One plan for pushing language resources to the web en-mass before a full django application is available is to use Hugo and its XML input process to parse OALC/OAI-PMH files (generated with an OAI-PMH bash script) and then convert bibtex entries to xml (using Jabref or commandline) or JSON. The content issue to overcome is that pages in Hugo have to have front matter. The XML and JSON are both content and front matter (or visible front matter). I think I need to generate an .md file for each entry meaning XML data is not really that useful. But there may be hope
I found the following resources really helpful with boolean operators and Venn diagrams.
Snarky Math (Director). (2021, October 21). Can you draw a Venn diagram for 4 sets? | Why Venn diagrams are not easy [Streamed]. Snarky Math.https://youtu.be/IekSOZIF5uI
I haddn't really thought about what they represent or the appropriateness of their use.
https://www.sciencedirect.com/science/article/abs/pii/B9780444529374500113
https://blog.jooq.org/say-no-to-venn-diagrams-when-explaining-joins/
https://github.com/tctianchi/pyvenn
This python lib is interesting for generating visualizations if they are accurate. I used an inaccurate visualization in my presentation on OLAC roles. Maybe this could be added to django to update automatically.
https://www.sciencedirect.com/topics/mathematics/venn-diagram
https://www.dubberly.com/concept-maps/visualizing-venn-diagrams.html
Django application to collect submitted DOIs, acquire their API provided metadata (Bibliographic metadata and citation graph metadata), allow limited (specified) annotation, and then make those records harvestable via OAI-PMH. Language Resource tagger—Adding a layer of language related metadata to published resources.
Some Django modules for OAI-PMH
https://github.com/saw-leipzig/foaipmh
https://github.com/jnphilipp/django_oai_pmh
https://pypi.org/user/jnphilipp/ his topic extraction module looks interesting.
Also look at the xsd schema here https://github.com/saw-leipzig/foaipmh/blob/5b15d5cc4700a3cccf497c47218c2fba6b3421d5/entrypoint.prod.sh#L5
Database Versioning
This depends on how the DB is set up. If we only have one record per item or one record per state... This needs more definition.
https://djangopackages.org/grids/g/versioning/
https://www.wpbeginner.com/beginners-guide/complete-guide-to-wordpress-post-revisions/
Form Builders
https://djangopackages.org/grids/g/form-builder/
Some Javascript tools for creating the specific forms needed:
https://github.com/HughP/dublin-core-generator
https://nsteffel.github.io/dublin_core_generator/generator.html
Markdown for documentation
https://neutronx.github.io/django-markdownx/
Bibtex
https://bibtexparser.readthedocs.io/en/master/
https://github.com/sciunto-org/python-bibtexparser
https://github.com/jnphilipp/bibliothek
https://github.com/lucastheis/django-publications <-- also check the network as "improvements" are all over the place.
Other names include:
* Babybib
* Pybtex
* Pybibliographer
ISSNs
ISSN.org is supposed to have an API.. but not sure if they do.
https://portal.issn.org/resource/ISSN/1904-0008
Any request to the portal may be automated thanks to the use of REST protocol. The download of results is also automated. This service is restricted to subscribing users. Please contact sales [at] issn.org for more information.
https://portal.issn.org/node/170
https://portal.issn.org/resource/ISSN/2549-5089#
https://portal.issn.org/resource/ISSN/2549-5089?format=json
We could also slurp the HTML for the sameAs links to other DBs if needed.
Views:
1. login with ORCID
2. query APIs (DOIs, ISBNs, ISSNs, ORCID, WikiData, etc.)
3. results display and annotation
4. submission
5. List of past submissions
6. update past submission screen (same as #3?)
If we ran a module like this:
https://pybliometrics.readthedocs.io/en/latest/classes/SerialTitle.html
Then we could take a reading on where the least spoken languages appear in the most highly ranked journals and determine if there was a bias or a loss to science.
Data Examples:
Have been moved to:
https://github.com/HughP/CrossRef-to-OLAC-data-examples
PDF Extraction:
https://levelup.gitconnected.com/scrap-data-from-website-and-pdf-document-for-django-app-fa8f37010085
https://towardsdatascience.com/how-to-extract-pdf-data-in-python-876e3d0c288
https://stackoverflow.com/questions/71850349/download-a-pdf-from-url-edit-it-an-render-it-in-django
https://stackoverflow.com/questions/48882768/django-reading-pdf-files-content
https://www.geeksforgeeks.org/working-with-pdf-files-in-python/
PDF Creation:
https://docs.djangoproject.com/en/4.1/howto/outputting-pdf/
https://jeltef.github.io/PyLaTeX/current/examples/header.html
