2004 Progress Report: Data Management and Analysis

EPA Grant Number: R829458C004
Subproject: this is subproject number 004 , established and managed by the Center Director under grant R829458
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: EAGLES - Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico
Center Director: Brouwer, Marius
Title: Data Management and Analysis
Investigators: Noble, Peter
Institution: University of Washington
Current Institution: University of Washington , University of Southern Mississippi
EPA Project Officer: Hiscock, Michael
Project Period: December 1, 2001 through November 30, 2005 (Extended to May 20, 2007)
Project Period Covered by this Report: December 1, 2003 through November 30, 2004
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Water , Ecosystems


The main objectives of this component of the Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico (CEER-GOM) are to: (1) develop a Web-driven database for the deposition of metadata and the CEER-GOM investigators’ data; and (2) conduct statistical analysis of ecological data.

Progress Summary:


Progress has been made towards publishing a methods paper describing Neuroet. The manuscript, entitled “Neuroet: an easy-to-use artificial neural network for ecological and biological modeling,” by P.A. Noble and E.H. Tribou has been accepted for publication in the journal Ecological Modelling. So far, more than 125 scientists have downloaded the Neuroet package from the Web site. The principal investigator (PI) of this subproject has submitted another manuscript, which demonstrates the utility of the Neuroet package using DNA microarray data. The manuscript entitled, “Evaluation of gel-pad oligonucleotide microarray technology using artificial neural networks” by A. Pozhitkov, H. Smidt, M. Konneke, and P.A. Noble, has been submitted to the journal Applied and Environmental Microbiology.

Collaboration With Other Participants

The PI analyzed microarray data (Brouwer), fish data (Thomas), and biofilm image and phytoplankton pigment data (Snyder/Morris). Analyses of microarray data using neural networks and principle component analyses indicated that there were no defined patterns in the microarray data by dissolved oxygen (DO). The report (Marius_report_3.doc) can be downloaded from the following ftp site: ftp://report:report@

Analysis of Dr. Thomas’s fish data was promising because there were clear differences between control and experimental fish based on DO in the laboratory experiments (Table 1). More analysis is necessary to confirm these findings. These results might be improved by using a balanced data set (i.e., even number of low and high DO samples). The low number of high DO samples might have biased the neutral network analysis.

Analysis of Dr. Thomas’s fish field data was less clear. The PI currently is analyzing the data using conventional statistics and neural network analyses.

The PI has analyzed all the biofilm image data from the Snyder laboratory (student Melissa Hagy) using my fractal dimension calculator. The fractal dimension calculator provides information on the roughness of the biofilm surface. The roughness of the biofilm surface might be correlated to DO, position in the water column (i.e. top or bottom), and/or nutrient availability. Although analysis of these data has been completed, the usefulness of the work is unknown because the PI was blinded to the type of surfaces provided by the Snyder laboratory. The report (Mellisa_reports_1_to_3.xls) can be downloaded at ftp://report:report@ Note: data files for this project are now available on the ftp site and awaiting further statistical analysis.

Analysis of the phytoplankton pigment composition of biofilms collected by Dr. Snyder and sent to post doctorate Helen Marshall (Morris’s laboratory) is complete. Conventional statistical analysis of the pigment composition by site and position (including possible site by position interactions) in the water column revealed that there were no obvious patterns in the data. General linear model regression analysis revealed that all relationships were not significant. A report was not made for this analysis because it was not significant. SAS files and output are available at ftp://report:report@ (Folder: Helen).

The PI has collaborated with Dr. Rose in integrating the CEER-GOM data, which involved converting all data into SAS format so that Dr. Rose could easily analyze the data. Attempts to put all the data into a single data set were unsuccessful.

Karen J. Jordon (a graduate student of Dr. Han at University of Alabama)has been taught the fundamentals of neural networks and introduced to the Neuroet package. She also has been shown how to extract equations from neural networks.

Web Site Developments

The Web site that contains “Tools for data analysis” is complete. Minor modifications to programs were made to accommodate users’ requests. The Web site provides an automated user-friendly interface where scientists can submit their data. Applications on the Web site automatically analyze submitted data and send the results back to users via email. Since January 2004, more than 3,100 jobs have been submitted and analyzed by tools on the Web site.

Estuarine and Great Lakes Program (EaGLe) Data Management Committee (EDMC)

The PI is a member of the EDMC and plays a role as a representative of the CEER-GOM group. The general objectives of the EDMC are to: (1) create metadata tables for every project; (2) ensure the Web-based metadata tables conform to federal standards; (3) guide the development of search and retrieval functions; and (4) submit the metadata files and data to a centralized database where they will be stored for perpetuity.

