60b7bb73-afde-4af6-a5bd-d83b289cf6e3.JPG

# Research Data ## What is Research Data? Research data is defined as recorded factual material commonly accepted in the scientific community as necessary to validate research findings. Many different things can be research data: * Spreadsheets * Lab notebooks * Code * Questionnaires *...

# Research Data ## What is Research Data? Research data is defined as recorded factual material commonly accepted in the scientific community as necessary to validate research findings. Many different things can be research data: * Spreadsheets * Lab notebooks * Code * Questionnaires * Audio files * Video files * Documents * Images Research data can exist in either digital or non-digital form. ## Why Manage Research Data? * **Ethical obligation**: The public has a right to see the data underlying research findings * **Reproducibility**: Allows others to reproduce and verify your work * **Increased impact**: Properly managed data is more visible and more useable, leading to more citations and collaborations * **Funders require it**: Many funders now require data management plans and data sharing ## Data Management Plan (DMP) A DMP is a formal document that outlines how you will handle your research data during and after your project. ### What Goes Into a DMP? * Types of data you will be collecting/creating * How you will organize, name, and store your data * Plans for data sharing and preservation * Roles and responsibilities ### DMPTool * Free, online tool that provides templates for creating DMPs * Templates for many different funding agencies * Provides guidance and best practices ## Data Organization * Use a consistent file naming scheme * `YYYYMMDD_ProjectName_Description.filetype` * Create a well-organized folder structure * One main folder for the overall project * Subfolders for different types of data, analyses, etc. * Include a `README` file * Explains the project, folder structure, data, etc. ## File Formats * Use open, non-proprietary formats whenever possible * Easier to share and preserve * Examples: `.txt`, `.csv`, `.pdf` * Avoid formats that require specific software * Examples: `.docx`, `.xlsx` ## Metadata * Data about data * Describes and contextualizes your data * Allows others to find, understand, and use your data * Examples: * Title, author, date created * Description of the data * Variables and their definitions * Methods used to collect the data ## Data Repositories * Provide long-term storage and access for your data * Make your data more visible and discoverable * Often required by funders * Examples: * Disciplinary repositories (e.g. PDB for protein structures) * General-purpose repositories (e.g. Zenodo, Dryad) * Institutional repositories ## Data Security * Secure your data to prevent loss, damage, or unauthorized access * Back up your data regularly * Follow the 3-2-1 rule: 3 copies of your data, on 2 different media, with 1 copy offsite * Use strong passwords * Encrypt sensitive data * Store physical data in a secure location ## FAIR Principles * **Findable**: Data and metadata are easy to discover * **Accessible**: Data can be obtained by a wide range of users * **Interoperable**: Data can be integrated with other data * **Reusable**: Data are well-described and can be used for future research