Update reference

Author: asogaard

paper/paper.bib

@@ -1,29 +1,13 @@
 // GraphNeT Zenodo
-@software{graphnet_zenodo:2022,
-  author       = {
-        Andreas Søgaard and
-        Rasmus F. Ørsøe and
-        Morten Holm and
-        Leon Bozianu and
-        Aske Rosted and
-        Troels C. Petersen and
-        Endrup Kaare Iversen and
-        Andreas Hermansen and
-        Tim Guggenmos and
-        Peter Andresen and
-        Ha Martin Minh and
-        Ludwig Neste and
-        Moust Holmes and
-        Axel Pontén and
-        Leonard Kayla DeHolton and
-        Philipp Eller
-    },
-  title        = {GraphNeT},
-  month        = may,
-  year         = 2023,
-  publisher    = {Zenodo},
-  doi          = {10.5281/zenodo.6720188},
-  url          = {https://doi.org/10.5281/zenodo.6720188}
+@software{Sogaard_GraphNeT_2023,
+author = {Søgaard, Andreas and F. Ørsøe, Rasmus and Holm, Morten and Bozianu, Leon and Rosted, Aske and C. Petersen, Troels and Endrup Iversen, Kaare and Hermansen, Andreas and Guggenmos, Tim and Andresen, Peter and Ha Minh, Martin and Neste, Ludwig and Holmes, Moust and Pontén, Axel and Leonard DeHolton, Kayla and Eller, Philipp},
+doi = {10.5281/zenodo.6720188},
+license = {Apache-2.0},
+month = may,
+title = {{GraphNeT}},
+url = {https://github.com/graphnet-team/graphnet},
+version = {1.0.0},
+year = {2023}
 }
 
 @incollection{NEURIPS2019_9015,

paper/paper.md

@@ -85,7 +85,7 @@ bibliography: paper.bib
 
 Neutrino telescopes, such as ANTARES [@ANTARES:2011hfw], IceCube [@Aartsen:2016nxy; @DeepCore], KM3NeT [@KM3Net:2016zxf], and Baikal-GVD [@Baikal-GVD:2018isr] have the science goal of detecting neutrinos and measuring their properties and origins. Reconstruction at these experiments is concerned with classifying the type of event or estimating properties of the interaction.
 
-`GraphNeT` [@graphnet_zenodo:2022] is an open-source Python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). `GraphNeT` makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques [@gnn_icecube].
+`GraphNeT` [@Sogaard_GraphNeT_2023] is an open-source Python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). `GraphNeT` makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques [@gnn_icecube].
 
 GNNs from `GraphNeT` are flexible enough to be applied to data from all neutrino telescopes, including future projects such as IceCube extensions [@IceCube-PINGU:2014okk; @IceCube:2016xxt; @IceCube-Gen2:2020qha] or P-ONE [@P-ONE:2020ljt]. This means that GNN-based reconstruction can be used to provide state-of-the-art performance on most reconstruction tasks in neutrino telescopes, at real-time event rates, across experiments and physics analyses, with vast potential impact for neutrino and astro-particle physics.