Processing, publication, access, insight. The future of data is frictionless.

Announcing the 2019 Fellows Programme

The Frictionless Data Reproducible Research Fellows Programme, supported by the Sloan Foundation, is recruiting and training early career researchers to become champions of the Frictionless Data tools and approaches in their field. Fellows will learn about Frictionless Data, lead workshops and events, and write blogs. In addition to mentorship, we are providing successful applicants with stipends of $5,000 to support their work and time during the nine-month long Fellows Programme. We welcome applications until 30th July 2019.

Read more and apply for the Fellows Programme here.


Lightweight containerisation formats for data that provide a minimal yet powerful foundation for data publication, transport, and consumption.

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Apps and integrations that make it easy to integrate Frictionless Data specifications into your data publication, access, and analysis workflows.

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Case Studies

Labs, libraries, governments, and companies are using Frictionless Data in their data workflows to reduce grunt work and move faster to insight.

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Get involved.


Learn how to get started using and developing with Data Packages through our guides, tutorials and documentation.

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We also have a chat room on Gitter dedicated to technical discussion about Frictionless Data. Feel free to stop by and introduce yourself.

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If you have a use case that you'd like to see supported, including integration with a particular tool or file format, leave a note.

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All our code is open source and made available under the CC-by license. Code and documentation contributions and adoptions are highly encouraged.

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Issue Tracker

If you have adopted Frictionless Data specs, and noticed a problem, open an issue in our tracker. Feature requests and suggestions are also welcome.

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Events Calendar

The Frictionless Data team runs workshops all year round. Find out where we'll be next and join us, or mail us an invite to your event: [email protected]

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Our Vision

Frictionless Data shortens the path from data to insight with a collection of specifications and software for the publication, transport, and consumption of data. At the heart of our approach is a deep understanding of the multi-faceted nature of data work, and an emphasis on platform-agnostic interoperability. From consumer spreadsheet software, through to cloud-based services for data analysis, the future of data is frictionless.

Informed by our work building and deploying CKAN and learning about various data publication workflows, we have learned that there is too much friction in working with data. The frictions we seek to remove---in getting, sharing, and validating data---stop people from truly benefiting from the wealth of data being opened up every day. This kills the cycle of find/improve/share that makes for a dynamic and productive data ecosystem.

We provide a simple wrapper and basic structure for transportation of data that significantly reduces the friction in data sharing and integration, supports automation and does this without imposing major changes on the underlying data being packaged. We focus on tabular data but any kind of data can be "packaged". Its lightweight and simple nature it is easy to adopt both for data publishers, data users and data tool makers.

We have been working on these and similar issues for nearly a decade, and we think that the time is right for frictionless data. Check the GET INVOLVED section above and learn how to help us get there.

Data "Containerization"

We see our approach as analogous to standardization efforts in the transport of physical goods. Historically, loading goods on a cargo ship was slow, manual, and expensive. The solution to these issues came in the form of containerization, the development of several ISO specifications specifying the dimensions of containers used in global shipping. Containerization dramatically reduced the cost and time required for transporting goods by enabling the automation of several elements of the transport pipeline.

We currently consider transporting data between and among tools to be comparable to shipping physical goods in the pre-containerization era. Currently, before you can properly begin an analysis of your data or build a data-intensive app, you have to extract, clean, and prepare your data: procedures that are often slow, manual, and expensive. Radical improvements in data logistics---through specialisation and standardisation---can get us to world where we spend less time sorting through and cleaning data and more time creating useful insight.


1. Focused: Focus on one part of the data chain, one specific feature (e.g. packaging), and a few specific types of data (e.g. tabular).

2. Web-oriented: Build for the web using formats that work naturally with HTTP such as JSON, a common data exchange format for web APIs, and CSV, which is easily streamable.

3. Distributed: Design for a distributed ecosystem with no centralized, single point of failure or dependence.

4. Open: Make things that anyone can freely and openly use and reuse with a community that is open to everyone.

5. Built Around Existing Software: Integrate with existing software while also designing for direct use---for example, when a Tabular Data Package integration is unavailable, fall back to CSV.

6. Simple: Keep the formats and metadata simple and lightweight, and make things easy to learn and use by doing the least required.

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