JuBiotech · tobiaslatour · Feb 3, 2026 · Oct 21, 2024 · Oct 26, 2024 · Nov 6, 2024
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -25,7 +25,7 @@ jobs:
       run: |
         pytest -v --cov-report xml --cov=peak_performance --cov-report term-missing peak_performance
     - name: Upload coverage
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         file: ./coverage.xml
     - name: Test Wheel build, install and import

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -19,7 +19,7 @@ repos:
   hooks:
     - id: black
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v1.4.1
+  rev: v1.19.1
   hooks:
   -   id: mypy
       exclude: 'test_.*?\.py$'

diff --git a/CITATION.cff b/CITATION.cff
@@ -0,0 +1,54 @@
+cff-version: "1.2.0"
+authors:
+- family-names: Nießer
+  given-names: Jochen
+  orcid: "https://orcid.org/0000-0001-5397-0682"
+- family-names: Osthege
+  given-names: Michael
+  orcid: "https://orcid.org/0000-0002-2734-7624"
+- family-names: Lieres
+  given-names: Eric
+  name-particle: von
+  orcid: "https://orcid.org/0000-0002-0309-8408"
+- family-names: Wiechert
+  given-names: Wolfgang
+  orcid: "https://orcid.org/0000-0001-8501-0694"
+- family-names: Noack
+  given-names: Stephan
+  orcid: "https://orcid.org/0000-0001-9784-3626"
+doi: 10.5281/zenodo.14261846
+message: If you use this software, please cite our article in the
+  Journal of Open Source Software.
+preferred-citation:
+  authors:
+  - family-names: Nießer
+    given-names: Jochen
+    orcid: "https://orcid.org/0000-0001-5397-0682"
+  - family-names: Osthege
+    given-names: Michael
+    orcid: "https://orcid.org/0000-0002-2734-7624"
+  - family-names: Lieres
+    given-names: Eric
+    name-particle: von
+    orcid: "https://orcid.org/0000-0002-0309-8408"
+  - family-names: Wiechert
+    given-names: Wolfgang
+    orcid: "https://orcid.org/0000-0001-8501-0694"
+  - family-names: Noack
+    given-names: Stephan
+    orcid: "https://orcid.org/0000-0001-9784-3626"
+  date-published: 2024-12-13
+  doi: 10.21105/joss.07313
+  issn: 2475-9066
+  issue: 104
+  journal: Journal of Open Source Software
+  publisher:
+    name: Open Journals
+  start: 7313
+  title: PeakPerformance - A tool for Bayesian inference-based fitting
+    of LC-MS/MS peaks
+  type: article
+  url: "https://joss.theoj.org/papers/10.21105/joss.07313"
+  volume: 9
+title: PeakPerformance - A tool for Bayesian inference-based fitting of
+  LC-MS/MS peaks
diff --git a/README.md b/README.md
@@ -2,13 +2,36 @@
 [![pipeline](https://github.com/jubiotech/peak-performance/workflows/pipeline/badge.svg)](https://github.com/JuBiotech/peak-performance/actions)
 [![coverage](https://codecov.io/gh/jubiotech/peak-performance/branch/main/graph/badge.svg)](https://app.codecov.io/gh/JuBiotech/peak-performance)
 [![documentation](https://readthedocs.org/projects/peak-performance/badge/?version=latest)](https://peak-performance.readthedocs.io/en/latest)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.07313/status.svg)](https://doi.org/10.21105/joss.07313)
 [![DOI](https://zenodo.org/badge/713469041.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
 
 # About PeakPerformance
 PeakPerformance employs Bayesian modeling for chromatographic peak data fitting.
 This has the innate advantage of providing uncertainty quantification while jointly estimating all peak parameters united in a single peak model.
 As Markov Chain Monte Carlo (MCMC) methods are utilized to infer the posterior probability distribution, convergence checks and the aformentioned uncertainty quantification are applied as novel quality metrics for a robust peak recognition.
 
