Workshops
The Center for Digital Scholarship offers workshops year-round, as well as an intensive Digital Humanities Summer Institute, which is typically offered in early June.
The summer institute is designed for Brown graduate students in the humanities and social sciences, but we welcome researchers of all levels (undergraduate to faculty) and all disciplines to join us for two week-long digital humanities workshops every summer. The first week focuses on tools for acquiring, analyzing, and visualizing data. The second week focuses on creating and critiquing projects in digital humanities. Each workshop can be taken independently, but both workshop weeks build on each other.
Below is a list of the workshops that we offer regularly. See the library calendar for upcoming scheduled workshops.
Data Management and Data Sharing
Data Management and Data Sharing covers classes on the fundamentals of research data management and provides participants with an overview of the data management activities applied at different stages of the research lifecycle. In addition, participants will learn the anatomy of data management and data sharing plans. Lastly, participants will plan for the appraisal of their data and the archiving of their data in a relevant repository, as well as the tools, people, and resources at Brown Library to support the publication of data at the end of their research project.
Managing Your Research Data
Learn best practices for the effective management, stewardship, publication and sharing of your research data. Participants will receive information on how to plan for the management of their data throughout the stages of their research projects, including best practices for establishing file naming conventions, folder structuring, annotating and describing data, versioning, backing up, storing, and securing data files, and sharing and archiving data in a repository.
Instructor: Andrew Creamer
How to Write a Data Management and Data Sharing Plan for Your Grant Proposals
Learn how to write a Data Management and Data Sharing Plan for your next research project or grant proposal, the people, tools, and resources available at Brown University to help you write these plans, and the expected best practices for the effective management, stewardship, publication and sharing of your research data. Compare and contrast researcher requirements for drafting National Institutes of Health’s (NIH) Data Sharing Plans and National Science Foundation’s (NSF) Data Management Plans (DMP). Become aware of Brown University, the Office of Research Integrity (ORI), and the major private and federal scientific research sponsors’ expectations for the management and sharing of your project’s data.
Instructor: Andrew Creamer
Citing and Publishing Your Research Data
This class looks at emerging conventions and tools for citing data and databases in scholarly publications. Participants will learn about the Force11 Data Citation Principles and the National Academies’ efforts to develop data attribution and citation practices and standards. In addition, the course will cover journal publishers’ expectations and policies for authors to make the data underlying their articles publicly available. Participants will review publisher requirements related to archiving data such as the PLoS data access policy and the The Joint Data Archiving Policy (JDAP) adopted by many leading journals across various disciplines, including those published by BMC, Nature and Science. Lastly, participants will learn about depositing their data into a relevant repository and the people, resources, and services available at Brown University Library to help them successfully archive, publish, and cite their data.
Instructor: Andrew Creamer
Managing your Digital Identity
You probably have a digital identity regardless of whether you’ve created or maintained social media profiles, websites, blogs or other online resources. But how do you utilize social media, blogging, and other publications to directly impact what people read to learn more about you? This workshop offers tips and tools to help you boost your social media presence, expand your online publications, and craft your digital narrative to bolster your career development.
Instructor: Ashley Champagne
Data Cleaning and Manipulation
These workshops can be taken as a series or individually. Together, they provide a good introduction to managing your research materials and cleaning/regularizing your data so that you can then proceed to analyze it using digital tools.
Regular Expressions: Search and Replace with Advanced Pattern Matching
Regular Expressions are a way of finding patterns in text and making changes en masse. This powerful and flexible approach is available in almost all text editors and processing tools, from Microsoft Excel to Python to Google Refine. This hands-on course is designed for anyone interested in cleaning up textual data, regardless of prior experience or discipline.
Instructor: Patrick Rashleigh | Course Page
Cleaning your Data with Google Refine / Open Refine
Google Refine is an easy to use open source tool for quickly cleaning up textual and numeric tabular data. It can be a lifesaver for scholars who need to work with data from scholarly databases, archives, and other sources and who want to manipulate formats and discover inconsistencies. This hands-on course is primarily aimed at social scientists and humanists, but all are welcome. Some familiarity with regular expressions will be helpful.
Data Analysis and Visualization
Introduction to Data Visualization
Poised to plunge into data visualization, making the latest-and-greatest fancy interactive extravaganzas? Well, hang on—before plunging into the tools, let’s take a step back and think about some basic principles of visual perception, design and representation, and communicating to an audience.
Instructor: Patrick Rashleigh
Using Excel for Data Visualizations
In an exploding universe of novel visualization tools, it’s easy to overlook Excel—or dismiss its graphics as boring or ugly. But in many instances, Excel is the most appropriate tool for producing simple static visualizations, and the results can be easily made attractive and in line with data visualization best practices. This 90-minute workshop will cover what Excel is good at, identify what to avoid, and go through the process of beautifying ugly default designs.
Instructor: Patrick Rashleigh
Tableau
Tableau is a software package that allows users to upload and explore their data, and quickly put together a variety of visualizations that can be published to the web. This is a hands-on workshop in which participants will learn Tableau by exploring a data set of their choosing and designing their own visualizations to communicate their findings.
Instructor: Patrick Rashleigh | Course Page
Thinking Critically About Data
Data sets can tell stories and support arguments, but they aren’t neutral. They reflect a limited amount of information, even as they are often used to bolster arguments on broader topics. So, how do you create a good dataset? How do you communicate what your data says, and what it doesn’t? In this workshop, we’ll use APIs to create datasets and explore what our data says and what it doesn’t.
Instructor: Ashley Champagne
Geographic and Spatial Tools
What are some different ways to represent information through maps? How can maps represent geographic changes and movement? Come think about maps and learn how to use geographic and spatial tools.
DH Tools and Methods
Introduction to Text Mining
Interested in exploring a large corpus of texts that would take years to read one by one? Come learn about different easy-to-use text mining tools designed to help you make sense of a large dataset. We’ll develop research questions from a sample corpus of texts by text mining with the following tools: Voyant, AntConc, and MALLET.
Instructor: Ashley Champagne
End-to-End Topic Modeling with MALLET
Topic modeling has a number of promising applications for the humanities. Unlike Key Word In Context (KWIC) lists and collocate lists, which require human supervision to parse one word from another, topic modeling is an unsupervised way to infer information about individual words based on how words co-occur or repeatedly appear in a corpus of texts.
In this workshop, will explore how to use MALLET from downloading the software to inputting our original dataset to creating a variety of topic models. This workshop will use the command line, but no prior experience is necessary.
Instructor: Ashley Champagne
Introduction to Network Analysis
This is a gentle introduction to network analysis with Gephi. Do you have a collection of letters and want to visualize the senders and receivers of the letters? Have Twitter data and want to explore which users follow each other? Network analysis can be a good tool to explore relational datasets. Come learn some of the basic principles of network analysis – and how to create a visualization with your own data. Feel free to bring questions and your own work.
Instructor: Ashley Champagne
Scalar
Scalar is a hosted platform for writing and publishing scholarship that takes advantage of images and time based media. It is optimal for sustained narrative or discussion, especially if the topic includes images or video. Come to this workshop to learn how to use Scalar, whether for scholarly publications or for incorporating it into class assignments. To see what Scalar can do, visit http://scalar.usc.edu