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Data Science

5 Fast Visualizations for Analyzing Publications in biblioshiny

This one hour online training will cover how to import publication data from Web of Science into biblioshiny to quickly create dynamic visualizations related to author, country, and topic/keyword trends for a collection of publications.  This training will introduce biblioshiny, a web interface for the R programming language package bibliometrix that can be used by non-coders to create analytics and plots for a collection of publications.  

Advanced Demonstration of Web of Science APIs

This one-hour online training will include live demonstrations of various Application Programming Interfaces (APIs) available for the Web of Science (WoS) platform and specific use cases, including research assessment, policy formation and tracking, and funding evaluation. Technical demonstrations will be performed using Python clients and a WoS API data parser/converter toolkit.

By the end of this training, attendees will be able to:  

Applying Findable, Accessible, Interoperable, Reproducible (FAIR) Principles for Reproducible Research

This one-hour online training covers various aspects of sharing code using MATLAB community tools like File Exchange and GitHub. Well-documented methods and workflows enable reproducible research by helping scientists follow each other’s experimental logic and interpret results.

By the end of this training, attendees will be able to: 

Bibliometric Services

As part of our Research Analytics services, the NIH Library’s Bibliometric Service provides publication analysis to NIH staff. The service uses analytics to understand and evaluate the publications produced by intramural NIH scientists and institutions at NIH.

Coding Macros in SAS

This one-hour online training, provided by a presenter from SAS, will demonstrate how 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. Macros are ways to use code to substitute in a value. Examples of macro code will be made available to attendees for modification and later use. 

By the end of this training, attendees will be able to:   

Cytoscape (Open Source, Freely Available)

Cytoscape is a free, open source visualization tool for modeling complex networks and integrating network points with attribute data.  Cytoscape can create visualizations of a variety of different types of networks, including molecular and genetic, social networks, and semantic Web.  Adding free plugins to Cytoscape also provides functionality including literature mining, network inference, topological clustering, and network comparison. 

Data Management and Sharing: Part 1 of 2

This one-hour and thirty minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series 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.   

Data Management and Sharing: Part 2 of 2

This hour and half online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series 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.  

Data Sharing and Discovery in Generalist Repositories: Resources and Real-World Examples

This one-hour online training offers an overview of the NIH-sponsored Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo), and the role of participating in these repositories in the NIH data repository landscape for intramural researchers. The session will highlight how these repositories support compliance with the NIH Data Management and Sharing Policy.

By the end of this training, attendees will be able to:  

Data Sources and Tools

Grant and funding information sources

NIH RePORTER: (Freely available) Grant information, associated publications, and associated patents (if available) for grants funded by NIH.

 

Publication data sources

PubMed: (Freely available) Extensive database of publications about or related to biomedicine. Especially useful for biomedical topic searching and for identifying publications funded by NIH Institutes and Centers.

Data Wrangling in Excel

This one-hour training will equip participants with data wrangling techniques using Excel and will tackle the challenges of messy datasets. Participants will learn how to clean, format, transform, standardize, and organize data in Excel. This class is for beginners: no advanced Excel experience is required but basic familiarity with Excel is expected. 

By the end of this training, attendees will be able to: 

From Excel to MATLAB: Boost Your Data Analysis

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: 

Introduction to Data Wrangling Using Python: Part 1 of 2

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.​

Introduction to Data Wrangling Using Python: Part 2 of 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.

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