Announcing the Frictionless Data Tool Fund

Hello everyone,

Great news: Open Knowledge International, through the Frictionless Data for Reproducible Research project, is now accepting applications for the Frictionless Data Tool Fund.

Offering one-off grants of $5,000, the mini-grant scheme is open to any persons or organizations interested in developing a tool for reproducible science built using Frictionless Data tooling, specs, or software.

Read more about the fund, apply as soon as you can and share widely in your networks: https://blog.okfn.org/2019/02/18/announcing-the-frictionless-data-tool-fund-2/

If you have any questions, feel free to ask them on Open Knowledge International’s Discuss, on our Gitter chat, or email the team at frictionlessdata@okfn.org.

Have a great rest of the day,

Best, Lieke Ploeger.

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Hi all,

Just a reminder, there is one month left to apply to our Frictionless Data for reproducible research tool fund grant. We are providing a number of mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible science or research built using the Frictionless Data specifications and software.

You can find more information at: http://bit.ly/2EfHvCV and http://bit.ly/2HNSNBq

Best, Lieke

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Hi, @liekeploeger.

I see that all the grantees have been announced already.

Have all their deliverables been set already, or is there room for suggetions on things that could be developed?

Hi @herrmann! I’m the product manager on the Tool Fund so I’ll answer for Lieke :slight_smile:
That’s a great question. Their deliverables have been set, but I would still be interested in hearing any ideas that you might have for suggestions. If you have any thoughts, please share them here!
Also, we will most likely have another round of Tool Fund projects in 2020, in case you have ideas for a new project you would like to implement.

Thanks, @lwinfree.

Yes, I do have an idea. Intake is a Python tool that helps loading large datasets in chunks, and can distribute processes to different cores or a cluster by using Dask. When working with tabular data, it does manage column types and other metadata, but does not yet support reading or writing Tabular Data Packages. Since Intake features an interface for writing plugins, I figured that writing a Tabular Datapackage specification plugin for Intake using datapackage-py could be helpful for people working with large datasets.

Do you think this would be a good idea, @vitorbaptista, @roll & @pwalsh?

I could write it myself, but I would take some time to learn the Intake interface and implement it. Then I remembered there had been a Frictionless Data Tool Fund some time ago and that this could be a good deliverable for someone working on that.

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Thanks, @herrmann, I think the idea is good. I guess there are no active Tool Fund mini-grants at the moment but we can help with datapackage-py usage if someone would take this project.

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I appreciate the libraries frictionlessdata has produced. I plan to incorporate it in a work project for data validation/transformation, which I see was a topic for one of the winning grantees. Is it safe to say this objective has not yet been incorporated into the current projects?

Hi @Stephen.Scheid! Welcome to the Frictionless Data discussion board!
Data validation is an integral part of the Frictionless software and tools, and all of the Tool Fund grantees will be incorporating it in one way or another.
That is great to hear that you will be incorporating Frictionless into your project! Please keep us updated on how it goes, and feel free to ask us any questions here, in our gitter chat, or open an issue in GitHub.

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For anyone who missed it, here’s a halfway point update from the 2019 Frictionless Data Tool Fund, from the Open Knowledge blog.

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