{"id":808,"date":"2026-06-09T02:11:33","date_gmt":"2026-06-09T02:11:33","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=808"},"modified":"2026-06-09T02:11:34","modified_gmt":"2026-06-09T02:11:34","slug":"research-data-management-how-to-make-a-data-management-plan-dmp","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/","title":{"rendered":"Research Data Management: How to Make a Data Management Plan (DMP)"},"content":{"rendered":"\n<p><strong>Contents<\/strong><\/p>\n\n\n\n<ul><li><a href=\"#_Toc231838211\">Glossary of Key Terms<\/a><\/li><li><a href=\"#_Toc231838212\">Key Takeaways<\/a><\/li><li><a href=\"#_Toc231838213\">What Is Research Data Management?<\/a><\/li><li><a href=\"#_Toc231838214\">The Research Data Lifecycle<\/a><\/li><li><a href=\"#_Toc231838215\">The FAIR Principles: The Foundation of Good RDM<\/a><\/li><li><a href=\"#_Toc231838216\">Step 1: Start with Policies, Ethics, and Legal Compliance<\/a><\/li><li><a href=\"#_Toc231838217\">Step 2: Build a Sound Data Collection Strategy<\/a><\/li><li><a href=\"#_Toc231838218\">Step 3: Organize Your Files and Folders<\/a><\/li><li><a href=\"#_Toc231838219\">Step 4: Document Everything: Metadata and README Files<\/a><\/li><li><a href=\"#_Toc231838220\">Step 5: Store Data Securely Using the 3-2-1 Rule<\/a><\/li><li><a href=\"#_Toc231838221\">Step 6: Develop a Data Management Plan (DMP)<\/a><\/li><li><a href=\"#_Toc231838222\">Step 7: Choose Open and Standard File Formats<\/a><\/li><li><a href=\"#_Toc231838223\">Step 8: Share Your Data: The Open Science Imperative<\/a><\/li><li><a href=\"#_Toc231838224\">Practical Implementation: Getting Started<\/a><\/li><li><a href=\"#_Toc231838225\">Continuous Self-Monitoring<\/a><\/li><li><a href=\"#_Toc231838226\">Frequently Asked Questions<\/a><\/li><\/ul>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Glossary_of_Key_Terms\" >Glossary of Key Terms<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Key_Takeaways\" >Key Takeaways<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_Is_Research_Data_Management\" >What Is Research Data Management?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Why_Should_Researchers_Care\" >Why Should Researchers Care?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#The_Research_Data_Lifecycle\" >The Research Data Lifecycle<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#The_FAIR_Principles_The_Foundation_of_Good_RDM\" >The FAIR Principles: The Foundation of Good RDM<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_Each_Principle_Means_in_Practice\" >What Each Principle Means in Practice<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_1_Start_with_Policies_Ethics_and_Legal_Compliance\" >Step 1: Start with Policies, Ethics, and Legal Compliance<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Applicable_Policies\" >Applicable Policies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Ethical_Regulations_and_Legislation\" >Ethical Regulations and Legislation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_2_Build_a_Sound_Data_Collection_Strategy\" >Step 2: Build a Sound Data Collection Strategy<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Reusing_Existing_Data\" >Reusing Existing Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Collecting_New_Data\" >Collecting New Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Ensuring_Data_Quality\" >Ensuring Data Quality<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_3_Organize_Your_Files_and_Folders\" >Step 3: Organize Your Files and Folders<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Folder_Structure\" >Folder Structure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#File_Naming_Conventions\" >File Naming Conventions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Version_Control\" >Version Control<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_4_Document_Everything_Metadata_and_README_Files\" >Step 4: Document Everything: Metadata and README Files<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_Metadata_Should_Include\" >What Metadata Should Include<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Data_Dictionaries\" >Data Dictionaries<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_5_Store_Data_Securely_Using_the_3-2-1_Rule\" >Step 5: Store Data Securely Using the 3-2-1 Rule<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_is_the_3-2-1_Backup_Rule\" >What is the 3-2-1 Backup Rule?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Access_Control_and_Security\" >Access Control and Security<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Data_Retention\" >Data Retention<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_6_Develop_a_Data_Management_Plan_DMP\" >Step 6: Develop a Data Management Plan (DMP)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Core_Questions_a_DMP_Should_Answer\" >Core Questions a DMP Should Answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Tools_to_Help_Create_a_DMP\" >Tools to Help Create a DMP<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_7_Choose_Open_and_Standard_File_Formats\" >Step 7: Choose Open and Standard File Formats<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Recommended_Formats_by_Data_Type\" >Recommended Formats by Data Type<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Step_8_Share_Your_Data_The_Open_Science_Imperative\" >Step 8: Share Your Data: The Open Science Imperative<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_Data_Should_be_Shared\" >What Data Should be Shared?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#De-identifying_Sensitive_Data\" >De-identifying Sensitive Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Choosing_a_Data_Repository\" >Choosing a Data Repository<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Licenses_and_Persistent_Identifiers\" >Licenses and Persistent Identifiers<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-36\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Practical_Implementation_Getting_Started\" >Practical Implementation: Getting Started<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-37\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#A_Realistic_Starting_Sequence\" >A Realistic Starting Sequence<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-38\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Choosing_a_Digital_Tool_Common_Approaches\" >Choosing a Digital Tool: Common Approaches<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-39\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Communicating_Change_Within_Your_Lab\" >Communicating Change Within Your Lab<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-40\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Continuous_Self-Monitoring\" >Continuous Self-Monitoring<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-41\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-42\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Do_I_need_a_Data_Management_Plan_even_if_my_funder_doesnt_require_one\" >Do I need a Data Management Plan even if my funder doesn\u2019t require one?