Data Services Classes and Customized Trainings

Classes Available on Request or Customized Training for your Group

To request a class, please contact HSLS Data Services.

Introduction to Python through Jupyter targets users of any experience level. If you have experience with another programming language or have never programmed at all, this workshop will get you off the ground running. This workshop approaches Python as a tool to complete data science tasks. Attendees will walk through Python at their own pace covering: types, operators, data structures, loops, flow control, comprehensions, and dealing with files. If you finish the introductory material you can continue to learn about Pandas. Pandas is a Python library which contains useful data structures for completing common data science tasks.

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.

Your Pitt Box account provides unlimited online/cloud storage and allows individual files, up to 15 GB, to be uploaded. In this class you will learn how to access your Box account, upload files, streamline your workflow with Box Drive, share files and folders (with or without password protection), plus more.

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.

This class focuses on the easy creation of publication-ready scientific illustrations using a drag-and-drop, web-based, HSLS-licensed software--ePath3D.

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 already conduct literature searches with PubMed and read free full-text articles from PubMed Central (PMC), so why try Europe PMC? Quite simply, because your current search strategy might not be finding all of the relevant information. This class will focus on two specific features of Europe PMC: (1) preprint searching and (2) direct linking within articles to public gene, protein, and chemical compound databases.

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.

OpenRefine (formerly Google Refine) is a powerful, free, open source, tool for working with messy tabular data.  It runs offline in a web browser and allows for reproducibility in data cleaning.

“What's in a name?” When you create a new file, do you give much thought to the name you save it as? This class focuses on best practices for naming files so that they are easily found, understood, and sharable in the future.

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 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.

This workshop, presented by Pitt CRC consultant Fangping Mu, will introduce the R and Bioconductor environment on the HTC cluster. The hands-on session will use Rstudio server on Open Ondemand.

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.