Teaching Reproducible Research with Real-World News: A Semester Plan Using These Ten Articles

Teaching Reproducible Research with Real-World News: A Semester Plan Using These Ten Articles

Hook: If you teach research methods or supervise student projects, you’ve likely seen the same three problems: students struggle to reproduce analyses, literature proliferates across paywalled and ephemeral sources, and workflows are not documented for future reuse. This semester syllabus turns ten contemporary news articles (tech, macroeconomics, automotive, policy, sports) into a scaffolded course that teaches reproducible workflows, critical appraisal, and open science practices using real-world data and tools.

Why news-based pedagogy for reproducibility matters in 2026

The last two years (late 2024–2026) magnified the need for reproducible research: AI-accelerated analyses, funder mandates for open data, and greater public scrutiny of policy and corporate claims have made traceable, reproducible workflows essential. In 2025 a wave of funder and institutional policies strengthened open-data and preprint expectations; classrooms are now ideal laboratories to teach these skills.

Use news articles as bounded, topical case studies: they are short, cross-disciplinary, and motivate methods with immediate real-world stakes.

Course overview and learning outcomes

• Course type: 14-week undergraduate/graduate seminar with lab sessions
• Credit hours: 3 (or modular short course)
• Prerequisites: Introductory statistics, basic programming (Python or R), or instructor-led bootcamp
• Learning outcomes:
  • Design and document a fully reproducible research pipeline (data → analysis → report)
  • Critically appraise news-based empirical claims using open-science standards
  • Produce a replication or partial replication of a news claim using public tools and FAIR data principles
  • Use citation managers, version control, containers, continuous integration, and data repositories in day-to-day research

Ten articles as the course backbone

We use the provided set of ten contemporary pieces spanning technology, macroeconomics, automotive forecasting, policy, and sports analytics. Each module pairs one (or more) article with a reproducibility task.

Selected articles and module pairings

1. SK Hynix’s PLC flash-memory innovation (tech) — replicate an engineering claim using vendor specs and benchmark data.
2. “The economy is shockingly strong” (macro) — reproduce headline macro indicators and critique data sources.
3. “Inflation could unexpectedly climb” (macro risk analysis) — reproduce a scenario analysis and sensitivity checks.
4. Toyota profile and production forecast to 2030 (automotive) — reproduce parts of the forecast using the downloadable Excel data as a practice dataset.
5. ABLE accounts expansion (policy) — reproduce eligibility population estimates from public census data.
6. College basketball surprise teams (sports) — reproduce basic win-share or performance metrics using public sport stats APIs.
7. SportsLine’s NFL divisional round model (sports) — rebuild a simplified simulation model and test robustness to seedings.
8. NBA picks from a 10,000-simulation model (sports) — reproduce Monte Carlo simulations, then containerize the analysis.
9. Bills vs. Broncos match prediction (sports) — build a data pipeline to fetch team-level stats and reproduce a model forecast.
10. Cavaliers vs. 76ers NBA pick (sports analytics) — compare two modeling approaches and document differences.

Semester modules: week-by-week scaffolded plan

Weeks 1–2: Foundations — open science, metadata & citation management

Start with transparency: what is reproducibility vs. replicability? Introduce FAIR principles and persistent identifiers (DOI, ORCID, DataCite). Practical labs:

• Set up a citation manager (Zotero recommended). Lab: create a shared Zotero group library with the ten articles and add metadata and tags.
• Assignment 1 (low-stakes): Curate annotated bibliographies for two assigned articles, export RIS/CSL JSON, and link to ORCID profiles.

Weeks 3–4: Version control & collaborative workflows

Teach Git basics, GitHub/GitLab organization, branching, pull requests, and code review. Introduce GitHub Classroom for assignment distribution.

• Lab: Initialize a repository for the SK Hynix replication, include README and citation file (CITATION.cff) for crediting data/code.
• Assessment: Peer code review of a short script (grading rubric provided).

Weeks 5–6: Reproducible analysis notebooks and literate programming

Compare R Markdown / Quarto and Jupyter notebooks; teach Jupytext and notebook best practices to avoid hidden state.

• Lab: Convert a messy .ipynb into a reproducible Quarto document; include a session on reproducible figures and caching.
• Assignment 2: Produce a reproducible notebook that reproduces a simple statistic from the “shockingly strong” economy article—include data retrieval and provenance.

