Questions and responses to USGS-wide poll on quality assurance practices for timeseries data, 2021

This data record contains questions and responses to a USGS-wide survey conducted to identify issues and needs associated with quality assurance and quality control (QA/QC) of USGS timeseries data streams. This research was funded by the USGS Community for Data Integration as part of a project titled ?From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making?. The poll targeted monitoring network managers and technicians and asked questions about operational data streams and timeseries data collection in order to identity opportunities to streamline data access, expedite the response to data quality issues, improve QA/QC procedures, reduce operations costs, and uncover other maintenance needs. The poll was created using an online survey platform. It was sent to 2326 systematically selected USGS email addresses and received 175 responses in 11 days before it was closed to respondents. The poll contained 48 questions of various types including long answer, multiple choice, and ranking questions. The survey contained a mix of mandatory and optional questions. These distinctions as well as full descriptions of survey questions are noted on the metadata.

本数据集收录了一项覆盖美国地质调查局（USGS）的全机构范围调查的问卷及回复内容，该调查旨在识别与美国地质调查局时序数据流的质量保证与质量控制（QA/QC）相关的问题与需求。本研究由美国地质调查局数据集成社区资助，作为题为《从被动维护到基于状态的维护：用于异常预测与运维决策的人工智能》项目的一部分开展。本次调查面向监测网络管理人员与技术人员，调研了运维数据流与时序数据采集相关的问题，以期梳理优化数据获取渠道、加快数据质量问题响应速度、完善QA/QC流程、降低运维成本，并挖掘其他运维需求。本次调查依托在线调查平台搭建，向经系统遴选的2326个美国地质调查局邮箱发送了问卷，并在11天内回收175份有效回复后停止接收答卷。本次调查共包含48道不同题型的题目，涵盖简答题、选择题与排序题；问卷同时设置必答题与选答题两类题型。上述题型区分及问卷题目完整说明均已在元数据中列明。