Relevant bibliographies by topics / Data quality

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data quality'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data quality"

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Vaníček, J. "Software and data quality." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (2012): 138–46. http://dx.doi.org/10.17221/5007-agricecon.

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The paper presents new ideas in the International SQuaRE (Software Quality Requirements and Evaluation) standardisation research project, which concerns the development of a special branch of international standards for software quality. Data can be considered as an integral part of software. The current international standard and technical report of the ISO/IEC 9126, ISO/IEC 14598 series and ISO/IEC 12119 standard covert the whole software as an indivisible entity. However, such data sets as databases and data stores have a special character and need a different structure of quality character
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Stoykov, Evgeni. "DATA QUALITY FOR HYDROGRAPHIC MEASUREMENTS." Journal Scientific and Applied Research 21, no. 1 (2021): 26–30. http://dx.doi.org/10.46687/jsar.v21i1.314.

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The topic of preservation of cultural heritage is very important and is an integral part of National Security. It is up-to-date and timely. Its significance is determined by the scale and intensity of criminal attacks on cultural heritage, which have caused an increase in the need to update the system of measures to safeguard cultural values and overcome the underestimation of the protection of cultural heritage as a national security factor.
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Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation betwee
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Andrienko, Gennady, Natalia Andrienko, and Georg Fuchs. "Understanding Movement Data Quality." Journal of Location Based Services 10, no. 1 (2016): 31–46. https://doi.org/10.1080/17489725.2016.1169322.

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Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results. Investigation of quality of movement data, due to their spatio-temporal nature, requires consideration from multiple perspectives at different scales. We review the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identify these types of problems.
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D.B.Shanmugam, J.Dhilipan, A.Vignesh, and T.Prabhu. "Challenges in Data Quality and Complexity of Managing Data Quality Assessment in Big Data." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 589–93. https://doi.org/10.35940/ijrte.C5643.099320.

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High Quality Data are the precondition for examining and making use of enormous facts and for making sure the estimation of the facts. As of now, far reaching exam and research of price gauges and satisfactory appraisal strategies for massive records are inadequate. To begin with, this paper abridges audits of Data excellent studies. Second, this paper examines the records attributes of the enormous records condition, presents high-quality difficulties appeared by large data, and defines a progressive facts exceptional shape from the point of view of records clients. This system accommodates o
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Reddy Desani, Nithin. "Enhancing Data Governance through AI - Driven Data Quality Management and Automated Data Contracts." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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Chicco, Joanne. "Data Quality." Health Information Management 27, no. 1 (1997): 12–21. http://dx.doi.org/10.1177/183335839702700106.

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Sadiq, Shazia, Tamraparni Dasu, Xin Luna Dong, et al. "Data Quality." ACM SIGMOD Record 46, no. 4 (2018): 35–43. http://dx.doi.org/10.1145/3186549.3186559.

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Glaze, William H. "Data quality." Environmental Science & Technology 36, no. 11 (2002): 225A. http://dx.doi.org/10.1021/es0223170.

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Fan, Wenfei. "Data Quality." ACM SIGMOD Record 44, no. 3 (2015): 7–18. http://dx.doi.org/10.1145/2854006.2854008.

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Dissertations / Theses on the topic "Data quality"

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Grillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.

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The main purpose of this thesis is to develop a data quality scorecard (DQS) that aligns the data quality needs of the Data warehouse stakeholder group with selected data quality dimensions. To comprehend the research domain, a general and systematic literature review (SLR) was carried out, after which the research scope was established. Using Design Science Research (DSR) as the methodology to structure the research, three iterations were carried out to achieve the research aim highlighted in this thesis. In the first iteration, as DSR was used as a paradigm, the artefact was build from the r
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Sýkorová, Veronika. "Data Quality Metrics." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-2815.

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The aim of the thesis is to prove measurability of the Data Quality which is a relatively subjective measure and thus is difficult to measure. In doing this various aspects of measuring the quality of data are analyzed and a Complex Data Quality Monitoring System is introduced with the aim to provide a concept for measuring/monitoring the overall Data Quality in an organization. The system is built on a metrics hierarchy decomposed into particular detailed metrics, dimensions enabling multidimensional analyses of the metrics, and processes being measured by the metrics. The first part of the t
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Yu, Wenyuan. "Improving data quality : data consistency, deduplication, currency and accuracy." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8899.

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Data quality is one of the key problems in data management. An unprecedented amount of data has been accumulated and has become a valuable asset of an organization. The value of the data relies greatly on its quality. However, data is often dirty in real life. It may be inconsistent, duplicated, stale, inaccurate or incomplete, which can reduce its usability and increase the cost of businesses. Consequently the need for improving data quality arises, which comprises of five central issues of improving data quality, namely, data consistency, data deduplication, data currency, data accuracy and
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Peralta, Veronika. "Data Quality Evaluation in Data Integration Systems." Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2006. http://tel.archives-ouvertes.fr/tel-00325139.

