Introduction to Reproducible Research
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.
Getting Started with Jupyter Notebooks
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.
Getting Started with RMarkdown
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.
Version Control: Using Git and GitHub
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.
Documenting Your Research Data
Documenting your research data is a prerequisite for data sharing and your own use of your data in the future. Good documentation helps your data be discoverable, understood, and trusted by others. Please view our individual modules for the training “Documenting Your Research Data” to learn documentation best practices by subtopic (e.g., code, tabular data, using documentation standards).
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.
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!