Class Type: Data Science

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Navigating the CDC to Locate Vital Statistics, Infectious and Chronic Diseases Data, Behavioral Data, and More

The US Centers for Disease Control and Prevention is the leader of public health in the US. It administers multiple surveys, gathers vital statistics, and tracks infectious diseases, and much more, all the while making this data readily available to the public. Like all Federal agencies, though, it is a complex organization which is reflected in their website.

During this class, we will explore key data initiatives of the CDC, focusing on publicly available data sites that allow you to find data through their interfaces. We will also review some of the CDC datasets that can be downloaded for free.

Note: Due to time constraints, we will not be able to download any of the data sites to run with statistical software.

All of Us Genomics Workshop: Investigating the Potential Impact of PCSK9 Variant on LDL Levels

Join us for our first-ever All of Us genomics workshop. In this workshop, we will focus on the PCSK9 gene and its connection to LDL levels for participants in the All of Us database. 

Using Electronic Health Record (EHR) data and provided genomic information, we will showcase a comprehensive approach that encompasses data cleaning and filtering to isolate specific genomic regions of interest. In addition to these preparatory steps, we will conduct a logistic regression analysis, utilizing disease status as the outcome variable and a single genetic variant as the predictor. These analytical skills serve as a solid foundation for future genome-wide association studies (GWAS) and provide a crucial resource for researchers. 

Target Audience: Researchers interested in the application of statistical methods to biological, genetic, and health-related data. 

Research Data Sharing

Sharing your research data allows your peers to validate your findings and build on your results, which is why it's increasingly required by funders and publishers. But many researchers are concerned about the work involved in preparing data for sharing, or worry that they will be scooped by others using their data. Come to this class to learn about best practices for sharing research data that will help both you and the research ecosystem make the best use of your data. We'll talk about data management and sharing policies, preparing your data for sharing, places to share data, and why "all data are available in the paper" isn't sharing data at all.

This class is 90 minutes to allow participants enough time to complete hands-on exercises and ask questions.

Planning for Data Management

Planning is a core component of managing your research data. The planning stage is when you decide how you will name and organize your files, identify who will need access to your data, and ensure that you're accounting for all of the data products your research will produce. In this class, we will work through hands-on planning exercises that you can apply in your own data management practice and adapt into a Data Management Plan (DMP) like those expected by funders or the University's new Research Data Management Interim Policy.

If you are currently conducting or planning to conduct research, please bring a description of your research project. We will also have some general scenarios available to work with. This is a hands-on class, so come prepared to talk with your colleagues about the kind of data you work with.

NIH Data Management and Sharing Plan Drop-In Office Hours

Are you applying for a NIH R01 grant with an upcoming deadline? Do you need help finalizing your Data Management and Sharing Plan (DMSP)? 

Physically or virtually drop-into the library with a draft of your DMSP to receive feedback from a HSLS librarian. Can’t make this scheduled time? No problem; contact Data Services or request feedback through the DMPTool as described in this newsletter article.

Registration not required, but if you register, you will receive event notifications.

Protecting Your Privacy Online

Have you ever clicked “I agree” without reading an app’s terms and conditions? Given a browser extension permission to read and change data on all sites you visit?

The aggregation of data collected by apps and websites can be used by advertisers, law enforcement, and propagators of misinformation to predict and shape human behavior.

In this 90-minute class, you will learn who is collecting your data, the types of data they’re collecting, and what they may be doing with it. Additionally, you will learn about options to increase your privacy and control over your own data.

Your data may be shared with third party vendors for purposes of advertisement, research, and product development.*

*Not really, but we are glad you read the fine print!

Writing a Data Management and Sharing Plan with DMPTool

Data management and sharing plans (DMSPs) are short documents that outline how a research team will organize, store, preserve, and share their research data during and after data collection. Attend this workshop to learn how DMPTool, a free online resource, can help you write your DMSP, through interactive templates and customized guidance, for grant or institutional requirements like the NIH Policy for Data  Management and Sharing or the University of Pittsburgh’s Research Data Management Interim Policy

RCR Session: NIH Data Management & Sharing Plans: Documentation, Metadata, and the "How" of Deposit

This is a presentation on NIH's new Policy for Data Management and Sharing (DMS Policy) that went into effect January 25, 2023. This Policy requires NIH-funded researchers to prospectively submit a plan outlining how scientific data from their research will be managed and shared. The Policy includes an expectation that researchers will maximize their data sharing within ethical, legal, or technical constraints, and explicitly encourages researchers to incorporate data sharing via deposit into a public repository into their standard research process. This session will focus on the pieces of information that will help other researchers find and reuse your data: metadata and documentation like READMEs, data dictionaries, and user manuals.