Experiment with zenodo file

.zenodo.json

@@ -1,7 +1,7 @@
 {
     "language": "eng",
     "license": "MIT",
-    "title": "pyeee: Parameter screening using Efficient/Sequential Elementary Effects, an extension of Morris' method.",
+    "title": "pyeee: Parameter screening using Efficient/Sequential Elementary Effects, an extension of Morris' method",
     "references": [
 	{
             "reference": "Cuntz, Mai et al. (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resources Research 51, 6417-6441, doi:10.1002/2015WR016907"
@@ -29,13 +29,13 @@
         {
             "scheme": "url",
             "identifier": "https://github.com/mcuntz/pyeee/",
-            "relation": "isDerivedFrom",
+            "relation_type": "isDerivedFrom",
             "resource_type": "software"
 	},
         {
             "scheme": "url",
             "identifier": "https://mcuntz.github.io/pyeee/",
-            "relation": "isDocumentedBy",
+            "relation_type": "isDocumentedBy",
             "resource_type": "publication-softwaredocumentation"
 	},
         {
@@ -47,7 +47,7 @@
         {
             "scheme": "url",
             "identifier": "https://anaconda.org/conda-forge/pyeee",
-            "relation": "isIdenticalTo",
+            "relation_type": "isIdenticalTo",
             "resource_type": "software"
 	}
     ],
@@ -73,6 +73,5 @@
             "name": "Juliane Mai"
 	}
     ],
-    "access_right": "open",
-    "description": "<p><strong>pyeee</strong> is a Python library for performing parameter screening of computational models. It uses Efficient or Sequential Elementary Effects, an extension of Morris&#39; method of Elementary Effects, published by:</p>\n\n<p>Cuntz M, Mai J, Zink M, Thober S, Kumar R, Sch&auml;fer D, Schr&ouml;n M, Craven J, Rakovec O, Spieler D, Prykhodko V, Dalmasso G, Musuuza J, Langenberg B, Attinger A, and Samaniego L (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, <em>Water Resources Research</em> 51, 6417-6441, doi:<a href=\"https://doi.org/10.1002/2015WR016907\">10.1002/2015WR016907</a></p>\n\n<p><strong>pyeee</strong> can be used with Python functions as well as external executables using libraries such as <a href=\"https://github.org/mcuntz/partialwrap/\">partialwrap</a>. Function evaluations can be distributed with Python&#39;s multiprocessing or via MPI.</p>\n\n<p>The complete documentation of pyeee is available at: <a href=\"https://mcuntz.github.io/pyeee/\">https://mcuntz.github.io/pyeee/</a></p>\n\n<p>A similar package (EEE) using a combination of bash and Python scripts is presented at: <a href=\"https://doi.org/10.5281/zenodo.3620894\">https://doi.org/10.5281/zenodo.3620894</a></p>\n\n<p>The version 4.0 modernised code structure and documentation, moving everything to Github, and version 4.1 added <strong>pyeee</strong> to conda-forge.</p>"
+    "access_right": "open"
 }