Principle:Testtimescaling Testtimescaling github io API Data Fetching

Metadata
Page Type	Principle
Concept Type	External Tool
Domain	Data_Retrieval, API_Integration
Namespace	Testtimescaling_Testtimescaling_github_io
Workflow	Automated_Citation_Tracking
Date Created	2026-02-14
Implementation	Implementation:Testtimescaling_Testtimescaling_github_io_Fetch_Arxiv_Citation_Count
Source	Semantic Scholar API

Overview

HTTP-based data retrieval from external REST APIs with structured error handling enables programmatic access to third-party data sources such as academic paper metadata.

Description

API data fetching is the pattern of making HTTP requests to external web services, parsing the structured responses (typically JSON), and extracting the relevant fields for downstream processing. This pattern is fundamental to any system that integrates with third-party data providers.

In the context of automated citation tracking, the target API is the Semantic Scholar Graph API, which provides academic paper metadata including citation counts. The API is queried using arXiv paper identifiers, and the response contains a JSON object with the requested fields.

Robust API data fetching must account for several failure modes: network timeouts, HTTP error status codes (4xx, 5xx), malformed JSON responses, missing fields in the response, and API rate limiting. A well-designed fetching function handles all these cases gracefully, returning a sensible default value (e.g., 0) rather than crashing the pipeline.

Usage

Use this pattern when you need to:

• Retrieve structured data from a web API -- particularly REST APIs that return JSON responses.
• Handle unreliable external dependencies -- wrap API calls in error handling to prevent pipeline failures.
• Fetch academic paper metadata -- citation counts, author lists, abstracts, or other bibliometric data from services like Semantic Scholar, CrossRef, or OpenAlex.
• Automate periodic data collection -- combine with scheduled triggers to keep local data in sync with external sources.

Theoretical Basis

The REST API consumption pattern follows a well-defined sequence:

1. URL Construction: Build the request URL by combining the API base URL with path parameters and query parameters. For the Semantic Scholar Graph API, the URL template is:

https://api.semanticscholar.org/graph/v1/paper/ARXIV:{arxiv_id}?fields=citationCount

The ARXIV: prefix tells the API to resolve the paper by its arXiv identifier rather than the Semantic Scholar internal ID. The fields=citationCount query parameter limits the response to only the citation count field, reducing response size and processing time.

2. HTTP GET Request: Send a GET request to the constructed URL. Key considerations:

• Timeout: Always set a timeout to prevent indefinite blocking. A timeout of 10 seconds is typical for external API calls in CI/CD contexts.
• Headers: Some APIs require authentication headers (API keys, bearer tokens). The Semantic Scholar public API does not require authentication for basic queries but may impose rate limits.

3. Response Validation: Check the HTTP status code using raise_for_status() or equivalent. Common status codes:

Status Code	Meaning	Action
200	Success	Parse the JSON body
404	Paper not found	Return default value (0)
429	Rate limited	Retry after delay or return default
500+	Server error	Log and return default

4. JSON Parsing and Field Extraction: Parse the response body as JSON and extract the target field. Using .get("citationCount", 0) (with a default) is safer than direct key access because it handles the case where the field is missing from the response.

5. Error Handling: Wrap the entire sequence in a try/except block to catch any unexpected exceptions (network errors, JSON decode errors, etc.). Log a warning message and return a default value. This ensures that a single failed API call does not crash the entire pipeline.

Design Principles:

• Fail gracefully: Return a default value instead of raising an exception.
• Log warnings: Record which calls failed so that issues can be diagnosed later.
• Set timeouts: Never make unbounded network calls in automated pipelines.
• Minimize response size: Request only the fields you need via query parameters.

Related Pages

• Implementation:Testtimescaling_Testtimescaling_github_io_Fetch_Arxiv_Citation_Count
• Principle:Testtimescaling_Testtimescaling_github_io_Badge_Data_Generation -- Consumes the citation count data to generate badge JSON
• Principle:Testtimescaling_Testtimescaling_github_io_Repository_Checkout -- Must run before the fetch script can execute