If you'd like to request a workshop for a class or organization, please send an email to library-collaboratory@ucsb.edu with a quarter's notice. If you are able to organize 10 participants to commit to attend, we will send a poll to our UCSB contact list to see which date/time is best. If we get 15 responses, we will organize a workshop and add it to on the Library's calendar.

Collaboratory Developed Workshops:

Introduction to Twitter Analysis using Twarc 

Twarc is a Python application developed by as part of Document the Now.  It allows users to gather larger amounts of Twitter data, either in realtime or for the previous 7-10 days.  (4 hours) 

Introduction to GIS using ArcGIS Desktop or Pro

Esri's ArcGIS is the industry standard geographic information systems software.  Used to create, edit, and analyze spatial data in a graphical environment, all UCSB faculty, staff, and students have access to these powerful desktop applications.  This 2 hour workshop provides a brief introduction to creating data-driven maps.

Qualitative Data Analysis Using NVivo 

NVIVO is software used to analyze text and survey data.  It can analyze data from a variety of sources: PDF's, web pages, audio and video transcriptions. It has tools built-in to aid in transcription, markup, and coding.  There is also a basic Twitter harvesting plug-in available.  This workshop can be customize for 1, 2, or 3 hour sessions.

Introduction to using Wikidata 

Learn the basics of the sparql query language, and the structure of Wikimedia Commons' Wikidata 'open knowledge base.' For example, you can run a query to return a list of popes who fathered children or graph popular human eye colors. (4 hours)

Anticipating US Census 2020 Data 

The 2020 Census is underway!  Learn about how the 2020 Census will be different than those in the past, and new data products that are anticipated in 2021. (80 minutes)

Working with International Census and Demographic Data

In addition to public census websites around the world, the Library owns or subscribes to census and other demographic datasets from China, India, and other countries.  Learn to access and analyze data from a variety of sources, and about complications you may encounter when comparing counries to each other.

Software and Data Carpentry

Introduction to R for Geospatial Data

R is a free software environment for statistical computing and graphics. This workshop is intended for learners working with geospatial data who have no prior experience using R. The workshop covers: working with R in the RStudio GUI, project management and file organization, importing data into R, introduction to R’s core data types and data structures, manipulation of data frames (tabular data) in R, introduction to visualization, and writing data to a file.

Plotting and Programming with Python

An introduction to programming in Python for people with little or no previous programming experience. The 8-hour workshop covers Python using Jupyter Lab notebooks and the pandas libraries. Programming concepts covered include Python data types, conditional statements and loops. By the end, learners will be able to produce simple time and scatter plots.

Introduction to the Unix Shell

The Unix shell is a ubiquitous program whose primary purpose is to read commands and run other programs. More importantly, it helps people combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including “high-performance computing” supercomputers).

Version Control with Git

Git works with the Unix shell to provide an efficient offline way to harness the version control benefits of Github for either personal or collaborative work. While most often used for code, Github offers a platform for any text-based writing or coding. It is a convenient way to preserve, version, and share code used for data-analysis.

Programming for R

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R.

R for Reproducible Scientific Analysis

This workshop builds on Programming for R to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Databases with SQL

Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets.