Good Enough Practices in Scientific Computing: All in One View

Last updated on 2023-08-31 | Edit this page

Estimated time: 15 minutes

Overview

Questions

How can we use computers more effectively in a scientific project?

Objectives

Discuss what can go wrong and right in scientific computing

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?

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.

Planning and designing
Collecting and capturing
Organizing and storing
Interpreting and analyzing
Managing and preserving
Publishing and sharing

Show me the 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.

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, and loses COVID test results…
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.

Key Points

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

Content from Data Management

Last updated on 2024-03-05 | Edit this page

Estimated time: 32 minutes

Overview

Questions

What is data management?
What data should I back up, and how?
How can I share my data effectively?

Objectives

Identify problems with data management practices
Understand what raw data is
Understand what backing up data means and why it is important to back up in more than one location
Be able to decide on appropriate file names and identifiers
Be able to create analysis ready datasets
Understand the importance of documenting your process
Understand what a DOI is and its usefulness

Data management

Data within a project may need to exist in various forms, ranging from what first arrives to what is actually used for the primary analyses. Data management is the process of storing, documenting, organizing, and sharing the data created and collected during a project. Our recommendations have two main themes. One is to work towards ready-to-analyze data incrementally, documenting both the intermediate data and the process. We also describe the key features of “tidy data”, which can be a powerful accelerator for analysis [wickham2014, hart2016].

Data management problems (5 minutes)

In your opinion, what can go wrong when you have poor data management? Write down 2 issues in the collaborative document.

Show me the solution

Data loss
Data corruption, making data unusable
Running out of storage capacity, making it hard to save data
Confusion: what does this data mean? where does it come from? what is its purpose?
Versioning issues: Which version of the data made this figure? Which version of the analysis script was used for the manuscript?

A comic strip titled 'The four stages of data loss' and subtitled 'dealing with accidental deletion of months of hard-earned data' by PhD Comics. It's based on the common theory of five stages of grief. The first panel is labelled 'stage 1: denial' and shows a man sitting behind a computer. A thought bubble above his head reads 'I did not just erase all my data. I surely made a back-up somewhere'. The second panel is labelled 'stage 2: anger' and shows the man shaking his computer. A speech bubble above his head shows he is shouting 'you stupid piece of crap! Where's my data?!'. The third panel is labelled 'stage 3: depression'. It shows the man crying, the broken computer beside him. He thinks 'Why? Why me?'. The fourth and final panel is labelled 'stage 4: acceptance'. It shows the man coming to terms with what has happened. He says 'I'm never going to graduate'.

Save the raw data

Backing up your data (5 minutes)

Which of the following do you believe are good ways and bad ways of backing up your data?

Commercial cloud service
In-house cloud service (operates similarly to a commercial cloud service but with servers and infrastructure maintained by your organization)
USB pen-drive
External hard-drive
My laptop
My workstation’s hard-disk
Network drive

Show me the solution

Commercial cloud service: it depends. Where are the servers located? How secure is it? How reliable is it? Do you have to pay for the service and what happens to your data if you can no longer afford it?
In-house cloud service: this is a good way to back up your data (usually). You have local support. It is probably compliant with funders and data security guidelines for most data sets. If you work with particularly sensitive data (for example data of patients), we suggest still discussing with IT and/or data security officers.
USB pen drive: definitely not! Pen-drives are prone to dying (and your data with it). It also raises data security issues and they can be easily lost.
External hard-drive: see above.
My laptop: it is good as a temporal storage solution for your active data. However, you should back it up appropriately.
My workstation’s hard-disk: it is good as a temporal storage solution for your active data. However, you should back it up appropriately.
Network drive: this is a good way to back up your data (usually). You have local support. It is probably compliant with funders and data security guidelines.

Backing up your data is essential, otherwise it is a question of when (not if) you lose it.

Where possible, save data as originally generated (i.e. by an instrument or from a survey). It is tempting to overwrite raw data files with cleaned-up versions, but faithful retention is essential for re-running analyses from start to finish; for recovery from analytical mishaps; and for experimenting without fear. Consider changing file permissions to read-only or using spreadsheet protection features, so it is harder to damage raw data by accident or to hand edit it in a moment of weakness.

Some data will be impractical to manage in this way. For example, you should avoid making local copies of large, stable databases. In that case, record the exact procedure used to obtain the raw data, as well as any other pertinent information, such as an official version number or the date of download.

If external hard drives are used, store them off-site of the original location. Universities often have their own data storage solutions, so it is worthwhile to consult with your local Information Technology (IT) group or library. Alternatively cloud computing resources, like Amazon Simple Storage Service (Amazon S3), Google Cloud Storage or Azure are reasonably priced and reliable. For large data sets, where storage and transfer can be expensive and time-consuming, you may need to use incremental backup or specialized storage systems, and people in your local IT group or library can often provide advice and assistance on options at your university or organization as well.

Working with sensitive data

Identify whether your project will work with sensitive data - by which we might mean:

Research data including personal data or identifiers (this might include names and addresses, or potentially identifyable genetic data or health information, or confidential information)
Commercially sensitive data or information (this might include intellectual property, or data generated or used within a restrictive commercial research funding agreement)
Data which may cause harm or adverse affects if released or made public (for example data relating to rare or endangered species which could cause poaching or fuel illegal trading)

It is important to understand the restrictions which may apply when working with sensitive data, and also ensure that your project complies with any applicable laws relating to storage, use and sharing of sensitive data (for example, laws like the General Data Protection Regulation, known as the GDPR). These laws vary between countries and may affect whether you can share information between collaborators in different countries.

Create the data you wish to see in the world

Discussion (2 minutes)

Which file formats do you store your data in? Enter your answers in the collaborative document.

Filenames: Store especially useful metadata as part of the filename itself, while keeping the filename regular enough for easy pattern matching. For example, a filename like 2016-05-alaska-b.csv makes it easy for both people and programs to select by year or by location. Common file naming conventions are discussed in the Turing Way and in the Project Organization episode of this lesson.

Variable names: Replace inscrutable variable names and artificial data codes with self-explaining alternatives, e.g., rename variables called name1 and name2 to first_name and family_name, recode the treatment variable from 1 vs. 2 to untreated vs. treated, and replace artificial codes for missing data, such as “-99”, with NA, a code used in most programming languages to indicate that data is “Not Available” [white2013].

File formats: Convert data from closed, proprietary formats to open, non-proprietary formats that ensure machine readability across time and computing setups [UIllinois]. Good options include CSV for tabular data, JSON, YAML, or XML for non-tabular data such as graphs, and HDF5 for certain kinds of structured data.

Create the dataset you wish you had received. The goal here is to improve machine and human readability, but not to do vigorous data filtering or add external information. Machine readability allows automatic processing using computer programs, which is important when others want to reuse your data. Specific examples of non-destructive transformations that we recommend at the beginning of analysis:

Create analysis-friendly data
Record all the steps used to process data
Record different data types in individual tables as appropriate (e.g. sample metadata may be kept separately from sequencing experiment metadata)
Use unique identifiers for every record in a table, allowing linkages between tables (e.g. sample identifiers are recorded in the sequencing experiment metadata)

Create analysis-friendly data

Discussion (2 minutes)

Which of the table layouts is analysis friendly? Discuss. Enter your answers in the collaborative document. Two tables of data appear side-by-side. The table on the left has columns named site, 1999, and 2000. The table on the right has columns named site, year, and cases.

Analysis can be much easier if you are working with so-called “tidy” data [wickham2014]. Two key principles are:

Make each column a variable: Don’t cram two variables into one, e.g., “male_treated” should be split into separate variables for sex and treatment status. Store units in their own variable or in metadata, e.g., “3.4” instead of “3.4kg”.

