Complete Course Catalog
This one-hour online session is intended to provide an overview, an in-depth description and comparison of three databases available to NIH researchers: Web of Science, PubMed, and Scopus. The instructors will share how to optimize the results of your search(es) in the three databases and utilize EndNote for final selection and deduplication of the most relevant results. This session pairs well with Introduction to EndNote Desktop, Introduction to PubMed, and Introduction to Web of Science.
By the end of this session, attendees will be able to:
- Understand the content and the coverage of PubMed, Scopus, and Web of Science
- Compare the results from the three databases using EndNote
- Determine the overlap and the unique citations in each database
- Select relevant results
Prior to this training, attendees should have EndNote 21 desktop installed on their computer. For directions on how to download EndNote watch this video. Prior experience with EndNote would be beneficial
This one-hour and a half online training introduces CAS SciFinder, a powerful research tool for exploring chemical and biological information. Attendees will learn how to search for and analyze compounds, investigate reaction pathways, and connect chemical structures to relevant scientific literature and bioactivity data. The session will highlight SciFinder's integration with BioFinder, providing researchers with comprehensive insights to support discovery and development.
By the end of this training, attendees will be able to:
- Explore SciFinder’s core capabilities and interface
- Perform structure-based and reference searches
- Master result navigation and efficient data retrieval
Requirements
Attendees are expected to have a basic understanding of chemical structures. To get the most from the session, participants should be familiar with drawing or interpreting molecular structures. No prior experience with SciFinder is required.
Note on Technology
Registrants will receive an email with instructions on how to access SciFinder before the training. Please ensure you have an active SciFinder account. If you register the day before the training, you may not have time to set up access. If you do not have an account, the session will be a demonstration only.
This one hour and half online training discusses how MATLAB enhances data analysis and visualization for technical professionals who typically use Excel. It highlights MATLAB's advantages, such as access to pre-built mathematical and analysis functions, powerful visualization tools, and the capability to automate analysis workflows, addressing the functional limitations often encountered with Excel.
By the end of this training, attendees will be able to:
- Learn the main steps required for performing data analysis with MATLAB, including data access and pre-processing, modeling, and deployment
- Gain an understanding of how to efficiently apply data analysis techniques using the MATLAB platform
- Share results with others by automatically creating reports
Attendees are not expected to have any prior knowledge of MATLAB and/or Excel. This training is an introductory level; no software installation required.
This one-hour online training, provided by a presenter from SAS, introduces the basics of accessing SAS 9.4 tools and setting up your environment.
By the end of this training, attendees will be able to:
Load data using SAS Studio or Enterprise Guide
Run simple programs using SAS Studio or Enterprise Guide
Generate reports using SAS Studio or Enterprise Guide
Describe technical aspects, such as understanding libraries, managing data sets, and using core SAS procedures for analysis
Attendees are not expected to have any prior knowledge of SAS to be successful in this training.
This one-hour training will cover the methods of conducting a rapid review, using rigorous systematic review methodology but in a shorter time frame. Attendees will learn best practices, guidelines and the required methodological steps on how to complete an effective rapid review within a faster pace than a systematic review.
By the end of this training, attendees will be able to:
Define the key characteristics of rapid reviews
Demonstrate the strengths and weaknesses of rapid reviews
Describe the key steps on how to conduct a rapid review
Attendees are not expected to have prior knowledge of rapid reviews to be successful in this training.
Want to learn more about the types of reviews before the training? Watch our short video series.
This one-hour training will cover the key steps on how to conduct an umbrella review. Umbrella reviews, sometimes referred to as review of reviews, are a review of other published systematic reviews and meta-analyses. Attendees will learn the benefits, best practices, guidelines, and key steps on how to successfully conduct an umbrella review.
By the end of this training, attendees will be able to:
Define the key characteristics of an umbrella review
Use resources for conducting an umbrella review
Describe the main steps on how to conduct an umbrella review
Attendees are not expected to have prior knowledge of umbrella reviews to be successful in this training.
Want to learn more about the types of reviews before the training? Watch our short video series.
This one-hour training will cover the key steps on how to successfully create an ORCiD profile. Attendees will learn the steps on how to easily add and update information to their profile including importing publications, reports, grants, and peer review scholarly work. The session 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.
By the end of this training, attendees will be able to:
Create ORCID iD, a permanent identifier for researchers to promote your scholarly output
Learn best practices to add content and auto import funding and references
Review ORCID iD profile examples and best practices for creating ORCID
Attendees are not expected to have an existing ORCiD profile to be successful in this training.
This one-hour online training is the first session in a two-part series that aims to assist researchers in writing a research paper. This training introduces the preliminary steps in the publishing process including generating an idea, choosing a journal, understanding journal criteria and format requirements, and writing a paper within the research manuscript framework. The training also provides best practices and tips for writing each section of the paper.
