This repository contains the resources (data and scripts) related to the version 2.* of the Softcite dataset, a corpus of 4971 scientific articles with software mention annotations. This is a gold standard corpus, resulting from multi-stage annotations by a team of annotators and reconciliation phases by curators to solve disagreements. The dataset is available under CC-BY license.
For the first versions of this dataset, see here. This new repository is dedicated to new annotation iterations on the dataset and is independent from the previous repository for simplification.
Find the Zenodo deposit of the new version here: 10.5281/zenodo.7995565
The Softcite dataset consist of the 4,971 full-texts in English (available in Open Access under CC-BY license), half in Life Sciences and half in Economics, for a total of around 46 million tokens.
Annotations from the previous version of the dataset (v1.0) and this new release (v2.0) are summarized below:
| Softcite dataset version | v1.0 (2020) | v2.0 (2023) |
|---|---|---|
| number of documents | 4,971 | 4,971 |
| --- | --- | --- |
| software name (total) | 4,093 | 5,134 |
| - environment | 1,089 | |
| - component | 88 | |
| - implicit | 106 | |
| --- | --- | --- |
| version | 1,258 | 1,478 |
| publisher | 1,111 | 1,311 |
| URL | 172 | 231 |
| programming language | 71 |
The additional software mentions were spotted in the articles with automatic and manual screening. They were then validated via the normal double process with reconciliation in case of disagreement.
The refinement of the type of mentioned software and the encoding of the possible relationships between software mentions in the same paragraph have been realized with the same gold-standard annotation approach as the first version of the Softcite dataset, relying on two independent "blind" annotation by two different annotators, followed by a reconciliation by a curator in case of disagreement.
The XML annotation guidelines describe the different mark-up and the annotation principles. They are available here.
For any have any suggestions, comments, contributions, welcome to start an issue or discussion in this repository, or contact us (see below).
The versions 2.* of the Softcite dataset contains the following resources:
- Under the
xml/subdirectory all the XML annotated corpus:
softcite_corpus-full.tei.xml: the dataset as a corpus with one TEI entry per document and paragraphs containing one software mention or more.softcite_corpus-holdout-full.tei.xml: for evaluation purpose, the so-calledholdout setcorresponding to 20% of the full texts with complete text content and software mentions. This holdout set represents a real distribution of mentions in papers and is, therefore, appropriate for evaluation.softcite_corpus-working.tei.xml: the subset of the corpus excluding the 20% of the full texts of the holdout set, for evaluation purposes.
Numerous XML tools and libraries allow to parse and extract information as needed.
- The
json/subdirectory contains converted JSON files for the XML corpus. The JSON format is provided for users more comfortable with JSON than XML. The name of the files is similar as for the above XML files but with.jsonextension. The JSON uses offsets for identifying the position of the annotation spans in paragraphs, which makes it less readable.
We include the Python script used to convert the master TEI XML corpus into JSON format under scripts/TEI2LossyJSON.py
Use like this:
python3 scripts/TEI2LossyJSON.py --tei-file xml/softcite_corpus-full.tei.xml --output json/We thank Alfred P. Sloan Foundation and NextGenerationEU/France Relance for supporting this work. We also thank our collaborators and student annotators for making this dataset gold-standard and available.
The Softcite dataset is distributed under CC-BY license. The Python scrips are distributed under Apache 2.0 license.
The documentation of the project is distributed under CC-0 license.
If you contribute to Softcite dataset project, you agree to share your contribution following these licenses.
Contact: James Howison (PI, @jameshowison), Patrice Lopez (patrice.lopez@science-miner.com)