🧹 Fix typos

NOTICE.md

@@ -20,8 +20,8 @@ Since the early access version `v0.6.0` it was used in scientific teaching by a
 
 As pyglotaran developers we strive to deliver high quality but also very reliable software, software you can trust as the basis for your scientific publications. How do we do this?
 
-The development process follows best practices in software engineering, we use git, automated testing, continuous integration, and code reviews. All code changes must pass a comprehensive (unit) test suite and is revied by at least one core maintainers before being merged. We use GitHub Actions to automatically run tests, linting, and other quality checks on every pull request and code push.
+The development process follows best practices in software engineering, we use git, automated testing, continuous integration, and code reviews. All code changes must pass a comprehensive (unit) test suite and is reviewed by at least one core maintainers before being merged. We use GitHub Actions to automatically run tests, linting, and other quality checks on every pull request and code push.
 
-But more importantly for quality assurance is the pyglotaran-validation framework. As described in Sebastian Weigand's MSc thesis, this framework allows for automated validation of pyglotaran's results against established known good results. It decouples result validation from the main project, allowing us to eaily compare results from earlier with newer versions. The validation framework compares pyglotaran outputs to manually validated reference results (or so-called gold standard).
+But more importantly for quality assurance is the pyglotaran-validation framework. As described in Sebastian Weigand's MSc thesis, this framework allows for automated validation of pyglotaran's results against established known good results. It decouples result validation from the main project, allowing us to easily compare results from earlier with newer versions. The validation framework compares pyglotaran outputs to manually validated reference results (or so-called gold standard).
 
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README.md

@@ -25,7 +25,7 @@ A common use case for the framework is the analysis of time-resolved spectroscop
 
 [in a nutshell] pyglotaran can be used from a Python script, or ideally Notebook, and involves specifying your desired analysis scheme consisting of a `model` and its `parameters` together with your `experiment_data`, and then letting it `optimize` this for you. This will fit your data while optimizing for the residuals given the model you specified, the constraints you specified therein, given the (free) parameters and its starting values you provided.
 
-We have prepared a number of comprehensive examples in the form of python notebooks in the [pyglotaran-examples](https://github.com/glotaran/pyglotaran-examples) which illustrate how to use the framework. Download the example that best aligns with your use case, and give it go, and perhaps soon you will also be looking at a georgous breakdown of your data like this.
+We have prepared a number of comprehensive examples in the form of python notebooks in the [pyglotaran-examples](https://github.com/glotaran/pyglotaran-examples) which illustrate how to use the framework. Download the example that best aligns with your use case, and give it a go, and perhaps soon you will also be looking at a gorgeous breakdown of your data like this.
 
 ![overview plot of the fluorescence case study from the pyglotaran-examples](docs/source/images/png/plot_overview_study_fluorescence.png)
 The results of a target analysis of Photosystem I (see [study_florescence](https://github.com/glotaran/pyglotaran-examples/tree/main/pyglotaran_examples/study_fluorescence) in the [examples](https://github.com/glotaran/pyglotaran-examples/tree/main/pyglotaran_examples).)