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

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

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

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

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

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.

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

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 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 dataset of their choosing and designing their own visualizations to communicate their findings.

Instructor: Patrick Rashleigh

Datasets 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

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

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.

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

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 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.