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.
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.
You've collected your data. Now what? In this class we will discuss the basic principles of data visualization and demonstrate a variety of visualization tools that you can use to establish the significance of your data.
This workshop will focus on LabArchives, the Electronic Research Notebook selected by the University of Pittsburgh. We will cover how to get started using it, including planning strategies, access, lab notebook creation and organization, adding and editing entries, linking, and sharing data.
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.
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.
Join the library to watch the webinar: "In scientific method we don’t just trust: or why replication has more value than discovery". In this lecture, Dr. Ioannidis will explain the current challenges of balancing discovery and replication in science at large, describe different forms of replication, and explain why reproducibility is important.
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.