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Merge pull request creativecommons#734 from creativecommons/dataviz-note
Add note to dataviz project blog posts
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content/blog/entries/2020-03-05-involucrate-gsoc-outreachy-es/contents.lr

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- [CC Search Browser extension](https://github.com/creativecommons/ccsearch-browser-extension) por Mayank Nader
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- [CC Vocabulary](https://github.com/creativecommons/vue-vocabulary) por Dhruv Bhanushali
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- [Revamping the CC Chooser](https://github.com/creativecommons/cc-chooser) por Ari Madian
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- [CC Catalog Data Visualization](https://github.com/creativecommons/cccatalog-dataviz) por Maria Belen Guaranda
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- [CC Catalog Data Visualization](https://github.com/cc-archive/cccatalog-dataviz) por Maria Belen Guaranda
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## Outreachy 2019-2020
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- [Improve License Chooser](https://github.com/creativecommons/cc-chooser) por Olga Bulat

content/blog/entries/2020-12-07-upcoming-changes-to-community/contents.lr

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- [CC Catalog](https://github.com/creativecommons/cccatalog)
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- [CC Catalog API](https://github.com/creativecommons/cccatalog-api)
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- [CC Search](https://github.com/creativecommons/cccatalog-frontend/)
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- [Linked Commons](https://github.com/creativecommons/cccatalog-dataviz/)
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- [Linked Commons](https://github.com/cc-archive/cccatalog-dataviz/)
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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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content/blog/entries/cc-datacatalog-data-processing-2/contents.lr

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---
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body:
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> ℹ️ **2023-08-31:** This project was archived along with the shuttering of CC
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Search (now [Openverse](https://openverse.org/)). Please also see the
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[Quantifying the Commons](https://github.com/creativecommons/quantifying)
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project.
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This is a continuation of my last blog post about the data processing part of the CC-data catalog visualization project. I recommend you to read that [last post](https://opensource.creativecommons.org/blog/entries/cc-datacatalog-data-processing/) for a better understanding of what I'll explain here.
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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

content/blog/entries/cc-datacatalog-data-processing-3/contents.lr

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---
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> ℹ️ **2023-08-31:** This project was archived along with the shuttering of CC
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Search (now [Openverse](https://openverse.org/)). Please also see the
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[Quantifying the Commons](https://github.com/creativecommons/quantifying)
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project.
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This is a continuation of my last blog post about the data processing part 2 of the CC-data catalog visualization project. I recommend you to read that [last post](https://opensource.creativecommons.org/blog/entries/cc-datacatalog-data-processing-2/) for a better understanding of what I'll explain here.
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Hello! In this post I am going to talk you about the extraction of unique nodes, and links, and the visualization of the force-directed graph with the processed data.
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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

content/blog/entries/cc-datacatalog-data-processing/contents.lr

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---
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> ℹ️ **2023-08-31:** This project was archived along with the shuttering of CC
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Search (now [Openverse](https://openverse.org/)). Please also see the
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[Quantifying the Commons](https://github.com/creativecommons/quantifying)
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project.
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Welcome to the data processing part of the GSoC project! In this blog post, I am going to tell you about my first thoughts with the real data, and give you some details of the implementation developed so far.
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### Data Extraction
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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

content/blog/entries/cc-datacatalog-data-thelinkedcommons/contents.lr

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> ℹ️ **2023-08-31:** This project was archived along with the shuttering of CC
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Search (now [Openverse](https://openverse.org/)). Please also see the
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[Quantifying the Commons](https://github.com/creativecommons/quantifying)
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project.
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This is a continuation of my last blog post about the data processing part 3 of the CC-data catalog visualization project. I recommend you to read that [last post](https://opensource.creativecommons.org/blog/entries/cc-datacatalog-data-processing-3/) for a better understanding of what I'll explain here.
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Hello! In this last post, I am going to talk you about the final visualization. First, I would like to talk about the data and share my recommendations.
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<br>
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You can check the whole project source code in the [Github repo](https://github.com/creativecommons/cccatalog-dataviz/tree/master/GSoC2019).
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You can check the whole project source code in the [Github repo](https://github.com/cc-archive/cccatalog-dataviz/tree/master/GSoC2019).
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### Final comments and future work
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This was my first experience with big data visualization, and I really enjoyed it!

