BioCypher submission

[slobentanzer]

Submitting Author: Sebastian Lobentanzer (@slobentanzer)
All current maintainers: (@slobentanzer)
Package Name: biocypher
One-Line Description of Package: framework for creating biomedical knowledge graphs
Repository Link: https://github.com/biocypher/biocypher
Version submitted: 0.5.14
Editor: @arianesasso
Reviewer 1: @Zethson
Reviewer 2: @Midnighter
Reviewer 3: Sinna
Archive:
Version accepted: v0.5.33
JOSS DOI: N/A
Date accepted (month/day/year): 11/14/2023

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Description

• Include a brief paragraph describing what your package does:
  BioCypher is a flexible and modular framework for the creation of knowledge graphs on the basis of ontologies, yielding a number of representations of the input knowledge sources. Its aim is to increase accessibility of biomedical knowledge graphs to a wide range of biomedical researchers.

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  Check out our package scope page to learn more about our
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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific & Community Partnerships

- [ ] Geospatial
- [ ] Education
- [ ] Pangeo

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[lwasser]

hi @slobentanzer thank you for this submission and welcome to pyOpenSci! I just wanted to say hello 👋 and to let you know that we see this submission. We will get back to you with the start of our initial checks next week! In the meantime, have a wonderful weekend. we will be in touch. ✨

[NickleDave]

Welcome to pyOpenSci @slobentanzer and thank you for your patience.

I'm happy to report that biocypher passes almost all of the initial editor checks.

There is one thing we need you to take care of before we start the review:

• README The package has a README.md file with clear explanation of what the package does, instructions on how to install it, and a link to development instructions.

Please add the following to the README:

• a clear explanation of what the package does, such that it could be understood by a general scientific audience.
  • What is a knowledge graph? How are they used? This can be brief with a link to a review or wikipedia article to supplement, that is understandable even to people that don't already work with knowledge graphs
  • It would really help to have a precise, succinct definition of what the package does and how it does it; use verbs. "Allows users to construct knowledge graphs from heterogeneous biomedical data, using adapters written in Python". This should ideally be the first thing in the README. The description that comes a bit lower has a helpful figure, but the language makes it sound very abstract without any detail on what or how, "this is our proposal for a framework..."
• instructions on how to install: this can be simple, pip install biocypher and if there is a package on a community conda channel, conda install biocypher -c conda-forge (or bioconda or whatever)

Please let me know once you have addressed this requirement.

The filled-out template with checks follows.

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci
review. Below are the basic checks that your package needs to pass
to begin our review. If some of these are missing, we will ask you
to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements below.

• Installation The package can be installed from a community repository such as PyPI (preferred), and/or a community channel on conda (e.g. conda-forge, bioconda).
  • pip install biocypher works. There is not a conda-forge package as far as I can tell
  • The package imports properly into a standard Python environment import package-name.
• Fit The package meets criteria for fit and overlap.
• Documentation The package has sufficient online documentation to allow us to evaluate package function and scope without installing the package. This includes:
  • User-facing documentation that overviews how to install and start using the package.
  • Short tutorials that help a user understand how to use the package and what it can do for them.
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    • There is a README but it does not have an explanation of what the package does or install instructions8
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• Package overlap The package doesn't entirely overlap with the functionality of other packages that have already been submitted to pyOpenSci.
• Archive (JOSS only, may be post-review): The repository DOI resolves correctly.
  • there is already a pre-print associated with the package, see link in README
• Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?

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• Initial onboarding survey was filled out
  We appreciate each maintainer of the package filling out this survey individually. 🙌
  Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. 🙌

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Editor comments

None of these are required but are just things I noted during the checks:

• In addition to adding the required clear explanation and install instructions to the README, you might consider adding more info from the index of the docs; there is a lot of very useful info / context there. For more on good practices for READMEs, see https://www.pyopensci.org/python-package-guide/documentation/repository-files/readme-file-best-practices.html
• It's a bit confusing that the install instructions (1) recommend a specific development tool and show adding the package as a dependency with that tool, and (2) use pip inside a conda environment. Typically one might show pip install package for the first and conda install package -c conda-forge for the second
• I noticed the package defaults to logging at the INFO level and writing a log file on import. Not sure that everyone will want that by default, especially if they are on a shared system

[slobentanzer]

Hi @NickleDave,
thanks for the feedback, I added the requested details to the README.

