Data are measurements or information that describe environmental processes, location, or conditions; ecological or health effects and consequences; or the performance of environmental technology. The resources on this page include best practices for data management planning, data quality, and data evaluation.

Environmental Data Management Best Practices (EDM-1)
Provides a series of fact sheets and case studies that summarize the latest science, engineering, and technologies surrounding environmental data management best practices.

Groundwater Statistics and Monitoring Compliance (GSMC-1)
Section 6 (Data Management Considerations)
Describes how input data files should provide statistical analysis deliverables (in electronic format) to allow for verification and cross-checking with different models.

Per- and Polyfluoroalkyl Substances (PFAS-1)
Section 11.3 (Data Evaluation)
Review of data evaluation factors and planning considerations for data usability.

Remediation Process Optimization (RPO-5)
Data from Field, pages 3-4
Each aspect of the environmental monitoring program, from sample collection to data management must address and meet applicable quality standards.

Soil Background and Risk (SBR-1)
Section 8.2 (Conceptual Site Model and Data Quality Objectives)
A systematic planning process is a key step in developing a successful sampling and analysis program to ensure the appropriate sampling, analyses, and data evaluations are conducted to meet program objectives.

Soil Background and Risk (SBR-1)
Section 10 (Analytical Methods)
A critical component in establishing soil background, whether it be default or site-specific, is to ensure that the soil samples are analyzed by laboratory methodologies that generate high-quality analytical data that meet the data quality objectives (DQOs) of the soil background study and are comparable to the site data being evaluated.

Unexploded Ordnance (QCMR-1)
Section 1 (Introduction)
Explains the decision logic used throughout a munitions response (MR) project and assists in developing the Quality Assurance and Quality Control (QA/QC) activities that ensure quality data and confidence in decisions.

Petroleum Vapor Intrusion (PVI-1)
Section 4.3 (Step 6)
Describes common data quality issues and factors to be considered to determine that data quality objectives have been met and data are usable to evaluate the vapor intrusion pathway.

Petroleum Vapor Intrusion (PVI-1)
Appendix G.1.3
Describes the process of defining project goals and data quality objectives when planning an environmental investigation.