Course Catalog

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.

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.

The purpose of this class is to introduce the fundamentals of conducting a meta-analysis. The focus will be on randomized clinical trials; however, the presenter will also briefly discuss the application of meta-analyses in laboratory and observational (epidemiological) studies. 

The audience should have an acquaintance with basic statistical concepts (including, but not limited to: dichotomous and continuous outcomes, odds ratios, standard deviation and error, weighted average, fixed and random effects and associated models, etc.). However, it will only be necessary to understand the principles and interpretation of these ideas, not the underlying mathematics and calculations to learn the principles presented. This class will not contain a “hands-on” portion or a set of exercises to be completed in a statistical software package during the training or afterwards.

This class will be taught by the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES).

For the Advanced Training, topics covered depend upon survey results: find key hubs using over-connectivity analysis; using Microarray repository for gene comparisons against public data; building networks with MetaCore; constructing your own pathway maps; performing toxicogenomic analysis in MetaCore; analyzing and building networks with miRNA and mRNA data; analyzing multi-omics data (RNA-seq, proteomics, metabolomics, etc).

For the Introductory Training we will cover basic topics such as: MetaCore overview; how to use MetaCore as a knowledge mining tool; how to upload data; running functional enrichments and exploring pathway maps; and running workflows.

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. 

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.

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. 

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature.

Part 1 will address considerations for the choice of statistical tests including the importance of study design and hypothesis, type of data and their distributions, and provide a brief review of important statistical features. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.

Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 2 of this class series.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature.

Part 2 will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and some nonparametric tests. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.

Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 1 of this class series.

This session teaches students to use SciFinder-n in their approach to infringement and patentability searches. Explore the science hidden in patents, search patent Markush structures, and check the chemical landscape. By the end of this training, students can expect to learn: about the patent content in SciFinder-n, how to use an AI-driven prior art analysis, how to explore a chemical landscape, how to find substance locations with PatentPak, and how to search Markush structures.

This introductory class will provide an overview of patent search tools, with a focus on search and analysis options in a variety of patent databases: Espacenet, Google Patents, PATENTSCOPE, Derwent Innovations Index, and Lens.org. During this session, the instructor will review patent search concepts including patent families, patent classification systems, and kind codes. Chemical structure and genetic sequence search options will also be covered.

The training will overview the current status of pathway tools, with focus on software available to NIH community.  It will discuss the biological interoperation of mutation and expression data in the context of pathways, pathway databases, and popular web-based pathway tools. The pathway software GSEA, g: Profiler, and PATHVIEW will be used to demonstrate how to run the pathway analysis of expression data against GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and MSigDB (Molecular Signatures Database).

This class provides a basic overview of best practices in data visualization and how these practices can be implemented in R. The focus of this class is on the effective use of space, how to handle overlapping points, common problems when using color, and ggplot themes. This class does not require downloading any software or packages.

Learn how you can promote your scholarly output using ORCiD, from creating an ORCiD iD that is a permanent identifier for researchers, to using the full site to help track your publications and your peer review work for journals. NIH encourages everyone who is engaged in research, grants, and research education to create an ORCiD identifier. Using ORCiD enables researchers to highlight their scholarly work more effectively. This class will review resources and best practices for creating an ORCiD profile that can be included in your CV, grant applications and bio to increase the visibility of your scholarly activities.

This class is designed for those who want to extend the basics of R Markdown and apply those skills in Quarto. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. In this intermediate-level class, participants will learn how to render documents in multiple format, and format citations and bibliographies using Zotero. Zotero is a free, easy-to-use tool to help collect, organize, annotate, cite, and share research. Some familiarity or experience in R and RStudio is recommended. Participants are encouraged to install R and RStudio before the class.