and Tricks for Setting Up STORET Data


Stations, also referred to as sites, identify or describe the physical location at which monitoring
occurs. All data collected in the field are linked to a specific location, or site, at which the field
work was conducted. Recording station data links water quality measurements to the place they

Precise location definition is very important to environmental analysis, and EPA data standards
for locational data are strictly followed in STORET. All applicable federal standards (e.g.,
Federal Information Processing Standard [FIPS], National Institute of Standards and Technology
[NIST], and others) are adhered to wherever possible.

Stations may be part of external reference schemes, and may carry a multitude of identifiers from
each of these schemes. For example, a station in STORET might have a National Pollutant
Discharge Elimination System (NPDES) number or a state regulatory program code.

Each monitoring station must include the following elements at a minimum  (note that other
metadata can be stored as well):

   •  ID Code (up to 15 characters, e.g., for Alameda Creek Volunteer Monitors, stations could
      be labeled "AC-001, AC-002, etc.),
   •   Station or Waterbody Type (e.g., Stream, Lake, Well, Estuary),
   •  Latitude and Longitude (does not have to be accurate to nearest square inch but try to be
      as precise as possible),
   •  Geopositioning Method (e.g., GPS, map interpolation),
   •  Datum (e.g., North American Datum 1983, etc.), and
   •   State and county.

The number of stations  reported by survey respondents ranges from 100 to over 18,000
depending on how the stations are set up. Table 2 shows different ways survey respondents have
set up naming conventions for STORET stations and the advantages and disadvantages users
described for each option. Please note that, in the examples,  site IDs and station numbers are
Tips and Tricks for Setting Up STORET Data

                  Table 2. Options for Defining Stations in STORET
Options for Defining Stations
Each individual program decides
how to input data in STORET l- 10
Program and Site ID 2
Reach IDs from Legacy STORET 3
County and site 4
Project, county, sequential number6
HUC, waterbody ID,
Stream names 8
ID from state Laboratory
Information Management System
Project ID, waterbody ID, site ID n
Sequential numbers 12
Waterbody ID, site ID 13

Easy to import into STORET 2
Consistent with Legacy
If site IDs are set, easy for
data collectors 4
Helps with biocriteria

Can track by watershed 7

Can easily link to source data9
Easy to understand n
Can track by waterbody : :
Easy to assign numbers to
new stations12
Easy to determine location13
Difficult to locate information :
No consistency10
Some state agencies prefer direction10

Difficult to calculate and retrieve 3
Easy to have duplicate IDs for streams 4
Difficult to separate combined
biological, chemical, and habitat data
from chemical-only data5
Easy to have duplicate IDs 6
Must verify and QC each HUC 7
Easy to have duplicate IDs 7
Too long for STORET 8
Not useful for querying based on
topics 9
Difficult to track multiple
Not always possible to assign
sequentially, so one waterbody may
have multiple numbers12

Tips and Tricks for Setting Up STORET Data


A field trip is a method of grouping actual visits to monitoring stations. One trip could involve a
single visit to a single station or multiple visits to several different stations. Trips also provide a
framework for storing "blank" samples and other QC activities.

Trip information includes the following at a minimum (Note that other metadata can be stored as

    •   An ID code for the trip,
    •   Date the trip began, and
    •   List of projects supported by the data collected on the trip.

It may be useful to define trips by a combination of geography and time. For example, the
Nevada Division of Environmental Protection (NDEP) monitors throughout the State of Nevada.
Nevada is divided into seven major river basins. A trip is defined as all visits to the stations
located within one basin for an entire year.  For example, the trip labeled "Carson 1999" included
all the monitoring stations visited in the Carson River Basin for the year 1999.

Table 3 shows different ways survey respondents have set up trips for STORET.
                     Table 3. Options for Defining Trips in STORET
Options for Defining
One year of sampling for a
particular program 1? 10> 12
"T" plus the station number2
Field crew, year, and week 5
Project and date6
One trip per day, month, or
Easy to understand :

Users often want to view one year of data 3
Limits the number of trips for easy data
Helps organize and load data7
Easy to see if data is already in STORET 7
Easy to find date by year and field office 5
Easy to find data for corrections 6
Accurate representation of actual trips 9
Can be auto-generated11
Easy to find data from a particular time period 13

May need to reformat data 2

IDs can be too long5
Time consuming to look up
the week a trip started 5

Tips and Tricks for Setting Up STORET Data

Station Visits
Station visits are the events that occur when a particular site or station is visited to conduct
monitoring activities.  Any number of activities can be done during a single visit. For example,
during a site visit, one field observation activity could include measurements of water
temperature, dissolved oxygen, and pH. Another field observation activity could involve
measuring vegetation cover as part of a habitat assessment.  Sampling activities could include
collection of a water sample or the collection of fish for tissue analysis.  Station visits in
STORET allow the user to track the frequency of visits to sampling stations.

Visits consist of the following information at a minimum (Note that other metadata can be stored
as well):

    •   Date and time of the visit,
    •   Station being visited, and
    •   Visit number.

A station can be visited any number of times during a single trip. However,  the visit number
must be different for each visit to a station.

Table 4 shows different ways survey respondents have set up station visits for STORET and the
advantages and disadvantages users mentioned for each choice.

                 Table 4. Options for Defining  Station Visits in STORET
Options for Defining
Station Visits
One station visit per
Sequential number2' 12
Allow STORET Import
Module (SIM) to assign3' 6' n
Date and station ID 5
Assign number based on visit
spanning several days 7> 8
Station, date, time 9
Easy to understand :
Creates a unique ID when combined
with date and time 2
Can assign by sampling date and time 3
Easy to understand5
Good for tracking multiday visits 7
Accurate tracking 9

May be duplicates later2
Resampling will create duplicates 3

Various activities on different days
may not appear to match7

Tips and Tricks for Setting Up STORET Data


Activities define a task accomplished during a visit to a monitoring station.  Activities include
collecting samples, taking field measurements (including habitat assessments), and making field
observations. Activities also document information about the sampling process, including
collection methods, sample preservation procedures, and personnel performing the activities.
Activities can be associated with specific monitoring projects.

Activity information should include the following at a minimum (Note that other metadata can
be stored as well):

    •   ID code of the activity (for a sample, it may be helpful to make the ID the same as the
       sample code),
    •   Activity type (e.g., sample, field observation/measurement [includes habitat assessment],
       or automatic data logger results),
    •   Medium (e.g.,  air, water, soil,  biological),
    •   Date and time  of the activity,
    •   Activity category (e.g., other sample information, such as routine sample, composite, or
    •   Activity location (monitoring  station where activities occur), and
    •   Collection procedure (for samples only).

Table 5 shows different ways survey respondents have set up activities for STORET.
Tips and Tricks for Setting Up STORET Data

                  Table 5. Options for Defining Activities in STORET
Options for Defining
Combine field measurements
and lab analyses and assign
numbers :
Month, day, and year (field
and lab results are separate)
along with parameter code 2
Field and lab results combined
for U.S. Geological Survey
(USGS) data and separate for
other data3
Lakes: Sample location,
depth, field or sample (F or S),
replicate (R)
Streams: Lab sample number
and descriptive code 4
Biological (B), chemical (C),
or field (F) plus a unique
Three different activity IDs:
water conditions, atmospheric
conditions, and water
samples 6
Unique activity ID: year, trip
ID, sequential number, and a
suffix representing a medium
code or sample type 7
Field measurements separate
from lab analyses 8> 9
One activity for date, time,
depth, activity type, and
Assign field measurements the
lab ID for a station with an
up, 12, 13
Efficient for data entry :
Keeps activities separate and unique
Easy to determine which data were
analyzed in the field or the lab, and
whether a result was created from a
sample or a field measurement 2
Separate results helps track and match
field and lab data3
Streams: system works well 4
Easy to group field or chemical data : :
Can help find data in STORET 6
Works well7
Can track field and lab data separately 9

Easy to determine what field conditions
existed when samples were taken 13
Difficult to track parameters in

Must know if trips and visits are
new or existing for entry into
Lakes: depth difficult to make
unique 4
ID may be too long for the activity
Field and chemical samples are
separate in STORET, but must be
considered together during analysis 5

More complicated to keep separate 9

Tips and Tricks for Setting Up STORET Data

Characteristic Groups

Characteristics are things that are actually measured and analyzed, for example, water
temperature, pH, arsenic, lead, DDT, total nitrogen, etc.  You can set up characteristic groups to
help group together the characteristics you use frequently.  Characteristics can be grouped by
medium, activity type, or any other useful category. Using characteristic groups allows you to
enter data with similar metadata as a group rather than providing the metadata for each piece of

Characteristic groups can be most helpful when using the batch entry function of STORET. In
addition, you can use characteristic groups to assign different metadata to a characteristic that is
collected in two different ways. For example, if dissolved oxygen is analyzed by two different
procedures, you can use a characteristic group to assign different metadata to each set of
dissolved oxygen results.

The information associated with each characteristic is dependent on the type of characteristic, but
generally the following are needed at a minimum to set up a characteristic group (Note that other
metadata can be stored as well):

    •   Group ID,
    •   Group name,
    •   Medium (e.g., water, biological, habitat assessment),
    •   Activity type the characteristic will be associated with (e.g., sample, field observation,
       automatic data logger),
    •   Characteristic name (select from STORET),
    •   Units of measurement for the characteristic (e.g., Mg/L, count, percentage), and
    •   Analytical method used with the characteristic.

One way to determine how characteristic groups may be helpful to you is to list the names of all
characteristics you will be  analyzing, and then group the names into logical categories.
Categories could include pesticides, field measurements, or biological measurements (e.g.,
taxonomic abundance).

Note that if the same characteristic is analyzed in more than one medium, it is necessary to set up
a separate group for each medium.  This is common with toxics, which may be measured in
water, sediment, and tissue.

In general, it is helpful not to be too restrictive when setting up characteristic groups.  For
example, if you only measure a few characteristics, you may find it easier to set up a single
characteristic group, even though it might combine characteristics from a field observation, (e.g.,
water temperature) with those from a sample (e.g.,  total nitrogen in water).  STORET will
prevent illogical groups, such as combining characteristics for taxonomic abundance with those
for automatic data loggers.

Table 6 shows different ways survey respondents have set up characteristic groups for STORET
and the advantages and disadvantages of each option.  One survey respondent suggests using old
STORET parameter codes as row IDs since other systems, like the Permit Compliance System
Tips and Tricks for Setting Up STORET Data                                                11

(PCS) and USGS/National Water Information System (NWIS), use them. Some respondents do
not recommend using characteristic groups because it complicates getting data back out of
            Table 6. Options for Defining Characteristic Groups in STORET
Options for Defining
Characteristic Groups
Activity type :
Standard Analysis Code (SAC) 2
Biological: by order/family
Chemical: by lab (except for
USGS data)
Fish: all in one group 3
One sample group (use lab codes
as row IDs) and a few groups for
field measurements 4
Medium; activity type (biological,
chemical or field) 5> 7
Medium, activity type, and
analysis groups 6
Collection, sample
preservation/transport, field or lab 8
One group for lab results, one
group for field analysis 10> 13
Do not use characteristic groups n
Easy to separate types of
measurements :
Easy to set up database :
If already associated with the data, easy
to set up for STORET2

Can associate characteristic groups
with specific samples through database
Easily corresponds to monitoring data 5
Can set defaults for methods and units 7

Quick to input data 13
Can associate metadata with each
activity using other methods n
Do not have to set up groups n

SACs may overlap 2
Creates too many groups 3
Characteristic groups are less
flexible than attributes 4
Using characteristic groups from
outside sources can compromise
defaults 7

Tips and Tricks for Setting Up STORET Data

Lab/Field Analytical Procedures and Equipment

Lab/field procedures and equipment provide information on how each piece of data was
analyzed or measured.  For example, for samples analyzed for total suspended solids (TSS), a lab
may use EPA method 160.2, "Non-Filterable Residue - TSS." The lab or labs you use should
have this information readily available.

Although it is a good idea to have an analytical procedure for each analyte, it is not required for
everything.  This is often the case for items measured in the field, such as water temperature,
dissolved oxygen, and pH.

Lab and field procedures are connected to results in STORET. Table 7 shows the way survey
respondents have set up analytical procedures and equipment for STORET.

Table 7. Options for Defining Lab/Field Analytical Procedures and Equipment in STORET
   Options for Defining Analytical
    Procedures and Equipment
 National procedures and some state-
 developed methods .'• 2< 3< 4<5' 6< 7< 8< 9<10<
Creating state-specific methods allows more
lab-specific methods to be defined1'3> 4> 13
Can use SIM translation feature to map
analytical procedures9
Some missing methods
in the national list2
Finding the correct
national procedure can
be time consuming7
Tips and Tricks for Setting Up STORET Data

Sample Collection Procedures
Sampling collection procedures describe how samples were collected, including information on
sampling gear, gear configuration, sample preservation, and storage. For measurements made at
the station, such as water temperature, this does not apply. You can create as many procedures
as necessary.

Collection procedures should include the following information at a minimum (Note that other
metadata can be stored as well):

   •   Sample collection procedure ID (up to 12 characters),
   •   Name of the procedure, and
   •   Type of sampling gear used (e.g., water sampler, electroshock, net).

Gear/equipment configurations describe the types of field measurement or sampling gear that are
used.  Once you select gear ID STORET, you can enter the gear configuration. This can include
serial  numbers, size, manufacturer, and any other information.

Sample preservation, transport, and storage describe how a sample was preserved and
transported to the lab for analysis. This basically consists of descriptions of containers, (e.g.,
glass bottles), and preservation, if any (e.g.,  dry ice, 864, etc.).

Table 8 shows different ways survey respondents have set up sample collection procedures for

         Table 8. Options for Defining Sample Collection Procedures in STORET
Options for Defining Sample
Collection Procedures
For gear configuration, describe tools
and methods? 1P
For sample preservation, describe
bottle used? 1P
Based on type of bottle used? 2' 8P
National procedures and some state-
developed procedures? 3P
Five collection procedures and six gear
configurations that cover all cases? 4P
Biological or grab sampling? 5P
Procedures unique to each project? *
Designate collection procedure but not
Do not define sample collection
procedures? 7P
Methods are linked to data? 1P
Easy to separate data? 2P
Easy to determine whether sample is a
grab or spatial composite? 3P
Captures necessary level of detailP 4P
Easy to trackP 5P

Quicker to set up STORETP 7P

May be difficult to ascertain
what bottle type was used?

Less descriptive metadata?
Tips and Tricks for Setting Up STORET Data

More Information

If you would like more information about setting up STORET, contact the EPA STORET
assistance hotline at 1-800-424-9067 or by e-mail at
Tips and Tricks for Setting Up STORET Data                                            15

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Tips and Tricks for Setting Up STORET Data                                                16

                                   Appendix A
         Tips and Tricks for Setting up STORET Data Survey

This appendix contains the survey questions sent to current STORET users and the full text of
their responses.  The first section, submitter information, includes the name and agency of each
respondent, and some other general information.  The remaining responses are organized by
STORET data categories (e.g., projects, stations, etc.).  In each section, the questions are listed
first with an assigned letter. The responses are numbered to correspond to the submitter
information in the first section and then further identified by the question letter.

A.   Survey Instructions

The purpose of this survey is to gather helpful ideas for setting up various aspects of STORET.
This information will be summarized, compiled and made available to the STORET community
to assist others in making decisions on how to customize STORET for efficient data import,
retrieval, and display. All setup information is helpful - even if it didn't work well.  Setting up
STORET can be daunting and a document summarizing how others have done it may save much
time and frustration for new users as well as provide great ideas for experienced ones.

Please fill out this survey and send it to: Thank you in advance
for taking the time to provide this valuable information and help others avoid "reinventing the
Submitter Information

1.  Name: Geoffrey Smith
   Agency: Delaware River Basin Commission
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x    Water Chemistry           	   Habitat Data               	Other
      	   Sediment Chemistry         	   Biological Data - Raw
      	   Tissue Chemistry           	   Biological Data - Metrics
        x    Physical Measurements      	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   2 Projects, -100 Stations

2.  Name: Carrie Wengert
   Agency: Pennsylvania Department of Environmental Protection
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x    Water Chemistry           	   Habitat Data               	Other
      	   Sediment Chemistry          x    Biological Data - Raw
        x    Tissue Chemistry           	   Biological Data - Metrics
      	   Physical Measurements      	   Data Loggers

Tips and Tricks for Setting up STORET Data Survey                             Appendix A

   Approximately how many projects and stations do you have in STORET now?
   3-soon 4 Projects, 570 Stations

3.  Name: Paul Morton
   Agency: New Jersey Dept of Environmental Protection
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
      	   Water Chemistry             x    Habitat Data              	Other
        x    Sediment Chemistry          x    Biological Data - Raw
      	   Tissue Chemistry          	   Biological Data - Metrics
        x    Physical Measurements     	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   37 Projects, 6000 Stations

4.  Name: Jim Porter
   Agency: Minnesota Pollution Control Agency
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x    Water Chemistry           	   Habitat Data              	Other
      	   Sediment Chemistry        	   Biological Data - Raw
      	   Tissue Chemistry          	   Biological Data - Metrics
        x    Physical Measurements     	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   33 Projects, 4421 Stations (Many stations are transfers  from Legacy with no new data. 1836
   stations have visits in the new system.)

5.  Name: Tavis C. Eddy
   Agency: Wyoming Department of Environmental Quality /Water Quality Division
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x    Water Chemistry           	   Habitat Data              	Other
      	   Sediment Chemistry          x    Biological Data B Raw
      	   Tissue Chemistry          	   Biological Data B Metrics
      	   Physical Measurements     	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   2 Projects, 	Stations

6.  Name: RickLangel
   Agency: Iowa Geological Survey (Iowa Department of Natural Resources)
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x    Water Chemistry           	   Habitat Data              	Other
      	   Sediment Chemistry        	   Biological Data - Raw
      	   Tissue Chemistry          	   Biological Data - Metrics
        x    Physical Measurements     	   Data Loggers

Tips and Tricks for Setting up STORET Data Survey                             Appendix A

   Approximately how many projects and stations do you have in STORET now?
   45 Projects, 326 Stations

7.  Name:  Deb Borland
   Agency: MT Department of Environmental Quality
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry             x    Habitat Data              	Other
        x     Sediment Chemistry          x    Biological Data - Raw
      	   Tissue Chemistry          	   Biological Data - Metrics
        x     Physical Measurements       x    Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   -200 Projects, 4000 Stations

8.  Name:  James Adkins
   Agency: Div. Water & Waste Management, WV Dept. Environmental Protection
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        P     Water Chemistry             F     Habitat Data              	Other
        P     Sediment Chemistry          F     Biological Data - Raw
      	   Tissue Chemistry            F     Biological Data - Metrics   P = Present
        P     Physical Measurements       F     Data Loggers              F = Future
   Approximately how many projects and stations do you have in STORET now?
   10 Projects, 3500 Stations

9.  Name: Dave Wilcox
   Agency: Gold Systems
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry             x    Habitat Data              	Other
        x     Sediment Chemistry          x    Biological Data - Raw
        x     Tissue Chemistry          	   Biological Data - Metrics
        x     Physical Measurements     	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   	Projects, 	Stations

10. Name: Julia Utter
   Agency: Florida Department Environmental Protection
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry           	   Habitat Data              	Other
      	   Sediment Chemistry          x    Biological Data - Raw
      	   Tissue Chemistry          	   Biological Data - Metrics
        x     Physical Measurements     	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   932 Projects, 18515 Stations

Tips and Tricks for Setting up STORET Data Survey                             Appendix A

11. Name: Rich Hanson
   Agency: South Dakota Dept. Env. & Nat. Res.
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry           	   Habitat Data               	Other
        x     Sediment Chemistry          x    Biological Data - Raw
      	   Tissue Chemistry           	   Biological Data - Metrics
        x     Physical Measurements      	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   66 Projects, 1105 Stations

12. Name: Joe Gross
   Agency: North Dakota Department of Health
   Email Address:
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry            x    Habitat Data               	Other
      	   Sediment Chemistry          x    Biological Data - Raw
      	   Tissue Chemistry           	   Biological Data - Metrics
      	   Physical Measurements      	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   110 Projects, 1275 Stations

13. Name: [respondent left blank]
   Agency: [respondent left blank]
   Email Address: [respondent left blank]
   What type of data are you putting into STORET? Check all that apply.
        x     Water Chemistry           	   Habitat Data               	Other
      	   Sediment Chemistry         	   Biological Data - Raw
      	   Tissue Chemistry           	   Biological Data - Metrics
        x     Physical Measurements      	   Data Loggers
   Approximately how many projects and stations do you have in STORET now?
   3 Projects, 383 Stations


A.   How did you define your projects? (By type (water treatment plants, volunteer river
     monitoring etc.), by calendar year (all data for a specific type broken out by calendar
     year), by length of applicable QAPP (data for type is added as long as under the same
     QAPP), by site or facility (i.e., one project = one facility's data), etc.)

B.   What were the advantages and disadvantages to setting up projects this way?

Tips and Tricks for Setting up STORET Data Survey                              Appendix A


      1 A. Project Name was defined by the program that the data is collected under.

      B. Advantage - Unambiguous results, ease of sorting once out of STORET.

      2A. I identified my projects based on the programs already designated. For example, one
          of our projects is WQN, which is all data associated with Pennsylvania's Fixed Water
          Quality Network program.  Similarly, WQF is associated with our Fish Tissue
          sampling program and GWN is associated with our Groundwater Network.

      B. The major advantage was that the data for these programs were already divided based
          on the program itself and that was one less step that I needed to perform when
          formatting the data.  I have not found any disadvantages of having my data broken
          down into these projects.

      3 A. Mix: USGS by "Water Year," multi-year projects by Watershed, Bio data by calendar

      B. USGS data matches "Water Year" books published by USGS, bio data matches
          reports, 303(d) data  easy to pull out by trip, but doesn't match QA Plans (which were
          by year).

      4A. A project may be a defined agency program, a more general ongoing monitoring
          effort, or a specific data provider, such as a local project that sends us its data.
          Examples: North Shore Load Project, Lake Trend Monitoring, Pipestone Creek
          TMDL Project. If staff want to be able to query data for a particular monitoring
          effort, we suggest they set up a STORET project for it.

      B. Data owners (monitoring and related staff) define how the data are organized, so it
          seems intuitive to them. It makes it easy to query data for a particular program or
          data provider.

      5A. Our program is specific enough in its use of STORET; for watershed sampling we
          have two projects: our REFERENCE Project which entails healthy systems that are
          used in our biocriteria  analyses for determining stream condition in general.  Our
          BURP (Benficial Use Recon. Project) Project is for the ambient monitoring of surface
          water across the state to determine if designated uses are being met.

      B. Some sites, or sampling events, are used for both projects, and we also have cases
          where a given sampling event (such as a water quality complaint) does not seem to fit
          into either category. The BURP project ends up being pretty all encompassing. The
          advantage is that these projects are also (somewhat) aligned with budget history of
          the same name.

      6A. Our projects are a combination the water-quality project and water year that the data
          is collected. For example, data collected as part of the Sny Magill 319 Monitoring

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

          project during Water Year 1999 would be assigned a project of SNY1999.  Our
          ambient data collected in Water Year 2002 would be assigned a project of AMB2002.

      B.  With our web-based retrieval, we can quickly retrieve project-specific data (for
          people only interested in a one project) and see any changes to our stations (for
          example, if an stations have been added or removed).

     7A.  Historic data migrated into the modernized STORET were migrated using projects
          categorized by type of project. The current data being generated and input into
          STORET is largely data collected for the TMDL program.  Although this could be
          considered one project with common DQO and data collection methods, the effort
          includes roughly 100 HUCS across Montana. For this reason, the projects were
          broken down by HUC or Watershed.  (We wanted "Project" to represent a long-term,
          ongoing grouping for results).

      B.  One advantage was that the project is always "known" to data management staff.
          Project corresponds to HUC, a required field for station establishment. (QC on the
          site location must be performed up front to determine HUC and Project, so it forces a
          QC check early on site locations).  A disadvantage is that it does not fully utilize the
          available metadata associated with the project designation.  Note that the use of
          watershed-based project groupings does  not preclude the creation of projects that are
          more consistent with STORET metadata capabilities, but did serve to  simplify the
          STORET start-up for our TMDL program.

     8A.  Usually, by purpose. Have Total Maximum Daily Load, Regular Ambient,  Intensive
          Survey, etc, projects. Duration is ongoing for most of our projects.

