I am asking around on different mailing lists to gain some insight into the archiving habits of linguists who use lexical databases. I am specifically interested in databases created by tools like FLEx, ToolBox, Lexus, TshwaneLex, etc.
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
The DCTerms provides some fields for use in the description of Collections. While it may be possible to use terms from specific vocabularies in the CLD (see a list of works) or see the whole application profile here. However, It is interesting to note that RDA has some of these terms too and even though purls were minted for CLD that linked data URIs exist for these concepts as well.
What are the User Task in OLAC and for an OLAC 2.0?
What overlap does DCTerms have with the IFLA-LRM/RDA models? That is, if OLAC stays with DCTerms, what user tasks is it known to not support? On the flip side, what user tasks do archives need to support beyond OLAC's capabilities?
I have a MediaWiki instance with 153 pages. It needs transferred to a Markdown based context with a git controlled environment. Here are some optioned:
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
I have been looking at Django for several projects. I've been looking at implementation details including hosting and database.
database options for Django and have settled on PostGIS as in PostgresSQL with GIS support.
https://stackoverflow.com/questions/3743632/gis-postgis-postgresql-vs-mysql-vs-sql-server
In general I have found reading through the following helpful
https://www.digitalocean.com/community/tutorials/how-to-create-a-django-app-and-connect-it-to-a-database
https://djangobuilder.io/#/
http://darrenoneill.co.uk/post/using-postgis-and-geodjango-find-nearest-neighbour/
https://medium.com/@amirm.lavasani/classic-machine-learning-in-python-k-nearest-neighbors-knn-a06fbfaaf80a
https://www.geeksforgeeks.org/find-the-nearest-node-to-a-point-using-osmnx-distance-module/
https://github.com/fabiocaccamo/django-treenode
https://github.com/peopledoc/django-ltree-demo