Weeks 7–8: Pipelines, workflow managers & CI

Introduce Makefiles and workflow tools (Snakemake, Nextflow). Show continuous integration with GitHub Actions to run tests and render reports on push.

• Lab: Build a Snakemake pipeline that ingests the Toyota Excel data and outputs basic visualizations; run it via GitHub Actions.
• Assignment 3: Implement CI that checks for missing data and renders an HTML report automatically when the repo is updated.

Weeks 9–10: Environment encapsulation—Docker & Binder

Teach container basics and reproducible computational environments. Demonstrate Binder, GitHub Codespaces, and lightweight containers (Apptainer) for HPC.

• Lab: Write a minimal Dockerfile or environment.yml; deploy to Binder to allow graders to re-run notebooks without local setup.
• Assignment 4: Containerize the NBA simulation from the provided SportsLine-style article and demonstrate identical outputs across environments.

Weeks 11–12: Data management & repositories

Focus on data licensing, anonymization, metadata (DataCite schema), and repository submission. Compare OSF, Zenodo, Dryad, and Figshare for different needs.

• Lab: Create an OSF project for the ABLE accounts exercise; attach cleaned data, metadata, and a DOI via Zenodo for code release.
• Assignment 5 (group): Publish a reproducible mini-study (code + data) that reproduces a claim from one of the sports articles. Submit to OSF and obtain a DOI.

Weeks 13–14: Communication, preregistration, and ethics

Cover preprints, preregistration, registered reports, and ethical considerations for scraping and personal data use. Final project presentations and reproducibility audits complete the course.

• Final project: Students select one article, produce a reproducible replication or robustness analysis, preregister a short analysis plan, and deposit artifacts (code, data, report) in a public repository with a DOI.
• Summative assessment: Each project undergoes a reproducibility audit by two peers using a structured rubric.

Practical, actionable mini-tutorials (classroom-ready)

1. Quick Zotero setup for a class library

1. Create a Zotero group and invite students—set group permissions to "Members can edit."
2. Add the ten articles, attach PDFs where available, and create tags by module (e.g., tech, macro, sports).
3. Export group library as RIS/CSL JSON for linking to course materials and reproducible references in Quarto/R Markdown.

2. Minimal reproducible pipeline with Snakemake

Teaching snippet (in plain language):

• Rule 1: download data from a single source (e.g., Toyota Excel or public sports API).
• Rule 2: clean data into standardized CSVs (include schema checks).
• Rule 3: run analysis scripts (R/Python) to create tables and figures.
• Rule 4: knit final Quarto HTML report.

Wrap in GitHub Actions: trigger on push, cache dependencies, run Snakemake, and upload artifacts to GitHub Pages or an OSF record.

3. Containerization checklist for reproducible grading

• Include a minimal environment file (conda/requirements.txt) and a Dockerfile.
• Test the container locally; ensure the container runs the pipeline end-to-end with the command documented in the README.
• Link to Binder for interactive re-runs and to the Docker image for deterministic outputs.

Assignments, assessment, and grading rubrics

Students need clear, measurable criteria. Use the following rubric components for each submission:

• Reproducibility (40%) — Can an independent grader run the exact commands to reproduce outputs? Are outputs stored and linked (DOI)?
• Documentation (20%) — README, CITATION.cff, code comments, and metadata completeness.
• Methodological critique (20%) — Quality of sensitivity analyses, justification of decisions, and data provenance.
• Open-science practices (10%) — Use of preprint/preregistration, licensing, and repository deposition.
• Communication (10%) — Clarity of the final report and accessibility of materials.

Case studies & in-class demos mapped to the ten articles

Use short in-class demos to illustrate common pitfalls and teach quick fixes:

• SK Hynix (tech): demonstrate how vendor datasheets can bias benchmark claims; run a microbenchmark and show variance across hardware; discuss reproducibility limits with proprietary firmware.
• Macro articles (economy and inflation risk): fetch time series (FRED), reproduce headline graphs, and show how different seasonality adjustments change narratives.
• Toyota forecast: show how baseline assumptions (market growth, EV adoption) influence model outputs; ask students to run scenario analyses with altered assumptions.
• Sports models (SportsLine/NBA): rebuild simplified Monte Carlo models; show sensitivity to priors and the danger of overfitting to a small playoff sample.
• ABLE accounts (policy): reproduce eligibility counts using public ACS microdata; discuss privacy and de-identification for policy research.

