Data science is concerned with finding answers to questions on the basis of available data, and communicating that effort. Besides showing the results, this communication involves sharing the data used, but also exposing the path that led to the answers in a comprehensive and reproducible way. It also acknowledges the fact that available data may not be sufficient to answer questions, and that any answers are conditional on the data collection or sampling protocols employed.
A Data Utopia for Science-of-Science
Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…
Cambria | Proceedings of the 8th Workshop on Principles and Practice of Consistency for Distributed Data
This summary was generated using automated tools and was not authored or reviewed by the article's author(s). It is provided to support discovery, help readers assess relevance, and assist readers from adjacent research areas in understanding the work. It is intended to complement the author-supplied abstract, which remains the primary summary of the paper. The full article remains the authoritative version of record. Click here to learn more.
Innovation in Data Science Education
Data science is a new field of research focuses on extracting knowledge and value from data. The radical growth in recent years in the availability of data and of the computational resources required to process them, has led to a corresponding increase ...
Dynamic Data Science
<p>Use our Common Online Data Analysis Platform (CODAP) to explore dynamic data science activities and gain fluency in data moves to examine large datasets.</p>
Related work | Data Counterfactuals
Adjacent research areas and starting papers, curated in Semble and synced into the site.
What makes something data?
This is a question I posted on BlueSky on Friday 11/21/25, inspired by a talk I recently attended about evaluation of “AI” systems. I think…
datasetpapers — a public research experiment
An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.
datasetpapers — a public research experiment
An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.
Adopting Foundational Data Science Curriculum with Diverse Institutional Contexts
This summary was generated using automated tools and was not authored or reviewed by the article's author(s). It is provided to support discovery, help readers assess relevance, and assist readers from adjacent research areas in understanding the work. It is intended to complement the author-supplied abstract, which remains the primary summary of the paper. The full article remains the authoritative version of record. Click here to learn more.
Proposal: User Intents for Data Reuse · bluesky-social atproto · Discussion #3617
This is a discussion thread for the User Intents for Data Reuse proposal.
Data Integration
This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem.
Data Feminism
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to d...
dc: data provenance and source attribution
Part of the datacounterfactuals.org reading lists. Research on data provenance, dataset documentation, licensing and attribution audits, and technical source-attribution methods for understanding which data sources are available, permitted, or responsible for model behavior.
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
Datasheets for Datasets
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
A large-scale audit of dataset licensing and attribution in AI