Regarding your comments, here's my opinion:

• I prefer not having dual maintenance of content, i.e., I do not want to keep a readme and the docs up to date simultaneously; so I am not in favour of packing things from the docs into the readme, because they need to be kept in sync in this case. What is the benefit of having it in the README, if users can just read the docs? BTW, that was also the reason that the docs were the first section of the README, as opposed to the description as suggested by you.
• I always use pip inside conda envs and did not know this was unusual. Happy for suggestions as to how to make that section clearer or more aligned with good practise.
• I am new to Python and find logging quite confusing; happy for suggestions (or a pointer to a best practise resource) here as well.

[NickleDave]

Thank you @slobentanzer for adding those details to the README. Looks great!

What is the benefit of having it in the README, if users can just read the docs?

I hear you. I also do not like the idea of duplicating content in the README that can go out of sync.
One way around this is to have the single source of truth be your README, and then get that into your docs by using an include directive in RST or in MyST

What is the benefit of having it in the README, if users can just read the docs?

There is an argument to be made that developers hyper-fixate on "Awesome" READMEs, mainly to impress other developers. Still, you don't want to lose a user because they found your GitHub repo first, and they couldn't figure out what your project really does, so they gave up without bothering to read the docs. What is the killer feature and what does it look like in code? If you can express that in one sentence and a single snippet with a few lines of code, at the top of both your README and on the index page of your docs, then you know you're making the best case for your package to new users. Of course that's not possible for every package, but seems like a good general goal to aim for.

I always use pip inside conda envs and did not know this was unusual. Happy for suggestions as to how to make that section clearer or more aligned with good practise.

I am not a conda expert but my understanding is that it's good practice to prefer conda install over pip install, to be able to reproduce environments. Basically because pip does things that conda doesn't do, or that conda doesn't know about. Maybe @jjhelmus can let us know if the advice in this blog post is still up-to-date:
https://www.anaconda.com/blog/using-pip-in-a-conda-environment

I am new to Python and find logging quite confusing; happy for suggestions (or a pointer to a best practise resource) here as well.

I am less new to Python but I still find logging confusing 😭

I'd suggest a quote from Hitchhiker's guide to Python is relevant here:

the user, not the library, should dictate what happens when a logging event occurs

https://docs.python-guide.org/writing/logging/#logging-in-a-library

For that reason I would avoid forcing a logging file and messages on your users.

I have looked several times but have never found a guide to logging that I felt answered my questions.

You probably know these already but:

• the HOWTO in the standard library docs is a good place to start, if you haven't read it already: https://docs.python.org/3/howto/logging.html
• Real Python usually has friendly introductions to topics, and logging is no exception: https://realpython.com/python-logging/
• If that's not enough, here is a rather opinionated blog post: https://ppwwyyxx.com/blog/2022/Effective-Python-Logging/

The main question I had as a library developer is how to use the logger in my modules, and how should I configure it. Through trial and error I have found it's "good enough" practice to make a new logger instance in each module, assigning it __name__ as its name (as you will read in most guides). I have also found that if I do want to configure those logger instances, it's easiest to achieve this through a function I call that checks if there are already existing Handlers attached, so that I don't stomp on a user's existing configuration. See here: vocalpy/vak#535

In my experience is that most scientific Python libraries do not include logging. There are exceptions I can think of:

• pytorch-lightning
• armory
• scikit-learn and see discussion on API Proposal for Logging scikit-learn/scikit-learn#17439)

You might find reading the code of those libraries helpful

[NickleDave]

Hope I did not just overwhelm you with advice.
I will find an editor and we will proceed with the review.
I know I got off to a bit of a slow start--thank you for your patience.

[NickleDave]

Hi again @slobentanzer, happy to inform you that @arianesasso will be editor for this review.
We are seeking appropriate reviewers now and she will tag them in once we have found them.

[arianesasso]

Hello @slobentanzer 👋🏼 . Thanks for the intro @NickleDave :). It is my pleasure to review BioCypher! We are now in the process of finding reviewers for the package. Hopefully, depending on their response, this should take one to two weeks. I will keep you posted on this thread, and if you have any questions, feel free to reach out!

[arianesasso]

Dear @slobentanzer, finding reviewers is taking a bit longer than expected. I hope that is ok. We might have someone already but he would only be able to start the review in a few weeks. Would that be acceptable?

[slobentanzer]

Hi @arianesasso,
sure, I have no immediate time pressures.