      B.  [respondent left blank]

     9A. We have managed projects in one of three ways:
          1.  One project for the entire organization.
          2.  Some organizations have pre-defined projects (studies, etc). In this case we
             simply use these projects.
          3.  For some organizations that have several different entities entering data into one
             org, we have assigned one or more projects to each entity.

       B. 1.  The obvious advantage of a single project is simplicity.  The disadvantage is that
             you limit your retrieval options.
          2.  Using  existing projects is probably the best option for most programs as it is easy
             to manage and provides a valuable way to retrieve their data.
          3.  This final option works well for programs managing data from many different
             entities. A good example of this would be a state hosting several volunteer
             monitoring groups.  If it is determined that all of the volunteer monitors will share
             a single org, then project becomes a good way  to segregate their data.

     10A. 1.  The type of monitoring plan the agency is using often defines project.
          2.  Abbreviate of the existing project name is often used.


Tips and Tricks for Setting up STORET Data Survey                              Appendix A
       B. No apparent disadvantages.

     11 A. A project is a specific sampling effort delineated by location(s) and sampling date(s).
          We created an ACCESS table containing projectlDs as well as project duration, start
          date and a brief description of the project.  This table is linked to other tables
          containing station IDs, sampling results, etc.

          Our project IDs are somewhat descriptive of the project with the last character
          identifying the type of project. Example The Lake Alvin Assessment Project is
          ALVINZZ1. The number "1" designates it as an assessment.  An implementation
          project will be designated by a "2" (Example - the Lake Alvin Implementation
          project ID would be ALVINZZ2. Dredging projects end in "D."  All following
          assessments on the same project  end in a different odd numbers all following
          implementation will end in a different even number. If we have something unique
          that doesn't fit the three typical projects we plan to add a different letter as the 8th

      B. Most environmental problems, assessment projects, or control efforts are usually
          defined by where and  when they occur so grouping data by project appears to be the
          most practical way to  group data.

          ID Set-up:  This way  we can sort data different ways. All Alvin data can be found by
          truncating the last letter off the ID. Or all assessment projects can be found by
          looking for the odd numbers, all  implementation projects can be queried by searching
          for even numbers.

     12A. For the most part they are defined by regionality (i.e., Watersheds, Counties,  etc.).

      B. A watershed may extend into multiple counties, and if defined by county, the project
          sponsor (319 sponsors) may not be willing to implement the project across county
          lines.  Takes more cooperation between counties/sponsors.

     13A. A project is often a study; so for example, one project may be a group of WQM
          stations along a certain stream. It should be noted that a  station might be in more than
          one project.

      B. Assigning data in this manner can be useful when pulling data from STORET.


A.   What type of labeling scheme did you develop for your station IDs?

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

B.   What were the advantages and disadvantages to setting up station IDs this way?


     1 A. It is dependent on who is program manager. We have everything from full numeric
          station id's to station id's that have a state abbreviation  and streams position in
          watershed longitudinally to collection organization and river mile of that site.

      B. Extensive research to determine exactly where site is.

     2A. As stated above, our programs are defined with defined goals, sites, etc.  The WQN
          program has stations set up simply by the letters WQNO### and the three digit
          number of the WQN site.  The fish tissue stations are set up a little differently.  They
          begin with WQF indicating the type of station, followed by the five-digit stream code
          followed by the river mile where sampling began.  So, an example of a fish tissue
          station would be WQF00002-17.8.  This indicates a Fish Tissue station on  the
          Delaware River starting at 17.8 RMI. Finally, the Groundwater Network stations
          were named in the following manner. They too are begun with a three-letter prefix in
          this case, GWN.  The next identifier is a letter indicating the drainage, D=Delaware,
          S=Susquehanna, etc.  The next grouping of numbers/letters indicates the sub-basin
          (based off of PA's State Water Plan) where the station is located (i.e., 02C = Lehigh
          sub-basin). Finally, the last digits indicate a numerical  sequence of stations assigned
          in that drainage.  So an example of a GWN station is GWND02C023 which tells us
          the station is a groundwater station located in the Delaware Drainage Lehigh
          Subbasin and it is the 23 site identified in this region.

      B. In all  cases, the advantage was that the sites were very similar and easy to format so
          that they could all be imported into STORET following the same template.  Again, I
          haven't  found any disadvantages using this system.

     3 A. Used  IDs from Legacy STORET, based on old River Reach system.

      B. Consistent with LDC (for as long as it's up) and stations come out in downsteam
          order. Disadvantages: Numbers are a pain to calculate, because they depend on miles
          up a segment and I don't have a route built in my  GIS coverage. I can't retrieve data
          on project specific station IDs (USGS, bio) as we could with old STORET secondary
          IDs.  Project specific Station IDs used in 305(b) and 303(d) reporting, so lists on EPA
          Web sites don't match what can be searched in STORET.

     4A. For lakes, we use commonly accepted IDs developed by the Minnesota Department
          of Natural Resources. Each ID consists of a county code and a serial number, e.g. 27-
          0016. To track specific monitoring sites on lakes, we include separate site codes on
          activity  IDs.  The site coordinates are tracked outside of STORET, although we hope
          to add them to STORET as "actual activity locations" eventually.

          For streams, we use a serial number in the form SOOO-000. We record aliases for
          stations, including Legacy STORET primary and  secondary codes, as external


Tips and Tricks for Setting up STORET Data Survey                               Appendix A

          reference scheme station IDs.  The goal is for each monitored point to have a single
          unique ID with separate aliases used by different data providers.

      B.  Lakes: Almost all data collectors in the state use the same IDs. Because a station is
          defined as an area and not a point, it simplifies things and reduces the number of
          station establishments.  Tracking the sites (actual activity locations) separately means
          maintaining another system and complicating our activity ID assignments.
          Streams: We do not currently have a good system in place to make sure that a
          monitored point gets established only once.  It can be time-consuming to determine
          whether stations  are co-located, and if so, how close is close enough to call it the
          same station.  The IDs are meaningless, which is good data practice but less intuitive
          for monitoring staff who use the IDs. We used to use a stream name abbreviation and
          a mile indicator,  such as CD-0.5. Increasingly detailed stream GIS coverages have
          rendered the mile values inaccurate. Assigning codes for new stations relative to old,
          inaccurate station codes got complicated.

     5A.  Ordinal by ecoregion, e.g., MRW26 means the 26th station for the Middle Rockies
          West ecoregion.

      B.  This is helpful in that it is easy for use to organize data by ecoregion, which is critical
          in our biocriteria development.  Our stations have traditionally been assigned for
          those locations where we have collected biological (benthic macroinvertebrates)
          chemical and habitat data, as a full triad.  This means that when we collect only
          chemical data (which is frequently) we have added the WQ (for water quality) onto
          the front of it e.g., WQMRW05. This has been cumbersome. Another issue with our
          station IDs is for those early stations; sometimes a MRW5 and a MRW05 both
          pertain to the same 5th station established for that ecoregion. This discrepancy has
          appeared and makes organization difficult.

     6A.  We use an 8-digit numeric code. The first 2 digits are for project identification.  The
          next 2 digits are the county number. The remaining 4 digits are a sequential number.

      B.  Since stations can be used on multiple projects, we have to take extra time to double
          check our current stations to make sure we do not duplicate stations with multiple
          STORET number.

     7A.  Our station ID convention is HUC based, again.  A three-character code designates
          the HUC.  This is followed by a five-character acronym for waterbody, and then a
          two-digit numeric for a total of 10 characters.

      B.  Disadvantage: The Cross-reference to HUC code must be known prior to assigning a
          station ID, and the HUC must have been verified as correct. Another disadvantage is
          that the station ID must be verified as unique in STORET prior to using to avoid
          assigning duplicate identifiers.

          Advantage is that all the stations in a single watershed group together in the STORET
          user interface and Report module, and the stations on a single waterbody group


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

          together within a given HUC if consistent acronyms have been used. Also people
          recognize their station IDs.

     8 A.  Most of all our streams have Alpha Numeric labels. Many labels are greater than 15
          characters allowed by STORET. So for a site, which have Id as WVABC-1-X-123-P-
          4-ZZ, Station Id could be ABC-001-0123, so it could fit in Station Id. Box.

      B.  Need for STORET allow for more than 15 Characters for Station IDs.  Then we can
          better link STORET to other databases in our agency. Now we only use EPA Key
          Identifiers for State Labels which can exceeds 16 characters allowed for Station Id for
          quite a number of our streams and waters.

