Data cleaning: detecting, diagnosing, and editing data abnormalities
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Inlier: Data value falling within
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Integrity signals
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Ambulatory data measured with wearable sensors, mobile phones, and surveys tend to be noisy.
“Ambulatory data measured with wearable sensors, mobile phones, and surveys tend to be noisy.”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [31]
The ‘Data Cleaning as a Process’ section (page unavailable) describes errors in questionnaire surveys and technical errors in measurement instruments, supporting the general point that collected data can be noisy. It does not address ambulatory data from wearable sensors or mobile phones, so the claim’s specific setting and devices are not supported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredChoice of data cleaning methods could affect study findings, repeatability of the study, and reproducibility of study findings.
“A clear and transparent description of data cleaning methods is important, as choice of methods could affect study findings, repeatability of the study, and reproducibility of study findings [50].”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [50]
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- Authors
- Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K.
- Affiliations
- University of Pennsylvania (US)
- ORCID
- Herbst, Kobus
- Venue
- PLoS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2005-09-06
- Volume
- 2
- Issue
- 10
- Pages
- e267
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC1198040
- OpenAlex
- W1971935224
- Cited by
- 446
- References
- 18
- Citations by year
- 2026: 21 · 2025: 39 · 2024: 35 · 2023: 35 · 2022: 32 · 2021: 47 · 2020: 40 · 2019: 47 · 2018: 36 · 2017: 23 · 2016: 25 · 2015: 17 · 2014: 9 · 2013: 13 · 2012: 5
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (063009)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- data cleaning, data quality, data anomalies, outlier detection, data editing, data validation
- MeSH terms
- Ethics, Professional, Humans, Peer Review, Statistics as Topic, Reproducibility of Results, Epidemiologic Studies, Guidelines as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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