change structure and follow Bjorn's idea of this lecture

lectures/good-practice/slides.qmd

@@ -3,65 +3,96 @@ title: "Good scientific and coding practice"
 author: "Bjorn Stevens and Theresa Mieslinger"
 ---
 
-# Good coding practice
-***consistency is the key***
+# Good Scientific Practice
+*Building trust in research. And in your own work.*
 
-* clean code
-* efficient code
-* understandable code
-* tracable code changes
+## What is it and why should we care?
+* Principles fomulated by the research community that define proper research behaviour with the aim to ensure a high quality, robustness and reproducibility of results (publications, data, code, software).
+* How it relates to this course: it provides rules for building software, using own and other software/data and for communicate the usage of software/data.
+
+## The pillars of Good Scientific Practice
+
+::: {.incremental}
+* **Reliability** in ensuring the quality of research, reflected in the design, methodology, analysis, and use of resources.
+* **Honesty** in developing, undertaking, reviewing, reporting, and communicating research in a transparent, fair, full, and unbiased way.
+* **Respect** for colleagues, research participants, research subjects, society, ecosystems, cultural heritage, and the environment.
+* **Accountability** for the research from idea to publication, for its management and organisation, for training, supervision, and mentoring, and for its wider societal impacts.
+:::
+*copied from [European Code of Conduct for Research Integrity](https://allea.org/wp-content/uploads/2023/06/European-Code-of-Conduct-Revised-Edition-2023.pdf)*
 
-## Style Guides
-***code is read much more often than it is written***
+## The pillars of Good Scientific Practice
 
-Each programing language has it's own guidlines:
+* Reliability / **Reproducibility**
+  * primary data
+  * data management and sharing
+* Honesty
+* **Respect & Accountability**
+  * authorship
+  * proper citation and referencing
 
-* [PEP8 for Python](https://peps.python.org/pep-0008/)
-* C++ does not have an official guidline, but [Stroustrup and Sutter](https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md) is a good starting point
+# Reproducibility
 
-## Clean Code
-* delete unused code blocks and only keep as few code as necessary
-* fewer lines of code -> fewer bugs (add source to this statement)
-* BUT: optimization should be in balance with code readability. Convoluted statements are ok if they improve the code efficiency, but might need to be accompanied by some sort of documentation.
+## What do we want to reproduce? {.special}
 
-## Efficient Code
-::: {.smaller}
-A collection and repetition of statements from previous lectures
+::: {.fragment}
+*the argument*
 :::
 
-* use math if you can, else, keep the order of complexity of your code in mind and check whether it behaves as you'd expect (complexity lecture)
-* reduce loops by vectorizing operations, e.g. in Python by using list comprehensions(?)
-* efficient memory usage (memory?)
-* use parallel processing if you can pinpoint the performance bottleneck to a task that can be split between multiple processors. (parallel programing?)
-* ...
+## What is needed to reproduce the argument? What do we need to save and how? {.special}
 
-## Understandable Code / Documentation
-code should be clear in itself, but also accompanied by a statement of its purpose and proper usage. Additional information could include input/output, author, or date information.
+## What should we document? {.special}
 
-* in-line documentation: docstrings are string literals written into your code (add example?)
-* comments
-* separate documentation: common format is a text file, e.g. README.txt, or a chapter in a linked documentation file or handbook
+## FAIR data
+* state the ideas and problems of FAIR data
 
-## Version control for code changes
-A version control software ensures a tracable record of code changes, it serves as a backup and is indispensible in any collaborative code development.
+## Which tools shall we use?
+* open source / development
+* trustworthy sources
 
-## Testing and Code Review
+## Summary on Reproducibility
+* save the primary data needed to reproduce the argument of your scientific study
 
-## Staying up to date
-* stay up to date with coding trends and libraries
-* be open and continue learning: new technologies typically improve your productivity
+# Respect & Accountability
+*What shall we credit and how?*
+
+## Authorship versus Acknowledgment
+
+## Licenses
+CC0 versus CC-BY
+
+## Intellectual Property (IP)
+
+## Using AI
+
+## Summary on Authorship and Credit
+
+# Good Coding Practice
+
+## Good Coding Practice
+* clean code: easier to understand for any reviewer and most important, fewer lines of code -> fewer bugs
+* efficient code
+  * use math if you can, else, keep the order of complexity of your code in mind and check whether it behaves as you'd expect
+  * use parallel processing if you can pinpoint the performance bottleneck to a task that can be split
+
+## Good Coding Practice
+* understandable code: code should be clear in itself, but also accompanied by a statement of its purpose and proper usage (documentation).
+* trustworthy code: testing and code review
+* tracable code changes: version control ensures a tracable record of code changes, it serves as a backup and is indispensible in any collaborative code development.
+
+# Summary
+Good Scientific Practice ensures research integrity and the advancement of knowledge.
 
-# Good scientific practice
 * Use trustworthy sources
 * understand your code
 * communicate the license
 * give credit to contributors
 * respect intellectual property (IP)
+* stay up to date with coding trends and libraries
+* be open and continue learning: new technologies typically improve your productivity :)
 
-## Licenses
-
-CC0 versus CC-BY
-
-## credit
+# Disclaimer
+*This lecture was designed with the help of the Large Language Model OpenAI GPT-4.*
 
-## IP
+# Further Reading
+* [European Code of Conduct for Research Integrity](https://allea.org/wp-content/uploads/2023/06/European-Code-of-Conduct-Revised-Edition-2023.pdf)
+* [DFG Guidlines for Safeguarding Good Research Practice. Code of Conduct](https://zenodo.org/records/6472827)