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API Portal: Open & Licensed Data Resources

What is an API?

APIs (Application Programming Interfaces) allow different software systems to communicate and exchange data. In research and data-driven environments, APIs are powerful tools for retrieving structured information from a wide range of sources, including government agencies, financial institutions, academic databases, and commercial platforms.

An API acts as a structured set of instructions that defines how users can request specific data or functionality from a provider’s system. It allows users to access information without needing to understand the internal structure or operations of the underlying database. APIs enable researchers, analysts, and developers to programmatically retrieve and integrate data into applications, reports, or analytical workflows in a consistent and efficient manner.

LinkedIn Learning Video Courses (USF NetID Required)

Introduction to Web APIs
Find out what an API is, what JSON is, and how you can use APIs and JSON to work with objects and parse data. Learn how to connect to an API, navigate API documentation, make a request, use parameters to filter the response, and more. 

Learning REST APIs
Discover what REST APIs are, why they matter, and how putting them to use can help you build faster, more efficient applications. Review how HTTP and REST APIs relate, explore the six constraints of REST, and learn about HTTP status messages.

Note: Access requires signing in to LinkedIn Learning using your USF email address and NetID credentials. 

Common Uses for APIs

  • Data Acquisition: Retrieve large volumes of structured data from trusted sources (e.g., government databases, financial market feeds, academic indexes) for quantitative or qualitative research.

  • Workflow Automation: Automate repetitive tasks such as data collection, cleaning, or reporting to enhance research efficiency and reproducibility.

  • Data Visualization and Reporting: Feed live or historical data into platforms such as Power BI or Tableau to create interactive dashboards and visual reports.

  • Computational Research: Support advanced applications such as machine learning, natural language processing, or large-scale text and data mining by integrating rich datasets from external APIs.

  • Academic Inquiry and Reproducibility: Improve transparency and ensure replicability in scholarly research by using standardized, well-documented data obtained directly from the source via APIs.

Example Software for API Access & Integration

  • Python: Widely used in data science and research. Libraries such as request, pandas, and json allow users to access and process API data efficiently.

  • R: A statistical programming language with packages like httr, jsonlite, and tidyverse commonly used for making API calls and conducting sophisticated data analysis.

  • Power BI: Microsoft’s business intelligence platform that supports API integration through Power Query and custom connectors for live data visualization.

  • Microsoft Excel (Power Query): A widely accessible spreadsheet tool with Power Query functionality, enabling users to connect to APIs and perform basic transformations—ideal for non-programmers.

  • Jupyter Notebooks: An open-source web application that combines live code, data, and narrative text—frequently used for documenting and sharing API-based research workflows in data science and computational fields.

Best Practices for Academic Use

When using APIs in research or academic projects, it is essential to review and follow each provider’s terms of service. Some APIs impose rate limits or restrict how the data can be stored, shared, or published. For large-scale text or data mining, ensure compliance with applicable copyright, data privacy, and fair use policies. Proper attribution and documentation of data sources also support academic integrity and reproducibility.