Course Catalog

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering several trainings that cover general concepts behind statistics and epidemiology. These trainings will help participants better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature. 

This four-hour online training will provide a brief review of the principles of epidemiology, outbreak investigations, implications in public health, key concepts and terms, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). Time will be devoted to questions from attendees and references will be provided for in-depth self-study. 

This one-hour online training will provide an overview of the CMS Virtual Library, an online resource designed specifically to support the information needs of CMS staff. This training will discuss CMS Virtual Library services and information resources. If you are unable to attend this training and would like an individual or small group orientation on CMS Virtual Library resources and services, please contact [email protected].   

By the end of this training, attendees will be able to:  

• Access the CMS Virtual Library
• Locate general library and CMS-specific resources
• Identify library services available to CMS staff 

Attendees are not expected to have any prior knowledge of the CMS Virtual Library to be successful in this training. 

This one-hour online training will cover some advanced features of using the PubMed interface. This training will demonstrate how to use special, customizable features of PubMed to improve their searches and save and manipulate search results.

By the end of this training, attendees will be able to: 

• Create a customized filter
• Use MyNCBI
• Apply Advanced features to build searches 

Attendees are expected to be familiar with the basic functions of the PubMed to be successful in this training. 

If no live trainings are available, you can watch a recording by clicking this link.

This one-hour online training 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. This training 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.  

By the end of this training, attendees will be able to:  

• Describe the difference between animal models, research organisms, and model organisms 

• Identify requirements for the NIH Model Organism Sharing Policy 

• Locate biomedical articles and patents related to animal models 

• Discover NIH-funded research projects, genetic information, and biomedical literature related to specific research organisms 

• Explore books on animal models and model organisms 

Attendees are not expected to have any prior knowledge of these resources to be successful in this training. 

This one-hour online training will compare data sources of citation data related to scientific publications for bibliometric analyses, including Web of Science, Scopus, Google Scholar, and Dimensions.ai. 

This one-hour online training, provided by a presenter from SAS, will demonstrate how using macros makes a code in SAS easier to read, easier to edit, less prone to errors, and often allows it to run more efficiently. Macros are ways to use code to substitute in a value. Examples of macro code will be made available to attendees for modification and later use. 

By the end of this training, attendees will be able to:   

• Identify what is a macro 

• Describe how macros work in SAS 

Attendees are expected to have some working experience with SAS 9.4 or to have attended an introductory SAS class, such as SAS® Programming 1: Essentials.  

This one-hour online training in the NIH Library Evidence Synthesis Review series provides an overview on establishing your inclusion and exclusion criteria and then using those criteria to conduct the screening steps to select relevant studies for your review.  

By the end of this training, attendees will be able to: 

• Describe what the eligibility criteria are and how to use them 

• Explain the process of screening  

• Name 2 tools for screening 

• Understand the importance of screening to the review process 

Attendees are not expected to have prior knowledge of how to conduct a review.  It is recommended that those planning to undertake a review, should register for the Evidence Synthesis series that take a deeper dive into the required methods for each step in a review.   

This training will provide a comprehensive look into critical steps of the review process – establishing your inclusion and exclusion criteria and then using those criteria to conduct the screening steps to select relevant studies for your review. Participants will receive resources and information on best practices for performing these steps to facilitate an effective and rigorous review. 

This one-hour online training session 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 training also highlights emerging AI related considerations related to plagiarism and copyright.  

By the end of the training, attendees will be able to:  

• Identify copyright and determine copyright status 

• Discuss different types of plagiarism and review how to avoid plagiarism 

• Highlight AI related considerations pertaining to plagiarism and copyright 

Attendees are not expected to have any prior knowledge of plagiarism or copyright to be successful in this training. The training is appropriate for early career as well as experienced researchers or administrators. 

This 45-minute online Lunch and Learn training will help attendees develop their own customized strategy for responsibly incorporating generative artificial intelligence (AI) tools, such as ChatGPT, into their workflows. 

By the end of this training, attendees will be able to: 

• Assess appropriate use cases for generative AI tools within their specific research/work context 

• Develop a customized generative AI usage strategy 

• Document their approach for using generative AI tools 

Attendees are not expected to have any prior knowledge of generative AI tools to be successful in this training. 

This one-hour online training will instruct participants on chart creation in Excel. 

By the end of this training, attendees will be able to: 

• Review and select chart types, layout, and style 

• Change colors and format options 

• Add titles and labels 

Attendees are not expected to have any prior knowledge of Excel. This is an introductory class for those who need to quickly learn basic Excel chart features, as well as a refresher for those with more experience. Basic familiarity of Excel is helpful, but not required.  

This one-hour online training will provide detailed information on how to create and manipulate pivot tables in Excel.  Pivot table is an interactive way to quickly summarize large amounts of data.   

By the end of this training, attendees will be able to:  

• Change labels and the pivot table layout 
• Update pivot tables  
• Group the pivot table by date  
• Create slicer filters to enable interactive filtering  
• Create pivot charts  

This is an introductory class, but basic familiarity with Excel is needed. This includes knowing basic Excel terminology and features like data entry, creating sheets, and moving within tabs.  

This one-hour and thirty minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.   

By the end of part one of this training series, attendees will be able to:   

• Understand data management best practices   

• Become familiar with data management tools  

• Have a solid knowledge of the resources, enabling data sharing  

During Part 2, attendees will learn about sharing and archiving data. You must register separately for Part 2 of this training. This training is introductory, no prior knowledge required.  

If there is no upcoming session listed below, please view our 3 part On Demand series.

This hour and half online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.  

By the end of part two of this training series, attendees will be able to:   

• Have a solid knowledge of the resources, enabling data sharing  

• Understand how data is archived and preserved  

Part 1 of this training covers understanding research data, how to manage research data, and how to work with data. During Part 2, attendees learn about sharing and archiving data. This training is introductory, no prior knowledge required.  

You must register separately for Part 1 of this training.  

If there is no upcoming session listed below, please view our 3 part On Demand series.

This one-hour online training introduces applying data science and artificial intelligence (AI) techniques to signals and time-series datasets using MATLAB. The training will cover the entire AI pipeline, from signal exploration to deployment. Participants will explore the fundamentals of processing, analyzing, and visualizing signal data, as well as implementing machine learning and AI algorithms tailored for time-series datasets. This training is designed for researchers, engineers, and data scientists who work with signals or temporal data and seek to enhance their analytical capabilities through MATLAB's data science and AI functionalities. 

By the end of this training, attendees will be able to: 

• Understand the unique challenges and opportunities in analyzing signals and time-series data. 

• Import, preprocess, and visualize signal and time-series datasets in MATLAB. 

• Apply machine learning techniques, including supervised and unsupervised algorithms, to create predictive models for time-series data. 

• Explore deep learning approaches, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, for advanced time-series analysis. 

• Deploy trained AI models and automate workflows to integrate insights into research or operational pipelines. 

• Utilize MATLAB’s documentation, online resources, and toolboxes to extend their data science and AI capabilities. 

Attendees are expected to be familiar with the basic functions of the MATLAB to be successful in this training. 

This one-hour training will equip participants with data wrangling techniques using Excel and will tackle the challenges of messy datasets. Participants will learn how to clean, format, transform, standardize, and organize data in Excel. This class is for beginners: no advanced Excel experience is required but basic familiarity with Excel is expected. 

By the end of this training, attendees will be able to: 

• Trim and transform data using UPPER and PROPER functions 

• Format dates 

• Clean data using filters and find/replace functions 

• Apply basic conditional formatting