Introduction to R for Absolute Beginners
R is an open source software for statistical computing and graphics. This Introduction to R workshop is for people who want to learn this data analysis tool but have little or no experience in any programming languages. The first half of this 3-hour workshop will focus on some basic concepts of coding and the second half will have several hands-on activities to learn basic R skills, such as installing R packages, importing and exploring data. Some troubleshooting tips and R resources will be provided at the end of this workshop.
Introduction to the Open Science Framework
Collaboration among researchers can be difficult in many ways. So too is the management of all the materials and documentation that go along with it. In this session we introduce a helpful platform designed to ease the difficulties of managing and sharing data and research products. The Open Science Framework (OSF) is a free, open source, online research project management platform where you can create a personal JHU affiliated account, manage multiple projects, and collaborate or share with other researchers when you are ready to do so. It has the capability of connecting to services you may already use for research including JHBox storage, or other 3rd party services such as Google Drive, Github, and Mendeley, to name a few. Over the hour we will tell you more about the OSF, how you can get started, explain basic concepts of the interface, and share some interesting examples of ways other researchers are using the OSF. You can find more information about OSF services on our website and you may also want to take a look at our Lab Organization Template and Electronic Lab Notebook Template to get an idea of how one might use the OSF. We hope that you can join us!
Open Science and Reproducible Research Tools
What is the open science movement and what does it mean for me as a researcher? In this workshop we will look at the growing importance of open science and explore the issues surrounding making research and data open. As we discuss definitions, strategies, barriers, and incentives to open research we will also begin to take a look at tools that assist users in working in a transparent and reproducible manner. Specifically we will demo the Open Science Framework (OSF), a free, open source, online research project management platform where you can create a personal JHU affiliated account, manage multiple projects, and collaborate or share with other researchers. A prime example of a service that promotes reproducibility, we will share more about ways that you can use the OSF to position yourself as a proponent in the open science movement.
Prerequisites: Some programming experience recommended (beginner level, preferably but not limited to R or Python) for the Jupyter Notebooks, RMarkdown and Git/GitHub sessions.
What is reproducible research and how can I make my research and code reproducible? In this one-day workshop with JHU Data Services, learn how open tools like R Markdown, Jupyter Notebooks, Git and GitHub can help make your research reproducible. Join JHU Data Services for this full-day workshop about concepts and tools for doing reproducible research. This workshop will be split into four separate interactive sessions: “Introduction to Reproducible Research,” “Getting Started with Jupyter Notebooks,” “Getting Started with RMarkdown” and “Version Control: Using Git and GitHub.” Sessions are run independently of each other. You may attend all sessions or only those that fit your interests.
9:30-10:30am: Introduction to Reproducible Research (Required)
A brief introduction to reproducible research. What is reproducible research? Why is it important? This session provides several good and not so good examples of reproducible research. We also provide several tips and useful resources for you to start making your research more reproducible.
10:45-12:15pm: Getting Started with Jupyter Notebooks (Optional)
You are already writing comments for your Python code so you may be wondering why you need to document code with a Notebook tool, such as Jupyter Notebooks. Notebook tools help you document and share your work with fellow Python coders, non-coders or non-Python coders. This workshop will teach you the basics of using Jupyter Notebooks and will focus on the features of Jupyter Notebooks that help you create reproducible research and code for technical and non-technical audiences. Many Python users choose to document and share code with Jupyter Notebooks, but this tool can also be used with other programming languages, including Julia and R. You can find examples of Jupyter Notebooks here.
1:30-3:00pm: Getting Started with RMarkdown (Optional)
You are already writing comments for your R code so you may be wondering why you need to document code with a Notebook tool, such as RMarkdown. You can find several RMarkdown examples here. Notebook tools are particularly useful when you share your code and analysis with non-coders or non-R coders. RMarkdown can help you create interactive web document with text, code and results of executing that chunk of code all in one place. Many R users choose to document and share code with RMarkdown, but it can be used to document scripts writing in other programming languages, such as Python and SQL.
3:30-5:00pm: Version Control: Using Git and GitHub (Optional)
Do you use different file names to track versions of your files? Are you wondering why everyone else is using GitHub? Do you comment out a chunk of code to test out code or debug? Do you want a system that can do version control automatically for you? If you answer yes for any of the questions above, then you should take this session to learn version control with Git and use GitHub as a git-hosting platform. We use a graphic user interface (GUI) tool, GitHub Desktop, to get your started with using Git and GitHub. No knowledge of command lines is needed for this session.
Register once here for access to all four sessions: https://jhu.libcal.com/event/7499496
Planning for Software Reproducibility and Reuse
This session helps make your research more efficient and impactful by presenting best practices for creating understandable, reproducible, reusable, and citable software and scripts. Additional topics include intellectual property considerations and ensuring long-term accessibility of code.
Contains 6 sub-modules, 22 mins in total. Delivered via interactive slideshow.
What is the open science movement and what does it mean for me as a researcher? This course, broken into 4 modules, covers the growing importance of open science and the issues surrounding making research and data open. Specifically, we discuss:
- Definitions of open science
- The incentives to making your research more open
- Some of the barriers to participating in open science
- Simple strategies for making your research more open, including information on people and resources at JHU that can help you.
Contains 4 modules, 41 minutes in total. Delivered via Blackboard.
Contact us to request a past workshop.
Open Tools for GIS and Mapping
This workshop will introduce participants: to basic terms and concepts of open source, open data; open access; several open tools for GIS and mapping work; and resources for learning more about each tool. Tools covered include: QGIS, Carto, Open Street Map, R, and Leaflet.