     9A.  In most cases, the stations already  have some type of ID from their use in the local
          LEVIS system. As there are many better ways to query a station that by ID, we
          typically use this existing ID or a simple numbering mechanism.

      B.  The advantage is to keep it simple and to be able to tie it back to the source of the
          data. The disadvantage would be to not have a logical key (i.e., with extra info coded
          into the ID itself) that could be user for querying.  In a robust database like STORET,
          however, you should simply be able to query a station by any of the many station
          fields and should not feel compelled to encrypt this information into the ID.

     10A. 1.  The station ids are based on delineation from a HUC code map used in the 1970s.
          2.  Each agency has developed their own  system for labeling station ids.

       B. 1.  The disadvantages are that the map is no longer in use and is not in an electronic
             format. It's hard to find and if you have gaps in your number set you're stuck
             having to create a new numbering system that doesn't fit with the old system.
          2.  Some agencies within Florida would like FDEP to mandate a consistent method
             for creating station_ids (i.e., lat/longs as the station_id). This would allow any
             user to understand the  specific labeling system during retrievals no matter which
             agency they are interested in.

    11 A.  The first seven letters are the first seven letters the Project ID. Subsequent letters or
          numeric characters (the last 8) refer to type of waterbody and site ID. Example:
          ALVINZZLA01 might refer to the Lake Alvin Project (ALVIN), a lake sample (LA)
          and site 01 (a specific sampling location in the lake). ALVINZZLAT01 might refer to
          a tributary site (T) at a specific sampling location (01) on a tributary.

          These sites can then be used for different types of projects for Lake Alvin such as an
          implementation project can have the same site ID as the assessment project. If more
          than one project was done at Lake Alvin then a numeric character could be inserted
          following the project name. So ALVIN1LA01 might refer to an in lake site from a
          Lake Alvin Assessment Project and an ALVIN2LA01 might refer to the same site
          used during an implementation project.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

       B. Simple logic and relatively easy to understand what the station ID refers to. This
          allows for quick recognition of which waterbody is involved and it is often a quick
          way to discern which project it is and what kind of project it is. It should be noted,
          however, that some projects involve more than one waterbody and so a project name
          may not always refer to a specific waterbody.  In those cases, one would need more
          information (location) to figure out the project name.

     12A. A 6-digit number beginning with the number 38. For example, our next station will
          be designated the number 385275.

       B. Advantage: Ease of assigning ID's to newly created stations.
          Disadvantage: Sequential  numbers are not always possible when ids are assigned at
          different times. For instance, Lake Isabel may have 2 ids associated with it, 384207
          and 384208.  If a new id is needed, it would be designated the next available number
          (i.e. 385275). This is not a huge problem. It would be less difficult for the field
          personnel sampling to remember the code if they were sequential.

     13 A. A 6-digit number/letter combination is assigned to each station.

       B. The letter portion of an ID is set up as an abbreviation of a specific waterbody or area
          of the state. This makes it easy to tell at a glance, in which region of the state, a
          station is located.  The number portion is unique for each station.
Station Visits


A.    How did you set up or define your station visits?

B.    What are the advantages and disadvantages to setting up the station visits this way?


      1 A.  One station visit per day.

       B.  It tends to idiot proof the system... and you know that we need that.

      2A.  The main identifier with Station Visits was our sequence number that was associated
          with the original sample. This along with the date and time creates a unique station

       B.  The advantages to doing things this way is that you do not need to create a field or re-
          create existing data. The disadvantage, and I have not run into this yet, is that I am
          not sure what the program will do if a sequence is ever repeated.

Tips and Tricks for Setting up STORET Data Survey                               Appendix A

     3 A.  Sequentially or allow SIM to define.

      B.  Allowing SIM caused problems if we had to go back to a site a second time for the
          same "round" of sampling.  Allowing SIM to assign keeps us from going crazy trying
          to keep it straight.

     4A.  One numeric ID per day per trip,  usually assigned automatically by SIM. The only
          exception is for one large volunteer monitoring project, in which we code visit IDs to
          keep each volunteer's data together.  This project uses three-character alphanumeric

      B.  This system seems to work well.

     5A.  Chronological by Station ID; most other cells left blank within the Station Visit

      B.  To be honest, we use this rarely to find/organize data; it does allow us to know how
          many times we have visited a site, but this ends being easy to discern in many ways.

     6A.  We let SIM automatically assign  station visits into STORET when we upload data.
          For most of our projects, we will  have only on visit per day. So, SIM is setup to
          assign  station visits by sampling date. But, some projects will have multiple visits on
          the  same day.  So, SIM is setup to assign visits on sampling date and time.

      B.  We do not use Station Visits with our web-based retrieval program.

     7A.  For our TMDL program, a station visit may span several days for practical purposes.
          Trips to distant part of the state may involve many hours of driving time. Monitoring
          staff may visit a number of sites repeatedly to complete assessment and  sample
          collection tasks. A visit spanning several days allows us to group related activities on
          a single trip into one visit "event."

      B.  One advantage is the ease of tracking the visit, and matching the visit when loading
          results for various activities for a given station on a given trip. Also the activities are
          grouped together. One disadvantage is that it is not always intuitive that various
          activities performed on different days involve the same visit. From the monitor's
          perspective, there may have been two or three distinct site visits during a trip to a
          given region of the state.

     8A.  For a given trip, we may visit many of our sites several times during a "trip."

      B.  [respondent left blank]

     9A.  For most programs, we have created a single station visit per station and date that the
          sampling occurred. If the program identifies that they may visit a station several
          times in one day, we will set it  up as one station visit per station per date and time.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

       B. Again, keep it simple if the program is not collecting this data. Of course, as soon as
          a program starts tracking their actual trips and station visits; these trips and visits
          should be imported into STORET to provide the most accurate tracking of this
          information possible.

     10A. Station visits are defined using the one visit per day default in SIM.

       B. None yet discernable. It is not an often-queried data layer.

     11 A. Station visits are auto-generated. If we need to query some data, we can query by
          Project/Station/Date and Time.

       B. Advantage - One less number to try to come up with. It might be easy querying with
          more descriptive station ID however with how we organize and use our data our
          system works for us.

     12A. By the visit number for that year.  Station 384321 was visited in May. That would be
          visit number 1.  Then visited again in June, that would be station visit number 2, and
          so on.

       B. Works well and is easy to understand.

     13A. A station visit is any sampling performed on one day.

       B. It seems the logical way to set up station visits.


A.   How did you set up (define) your trips (i.e. by day, month, year etc.)?

B.   What were the advantages and disadvantages to setting up trips this way?


     1 A.  We define trip as one year of sampling under that specific program.

      B.  Advantage - Easy to parse out each sampling season.
          Disadvantage - Is a rather broad "trip" designation.

     2A.  Currently the trips are identified by T and then the station number. Activities are set
          up using date, month, year, etc.).

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

      B.  Several times, the system rejected a trip ID for some reason unknown to me and I had
          to reformat the data to "make it work."  I haven't found any other advantages or
          disadvantages, and I'm still confused as to the significance of the trip.

     3 A.  Usually by year.

      B.  People usually want to see a whole year's worth of data and I don't have a billion
          trips in my list as I would if I used matrix or season as the grouping item.

     4A.  By project year. For example,  the Milestone project's 2002 water year data is under
          the trip MILE-2002WY.  The 2002 calendar year Lake Trend data is  under the trip
          LAKETRND-2002CY.  Project staff determine whether they want the data stored by
          water year or calendar year.

      B.  This system seems to work well. The only advantage we see to using trips in the
          literal sense is to track trip blanks.  We decided it was not worth the hassle of creating
          so many trips for that one function.

     5 A.  We created Trips for the sole purpose of STORET. They  are specified by the field
          crew, year and week; for example: LA980831 pertains to the trip taken by the Lander
          Crew, in 1998, on the week starting 8/31.

      B.  Advantage: we can isolate by year and field office.
          Disadvantage: the ids get long, and for previous years it is time consuming to look up
          the beginning of the week that  a trip went out.

     6A.  Trips are a combination of project and date (dependent on the number of samples
          received in a month). For projects that we expect few  samples each month, the trip
          would be a combination of the  project and the year that the samples were collected.
          For projects that we expect many samples each month, the trip would be a
          combination of the project, month,  and year.

