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01-introduction.md

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+---
+title: Introduction
+teaching: 5
+exercises: 10
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Discuss what can go wrong and right in scientific computing
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can we use computers more effectively in a scientific project?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Computers are now essential in all branches of science.
+We use computers to collect, analyze, and store data, to collaborate, and to write manuscripts.
+Yet most researchers are never taught the equivalent of basic lab skills for research computing.
+
+This lesson covers a set of good computing practices that **every** researcher can adopt,
+regardless of computational skill.
+Informally, it teaches how to not lose your stuff, and generally how to be more efficient.
+
+Many of our recommendations are for the benefit of the collaborator every researcher cares about most:
+their future self (as the joke goes, yourself from 3 months ago doesn't answer email…).
+
+Practically, future you will either:
+
+- curse current you (bad), or
+- thank current you (better).
+
+Adopting good practices is kind to your future self.
+
+So, which habits and practices can save you time in the future and improve your work?
+
+:::::::::::::::::::::::::::::::::::::::  challenge
+
+## Discuss in groups
+
+- What can go wrong in research computing?
+- What can go right in research computing?
+
+Consider the entire data life cycle of a project, as well as the actual analysis.
+
+1. Planning and designing
+2. Collecting and capturing
+3. Organizing and storing
+4. Interpreting and analyzing
+5. Managing and preserving
+6. Publishing and sharing
+
+:::::::::::::::  solution
+
+## Solution
+
+- You can lose your data
+- You can re-analyze your data in 1 year's
+  time and learn something important
+- And many, many, more examples
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Challenges in data-heavy biology
+
+For example, consider biology.
+
+![](fig/ew-intro.png){alt='This image shows the many different levels of data that biologists might work with. On the highest level, they work with organisms, shown in the image are a wasp, a tree, and a bacteria. On an intermediate level, they may collect raw data from these organisms. Shown in the image are a picture of dividing yeast cells, a western blot, and a floppy disk containing text data. On the final level, they will have visualisations of the raw data. Shown in the image are a picture of dividing cells with fluorescence at the boundary, a bar plot, and a cartoon of gene transcription.'}
+
+Biologists study organisms, and have to deal with many kinds of data.
+In biology, as in other sciences:
+
+- All your raw data are digital files
+  - How do you manage them?
+- There are many tools to process data
+  - Too much choice, many bad choices
+  - e.g. [Excel changes gene names](https://doi.org/10.1186/s13059-016-1044-7), and [loses COVID test results](https://www.bbc.co.uk/news/technology-54423988)...
+- There are many levels at which to study a problem and many steps to understand
+  - Where do you even start?
+
+## Good news: everyone has these problems!
+
+- Other people have thought about good practices and created good tools.
+- You don't have to reinvent practices and tools.
+- You can learn to be "good enough" in scientific computing.
+- This is an ongoing process through your career.
+
+## Principles: planning, modular organization, names, documentation
+
+This lesson has episodes covering data management, software, project organization, collaboration, keeping track of changes, and manuscripts.
+
+Good Enough Practices rely on a shared set of principles that span these areas:
+
+- Planning: plan out how to work. Any plan that you can stick to is better than no plan.
+- Modular organization: organize your data, code, and projects into coherent modules.
+- Names: give good names to your files, folders, and functions, that make them easy to find and to understand.
+- Documentation: explicitly write down everything you and your collaborators need to know in the future.
+
+
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Computing is essential in science and (almost) all data are digital
+- A set of good enough practices can make you more efficient
+- Future you will thank past you for adopting good practices
+- Shared Principles: planning, modular organisation, names, documentation
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+