W10-11 Blog

In our recent group collaboration, we delved into the topic of Representing Data in our course. Our main focus was creating interactive visualizations using Python based on the publishing data from the National Library of Scotland (NLS), aiming to engage younger people who are not necessarily “artists” and have yet to discover the relevance of their lives to cultural heritage organizations.

The integrated application of library data visualization is transforming the production methods and dissemination patterns of cultural collections, presenting new opportunities for cultural development. As the younger generation becomes increasingly intertwined with digital resources, they are more inclined towards digital and visual means to access culture. Therefore, our project utilized various visual aids such as bar charts, pie charts, maps, heat maps, word clouds, word frequency charts, and knowledge network graphs to present relevant information on the temporal trend of publishing quantity, language distribution, publishing types, the geographic distribution of publishing, as well as commonly used publishing themes and keywords.

The dataset we worked with was obtained from the official website of the NLS and contained bibliographic records from the library’s catalogue of published material such as books, maps, music, journals, newspapers, pamphlets, leaflets, etc. The dataset also included records of print and digital publications.

Our visualizations provided valuable insights into publication volume, language distribution, publication region distribution, publication types, themes, subject heat maps, keywords, and knowledge mapping. Our intention was to make the library’s collections more engaging and accessible to young people, enabling them to explore and connect with the materials in a meaningful way.

We encountered some limitations and challenges in the final data representation and design process. The original dataset had significant missing and redundant information, which required considerable time and effort to clean and process. Improved data collection methods and management could help reduce these challenges and make the visualization process more efficient for future projects.

We also faced challenges with the accuracy of our publication region distribution map, as we were unable to find accurate data on the actual locations of book publications. Instead, we generated the map based on the locations of publishers, which may not be an accurate representation. Future visualizations could incorporate more reliable data sources, such as utilizing book metadata to identify the true publication locations.

In the context of inequality, studying Representing Data can help address social disparities in access to information. By utilizing visual aids and interactive tools, we can present information in a more accessible way, promoting inclusivity and diversity. Our project’s aim to engage younger people who are not necessarily “artists” and have yet to discover the relevance of their lives to cultural heritage organizations exemplifies our effort to promote inclusivity and accessibility.

By using data visualization to showcase various aspects of the National Library of Scotland’s collections, we can increase awareness and understanding of cultural heritage, particularly among younger and underrepresented communities. This increased engagement can help promote social cohesion and a sense of shared identity, contributing to a more equitable society.

In conclusion, our project successfully showcased various aspects of the NLS’s collections while providing valuable insights into the publication data from multiple perspectives. By continually refining the design process and addressing the limitations and challenges we faced, we can enhance the effectiveness and impact of these visualizations, ultimately providing a more engaging and meaningful experience for young people exploring the library’s resources.