Organizing Research Data: A Folder Structure Guide for PhD Students
Dockhorn, Ron; Demerdash, Yasmin; Wilbrandt, Jeanne
- Publisher
- Zenodo
- Language
- en
Abstract
The organization of research data is an important aspect for the reproducibility and traceability of research results. A well-structured and documented research data management (RDM) is crucial for adhering to the FAIR principles and is an essential part of good research practice throughout the entire data lifecycle. Good RDM leads to well-organized, annotated, and clear data, reduces the risk of data loss, and saves time in research practice. An appropriate folder structure is essential foreffective data organization, but this already requires years of professional experience, which is particularly lacking for early-career researchers. Existing solutions, such as templates for individual projects, often do not cover the extensive organizational requirements of a doctoral project and usually provide little guidance or background knowledge. To address this challenge, we present a comprehensive folder structure template specifically for doctoral candidates in the life and naturalsciences. Our template includes folders for the most important use cases in research practice: results, figures, manuscripts, code, and administrative documents. It offers a practical solution for digital data organization and contains recommendations and further information for best RDM practices, as well as guidelines for capturing standardized metadata. By applying this folder structure at the beginning of a project, researchers can organize and manage their data more efficiently, leading to more FAIR data and higher research quality. Our template aims to help early-career researchers develop good practices in research data management and preserve their research data in the long term by addressing the challenges of data organization for doctoral projects. The poster was conducted and presented at the "First International Symposium & Autumn School on “Chemistry, Physics & Devices of Organic 2D Crystals" in Dresden, 06-10. Oct. 2025 and additionally presented at the "6. SaxFDM-Tagung" in Dresden, 20. – 21. Nov. 2025. Link to "PhD Project Folder Template for the Life Sciences": https://doi.org/10.5281/zenodo.15835125 and https://github.com/RDMJeanne/FolderStructure Link to README.md: https://doi.org/10.5281/zenodo.14848834Link to Markdown-to-JSON-Parser: https://doi.org/10.5281/zenodo.14942696 and https://github.com/Bondoki/ParsingMetadataMD2JSON
Full text
CRC 1415 - Chemistry of Synthetic Two-Dimensional Materials Organizing Research Data: A Folder Structure Guide for PhD Students Folder Structure Template Ron Dockhorn¹ ¹ CIDS - Center for Interdisciplinary Digital Sciences Informationsdienste und Hochleistungsrechnen (ZIH) Technische Universität Dresden, Dresden, Germany 0000-0002-5268-5430 Yasmin Demerdash² ² Administration Safety and Compliance Institute of Molecular Biology (IMB) Mainz, Germany 0000-0002-3246-7604 Jeanne Wilbrandt³ ³ Core Facility Life Science Computing Leibniz Institute on Aging – Fritz Lipmann Institute e.V. Jena, Germany 0000-0002-0363-3837 CONTENT References Source Code 0 00 1 1 10 01 01 1 1 1 0 1 00 1 Motivation Challenge: •Researchers lack standardized documentation practices in RDM, impeding data sharing, collaboration and open science. Insufficient structured documentation and appropriate metadata hinders the findability, attribution, and reusability of datasets. • Goal: •Facilitate a comprehensive and practical folder structure template supporting doctoral candidates in life and natural sciences in organizing and managing their research data efficiently.[1] Facilitate common standard for data description using generic "ReadMe" text files for human and machine readability.[2,3] Promote FAIR data principles for reproducible research developing good research data management habits and practice. • • Four Steps in Data Organization Identify current and potential file types for research considering formats and content, also. Based on content relationships and workflow stages. Enrich with metadata Importance of file types and clusters avoiding overlap, and maintaining manageable folder sizes. Overview: • • • • • Five top-level folders for quick access: More details on GitHub[1] G_PhD_Readme.md Guidance README files (G_*_README) and metadata prototypes (M_*_README) provided. Using plain human-readable Markdown format. Single-project templates as .zip file for quick ready-to-use of standard structures. Consistent naming scheme using ISO 8601 chronological order and two-number digits. 03_Presentations • • Helps locate material for future presentations. Stores third-party and self-created images, along with their source and license. 04_Publications • • • • Collects manuscripts and written research output in individual subfolders. Store "colder" (semi) final figure/table versions along with data lineage and code/methods. Stores draft and final version of manuscript, allowing for easy access and version tracking. Ready-to-archive for public repositories. 05_Thesis • • Dedicated thesis folder at the start of the PhD helps organize ideas and thoughts. Organizing notes and sources by section (e.g. introduction, discussion) of PhD thesis. Guidance README •Provide explanations and best practices. Metadata prototypes • • Used for structured documentation of data following the Dublin Core metadata standard Written in Markdown - can easily be parsed[2,3] 01_Documents •Manage various research non-experimental documents (grant proposals, meeting notes...). Familiarize with funding data policies including personal data management plan (DMP). Maybe secure encryption due to personal data. 02_Projects •Main working folder contains actively used "hot" and often accessed data. Each project folder follows a "one project, one folder" principle organized by experiments. Data is organized into raw (unaltered) and processed (transformed) files, with code and results stored separately. [1] Y. Demerdash, R. Dockhorn, J. Wilbrandt, "Data Organization Made Easy: Comprehensive Folder Structure Template for Early Career Life/Natural Science Researchers", Data Science Journal 2025 (in rev.) Also: https://github.com/RDMJeanne/FolderStructure [2] R. Dockhorn, "From Data to Credits: Using ReadMe, Markdown, and Dublin Core for Better Documentation", Zenodo (2025) https://doi.org/10.5281/zenodo.14848834 [3] R. Dockhorn, ParsingMetadataMD2JSON (v1.0.0), Zenodo (2025) https://doi.org/10.5281/zenodo.14942696 https://github.com/Bondoki/ParsingMetadataMD2JSON All licensed under CC0 v1.0 Universal and made public available free of charge
