metadata

dataset_info:
  features:
    - name: page_content
      dtype: string
    - name: metadata_json
      dtype: string
  splits:
    - name: train
      num_bytes: 930431
      num_examples: 269
  download_size: 381374
  dataset_size: 930431
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
language:
  - en
license: mit
task_categories:
  - text-retrieval
  - question-answering
  - text-classification
tags:
  - document-processing
  - financial-aid
  - education
  - government-documents
  - langchain
  - prefect
  - rag
  - educational
pretty_name: Federal Student Aid Document Loader Dataset

Federal Student Aid Document Loader Dataset

This dataset demonstrates document loading and processing techniques using LangChain's document loaders in an educational pipeline. It contains processed pages from official Federal Student Aid handbooks for the 2025-26 processing year.

Dataset Details

Dataset Description

This dataset contains 269 processed document pages extracted from four Federal Student Aid handbook volumes using LangChain's PyPDFDirectoryLoader. Each record includes the extracted text content and comprehensive metadata about the source document and processing pipeline.

The dataset serves as an educational example of:

Document loading with LangChain
Workflow orchestration with Prefect
Metadata standardization across document sources
Preparation for RAG (Retrieval Augmented Generation) systems
Curated by: Educational demonstration project (dwb2023)
Language(s): English
License: MIT
Source Documents: Federal Student Aid Handbooks 2025-26

Dataset Sources

Repository: langchain-dataset-loaders
Original Documents: Federal Student Aid Partner Connect Knowledge Center
Processing Pipeline: LangChain + Prefect orchestration example
Related Tutorial: Document Loader Pipeline Guide

Uses

Direct Use

This dataset is designed for educational purposes to demonstrate:

Document Processing Pipelines
- Learning LangChain document loader patterns
- Understanding metadata enrichment
- Practicing document chunking and retrieval
RAG System Development
- Building question-answering systems over financial aid documentation
- Creating semantic search applications
- Developing document intelligence tools
Educational Research
- Studying federal student aid policies and procedures
- Analyzing government document structure
- Understanding financial aid eligibility requirements

Out-of-Scope Use

Not for production financial aid decisions - Use official FSA sources for actual student aid determinations
Not for legal advice - Consult official regulations and qualified professionals
Not for current academic year processing - This dataset reflects 2025-26 guidelines which may be outdated

Dataset Structure

Each record contains two main fields:

page_content (string): Extracted text content from PDF pages (277-5,300 characters)
metadata_json (string): JSON-formatted metadata including:
- Source file information (source, total_pages, page, page_label)
- Processing details (loader_type, load_timestamp)
- Document creation metadata (producer, creator, creationdate, moddate)

Example Record

{
  "page_content": "Volume 7 The Federal Pell Grant Program Introduction This volume of the Federal Student Aid Handbook provides information to assist schools in determining student eligibility...",
  "metadata_json": "{\"producer\": \"GPL Ghostscript 10.00.0\", \"creator\": \"wkhtmltopdf 0.12.6\", \"source\": \"data/raw/The_Federal_Pell_Grant_Program.pdf\", \"total_pages\": 65, \"page\": 0, \"loader_type\": \"pdf\", \"load_timestamp\": \"2025-07-03T15:05:37.091391\"}"
}

Dataset Creation

Curation Rationale

This dataset was created to provide a realistic, domain-specific example for learning document processing techniques. Federal Student Aid handbooks were chosen because they:

Represent real-world government documents with complex formatting
Contain structured information suitable for Q&A applications
Provide educational value about financial aid processes
Demonstrate handling of large, multi-volume document sets

Source Data

Data Collection and Processing

The dataset was created using a LangChain + Prefect pipeline with the following steps:

Source Documents: Four PDF volumes from the Federal Student Aid Handbook 2025-26:
- Volume 3: Applications and Verification Guide
- Volume 6: Academic Calendars, Cost of Attendance and Packaging
- Volume 7: The Federal Pell Grant Program
- Volume 8: The Direct Loan Program
Processing Pipeline:
- PyPDFDirectoryLoader for PDF text extraction
- Metadata enrichment with source tracking
- Timestamp recording for provenance
- JSON serialization for storage
Quality Assurance:
- Validation of extracted content length
- Metadata consistency checks
- Source file verification

Who are the source data producers?

Original Authors: U.S. Department of Education, Federal Student Aid
Document Type: Official federal government handbooks and guidance
Publication Year: 2025-26 processing year
Processing: Educational demonstration project

Personal and Sensitive Information

The source documents are public federal guidance materials that do not contain personal information. The dataset includes:

Public Policy Information: Federal student aid regulations and procedures
No Personal Data: No student records, SSNs, or individual information
No Sensitive Content: Standard government procedural documentation

Bias, Risks, and Limitations

Technical Limitations

Processing Year: Reflects 2025-26 guidelines which may become outdated
Extraction Accuracy: PDF text extraction may contain formatting artifacts
Scope: Limited to four handbook volumes, not comprehensive FSA guidance
Language: English-only content

Content Considerations

Policy Changes: Federal student aid policies evolve; this represents a snapshot
Interpretation: Documents reflect official policy but may require expert interpretation
Completeness: Does not include all FSA guidance documents or updates

Recommendations

Users should:

Verify current regulations when using for research
Understand this is educational data, not authoritative guidance
Consider potential extraction errors when analyzing content
Use official FSA sources for actual student aid decisions

Educational Value

This dataset demonstrates:

Document Processing Concepts

LangChain Integration: Practical use of document loaders
Metadata Management: Systematic tracking of document provenance
Workflow Orchestration: Using Prefect for pipeline management

Real-World Applications

Government Document Processing: Handling official publications
RAG System Development: Preparing documents for retrieval systems
Text Analytics: Analyzing structured government content

Citation

APA: dwb2023. (2025). Federal Student Aid Document Loader Dataset [Data set]. Hugging Face. https://huggingface.co/datasets/dwb2023/document-loader-jul2025

BibTeX:

@dataset{dwb2023_federal_student_aid_2025,
  title={Federal Student Aid Document Loader Dataset},
  author={dwb2023},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/dwb2023/document-loader-jul2025}
}

More Information

Related Resources

Learning Path

Explore the source repository
Run the document loading pipeline
Experiment with RAG applications using this dataset
Extend to other document types and sources

Dataset Card Authors

Primary: Educational demonstration project (dwb2023) Contributors: LangChain + Prefect learning community

Dataset Card Contact

For questions about this educational dataset or the document loading pipeline, please refer to the source repository issues.