Farm-to-Table Data Pipeline

• Farm-to-Table Data Analysis Pipeline is a modular workflow that automates end-to-end processing of public datasets using scalable queries and identifier-based integration.
• It sequentially processes data through harvesting, reprocessing, visualization, and optional social media export to support reproducible Big Data analysis.
• The pipeline integrates arXiv and inSpireHEP records, demonstrating practical applications in both educational settings and real-time research analytics.

A farm-to-table data analysis pipeline in the context of contemporary scientific computing refers to a modular workflow capable of automating the collection, processing, analysis, and visualization of large-scale datasets sourced directly from public repositories, with the intent to serve quantitative insights rapidly and reproducibly to researchers, analysts, and sometimes broader audiences. As outlined in (Delorme et al., 11 Sep 2025), such a pipeline is both a teaching tool—for introducing modern Big Data practices in undergraduate courses—and a practical engine for real-time or batch data-driven decision making.

1. Modular Pipeline Structure and Workflow

The pipeline described is architected in discrete, sequential phases to move data from raw public sources ("farming") through staged processing to visualization and dissemination ("table"), as follows:

• Phase 0: Initialization Establishes pipeline parameters (e.g., date ranges, file paths, subject filters) in a dedicated state object (APIQueryInputs) for downstream reference.
• Phase 1: Data Harvesting from arXiv Utilizes the arXiv API (http://export.arxiv.org/api) to submit partitioned, micro-queries (typ. three-month slices) and efficiently manage large-scale data pulls (up to 800,000 entries). Results, returned in Atom XML, are parsed into tab-separated text files containing manuscript metadata.
• Phase 2: Data Harvesting from inSpireHEP For each manuscript (as identified by arXiv ID), constructs targeted queries for the inSpireHEP API (https://inspirehep.net/api) to collect bibliometric records—citation stats, authorship, and related metadata—typically in JSON format. Integration links records using the manuscript identifier as a key.
• Phase 3: Data Reprocessing Reloads raw outputs, uses pandas to filter relevant attributes, addresses missing fields, computes derived metrics (ratios, averages).
• Phase 4: Plotting and Visualization Employs matplotlib to create visual summaries (histograms, line plots), quantifies composite measures (e.g., mean citations per weekday, category breakdowns), and saves graphical outputs.
• Phase 5: Export to Social Media (Advanced) Optionally, results (e.g., images) are transmitted to public platforms (Twitter/X, Bluesky) using client libraries (tweepy, atproto) and secured via token-based authentication.

This clear separation fosters both pedagogical use (isolated paper of each function) and professional maintainability.

2. Data Sources and Integration Strategy

The pipeline interlinks two primary open repositories:

Repository	Data Type	Retrieval API
arXiv	Manuscript metadata (XML/Atom)	http://export.arxiv.org/api
inSpireHEP	Bibliometric/citation records (JSON)	https://inspirehep.net/api

Records from arXiv become keys for bibliometric augmentation from inSpireHEP. Integration at the manuscript ID level ensures join consistency and robust data enrichment. The strategy exemplifies best practices in Big Data integration—partitioned queries, format-aware parsing, and identifier-based linking.

3. Technologies and Open-Source Tools

Key computational infrastructure comprises widely-used open-source Python libraries:

• urllib, feedparser for HTTP/XML communications.
• pandas for tabular manipulation and composite metric calculations.
• matplotlib for plotting.
• numpy for vectorized numerical routines.
• tweepy, atproto for social media API calls.

Code is modular, with phases encapsulated as functions and state maintained in objects for reproducibility and parameterization.

4. Implementation Details and Analytical Methods

Scripted in files such as bibAPI.py, the pipeline leverages object-oriented and functional paradigms to structure execution. For example:

apiInputs = APIQueryInputs() doPhase1(apiInputs) doPhase2(apiInputs) ...

Query construction respects API rate limits and output sizes via temporal micro-partitioning. The pipeline adapts to missing data (assigning "Not-Given") and performs aggregation operations (e.g., mean citations per weekday). Final visualizations are dynamically labeled and file-path directed by initial parameters.

5. Educational and Practical Contexts

The pipeline serves dual roles:

• Pedagogical Designed for undergraduate scientific computing courses, it demystifies Big Data practices via hands-on experience with real, research-scale datasets. Its modular architecture and open-source base facilitate incremental mastery.
• Practical Utility Adaptable for online data acquisition (continuous DAQ monitoring analogous to CERN workflows), it can feed real-time dashboards, connect with external communication platforms, and serve as a launchpad for more advanced statistical and machine learning analyses via tools like scipy and sklearn.

Challenges noted include handling large file sizes, mastering API protocols, and integrating theory with real data—all haLLMarks of scalable computational science.

6. Example Implementation and Outcomes

The proof-of-concept implementation—developed by undergraduate students in a four-week research program—demonstrates pragmatic feasibility. Processing hundreds of thousands of records, it enabled statistically robust exploration of, for example, weekday submission effects on citations. Code volume (100–250 lines per phase) and laptop-scale resource requirements confirm accessibility.

Significant outcomes included:

• Refutation or affirmation of scientific myths (e.g., citation-day correlation).
• Templates for expansion to analogous datasets (e.g., socioeconomic statistics).
• Bridging spreadsheet familiarity to Big Data proficiency.

7. Conclusion and Implications

The farm-to-table data analysis pipeline, as implemented and described in (Delorme et al., 11 Sep 2025), is a rigorously modular, open-source framework for integrating, processing, and visualizing Big Data from public repositories. Its design is tractable for undergraduate pedagogical use and scalable for research needs, illustrating how computational workflows for data analysis can be effectively democratized. Adaptability to diverse contexts—classroom, online monitoring, scientific communication—suggests broad relevance for future Big Data pipelines in quantitative disciplines.

A plausible implication is that such modular, public-data pipelines could be generalized across domains where rapid, reproducible ingestion and analysis of large, heterogeneous datasets are required.