Sensor Resources

From Earth Science Information Partners (ESIP)

Publications about Sensors and Sensor Networks

This page contains resources (peer-reviewed publications) that pertain to sensors, equipment, or data collection. For publications pertaining to quality control (statistics, data storage and versioning, etc.) please upload to the Quality Control Resources page.

(abstract) For users to trust and interpret the data in scientific digital libraries, they must be able to assess the integrity of those data. Criteria for data integrity vary by context, by scientific problem, by individual, and a variety of other factors. This paper compares technical approaches to data integrity with scientific practices, as a case study in the Center for Embedded Networked Sensing (CENS). The goal of this research is to identify functional requirements for digital libraries of scientific data that will serve this community. Data sources include analysis of documents produced by the CENS data integrity group and interviews with science and technology researchers within CENS.

(abstract) PA is conducting a National Study of Chemical Residues in Lake Fish Tissue. The study involves five analytical laboratories, multiple sampling teams from each of the 47 participating states, several tribes, all 10 EPA Regions and sev- eral EPA program offices, with input from other federal agencies. To fulfill study objectives, state and tribal sampling teams are voluntarily collecting pre- dator and bottom-dwelling fish from approximately 500 randomly selected lakes over a 4-year period. The fish will be analyzed for more than 300 pollu- tants. The long-term nature of the study, combined with the large number of participants, created several QA challenges: (1) controlling variability among sampling activities performed by different sampling teams from more than 50 organizations over a 4-year period; (2) controlling variability in lab pro- cesses over a 4-year period; (3) generating results that will meet the primary study objectives for use by OW statisticians; (4) generating results that will meet the undefined needs of more than 50 participating organizations; and (5) devising a system for evaluating and defining data quality and for report- ing data quality assessments concurrently with the data to ensure that assess- ment efforts are streamlined and that assessments are consistent among organizations. This paper describes the QA program employed for the study and presents an interim assessment of the program’s effectiveness