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 session will provide a demonstration on locating publications in Web of Science, analyzing the citation data in Web of Science and InCites to locate country-specific metrics, first/corresponding authorship data, and creating shareable online reports and dashboards in InCites.

This session will provide a demonstration on locating publications in Web of Science and creating co-authorship and keyword co-occurrence networks in VOSviewer.

Note: VOSviewer (https://www.vosviewer.com/ ) is an open-source software tool, that attendees can request their IT department to install on their computer prior to the class, if they would like to follow along with the demonstration.

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 needed. This includes knowing basic Excel terminology and features like data entry, creating sheets, and moving within tabs.

In this webinar, participants will apply deep learning to brain MRI images. They will explore multiple methods of generating models, as well as interrogate them with explainability techniques, such as applying artificial intelligence (AI) to data, using apps to train AI models for prediction, and sharing results with collaborators.

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

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.

This class provides a basic overview of creating plots using ggplot. ggplot is a part of the Tidyverse, a collection of R packages designed for data science. This class will focus on identifying the appropriate packages for plotting, defining plot aesthetics, and demonstrating how to add layers to ggplot graphs.  You must have taken Introduction to R and RStudio class to be successful in this class. 

By the end of this class, participants should be able to discuss the connection between data, aesthetics, & the grammar of graphics, describe how ggplot works, define geoms, and distinguish between individual geoms and collective geoms.

This class provides an overview of options for customizing a ggplot graph. This class will focus on methods for creating small multiples, options for customizing a graph, and how to apply ggplot themes. You must have taken Data Visualization in R: ggplot class to be successful in this class.

By the end of this class, participants should be able to describe options for time series data, create a line plot in ggplot, learn how to facet a plot, demonstrate options for customizing the title and axis, and apply different ggplot themes.

This class provides an overview of the methods used to visualize the association among two or more quantitative variables. This class will focus on scatterplots, scatterplot matrix, and visualizing paired data. Upon completion of this class, participants should be able to define bivariate data, create a scatterplot using ggplot, define linear regression, and demonstrate how to perform a simple linear regression in R. You must have taken Data Visualization in R: ggplot class to be successful in this class.

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 in-person workshop will focus on data wrangling using tidy data principles. Tidy data describes a standard way of storing data that facilitates analysis and visualization within the tidyverse ecosystem. There will be a discussion of what makes data "tidy," and methods for reshaping your data using dplyr and tidyr functions. Prior to attending this class, you will need to have:

1. Installed R and RStudio
2. Taken the Introduction to R and RStudio class. If not, here are some resources for getting started:
   1. Introduction to R
   2. Introduction to RStudio
   3. Introduction to Scripts in RStudio

By the end of this class, attendees will be able to demonstrate how to describe the purpose of the dplyr and tidyr packages, select certain columns in a data frame, select certain rows in a data frame according to filtering conditions, and add new columns to a data frame that are functions of existing columns.

Note on Technology

The NIH Library has 24 pre-configured Windows laptops that you are welcome to use during this training on a first come, first served basis. You are also welcome to bring your own laptop (PC or Mac). NIH Staff bringing their own NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH-Guest-Network Wi-Fi.

Registrants will receive an email with information and instructions to install and verify access to R and RStudio 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.

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.

During this one-hour session, learn how the Division of Health Care Statistics (DHCS) in the Centers for Disease Control and Prevention’s (CDC) National Center for Health Statistics (NCHS) modernized their approach to disseminating vital information on the nation’s utilization of hospitals, and post-acute and long-term care facilities.  Learn about web dashboards that provide dynamic results on patient care received in emergency departments, drug-use-involved hospital encounters, and characteristics of post-acute and long-term care providers and their service users.

Electronic lab notebooks (ELNs) are digital tools that allow researchers to record, manage, and share their data and results in a secure and efficient way. The virtual roundtable discussion will cover various aspects of using ELNs in biomedical research, such as general best practices and use cases; specific considerations for technology transfer, records management, and pre-clinical studies; and protecting against allegations of scientific misconduct. The participants will learn from the presentations of experts and practitioners with experience and knowledge of different ELN platforms and solutions.

Please Note: This event is for NIH Staff only

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.

Learn about the powerful features and functions in Excel to organize and manipulate data you've captured in BTRIS. Excel gives you the flexibility to sort, search, view and manipulate discrete and text data. Using your own protocol data, attendees will walk through Excel features and functions so you can effectively work with data downloaded from BTRIS.

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 workshop will cover exome sequencing data analysis, followed by tutorials showing the use of exome analysis workflow. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. This workshop will be taught by NCI staff and is open to NIH and HHS staff. 

Participants will have a chance to: independently run basic exome analysis for variant detection, run quality control check on sequencing data, align the sequencing reads to a reference genome, generate alignment statistics and check mapping quality, identify variants, and visualize the exome sequencing data and variants. 

This class is 3 hours and is a mix of lecture and hand-on exercise.