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DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data
9 references·0 citing·34 second hop
DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data (2019)DataGauge: A Practical Process for System…Data quality assessment framework to assess electronic medical record data for use in research (2016)Data quality assessment frame…The Challenges of Data Quality and Data Quality Assessment in the Big Data Era (2015)The Challenges of Data Qualit…A Review of Data Quality Assessment Methods for Public Health Information Systems (2014)A Review of Data Quality Asse…Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities (2005)Data Cleaning: Detecting, Dia…A Formal Definition of Data Quality Problems. (2005)A Formal Definition of Data Q…AIMQ: a methodology for information quality assessment (2002)AIMQ: a methodology for infor…A product perspective on total data quality management (1998)A product perspective on tota…Beyond Accuracy: What Data Quality Means to Data Consumers (1996)Beyond Accuracy: What Data Qu…Introduction to Quality Engineering: Designing Quality into Products and Processes (1986)Introduction to Quality Engin…Anchoring data quality dimensions in ontological foundations (1996)Data Cleaning: Problems and Current Approaches. (2000)Data quality assessment (2002)A Taxonomy of Dirty Time-Oriented Data (2012)Big Data Quality: A Quality Dimensions Evaluation (2016)Big Data Quality: A Survey (2018)Out of the Crisis (1982)Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation (1991)Information Systems Success: The Quest for the Dependent Variable (1992)The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach (2002)Measuring information systems success: models, dimensions, measures, and interrelationships (2008)A framework for analysis of data quality research (1995)E-government success factors: Mapping practical tools to theoretical foundations (2005)Methodologies for data quality assessment and improvement (2009)Data fusion (2009)Assessing the quality of administrative data for research: a framework from the Manitoba Centre for Health Policy (2017)An Overview of Data Quality Frameworks (2019)Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R (2021)Analysis of a probabilistic record linkage technique without human review. (2003)A Framework for Data Quality Assessment in Clinical Research Datasets. (2017)How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning (2018)Quality of input data in emergency department simulations: Framework and assessment techniques (2018)The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East (2012)Big data: Issues, challenges, tools and Good practices (2013)Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? (2019)Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female (2020)Signal-to-Noise Ratios, Performance Criteria, and Transformations (1988)Economical Experimentation Methods for Robust Design (1991)AN OVERVIEW OF TAGUCHI METHOD AND NEWLY DEVELOPED STATISTICAL METHODS FOR ROBUST DESIGN (1992)The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement (2015)Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018)Politics, sentiments, and misinformation: An analysis of the Twitter discussion on the 2016 Austrian Presidential Elections (2018)WebQual: An Instrument for Consumer Evaluation of Web Sites (2007)Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (2014)
this work references cited by second hop

References

  • 1996Beyond Accuracy: What Data Quality Means to Data Consumers4,663 cited
  • 2002AIMQ: a methodology for information quality assessment1,626 cited
  • 1986Introduction to Quality Engineering: Designing Quality into Products and Processes1,180 cited
  • 2015The Challenges of Data Quality and Data Quality Assessment in the Big Data Era1,044 cited
  • 1998A product perspective on total data quality management911 cited
  • 2005Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities446 cited
  • 2014A Review of Data Quality Assessment Methods for Public Health Information Systems354 cited
  • 2005A Formal Definition of Data Quality Problems.109 cited
  • 2016Data quality assessment framework to assess electronic medical record data for use in research86 cited

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