All workshops will be held on Tuesday, May 30, at the Charles Library on Temple University’s campus at 1900 N. 13th St., Philadelphia, PA
Morning Workshops (9:00am-12:00am)
A Friendly Introduction to Python for Absolute Beginners
Presenters: Kara Handren, University of Toronto and Kelly Schultz, University of Toronto
Interested in learning to program but don’t know where to start? This hands-on workshop will introduce you to the basic concepts of one of the world’s most popular programming languages, Python! This introduction to Python will include concepts such as data types, variables, operators and loops. You will also learn how to use Jupyter Notebooks to read and write code. This workshop will establish a foundation to start exploring Python, and help to get rid of any nervousness you might have about learning to code. There will be plenty of opportunities to ask any questions and practice as we go!
Track: Data Literacy
Understanding Data Anonymization
Presenter: Kristi Thompson, Western University
Data curators should have a basic understanding of data anonymization so they can support safe sharing of sensitive data and avoid sharing data that accidentally violates confidentiality. This workshop will consist of a lecture followed by an interactive hands-on session using R. The first half will cover the mathematical and theoretical underpinnings of guaranteed data anonymization. Topics covered include an overview of identifiers and quasi-identifiers, an introduction to k-anonymity, a look at some cases where k-anonymity breaks down, and a discussion of various enhancements of k-anonymity.
The second half will walk participants through some steps to assess the disclosure risk of a dataset and anonymize it using R and the R package SDCMicro.
Much of the academic material looking at data anonymization is quite abstract and aimed at computer scientists, while material aimed at data curators does not always consider recent developments. This session is intended to help bridge the gap.
Track: Data management and archiving
Afternoon Workshops (1:00pm-4:00pm)
Analyzing donations of digital trace data: Starting with your own search behavior
Presenter: Ericka Menchen-Trevino, American University
Researchers who collect digital trace data often rely on APIs that can change at the whim of the platforms that create them. While personal data downloads require more effort to collect, they are mandated by European regulation and thus less likely to be discontinued, and can be combined with traditional participant-centered methods. This workshop will introduce researchers to how to work with research participants to collect and analyze the individual digital trace data that people can potentially donate for research purposes (e.g. search queries, web browsing history, social media activity, and screenshots). While these data are typically incorporated into quantitative projects, we will also discuss how qualitative researchers can incorporate these traces into their projects as well.
In the workshop, participants will see examples of how the ethical principles of data minimization and informed consent to data donation research have been implemented. Participants will also be introduced to and provided resources about different sampling strategies and analytical approaches for working with digital trace data. These approaches include network analysis, Markov chains, and multi-level modeling for quantitative analysis, and data-prompted interviews or observation for qualitative analysis.
The workshop will include a hands-on component where participants will have the opportunity to download and analyze their own Google search traces, or use example data if they decide not to or run into technical problems. We will clean, extract, and summarize variables from this search data to explore how search query topics change over time. Overall, this workshop will introduce participants to the skills and knowledge necessary to effectively collect, analyze, and make sense of digital trace data for research purposes, while also considering ethics and appropriate analytical approaches.
Track: Data Literacy
Introduction to the Dataverse software for managing and sharing your research data
Presenter: Sonia Barbosa, Harvard University
The Dataverse Project is an open-source web application to share, preserve, cite, explore, and analyze research data. It facilitates making data available to others and allows you to replicate others’ work more easily. Researchers, journals, data authors, publishers, data distributors, and affiliated institutions all receive academic credit and web visibility.
A Dataverse repository is the software installation, which then hosts multiple virtual archives called Dataverse collections. Each Dataverse collection contains datasets, and each dataset contains descriptive metadata and data files (including documentation and code that accompany the data). As an organizing method, Dataverse collections may also contain other Dataverse collections.
The central insight behind the Dataverse Project is to automate much of the job of the professional archivist, and to provide services for and to distribute credit to the data creator.
This workshop will provide hands-on training in managing and sharing your research data in a Dataverse-based repository.
Track: Data Management and Archiving
Incorporating Critical Data Literacy into Data Visualization Pedagogy
Presenter: Subhanya Sivajothy, McMaster University
From understanding climate change to COVID-19, data visualizations and infographics are an important tool for making sense of complex issues and data. Visualizations are ubiquitous as we encounter them in news sources, social media, scientific papers and more; however, they are often treated as a neutral, and objective source of information. In this workshop we will look at methods of teaching data visualization skills that encourage participants to both read and create visualizations in a critical manner.
The first part of the workshop will cover foundational concepts in visualization in design principles, as well as provide a critical understanding of the history of data visualizations and how they’ve been mobilized in both beneficial and harmful ways. This section will also provide an overview on how to incorporate principles of data justice, data ethics and accessibility into your pedagogical practice during each building block of teaching the data visualization process.
The second half of this section will bring into focus, the larger data justice concepts from other fields such as using counter-data, participatory research techniques, anonymity by design and explore how they may apply to teaching data visualizations. We will also explore how as data practitioners we can manage complexity and uncertainty in our data in ethical ways.
We will end the session by working together on some instruction design activities that participants will be able to workshop with each other in groups.
This workshop is suitable for people who are interested in both learning about data visualizations and teaching data visualizations at all levels.
Track: Data Literacy
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