add blog post and authors

Author: TimidRobot

content/blog/authors/anthony_ho/contents.lr

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+username: anthony_ho
+---
+name: Anthony Ho
+---
+md5_hashed_email:
+---
+about:
+As of 2023, Anthony Ho is a senior studying information analysis and economics
+at the University of Michigan. Upon graduation, he is returning to Hong Kong
+and pursuing a career in consulting.

content/blog/authors/claire_wan/contents.lr

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+username: claire_wan
+---
+name: Claire Wan
+---
+md5_hashed_email:
+---
+about:
+As of 2023, Claire Wan is a senior studying Information and Cognitive Science
+with a minor in Computer Science at the University of Michigan. She is working
+as a software engineer in Chicago after graduation.

content/blog/authors/grace_coleman/contents.lr

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+username: grace_coleman
+---
+name: Grace Coleman
+---
+md5_hashed_email:
+---
+about:
+As of 2023, Grace Coleman is a senior studying information analysis at the
+University of Michigan. She is staying at Michigan to pursue a Masters in
+Business Analytics at the Ross School of Business upon graduation.

content/blog/authors/tyler_phillips/contents.lr

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+username: tyler_phillips
+---
+name: Tyler Phillips
+---
+md5_hashed_email:
+---
+about:
+As of 2023, Tyler Phillips is a graduating senior at the University of Michigan
+School of Information (UMSI), receiving his Bachelor of Science in Information
+Analysis accompanied with a minor in Art & Design - Sonic & Visual Arts
+Integration. Tyler will continue his graduate studies in the fall, with
+reasonable expectations of a duel-degree, heading back to UMSI to obtain a
+Masters of Science in Information - Big Data Analytics as well as a Masters in
+Business Analytics, at the UM Ross School of Business.

