Provoking a Cultural Shift in Data Quality

McCord, Sarah E.; Webb, Nicholas P.; Zee, Justin W. Van; Burnett, Sarah H.; Christensen, Erica M.; Courtright, Ericha M.; Laney, Christine; Lunch, Claire; Maxwell, C.; Karl, Jason W.; Slaughter, Andrew; Stauffer, Nelson G.; Tweedie, C. E.

doi:10.1093/biosci/biab020

Cited by 20 publications

(12 citation statements)

References 48 publications

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“…This analysis found that 13% of the days with rainfall and 7% of the runoff events sampled had errors indicating that the prior or existing QAQC protocols are relatively robust. However, considering the widespread use of WGEW data and the substantial investment in data collection, continued investment to improve and develop measurement protocols and QAQC procedures are warranted, e.g., [62].…”

Section: Discussionmentioning

confidence: 99%

Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets

Bitew

Demaria

Heilman

et al. 2022

Water

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The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets.

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Section: Discussionmentioning

confidence: 99%

Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets

Bitew

Demaria

Heilman

et al. 2022

Water

View full text Add to dashboard Cite

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“…Decision-support framework Shafer et al (2000); Schultz et al (2015) Integrated workflows among teams and operational processes Abeysirigunawardena et al (2015); Hudson et al (1999);McCord et al (2021); Shafer et al (2000) Proper sensor procurement, configuration and routine calibration traceable to applicable standards Assessments and audits against performance targets Hudson et al (1999); Schultz et al (2015)…”

Section: Literature Referencementioning

confidence: 99%

“…Yet, there is comparatively very little scientific literature on QMS frameworks for sensor networks and how they are applied effectively and efficiently at the network scale (although some examples exist, e.g. Fiebrich et al, 2010; McCord et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A process approach to quality management doublesNEONsensor data quality

et al. 2022

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1. A quality management system is critical for ensuring that the data and services provided by an organization meet the needs of its mission. With a mission to collect long-term open-access ecological data to better understand how US ecosystems are changing, the National Ecological Observatory Network (NEON) is a highly standardized measurement network distributed across the United States and Puerto Rico collecting data on the biosphere and its interfaces with the pedosphere, hydrosphere and atmosphere.2. In order to achieve high-quality, comparable data across the network, a quality management system was developed by applying the seven ISO 9001:2015 principles of quality management: customer focus, leadership, engagement of people, process approach, improvement, evidence-based decision making and relationship management. The resultant system is integrated throughout NEON's organizational structure with an approach that connects people and operational processes throughout the data life cycle (process approach).3. We describe the system with respect to sensor data (automated measurements), demonstrating its effectiveness through examples, lessons learned and a continuous history of improvement towards quality goals, including a doubling of data quality in NEON's meteorological and soil datasets since 2015 and substantial gains in other sensor datasets.4. Owing to a focus on quality management principles and particularly the interconnectedness of human and information systems, NEON's quality management system can serve as a model for networks with a variety of organizational structures and sizes.

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“…Big data is becoming a vital resource to address unique customer requirements and gain competitive advantages. The development of big data is still in the initial stage, and relevant studies focused on the definition and particular technology or solution of big data [25][26][27][28][29][30][31][32][33]. Laney [25] proposed a well-known definition of 3Vs based on the attributes of big data, including volume, velocity, and variety.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The development of big data is still in the initial stage, and relevant studies focused on the definition and particular technology or solution of big data [25][26][27][28][29][30][31][32][33]. Laney [25] proposed a well-known definition of 3Vs based on the attributes of big data, including volume, velocity, and variety. Later studies [26,27], except 3Vs, veracity, and value, were added to explain the definition of big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation

et al. 2022

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This study discusses how to create strategic value through energy big data and how to promote stakeholder interaction mechanisms in the evolution of the energy big data ecosystem. We use differential game methods to study the interaction between one power grid enterprise (PG) and one technology supplier (TS) under three different cost-sharing contracts: without cost-sharing contract, cost-sharing contract, and two-way subsidy contract. The effectiveness of the dynamic equilibrium strategies under different contracts is verified via numerical simulations. The results show that under the centralized decision scenario, the technological innovation investment, the degree of technological advancement of PG and TS, and the total profit of the supply chain system are superior to the decentralized decision scenario. The extent of TS technology innovation investment depends on the share rate of PG. Technology innovation investment and the profits of energy big data service supply chain stakeholders will increase with the sensitivity coefficient of technological advancement. Compared with contracts without cost-sharing and with cost-sharing, the two-way subsidy contract can provide the Pareto optimal solution for the investment trajectory of technological innovation and long-term profits. Theoretically, this study reveals a new perspective in the research on the relationship between power grid enterprises and technology suppliers under dynamic technology innovation. In practice, this study facilitates power grid enterprises and technology suppliers to form a closer cooperative relationship in the energy big data ecosystem. More importantly, it is helpful for power grid enterprises to make optimal transaction decisions at different stages of energy big data ecosystem evolution.

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Provoking a Cultural Shift in Data Quality

Cited by 20 publications

References 48 publications

Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets

Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets

A process approach to quality management doublesNEONsensor data quality

Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation

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