Make each row an observation: Data often comes in a wide format, because that facilitated data entry or human inspection. Imagine one row per field site and then columns for measurements made at each of several time points. Be prepared to gather such columns into a variable of measurements, plus a new variable for time point. The figure above presents an example of such a transformation.

Record all the steps used to process data

Data manipulation is as integral to your analysis as statistical modeling and inference. If you do not document this step thoroughly, it is impossible for you, or anyone else, to repeat the analysis.

The best way to do this is to write scripts for every stage of data processing. This might feel frustratingly slow, but you will get faster with practice. The immediate payoff will be the ease with which you can re-do data preparation when new data arrives. You can also re-use data preparation steps in the future for related projects. For very large data sets, data preparation may also include writing and saving scripts to obtain the data or subsets of the data from remote storage.

Some data cleaning tools, such as OpenRefine, provide a graphical user interface, but also automatically keep track of each step in the process. When tools like these or scripting is not feasible, it’s important to clearly document every manual action (what menu was used, what column was copied and pasted, what link was clicked, etc.). Often you can at least capture what action was taken, if not the complete why. For example, choosing a region of interest in an image is inherently interactive, but you can save the region chosen as a set of boundary coordinates.

How, when and why do you document?

As much as possible, always and to help you future self.

Use multiple tables as necessary, and use a unique identifier for every record

Raw data, even if tidy, is not necessarily complete. For example, the primary data table might hold the heart rate for individual subjects at rest and after a physical challenge, identified via a subject ID. Demographic variables, such as subject age and sex, are stored in a second table and will need to be brought in via merging or lookup. This will go more smoothly if subject ID is represented in a common format in both tables, e.g., always as “14025” versus “14,025” in one table and “014025” in another. It is generally wise to give each record or unit a unique, persistent key and to use the same names and codes when variables in two datasets refer to the same thing.

Submit data to a reputable DOI-issuing repository so that others can access and cite it

Show me the solution

Personal/lab web-site: this is not the best place to store your data long-term. These websites are not hosted long term. You can have a link to the repo, though.
GitHub: in itself it is not proper for sharing your data as it can be modified. However, a snapshot of a Github repository can be stored in Zenodo and be issued a DOI.
General repo (i.e.: Zenodo, Data Dryad, etc.): good option to deposit data that does not fit in a specific repository. Best if the service is non-commerical, has long-termdata archival and issues DOIs, such as Zenodo.
Community specific repo (i.e.: ArrayExpress, SRA, EGA, PRIDE, etc.): best option to share your data, if your research community has come up with a sustainable long-term repository.

Your data is as much a product of your research as the papers you write, and just as likely to be useful to others (if not more so). Sites such as Dryad and Zenodo allow others to find your work, use it, and cite it; we discuss licensing in the episode on collaboration. Follow your research community’s standards for how to provide metadata. Note that there are two types of metadata: metadata about the dataset as a whole and metadata about the content within the dataset. If the audience is humans, write the metadata (the README file) for humans. If the audience includes automatic metadata harvesters, fill out the formal metadata and write a good README file for the humans [wickes2015].

What is a DOI?

A digital object identifier is a persistent identifier or handle used to identify objects uniquely.
Data with a persistent DOI can be found even when your lab website dies.
doi-issuing repositories include: zenodo, figshare, dryad.

Data management plans

Many universities and funders require researchers to complete a data management plan (DMP). A DMP is a document which outlines information about your research data and how it will be processed. Many funders provide basic templates for writing a DMP, along with guidelines on what information should be included but the main compoments of a DMP are:

Information about your data
Information about your metadata and data formats
Information on how data can be accessed, shared and re-used
Information on how data will be stored and managed, including long-term storage and maintenance after your project is complete

Discussion (2 minutes)

Aside from being a requirement, there are many benefits of writing a DMP to researchers. What sort of benefits do you think there are? Enter your answers in the collaborative document.

Show me the solution

Find and understand data easily
Allows continuity of work when colleagues leave or join the lab
It helps you consider issues about your data before they arise and come up with solutions

Writing your first data management plan can be a daunting task but your future self will thank you in the end. It’s best to speak to other members of your lab about any existing lab group or grant data management plans. If your lab group doesn’t have a data management plan, it may be helpful to work on it together to identify any major considerations. Often research institutions provide support for DMPs, e.g. through library services or a data steward.

More resources on data management plans are available at DMP online.

What’s your next step in data management?

Which recommendations above are most helpful for your current project? What could you try this week?
Does your next project have a data management plan? Could you draft one?

Summary

Taken in order, the recommendations above will make it easier to keep track of your data and to work with it. Saving the raw data along with clear metadata, backed up, is your insurance policy. Creating analysis-friendly data, and recording all the steps used to process data, means that you and others can reproduce your analysis. Sharing your data via DOI-issuing repository allows others can access and cite it, which they will find easier if your data are analysis-friendly, clearly named, and well-documented.

These recommendations include explicitly creating and retaining of intermediate data files at intermediate steps of the analysis, with increasing levels of cleanliness and task-specificity. Saving intermediate files makes it easy to re-run parts of a data analysis pipeline, which in turn makes it less onerous to revisit and improve specific data processing tasks. Breaking a lengthy analysis workflow into modular parts makes it easier to understand, share, describe, and modify.

Modifying and sharing your data analysis is only possible if you still have the raw data: back up your data!!!

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Raw data is the data as originally generated – it should be kept read-only
Raw data has to be backed up in more than one location
Create the data you wished you have received
Keeping track of your actions is a key part of data management
The Digital object identifiers (DOIs) is a unique identifier that permanently identifies data and makes it findable
Finding a repository tailored to your data is key to making it findable and accessible by the broader community

Content from Code and Software

Last updated on 2023-08-31 | Edit this page

Estimated time: 40 minutes

Overview

Questions

What is research code and software?
What can you do to make code usable and reusable?
What are the characteristics of readable code?

Objectives

Describe what is research software and its purposes
Decompose a workflow into identifiable components
Know when to separate a script into several functions
Write code that is easy to run by others by including all dependencies, requirements, documentation, and examples

What your future self may think…

A tweet from Neil Ferguson, posted on March 22nd 2020. It reads 'I'm conscious that lots of people would like to see and run the pandemic simulation code we are using to model control measures against COVID-19. To explain the background — I wrote the code (thousands of lines on undocumented C) 13 plus years ago to model flu pandemics…'

The twitter thread illustrates a real example of research software problems, from Prof. Neil Ferguson. In that case, the research software written for one purpose was suddenly in high demand, for important public health reasons, over a decade later. And that software was hard for others to use.

Smaller-scale versions of this problem are more common:

you want to re-run a data analysis that you did six months ago
a new post-doc starts working on a related project and needs to adapt your analysis to a new dataset
you publish a paper, and a masters student from the other side of the world emails you to reproduce the results for their project

What is research code and software?

There are many different shapes and sizes of research software:

Any code that runs in order to process your research data.
A record of all the steps used to process your data (scripts and workflow such data analysis are software).
R, Python, MATLAB, unix shell, OpenRefine, ImageJ, etc. are all scriptable. So are Microsoft Excel macros.
Standalone programs or scripts that do particular research tasks are also research software.

There are extended discussions about research software at the Software Sustainability Institute.

Potential problems writing code

Discussion

What can go wrong with writing research code?

