Research Data Management

A research guide detailing NYU's membership in the Carpentries organization.

Course materials on the basics of what comprise RDM and how you can implement them in your daily practice.

Course materials on using OpenRefine, a powerful tool for cleaning and transforming tabular data.

A class on how to extract and manage data from text and HTML files, interface with the web, and perform search functions on large datasets using introductory Python techniques.

An introduction to using OCR to transform images of documents into machine-readable text, including the use of pattern training.

This class is designed for first-time and longer-term users of Jupyter Notebooks, a workspace for writing code. The class focuses on using Notebooks to facilitate sharing and publishing of script workflows. It aims to provide users with knowledge about shortcuts, plugins, and best practices for maximizing re-usability and shareability of Notebook contents.

This workshop introduces the basic concepts of Git version control. Whether you're new to version control or just need an explanation of Git and GitHub, this two hour tutorial will help you understand the concepts of distributed version control. Get to know basic Git concepts and GitHub workflows through step-by-step lessons. We'll even rewrite a bit of history, and touch on how to undo (almost) anything with Git. This is a class for users who are comfortable with a command-line interface.

A class on setting up and managing research materials; caring for digital files to enable collaboration, sharing, and re-use; and helpful software/digital tools for organizing personal research files.

This session is an intermediate-to-advanced level class that offers some ideas for how to approach the following common data wrangling needs in research: 1) Obtain data and load it into a suitable data "container" for analysis, often via a web interface, especially an API, 2) parse the data retrieved via an API and turn it into a useful object for manipulation and analysis, and 3) perform some basic summary counts of records in a dataset and work up a quick visualization.

An introduction to managing, annotating, organizing, archiving, and publishing research data using the Open Science Framework.

A class covering the basics of writing a successful data management plan for federal funding agencies such as the NEH, NSF, NIH, NASA, and others.

A session on how to cite code and data, and how to enable your data and code to be cited by others.

A session from a class on the basics of using Gephi to visualize network data.

A session devoted to tools and repositories that can help connect your publications with the corresponding data.

Have you heard about the reproducibility crisis in science (ex. in Nature and Economist)? Do you wonder how you could increase the reproducibility of your own work? This session will show you some hands-on, practical steps and tools that can help make your research reproducible in your field.