Data Services

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).

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature. 

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. NHANES is a major program of the National Center for Health Statistics (NCHS), part of the Centers for Disease Control and Prevention (CDC). This introductory one-hour webinar by Jane Gwira Baumblatt, MD, MPH,  will provide an overview of the content of NHANES and how to access the data. 

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read and edit, less prone to errors, and allows it to run more efficiently. This 90-minute advanced class will provide an in-depth look at using and writing macros in SAS. Topics covered in this class include macro function, using SQL and Data Step to create macro variables, indirect references to macro variables, defining and calling a macro, macro variable scope, conditional processing, and iterative processing. 

This webinar will include live demonstrations of various Application Programming Interfaces (APIs) and specific use cases.

The live demonstrations of APIs will cover:

• Research assessment
• Policy formation and tracking
• Funding evaluation

A Clarivate instructor will take a deep dive into APIs and share technical examples using APIs, as well as suggest practical methods to apply APIs within your organization.

Large language models (LLMs) are artificial intelligence (AI) algorithms that employ deep learning and extensive data sets to create new content. LLMs offer many possible applications in the biomedical field, such as the development of chatbots for use by clinicians, patients, and researchers. Join this roundtable discussion to learn about current use cases of LLMs at NIH. The program will begin with brief presentations by our panelists, followed by an open discussion:

Alicia Lillich, NIH Library 
Introduction to Large Language Models (LLMs)

This first of five webinars will introduce NIH’s All of Us Research Program, including the program’s mission and core values. Learn about the current size and diversity of the participant cohort and the data types and tools available to researchers. Attendees will also see examples of recent research using the All of Us dataset. 

This session will outline the All of Us Researcher Workbench registration process for NIH researchers. Access to the Researcher Workbench is free, and all registered researchers are provided $300 initial computational credits. Some analyses in the cloud may incur additional costs beyond these credits. Attendees will also learn how to create a Google billing account in case they use up their initial credits. Finally, attendees will hear about funding opportunities that can support using the All of Us dataset. 

In this webinar, attendees will learn the basics of using the All of Us Researcher Workbench’s point-and-click research tools, including how to create a workspace, how to build a cohort of All of Us participants using the Cohort Builder, and more. 

Webinar attendees will hear tips and tricks to code efficiently in the Researcher Workbench using R and RStudio. Although Python and SAS are alternative programming languages available on the Researcher Workbench, this session will only focus on using R and RStudio. Participants should already have a general understanding of how to code in R prior to attending the session. This session will not cover the basics of coding in R.

Webinar attendees will learn how to use and navigate the All of Us Researcher Workbench’s User Support Hub, which provides video tutorials, help articles, and more. Attendees will also learn about opportunities to get support from the Researcher Workbench help desk and how to stay involved with the All of Us Research Program through the program’s network of partners. 

Presenters:

This training on Application Programming Interfaces (APIs) from Clarivate will focus on the metrics and analytics APIs from InCites Benchmarking & Analytics and Journal Citation Reports. This session will include demonstrations on harvesting data from these products with various toolkits.

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.

Macros are ways to use code to substitute in a value, and 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. This intermediate class will provide an overview of what is a macro and how macros work in SAS, including the macro facility. Examples of macro code will be made available to class participants for modification and later use.

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.  

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.

This course 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 this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This course 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 this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

During this 90-minute training session, attendees will be introduced to the interface of MATLAB, develop a solid understanding of Artificial Intelligence (AI) fundamentals, and discover additional resources and support tailored for beginners.

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

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. 

In this webinar, the participants will learn about application of machine learning methods, specifically geared towards predicting the toxicity of target molecules. They will gain insights into various machine learning techniques, fostering a comprehensive understanding of their application in this critical domain. Furthermore, attendees will acquire the skills to apply machine learning to their data, utilize applications to train artificial intelligence (AI) models for toxicity prediction, and effortlessly share the results with collaborators.

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.

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.

Generalist repositories offer NIH researchers a flexible, trusted resource to share data for which there is no appropriate discipline specific repository as well as to share many other research outputs valuable for reproducibility and open science. This webinar, presented by participants of the NIH Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo) will share generalist repository use cases and best practices for sharing and finding data in generalist repositories.