ReviewPatterns (New York, N.Y.)2021
Data and its (dis)contents: A survey of dataset development and use in machine learning research.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Adherence of Studies on Large Language Models for Medical Applications Published in Leading Medical Journals According to the MI-CLEAR-LLM Checklist.Korean journal of radiology · 2025Pooled it
- Machine learning and artificial intelligence in cardiac transplantation: A systematic review.Artificial organs · 2022Pooled it
- AP-Lab: An AI-Driven Autonomous Pilot-Scale Platform Bridging Materials Discovery and Industrial Manufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A novel point cloud completion model for three-dimensional reconstruction of complex, dynamic population-level crop canopy architecture.Plant communications · 2026Article
- Cut-SOAP: A Machine Learning Descriptor for Rapid Screening of Molecular Adsorption Energetics.ACS omega · 2026Article
- Article
- Unraveling bioactive potential and production inChinese herbal medicines · 2025Review
- Uncovering ethical biases in publicly available fetal ultrasound datasets.NPJ digital medicine · 2025Article
- Review
- Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery.Plant physiology · 2025Article
- Tradeoffs Between Richness and Bias of Augmented Data in Long-Tail Recognition.Entropy (Basel, Switzerland) · 2025Article
- Applications of deep learning in intracranial aneurysm imaging: A scoping review of detection, risk prediction, and emerging prognostic models.Current journal of neurology · 2025Review
- Assessing the practicality of using freely available AI-based GPT tools for coach learning and athlete development.Frontiers in sports and active living · 2025Article
- The effects of mismatched train and test data cleaning pipelines on regression models: lessons for practice.PeerJ. Computer science · 2025Article
- Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool.Scientific reports · 2024Article
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers.Science and engineering ethics · 2024Article
- Article
- Avoiding common machine learning pitfalls.Patterns (New York, N.Y.) · 2024Review
- Data of history: An open-source and multiformat wall image dataset of Panam city, a historical place.Data in brief · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.