      B.  We use trips to find data in STORET that may need corrections. Otherwise, we do
          not use trips for data retrieval.

     7A.  Trips were defined by year. Generally we use a project year, except in the case of
          TMDL program where an entire major basin or region of the state is combined into a
          single Trip. For monitoring projects conducted external to our agency, a separate trip
          helps distinguish the data and allows us to designate a "trip leader" without
          maintaining external personnel in our Organization personnel list.

      B.  The Trip by Year concept helps greatly in organizing and  loading current and historic
          data, and ensuring completeness without duplication. If data from a given data source
          belongs in a designated Trip, we know right where to look to see if the data exists in
          STORET.  Related data also tends to be grouped together, rather than a statewide
          grouping based on all samples  collected in June, for example.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

     8A.  If for a specific watershed during a given year, we consider a trip for 12 months for
          that watershed for one Project, like TMDL. Next year we will be sampling another
          watershed or body for 12 months for TMDL. For our Ambient Sites, we use quarter
          of a year in which all our sites are visited just once.

      B.  [respondent left blank]

     9A.  For programs tracking trips, we simply import what they provide. For all others we
          have created either one trip per day, month, or year.

      B.  For legacy data, one trip per month or year works well to organize their data without
          an enormous number of trips.  The more years of data they have, the better the one
          trip per year option gets. For programs that do monthly monitoring, one trip per
          month works well as this provides a pretty accurate representation of their actual

    10A.  Trips are defined by using the one trip per year default in SIM.

      B.  The advantages are that the STORET interface can handle browsing the number of
          trips generated by the one-per-year default. Other than that it is not an often-queried
          data layer.

    11 A.  Trips are auto-generated by day.  One trip per day per  project.

      B.  Advantage - Easy for us to generate trip ID.
          Disadvantage - None really for us.

    12A.  By the year.

      B.  Have not discovered any major advantages or disadvantages with this method of

    13A.  Each month's worth of WQM data is considered a trip. All  data for all stations is
          included in that trip.

      B.  Assigning data to monthly "trips" makes it easy to find data from a particular period
          of time.


A.   How do you set up (define) activities (i.e., keep field measurements separate from
     laboratory analyses or combine together, etc.)?
B.   What were the advantages and disadvantages to setting up activities this way?


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

      1A.  We combine all together (I just give them arbitrary numbers) and the designation lies
          in the Activity type.

       B.  Advantage - More time efficient to number this way for data entry.
          Disadvantage - When open screens in STORET you can't really tell what you are
          looking at in terms of parameters.

      2A.  Activities are defined by month, day and year.  As far as specific activities, field and
          lab results are separated using our parameter codes assigned to the sample.

       B.  The advantage to this is that it keeps all activities separate and unique and allows an
          end user to clearly see which data was analyzed in the field and which data was
          analyzed in a lab.

      3 A.  Mix again. Data from USGS is combined; data we do internally is separate.

       B.  Separate forces me to keep track of Activity IDs so the field and lab stuff matches up,
          and also is unique. Also a pain to keep separate because I need to know if the
          Trip/Visit is new or existing to tell SIM to create a new one or use an existing one.

      4A.  Activities were more difficult to define than most other STORET concepts.  Because
          field and lab data had to be separated by type into different activities, we had to
          devise a system to keep sampling events together.  An explanation of our ID scheme
          follows. It might offer some insight into how we did this.

          Lakes:  Activity IDs consist of a three- to five-digit site code indicating the sample
          location on the lake, plus a hyphen, plus a two-digit depth to the nearest meter, plus F
          for field msr/obs or S for sample, plus R if a replicate, plus a digit if necessary for
          uniqueness. For 2-meter-integrated samples, the suffix following the site code is -
          I2S.  Examples:  201-OOF1, 102-03S, 102-03SR, 401-I2S.

          Streams: Activity IDs are usually the lab sample number followed by one or more
          descriptive codes.  The field data corresponding to the lab data also uses the same
          sample number for the core of the ID. If a lab sample number is unavailable, we use
          a separate serial number in its place.  The suffix codes for regular samples are F for
          field msr/obs, S for sample, plus R if it's a replicate.  For QC samples, the suffix code
          is Q, plus either E for equipment blank or R for reagent blank. Examples:
          200215678F, 200215678S, 200215678SR, 200215678QE.

       B.  Lakes:  We set it up this way to track site codes and to keep profile data organized.
          There are some significant disadvantages, however, due to the rounded-off depths.
          Say a person uses a Hydrolab or YSI to collect profile data at site 103, resulting in
          readings at 3.7m, 4.0m, and 4.4m, among others.  Under our scheme, each would
          have an activity ID of 103-04F. I have to add a digit to each to make it unique: 103-


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

          04F1, 103-04F2, and 103-04F3.  The activity ID field is not long enough for us to use
          more decimal places and still accommodate the rest of our coding scheme.

          Streams: This system seems to work well.

     5A.  Our activities are as follows: we conduct biological, chemical and field data
          collection.  Field pertains to that which was directly observed in the field, such as
          pH, Dissolved Oxygen, etc. So an activity ID of B67 refers to a biological activity,
          and the same station and date would have a C67 for the chemical data.

      B.  There is clarity to our approach,  although the chronological ID does not aid us in any
          major way, and by splitting field and chemical samples we treat them separately in
          database organization, while we  need to consider them together in analyses.

     6A.  We keep activities separated.  Currently, we use 3 different activity Ids. Two are for
          field measurements  (one for measurements related to water conditions, the other for
          atmospheric conditions that are recorded) and the final one for water samples that are

      B.  Activities are only used to help us find data in STORET. Like trips, activities are not
          used in our web-based program for data retrieval.

     7A.  We ordered sheets of pre-printed, color-coded labels from (Shamrock). Each sheet
          may have, say 10 identical labels.  These labels are used to identify the related
          activities for a given site visit. To  ensure uniqueness for each activity ID, a suffix is
          added that represents a medium-code or sample type. For example, "M" for
          macroinvertebrate, "W" for water, "S" for sediment, "F" for Field Msr/Obs, etc.
          Typically, one label is used in the field book, and/or on the site form.  This gives all
          activities related to a site-visit similar activity ID(s), and all group together. The
          activity ID  has a two-digit prefix for year, an alpha character corresponding to a given
          trip, a three-digit numeric (sequential), then a hyphen and room for the medium code
          suffix. Example: 02-L127-M. Our activity ID is typically 9 characters, though the
          suffix for sample type, or the sequential numeric could be longer if necessary.

      B.  This convention has many advantages, and is working well for us.

     8A.  Field measurements are separated from laboratory analysis. Activities, like Nutrients,
          are kept separated from Metals, etc.

      B.  [respondent left blank]

     9A.  In almost all cases we have taken the effort to break out the field measurements from
          the lab analyses.

      B.  This takes a considerable amount of work, compared to leaving them as a single
          activity, but the data is ultimately modeled much better. If this is not completed, then
          users finds  themselves being forced to add a sample collection procedure for an


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

          activity that may have only had field measurements.  Keeping field and lab results in
          a single activity also causes the problem that you lose an easy way to track if the
          result was created from a sample or a field measurement.

          A similar situation exists for medium. Often Air temperature is taken with a water
          activity. As the characteristic actually includes the word 'Air' in the characteristic
          description, we will sometimes include this with the water activity just to keep things

    10A.  Activities are defined as a composite set; one activity for each sample date, time,
          depth, activity type and category.

      B.  It seems that this is the only way to set up an activity short of ignoring the activity
          type data layer and defaulting everything to Sample or Field Msr/Obs. It would help if
          the Activity ID field were longer than 12 characters so that it would be easier to
          construct an ID given that there are so many pieces of data that have to be considered
          in one activity id.

    11 A.  The activity ID for lab analysis is the same as it is for Measurements/Observations
          except measurements and observation activities have an "F" after the Activity ID to
          designate as a field measurement.  We use separate ACCESS tables/forms to group
          field data, chemical data, biological data, etc. The ID for contaminate data has "C"
          after the lab ID, Elutriate data has an "E" after the ID, the ID for the Algae data has an
          "A" after the ID and so forth.

      B.  As an advantage we can group  our field and chemical data easily by comparing
          similar activity IDs.  As STORET dictates each kind of data (i.e. chemical,
          biological, field, etc.) has certain attributes and this uniqueness of each data type led
          us to group the data in our ACCESS table by sheet. In our ACCESS data entry forms
          we can then keep unique biological sampling or chemical  sampling and link them to
          the field data sheet in the same form.