Suggestions

I don’t remember what this code does
I don’t remember why I made this choice
This code doesn’t work any more
This code doesn’t work on an updated version of my dataset
This code doesn’t work on a different computing system
I’m not sure if this calculation is correct

If you or your group are creating ten thousands lines of code for use by hundreds of people you have never met, you are doing software engineering. If you’re writing a few dozen lines now and again, and are probably going to be its only user, you may not be doing engineering, but you can still make things easier on yourself by adopting a few key engineering practices. What’s more, adopting these practices will make it easier for other people and your future self to understand and (re)use your code.

The core realization in these practices is that readable, reusable, and testable are all side effects of writing modular code, i.e., of building programs out of short, single-purpose functions with clearly-defined inputs and outputs [hunt1999]. Much has been written on this topic, and this section focuses on practices that best balance ease of use with benefit for you and collaborators.

Programs themselves are modular, and can be written in scripts that run a clearly-defined set of functions on defined inputs.

Place a brief explanatory comment at the start of every script or program

Short is fine; always include at least one example of how the program is used. Remember, a good example is worth a thousand words. Where possible, the comment should also indicate reasonable values for parameters like in this example.

    Synthesize image files for testing circularity estimation algorithm.

    Usage: make_images.py -f fuzzing -n flaws -o output -s seed -v -w size

    where:
    -f fuzzing = fuzzing range of blobs (typically 0.0-0.2)
    -n flaws   = p(success) for geometric distribution of # flaws/sample (e.g. 0.5-0.8)
    -o output  = name of output file
    -s seed    = random number generator seed (large integer)
    -v         = verbose
    -w size    = image width/height in pixels (typically 480-800)
    -h         = show help message

The reader doesn’t need to know what all these words mean for a comment to be useful. This comment tells the reader what words they need to look up: fuzzing, blobs, and so on.

Writing helpful explanatory comments

Multiple Choice

An example function GetData reads in data files of a particular type. Which of the following should be included in an explanatory comment for this function?

Options

“this function reads a file”
file name
file type
output type
date
function author
function version
data columns or other properties
expected file path / address (for example a specific directory or web address)
all of the above

Show me the solution

file type - it is good to know what the function is tailored to process
output type - it is good to know how to integrate a function in a workflow
data columns or other properties - it is good to know the internal structure of the data
expected file path / address (for example a specific directory or web address) - it is good to know the origin to evaluate the validity of the data

Decompose programs into functions

A function is a reusable section of software that can be treated as a black box by the rest of the program. This is like the way we combine actions in everyday life. Suppose that it is teatime. You could get a teabag, put the teabag in a mug, boil the kettle, pour the boiling water into the mug, wait 3 minutes for the tea to brew, remove the teabag, and add milk if desired. It is much easier to think of this as a single function, “make a cup of tea”.

Software programming languages also allow you to combine many steps into a single function. The syntax for creating functions depends on programming language, but generally you:

name the function
list its input parameters
describe what information it produces
write some lines of code that produce the desired output.

Good functions should have only one main task: for example, “make a cup of tea” does not also specify how to make a sandwich. Functions can also be built up from other functions: for example, “boil the kettle” involves checking if there is water in the kettle, filling the kettle if not, and then turning the kettle on. Having one main task means that functions should take no more than five or six input parameters and should not reference outside information. Functions should be no more than one page (about 60 lines) long: you should be able to see the entire function in a standard (~10pt) font on a laptop screen. If your function grows larger than this, it is usually best to break that up into simpler functions.

The key motivation here is to fit the program into the most limited memory of all: ours. Human short-term memory is famously incapable of holding more than about seven items at once [miller1956]. If we are to understand what our software is doing, we must break it into chunks that obey this limit, then create programs by combining these chunks. Putting code into functions also makes it easier to test and troubleshoot when things go wrong.

Pseudocode is a plain language description of code or analysis steps. Writing pseudocode can be useful to think through the logic of your analysis, and how to decompose it into functions.

The “make a cup of tea” example above might look like this:

    make_cup_of_tea = function(sugar, milk)

        if kettle is not full
            fill kettle

        boil kettle
        put teabag in cup
        add water from kettle to cup
        wait 2 minutes

        if sugar is true
            add sugar to cup
        if milk is true
            add milk to cup

        stir with spoon

        return cup

Using pseudocode

In this scenario, you’re managing fruit production on a set of islands. You have written a pseudocode function that tells you how to count how much fruit of a particular type is available to harvest on a given island.

count_fruit_on_island = function(fruit type, island)
    total fruit = 0
    for every tree of fruit type on the island
        total fruit = total fruit + number of fruit on tree
    end for loop
    return total fruit

Write the commands to call this function to count how many coconuts there are on Sam’s island, how many cherries there are on Sam’s island, and how many cherries there are on Charlie’s island.

Write a pseudocode for loop like the one above that uses this function to count all the cherries on every island.

Show me the solution

sams coconuts = count_fruit_on_island(coconuts, Sam's island)
sams cherries = count_fruit_on_island(cherries, Sam's island)
charlies cherries = count_fruit_on_island(cherries, Charlie's island)

To count all the cherries on every island:

total cherries = 0
for every island
    total cherries = total cherries + count_fruit_on_island(cherries, island)
end for loop
print "There are " + total cherries + " cherries on all the islands"

Be ruthless about eliminating duplication

Write and re-use functions instead of copying and pasting code, and use data structures like lists instead of creating many closely-related variables, e.g. create score = (1, 2, 3) rather than score1, score2, and score3.

Also look for well-maintained libraries that already do what you’re trying to do. All programming languages have libraries that you can import and use in your code. This is code that people have already written and made available for distribution that have a particular function. For instance, there are libraries for statistics, modeling, mapping and many more. Many languages catalog the libraries in a centralized source, for instance R has CRAN, Python has PyPI, and so on. So always search for well-maintained software libraries that do what you need before writing new code yourself, but test libraries before relying on them.

Give functions and variables meaningful names

Meaningful names for functions and variables document their purpose and make the program generally easy to read. As a rule of thumb, the greater the scope of a variable, the more informative its name should be: while it’s acceptable to call the counter variable in a loop i or j, things that are re-used often, such as the major data structures in a program should not have one-letter names.

Name that function

An example function is defined in the format functionName (variableName) This function cubes every third number in a sequence. What are the most meaningful names for functionName and variableName? Choose one from each of the following sections:

functionName

processFunction
computeCubesOfThird
cubeEveryThirdNumberInASequence
cubeEachThird
3rdCubed

variableName

arrayOfNumbersToBeCubed
input
numericSequence
S

Show me the solution

functionName

processFunction - incorrect, too vague
computeCubesOfThird - incorrect, doesn’t imply every third in sequence
cubeEveryThirdNumberInASequence - incorrect, too long
cubeEachThird - correct, short and includes information on the data and calculation performed
3rdCubed - incorrect, bad practice to put a number at the beginning of a function name (and not allowed by some programming languages)

variableName

arrayOfNumbersToBeCubed - incorrect, too long
input - incorrect, too vague
numericSequence - correct, short and included information about the type of input
S - incorrect, too vague

Language style guides

Remember to follow each language’s conventions for names, such as net_charge for Python and NetCharge for Java. These conventions are often described in “style guides”, and can even be checked automatically.

Callout

Tab Completion

Almost all modern text editors provide tab completion, so that typing the first part of a variable name and then pressing the tab key inserts the completed name of the variable. Employing this means that meaningful longer variable names are no harder to type than terse abbreviations.

Make dependencies and requirements explicit.

This is usually done on a per-project rather than per-program basis, i.e., by adding a file called something like requirements.txt to the root directory of the project, or by adding a “Getting Started” section to the README file.

Do not comment and uncomment sections of code to control a program’s behavior

This is error prone and makes it difficult or impossible to automate analyses. Instead, put if/else statements in the program to control what it does, and use input arguments on the command line to select particular behaviour. For example, including the input argument --option and corresponding if/else statements to control running an optional piece of the program. Remember to use descriptive names for input arguments.