content/blog/entries/cc-datacatalog-visualization/contents.lr

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> ℹ️ **2023-08-31:** This project was archived along with the shuttering of CC
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Search (now [Openverse](https://openverse.org/)). Please also see the
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[Quantifying the Commons](https://github.com/creativecommons/quantifying)
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project.
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_“By visualizing information, we turn it into a landscape that you can explore with your eyes.”_ – David McCandless.
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The landscape of licensed content is wide and varied. We have domains linking to other domains, different license types, and some metadata. This information is extracted from the Internet monthly by [Common Crawl](http://commoncrawl.org/). It is fair to mention that we have 250 million works and growing! If you didn't know we had so much licensed content, well then, this is one of the goals of this project: show the users how licensers are connected, their licensed content, and show how the licensing wave is expanding.
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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

content/blog/entries/linked-commons-autocomplete-feature/contents.lr

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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. :)

content/blog/entries/linked-commons-data-update/contents.lr

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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! 🚀
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___

content/blog/entries/linked-commons-gsoc-wrap-up/contents.lr

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### My Code Contributions
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**Repository:** https://github.com/creativecommons/cccatalog-dataviz/
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**Repository:** https://github.com/cc-archive/cccatalog-dataviz/
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**Commits:** https://github.com/creativecommons/cccatalog-dataviz/commits/master
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**Commits:** https://github.com/cc-archive/cccatalog-dataviz/commits/master
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**Contributors:** https://github.com/creativecommons/cccatalog-dataviz/graphs/contributors
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**Contributors:** https://github.com/cc-archive/cccatalog-dataviz/graphs/contributors
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[**Migrate frontend to React #28**](https://github.com/creativecommons/cccatalog-dataviz/pull/28)
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[**Migrate frontend to React #28**](https://github.com/cc-archive/cccatalog-dataviz/pull/28)
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- Add client-side graph filtering method to enable users to interact with the loaded graph.
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[**Add server-side filtering #29**](https://github.com/creativecommons/cccatalog-dataviz/pull/29)
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[**Add server-side filtering #29**](https://github.com/cc-archive/cccatalog-dataviz/pull/29)
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- 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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[**Design upgrade #33**](https://github.com/creativecommons/cccatalog-dataviz/pull/33)
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[**Design upgrade #33**](https://github.com/cc-archive/cccatalog-dataviz/pull/33)
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</figure>
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[**Add node suggestions feature #35**](https://github.com/creativecommons/cccatalog-dataviz/pull/35)
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[**Add node suggestions feature #35**](https://github.com/cc-archive/cccatalog-dataviz/pull/35)
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- Added query autocomplete feature, to enable users to explore all the nodes in the database.
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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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[**Fix filtering module #38**](https://github.com/creativecommons/cccatalog-dataviz/pull/38)
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[**Fix filtering module #38**](https://github.com/cc-archive/cccatalog-dataviz/pull/38)
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- Refer to [this blog](/blog/entries/linked-commons-data-update/) for a piece of detailed information on data update.
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[**Database upgrade and core enhancements #39**](https://github.com/creativecommons/cccatalog-dataviz/pull/39)
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[**Database upgrade and core enhancements #39**](https://github.com/cc-archive/cccatalog-dataviz/pull/39)
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- Dockerizes the frontend and backend for both dev and prod environments for higher application portability.
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[**Frontend enhancements #40**](https://github.com/creativecommons/cccatalog-dataviz/pull/40)
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[**Frontend enhancements #40**](https://github.com/cc-archive/cccatalog-dataviz/pull/40)
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content/blog/entries/linked-commons-whats-new/contents.lr

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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. :)

content/blog/entries/meet-gsoc-2019-students/contents.lr

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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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</div>
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<div class="col pt-3">
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<img src="maria.jpg" alt="María Belén Guaranda Cabezas" /><br />

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