+# Installation
+
+It is highly recommended to follow the following steps and install ``PeakPerformance`` in a fresh Python environment:
+1. Install the package manager [Mamba](https://github.com/conda-forge/miniforge/releases).
+Choose the latest installer at the top of the page, click on "show all assets", and download an installer denominated by "Mambaforge-version number-name of your OS.exe", so e.g. "Mambaforge-23.3.1-1-Windows-x86_64.exe" for a Windows 64 bit operating system. Then, execute the installer to install mamba and activate the option "Add Mambaforge to my PATH environment variable".
+
+⚠ If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.
+
+ℹ The newest conda version should also work, just replace `mamba` with `conda` in step 2.
+
+2. Create a new Python environment in the command line using the provided [`environment.yml`](https://github.com/JuBiotech/peak-performance/blob/main/environment.yml) file from the repo.
+   Download `environment.yml` first, then navigate to its location on the command line interface and run the following command:
+```
+mamba env create -f environment.yml
+```
+
+Naturally, it is alternatively possible to just install ``PeakPerformance`` via pip:
+
+```bash
+pip install peak-performance
+```
+
 # First steps
 Be sure to check out our thorough [documentation](https://peak-performance.readthedocs.io/en/latest). It contains not only information on how to install PeakPerformance and prepare raw data for its application but also detailed treatises about the implemented model structures, validation with both synthetic and experimental data against a commercially available vendor software, exemplary usage of diagnostic plots and investigation of various effects.
 Furthermore, you will find example notebooks and data sets showcasing different aspects of PeakPerformance.
@@ -19,4 +42,5 @@ When contributing code, create a local clone of PeakPerformance, create a new br
 
 # How to cite
 Head over to Zenodo to [generate a BibTeX citation](https://doi.org/10.5281/zenodo.10255543) for the latest release.
-A publication has just been submitted to a scientific journal. Once published, this section will be updated.
+In addition to the utilized software version, please cite our scientific publication over at the Journal of Open Source Software (JOSS).
+A detailed citation can be found in CITATION.cff and in the sidebar.
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -21,7 +21,7 @@
 
 project = "PeakPerformance"
 copyright = "2023, Forschungszentrum Jülich GmbH"
-author = "Michael Osthege"
+author = "Jochen Nießer, Michael Osthege"
 
 # The full version, including alpha/beta/rc tags
 release = __version__

diff --git a/docs/source/index.md b/docs/source/index.md
@@ -9,32 +9,51 @@ title: PeakPerformance documentation
 [![](https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
 
 
-``peak_performance`` is a Python toolbox for Bayesian inference of peak areas.
+``PeakPerformance`` is a Python toolbox for Bayesian inference of peak areas.
 
 It defines PyMC models describing the intensity curves of chromatographic peaks.
 
 Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
 
+This documentation features various notebooks that demonstrate the usage.
+
 # Installation
 
+It is highly recommended to follow the following steps and install ``PeakPerformance`` in a fresh Python environment:
+1. Install the package manager [Mamba](https://github.com/conda-forge/miniforge/releases).
+Choose the latest installer at the top of the page, click on "show all assets", and download an installer denominated by "Mambaforge-version number-name of your OS.exe", so e.g. "Mambaforge-23.3.1-1-Windows-x86_64.exe" for a Windows 64 bit operating system. Then, execute the installer to install mamba and activate the option "Add Mambaforge to my PATH environment variable".
+
+```{caution}
+If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.
+```
+
+```{note}
+The newest conda version should also work, just replace `mamba` with `conda` in step 2.)
+```
+
+2. Create a new Python environment in the command line using the provided [`environment.yml`](https://github.com/JuBiotech/peak-performance/blob/main/environment.yml) file from the repo.
+   Download `environment.yml` first, then navigate to its location on the command line interface and run the following command:
+```
+mamba env create -f environment.yml
+```
+
+Naturally, it is alternatively possible to just install ``PeakPerformance`` via pip:
+
 ```bash
 pip install peak-performance
 ```
 
 You can also download the latest version from [GitHub](https://github.com/JuBiotech/peak-performance).
 
-
-The documentation features various notebooks that demonstrate the usage.
-
 ```{toctree}
 :caption: Tutorials
 :maxdepth: 1
 
-markdown/Installation
-markdown/Preparing_raw_data
+notebooks/Preparing_raw_data_for_PeakPerformance
 markdown/Peak_model_composition
-markdown/PeakPerformance_validation
 markdown/PeakPerformance_workflow
+markdown/PeakPerformance_validation
+notebooks/Recreate_data_from_scratch
 markdown/Diagnostic_plots
 markdown/How_to_adapt_PeakPerformance_to_your_data
 ```