    12A.  The lab assigns a Chem-Log number for every sample (i.e. 03-R0001). This log
          number is used as the activity ID. To keep the field measurements associated with
          that sample separated, we place an "F" at the end of the log number to differentiate
          the field measurement results (i.e., 03-R0001F) from the lab results.

      B.  No major advantages or disadvantages.

    13 A.  We assign all field measurements the ID number that the laboratory assigned the
          laboratory data for the station.  We add an "F" to the end of that lab number.

      B.  Using the lab ID number for field data makes it easy for us to tell that certain field
          conditions existed when certain samples for lab analysis were taken. Nevertheless,
          because field data has an "F" at the end of the number, STORET does not actually
          combine the lab and field data.   Therefore, we hope to eliminate any confusion about
          what analysis was performed in the field and what analysis was performed in the lab.


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

Characteristic Groups


A.   How do you set up characteristic groups?  Do you group characteristics by medium,
     activity type, i.e., Field Measurements vs. Sample, or do you find it more convenient
     to combine different types of characteristics into a few or even one group.

B.   What were the advantages and disadvantages to setting up characteristic groups this


     1A. We tend to group by activity type.

       B. Advantage - It allows us to separate out the different types of measurements quickly,
          eases database set up when using SIM.
          Disadvantage - When in STORET App. it is not possible to see all at once when done
          this way.

     2A. Our characteristic groups are defined by the SAC (standard analysis code) used in
          analyzing the specific samples at the lab. So, for example, if a sample is sent to the
          lab labeled SAC 10, SAC 10 may consist of testing for Metals.  So, under
          characteristic group SAC, all of the metals would appear as Characteristics.

       B. As is the case with many of the things I've used, this already existed and was
          associated with the data so therefore, I did not need to create a new way to group the
          samples. The disadvantage was that the SAC's overlap as far as characteristics so it
          was timely in setting up initially.

     3A. Started when we were limited to 100 Row IDs. Bio by Order/Family. Chem by lab
          except for USGS, which is by groupings (field, routine, metals, organics,  etc).
          Field/Lab separate. Fish all in one group (created after the 100 Row ID limit).

       B. Creates too many groups, hard to keep track of. Worried that a few big groups would
          slow SIM.

     4A. We set up one large sample-type characteristic group for our main lab using the lab's
          analysis codes as row IDs.  We also set up a few field-type characteristic groups.  I
          had planned to set up one characteristic group per lab, but lately I have been moving
          away from characteristic groups. Instead, I created something like a characteristic
          group table in a separate database, and I import data through SIM using the
          characteristic-by-attributes method. I find it gives me more flexibility to handle
          special cases that may not exactly fit STORET's characteristic group templates.

Tips and Tricks for Setting up STORET Data Survey                               Appendix A

      B.  Setting up sample-type characteristic groups by lab organizes procedures logically.
          Characteristic groups in general are somewhat less flexible than storing
          characteristics by attribute through SIM.

     5 A.  We break them  out by medium; biological, chemical and field parameters.

      B.  This has worked well, and philosophically corresponds to our monitoring agenda.
          One initial problem, that presently needs to be fixed, is that at the time we developed
          our CGs only 99 characteristics were allowed in each group.  Since our taxa list for
          invertebrates is  over 400 taxa, we had to make 5 different biological CGs. Now that
          we can have more characteristics per group, I have to decide whether to retrofix the
          older data, or make a decision to choose a year to move forward with a single new
          CG to cover all  bug data.

     6A.  We set up or characteristics on a combination of medium, activity type, and analysis
          groups.  For example, we have one category for atmospheric field measurements (i.e.,
          air temperature, cloud cover, etc.). We have a category for water field measurements
          (i.e., water temperature, pH, etc.). For water samples that we have analyzed in a lab,
          we have characteristic groups for nutrients, bacteria, pesticides, semi-volatiles, etc.

      B.  [respondent left blank]

     7A.  Our characteristics are grouped by medium, then activity type.

      B.  It is convenient for us to use characteristic groups for work where agency staff and set
          procedures ensure defaults like units of measure and methods will be consistent. We
          try to avoid the use of characteristic groups in receiving data from projects or data
          sources external to our agency, as we are concerned about having a number of
          defaults as "understood."  A change in methodology or units cannot be easily
          accommodated in these cases.

     8 A.  Group characteristics, by  collection, sample preservation/transport, or if they are
          field, or lab types.

      B.  [respondent left blank]

     9A.  We have worked with an  even mix of programs that have setup characteristic groups
          and those that just pass all of the characteristic information directly to STORET.
          When using characteristic groups and rows, the most important thing is to set them up
          in a way that can easily be mapped from the  source data.  You do not want to have to
          work backward and add the group and row to the source data set.

      B.  [respondent left blank]

    10A.  Characteristic groups are set up with activity type as the dependency. All
          characteristics that are analyzed in the lab are in one group; all that are analyzed in the
          field are in another.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A
       B.  Aside from not being able to default the characteristic group value when creating a
          batch file, nothing.

     11 A.  We do not set up characteristic groups.

          There are also physical limitations to the ACCESS data entry forms that predispose
          us to split the data into different data types. There is only so much information that
          can fit in a single data entry form. So our contaminant data while considered
          chemical data is on a different sheet.  Special chemical parameters that are not
          sampled on a regular basis are on a different sheet. Even thought they are on separate
          sheets all water chemical samples will have the same ID with just a different letter at
          the end of the ID.

          In addition, our chemical data typically comes to us from the laboratory separate from
          the biological data and sometimes separate from all of the field data.  So it also makes
          sense to separate the data types simply because we get the data separately anyway.

          We do not use characteristic groups, we use our own built sample collection
          procedures and all meta data is added to each activity ID via an ACCESS process.

       B.  Advantages - Didn't have to set up Characteristic groups.  More flexible.

     12A.  [respondent left blank]

       B.  [respondent left blank]

     13 A.  We set up characteristic groups by laboratory (one for each lab) and by field data.

       B.  The advantage to doing it this way is that when you enter data through SIM, all data
          from one lab can go in with one characteristic group.  It makes data input go faster.
Lab/Field Analytical Procedures and Equipment


A.   How do you set up lab/field analytical procedures and equipment? Do you rely on the
     "National Procedures" provided by STORET or do you create your own?

B.   What were the advantages and disadvantages to setting up lab/field analytical
     procedures and equipment groups this way?


     1 A.  National Procedures where apply. For gear and collection we designate our own.


Tips and Tricks for Setting up STORET Data Survey                               Appendix A
      B.  Analytical - very time consuming to search for and designate manually not being
          familiar with methodologies.
          Gear/Collection - designating our own allowed us to get more specific on our actual
          sample collection procedures.

     2A.  Our lab has given me a listing of all of the methods they use in analyzing the samples
          and I've in turn relied mostly on the National Procedures. However, DEP has several
          methods that they've developed or adopted that were not on these lists so I also
          created my own.

      B.  I think the National Procedures are a good idea, but they need to be kept current with
          new releases of publications. The advantage to using them is that it took a lot less
          time to adopt and already existing procedure, but the disadvantage was that there
          were some methods missing.

     3 A.  Use national if they match, otherwise created my own (especially for USGS Denver
          lab methods).  Set up both field  and lab methods.

      B.  Creating my own lets users know where we deviated from national procedures and
          add procedures not supplied by EPA-HQ. National save me time, especially with

     4A.  We started out by creating our own procedures, and we still do for our main lab.
          Increasingly we rely on the national procedures as we collect data from a myriad of
          data providers and labs.

      B.  Using the national procedures is less hassle. Creating your own lets you reference
          lab-specific methods that may deviate from the national procedures.

     5 A.  Most of this was done in communication with our lab manager. We mainly used the
          National Procedures.

      B.  It was very organized, and all the relevant material is in one place.

     6A.  We rely heavily on the national  procedures. However, we will create the method only
          if the parameter does not have an established national method.

      B.  Most parameters that we test for have established  methods, so it is easiest to use the
          national procedures that STORET provides.

     7A.  Whenever possible we try to reference National Procedures in use.

