Open research

Open models, dataset licences, reproducibility and documentation: what it takes for NLP research to be checkable and reusable.

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  Dataset Licences for NLP: Creative Commons, Open Data Commons and the EU Rules

  A practical guide to licensing text datasets in NLP research: Creative Commons and CC0, ShareAlike and NonCommercial clauses, Open Data Commons, LDC and ELRA agreements, EU database rights and text and data mining exceptions, and how to document licences.

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  Datasheets, Data Statements and Model Cards: Documenting NLP Data and Models

  What datasheets for datasets, data statements for NLP and model cards are, where they came from, what they ask, how they relate to the EU AI Act's documentation duties, and how to write useful ones without boilerplate.

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  Reproducibility in NLP: Why Results Fail to Repeat and How to Fix It

  Why natural language processing results are often hard to reproduce, from preprocessing details and random seeds to changing API models, what reproducibility studies have found, the checklists conferences now use, and a practical routine for researchers.

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  What Makes a Language Model "Open"? Weights, Data, Code and Licences

  Open weights, open source and open science are not the same thing. A guide to the components of a language model release, the Open Source AI Definition 1.0, common licences with use restrictions, and how the EU AI Act treats open-source models.