Blog

Reviewing Reproducible Code in Gigantum

Posted by The Gigantum Team on May 18, 2020 5:16:07 PM

This post is a high-level overview to show how to Gigantum to inspect code for a manuscript under review. 

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Topics: Reproducibility, Open Science, Peer Review

Submitting Reproducible Code for Peer Review in Gigantum

Posted by The Gigantum Team on May 18, 2020 4:18:42 PM

This post is a high-level overview for using Gigantum to submit reproducible code in support of a manuscript under peer review.

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Topics: Science, Reproducibility, Open Science

Scaling On the Cheap with Dask, Gigantum, and DigitalOcean

Posted by Dav Clark on May 14, 2020 7:13:44 AM

Below, we’ll sketch out a smart approach for using lots of CPU cores without breaking the bank: using your laptop when feasible along with a DIY approach to working on bigger cloud resources as needed. We’ll use Gigantum to automate Git and Docker, along with most details of our cloud environment. With the following approach, you can be up and running Dask on 32 CPU cores on DigitalOcean in about 10 minutes - look at those tasks fly in parallel!

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Topics: Reproducibility, Data Science, Open Science

Rebooting reproducibility: From re-execution to replication

Posted by Tyler Whitehouse, Dav Clark and Emmy Tsang on Jul 12, 2019 12:26:00 PM

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Computational reproducibility should be trivial but it is not. Though code and data are increasingly shared, the community has realised that many other factors affect reproducibility, a typical example of which is the difficulty in reconstructing a work’s original library dependencies and software versions. The required level of detail documenting such aspects scales with the complexity of the problem, making the creation of user-friendly solutions very challenging.

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Topics: Reproducibility, Data Science

Making Reproducibility Reproducible

gigantum blog post 12

Reproducibility doesn’t have to be magic, anymore. This image is provided by Abstruse Goose under the Creative Commons License

TL;DR - We believe the following

  • Approaches to the transmission of scientific knowledge are currently broken, mainly due to the criticality of software in modern research.
  • Calling re-execution of static results “reproducibility” isn’t enough. Reproducibility should be functionally equivalent to collaboration.
  • Academic emphasis on best practices is ineffective and should switch to a product based approach that minimizes effort rather than maximizes it.
  • By focusing on the needs of the end user, people can actually improve how scientific knowledge is communicated and shared.
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Topics: Science, Reproducibility, Data Science, Containers, Jupyter

Gigantum – a simple way to create and share reproducible data science and research

Today, we present Gigantum, an open source platform for creating and collaborating on computational and analytic work, complete with:

  • Automated, high-resolution versioning of code, data and environment for reproducibility and rollback
  • Work and version history illustrated in a browsable activity feed
  • Streamlined environment management with customization via Docker snippets
  • One-click transfer between laptop and cloud for easy sharing
  • Seamless integration with development environments such as JupyterLab
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Topics: Data Science, Software

Extending Git Commit Metadata In Gigantum

Posted by Dean Kleissas - Co-founder and CTO at Gigantum on Jul 20, 2018 12:27:00 PM

At Gigantum, we are building an open-source tool for developing, executing, and sharing data science projects that automates the creation of versioned and containerized code. This way your work is always accessible, reproducible, and transparent. Our ultimate goal is to make science and data science more efficient and reproducible, and we want people to directly access and build on each other’s work without all of the technical hassles. You can learn more about Gigantum, try the Client in the cloud, or download and install it locally at our website: https://gigantum.com

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Topics: Open Science, Git, Software