Provide a simple example or test data set

Users (including yourself) can run your program on this set to determine whether it is working and whether it gives a known correct output for a simple known input. Such a test is particularly helpful when supposedly-innocent changes are being made to the program, or when it has to run on several different machines, e.g., the developer’s laptop and the department’s cluster. This type of test is called an integration test.

Code can be managed like data

Your code is like your data and also needs to be managed, backed up, and shared.

Your software is as much a product of your research as your papers, and should be as easy for people to credit. Submit code to a reputable DOI-issuing repository, just as you do with data. DOIs for software are provided by Figshare and Zenodo, for example. Both Figshare and Zenodo integrate directly with GitHub.

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Any code that runs on your research data is research software
Write your code to be read by other people, including future you
Decompose your code into modules: scripts and functions, with meaningful names
Be explicit about requirements and dependencies such as input files, arguments and expected behaviour

Content from Collaboration

Last updated on 2023-09-23 | Edit this page

Estimated time: 37 minutes

Overview

Questions

What do collaborators need to know to contribute to my project?
How can documentation make my project more efficient?
What is a license and does my project need one?

Objectives

Facilitate contributions from present and future collaborators
Learn to treat every project as a collaborative project
Describe a project in a README file
Understand what software licenses are and how they might apply to your project

You may start working on projects by yourself or with a small group of collaborators you already know, but you should design it to make it easy for new collaborators to join. These collaborators might be new grad students or postdocs in the lab, or they might be you returning to a project that has been idle for some time. As summarized in [steinmacher2015], you want to make it easy for people to set up a local workspace so that they can contribute, help them find tasks so that they know what to contribute, and make the contribution process clear so that they know how to contribute. You also want to make it easy for people to give you credit for your work.

Collaboration opportunities and challenges

Discussion

How does collaboration help in scientific computing?
What goes wrong with collaboration?
How can you prepare to collaborate?

Suggestions

How collaboration can help:

Collaboration brings other ideas and perspectives on your project
Describing your project to (potential) collaborators can help to focus the project
Thinking about other people helps you to return to your project later

What can go wrong with collaboration:

People can be confused about:
Goals: what are we trying to do?
Process: what tools will we use, how will we do it?
Responsibilities: whose job is it to do this thing?
Credit: how are contributions going to be recognized?
Data: how do we share sensitive data?
Timelines: when will people finish their tasks?

How to prepare for collaboration:

Document important things
Decide on goals and a way of working (process)
Clarify the scope and audience of your project
Highlight outstanding issues

Create an overview of your project

Written documentation is essential for collaboration. Future you will forget things, and your collaborators will not know them in the first place. An overview document can collect the most important information about your project, and act as a signpost. The overview is usually the first thing people read about your project, so it is often called a “README”. The README has two jobs: describing the contents of the project, and explaining how to interact with the project.

Create a short file in the project’s home directory that explains the purpose of the project. This file (generally called README, README.txt, or something similar) should contain :

The project’s title
A brief description
Up-to-date contact information
An example or two of how to run the most important tasks
Overview of folder structure

Describe how to contribute to the project

Because the README is usually the first thing users and collaborators on your project will look at, make it explicit how you want people to engage with the project. If you are looking for more contributors, make it explicit that you welcome contributors and point them to the license (more below) and ways they can help.

A separate CONTRIBUTING file can also describe what people need to do in order to get the project going and use or contribute to it:

Dependencies that need to be installed
Tests that can be run to ensure that software has been installed correctly
Guidelines or checklists that your project adheres to.

This information is very helpful and will be forgotten over time unless it’s documented inside the project.

Comparing README files

Here is a README file for a data project and one for a software project. What useful and important information is present, and what is missing? Data Project README
Software Project README

Show me the solution

This Data Project README:

Contains a DOI
Describes the purpose of the code and links to a related paper
Describes the project structure
Includes a license
DOES NOT contain requirements
DOES NOT include a working example
DOES NOT include a explicit list of authors (can be inferred from paper though)

This Software Project README:

Describes the purpose of the code
Describes the requirements
Includes instructions for various type of users
Describes how to contribute
Includes a working example
Includes a license
DOES NOT include an explicit DOI
DOES NOT describe the project structure

Create a shared “to-do” list

This can be a plain text file called something like notes.txt or todo.txt, or you can use sites such as GitHub or Bitbucket to create a new issue for each to-do item. (You can even add labels such as “low hanging fruit” to point newcomers at issues that are good starting points.) Whatever you choose, describe the items clearly so that they make sense to newcomers.

Decide on communication strategies

Make explicit decisions about (and publicize where appropriate) how members of the project will communicate with each other and with external users / collaborators. This includes the location and technology for email lists, chat channels, voice / video conferencing, documentation, and meeting notes, as well as which of these channels will be public or private.

Collaborations with sensitive data

If you determine that your project will include work with sensitive data, it is important to agree with collaborators on how and where the data will be stored, as well as what the mechanisms for sharing the data will be and who is ultimately responsible for ensuring these are followed.

Make the license explicit

What is a licence?

Specifies allowable copying and reuse
Without a licence, people cannot legally reuse your code or data
Different options for different goals and funder requirements (Apache, MIT, CC, …)
For example, this lesson is reusable with attribution under a Creative Commons Attribution (CC BY) 4.0 licence
Applies to all material in a project, e.g. data, text and code

Have a LICENSE file in the project’s home directory that clearly states what license(s) apply to the project’s software, data, and manuscripts. Lack of an explicit license does not mean there isn’t one; rather, it implies the author is keeping all rights and others are not allowed to re-use or modify the material. A project that consists of data and text may benefit from a different license to a project consisting primarily of code.

We recommend Creative Commons licenses for data and text, either CC-0 (the “No Rights Reserved” license) or CC-BY (the “Attribution” license, which permits sharing and reuse but requires people to give appropriate credit to the creators). For software, we recommend a permissive open source license such as the MIT, BSD, or Apache license [laurent2004]. A useful resource to compare different licenses is available at tldrlegal. More advice for how to use licences for research data is available at openaire.

What Not To Do

We recommend against the “no commercial use” variations of the Creative Commons licenses because they may impede some forms of re-use. For example, if a researcher in a developing country is being paid by her government to compile a public health report, she will be unable to include your data if the license says “non-commercial”. We recommend permissive software licenses rather than the GNU General Public License (GPL) because it is easier to integrate permissively-licensed software into other projects, see chapter three in [laurent2004].

Make the project citable

A CITATION file describes how to cite this project as a whole, and where to find (and how to cite) any data sets, code, figures, and other artifacts that have their own DOIs. The example below shows the CITATION file for the Ecodata Retriever; for an example of a more detailed CITATION file, see the one for the khmer project.

Please cite this work as:

Morris, B.D. and E.P. White. 2013. "The EcoData Retriever:
improving access to existing ecological data." PLOS ONE 8:e65848.
http://doi.org/doi:10.1371/journal.pone.0065848

Recommended resources

The Turing Way Guide for Collaboration

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Create an overview of your project
Create a shared “to-do” list
Decide on communication strategies
Make the license explicit
Make the project citable

Content from Project Organization

Last updated on 2023-09-23 | Edit this page

Estimated time: 25 minutes

Overview

Questions

How should I name my files?
How does folder organization help me

Objectives

Understand elements of good naming strategy
Evaluate pros and cons of different project organizations

Organizing the files that make up a project in a modular, logical, and consistent directory structure will help you and others keep track of them.

