  In recent years, large-scale data collection efforts have prioritized the
amount of data collected in order to improve the modeling capabilities of large
language models. This prioritization, however, has resulted in concerns with
respect to the rights of data subjects represented in data collections,
particularly when considering the difficulty in interrogating these collections
due to insufficient documentation and tools for analysis. Mindful of these
pitfalls, we present our methodology for a documentation-first, human-centered
data collection project as part of the BigScience initiative. We identified a
geographically diverse set of target language groups (Arabic, Basque, Chinese,
Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages,
Portuguese, Spanish, and Vietnamese, as well as programming languages) for
which to collect metadata on potential data sources. To structure this effort,
we developed our online catalogue as a supporting tool for gathering metadata
through organized public hackathons. We present our development process;
analyses of the resulting resource metadata, including distributions over
languages, regions, and resource types; and our lessons learned in this
endeavor.
