Ensuring Reproducibility: Publishing Both Data and Code Under an Institutional Repository

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Publishing data and code for reproducibility in research demands that you share not only your findings but also the underlying data and analysis code. Depositing these materials in a trusted repository (institutional or open‐access) with persistent identifiers (DOIs) ensures transparency, credit, and reusability. Below, find a step‐by‐step guide, DOI assignment tips, and a sample folder structure to get you started.

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1. Choosing a Repository for publishing data and code for reproducibility

• Institutional Repository
  • Pros: University‐branded, often free, integrates with campus IP management.
  • Cons: May lack granular DOI support or community discoverability.
• Open‐Access Platforms
  • Zenodo (CERN): Free, supports unlimited files up to 50 GB, mints DOIs automatically.
  • Figshare: Free up to 5 GB; makes items citable via DOIs; integrates with GitHub.
  • Dryad, OSF: Discipline‐specific features, various pricing models.

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2. Step-by-Step Deposit Guide for publishing data and code for reproducibility

1. Prepare Your Materials
   • Anonymized Dataset (.csv or .xlsx): Remove direct identifiers; include a README explaining variables.
   • Analysis Scripts (.R, .py, or Jupyter notebooks): Ensure they run from raw data to final tables/figures.
   • Documentation:
     • README.md with project overview, dependencies (e.g., R‐package list), and usage instructions.
     • LICENSE file (e.g., CC BY 4.0 for data; MIT for code).
2. Log In & Create New Submission
   • For Zenodo: Sign in via ORCID/GitHub → “New Upload” → drag & drop files.
   • For Institutional: Navigate to your university archive portal → “Deposit Research Output” form.
3. Fill Metadata Fields
   • Title: Reflects your study (e.g., “Survey Data and R Code for Hybrid Instruction Study, 2025”).
   • Authors & Affiliations: Include your ORCID iD.
   • Description/Abstract: Briefly summarize data collection, study scope, and code purpose.
   • Keywords/Subjects: Enhance discoverability (e.g., “educational research,” “R scripts,” “student surveys”).
   • License Selection: Choose open license for maximum reuse.
4. Review & Publish
   • Preview your submission; fix any warnings.
   • Click “Publish” (or “Submit for Review” if institutional).
   • A DOI will be minted (Zenodo/ Figshare) or you’ll receive one soon via your university repository.
5. Link From Your Dissertation & Manuscripts
   • In your methods or data-availability section, include:

The anonymized dataset and analysis scripts are available at Zenodo: DOI 10.5281/zenodo.1234567

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3. Assigning & Using DOIs

• Automatic DOI Minting
  • Zenodo and Figshare provide a DOI upon publication.
• Institutional DOI Requests
  • Contact your library or digital‐scholarship office to reserve a DOI prefix and suffix for your dataset.
• Citation Format

Gupta, A. (2025). Hybrid Instruction Survey Data & R Code [Data set]. Zenodo. <Enter the link to the artifact here>

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4. Sample Folder Structure

Hybrid_Instruction_Study_2025/
├── data/
│   ├── raw/
│   │   └── survey_responses_raw.csv
│   ├── processed/
│   │   └── survey_responses_anonymized.csv
│   └── README_data.md
├── code/
│   ├── 01_data_cleaning.R
│   ├── 02_analysis.R
│   └── 03_visualizations.R
├── docs/
│   ├── README.md           # Overview & instructions
│   ├── LICENSE             # Data: CC BY 4.0; Code: MIT
│   └── CITATION.cff        # Citation metadata
└── outputs/
    ├── tables/
    │   └── table1_summary.csv
    └── figures/
        └── fig1_trend.png

• data/raw/: Untouched original files (for archive only).
• data/processed/: Anonymized, analysis‐ready datasets.
• code/: Modular scripts following a logical pipeline.
• docs/: Documentation, license, and citation files.
• outputs/: Exported tables and figures ready for publication.

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5. Quick‐Start Checklist

• Anonymize all personal identifiers before deposit.
• Include clear README and license files.
• Ensure code runs end‐to‐end on a fresh machine.
• Choose an open license (CC BY, MIT) for maximum reuse.
• Deposit data and code together; link with a DOI in your dissertation.
• Announce your repository link in conference presentations and your ORCID profile.

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By depositing your data and code in a reputable repository—complete with a DOI, comprehensive metadata, and clear folder organization—you cement the reproducibility and impact of your PhD research. Future scholars will thank you, and your work will stand on a foundation of openness and trust.

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