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Catalog, API, and Linked Commons contributors are encouraged to contribute to our other Python projects such as the [CC Legal Database](https://github.com/creativecommons/legaldb) or the upcoming [CC Licenses](https://github.com/creativecommons/cc-licenses) project. If you are a CC Search contributor, we recommend checking out frontend projects such as the [CC Chooser](https://github.com/creativecommons/chooser) or [Vocabulary](https://github.com/creativecommons/vocabulary).
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- Visualization with the data.
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- Development or modification of pruning/filtering rules.
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You can follow the project development in the [Github repo](https://github.com/creativecommons/cccatalog-dataviz).
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You can follow the project development in the [Github repo](https://github.com/cc-archive/cccatalog-dataviz).
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CC Data Catalog Visualization is my GSoC 2019 project under the guidance of [Sophine
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Clachar](https://creativecommons.org/author/sclachar/), who has been greatly helpful and considerate since the GSoC application period. Also, my backup mentor, [Breno Ferreira](https://creativecommons.org/author/brenoferreira/), and engineering director [Kriti
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- Visualization of the pie chart
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- Development or modification of pruning/filtering rules.
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You can follow the project development in the [Github repo](https://github.com/creativecommons/cccatalog-dataviz).
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You can follow the project development in the [Github repo](https://github.com/cc-archive/cccatalog-dataviz).
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CC Data Catalog Visualization is my GSoC 2019 project under the guidance of [Sophine
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Clachar](https://creativecommons.org/author/sclachar/), who has been greatly helpful and considerate since the GSoC application period. Also, my backup mentor, [Breno Ferreira](https://creativecommons.org/author/brenoferreira/), and engineering director [Kriti
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- Data aggregation
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- Visualization with the data + perfectioning pruning/filtering rules
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You can follow the project development in the [Github repo](https://github.com/creativecommons/cccatalog-dataviz).
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You can follow the project development in the [Github repo](https://github.com/cc-archive/cccatalog-dataviz).
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CC Data Catalog Visualization is my GSoC 2019 project under the guidance of [Sophine
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Clachar](https://creativecommons.org/author/sclachar/), who has been greatly helpful and considerate since the GSoC application period. Also, my backup mentor, [Breno Ferreira](https://creativecommons.org/author/brenoferreira/), and engineering director [Kriti
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- Reviewing of part of the dataset
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- Implementation of a module for cleaning and parsing the data
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You can follow the project development in the [Github repo](https://github.com/creativecommons/cccatalog-dataviz).
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You can follow the project development in the [Github repo](https://github.com/cc-archive/cccatalog-dataviz).
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CC Data Catalog Visualization is my GSoC 2019 project under the guidance of [Sophine
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Clachar](https://creativecommons.org/author/sclachar/), who has been greatly helpful and considerate since the GSoC application period. Also, my backup mentor, [Breno Ferreira](https://creativecommons.org/author/brenoferreira/), and engineering director [Kriti
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In the next blog, we will be covering the long awaited data update and the new architecture.
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## Conclusion
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Overall, I enjoyed working on this feature and it was a great learning experience. This feature has been successfully integrated to the development version, do check it out. Now that you have read this blog till the end, I hope that you enjoyed it. For more information please visit our [Github repo](https://github.com/creativecommons/cccatalog-dataviz/). We are looking forward to hearing from you on linked commons. Our [slack](https://creativecommons.slack.com/channels/cc-dev-cc-catalog-viz) doors are always open to you, see you there. :)
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Overall, I enjoyed working on this feature and it was a great learning experience. This feature has been successfully integrated to the development version, do check it out. Now that you have read this blog till the end, I hope that you enjoyed it. For more information please visit our [Github repo](https://github.com/cc-archive/cccatalog-dataviz/). We are looking forward to hearing from you on linked commons. Our [slack](https://creativecommons.slack.com/channels/cc-dev-cc-catalog-viz) doors are always open to you, see you there. :)
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## Conclusion
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This task was really challenging and I learnt a lot. It was really mesmerizing to see the **Linked Commons grow and evolve**. I hope you enjoyed reading this blog. You can follow the project development [here](https://github.com/creativecommons/cccatalog-dataviz/), and access the stable version of linked commons [here](http://dataviz.creativecommons.engineering/).
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This task was really challenging and I learnt a lot. It was really mesmerizing to see the **Linked Commons grow and evolve**. I hope you enjoyed reading this blog. You can follow the project development [here](https://github.com/cc-archive/cccatalog-dataviz/), and access the stable version of linked commons [here](http://dataviz.creativecommons.engineering/).
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Feel free to report bugs and suggest features. It will help us improve this project. If you wish to join the our team, consider joining our [slack](https://creativecommons.slack.com/channels/cc-dev-cc-catalog-viz) channel. Read more about our community teams [here](https://opensource.creativecommons.org/community/). See you in my next blog! 🚀
- We realized that the client-side graph filtering method is not very scalable. This PR adds the basic structure for the backend server and adds server-side graph filtering logic.
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- Added a parser to convert the input JSON file from `{nodes:[], links:[]}` schema to the distance list format.
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- Added query autocomplete feature, to enable users to explore all the nodes in the database.
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- This functionality aims to minimize the number of misspelt filtering tries from the client.
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- Refer to [this blog](/blog/entries/linked-commons-autocomplete-feature/) for the motivation and detailed report on why we added autocomplete aka node suggestions feature.
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* Update the visualization with a more recent and bigger dataset
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## Conclusion
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Overall, it was fantastic and rejuvenating experience working on these tasks. Now that you have read this blog till the end, I hope that you enjoyed it. For more information visit our [Github repo](https://github.com/creativecommons/cccatalog-dataviz/). We are looking forward to hearing from you on linked commons. Our [slack](https://creativecommons.slack.com/channels/cc-dev-cc-catalog-viz) doors are always open to you, see you there. :)
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Overall, it was fantastic and rejuvenating experience working on these tasks. Now that you have read this blog till the end, I hope that you enjoyed it. For more information visit our [Github repo](https://github.com/cc-archive/cccatalog-dataviz/). We are looking forward to hearing from you on linked commons. Our [slack](https://creativecommons.slack.com/channels/cc-dev-cc-catalog-viz) doors are always open to you, see you there. :)
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the most content, which CC licenses are used the most, and much more.
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She will be mentored by our Data Engineer <a href="https://creativecommons.org/author/sclachar/">Sophine Clachar</a> with backup from Breno Ferreira.</p>
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<p>You can follow the progress of this project through the <a href="https://github.com/creativecommons/cccatalog-dataviz">GitHub repo</a> or on the <code>#gsoc-cc-catalog-viz</code> channel on our <a href="https://creativecommons.github.io/community/">Slack community</a>.</p>
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<p>You can follow the progress of this project through the <a href="https://github.com/cc-archive/cccatalog-dataviz">GitHub repo</a> or on the <code>#gsoc-cc-catalog-viz</code> channel on our <a href="https://creativecommons.github.io/community/">Slack community</a>.</p>
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