Add LICENSE and CONTRIBUTING.md

CONTRIBUTING.md

@@ -0,0 +1,46 @@
+# Contributing
+
+Contributions to the PEtab SciML test suite are welcome. Please either:
+
+- (Preferred) open a
+  [pull request](https://github.com/sebapersson/petab_sciml_testsuite/pulls) (drafts
+  welcome) adding one or more test cases.
+- Or open an [issue](https://github.com/sebapersson/petab_sciml_testsuite/issues) describing
+  the missing coverage.
+
+## Adding a new test case
+
+To add a new test case, create a subdirectory in the relevant suite under `test_cases/`,
+named with the next three-digit ID (e.g., `003`). Include a short README describing what the
+test covers. Most test files should be generated automatically using utility functions from
+this repository’s associated Julia library. We use Julia for its strong SciML ecosystem and
+mature tooling for computing reference gradients (high-order finite-difference methods). To
+install this Julia library, with Julia ≥1.10, from the root directory of repository start
+Julia and run:
+
+```julia
+import Pkg; Pkg.instantiate()
+```
+
+Which files to add depends on the test type.
+
+For **ML model import tests**, provide both `net.jl` and `net.py` script. The `net.jl`
+script should generate the test inputs, ML parameters, expected outputs, and
+`solutions.yaml`. The `net.py` script should create the PEtab SciML ML-model YAML file and
+checks consistency with the ML model used to produce the Julia reference outputs. This
+ensures the case is importable in both Python (e.g., PyTorch/Equinox) and Julia, aligning
+with the goal that the PEtab SciML YAML is exchangeable across ecosystems. An example can be
+found
+[here](https://github.com/sebapersson/petab_sciml_testsuite/tree/main/test_cases/net_import/001).
+
+For **PEtab SciML import problem tests**, provide a `create.jl` script. It should generate
+the PEtab problem files and the reference values. For the PEtab problem files, the mapping
+and hybridisation PEtab tables must be provided manually, the other components can be
+selected from predefined assets (`assets/` for SBML models, ML-model YAMLs, ML parameters,
+and array inputs) and defaults in `src/` (new values can be added if needed). For the
+reference values, include an `llh_id` that maps to a likelihood implementation in
+`src/test_values/nllh/`. Because these SciML problems are not analytically solvable, the
+likelihood must be implemented explicitly. An example can be found
+[here](https://github.com/sebapersson/petab_sciml_testsuite/tree/main/test_cases/hybrid/001).
+
+**Initialization tests** are specified similarly to PEtab SciML import problem.

LICENSE

@@ -0,0 +1,20 @@
+MIT License
+
+Copyright (c) 2026, PEtab - an SBML and TSV-based data format for parameter estimation
+problems in systems biology
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this
+software and associated documentation files (the "Software"), to deal in the Software
+without restriction, including without limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
+to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
+PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
+FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.

README.md

@@ -109,50 +109,3 @@ memory access). For example, for images PyTorch expects `(C, H, W)`, whereas Jul
 `(H, W, C)`; accordingly, test case 018 sets `input_order_py = (C, H, W)` and
 `input_order_jl = (H, W, C)`. If you add an importer for another language (e.g., R), provide
 the corresponding input/output orders in `solutions.yaml` for the ML import tests.
-
-## Contributing
-
-Contributions to the PEtab SciML test suite are welcome. Please either:
-
-- (Preferred) open a
-  [pull request](https://github.com/sebapersson/petab_sciml_testsuite/pulls) (drafts
-  welcome) adding one or more test cases.
-- Or open an [issue](https://github.com/sebapersson/petab_sciml_testsuite/issues) describing
-  the missing coverage.
-
-### Adding a new test case
-
-To add a new test case, create a subdirectory in the relevant suite under `test_cases/`,
-named with the next three-digit ID (e.g., `003`). Include a short README describing what the
-test covers. Most test files should be generated automatically using utility functions from
-this repository’s associated Julia library. We use Julia for its strong SciML ecosystem and
-mature tooling for computing reference gradients (high-order finite-difference methods). To
-install this Julia library, with Julia ≥1.10, from the root directory of repository start
-Julia and run:
-
-```julia
-import Pkg; Pkg.instantiate()
-```
-
-Which files to add depends on the test type.
-
-For **ML model import tests**, provide both `net.jl` and `net.py` script. The `net.jl`
-script should generate the test inputs, ML parameters, expected outputs, and
-`solutions.yaml`. The `net.py` script should create the PEtab SciML ML-model YAML file and
-checks consistency with the ML model used to produce the Julia reference outputs. This
-ensures the case is importable in both Python (e.g., PyTorch/Equinox) and Julia, aligning
-with the goal that the PEtab SciML YAML is exchangeable across ecosystems. An example can be
-found
-[here](https://github.com/sebapersson/petab_sciml_testsuite/tree/main/test_cases/net_import/001).
-
-For **PEtab SciML import problem tests**, provide a `create.jl` script. It should generate
-the PEtab problem files and the reference values. For the PEtab problem files, the mapping
-and hybridisation PEtab tables must be provided manually, the other components can be
-selected from predefined assets (`assets/` for SBML models, ML-model YAMLs, ML parameters,
-and array inputs) and defaults in `src/` (new values can be added if needed). For the
-reference values, include an `llh_id` that maps to a likelihood implementation in
-`src/test_values/nllh/`. Because these SciML problems are not analytically solvable, the
-likelihood must be implemented explicitly. An example can be found
-[here](https://github.com/sebapersson/petab_sciml_testsuite/tree/main/test_cases/hybrid/001).
-
-**Initialization tests** are specified similarly to PEtab SciML import problem.