Selecting and Using Data Extraction Tools for Systematic Reviews (Recording)

Gain powerful knowledge and skills in finding, assessing, using, and advising on data extraction tools that will significantly increase your value on review teams.

Sharing it Ethically: Research Lifecycle Support for Sharing Human Participants Data (Recording)

Health sciences librarians: Learn how to take advantage of your unique position to guide researchers through the complicated process of ethically sharing human subjects data.

Standardize Your Research Data with the NIH Common Data Elements Repository - On Demand

This interactive, online course is intended to introduce researchers and librarians to common data elements (CDEs) and how to use the NIH Common Data Elements Repository.

The Basics of Scholarly Communication

Learn the basics of scholarly communications!

Timelines, Teams, and Tools: Helping Review Teams Organize and Coordinate Their Systematic Review Projects (Recording)

Do you work on systematic reviews and want to increase your publication numbers and status? Two highly experienced librarians on review teams will show how you can accomplish this by adding review team selection, organization, and project management to your skillset.

Uninformed Participants and Informed Choices: Social Media Data in Health Sciences Research (Recording)

If you work with data or support researchers who do, this webinar will prepare you to address legal and ethical issues in the use of social media data in health sciences research.

Working with Research Partners to Coordinate Domain-Specific Data Services (Recording)

Explore service examples, identify scientific research data challenges, and leave with checklists you can tailor to your community.

Citizen Science & Libraries: Advance Alzheimer's Research Online Presentation and Q&A*

Register for this event for an introduction to citizen science, to learn more about libraries as hubs for citizen science, and to learn how to participate in the Stall Catchers citizen science project through an engaging presentation and online Q&A.

Clean & Tidy Data: Getting Started with Spreadsheet Data*

In this webinar, you will learn the best data principles while working with spreadsheets, identify how to curate and identify common issues with spreadsheets, and set the foundations for getting data more interoperable with downstream processes.

Common Data Elements: Standardizing Data Collection*

This online, self-paced course describes how librarians and other information professionals can facilitate data standardization and research data sharing using common data elements (CDEs)

Community-Based Health Information*

In this online course, learn about consumer and public health information services and programs that can impact the health of the communities you serve.

Consumer Health Collection Management - On demand*

This 4 credit-hour, on-demand class reviews collection management principles and resources for health-related collections with a focus on collections in public libraries and consumer health.

Creating Data Management Plans with DMPTool*

This webinar focuses on the NIH Data Management and Sharing Policy. The class will provide training on the DMPTool to librarians who help researchers with data management and policy adherence.

Creating NIH Data Management and Sharing Plans with DMPTool*

This webinar will also cover the basics of data management planning, NIH and NLM data management resources, and practice building a plan with DMPTool online.

Disaster/Emergency Management for Information Professionals*

Want to learn how you can play a role in all the stages of disaster management as an information profession? If so, this is the online course for you.

Drug Terminologies and RxNorm*

This course will introduce you to the basic concepts of medical terminologies (including what they are and why they are important), followed by a deep dive into RxNorm, an NLM-authored medical terminology specializing in drug information.

Fundamentals of Health Data Science*

This online class covers the basics of programming in Python for data science projects, with a general look at data science and algorithmic concepts. It also looks at coding topics such as the understanding and tools needed to clean data, create data visualizations, and share reproducible results.