- Come up with a great bacronym for Habeas e.g. Helpfully Annotated Basic Encyclopedia of Academic Software
- Take the CORD-19 Software Mentions dataset and clean it
- Sort the dataset in order of popularity
- Starting with the most popular software, split the software between us and manually identify whether the software has an associated code repository
- Create the first Habeas Corpus from this exercise, which will just contain: name of the software, URL of associated code repository, list of identifiers of papers that mention it
- Use this version of Habeas Corpus to test analyses that answer the questions we'd like to pose, and identify what information/metadata is missing
- Go back to incorporate that metadata where simple
- Present some of the questions we're now able to answer using Habeas Corpus
- Based on our experience of 1. create instructions for cleaning the CORD-19 Software Mentions dataset
- Based on our experience of 1. create a "software name deduplication mapping tool" which lets people know the aliases of common software titles
- Create entries in Wikidata for each piece software, including its aliases, to give it a unique identifier
- Create tools to make it easier to automatically
| Question | Data / Metadata required | Notes |
|---|---|---|
| Most popular software | Software mentions, software aliases | |
| Usage | ? | How do we identify number of users? Number of unique publication authors who mention it? Number of unique publications that mention it? |
| Type of License | Link to code repository or license file | Should this be dynamically fetched? |
| Filter software by purpose | ? | Requires classification of purpose |
| How software is being used | Where software is mentioned, other context? | Is there a good way of doing this in an automated way? |
| Analyse dependencies | Link to code repo | |
| Software metrics | Link to code repo | |
| Software project metrics (e.g. how often developed, contributors) | Link to code repo | |
| Academic disciplines a piece of software is used in | Links to paper IDs that mention it | |
| Who funded the software | Links to grants, funders | Perhaps through acknowledgements in the paper that mentions it? Only works if author created |
| Who funded the use of the software to do a piece of research | Link to paper ID and ability to extract research funder info from that | |
| How detailed is the mention of the software (e.g. citation, version number, release year) | Full text encoding? | This might be better applied to the SoftCite dataset which has this information already coded and annotated |
| Summarise by platform, e.g. all Python packages | Platform metadata | Or some external source that allows understanding of what software is "similar" |
- Software Title (String)
- Number of unique papers that mention it at least once (Integer)
- Link to code repository (if available) (URI)
- List of papers which mention software title (list of DOIs/URIs)
- List of titles that map to this (list of Strings)
- Dependencies: we could create a utility that leverages GitHub's support for understanding dependencies
- License type: we could use existing utilities for finding the license of a GitHub repo
- Academic disciplines: we could create a tool that extracts JATS4R Subject and Keyword tags: https://www.niso.org/publications/rp-32-2019-jats4r-subjects-and-keywords-v1
- Funders: we could use PID Graph / OpenAIRE funder database, or scrape from papers
- What software is used on national Tier-1 / Tier-2 facilities
- What software depends / is a dependency of this
- Canonical list of disciplines
- Wikidata