mlsanityData to 14 Oct 2026

Data Excellence for AI: Why Should You Care

arXiv 2111.10391 ↗First posted 19 Nov 2021cs.LG cs.AIcited by 14

Not on the map. Why?

Abstract (arXiv)

The efficacy of machine learning (ML) models depends on both algorithms and data. Training data defines what we want our models to learn, and testing data provides the means by which their empirical progress is measured. Benchmark datasets define the entire world within which models exist and operate, yet research continues to focus on critiquing and improving the algorithmic aspect of the models rather than critiquing and improving the data with which our models operate. If "data is the new oil," we are still missing work on the refineries by which the data itself could be optimized for more effective use.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 0 of its 7 references that are in the map. First posted on arXiv 19 Nov 2021.

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Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 3.

14 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 203
  2. 2022DataPerf: Benchmarks for Data-Centric AI Developmentcited by 90
  3. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  4. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  5. 2022Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Taskscited by 17
  6. 2023A Bag-of-Prototypes Representation for Dataset-Level Applicationscited by 11
  7. 2022Striving for data-model efficiency: Identifying data externalities on group performancecited by 4
  8. 2024Improving Label Error Detection and Elimination with Uncertainty Quantificationcited by 4
  9. 2024Deep Learning methodology for the identification of wood species using high-resolution macroscopic imagescited by 3
  10. 2025Core-Set Selection for Data-efficient Land Cover Segmentationcited by 3
  11. 2025Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutionscited by 1
  12. 2023Class-Adaptive Sampling Policy for Efficient Continual Learningcited by 0
2 more
  1. 2024Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive Replaycited by 0
  2. 2025What's the next frontier for Data-centric AI? Data Savvy Agentscited by 0