Merge pull request #405 from afuetterer/python-311

chore: drop support for python < 3.11

Dockerfile

@@ -1,5 +1,5 @@
 # syntax=docker/dockerfile:1.5
-FROM python:3.8-slim
+FROM python:3.11-slim
 
 WORKDIR /usr/src/app
 

README.md

@@ -8,8 +8,8 @@ Developers: [Robert Huber](mailto:[email protected]), [Anusuriya Devaraju](mailto:
 
 ## Overview
 
-F-UJI is a web service to programatically assess FAIRness of research data objects based on [metrics](https://doi.org/10.5281/zenodo.3775793) developed by the [FAIRsFAIR](https://www.fairsfair.eu/) project. 
-The service will be applied to demostrate the evaluation of objects in repositories selected for in-depth collaboration with the project.  
+F-UJI is a web service to programatically assess FAIRness of research data objects based on [metrics](https://doi.org/10.5281/zenodo.3775793) developed by the [FAIRsFAIR](https://www.fairsfair.eu/) project.
+The service will be applied to demostrate the evaluation of objects in repositories selected for in-depth collaboration with the project.
 
 The '__F__' stands for FAIR (of course) and '__UJI__' means 'Test' in Malay. So __F-UJI__ is a FAIR testing tool.
 
@@ -26,28 +26,24 @@ An R client package that was generated from the F-UJI OpenAPI definition is avai
 An open source web client for F-UJI is available at https://github.com/MaastrichtU-IDS/fairificator.
 
 ## Assessment Scope, Constraint and Limitation
-The service is **in development** and its assessment depends on several factors. 
-- In the FAIR ecosystem, FAIR assessment must go beyond the object itself. FAIR enabling services and repositories are vital to ensure that research data objects remain FAIR over time. Importantly, machine-readable services (e.g., registries) and documents (e.g., policies) are required to enable automated tests. 
-- In addition to repository and services requirements, automated testing depends on clear machine assessable criteria. Some aspects (rich, plurality, accurate, relevant) specified in FAIR principles still require human mediation and interpretation. 
+The service is **in development** and its assessment depends on several factors.
+- In the FAIR ecosystem, FAIR assessment must go beyond the object itself. FAIR enabling services and repositories are vital to ensure that research data objects remain FAIR over time. Importantly, machine-readable services (e.g., registries) and documents (e.g., policies) are required to enable automated tests.
+- In addition to repository and services requirements, automated testing depends on clear machine assessable criteria. Some aspects (rich, plurality, accurate, relevant) specified in FAIR principles still require human mediation and interpretation.
 - The tests must focus on generally applicable data/metadata characteristics until domain/community-driven criteria have been agreed (e.g., appropriate schemas and required elements for usage/access control, etc.). For example, for some of the metrics (i.e., on I and R principles), the automated tests we proposed only inspect the ‘surface’ of criteria to be evaluated. Therefore, tests are designed in consideration of generic cross-domain metadata standards such as dublin core, dcat, datacite, schema.org, etc.
 - FAIR assessment is performed based on aggregated metadata; this includes metadata embedded in the data (landing) page, metadata retrieved from a PID provider (e.g., Datacite content negotiation) and other services (e.g., re3data).
 
 ![alt text](https://github.com/pangaea-data-publisher/fuji/blob/master/fuji_server/static/main.png?raw=true)
 
 ## Requirements
-Python 3.8+
-
-### 308 redirects
-In order to deal with 308 redirects, the following patch has to be applied on urrlib:
-https://github.com/python/cpython/pull/19588/commits
+[Python](https://www.python.org/downloads/) `3.11+`
 
 ### Google Dataset Search
 * Download the latest Dataset Search corpus file from: https://www.kaggle.com/googleai/dataset-search-metadata-for-datasets
 * Open file fuji_server/helper/create_google_cache_db.py and set variable 'google_file_location' according to the file location of the corpus file
 * Run create_google_cache_db.py which creates a SQLite database in the data directory. From root directory run `python3 -m fuji_server.helper.create_google_cache_db`.
 
 The service was generated by the [swagger-codegen](https://github.com/swagger-api/swagger-codegen) project. By using the
-[OpenAPI-Spec](https://github.com/swagger-api/swagger-core/wiki) from a remote server, you can easily generate a server stub.  
+[OpenAPI-Spec](https://github.com/swagger-api/swagger-core/wiki) from a remote server, you can easily generate a server stub.
 The service uses the [Connexion](https://github.com/spec-first/connexion) library on top of Flask.
 
 ## Usage

pyproject.toml

@@ -11,9 +11,8 @@ classifiers = [
   "Development Status :: 5 - Production/Stable",
   "Intended Audience :: Science/Research",
   "License :: OSI Approved :: MIT License",
-  "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.8",
-  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3.11",
   "Topic :: Scientific/Engineering :: Information Analysis"
 ]
 dependencies = [
@@ -58,7 +57,7 @@ keywords = [
 license = "MIT"
 name = "fuji"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 version = "3.0.0"
 
 [project.optional-dependencies]
@@ -94,7 +93,7 @@ Source = "https://github.com/pangaea-data-publisher/fuji"
 
 [tool.black]
 line-length = 120
-target_version = ["py38", "py39", "py310", "py311"]
+target_version = ["py311"]
 
 [tool.coverage.report]
 skip_empty = true
@@ -144,7 +143,7 @@ line-length = 120
 select = [
   "I"  # isort
 ]
-target-version = "py38"
+target-version = "py311"
 
 [tool.ruff.isort]
 known-first-party = ["fuji_server", "tests"]