Project organisation problems

Discuss what can go wrong with project organisation:

Struggling to find the code that creates a particular figure
Hating to look at or even think about your project because of how badly organised it is

README files are magic

You look at a directory (or project), you read it, and it tells you what you need to know

… as long as you keep it updated!

Put each project in its own directory, which is named after the project

Similar to deciding when a chunk of code should be made a function, the ultimate goal of dividing research into distinct projects is to help you and others best understand your work. Some researchers create a separate project for each manuscript they are working on, while others group all research on a common theme, data set, or algorithm into a single project.

As a rule of thumb, divide work into projects or modules based on the overlap in data and code files. If two research efforts share no data or code, they will probably be easiest to manage independently. If they share more than half of their data and code, they are probably best managed together, while if you are building tools that are used in several projects, the common code should probably be in a project of its own.

Projects do often require their own organizational model. The below recommendations on how you can structure data, code, analysis outputs and other files, are drawn primarily from [noble2009, gentzkow2014]. Other structures to consider are:

The important concepts are that is useful to organize the project in modules by the types of files and that consistent planning and good names help you effectively find and use things later. Your lab or organization may have a template to use.

Put text documents associated with the project in the `doc` directory.

This includes files for manuscripts, documentation for source code, and/or an electronic lab notebook recording your experiments. Subdirectories may be created for these different classes of files in large projects.

Put raw data and metadata in a `data` directory, and files generated during cleanup and analysis in a `results` directory

When we refer to “generated files”, this includes intermediate results, such as cleaned data sets or simulated data, as well as final results such as figures and tables.

The results directory will usually require additional subdirectories for all but the simplest projects. Intermediate files such as cleaned data, statistical tables, and final publication-ready figures or tables should be separated clearly by file naming conventions or placed into different subdirectories; those belonging to different papers or other publications should be grouped together. Similarly, the data directory might require subdirectories to organize raw data based on time, method of collection, or other metadata most relevant to your analysis.

Put project source code in the `src` directory

src contains all of the code written for the project. This includes programs written in interpreted languages such as R or Python; those written compiled languages like Fortran, C++, or Java; as well as shell scripts, snippets of SQL used to pull information from databases; and other code needed to regenerate the results.

This directory may contain two conceptually distinct types of files that should be distinguished either by clear file names or by additional subdirectories. The first type are files or groups of files that perform the core analysis of the research, such as data cleaning or statistical analyses. These files can be thought of as the “scientific guts” of the project.

The second type of file in src is controller or driver scripts that contains all the analysis steps for the entire project from start to finish, with particular parameters and data input/output commands. A controller script for a simple project, for example, may read a raw data table, import and apply several cleanup and analysis functions from the other files in this directory, and create and save a numeric result. For a small project with one main output, a single controller script should be placed in the main src directory and distinguished clearly by a name such as “runall”. The short example below is typical of scripts of this kind; note how it uses one variable, TEMP_DIR, to avoid repeating the name of a particular directory four times.

    TEMP_DIR = ./temp_zip_files

    echo "Packaging zip files required by analysis tool..."
    mkdir $(TEMP_DIR)
    ./src/make-zip-files.py $(TEMP_DIR) *.dat

    echo "Analyzing..."
    ./bin/sqr_mean_analyze -i $(TEMP_DIR) -b "temp"

    echo "Cleaning up..."
    rm -rf $(TEMP_DIR)

Put compiled programs in the `bin` directory

bin contains executable programs compiled from code in the src directory. Projects that do not have any will not require bin.

Scripts vs. Programs

We use the term “script” to mean “something that is executed directly as-is”, and “program” to mean “something that is explicitly compiled before being used”. The distinction is more one of degree than kind—libraries written in Python are actually compiled to bytecode as they are loaded, for example—so one other way to think of it is “things that are edited directly” and “things that are not”.

External Scripts

If src is for human-readable source code, and bin is for compiled binaries, where should projects put scripts that are executed directly—particularly ones that are brought in from outside the project? On the one hand, these are written in the same languages as the project-specific scripts in src; on the other, they are executable, like the programs in bin. The answer is that it doesn’t matter, as long as each team’s projects follow the same rule. As with many of our other recommendations, consistency and predictability are more important than hair-splitting.

Name all files to reflect their content or function.

For example, use names such as bird_count_table.csv, manuscript.md, or sightings_analysis.py. Do not use sequential numbers (e.g., result1.csv, result2.csv) or a location in a final manuscript (e.g., fig_3_a.png), since those numbers will almost certainly change as the project evolves.

File names should be:

Machine readable

Human readable

Descriptive of their contents

Optional: Consistent

Optional: Play well with default ordering

Example

The diagram below provides a concrete example of how a simple project might be organized following these recommendations:

    .
    |-- CITATION
    |-- README
    |-- LICENSE
    |-- requirements.txt
    |-- data
    |   -- birds_count_table.csv
    |-- doc
    |   -- notebook.md
    |   -- manuscript.md
    |   -- changelog.txt
    |-- results
    |   -- summarized_results.csv
    |-- src
    |   -- sightings_analysis.py
    |   -- runall.py

The root directory contains a README file that provides an overview of the project as a whole, a CITATION file that explains how to reference it, and a LICENSE file that states the licensing. The requirements.txt file lists the software that is required to run the data analysis. The data directory contains a single CSV file with tabular data on bird counts (machine-readable metadata could also be included here). The src directory contains sightings_analysis.py, a Python file containing functions to summarize the tabular data, and a controller script runall.py that loads the data table, applies functions imported from sightings_analysis.py, and saves a table of summarized results in the results directory.

This project doesn’t have a bin directory, since it does not rely on any compiled software. The doc directory contains two text files written in Markdown, one containing a running lab notebook describing various ideas for the project and how these were implemented and the other containing a running draft of a manuscript describing the project findings.

Naming and sorting (5 minutes)

Have a look at the example files from a project, similar to the one from the previous metadata episode.

All the files have been sorted by name and demonstrate consequences of different naming strategies.

For your information, to encode experimental details the following conventions were taken:

phyB/phyA are sample genotypes (that is, which gene is mutated)
sXX is the sample number
LD/SD are different light conditions (long or short day)
on/off are different media (on sucrose, off sucrose)
measurement date
other details are timepoint and raw or normalized data

2020-07-14_s12_phyB_on_SD_t04.raw.csv
2020-07-14_s1_phyA_on_LD_t05.raw.csv
2020-07-14_s2_phyB_on_SD_t11.raw.csv
2020-08-12_s03_phyA_on_LD_t03.raw.csv
2020-08-12_s12_phyB_on_LD_t01.raw.csv
2020-08-13_s01_phyB_on_SD_t02.raw.csv
2020-7-12_s2_phyB_on_SD_t01.raw.csv
AUG-13_phyB_on_LD_s1_t11.raw.csv
JUL-31_phyB_on_LD_s1_t03.raw.csv
LD_phyA_off_t04_2020-08-12.norm.csv
LD_phyA_on_t04_2020-07-14.norm.csv
LD_phyB_off_t04_2020-08-12.norm.csv
LD_phyB_on_t04_2020-07-14.norm.csv
SD_phyB_off_t04_2020-08-13.norm.csv
SD_phyB_on_t04_2020-07-12.norm.csv
SD_phya_off_t04_2020-08-13.norm.csv
SD_phya_ons_t04_2020-07-12.norm.csv
ld_phyA_ons_t04_2020-08-12.norm.csv

What are the problems with having the date first?
How do different date formats behave once sorted?
Can you tell the importance of a leading 0 (zeros)?
Is it equally easy to find all data from LD conditions as ON media?
Can you spot the problem when using different cases (upper/lower)?
Do you see benefits of keeping consistent lengths of the naming conventions?
Do you see what happens when you mix conventions?