      B.  Tracking down the correct National Procedure in use can be time consuming and
          frustrating, especially if STORET makes a distinction between methods that does not
          seem to correspond to method numbers in a laboratory's method documentation.
          Nevertheless, we try to strive for a high level of data comparability, and approved
          national methods are an element of that.


Tips and Tricks for Setting up STORET Data Survey                              Appendix A
     8A.  Mostly, we rely on National Procedures. A few we have created for visual inspection,
          in-stream flow measurement, etc, where we could not find any in National

      B.  [respondent left blank]

     9A.  Most programs have some field in their source data that can be mapped to the
          analytical procedure.  We use the SIM translation feature to do this mapping. Some
          programs have utilized several  of the National procedures, but just about everyone we
          have worked with has had to add some of their own as well. Very few programs have
          done much with Sample collection procedures or Equipment. In almost all cases, we
          have created a single sample collection procedure that does not require equipment and
          loaded all samples with this procedure.

      B.  The big thing here is that we simply had to make due with the information available
          in the  source data. By defaulting to a single Sample collection procedure that does
          not require equipment, we have solved the problem of not having the necessary data.
          In some cases, the program did provide multiple sample collection procedures and
          equipment, which is nice if you have the data.

    10A. National procedures are used when possible, otherwise they are created as org specific

      B. None.

    11 A. We have an Analytical Codes ACCESS table that contains information about a
         specific parameter, its STORET character, fraction analyzed, units, analytical
         procedure, and detection limits. The analytical procedures follow national procedures
         and use the same code numbers. All of this information is listed for each laboratory
         because different laboratories may analyze parameters differently and even have
         different detection limits for the same  analytical procedure.
          We try to use only recognized methods. If we can't find a method that works we
          develop our own, enter it into STORET and then it gets added through our ACCESS
          automated process.

      B. Simple logic.

    12A. Both.  If our lab procedure deviates from the National Procedure in any way, it is
         noted by defining a new state defined analytical procedure.

      B. [respondent left blank]

    13 A. We do it both ways.

      B. You can customize.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

Sample Collection Procedures


A.   How do you set up your sample collection procedures, i.e., sampling gear, gear
     configuration, sample preservation and storage?

B.   What were the advantages and disadvantages to setting up sample collection
     procedures this way?


     1 A.  Gear Config. - Just described the tools we used for collection and the method of
          sampling (Bottles/Bridge).
          Sample pres/storage - The bottle each sample was in (acidSOOml, icedlL, etc.).

      B.  Advantage - Do not have to go back into STORET to look at our methods; it's
          ingrained in the data request.

     2A.  The sample collection procedures were set up based on what type of bottle is used to
          collect a certain type of sample.  For example, CONT-5 is the bottle and method used
          to collect all metals, CONTlO-Phsphorus, Ammonia and TOC, etc.

      B.  The advantages to this were that everything stayed separate based on the type of
          sample being collected.  The disadvantage was that you needed to know what type of
          equipment was used when collecting the sample and that info might not be easily

     3 A.  Creating my own lets users know where we deviated from national procedures and
          add procedures not supplied by EPA-HQ.  National save me time, especially with

      B.  People basically want to know if it was a grab or a spatial composite, not as
          concerned  about the exact technique.  Since we use a certified lab, they're not as
          concerned  about the preservation, transport and storage.

     4A.  We have five collection procedures that seem to cover all cases: composite (flow-
          weighted with auto-sampler), composite (multiple locations), grab, lake depth point,
          lake surface 2-meter integrated.

          We have six gear configurations. We set up one, catch-all, sample preservation and
          storage default which we plan to refine further later.

      B.  It captures the level of detail we want without being too onerous.

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

     5 A.  We broke it out as being either biological sampling, or grab sampling, and the
          sampling procedures were described in the boxes provided.

      B.  This works well for us, having a basic list. However, for when there are changes in
          our approach, we will need to be able to change this section easily.

     6A.  Sampling collection procedures are unique for each project and are based on the
          project's QAPP or standard operating procedures.

      B.  [respondent left blank]

     7 A.  To speed start up, we chose to bypass some of the metadata that was not required
          here, such as detailed sample preservation descriptions. We did not associate gear
          with sample types (as for grab samples), and defaulted other gear types according to
          well-established sampling protocols.
      B.  This was quicker, and placed less burden on monitors and data providers.
          Disadvantage is less descriptive metadata.

     8A.  Most of our samples are grab. Then the waters are put into different containers—one
          for nutrients, another for total metals, dissolved metals, according to sample
          preservation/storage and transport.

      B.  Need to have STORET to indicate date time that samples are delivered to labs.

     9A.  See above.

      B.  [respondent left blank]

    10A.  Usually there is just a simple  sample collection procedure with no associated gear.
          The dependency is activity type.

      B.  Don't know yet.

    11 A.  We use an ACCESS table containing media, projectID, waterbody type, activity class,
          sample collection procedure, configuration name, and gear ID.  Sample preservation
          and  storage is not addressed, other than it is  implied through the AnalyticalCodes
          table and the analytical procedures code number.

      B.  [respondent left blank]

    12A.  [respondent left blank]

      B.  [respondent left blank]

    13A.  Very general.

      B.  [respondent left blank]


Tips and Tricks for Setting up STORET Data Survey                              Appendix A

One Final Question...


Are there any other aspects of Modernized STORET that you set up one way originally but
now wish you had done it differently or could start over?


I.   Yes and no. I have started out setting up my data entry in a SIM ready format which
    conflicts with data analysis format so I have to crosstab in access for analysis. What I've
    done lately is wait until all my data is in STORET so I can pull it out in a single format then
    get it ready for analysis.  Its makes entry EASY but it compounds analysis... quite a little

2.   Yes.  Originally, I couldn't figure out the Activity ID aspect of things.  It had  to be a unique
    number for every sample.  I talked with several different folks who assigned a random
    number for the activity ID.  This is what I did  also initially. I didn't like the fact that you
    could not determine which activities were what when downloading information  from the
    internet so I actually did  start over and now give my samples an activity ID based off of the
    parameter code and a random number.

3.   Don't get me started. I would have dumped the migrated stations from LDC and imported
    new stations off of GIS coverages since I later found the coordinates were off. We did the
    former before SIM, when it took forever to create new stations.  I would have created one
    Characteristic Group per lab and one for Field Measurements/Observations, one for fish and
    one for bugs "macroinvertebrates." I would have been consistent with Projects (match QA
    Plan) and Trips. I would have used STORET  parameter codes for Row IDs since other
    systems (PCS, USGS) still use them. I would not have gotten so hung up about filling in
    every field I could on stations. I would have "broken" ties with LDC and established
    multiple Orgs, one for each physical Bureau in NJDEP, so they could administer their own
    data and I wouldn't have to look at their stations or Characteristic Groups when searching for
    information (and get faster response times).

4.   Only as noted above, under advantages and disadvantages.  Just as important as  defining
    these concepts and IDs was to develop our "pre-STORET" Access database to create SIM
    files based on them.

5.   I wish I could start over with the establishment of the Char. Groups for the Benthic
    Macroinvertebrates, now that the ITIS list has grown, and the C. Group can take a larger
    number of characteristics.  But looking back, I would not have done anything  differently,
    since I worked with what I had at the time. I wish we had utilized personnel names more
    frequently in our spreadsheet days. I have gotten confused with some of our data that was
    designated non-detect while other data was labeled present below quantification limit; I wish

Tips and Tricks for Setting up STORET Data Survey                              Appendix A

   I had labeled it all non-detect, since now I have to verify which is which, and confront what
   this difference truly means for when we serve the data.

6.  [respondent left blank]

7.  Not really. We are doing the best we can to implement the system with the resources and
   staff we have.  As time goes on we hope to improve not only our business process, but also
   enhance the metadata we are providing with the data, and more fully utilize the potential of
   the STORET system.  The "trade-offs" we have made to simplify use are seen as necessary
   to get us going on the  system.

8.  [respondent left blank]

9.  Each migration gets a  bit easier. We have loaded data many different ways depending on the
   needs of the program and the quality of their data.  My big advice would be that you need to
   approach each project  based on its merits, and not try to force a one-size-fits-all approach.

10.1 would not necessarily use characteristic groups because querying data from STORET
   becomes more complicated and there are  errors in the database that are introduced by the use
   of characteristic groups on occasion.

11. [respondent left blank]

12. None as of yet. There may be things discovered in the future, as we get into this a little

13. No.