Seismic Data Quality Assurance Using IRIS MUSTANG Metrics

Key Info
URL - the landing page for the learning resource: 
https://ds.iris.edu/ds/nodes/dmc/tutorials/seismic-data-quality-assurance-using-…
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

Seismic data quality assurance involves reviewing data in order to identify and resolve problems that limit the use of the data – a time-consuming task for large data volumes! Additionally, no two analysts review seismic data in quite the same way. Recognizing this, IRIS developed the MUSTANG automated seismic data quality metrics system to provide data quality measurements for all data archived at IRIS Data Services. Knowing how to leverage MUSTANG metrics can help users quickly discriminate between usable and problematic data and it is flexible enough for each user to adapt it to their own working style.
This tutorial presents strategies for using MUSTANG metrics to optimize your own data quality review. Many of the examples in this tutorial illustrate approaches used by the IRIS Data Services Quality Assurance (QA) staff.
 

Authoring Organization(s) Name: 
Incorporated Research Institutions for Seismology (IRIS)
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Big data
Cyberinfrastructure to enable FAIR data principles
Data analysis
Data quality
Data quality - Core Trustworthy Data Repositories Requirements
Earthquake data
Scientific reproducibility
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Physical Sciences and Mathematics: Earth Sciences
Published / Broadcast: 
Tuesday, April 26, 2016
Publisher - organization credited with publishing or broadcasting the learning resource: 
Incorporated Research Institutions for Seismology (IRIS)
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Text - an explanation of a concept or a story using human readable characters formed into words, usually distinguished from graphical images.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Early-career research scientist
Graduate student
Mid-career research scientist
Research faculty
Research scientist
Software engineer
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 Day (but less than 1 week)
Framework - A community-based organization plan or set of steps for education or training: 
FAIR Data Principles