# A domain-specific language for describing machine learning datasets

arXiv: https://arxiv.org/abs/2207.02848 · HTML: https://mlsanity.com/paper/2207.02848/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)
- [TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning](https://mlsanity.com/paper/1903.01855/) (2019)
- [Attribute Privacy: Framework and Mechanisms](https://mlsanity.com/paper/2009.04013/) (2020)
- [A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication](https://mlsanity.com/paper/2111.04424/) (2021)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)](https://mlsanity.com/paper/2009.10632/) (2020)
- [StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs](https://mlsanity.com/paper/2106.05373/) (2021)
- [DAG Card is the new Model Card](https://mlsanity.com/paper/2110.13601/) (2021)
- [Sciunits: Reusable Research Objects](https://mlsanity.com/paper/1707.05731/) (2017)

## Built on by (most cited first, 12 of 12)

- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Unpacking Human-AI interactions: From interaction primitives to a design space](https://mlsanity.com/paper/2401.05115/) (2024)
- [A Standardized Machine-readable Dataset Documentation Format for Responsible AI](https://mlsanity.com/paper/2407.16883/) (2024)
- [Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning](https://mlsanity.com/paper/2404.15320/) (2024)
- [Towards Sustainability Model Cards](https://mlsanity.com/paper/2507.19559/) (2025)
- [AgentSLA : Towards a Service Level Agreement for AI Agents](https://mlsanity.com/paper/2511.02885/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning](https://mlsanity.com/paper/2401.10304/) (2024)
- [CONCORD: Towards a DSL for Configurable Graph Code Representation](https://mlsanity.com/paper/2401.17967/) (2024)
- [Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets](https://mlsanity.com/paper/2406.13552/) (2024)
- [MRM3: Machine Readable ML Model Metadata](https://mlsanity.com/paper/2505.13343/) (2025)

## Abstract (arXiv)

Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift where data issues are given the attention they deserve, and more standard practices around the gathering and processing of datasets start to be discussed and established. So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets. In this sense, and inspired by these proposals, we define a new domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, data provenance, and social concerns. We believe this DSL will facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The DSL is implemented as a Visual Studio Code plugin, and it has been published under an open source license.