Datasheet for Dataset Template

Summary

This LaTeX template transforms dataset documentation from an afterthought into a structured, professional process. Based on the influential "Datasheets for Datasets" paper by Timnit Gebru and colleagues, it provides a comprehensive framework for documenting everything from data collection methodology to ethical considerations. Rather than starting from scratch or using ad-hoc documentation approaches, data scientists and researchers can use this template to create standardized, publication-ready datasheets that meet emerging industry expectations for transparency.

The backstory: Why datasheets became essential

The concept of datasheets for datasets emerged from a simple but powerful analogy: electronic components come with detailed specification sheets, so why don't datasets? As AI systems increasingly drive critical decisions in hiring, lending, healthcare, and criminal justice, the datasets that train these models have come under scrutiny. The 2018 paper that inspired this template argued that standardized documentation could prevent many AI failures by making dataset limitations, biases, and appropriate use cases explicit upfront.

This template operationalizes those insights, turning academic concepts into practical documentation that can be integrated into existing research and development workflows.

Who this resource is for

Primary users:

• Data scientists and ML engineers creating datasets for internal use or public release
• Academic researchers preparing datasets for publication or conference submission
• Product managers overseeing AI development who need to ensure proper dataset documentation
• Compliance teams in regulated industries requiring detailed data lineage and bias documentation

Secondary users:

• Data consumers who need to evaluate whether a dataset fits their use case
• Auditors and regulators reviewing AI systems for compliance or risk assessment
• Open source maintainers releasing datasets to the research community

What's actually in the template

The template structures documentation around seven core sections, each with specific prompts and formatting:

Motivation section covers why the dataset was created, what problems it addresses, and who funded its development. This context helps users understand potential biases or limitations inherent in the dataset's purpose.

Composition section details what's actually in the dataset - data types, number of instances, relationships between data points, and any missing information. Critically, it includes prompts for documenting sensitive data and potential identification risks.

Collection process section documents how data was gathered, who did the collection, what timeframe it covers, and what quality control measures were applied. This section often reveals sampling biases or collection artifacts that affect model performance.

Preprocessing and labeling covers any transformations applied to raw data, who performed labeling tasks, and what instructions or guidelines they followed. For datasets with human annotations, this section captures potential annotator bias.

Distribution and maintenance addresses how the dataset will be shared, updated, or deprecated over time. This forward-looking section helps users understand the dataset's lifecycle and sustainability.

Uses and limitations explicitly states recommended and prohibited uses, known biases or performance gaps across different populations, and technical limitations that might affect model training.

Legal and ethical considerations covers privacy protections, consent mechanisms, intellectual property rights, and any ethical review processes applied during dataset creation.

Getting started with the template

Since this is a LaTeX template hosted on Overleaf, you can start documenting immediately without installing software. Click the template link, create a copy in your Overleaf account, and begin filling in the structured sections. The template includes helpful comments and examples throughout.

For teams new to dataset documentation, consider completing the template collaboratively - different team members likely have unique insights into data collection, preprocessing, and intended uses. The process of filling out the template often reveals undocumented assumptions or practices that could affect downstream model performance.

The template generates professional-looking PDFs suitable for academic publication, regulatory submission, or internal documentation standards. Many organizations now require datasheet completion before deploying models trained on new datasets.

Common pitfalls when creating datasheets

Treating documentation as a checkbox exercise rather than a genuine reflection on dataset properties and limitations. The most valuable datasheets honestly acknowledge what's unknown or problematic about a dataset.

Focusing only on technical specifications while glossing over potential biases, ethical concerns, or inappropriate use cases. These "soft" factors often determine whether a dataset should be used for a particular application.

Creating datasheets only for external datasets while skipping documentation for internal or proprietary data. Internal datasets often carry more risk since they receive less external scrutiny.

Assuming datasheets are one-time documents rather than living resources that should be updated as understanding of dataset properties evolves.