Replace Zenodo badge/ref by JOSS

README.md

@@ -28,20 +28,19 @@ and then estimate that truth given the models and noisy observations.
 
 [Install](#installation), then
 read, run and try to understand `examples/basic_{1,2,3}.py`.
-Some of the examples can also be opened in Jupyter, and thereby run in the cloud
-(i.e. *without installation*, but requiring Google login): [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/nansencenter/DAPPER).
+Some of the examples also exist as Jupyter notebooks, and can be run in the cloud
+*without installation* (but requiring Google login): [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/nansencenter/DAPPER).
 This [screencast](https://www.youtube.com/watch?v=YtalK0Zkzvg&t=6475s)
 provides an introduction.
 The [documentation](https://nansencenter.github.io/DAPPER)
-includes general guidelines and the API,
-but for any serious use you will want to read and adapt the code yourself.
-If you use it in a publication, please cite, e.g.,
+includes general guidelines as well as the API reference,
+but for extensive use you must expect you read the code as well.
+If used for a publication, please cite, e.g.,
 *The experiments used (inspiration from) DAPPER [ref], version 1.6.0*,
-where [ref] points to [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10710355.svg)](https://doi.org/10.5281/zenodo.10710355).
-Lastly, for an introduction to DA theory also using Python,
+where [ref] points to [![DOI](https://joss.theoj.org/papers/10.21105/joss.05150/status.svg)](https://doi.org/10.21105/joss.05150).
+Lastly, for an introduction to DA *theory* also using Python,
 see these [tutorials](https://github.com/nansencenter/DA-tutorials).
 
-
 ## Highlights
 
 DAPPER enables the numerical investigation of [DA methods](#da-methods)
@@ -50,26 +49,30 @@ through a variety of typical [test cases](#test-cases-models) and statistics. It
 (b) facilitates comparative studies, thus promoting the
 (a) reliability and
 (b) relevance of the results.
-For example, this figure is generated by `examples/basic_3.py`,
-making use of built-in tools for experiment and result management,
+For example, the figure below is generated by `examples/basic_3.py`,
 reproduces figure 5.7 of [these lecture notes](http://cerea.enpc.fr/HomePages/bocquet/teaching/assim-mb-en.pdf).
-
-![Comparative benchmarks with Lorenz'96 plotted as a function of the ensemble size (N)](./docs/imgs/ex3.svg)
-
-DAPPER is (c) open source, written in Python, and (d) focuses on readability;
-this promotes the (c) reproduction and (d) dissemination of the underlying science,
+DAPPER is
+(c) open source, written in Python, and
+(d) focuses on readability;
+this promotes the
+(c) reproduction and
+(d) dissemination of the underlying science,
 and makes it easy to adapt and extend.
 
-It also illustrates how to parallelise ensemble forecasts (e.g. the QG model),
-local analyses (e.g. the LETKF), and independent experiments (e.g. `examples/basic_3.py`).
-It comes with a battery of diagnostics and statistics.
-These all get averaged over subdomains (e..g "ocean" and "land") and then in time.
+![Comparative benchmarks with Lorenz-96 plotted as a function of the ensemble size (N)](./docs/imgs/ex3.svg)
+
+DAPPER demonstrates how to parallelise ensemble forecasts (e.g., the QG model),
+local analyses (e.g., the LETKF), and independent experiments (e.g., `examples/basic_3.py`).
+It includes a battery of diagnostics and statistics,
+which all get averaged over subdomains (e.g., "ocean" and "land") and then in time.
 Confidence intervals are computed, including correction for auto-correlations,
 and used for uncertainty quantification, and significant digits printing.
 Several diagnostics are included in the on-line "liveplotting" illustrated below,
 which may be paused for further interactive inspection.
+In summary, DAPPER is well suited for teaching and fundamental DA research.
+Also see its [drawbacks](#similar-projects).
 
-![EnKF - Lorenz'63](./docs/imgs/ex1.jpg)
+![EnKF - Lorenz-96](./docs/imgs/ex1.jpg)
 
 <!-- Non-highlighted features:
 - Time sequences use via `tools.chronos.Chronology` and `tools.chronos.Ticker`.
@@ -79,12 +82,9 @@ which may be paused for further interactive inspection.
   provides input flexibility/overloading,
   lazy eval that facilitates the use of non-diagonal
   covariance matrices (whether sparse or full).
+- built-in tools for experiment and result management,
 -->
 
-In summary, DAPPER is well suited for teaching and fundamental DA research.
-Also see its [drawbacks](#similar-projects).
-
-
 ## Installation
 
 Successfully tested on Linux/Mac/Windows.
@@ -172,7 +172,7 @@ The particle filter is tuned with "effective-N monitoring",
 "regularization/jittering" strength, and more.
 
 For a list of ready-made experiments with suitable,
-tuned settings for a given method (e.g. the `iEnKS`), use:
+tuned settings for a given method (e.g., the `iEnKS`), use:
 
 ```sh
 grep -r "xp.*iEnKS" dapper/mods