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Celebrating the 10 year anniversary of being the Red Wolves. A brief history of campus life and culture when the IU East mascot was the Pioneers and the reason we changed to the Red Wolves and the introduction of Rufus, the Red Wolf. Being interviewed is the Director of Campus Life Rebekah Hester and NSM faculty member Neil Sabine.
Indiana University's Lilly Library acquired a large collection of the papers of Orson Welles in the late 1970s, and with it nearly six hundred recordings of his iconic series First Person Singular, Mercury Theatre on the Air, and Campbell Playhouse, as well as more obscure gems, mostly originals cut directly from the broadcasts as they aired. And yet the collection guide listed only "tapes," reformatted from the unmentioned originals. The presentation will discuss how the discs were 'rediscovered,' the problem of multiple formats in traditional archival descriptive practices, and IU's project to digitize and make publicly available the original disc recordings.
The Orson Welles on the Air project has digitized the discs and associated scripts. In creating the publicly available web site, the project team used Omeka, an application that the group had a lot of experience with, but this time faced a new use case that required the integration of audio and image interfaces. Omeka has a plugin that works with the audio in Media Collections Online (Avalon Media Systems), but how to integrate the scripts? And how to handle playback of radio programs spread across multiple files/disc sides?
Using standard plugins for Omeka, we were able to create a web site that would allow audio playback while simultaneously allowing the user to page through images of the script. In this presentation, we will demo the new site and show how we added the linked audio and print pages.
There are many tools and platforms for creating data visualizations, but in order to ensure they communicate in an effective way, your visualizations must be grounded in the appropriate quantitative methods. In this workshop, we will present some problematic humanities datasets and case studies, and use them to walk through the structure and assumptions your data will need to meet in order to create effective data visualizations. Introductory quantitative methods and vocabularies will be presented.
At times more complex data visualizations are necessary to communicate your argument and explore the multiple dimensions of your dataset. This hands-on session will start you down the path towards employing statistical methods to communicate your argument, and will give you a chance to bring your own data and work through options for visualizations. During the workshop we will use two sample datasets to discuss how they were prepared and structured to enable comparison with regression analysis. We'll discuss regression analysis and how you can compare two datasets in a way that ensures you're getting useful information.
Digital tools for mapping, data visualization, and network analysis offer opportunities to discover, answer, and present research for scholars working in the arts and humanities. But these methods require moving your evidence and research into a data structure appropriate for your chosen tool. In this workshop, we'll discuss the types of decisions you'll encounter when representing your humanities evidence in a digital environment and best practices for structuring your research data for use in a number of digital tools.
Digital image manipulation, social network analysis, and data mining can change our perceptions of the world around us, but they also require careful, critical use. This presentation will take arts & humanities practitioners through mapping, data mining, network analysis, data visualization, 3D rendering, computationally aided vision, and other digital methods in a variety of disciplines and tackle some of the critical issues for digital arts and humanities practitioners.
This workshop will introduce basic information visualization concepts and discuss their implementation within R analyses (ggplot2) and for Web (D3, Shiny, and Jupyter).
Python has become the lead instrument for data scientists to collect, clean, and analyze data. As a general purpose programming language, Python is flexible and well-suited to handle large datasets. This workshop is designed for social scientists, who are interested in using Python, but have no idea where to start. Our goal is to "de-mystify" Python and to teach social scientists how to manipulate and examine data that deviate from the clean, rectangular survey format. Computers with Python pre-loaded are available in the SSRC on a first-come, first-served basis. This workshop is intended for social scientists who are new to programming. No experience required.
