Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

By J. van den Broeck, Solveig Argeseanu Cunningham, R. Eeckels, K. Herbst. PLoS Medicine, 2005

463 citations recorded by the source corpus.

In this policy forum the authors argue that data cleaning is an essential part of the research process, and should be incorporated into study design.

Subfields: Artificial Intelligence, Management Science and Operations Research, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/261158c5f12b4159ea98f0013286083f2eb43f8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Andreas Holzinger
2. Chris Biemann
3. Heimo Müller
4. Kurt Zatloukal
5. Saurav Prakash
6. Amir Salman Avestimehr

In Management Science and Operations Research

1. Andreas Holzinger

Citations in the corpus

• Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research (2016, background): “In general, data quality in medical research project is not a well-researched topic [23].”
• Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Application and Benchmark While mostly applied in data mining tasks [77], [78], [79], outlier detection is also used in the real-world computer vision applications such as video surveillance [80] and dataset cleaning [81], [82], [83].”