Course Catalog

This one-hour training will provide detailed information and demonstrations on how to manage data in Excel. By the end of this course, the participants will recognize how to filter data by text, numbers, and date; how to sort data alphabetically and by color; how to remove duplicates; how to split and combine columns; and how to create customs lists.

This is an introductory class for those who need to quickly learn basic Excel data management features and for those who are interested in a refresher.

Basic knowledge of Excel is required.

Labeling signal data is a very important step in creating AI-based signal processing solutions.  However, this step can be very time consuming and manual. In this beginner/intermediate one-hour session, the attendees will be introduced to signal labeling for use in AI applications and discuss how MATLAB can be used to speed up and simplify the process, from preprocessing to extracting information from signals. The session will cover different approaches for signal labeling, including using algorithms and automating with deep learning models. It will also discuss an iterative method of building deep learning models and reducing human effort in labeling. 

When it comes to data analysis and visualization, technical professionals who use Excel often encounter functional limitations. MATLAB supplements the capabilities of Excel by providing access to pre-built mathematical and analysis functions, visualization tools, and the ability to automate analysis workflows. Attendees will learn the main steps required for performing data analysis with MATLAB, including data access and pre-processing, modeling, and deployment. Attendees will gain an understanding of how to efficiently apply data analysis techniques using the MATLAB platform. This session is for beginners; no software installation required.

In this webinar, participants will learn how to tell a story with their data using notebook-style live scripts and how to integrate MATLAB with Python code. Attendees will also learn how to share code and take advantage of MATLAB community tools such as File Exchange and GitHub. Finally, the participants will find out how they can host MATLAB offerings at their High-Performance Computing Centers or a Science Gateway. This session is for beginners; no software installation required.

Learn about a wide range of capabilities for image processing and computer vision including machine and deep learning using deep convolutional neural networks (CNNs). Transitioning image models from pixel-based to feature-based allows us to extract information from images and video at a high level, to detect, classify, and track objects, co-register images, or understand a real-world scene. Using collections of features, we can train computers to recognize objects, with user-specified or automatically determined features. This is an introductory class, but familiarity with MATLAB or image processing is recommended.

During this webinar, participants will enhance their technical skills and proficiency with MATLAB by navigating online MATLAB resources designed to augment the learning experience and problem-solving capabilities, including documentation, examples, and community forums. In addition, this webinar will also present a preview of upcoming webinars, featuring cutting-edge topics and expert insights. 

This is an introductory-level class taught by MathWorks. No installation of MATLAB is necessary. 

MATLAB provides two-way integration with many programming languages such as Python, which allows for greater collaboration between investigators. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB. Attendees will learn how to use MATLAB’s Python integration to improve the compatibility and usability of their code. This webinar will be helpful for participants who have collaborators using Python with MATLAB, or for attendees who want to integrate MATLAB’s capabilities in a Python program. This session is for beginners; no software installation required.

Individual authors are increasingly being asked to demonstrate the impact of their published research using various citation-based metrics like the H-Index. Although such metrics have significant limitations, when used properly they can assist in the evaluation of individual authors for promotion, tenure, and green card applications. In this class, participants will gain an understanding of how these metrics are calculated, why certain metrics like the Journal Impact Factor should not be used to evaluate an author’s work, and how to obtain appropriate citation metrics.

This webinar introduces SimBiology as a modeling environment for mechanistic pharmacokinetic (PK), pharmacodynamic (PD), and quantitative systems pharmacology (QSP) modeling and simulation. Participants will learn how to use the SimBiology Model Builder app to build a mechanistic model and how to use the SimBiology Model Analyzer app to calibrate the model to experimental data, as well as perform model predictions. 

This is an introductory-level class taught by MathWorks. No installation of MATLAB is necessary. 

Learn how the National Survey of Family Growth (NSFG), a program of the National Center for Health Statistics (NCHS) from the Centers for Disease Control and Prevention (CDC), collects information on pregnancies, marriages and other relationships, use of contraception, and women’s and men’s general and reproductive health. NSFG data are used to help understand health and health behaviors in the United States; plan health services and education programs; and do statistical analyses of fertility, families, and reproductive health. This one-hour webinar by Anjani Chandra, Ph.D., Principal Investigator and Team Lead for NSFG, will provide an overview of the survey, how the data have been used, and how to access the data for statistical studies.

This session describes the application of the web-based interactive OmicCircos in R Shiny to construct circular plots with desired biological features. Example data from human and mouse genomes will be used to demonstrate over thirty plot functions along with the color selection, annotation, labeling, and zoom capabilities. User-guide, take-home video and sample plots from publications will be provided. No R Programming experience is required. By the end of this training,  students will be able to format data for omicCircos in R Shiny, use the point-and-click interface to set the parameters and generate circular plots, and export the plot for presentation and publication. 