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Les besoins d'accéder, de façon uniforme, à des sources de données multiples, sont chaque jour plus forts, particulièrement, dans les systèmes décisionnels qui ont besoin d'une analyse compréhensive des données. Avec le développement des Systèmes d'Intégration de Données (SID), la qualité de l'information est devenue une propriété de premier niveau de plus en plus exigée par les utilisateurs. Cette thèse porte sur la qualité des données dans les SID. Nous nous intéressons, plus précisément, aux problèmes de l'évaluation de la qualité des données délivrées aux utilisateurs en réponse à leurs re
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Peralta, Costabel Veronika del Carmen. "Data quality evaluation in data integration systems." Versailles-St Quentin en Yvelines, 2006. http://www.theses.fr/2006VERS0020.

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This thesis deals with data quality evaluation in Data Integration Systems (DIS). Specifically, we address the problems of evaluating the quality of the data conveyed to users in response to their queries and verifying if users’ quality expectations can be achieved. We also analyze how quality measures can be used for improving the DIS and enforcing data quality. Our approach consists in studying one quality factor at a time, analyzing its impact within a DIS, proposing techniques for its evaluation and proposing improvement actions for its enforcement. Among the quality factors that have been
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Deb, Rupam. "Data Quality Enhancement for Traffic Accident Data." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/367725.

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Death, injury, and disability resulting from road traffic crashes continue to be a major global public health problem. Recent data suggest that the number of fatalities from traffic crashes is in excess of 1.25 million people each year with non-fatal injuries affecting a further 20-50 million people. It is predicted that by 2030, road traffic accidents will have progressed to be the 5th leading cause of death and that the number of people who will die annually from traffic accidents will have doubled from current levels. Both developed and developing countries suffer from the consequences of t
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He, Ying Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "Spatial data quality management." Publisher:University of New South Wales. Surveying & Spatial Information Systems, 2008. http://handle.unsw.edu.au/1959.4/43323.

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The applications of geographic information systems (GIS) in various areas have highlighted the importance of data quality. Data quality research has been given a priority by GIS academics for three decades. However, the outcomes of data quality research have not been sufficiently translated into practical applications. Users still need a GIS capable of storing, managing and manipulating data quality information. To fill this gap, this research aims to investigate how we can develop a tool that effectively and efficiently manages data quality information to aid data users to better understand a
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Redgert, Rebecca. "Evaluating Data Quality in a Data Warehouse Environment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208766.

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The amount of data accumulated by organizations have grown significantly during the last couple of years, increasing the importance of data quality. Ensuring data quality for large amounts of data is a complicated task, but crucial to subsequent analysis. This study investigates how to maintain and improve data quality in a data warehouse. A case study of the errors in a data warehouse was conducted at the Swedish company Kaplan, and resulted in guiding principles on how to improve the data quality. The investigation was done by manually comparing data from the source systems to the data integ
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Bringle, Per. "Data Quality in Data Warehouses: a Case Study." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-404.

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<p>Companies today experience problems with poor data quality in their systems. Because of the enormous amount of data in companies, the data has to be of good quality if companies want to take advantage of it. Since the purpose with a data warehouse is to gather information from several databases for decision support, it is absolutely vital that data is of good quality. There exists several ways of determining or classifying data quality in databases. In this work the data quality management in a large Swedish company's data warehouse is examined, through a case study, using a framework speci
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Li, Lin. "Data quality and data cleaning in database applications." Thesis, Edinburgh Napier University, 2012. http://researchrepository.napier.ac.uk/Output/5788.

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Today, data plays an important role in people's daily activities. With the help of some database applications such as decision support systems and customer relationship management systems (CRM), useful information or knowledge could be derived from large quantities of data. However, investigations show that many such applications fail to work successfully. There are many reasons to cause the failure, such as poor system infrastructure design or query performance. But nothing is more certain to yield failure than lack of concern for the issue of data quality. High quality of data is a key to to
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Books on the topic "Data quality"

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Tarpey, Simon. Data quality. NHS Executive, 1996.

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Wang, Richard Y. Data quality. Kluwer Academic Publishers, 2001.

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Wang, Richard Y. Data quality. Kluwer Academic Publishers, 2002.

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Willett, Terrence, and Aeron Zentner. Assessing Data Quality. SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071858769.

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Otto, Boris, and Hubert Österle. Corporate Data Quality. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-46806-7.

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Fisher, Peter F., and Michael F. Goodchild. Spatial Data Quality. Edited by Wenzhong Shi. Taylor & Francis, 2002. http://dx.doi.org/10.4324/9780203303245.

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Wenzhong, Shi, Goodchild Michael F, and Fisher Peter, eds. Spatial data quality. Taylor & Francis, 2002.