Show me the solution

Using dates up front makes it difficult to quickly find data for particular conditions or genotypes. It also masks the “logical” order of samples or timepoints.
Named months break the “expected” sorting, same as dates without leading 0
Without leading zeros, ‘s12’ appear before s1 and s2
the first (and second) part of the name are easiest to spot
the last file is also from LD conditions, but appears after SD, same with ‘phya’ genotypes
the last 3 file names are easiest to read as all parts appear on top of each other due to the same 3 letter-length codes ons and off
The lack of consistency makes it very difficult to get data from related samples/conditions.

Some helpful organisation tools

Integrated Development Environments (IDEs) combine many features to write code and organise projects:
- PyCharm for python
- RStudio for R
- VSCode for python and other languages, etc.
Notebooks collect data, code, results, thinking in single documents:
- jupyter
- R markdown
- quarto, etc.
Cookie Cutter project templates for reproducible science

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Some content was adapted from FAIR in Biological Practice episode on files and organisation. That material gives a slightly different and also useful perspective.

Key Points

A good file name suggests the file content
Good project organization saves you time

Content from Keeping Track of Changes

Last updated on 2023-09-07 | Edit this page

Estimated time: 40 minutes

Overview

Questions

How do I make changes to a project without losing or breaking things?
Why does GitHub exist?

Objectives

List common problems with introducing changes to files without tracking
Understand good practices in tracking changes
Write a good change description

A comic strip titled "final.doc" by PhD Comics. The first panel shows a student saving a document on their computer and naming the file "final.doc". The second panel shows their professor editing the document on a printed piece of paper. The third panel shows the student making the edits and naming the new document "final_rev2.doc". The fourth to ninth panels go back and forth between the professor and the student, with increasingly complex file names. By the end the student is exasperated and hitting their head on their computer screen.

Problems with change

Which of this issues can you relate to?

I have fifteen versions of this file and I don’t know which is which
I can’t remake this figure from last year
I modified my code and something apparently unrelated does not work anymore
I have several copies of the same directory because I’m worried about breaking something
Somebody duplicated a record in a shared file with samples
You remember seeing a data file but cannot find it anymore: is it deleted ? Moved away ?
I tried multiple analysis and I don’t remember which one I chose to generate my output data
I have to merge changes to a paper from mails with collaborators
I accidently deleted a part of my work
I came to an old project and forgot where I left it
I have trouble to find the source of a mistake in an experiment
My directory is polluted with a lot of unused/temporary/old folders because I’m afraid of losing something important
I made a lot of changes to my paper but only want to bring back one of paragraph

Keeping track of changes that you or your collaborators make to data and software is a critical part of research. Being able to reference or retrieve a specific version of the entire project aids in reproducibility for you leading up to publication, when responding to reviewer comments, and when providing supporting information for reviewers, editors, and readers.

We believe that the best tools for tracking changes are the version control systems that are used in software development, such as Git, Mercurial, and Subversion. They keep track of what was changed in a file when and by whom, and synchronize changes to a central server so that many users can manage changes to the same set of files.

While these version control tools make tracking changes easier, they can have a steep learning curve. So, we provide two sets of recommendations:

a systematic manual approach for managing changes and
version control in its full glory,

and you can use the first while working towards the second, or just jump in to version control.

Whatever system you chose, we recommend that you:

Back up (almost) everything created by a human being as soon as it is created

This includes scripts and programs of all kinds, software packages that your project depends on, and documentation. A few exceptions to this rule are discussed below.

Keep changes small

Each change should not be so large as to make the change tracking irrelevant. For example, a single change such as “Revise script file” that adds or changes several hundred lines is likely too large, as it will not allow changes to different components of an analysis to be investigated separately. Similarly, changes should not be broken up into pieces that are too small. As a rule of thumb, a good size for a single change is a group of edits that you could imagine wanting to undo in one step at some point in the future.

Everyone working on the project should share and incorporate changes from others on a regular basis. Do not allow individual investigator’s versions of the project repository to drift apart, as the effort required to merge differences goes up faster than the size of the difference. This is particularly important for the manual versioning procedure described below, which does not provide any assistance for merging simultaneous, possibly conflicting, changes.

The list should include writing log messages that clearly explain any changes, the size and content of individual changes, style guidelines for code, updating to-do lists, and bans on committing half-done work or broken code. See [gawande2011] for more on the proven value of checklists.

Store each project in a folder that is mirrored off the researcher’s working machine

This may include:

using a shared system such as a (institutional) cloud or shared drive, or
a remote version control repository such as GitHub.

Synchronize that folder at least daily. It may take a few minutes, but that time is repaid the moment a laptop is stolen or its hard drive fails.

How to document a change

A good entry that documents changes should contain:

Date of the change
Author of the change
List of affected files
A short description of the nature of the introduced changes AND/OR motivation behind the change.

Examples of the descriptions are:

Added flow cytometry data for the control and starvation stressed samples
Updated matplot library to version 3.4.3 and regenerated figures
Added pane with protein localization to the Figure 3 and its discussion in the text
Reverted to the previous version of the abstract text as the manuscript reached word limits
Cleaned the strain inventory: Recent freezer cleaning and ordering indicated a lot of problem with the strains data. The missing physical samples were removed from the table, the duplicated ids are marked for checking with PCR. The antibiotic resistance were moved from phenotype description to its own column.
New regulation heatmap: As suggested by Will I used the normalization and variance stabilization procedure from Hafemeister et al prior to clustering and heatmap generation

The larger the project (measured either in: collaborators, file numbers, or workflow complexity) the more detailed the change description should be. While your personal project can get away with one liner descriptions, the largest projects should always contain information about motivation behind the change and what are the consequences.

Manual Versioning

Our first suggested approach, in which everything is done by hand, has two additional parts:

Add a file called CHANGELOG.txt to the project’s docs subfolder, and make dated notes about changes to the project in this file in reverse chronological order (i.e., most recent first). This file is the equivalent of a lab notebook, and should contain entries like those shown below.

## 2016-04-08

* Switched to cubic interpolation as default.
* Moved question about family's TB history to end of questionnaire.

## 2016-04-06

* Added option for cubic interpolation.
* Removed question about staph exposure (can be inferred from blood test results).

Copy the entire project whenever a significant change has been made (i.e., one that materially affects the results), and store that copy in a sub-folder whose name reflects the date in the area that’s being synchronized. This approach results in projects being organized as shown below:

.
|-- project_name
|   -- current
|       -- ...project content as described earlier...
|   -- 2016-03-01
|       -- ...content of 'current' on Mar 1, 2016
|   -- 2016-02-19
|       -- ...content of 'current' on Feb 19, 2016

Here, the project_name folder is mapped to external storage (such as Dropbox), current is where development is done, and other folders within project_name are old versions.

Data is Cheap, Time is Expensive

Copying everything like this may seem wasteful, since many files won’t have changed, but consider: a terabyte hard drive costs about \$50, which means that 50 GByte costs less than \$5. Provided large data files are kept out of the backed-up area (discussed below), this approach costs less than the time it would take to select files by hand for copying.

This manual procedure satisfies the requirements outlined above without needing any new tools. If multiple researchers are working on the same project, though, they will need to coordinate so that only a single person is working on specific files at any time. In particular, they may wish to create one change log file per contributor, and to merge those files whenever a backup copy is made.

Version Control Systems

What the manual process described above requires most is self-discipline. The version control tools that underpin our second approach—the one we use in our own projects–don’t just accelerate the manual process: they also automate some steps while enforcing others, and thereby require less self-discipline for more reliable results.

Use a version control system, to manage changes to a project.

