This class is intended for those who have already take the Exploring and Cleaning Data with OpenRefine class or have an understanding of the basic functions and layout of OpenRefine. Topics covered will include data reconciliation, web scraping, and html parsing.
Please bring a laptop.
In this workshop, attendees will work at their own pace to learn basic data science tasks in Pandas. Pandas is a fantastic Python package which provides data structures and analysis tools for data science tasks. The workshop will cover the data structures, selection, mapping functions, reductions, statistics, input/output, pivot tables, grouping, and time-series data. Basic knowledge of Python is required. Attendees should be familiar with the syntax, using lists, and basic knowledge of lambdas.
What is a Data Management Plan? This session will answer that question, as well as describe the steps to creating a DMP, tools that can help with DMP development, and post-award management issues. University of Pittsburgh-specific guidelines and support resources will also be shared.
Many funders, publishers, and institutions require researchers to make their research data public, but practical challenges can act as a barrier to sharing data, especially in the health sciences. This hands-on workshop will guide participants through the data sharing process, from initial study design to data deposit. Exercises will prompt participants to think through issues of data documentation, reuse value, and promotion of their own research projects.
Microsoft Excel is a commonly used program to record and store datasets with headings, rows, and columns. In this class, we will look at transforming that data into summary tables and charts. You will work through data examples to create pivot tables and pivot charts, apply conditional formatting to your tables and sheets, and prepare your figures for use in other programs.
Learn how to keep your data safe AND preserve it for future use by following a few simple rules. File formats, file-naming conventions, repositories, storage options and more will be discussed.
In this hands-on workshop, learn how to manage your work with the version control system Git. Git helps keep your files safe from accidental deletion, tracks who made what change when, and lets multiple people work on the same project without overwriting each other's work. We'll cover using Git from the Unix shell and through Github online. No previous experience with the command line is necessary, although some basic knowledge is recommended.
In this class, learn the fundamentals of keeping your data secure and organized through brief introductions to the core areas of data management: file storage and organization, file documentation, data preservation, and data publication and/or data sharing. This class is intended for graduate students and researchers who are working on long-term research projects, or for anyone who wants to make sure their personal files are safe for the long-term.
You've collected your data. Now what? In this class we will learn how to use Tableau to demonstrate the significance of your data.
Have you shared your data in an open-access repository or to accompany a publication? Are you interested in sharing data with potential collaborators, but you need to maintain control over who can access it? Come share your experiences in this drop-in session and learn about a new data-discovery platform from the Pitt HSLS team that can help you increase the visibility of your datasets without necessarily making them public.
Need to find a dataset to act as a control for your study? Or do you want to reuse open access data? This class will offer tips for locating and citing data and include hands-on exercises to explore directories of data repositories and data journals.
Do you have data that require bioinformatics analysis? Are you concerned about scientific rigor and reproducibility? Come learn about the “4 C’s” available to Pitt researchers: Core facilities, Collaboration with bioinformaticians, Coding, and Commercially-licensed tools. Make an informed decision on the best option(s) for your data needs.
Need to find a dataset to act as a control for your study? Or do you want to reuse open access data? This workshop offers tips for locating and citing data, and includes hands-on exercises to explore directories of data repositories and data journals.
Did you know that for each minute of planning at the beginning of a project, you will save yourself roughly 10 minutes of headache later? This session will provide practical tips for organizing, naming, documenting, storing and preserving your data.