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Wang, Y. Richard. Quality data objects. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1992.

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O'Day, James. Accident data quality. National Academy Press, 1993.

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Pfeil, Buttenfield Barbara, ed. Mapping data quality. University of Toronto Press, 1993.

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Book chapters on the topic "Data quality"

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Fürber, Christian. "Data Quality." In Data Quality Management with Semantic Technologies. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-12225-6_3.

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Treder, Martin. "Data Quality." In The Chief Data Officer Management Handbook. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6115-6_10.

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Nahler, Gerhard. "data quality." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_352.

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Piedmont, Ralph L. "Data Quality." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_667.

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Fleckenstein, Mike, and Lorraine Fellows. "Data Quality." In Modern Data Strategy. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68993-7_11.

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Shekhar, Shashi, and Hui Xiong. "Data Quality." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_248.

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Solberg Söilen, Klaus. "Data Quality." In Springer Texts in Business and Economics. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69518-6_3.

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van Gils, Bas. "Data Quality." In The Enterprise Engineering Series. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35539-4_17.

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Barksdale, George “Buster”, and Kecia Pierce. "Data Quality." In Using Metering to Perform Energy Management. River Publishers, 2024. http://dx.doi.org/10.1201/9781003467113-7.

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Piedmont, Ralph L. "Data Quality." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_667.

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Conference papers on the topic "Data quality"

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Peixoto, Teresa, Bruno Oliveira, Óscar Oliveira, and Fillipe Ribeiro. "Real-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics." In 10th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013242900003944.

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Shepperd, Martin. "Data quality." In Proceeding of the 2nd international workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1985374.1985376.

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Sidi, Fatimah, Payam Hassany Shariat Panahy, Lilly Suriani Affendey, Marzanah A. Jabar, Hamidah Ibrahim, and Aida Mustapha. "Data quality: A survey of data quality dimensions." In 2012 International Conference on Information Retrieval & Knowledge Management (CAMP). IEEE, 2012. http://dx.doi.org/10.1109/infrkm.2012.6204995.

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Mehmood, Kashif, Samira Si-Said Cherfi, and Isabelle Comyn-Wattiau. "Data quality through model quality." In Proceeding of the first international workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1651415.1651421.

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Arruda, Darlan, and Nazim H. Madhavji. "QualiBD: A Tool for Modelling Quality Requirements for Big Data Applications." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006294.

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He, Tianxing, Shengcheng Yu, Ziyuan Wang, Jieqiong Li, and Zhenyu Chen. "From Data Quality to Model Quality." In Internetware '19: The 11th Asia-Pacific Symposium on Internetware. ACM, 2019. http://dx.doi.org/10.1145/3361242.3361260.

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Johnson, Theodore, and Tamraparni Dasu. "Data quality and data cleaning." In the 2003 ACM SIGMOD international conference on. ACM Press, 2003. http://dx.doi.org/10.1145/872757.872875.

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Fu, Qian, and John M. Easton. "Understanding data quality: Ensuring data quality by design in the rail industry." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258380.

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Schieferdecker, Ina. "(Open) Data Quality." In 2012 IEEE 36th Annual Computer Software and Applications Conference - COMPSAC 2012. IEEE, 2012. http://dx.doi.org/10.1109/compsac.2012.120.

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Pon, Raymond K., and Alfonso F. Cárdenas. "Data quality inference." In the 2nd international workshop. ACM Press, 2005. http://dx.doi.org/10.1145/1077501.1077519.

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Reports on the topic "Data quality"

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DEFENSE LOGISTICS AGENCY ALEXANDRIA VA. Data Quality Engineering Handbook. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada315573.

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Ichinose, G. Waveform Data Quality Assessment. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1863669.

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Lockrem, L. L. Quality assurance and data management. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/362612.

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Hovorka, Susan. SECARB-USA: Data Quality Methodology. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1836835.

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Stewart, Ron, and Sharath Kallaje. FedFleet 2024: Fleet Data Quality - Understanding and Improving Your Agency's FAST Fleet Data Quality Metric? Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2305390.

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Lombardini, Simone, Alexia Pretari, and Emily Tomkys Valteri. Going Digital: Improving data quality with digital data collection. Oxfam GB, 2018. http://dx.doi.org/10.21201/2018.3071.

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Matejka, L. A. Jr. Quality data validation: Comprehensive approach to environmental data validation. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10185693.

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Woods, Taylor. Data from GSSE Water Quality 2020. University of Tennessee, Knoxville Libraries, 2020. http://dx.doi.org/10.7290/91mdbwflvd.

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Williams, R. J. Data Quality Statements for Spatial Databases. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada264125.

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Melito, Ivano, and Michael J. Briggs. Data Quality Assurance for Ribs XM2000. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383556.

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