Box 2 briefly explains how version control systems work. It’s hard to know what version control tool is most widely used in research today, but the one that’s most talked about is undoubtedly Git. This is largely because of GitHub, a popular hosting site that combines the technical infrastructure for collaboration via Git with a modern web interface. GitHub is free for public and open source projects and for users in academia and nonprofits. GitLab is a well-regarded alternative that some prefer, because the GitLab platform itself is free and open source. Bitbucket provides free hosting for both Git and Mercurial repositories, but does not have nearly as many scientific users.

Box 2: How Version Control Systems Work

A version control system stores snapshots of a project’s files in a repository. Users modify their working copy of the project, and then save changes to the repository when they wish to make a permanent record and/or share their work with colleagues. The version control system automatically records when the change was made and by whom along with the changes themselves.

Crucially, if several people have edited files simultaneously, the version control system will detect the collision and require them to resolve any conflicts before recording the changes. Modern version control systems also allow repositories to be synchronized with each other, so that no one repository becomes a single point of failure. Tool-based version control has several benefits over manual version control:

Instead of requiring users to make backup copies of the whole project, version control safely stores just enough information to allow old versions of files to be re-created on demand.
Instead of relying on users to choose sensible names for backup copies, the version control system timestamps all saved changes automatically.
Instead of requiring users to be disciplined about completing the changelog, version control systems prompt them every time a change is saved. They also keep a 100% accurate record of what was actually changed, as opposed to what the user thought they changed, which can be invaluable when problems crop up later.
Instead of simply copying files to remote storage, version control checks to see whether doing that would overwrite anyone else’s work. If so, they facilitate identifying conflict and merging changes.

Changelog in action

Have a look at one of the example github repositories and how they track changes:

Give examples of:

what makes their changelogs good?
what could be improved?

Also, what would be the most difficult feature to replicate with manual version control?

Show me the solution

Some good things:

all log entries contain date and author
all log entries contain list of files that have been modified
for text files the actual change can be visible
the description text gives an idea of the change

Some things that could be improved:

The pigs files should probably be recorded in smaller chunks (commits). The raw data and cleaned data could be added separetely unless they all were captured at the same time.
Rather than general “Readme update” a more specific descriptin could be provied “Reformated headers and list”
Some of the Ballou et al changes could do with more detailed descriptions, for example why the change took place in case of IQ_TREE entries

Something difficult to replicate manually:

The changelog is linked to a complete description of the file changes.
Click on an entry, for example Clarify README.md or update readme file, and you’ll see the file changes with additions marked with + (in green) and deletions marked with - (in red).

What Not to Put Under Version Control

The benefits of version control systems don’t apply equally to all file types. In particular, version control can be more or less rewarding depending on file size and format. First, file comparison in version control systems is optimized for plain text files, such as source code. The ability to see so-called “diffs” is one of the great joys of version control systems. Unfortunately, Microsoft Office files (like the .docx files used by Word) or other binary files, e.g., PDFs, can be stored in a version control system, but it is not always possible to pinpoint specific changes from one version to the next. Tabular data (such as CSV files) can be put in version control, but changing the order of the rows or columns will create a big change for the version control system, even if the data itself has not changed.

Second, raw data should not change, and therefore should not require version tracking. Keeping intermediate data files and other results under version control is also not necessary if you can re-generate them from raw data and software. However, if data and results are small, we still recommend versioning them for ease of access by collaborators and for comparison across versions.

Third, today’s version control systems are not designed to handle megabyte-sized files, never mind gigabytes, so large data or results files should not be included. (As a benchmark for “large”, the limit for an individual file on GitHub is 100MB.) Some emerging hybrid systems such as Git LFS put textual notes under version control, while storing the large data itself in a remote server, but these are not yet mature enough for us to recommend.

Some git resources

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Small, frequent changes are easier to track
Tracking change systematically with checklists is helpful
Version control systems help adhere to good practices

Content from Manuscripts

Last updated on 2023-08-31 | Edit this page

Estimated time: 20 minutes

Overview

Questions

How do I write a collaborative paper?

Objectives

Know how to frame writing manuscripts as a computing project
Compare the benefits and drawbacks of 3 approaches
Know where to start with text-based version control as a good practice for writing manuscripts

An old joke says that doing the research is the first 90% of any project; writing up is the other 90%. While writing is rarely addressed in discussions of scientific computing, computing has changed scientific writing just as much as it has changed research.

Writing manuscripts is often collaborative, and so a team with diverse backgrounds, skills, and expectations must work together. In our experience, setting explicit expectations for writing is essential, just like other collaborations.

Discussion (3 mins)

Whether or not you have written a scientific manuscript before, you probably have experience of group work or writing. Discuss on the collaborative document:

What tools have you used before for group writing?
What’s gone wrong with group writing you’ve been involved with in the past?

The First Rule Is…

The workflow you choose is less important than having all authors agree on the workflow before writing starts. Make sure to also agree on a single method to provide feedback, be it an email thread or mailing list, an issue tracker, or some sort of shared online to-do list.

We suggest having a meeting (or online thread) of all authors at the beginning of the writing process. Ask everyone how they would prefer to write a manuscript. Then agree on a decision and process, and put the outcome in writing. If co-authors are learning new tools, ask someone familiar with those tools to support them!

Making email-based workflows work

A common practice in academic writing is for the lead author to email successive versions of a manuscript to coauthors to collect feedback, which is returned as changes to the document, comments on the document, plain text in email, or a mix of all three. This allows co-authors to use familiar tools, but results in a lot of files to keep track of, and a lot of tedious manual labor to merge comments to create the next master version.

However, if a (senior) co-author insists on using a particular format, like word or LaTeX, or on sending comments by email or written on printouts, in our experience it can be very difficult to convince them to change. Two principles make an email-based workflow work: informative filenames with date and initials, and a single lead author who co-ordinates.

Top tips for writing manuscripts via email

Give your manuscript file an informative name, and update the date and initials of last edit, for example best_practices_manuscript_2013-12-01_GW.doc would be the version edited by GW on 1st December 2013.

Choose one person to co-ordinate (i.e. the lead author), who is responsible for merging comments and sending out updated manuscripts to all other co-authors.

Good practices beyond an email-based workflow

Instead of an email-based workflow, we recommend mirroring good practices for managing software and data to make writing scalable, collaborative, and reproducible. As with our recommendations for version control in general, we suggest that groups choose one of two different approaches for managing manuscripts. The goals of both are to:

Ensure that text is accessible to yourself and others now and in the future by making a single master document that is available to all coauthors at all times.
Reduce the chances of work being lost or people overwriting each other’s work.
Make it easy to track and combine contributions from multiple collaborators.
Avoid duplication and manual entry of information, particularly in constructing bibliographies, tables of contents, and lists.
Make it easy to regenerate the final published form (e.g., a PDF) and to tell if it is up to date.
Make it easy to share that final version with collaborators and to submit it to a journal.

Single Master Online

Our first alternative will already be familiar to many researchers:

Write manuscripts using online tools with rich formatting, change tracking, and reference management, such as Google Docs or MS OneDrive. With the document online, everyone’s changes are in one place, and hence don’t need to be merged manually.

We realize that in many cases, even this solution is asking too much from collaborators who see no reason to move forward from desktop GUI tools. To satisfy them, the manuscript can be converted to a desktop editor file format (e.g., Microsoft Word .docx or LibreOffice .odt) after major changes, then downloaded and saved in the doc folder. Unfortunately, this means merging some changes and suggestions manually, as existing tools cannot always do this automatically when switching from a desktop file format to text and back (although Pandoc can go a long way).