As a representative of the CEER-GOM group, the PI’s role is to assist researchers in depositing their data and metadata to the U.S. Environmental Protection Agency Web Site by providing researchers with the metadata requirements, helping researchers assemble their data and metadata, uploading their data to a intermediate Web site where the contents and organization of the metadata and data will be validated, and sending the completed package to the centralized database.

Future Activities:

For 2005, I plan to analyze the CEER-GOM data, determine links (integrate) among group members, and upload the metadata and data for CEER-GOM to the centralized database. I also will continue to analyze data provided to me by other members of CEER-GOM.

Journal Articles on this Report : 4 Displayed | Download in RIS Format

Other subproject views: All 18 publications 10 publications in selected types All 10 journal articles
Other center views: All 171 publications 54 publications in selected types All 48 journal articles
Type Citation Sub Project Document Sources
Journal Article Gough HL, Dahl AL, Tribou E, Noble PA, Gaillard J-F, Stahl DA. Elevated sulfate reduction in metal-contaminated freshwater lake sediments. Journal of Geophysical Research: Biogeosciences 2008;113(G4):G04037, doi:10.1029/2008JG000738. R829458 (2005)
R829458C004 (2003)
R829458C004 (2004)
  • Abstract: Wiley - Abstract
  • Journal Article Lewitus AJ, White DL, Tymowski RG, Geesey ME, Hymel SN, Noble PA. Adapting the CHEMTAX method for assessing phytoplankton taxonomic composition in Southeastern U.S. estuaries. Estuaries 2005;28(1):160-172. R829458 (2005)
    R829458C004 (2004)
    R829458C004 (2005)
  • Abstract: Estuarine Research Federation Abstract
  • Journal Article Noble PA, Tribou EH. Neuroet: an easy-to-use artificial neural network for ecological and biological modeling. Ecological Modelling 2007;203(1-2):87-98. R829458 (2005)
    R829458C004 (2004)
  • Full-text: Science Direct
  • Abstract: Science Direct
  • Other: Science Direct PDF
  • Journal Article Pozhitkov A, Chernov B, Yershov G, Noble PA. Evaluation of gel-pad oligonucleotide microarray technology by using artificial neural networks. Applied and Environmental Microbiology 2005;71(12):8663-8676. R829458 (2005)
    R829458C004 (2004)
    R829458C004 (2005)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Applied and Environmental Microbiology-Full Text HTML
  • Abstract: Applied and Environmental Microbiology-Abstract
  • Other: Applied and Environmental Microbiology-Full Text PDF
  • Supplemental Keywords:

    population, community, ecosystem, watersheds, estuary, estuaries, Gulf of Mexico, nutrients, hypoxia, innovative technology, biomarkers, water quality, remote sensing, geographic information system, GIS, integrated assessment, risk assessment, fisheries, conservation, restoration, monitoring/modeling, benthic indicators, ecological exposure, ecosystem monitoring, environmental indicators, environmental stress, estuarine ecoindicator, estuarine integrity,, RFA, Scientific Discipline, ECOSYSTEMS, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Ecology, Ecosystem/Assessment/Indicators, Ecosystem Protection, Aquatic Ecosystem, Aquatic Ecosystems, Ecological Effects - Environmental Exposure & Risk, Environmental Monitoring, Ecological Monitoring, Ecology and Ecosystems, Biology, Ecological Indicators, Gulf of Mexico, monitoring, ecoindicator, ecological exposure, neural network software, estuaries, estuarine integrity, CEER-GOM, estuarine ecoindicator, data management, environmental indicators, environmental stress, water quality

    Relevant Websites:

    http://www.usm.edu/gcrl/ceer_gom/ Exit
    http://noble.ce.Washington.edu/Neuroet.htm Exit

    Progress and Final Reports:

    Original Abstract
  • 2002 Progress Report
  • 2003 Progress Report
  • 2005 Progress Report
  • 2006
  • Final

  • Main Center Abstract and Reports:

    R829458    EAGLES - Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R829458C001 Remote Sensing of Water Quality
    R829458C002 Microbial Biofilms as Indicators of Estuarine Ecosystem Condition
    R829458C003 Individual Level Indicators: Molecular Indicators of Dissolved Oxygen Stress in Crustaceans
    R829458C004 Data Management and Analysis
    R829458C005 Individual Level Indicators: Reproductive Function in Estuarine Fishes
    R829458C006 Collaborative Efforts Between CEER-GOM and U.S. Environmental Protection Agency (EPA)-Gulf Ecology Division (GED)
    R829458C007 GIS and Terrestrial Remote Sensing
    R829458C008 Macrobenthic Process Indicators of Estuarine Condition for the Northern Gulf of Mexico
    R829458C009 Modeling and Integration