diff --git a/.zenodo.json b/.zenodo.json
index 191c974..f89457e 100644
--- a/.zenodo.json
+++ b/.zenodo.json
@@ -7,48 +7,28 @@
             "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"
 	}
     ],
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-            "scheme": "doi",
-            "identifier": "10.1002/2015WR016907",
-            "relation_type": {
-		"id": "isderivedfrom",
-		"title": {
-		    "de": "Wird abgeleitet von",
-		    "en": "Is derived from"
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-		"title": {
-		    "de": "Zeitschriftenartikel",
-		    "en": "Journal article"
-		}
-            }
-	},
-        {
-            "scheme": "url",
-            "identifier": "https://github.com/mcuntz/pyeee/",
-            "relation_type": "isDerivedFrom",
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-            "identifier": "https://mcuntz.github.io/pyeee/",
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-            "resource_type": "publication-softwaredocumentation"
-	},
+    "upload_type": "software",
+    "keywords": [
+        "Python utilities",
+        "Optimization",
+        "Screening",
+        "Morris",
+        "Elementary Effects",
+        "Morris method",
+        "Python"
+    ],
+    "creators": [
         {
-            "scheme": "url",
-            "identifier": "https://pypi.org/project/pyeee/",
-            "relation": "isIdenticalTo",
-            "resource_type": "software"
+            "orcid": "0000-0002-5966-1829",
+            "affiliation": "Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement - INRAE, Nancy, France",
+            "name": "Matthias Cuntz"
 	},
         {
-            "scheme": "url",
-            "identifier": "https://anaconda.org/conda-forge/pyeee",
-            "relation_type": "isIdenticalTo",
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+            "orcid": "0000-0002-1132-2342",
+            "affiliation": "University of Waterloo, ON, Canada",
+            "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>"
 }