Text-based Documents Under Version Control

The second approach treats papers exactly like software, and has been used by researchers in mathematics, astronomy, physics, and related disciplines for decades:

Write the manuscript in a plain text format that permits version control such as LaTeX or Markdown, and then convert them to other formats such as PDF as needed using scriptable tools like Pandoc.

Using a version control system provides good support for finding and merging differences resulting from concurrent changes. It also provides a convenient platform for making comments and performing review.

This approach re-uses the version control tools and skills used to manage data and software, and is a good starting point for fully-reproducible research. However, it requires all contributors to understand a much larger set of tools, including markdown or LaTeX, make, BiBTeX, and Git/GitHub.

It is even possible using this approach to combine manuscripts and data analysis, e.g. through Rmarkdown.

Top tips for writing manuscripts via text-based version control

Project organization is crucial here, structure your folder thoughtfully.

Make a project/manuscript README file, including the agreed workflow.

Separate sentences by linebreaks in your plain-text document, to make comparisons and merging easier.

Benefits and drawbacks of each approach

Things to consider	Email based workflow	Single master online	Text-based under version control
Previous user experience/comfort	High	Medium	Low
Visible tracking of changes	Low	Variable	High
Institutional support	Low	High	Low
Ease of merging changes and suggestions	Low	Medium	High
Distributed control	Low	High	High
Ease of formatting changes for re-submission	Low	Low	High

While we feel that text-based version control is a superior method, the barriers to entry may be too high for many users. The single master online approach is a good compromise. If your institution has invested in an environment (Google Docs / MS Office), users can stay within their familiar desktop GUI applications while still taking advantage of automatic file versioning and shared editing.

Discussion: Approaching your next manuscript (7 mins)

In groups, discuss:

What’s the next manuscript you’ll work on, and who with?
Which approaches will you use to collaborate on this next manuscript?

Getting started writing text-based version control

Version Control with Git Carpentries lesson introduces text-based version control, that you could use for a collaborative manuscript.

Manubot is an open-source system for writing scholarly manuscripts via GitHub, with tutorials.

Supplementary Materials

Supplementary materials often contain much of the work that went into the project, such as tables and figures or more elaborate descriptions of the algorithms, software, methods, and analyses. In order to make these materials as accessible to others as possible, do not rely solely on the PDF format, since extracting data from PDFs is notoriously hard. For the same reason, Excel is not a suitable file format for table data that others may want to re-analyze. It is acceptable for summary statistics tables so long as the underlying data is also available in a text file format such as CSV.

We recommend separating the results that you may expect others to reuse (e.g., data in tables, data behind figures) into separate, text-format files in formats such as CSV, JSON, YAML, XML, or HDF5. The same holds for any commands or code you want to include as supplementary material: use the format that most easily enables reuse (source code files, Unix shell scripts etc).

Attribution

This episode was adapted from and includes material from Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Have all authors agree on a workflow before the writing starts
Email-based workflows work better with informative filenames and clear co-ordination
Text-based documents with version control scale better, if co-authors are familiar with the tools
Single Master Online approaches can be an effective compromise

Content from What To Do Next

Last updated on 2023-08-31 | Edit this page

Estimated time: 10 minutes

Overview

Questions

How can you improve practices after this workshop?

Objectives

Learn about other resources to improve good practices
Reflect on good enough practices appropriate for your career stage

Heads up

When you lose your data, or stare, disgusted, at a horrible file, think of today.

Learning good practices is a long-term process

We have outlined a series of good enough practices for scientific computing. These practices are pragmatic, broadly accessible, and can be applied by both individuals and groups. Most importantly, these practices make researchers more productive individually by enabling them to get more done in less time and with less pain. They also accelerate research as a whole by making work more reproducible.

We have covered good practices in several areas:

Data management
Software
Collaboration
Project organization
Keeping track of changes
Manuscripts

We have explained the shared set of principles behind good practices:

Planning
Modular organization
Names
Documentation

Collectively, these practices make your work easier to use for other people, including for your future self. Thinking through your work from a collaborator’s point of view is helpful, and ultimately kinder to your future self.

What we left out, and where to learn more

Learning good practices is a long-term process that never stops. We left out many good practices that, although useful, have more niche applications.

We recommend the paper Best Practices in Scientific Computing, especially for those gaining more experience with coding. There are many other useful papers and resources that we have selected.

One of your best resources is your colleagues - talk to them! In our experience, people who have learned good practices themselves are eager to teach them to others.

What other resources are available?

Where can you look for more help?
What resources are available in your research group? Your institution?

Different people make different contributions to good practice

Progress in computational good practices comes from different places in the scientific community:

Funders can require data management plans and provide support for data management.
Institutions can value the time that goes into data sharing and training.
Research librarians, research software engineers, and data managers support others in learning and applying good practices.
PIs and lab heads can require that lab members share code and data, and make it easy for them to do so.
Lab members can organise “data curation days” and training sessions to share good practices.
Self-organised groups led by students and postdocs can share ideas and train each other.
Global organisations like The Carpentries can co-ordinate training and support training materials.
Professional societies can help to organise training.

It takes time to learn good practices, and time to train others. This time has to be valued by individuals, funders, institutions, and the scientific community.

Developing your own good practices is ultimately a personal journey that will impact your career and impact the people around you. Maybe today you will learn to write README files, or next year you might start using version control, or in the future you could find yourself teaching students to write data management plans for their projects.

What will you do next?

Which good enough practices will you use in your next project?
How can you help your colleagues in using good practices?
What could your longer-term contribution to good practices be?

Attribution

Content of this episode was adopted after Wilson et al. Good Enough Practices for Scientific Computing.

Key Points

Learning good practices is a long-term process
Different people make different contributions to good practice

Overview

Questions

Objectives

Discuss in groups

Show me the solution

Challenges in data-heavy biology

Good news: everyone has these problems!

Principles: planning, modular organization, names, documentation

Key Points

Overview

Questions

Objectives

Data management

Data management problems (5 minutes)

Show me the solution

Save the raw data

Backing up your data (5 minutes)

Show me the solution

Working with sensitive data

Create the data you wish to see in the world

Discussion (2 minutes)

Create analysis-friendly data

Discussion (2 minutes)

Record all the steps used to process data

How, when and why do you document?

Use multiple tables as necessary, and use a unique identifier for every record

Submit data to a reputable DOI-issuing repository so that others can access and cite it

Sharing your data with the world (5 minutes)

Show me the solution

What is a DOI?

Places to share Data, with DOIs

Data management plans

Discussion (2 minutes)

Show me the solution

What’s your next step in data management?

Summary

Attribution

Key Points

Overview

Questions

Objectives

What your future self may think…

What is research code and software?

Potential problems writing code

Suggestions

Place a brief explanatory comment at the start of every script or program

Writing helpful explanatory comments

Options

Show me the solution

Decompose programs into functions

Using pseudocode

Show me the solution

Be ruthless about eliminating duplication

Give functions and variables meaningful names

Name that function

Show me the solution

Language style guides

Callout

Make dependencies and requirements explicit.

Do not comment and uncomment sections of code to control a program’s behavior

Provide a simple example or test data set

Code can be managed like data

Attribution

Key Points

Overview

Questions

Objectives

Collaboration opportunities and challenges

Suggestions

Create an overview of your project

Describe how to contribute to the project

Comparing README files

Show me the solution

Create a shared “to-do” list

Decide on communication strategies

Collaborations with sensitive data

Make the license explicit

What is a licence?

Make the project citable

Recommended resources

Put text documents associated with the project in the `doc` directory.

Put raw data and metadata in a `data` directory, and files generated during cleanup and analysis in a `results` directory

Put project source code in the `src` directory

Put compiled programs in the `bin` directory