Update nomad citation and links

Author: domna

paper/paper.bib

@@ -211,33 +211,45 @@ @misc{byrnes2020multilayer
   primaryClass={physics.comp-ph}
 }
 
-@Article{Scheffler2022,
-  author={Scheffler, Matthias
-  and Aeschlimann, Martin
-  and Albrecht, Martin
-  and Bereau, Tristan
-  and Bungartz, Hans-Joachim
-  and Felser, Claudia
-  and Greiner, Mark
-  and Gro{\ss}, Axel
-  and Koch, Christoph T.
-  and Kremer, Kurt
-  and Nagel, Wolfgang E.
-  and Scheidgen, Markus
-  and W{\"o}ll, Christof
-  and Draxl, Claudia},
-  title={FAIR data enabling new horizons for materials research},
-  journal={Nature},
-  year={2022},
-  month={Apr},
-  day={01},
-  volume={604},
-  number={7907},
-  pages={635-642},
-  abstract={The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.},
-  issn={1476-4687},
-  doi={10.1038/s41586-022-04501-x},
-  url={https://doi.org/10.1038/s41586-022-04501-x}
+@article{Scheidgen2023,
+  doi = {10.21105/joss.05388},
+  url = {https://doi.org/10.21105/joss.05388},
+  year = {2023},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {90},
+  pages = {5388},
+  author = {Markus Scheidgen
+    and Lauri Himanen
+    and Alvin Noe Ladines
+    and David Sikter
+    and Mohammad Nakhaee
+    and Ádám Fekete
+    and Theodore Chang
+    and Amir Golparvar
+    and José A. Márquez
+    and Sandor Brockhauser
+    and Sebastian Brückner
+    and Luca M. Ghiringhelli
+    and Felix Dietrich
+    and Daniel Lehmberg
+    and Thea Denell
+    and Andrea Albino
+    and Hampus Näsström
+    and Sherjeel Shabih
+    and Florian Dobener
+    and Markus Kühbach
+    and Rubel Mozumder
+    and Joseph F. Rudzinski
+    and Nathan Daelman
+    and José M. Pizarro
+    and Martin Kuban
+    and Cuauhtemoc Salazar
+    and Pavel Ondračka
+    and Hans-Joachim Bungartz
+    and Claudia Draxl},
+  title = {NOMAD: A distributed web-based platform for managing materials science research data},
+  journal = {Journal of Open Source Software}
 }
 
 @Inbook{Hilfiker2018,

paper/paper.md

@@ -79,9 +79,9 @@ An open-source toolkit, **PyElli** has many inherent benefits over proprietary s
 For SE, optical models vary between manufacturers and translation can be difficult without comprehensive documentation.
 **PyElli's** open-source nature makes optical models extendable, auditable, and fully comprehensive.
 Each version of **PyElli** is associated with a DOI and a Zenodo upload, allowing for reliable referencing and reproducibility of analysis results.
-It supports [NXellipsometry](https://fairmat-experimental.github.io/nexus-fairmat-proposal/9636feecb79bb32b828b1a9804269573256d7696/classes/contributed_definitions/NXellipsometry.html#nxellipsometry), a recent advancement in SE data standardization.
+It supports [NXellipsometry](https://manual.nexusformat.org/classes/contributed_definitions/NXellipsometry.html), a recent advancement in SE data standardization.
 This standard is developed by the German consortium [FAIRmat](https://www.fair-di.eu/fairmat) within the German National Research Data Infrastructure (NFDI) and is accepted by the [NeXus International Advisory Committee (NIAC)](https://www.nexusformat.org/NIAC.html), which has been maintaining beamline standards in the [NeXus format](https://www.nexusformat.org) for over 20 years [@Konnecke2015].
-**PyElli** is also available as an [example](https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-remote-tools-hub/-/tree/develop/docker/ellips/example) and a cloud-based [analysis tool](https://nomad-lab.eu/prod/v1/staging/gui/analyze/north) in the research data management software [NOMAD](https://nomad-lab.eu/nomad-lab/) [@Scheffler2022].
+**PyElli** is also available as an [example](https://gitlab.mpcdf.mpg.de/nomad-lab/north/ellips/-/tree/main/example?ref_type=heads) and a cloud-based [analysis tool](https://nomad-lab.eu/prod/v1/staging/gui/analyze/north) in the research data management software [NOMAD](https://nomad-lab.eu/nomad-lab/) [@Scheidgen2023].
 
 In summary, **PyElli** aims to provide the means of more straight-forward data analysis, reproducibility, and FAIR data management within the ellipsometry community.