content/blog/entries/2023-04-27-umsi-how-many-mona-lisas/contents.lr

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+title: Many Mona Lisas? Artistic Data Quantification and Assessment
+---
+categories:
+cc-dataviz
+collaboration
+community
+quantifying-the-commons
+---
+author:
+grace_coleman
+anthony_ho
+tyler_phillips
+claire_wan
+---
+pub_date: 2023-04-27
+---
+body:
+
+Quantifying the Commons
+
+University of Michigan, School of Information
+
+
+## Project Objective and Problem Statement
+
+Creative Commons (CC) has over one billion licensed works. However, there is no
+central data or organization of CC’s licensed works, making it difficult to
+quantify the number of works and to analyze which licenses are useful or should
+be retired. The goal of this project is to help CC staff identify redundant
+licenses and use quantitative data in marketing its impact. It focuses on Open Education Resources (OER).
+
+
+## Data Collection
+
+Data was collected from [OER Commons][oercommons], which is one of CC’s
+platforms and a library containing digital education resources. The first step
+in data collection was identifying which licenses this data source uses and how
+many works are under each license within OER Commons. OER Commons uses the
+licenses CC-BY, CC-BY-SA, CC-BY-ND, CC-BY-NC, CC-BY-NC-SA, and CC-BY-NC-ND
+which contribute to both ‘fair use’ and ‘commercial use’ assets, respectively.
+The next step in data collection was querying the Application Programming
+Interface (API) by license. In order to retrieve all works for a license,
+queries are batched by a maximum of 50 works retrieved at once.  This process
+is repeated until all works for a license are retrieved. These steps are run
+for every license. For every API call, the response is in XML which is parsed
+for features including education level, subject area, material type, media
+format, languages, primary user, and educational use. The results are outputted
+to a tab-separated CSV file.
+
+[oercommons]: https://www.oercommons.org/
+
+
+## Exploratory Data Analysis (EDA)
+
+After collecting all of our data, we began exploring the different columns in
+our dataframe. In particular, we looked at the distribution of different
+languages, the distribution of items by license type, and when items were added
+to the OER Commons API. Through this exploration, we were able to further
+specify our analysis and dig deeper into the different relationships of the
+data.
+
+
+### Diagram #1:
+
+![img](diagram_01.png)
+
+Diagram #1 shows the distribution of items taken from OER Commons by license
+type. It is clear that the CC-BY license type is the most popular, with 43% of
+the items having that license type. The CC-BY-SA license is also fairly
+popular, accounting for 27% of the items collected.
+
+
+### Diagram #2:
+
+![img](diagram_02.png)
+
+Diagram #2 shows when items have been added to the OER Commons API. There is
+little activity from December 2015, up to the beginning of 2023. However, close
+to 30,000 items were added to the API in early 2023.
+
+
+### Diagram #3:
+
+![img](diagram_03.png)
+
+Diagram #3 shows the percentage of items by language. English is the most used
+language, with about 86% of the items being in English. The other languages
+each have a small amount of the items.
+
+
+### Diagram #4:
+
+![img](diagram_04.png)
+
+Since English is clearly the most popular language, we decided to see the
+license distribution for items that are in English. Diagram #4 shows a similar
+distribution to the pie chart depicting the overall license distribution; this
+is to be expected since items in English account for 86% of all items, so the
+distribution of licenses is similar to the overall distribution.
+
+
+### Diagram #5:
+
+![img](diagram_05.png)
+
+We continued to look at the distribution of licenses by each language.
+Diagram #5 shows that for the items in French, CC-BY license is the most
+popular at 49%, with CC-BY-SA being right behind it at 32%.
+
+
+## Visualizations
+
+
+### Diagram #6:
+
+![img](diagram_06.png)
+
+Diagram #6 shows the distribution of items on OER commons by primary user and
+broken down by license type. The platform predominantly contains items designed
+for teachers and students, with the rest for parents, administrators,
+librarians among others. The breakdown of licenses for each primary user is
+relatively consistent with the overall breakdown of the platform, as seen from
+the charts below (Diagram #7 and Diagram #8).
+
+
+### Diagram #7:
+
+![img](diagram_07.png)
+
+
+### Diagram #8:
+
+![img](diagram_08.png)
+
+
+### Diagram #9:
+
+![img](diagram_09.png)
+
+Another aspect analyzed was inspecting the subject areas and the licenses that
+they hold as shown in Diagram #9. Some preliminary data cleaning had to be
+conducted as there were too many subjects on the platform, while some subjects
+had very low counts. The team grouped similar subjects into nine different
+categories, for example, social science, anthropology, sociology,
+communication, world cultures, psychology, women’s studies, and social work
+were grouped into social sciences.
+
+It can be seen from Diagram #9 that the most popular subject areas on the
+platform are health sciences, language/arts and other sciences. Diving deeper
+into these subject areas, health sciences and language/arts have a higher
+proportion of items with the CC-BY-NC-SA license.
+
+
+### Diagram #10:
+
+![img](diagram_10.png)
+
+Finally, the team analyzed the material types of the items and sorted it by
+education level that the items were created for. Again, some data cleaning was
+required as there were too many material types to analyze and some also had
+very small data counts. The seven material types shown in Diagram #10 were the
+most popular, and represented roughly 2/3 of the total.
+
+After sorting the education levels in chronological order, an interesting trend
+that emerged is that the number of items increases with education level from
+preschool, hits a peak at the community college level, and then decreases
+afterwards. A shift in the material types can also be drawn from the graph, as
+lesson plans represent a large proportion of items from preschool to high
+school, but become insignificant from the college level onwards. On the other
+hand, this is replaced by a higher proportion of readings. Another observation
+worth remarking is that there is also a higher proportion of items at the
+college level for textbooks.
+
+
+## Key Value
+
+The insights created through the analysis of this project will be helpful for
+CC’s marketing efforts. The ability to understand the distribution of license
+types in different contexts such as education level, will help CC be better
+equipped to target their marketing toward key demographics such as preschool
+education materials for example. Another take away in terms of key value was
+CC’s initiative to long term preservation. CC’s need to centralize their
+collaborators' content into a database warehouse system has been an identified
+direction since the start of this project. Our prototype database of the OER
+Commons has contributed to these efforts in both small scale implementation as
+well as meeting the scope of our database system modeling. As other CC cohort
+chapters contribute their own databases of licenced works, there is a hopeful
+expectation that a merger of acquisition will take place with other CC chapters
+in the future.
+
+
+## Next Steps
+
+As CC expands its contributing members into the open-source initiative of
+bringing licensed works to the world, other internal systems of data
+preservation and maintenance start to become a point of serious interest as the
+databases start to become an integrated endeavor in the future. Running our
+prototype case study of the OER-Commons database has given us insights on the
+direction of CC current database system and how this system will be better
+suited to evolve into a data warehouse hub as a long-term solution. When we
+started the process of data mining and data analysis, using Python3 has been a
+staple in both our groups efforts as well as CC’s previous protocols with Git.
+So, complementing this framework with other Python libraries that allow for
+easier database querying will be a step in the right direction for the next
+cohort of CC contributors to further this process along. An example of this
+library integration would be pandasql to utilize the family pandas library
+methods along with the SQL command logic that makes database maintenance easy
+and manageable. Besides updating the data storage, future work can continue to
+collect data from other sources with CC licensed work including the GLAM and
+Internet Archive.
+
+
+## Acknowledgements
+
+We would like to express our gratitude towards Timid Robot Zehta, our client,
+for working on behalf of CC, as well as [OER Commons][oercommons] for their
+valuable contributions towards the development of digital licensing and open
+source databasing initiatives. Without them, this project would not have been
+possible. Their efforts have been instrumental in giving us the tools and
+resources to help progress in the open-source initiative by allowing us to
+promote the free exchange of ideas, knowledge, and resources within the art,
+health, and education sectors of non-profit endeavors. Open source projects are
+important because they allow the public to use and work on projects without
+restrictions or keys. Since this initiative is open source, our efforts can be
+added to and built upon, allowing the project to continue through the addition
+of new contributors with fresh perspectives. Both of their commitment to
+promoting accessible and inclusive content has enabled individuals and
+organizations to create and distribute digital assets without facing any legal
+restrictions around the world. It has been an absolute pleasure to work with
+these organizations and be a part of their mission to democratize access to
+information.
+
+[oercommons]: https://www.oercommons.org/