2026 trends and future-facing teaching points

Integrate these 2026 developments into course discussions and final reflections:

• AI-assisted reproducibility: GitHub Copilot and LLMs can scaffold scripts, but students must validate outputs and track prompts to avoid hidden provenance gaps.
• Stronger open-data mandates: By 2025–26 many funders require machine-readable metadata and persistent identifiers—teach students to deposit and cite data properly.
• Container-first grading: Increasing use of containerized assignments in journals and courses means grading reproducibility is becoming automated and scalable.
• Reproducibility badges and badges adoption: Journals and preprint servers have normalized badges for open data, open code, and preregistration. Include badge criteria in assignments.

Common teaching challenges and mitigation strategies

• Paywalled data or proprietary tools: Use public analogs or provide synthetic data that matches the structure of the paywalled source.
• Diverse student technical skill levels: Run a pre-course bootcamp and provide tiered assignments (basic reproducibility → advanced containerization).
• Resource constraints: Use Binder and GitHub Actions free tiers; provide shared cloud credits for heavier workloads if necessary.
• Ethics and scraping: Teach robots.txt, ToS checks, and institutional review when using personal or sensitive data.

Instructor resources, templates and starter kits

Provide students with starter repositories that include:

• README template with a reproduction checklist
• CITATION.cff and LICENSE templates
• Conda environment.yml and minimal Dockerfile
• Snakemake example pipeline and GitHub Actions CI file
• OSF/Zenodo deposit how-to guide

Final project: reproducibility audit workflow (deliverables)

Each student team submits:

1. Preregistration (short analysis plan) with a timestamped record
2. Repository with code, environment specification, data (or synthetic analog), and a CITATION.cff
3. Automated CI that runs core tests and builds the report
4. Public deposit (OSF/Zenodo) with a DOI
5. Peer reproducibility audit report (completed by two classmates)

Evidence of impact and metrics to evaluate course success

Collect evidence of learning outcomes with these indicators:

• Proportion of final projects that fully reproduce the reported outputs
• Number of student repositories with DOIs and open licenses
• Peer audit scores over time (show improvement)
• Student self-reported confidence in reproducible tools and methods (pre/post survey)

Closing: why this syllabus works for 2026 and beyond

This scaffolded, news-driven semester plan gives students practical, transferable skills: they learn to assemble reproducible pipelines, critique public claims, and communicate transparent results. By anchoring technical lessons to timely articles (from SK Hynix’s hardware claims to SportsLine’s simulation models and Toyota’s forecast), students practice the exact workflows modern researchers need in 2026—version control, containerization, CI, metadata, and open data deposition.

Actionable takeaways:

• Start your course with a shared Zotero library and a one-week environment bootcamp.
• Use short, news-focused replication tasks to teach pipeline components incrementally.
• Require a DOI deposit and a reproducibility audit for the final project.
• Automate grading where possible with GitHub Actions and containerized tests.

Get the course starter kit

If you want a ready-to-run package for your class, I’ve prepared a starter kit that includes templates (README, CITATION.cff, Dockerfile), Snakemake examples, and a sample schedule mapped to the ten articles listed in this syllabus. Download, adapt, and reuse under an open license—then share student project DOIs to build a reproducibility portfolio for your program.

Call to action: Request the starter kit, sample rubric, and a live demo session by emailing the course maintainer or visiting our reproducibility resources page. Run one module this term—your students will graduate with skills that matter.

Related Reading

• Do 3D-Scanned Insoles Make Long Drives More Comfortable? A Driver-Focused Test
• Are 3D-Scanned Custom Hairpieces Worth It? What the ‘Placebo Tech’ Debate Means for Wigs and Caps
• How to Host Community-Run Servers for a Shuttered MMO (Legal & Technical Checklist)
• Rebuilding a Deleted Island: Step-by-Step Design Plan Inspired by the Infamous ACNH Adult Island
• Speed Up Your Wi‑Fi Without Breaking the Bank: When to Buy a Mesh System