This workshop will be taught by NCI staff and is open to NIH and HHS staff. This class is 2 hours and is a mix of lecture and demo. 

Spend 30 minutes with the NIH Library over your lunch break and learn how to cite data. In this session, we discuss the importance of data citation, identify key components of a data citation, and locate style guidelines for data citations. Participants should come prepared with a data set they would like to cite as a working example for this session.

This class complements NIH Library trainings on FAIR Data Principles and Resources for Finding and Sharing Research Data.

During this session we will introduce our services, answer basic questions, and arrange follow-up support for more complex queries. The NIH Library Research Analytics team provides support for bibliometrics, bioinformatics, data analysis, data visualization, and statistics.  Participants will be able to post questions and “upvote” questions from other users that they would like to see answered.

This class will provide an overview of NIH Library services and information resources for HHS staff. By the end of this class, participants will be able to demonstrate how to access/login to the NIH Library online; describe available information resources for HHS staff; describe how to access online journals and access full text articles and access/search databases; demonstrate how to order articles and other documents; and discuss additional services available for HHS staff including manuscript preparation, document editing, and literature searching.

This class will provide an overview of NIH Library services and information resources for NIH staff. By the end of this class, participants will be able to demonstrate how to remotely access/login to the NIH Library online; describe available information resources for NIH staff; describe how to access online journals and access full text articles and access/search databases; demonstrate how to order articles and other documents; and discuss additional services available for NIH staff including manuscript preparation, document editing, and literature searching.

This in-person hands-on workshop will introduce the Ingenuity Pathway Analysis (IPA), which is available to access from the NIH Library. IPA can be used identify biological relationships, mechanisms, pathways, functions, and diseases most relevant to experimental datasets.

Upon completion of this workshop, participants will  be to able compare different groups at different time points and treatments, perform Analysis Match to compare user data with public data sources, and generate IPA Networks using genes and diseases of interest. 

Session 1 (IPA): 10:00 AM to 12:00 PM

In this session, participants will learn about bioinformatics resources from the NIH Library and learn how to perform pathway analysis using IPA.

Lunch: 12:00 PM to 12:45 PM

Lunch on your own

Session 2 (IPA): 1:00 PM to 2:30 PM

In this session, participants will extend the learning from Session 1 and learn how to mine IPA database for novel discoveries.

Session 3 (CLC): 2:30 PM to 4:00 PM

In this session, participants will learn about CLC Genomics Workbench, including a live demo of the basic features and main functionalities.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

Registrants will receive an email with information and instructions to install and verify access to IPA before the class.  If you register the day before the class, you may not have time to download and properly install the necessary software. If you do not have the software installed, this training will be demo only.

In this in-person session, participants will have an opportunity to discuss their own research and use of Qiagen products with Qiagen scientists.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

This in-person workshop will show participants how to identify cell types based on statistics, visualization, and canonical markers. One Peripheral blood mononuclear cells (PBMCs) sample will be used to illustrate a basic Single Cell RNA-Seq analysis pipeline. Partek Flow is a web-based application for the analysis of next generation sequencing (NGS) including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface and the ability to build custom analysis pipelines, Partek Flow enables users to carry out routine NGS data analysis using dozens of popular algorithms without writing codes or running command lines tools.  Attendees will need to have taken the Partek Flow Basic Components class before registering or be comfortable with Partek Flow.

By the end of this class, attendees will be able to demonstrate how to access Partek Flow from the NIH Library, discuss the Quality Control (QC) and Quality Assurance (QA) tools, identify pre- and post-alignment tools, describe options for quantification and normalization, and perform pathway analysis and visualization.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

Registrants will receive an email with information and instructions to install and verify access to Partek Flow before the class.  If you register the day before the class, you may not have time to download and properly install the necessary software. If you do not have the software installed, this training will be demo only.

In this webinar, attendees will learn how to share their code, taking advantage of MATLAB community tools such as File Exchange & GitHub. They will also find out how to host MATLAB offerings at their HPC center or a Science Gateway. In addition, the participants will create notebook-style Live Scripts using MATLAB Live Editor; leverage MATLAB Community Resources to make code, projects, and toolboxes available; and learn how to access MATLAB through the browser and share licenses with collaborators.

This is an introductory-level class taught by MathWorks. No installation of MATLAB is necessary.

In this 90-minute session, participants will learn to improve and optimize their MATLAB code to boost execution speed by orders of magnitude. The course covers common pitfalls in writing MATLAB code, explores the use of the MATLAB Profiler to find bottlenecks, and introduces the use of Parallel Computing Toolbox. The course also addresses: vectorization and best coding practices in MATLAB; incorporating compiled languages, such as C, into MATLAB applications; utilizing additional hardware, such as multicore processors and GPUS to improve performance; as well as scaling up to a computer cluster, grid environment or cloud. This session is for beginners through experienced; no software installation required.