By the end of this training, attendees will be able to:
Generate an idea and a research topic for a manuscript
Identify resources for choosing an appropriate journal
Understand the biomedical research paper structure and framework
Describe information to be included in each section of the biomedical research paper framework
Attendees are not expected to have prior knowledge on writing a research paper. The training may be informative for early career researchers or anyone needing a refresher on how to write a research paper. The training is a companion to How to Write a Research Paper: Part 2, and is a logical follow-up for the How to Write an Abstract training.
This one-hour online training is the second session in a two-part series that aims to assist researchers in writing a research paper. This training will cover what happens after a manuscript is submitted to a journal including the peer review process, responding to journal editor and peer reviewer comments, and understanding open access and data sharing requirements. This training also provides best practices and tips to make the manuscript writing and submission process easier.
By the end of this training attendees will be able to:
Describe the peer review process
Formulate well-written and robust responses to journal editors and peer reviewers
Understand appropriate next steps after manuscript acceptance or rejection
Identify resources to help meet NIH public access requirements for manuscripts and data
Attendees are not expected to have prior knowledge on writing a research paper. The training may be informative for early career researchers or anyone needing a refresher on how to write a research paper. The training is a companion to How to Write a Research Paper: Part 1.
This one-hour online training will cover bibliometric theories and concepts, provide an overview of bibliometric indicators, and explore limitations and potential misuses of bibliometrics. Bibliometrics uses quantitative analysis of scientific publications to measure productivity, collaboration, research topics, and impacts of authors, groups, institutions, and grant portfolios.
By the end of this training, attendees will be able to:
Discuss responsible use of bibliometrics methods and data
List corresponding indicators for author, journal, and article-level bibliometrics analyses
Choose resources and training for further learning
This is an introductory training. Attendees are not expected to have any prior knowledge of bibliometrics.
Requirements:
The instructor will email pre-training readings one week prior training. Expected reading time: 15 to 20 minutes.
If no live trainings are available, you can watch a recording by clicking this link.
This one-hour online training, is the first of a two-part series, which introduces participants to cleaning and exploring a patient health dataset using Python and pandas. Attendees will load tabular data, inspect structure and data types, summarize columns, and identify common data quality problems such as missing values, inconsistent formats, and duplicate records. They will then apply practical fixes, including standardizing height and weight units, parsing and normalizing dates of birth, splitting combined fields, and using Boolean masks to flag or correct implausible values.
By the end of this session students will be able to:
- Import CSV data into pandas DataFrames and quickly understand column types, basic statistics, and overall data quality.
- Identify duplicate or repeated patient records and decide whether to keep, correct, or remove them.
- Detect and handle missing or inconsistent values using methods such as isna, fillna, filtering, and conditional replacement.
- Standardize mixed formats (for example, heights with and without units, date strings in different formats, and numeric values embedded in text).
- Create derived columns such as systolic and diastolic blood pressure, and use logical conditions to flag questionable or out-of-range values.
Attendees are expected to have:
- Basic Python coding knowledge
- Familiarity with an IDE and loading script and data files into the IDE. (Colab, Jupyter Notebooks)
Requirements:
- Participants will receive a script file and data files prior to the training. These should be loaded and ready to use before the training session begins.
You can register for Part 2 in this series via the link below:
https://www.nihlibrary.nih.gov/training/introduction-data-wrangling-using-python-part-2-2
This one-hour online training, the second session of the two-part series, focuses on reshaping and enriching the cleaned patient dataset to prepare it for analysis and reporting. Attendees will practice splitting and recombining columns (for example, separating full names into first and last names), converting columns to appropriate data types, and engineering new fields such as outlier indicators and blood pressure status labels. The session also covers merging multiple tables (patient details, contact information, and subsets of records) and filtering or subsetting data to answer specific analytical questions.
By the end of this training, attendees will be able to:
- Reshape and restructure data by splitting and combining columns, changing data types, and reordering or selecting relevant fields.
- Engineer clinically useful features, including z-score–based outlier flags, hypertension indicators, and combined status columns for downstream models or dashboards.
- Merge and join DataFrames using common keys (such as patient ID) to bring together core data with supplemental tables like contact information.
- Filter and subset records based on multiple conditions (for example, patients with diabetes and abnormal blood pressure) to create analysis-ready datasets.
Attendees are expected to have:
- To have attended Intro to Data Wrangling Using Python - Part 1 of the series
- Basic Python coding knowledge
Familiarity with an IDE and loading script and data files into the IDE. (Colab, Jupyter Notebooks)
Requirements:
- Participants will receive a script file and data files prior to the training. These should be loaded and ready to use before the training session begins.
You can register for Part 1 in this series via the link below:
https://www.nihlibrary.nih.gov/training/introduction-data-wrangling-using-python-part-1-2
This one-hour online training will cover importing records into EndNote Desktop, creating, managing, and sharing EndNote libraries, and inserting formatted citations into a Word document using the “Cite While You Write” feature.
