The gains and hurdles of making science a community effort

The case of Physiopy

smoia
@SteMoia
s.moia.research@gmail.com

MRITogether 08.12.2023

Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands;    physiopy (https://github.com/physiopy)

Stefano Moia, 2023

Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands;    physiopy (https://github.com/physiopy)

The gains and hurdles of making science a community effort

The case of Physiopy

MRITogether 08.12.2023

Disclaimers

1. I am a member of the physiopy community

2. I have a bias towards the core tenets of Open Science as better scientific practices.

3. I am also fairly biased by my own experience - what I am saying as a whole might not hold for everyone, but hopefully you can get some inspiration from it.

0. Rules & Materials

This is a new chapter

Take home #0

This is a take home message

1. physiopy

Why physiopy

In neuroimaging, integration of physiological measures to data collection and analyses are still a niche topic. By raising awareness, we can inspire researchers and clinicians become interested in the topic.

*Open Source Software Development is the idea of developing a software publicly, sharing it from the beginning of the development, fostering a democratic community of contributors in support of the project, using version control and software testing.

physiopy adopts a Community driven, BIDS-based, Open Development* approach.

Sharing physiological data, toolboxes, and documentation following the concepts of Open Science could improve the exposition to this topic.

Community practices meetings, community consensus, and community guidelines.

Spark interest!

The more we share, the better it becomes

This is (not)
the way!

Of the People, by the People, for the People

physiopy's aims

The main goal of physiopy is to help collect, analyze and share physiological data by:

  1. Writing packages to make user-friendly pipelines to work with physiological data.
  2. Specializing in physiological data use in neuroimaging (i.e. MRI) data analysis.
  3. Providing documentation containing tips and strategies on how to collect such data and use our packages.
  4. Helping set a standard for these data, albeit without forcing users to use it.

Core components

A set of easily adoptable toolboxes

Community of users, developers, and researchers interested in physiology

Clear and approachable documentation

Community practices based on consensus

Core components

Core components

physiopy's libraries

Raw data

BIDSification

physiological data preprocessing

phys. denoising

phys. imaging

Data acquisition

Process description

QA/QC

physiopy's libraries

Raw data

phys2bids


peakdet
 

phys2denoise

phys. imaging

Data acquisition

physiopy's documentation

physioQC

Coordinated testing suite

BIDS Extension Proposal

physiopy's status

  • All volunteers are... volunteering (so far)
  • The project has virtually no funds (so far)
  • Founded in 2019 by 9 people in 3 labs
  • In 2023 it counts over 30 contributors from over 24 institutions

2. Communities

Adapt & juggle

  • Stir the contributors, but follow them
  • Be ready to switch focus when needed

Keep track of everything

Keep track of everything

Keep track of everything

Keep track of everything

Attribution

Engage (others) & disengage (yourself)

  • Keep meeting regularly, even if nothing is happening
  • Give space to other contributors by distancing yourself
  • Eventually, discuss a governance

Contributions and communities

  • Development is a straight(er) line
  • Time effective (on the short run)
  • Low(er) engagement
  • Less mentors
  • Small(er) user base
  • Less tests
  • Consensus not guaranteed
  • Clear attribution
  • Development takes its own path
  • (More) Time consuming
  • High(er) engagement
  • More mentors
  • Big(ger) user base
  • More tests
  • Consensus based
  • Fuzzier attribution

Take home #1

There are advantages and disadvantages in working within a community - choose what better fit your aims and interest.

3. (Some) Do's

Discuss good practices and/or LEARN

Recognise the contributors

Depending on the community and the governance scheme, contributions might be recognised differently.

Be clear about how you will recognise contributions.

One way of recognising contributors is the all-contributors specification.
(authorship is more complicated)

Create meaningful deliverables
when you can
(even multiple times)

Aim for readability

a, b = rui()

c = s(a, b)

p(c)
a, b = read_user_input()

c = sum_two_numbers(a, b)

print(c)
def very_important_function(template: str, *variables, file: os.PathLike, engine: str, header: bool = True, debug: bool = False):
    """Applies `variables` to the `template` and writes to `file`."""
    with open(file, 'w') as f:
        ...
        
        
        
        
        
        
        
def very_important_function(
    template: str,
    *variables,
    file: os.PathLike,
    engine: str,
    header: bool = True,
    debug: bool = False,
):
    """Applies `variables` to the `template` and writes to `file`."""
    with open(file, "w") as f:
        ...

Automate as much as you can

  • Pre-commit [local, remote]: Automate code checks and styling on git commit
  • Pytest, pytest-cov [local, remote]: (automated) testing and coverage
  • Codecov [remote]: automated code coverage change check
  • Auto [remote]: automated version update, tag, release, and changelog
  • Zenodo, PyPI [remote]: automated DOI and package publishing
  • Readthedocs [remote]: (automated) documentation publishing

Take home #2

Engage your community
to learn more/better.


Especially, learn how
to automate your work!

Thanks to...

...you for the (sustained) attention!

That's all folks!

...the organisers, for having me here

...the Physiopy contributors

smoia
@SteMoia
s.moia.research@gmail.com
github.com/physiopy
physiopy.github.io
physiopy.community@gmail.com

Open meetings every third Thursday of the month at 16h00 UTC

Stefano Moia, 2023

Any question [/opinions/objections/...]?

Stefano Moia, 2023

BIDSifying physiological data: phys2bids

physiopy/phys2bids is our flagship repository, introduced in December 2019.

Its aim is reorganising physiological recordings into BIDS format, and it currently supports AcqKnowledge (BIOPAC), Labchart (ADInstruments), Spike2, and GE files.

BIDScoin¹ integrates phys2bids as a plugin, granting it a GUI and facilitating its use.

1. Zwiers, Moia, & Oostenveld, 2022 (Front Neuroinform)

Preprocess physio data: peakdet

physiopy/peakdet was "donated" by its maintaner, Ross Markello, and is currently maintained by physiopy.

Its aim is denoising physiological data and detect peaks in the signal.

It supports automatic peak detection and manual result correction.

Denoising fMRI data: phys2denoise

physiopy/phys2denoise is our second repository, introduced in May 2020.

Its aim is creating denoising regressors for fMRI from physiological recordings.

It is in alpha stage (partially tested code), and it supports common denoising methods based (at the moment) on cardiac and respiratory data.

physiopy's documentation

physiopy's documentation is an important pillar of physiopy's aim, as it is meant to guide new users in their physiological data approach.

 

We are compiling best practices based on bimonthly discussions within the community.