Skip to main content

Reproducibility simplified.

Project description

Calkit

Calkit simplifies reproducibility, acting as a layer on top of Git, DVC, Zenodo, and more, such that all all aspects of the research process can be fully described in a single repository.

Why does reproducibility matter?

If your work is reproducible, that means that someone else can "run" it and get the same results or outputs. This is a major step towards addressing the replication crisis and has some major benefits for both you as an individual and the research community:

  1. You will avoid mistakes caused by, e.g., running an old version of a script and including a figure that wasn't created after fixing a bug in the data processing pipeline.
  2. Since your project is "runnable," it's more likely that someone else will be able to reuse part of your work to run it in a different context, thereby producing a bigger impact and accelerating the pace of discovery. If someone can take what you've done and use it to calculate a prediction, you have just produced truly useful knowledge.

Why another tool/platform?

Git, GitHub, DVC, Zenodo et al. are amazing tools/platforms, but their use involves multiple fairly difficult learning curves. Our goal is to provide a single tool and platform to unify all of these so that there is a single, gentle learning curve. However, it is not our goal to hide or replace these underlying components. Advanced users can use them directly, but new users aren't forced to, which helps them get up and running with less effort and training. Calkit should help users understand what is going on under the hood without forcing them to work at that lower level of abstraction.

Installation

Simply run

pip install calkit-python

Cloud integration

The Calkit cloud platform (https://calkit.io) serves as a project management interface and a DVC remote for easily storing all versions of your data/code/figures/publications, interacting with your collaborators, reusing others' research artifacts, etc.

After signing up, visit the settings page and create a token. Then run

calkit config set token ${YOUR_TOKEN_HERE}

Then, inside a project repo you'd like to connect to the cloud, run

calkit config setup-remote

This will setup the Calkit DVC remote, such that commands like dvc push will allow you to push versions of your data or pipeline outputs to the cloud for safe storage and sharing with your collaborators.

How it works

Calkit creates a simple human-readable "database" inside the calkit.yaml file, which serves as a way to store important information about the project, e.g., what question(s) it seeks to answer, what files should be considered datasets, figures, publications, etc. The Calkit cloud reads this database and registers the various entities as part of the entire ecosystem such that if a project is made public, other researchers can find and reuse your work to accelerate their own.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

calkit_python-0.0.9.tar.gz (15.8 kB view details)

Uploaded Source

Built Distribution

calkit_python-0.0.9-py3-none-any.whl (20.0 kB view details)

Uploaded Python 3

File details

Details for the file calkit_python-0.0.9.tar.gz.

File metadata

  • Download URL: calkit_python-0.0.9.tar.gz
  • Upload date:
  • Size: 15.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for calkit_python-0.0.9.tar.gz
Algorithm Hash digest
SHA256 b0f7d35f058b8b4886229db32a70a6d31071fb02a341bf36ffb420e5e7d8b464
MD5 9514f7fb30990ee979ca905d85c3faf4
BLAKE2b-256 4edd5f527a07cec7819e0294891be4d90bead218682470c43ecf3de4494f49ed

See more details on using hashes here.

File details

Details for the file calkit_python-0.0.9-py3-none-any.whl.

File metadata

File hashes

Hashes for calkit_python-0.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 276357bef8ab7f6067df8f48d85d18b8bdba94566aa1c3db79abe0025f4583c4
MD5 b7737c99afdee99cdb559eb0781f1afe
BLAKE2b-256 ba5d4e6ec61ad609d203fbb053c826e4cacd98d39913ca31d1a5bf8ef615eb8f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page