English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Advances in machine learning are closely tied to the creation of datasets.While data documentation is widely recognized as essential to the reliability,reproducibility, and transparency of ML, we lack a systematic empiricalunderstanding of current dataset documentation practices. To shed light on thisquestion, here we take Hugging Face -- one of the largest platforms for sharingand collaborating on ML models and datasets -- as a prominent case study. Byanalyzing all 7,433 dataset documentation on Hugging Face, our investigationprovides an overview of the Hugging Face dataset ecosystem and insights intodataset documentation practices, yielding 5 main findings: (1) The dataset cardcompletion rate shows marked heterogeneity correlated with dataset popularity.(2) A granular examination of each section within the dataset card reveals thatthe practitioners seem to prioritize Dataset Description and Dataset Structuresections, while the Considerations for Using the Data section receives thelowest proportion of content. (3) By analyzing the subsections within eachsection and utilizing topic modeling to identify key topics, we uncover what isdiscussed in each section, and underscore significant themes encompassing bothtechnical and social impacts, as well as limitations within the Considerationsfor Using the Data section. (4) Our findings also highlight the need forimproved accessibility and reproducibility of datasets in the Usage sections.(5) In addition, our human annotation evaluation emphasizes the pivotal role ofcomprehensive dataset content in shaping individuals' perceptions of a datasetcard's overall quality. Overall, our study offers a unique perspective onanalyzing dataset documentation through large-scale data science analysis andunderlines the need for more thorough dataset documentation in machine learningresearch.

Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face