By the end of this training, attendees will be able to:- Create an EndNote Desktop Library
- Import records into EndNote
- Use the Cite While you Write feature to format bibliographies
- Sync your EndNote Desktop Library with EndNote online
Attendees are not expected to have any prior knowledge of the product. Prior this introductory training, attendees should have EndNote desktop installed on their computer. To learn how to download EndNote, watch this tutorial from the NIH Library.
This one-hour online introductory training in the NIH Library Evidence Synthesis Review Series provides a high-level overview of the complete process for conducting a Systematic Review. This training will cover each step for conducting a systematic review and introduce resources for conducting and reporting a systematic review.
By the end of this training, attendees will be able to:
Distinguish between a systematic review and other review types
Understand the definition of a systematic review
Identify the recommended resources for conducting and reporting a systematic review
Demonstrate a high-level understanding of the steps involved in a systematic review
Attendees are not expected to have prior knowledge of how to conduct a systematic review. This training does not take the place of more in-depth trainings in the NIH Library Evidence Synthesis Review series. It is recommended that those planning to undertake a systematic review, should register for the Evidence Synthesis series that take a deeper dive into the required methods for each step in a systematic review.
This hour and a half online training covers how to analyze and model data using interactive tools in MATLAB. Through live demonstrations and examples, attendees will learn to solve many steps in a data analysis workflow without writing any code. The interactive tools can generate the MATLAB code needed to reproduce the work programmatically.
By the end of this training, attendees will be able to:
- Use interactive tools for data visualization, cleaning, and modeling
- Automatically generate code to replicate interactive work
- Capture work in easy-to-write scripts and functions
- Share results by automatically creating reports
This training taught by MathWorks. Attendees are not expected to have any prior knowledge of MATLAB, but experienced users will also benefit from new tools, tips, and tricks from the latest releases. This training is an introductory level; no software installation required.
This one-hour online training will provide detailed information and demonstrations on how to manage data in Excel.
By the end of this training, attendees will be able to:
Filter data by text, numbers, and date
Sort data alphabetically and by color
Remove duplicates
Split and combine columns
Create custom lists
This is an introductory training 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. This training will be presented using a PC.
This two-and-a-half hour online training will introduce the fundamentals of conducting a meta-analysis. The focus will be on randomized clinical trials; however, the instructor will also briefly discuss the application of meta-analyses in laboratory and observational (epidemiological) studies. Time will be devoted to questions from attendees and references will be provided for in-depth self-study.
By the end of this training, attendees will be able to:
Describe hypothesis testing and different study designs
Define what is a meta-analysis
List the steps to conduct
Explain where to find additional resources
Attendees 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).
This one-hour online training introduces attendees to modeling and simulation of biological systems using MATLAB’s SimBiology and BioPipeline Designer toolboxes. SimBiology is a versatile toolbox for modeling, simulating, and analyzing dynamic biological systems such as metabolic pathways, signaling cascades, and pharmacokinetics/pharmacodynamics (PK/PD) models. BioPipeline Designer complements this by streamlining workflows for integrating biological data and automating computational analyses.
By the end of this training, attendees will be able to:
Describe the capabilities and applications of SimBiology and BioPipeline Designer for modeling and analyzing biological systems.
Construct and parameterize basic models of biological processes using SimBiology’s graphical and programmatic interfaces.
Simulate dynamic behaviors of biological systems, such as time-course analyses, and interpret simulation results.
Automate and streamline data integration workflows using BioPipeline Designer to enhance reproducibility and efficiency.
Access and utilize resources for further learning, including tutorials, user guides, and MATLAB community forums
Attendees are expected to be familiar with the basic functions of the MATLAB to be successful in this training.
This one-hour online training will provide an overview of NIH Library services and information resources provided for HHS staff via the HHS Virtual Library.
By the end of this training, attendees will be able to:
Demonstrate how to remotely access and login to the HHS Virtual Library online
Identify the NIH Librarian assigned to their agency
Describe information resources and services available for HHS staff
Search for and link to full text journal articles
Create a document delivery account and order documents
Attendees are not expected to have prior knowledge of resources and services provided by the NIH Library for HHS staff. This training is an excellent jumping off point for those interested in learning to use resources introduced in this class, such as an Introduction to EndNote Desktop, an Introduction to PubMed, and an Introduction to Scopus.
This one-hour online training will provide an overview of NIH Library services and information resources provided for HHS staff via the HHS Virtual Library.
By the end of this training, attendees will be able to:
Demonstrate how to remotely access and login to the HHS Virtual Library online
Identify the NIH Librarian assigned to their agency
Describe information resources and services available for HHS staff
Search for and link to full text journal articles
Create a document delivery account and order documents
Attendees are not expected to have prior knowledge of resources and services provided by the NIH Library for HHS staff. This training is an excellent jumping off point for those interested in learning to use resources introduced in this class, such as an Introduction to EndNote Desktop, an Introduction to PubMed, and an Introduction to Scopus.