paper: try @misc for software references

according to https://joss.readthedocs.io/en/latest/example_paper.html

paper.bib

@@ -144,14 +144,15 @@ @software{TheSparseGridsMatlabKit
 It is lightweight, high-level and (hopefully) easy to use, good for quick prototyping and teaching. It comes with a very extensive documentation and examples (8800 lines of code, 4800 lines of comments).}
 }
 
-@software{DisCoTeccombischemeutilities2022,
+@misc{DisCoTec-combischeme-utilities,
+  author = {SG++ development team},
   title = {{{DisCoTec-combischeme-utilities}}},
-  date = {2022-10-18T05:44:12Z},
-  origdate = {2022-10-07T08:29:19Z},
-  url = {https://github.com/SGpp/DisCoTec-combischeme-utilities},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/SGpp/DisCoTec-combischeme-utilities}, 
   urldate = {2023-03-21},
   abstract = {Python utilities to create (combination technique schemes) input files for the DisCoTec code},
-  organization = {SG++ development team}
 }
 
 @article{verboncoeurParticleSimulationPlasmas2005,
@@ -168,23 +169,26 @@ @article{verboncoeurParticleSimulationPlasmas2005
   urldate = {2024-12-04},
   issue = {5A},
 }
-@software{SGppSGpp2024,
-  title = {{{SGpp}}/{{SGpp}}},
-  date = {2024-12-05T10:19:33Z},
-  origdate = {2018-03-05T12:33:52Z},
+
+@misc{SGpp,
+  title = {{SGpp}},
+  author = {SG++ development team},
+  title = {Fidgit: An ungodly union of GitHub and Figshare},
+  year = {2018},
+  publisher = {GitHub},
+  journal = {GitHub repository},
   url = {https://github.com/SGpp/SGpp},
   urldate = {2024-12-10},
-  abstract = {SG⁺⁺ – the numerical library for Sparse Grids in all their variants.},
-  organization = {SG++ development team},
-  keywords = {adaptive-sparse-grids,b-splines,cpp,interpolation,java,machine-learning,matlab,numerics,optimization,pde,python,quadrature,regression,sparse-grids}
+  abstract = {SG⁺⁺ -- the numerical library for Sparse Grids in all their variants.}
 }
 
-@software{obersteinerSparseSpACESparseGrid2023,
-  title = {{{sparseSpACE}} - {{The Sparse Grid Spatially Adaptive Combination Environment}}},
+@misc{sparseSpACE,
   author = {Obersteiner, Michael},
-  date = {2023-05-09T06:55:23Z},
-  origdate = {2019-09-02T09:22:49Z},
-  url = {https://github.com/obersteiner/sparseSpACE},
+  title = {{{sparseSpACE}} - {{The Sparse Grid Spatially Adaptive Combination Environment}}},
+  year = {2019},
   urldate = {2023-08-07},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/obersteiner/sparseSpACE},
   abstract = {sparseSpACE - the Sparse Grid Spatially Adaptive Combination Environment implements different variants of the spatially adaptive combination technique.}
 }

paper.md

@@ -162,7 +162,7 @@ sparse grids and the combination technique.
 We will give a brief overview and outline the differences and
 application areas of the codes.
 
-The C++ code `SG++` [@SGppSGpp2024] provides a direct interface to
+The C++ code @SGpp provides a direct interface to
 sparse grids and applying them to a variety of different tasks such as interpolation,
 quadrature, optimization, PDEs,  regression, and classification.
 With the help of wrappers, the framework can be used from various other programming
@@ -172,7 +172,7 @@ implementation of the combination technique.
 Although offering parallelization for some of the tasks, the code
 mainly targets single-node computations.
 
-The `Sparse Grids Matlab Kit` [@TheSparseGridsMatlabKit]
+@TheSparseGridsMatlabKit
 by Piazzola and Tamellini was originally designed for teaching purposes and
 uncertainty quantification with the combination technique [@piazzolaSparseGridsMatlab2024].
 It offers a user friendly MATLAB interface for the combination technique.
@@ -181,7 +181,7 @@ of component grid collocation points.
 The code is designed for usage on a single node which limits the parallelism
 to shared memory.
 
-The `sparseSpACE` [@obersteinerSparseSpACESparseGrid2023] project offers
+The @sparseSpACE project offers
 different variants of the combination technique including a spatially adaptive
 combination technique.
 It provides implementations for various applications such as numerical integration,