Course Catalog

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a review of core concepts in epidemiology. This session will cover the principles of epidemiology, key concepts and terms, brief review of outbreak investigations and study designs, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). The instructor will present a set of exercises to work through during the lesson (a calculator will be needed). 

Embase is a biomedical and pharmacological database covering international literature from 1947 to present. Embase includes citations and unique indexing for drugs not found in PubMed. This advanced training session will focus on using Embase for general literature searching, and will cover how to use the database’s embedded tools to design advanced queries for systematic reviews and adverse drug reactions. Participants will also learn how to combine queries to increase search precision, and how to use the PICO (population, intervention, comparison, and outcome) and the PV (Pharmacovigilance) wizards.

PubMed comprises tens of millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. This class introduces advanced PubMed features that allow for focused searches. Participants will learn how to personalize PubMed using My NCBI, use internal PubMed vocabulary lists (MeSH) to create more efficient search strategies, truncation use, and searching with Boolean operators. Additionally, skills in applying search filters, creating subject alerts, and exporting search results to bibliographic management software such as EndNote will be discussed.

Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. This introductory class will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. The class will also discuss the differences between animal models, research organisms, and model organisms, and will review requirements and resources for the NIH Model Organism Sharing Policy.

VSClinical is designed for researchers to efficiently process the clinical interpretation of cell variants based on Association for Molecular Pathology (AMP) and American College of Medical Genetics and Genomics (ACMG) guidelines. This class will demonstrate how VSClinical enables labs to test for both germline and somatic cell variants according to the American College of Medical Genetics (ACMG) and AMP guidelines in an automated fashion. With the VSClinical interpretation hub, participants will learn how to produce customized reports, use lab knowledge databases to streamline variant classifications, and evaluate somatic and germline cell variants.

This class focuses on structuring spreadsheet data so that it is ready for data analysis, or for importing into R or Python. By the end of this class, students should be able to: describe best practices for data entry, and formatting data in spreadsheets; list common formatting mistakes; describe different approaches for handling dates in spreadsheets; identify tips for quality control and data manipulation in spreadsheets; exporting data from spreadsheets.

Biosketches are used more and more in NIH internal reports. Participants will learn how to create a Biosketch with efficiency. Participants will also learn the appropriate components needed to put their best foot forward.

Learn how to use the Biomedical Translational Research Information System (BTRIS) to streamline the task of gathering and reporting active protocol data. BTRIS staff will demonstrate how to run queries in BTRIS and then guide attendees through running reports for their protocols.

Class Prerequisites:
You must have an established BTRIS user account for access to identified data. These accounts are given to all Principal Investigators or with permission to their designees. If you do not have an account, please complete the BTRIS Access form.

For additional information on these sessions, go to the BTRIS webpage.

Galaxy is a scientific workflow, data integration, data analysis, and publishing platform that makes computational biology accessible to research scientists that do not have computer programming experience. This training will introduce ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow.  This workshop will be taught by NCI staff and is open to NIH and HHS staff.

This class is a mixture of lecture and hands-on exercises. By the end of this class, students will be able to: independently run basic ChIP-seq analysis for peak calling, run quality control on ChIP-seq data, map raw reads to a reference genome, generate alignment statistics and check mapping quality, call peaks using MACS, annotate peaks, and visualize the enriched regions.

Choosing a quality journal to publish in can be a daunting task. In this class, participants will become familiar with the available resources and tools to assist in targeting quality journals, as well as ways to recognize questionable or predatory journals. Additional topics include tips to avoid a predatory conference, evaluating invitations to serve as a peer review or editorial board member, preprint servers, and what to do if you published with a questionable journal will be covered.

This class will give you an in-depth look at tools for evidence-based clinical information.  Explore our available resources and learn search tips for DynaMed (clinical decision support tool), Access Medicine (decision making tool), Cochrane (evidence to inform healthcare decisions), EBMcal (clinical criteria sets and decision tree analysis tools), and PubMed shortcuts to evidence based clinical information. Learning Objectives: become familiar with the NIH Library’s clinical resource tools and know how to use them; and do better, more efficient searching using clinical resource tools.

This class will help authors navigate the legal and ethical issues surrounding copyright and plagiarism, identify and avoid potential copyright infringement issues, and ensure the integrity of their work as a component of their publishing process. This is part of NIH LIbrary's writing and publishing class series that supports writing, publishing, and scholarly communication.

This one-hour training will provide detailed information on how to create charts in MS Excel, including reviewing and selecting chart types, layouts, and styles. The training will also cover changing colors and format options, as well as how to make changes to titles and labels. This is an introductory class for those who need to quickly learn basic Excel chart features and for those who are interested in a refresher.  

No previous experience with Excel is required, but basic familiarity with Microsoft Office is helpful. 

This one-hour training will provide detailed information on how to create and manipulate pivot tables in Excel. Participants will learn how to change labels and the pivot table layout, update the pivot table, group the pivot table by date, create slicers, and create pivot charts. This is an introductory class, but basic familiarity with Excel is helpful. 

Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Data Wrangling is another class in the NIH Library Introduction to R and RStudio Series. A basic understanding of using R and RStudio to manage data is expected. This one-hour class will introduce the R tidyverse package and how to use it to manipulate, analyze and export data. This class will explore options for using the tidyverse functions select, filter, pipes, mutate, head, is.na, and export. Learning these functions will enable users to have well formatted data frames for future clean and consistent analysis and visualizations.

Data Wrangling is another class in the NIH Library Introduction to R and RStudio Series. A basic understanding of using R and RStudio to manage data is expected. This one-hour class will build on the introduction provided in Data Wrangling Part 1 to further explore the R tidyverse package and how to use it to manipulate, analyze and export data. This class will explore options for using the tidyverse functions group_by, summarize, count, arrange, and export. Learning these functions will enable users to have well formatted data frames for future clean and consistent analysis and visualizations.

This session focuses on defining the scope of your review by applying techniques to formulate a workable research question. The class introduces various frameworks used for developing a research question, and presents the requirements and steps involved in conducting the literature search for the systematic review. Useful resources are introduced throughout the session.

This class will show participants how to effectively search for nursing and allied health evidence-based information using PubMed and CINAHL databases. Class objectives: learn how to develop evidence-based search strategies using keywords and database subject headings; acquire tips to use limits in your search; and locate the full-text of articles identified in searches.