Total Error Sheets for Datasets (TES-D) -- A Critical Guide to Documenting Online Platform Datasets

TL;DR

This paper introduces TES-D, a comprehensive template for documenting online platform datasets to enhance data quality reflection and transparency in research, building upon prior error frameworks.

Contribution

It develops a structured documentation template, TES-D, for online platform datasets, facilitating critical reflection on data quality and transparency in research.

Findings

Provides a detailed TES-D template and manual

Builds upon prior Total Error Framework for digital traces

Aims to improve dataset documentation and transparency

Abstract

This paper proposes a template for documenting datasets that have been collected from online platforms for research purposes. The template should help to critically reflect on data quality and increase transparency in research fields that make use of online platform data. The paper describes our motivation, outlines the procedure for developing a specific documentation template that we refer to as TES-D (Total Error Sheets for Datasets) and has the current version of the template, guiding questions and a manual attached as supplementary material. The TES-D approach builds upon prior work in designing error frameworks for data from online platforms, namely the Total Error Framework for digital traces of human behavior on online platforms (TED-On, https://doi.org/10.1093/poq/nfab018).