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-43\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Whats_the_difference_between_a_data_repository_and_cloud_storage_like_Google_Drive_or_Dropbox\" >What\u2019s the difference between a data repository and cloud storage like Google Drive or Dropbox?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-44\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#How_do_I_handle_data_that_belongs_to_multiple_collaborators_or_institutions\" >How do I handle data that belongs to multiple collaborators or institutions?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-45\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#My_lab_generates_very_large_datasets_terabytes_of_imaging_or_sequencing_data_Does_RDM_still_apply\" >My lab generates very large datasets (terabytes of imaging or sequencing data). Does RDM still apply?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-46\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Is_anonymized_data_always_safe_to_share\" >Is anonymized data always safe to share?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-47\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#How_long_should_I_keep_my_research_data_after_a_project_ends\" >How long should I keep my research data after a project ends?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-48\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#What_happens_to_research_data_when_a_PhD_student_or_postdoc_leaves_the_lab\" >What happens to research data when a PhD student or postdoc leaves the lab?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-49\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/#Can_I_use_AI_tools_to_help_with_data_documentation_or_organization\" >Can I use AI tools to help with data documentation or organization?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Glossary_of_Key_Terms\"><\/span><a id=\"_Toc231838211\">Glossary of Key Terms<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Term<\/strong><\/td><td><strong>Definition<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Research Data Management (RDM)<\/td><td>The process of collecting, organizing, storing, securing, sharing, and preserving research data across all stages of the research lifecycle<\/td><\/tr><tr><td>Data Lifecycle<\/td><td>The sequence of stages data passes through: creation\/collection \u2192 processing \u2192 analysis \u2192 storage \u2192 sharing \u2192 preservation\/reuse<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/everything-you-need-to-know-about-making-research-data-open-and-fair\">FAIR Principles<\/a><\/td><td>A framework stating that data should be Findable, Accessible, Interoperable, and Reusable<\/td><\/tr><tr><td>Data Management Plan (DMP)<\/td><td>A formal document outlining how data will be collected, stored, shared, and preserved throughout and after a research project<\/td><\/tr><tr><td>Metadata<\/td><td>Structured data that describes other data: for example, who created a file, when, using what method, and in what format<\/td><\/tr><tr><td>Data Repository<\/td><td>A digital archive where datasets can be deposited and made accessible to other researchers<\/td><\/tr><tr><td>Open File Format<\/td><td>A non-proprietary file format that can be opened without specific paid software and is more likely to remain accessible over time<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/future-proofing-scholarly-publishing-the-role-of-pids\">Persistent Identifier<\/a> (PID)<\/td><td>A long-lasting reference to a digital resource: such as a DOI (Digital Object Identifier): that makes data reliably citable and findable<\/td><\/tr><tr><td>De-identification<\/td><td>The process of removing or masking personally identifiable information from a dataset to protect participant privacy<\/td><\/tr><tr><td>Version Control<\/td><td>A system for tracking changes to files over time, allowing earlier versions to be retrieved<\/td><\/tr><tr><td>Electronic Lab Notebook (ELN)<\/td><td>A digital tool that replaces or supplements traditional paper lab notebooks, enabling structured, searchable documentation of research activities<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/adopting-open-science-practices-a-primer\">Open Science<\/a><\/td><td>A movement that promotes transparent, reproducible, and openly accessible research: including open data, open methods, and open publications<\/td><\/tr><tr><td>Data Governance<\/td><td>The set of policies, roles, and processes that determine how data is managed, accessed, and protected within an organization<\/td><\/tr><tr><td>3-2-1 Backup Rule<\/td><td>A data backup strategy: maintain 3 copies of data, on 2 different storage media, with 1 copy stored offsite<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Key_Takeaways\"><\/span><a id=\"_Toc231838212\">Key Takeaways<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul><li>Research Data Management is the process of providing appropriate labeling, storage, and access for data at all stages of a research project.<\/li><li>RDM encompasses all data-related activities across the entire data lifecycle: from collection and processing through to storage, sharing, and long-term preservation.<\/li><li>Well-organized research data can help researchers improve efficiency, meet funding and regulatory requirements, and optimize the potential of their research: leading to publication, funding, and collaboration opportunities.<\/li><li>The FAIR principles: Findable, Accessible, Interoperable, and Reusable: serve as the foundational framework for modern RDM and should guide every decision about how data is documented, stored, and shared.<\/li><li>A Data Management Plan (DMP) is not optional busywork: it is a living document that helps researchers anticipate needs, stay compliant, and maximize the long-term value of their data.<\/li><li>Open and standard file formats ensure that research data remain accessible and usable over time by avoiding dependencies on proprietary software that may not be supported in the future.<\/li><li>Data publication promotes transparency, credibility, long-term accessibility, reproducibility, and collaboration. Researchers stand to benefit personally through greater recognition of their work.<\/li><li>Continuous self-monitoring (setting regular intervals to review data practices, assess progress, and refine workflows) is just as important as the initial setup of any RDM system.<\/li><\/ul>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"What_Is_Research_Data_Management\"><\/span><a id=\"_Toc231838213\">What Is Research Data Management?<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Research data is changing in scale and complexity at a pace that was hard to imagine two decades ago. In the 20th century, it was common for a study or experiment to yield a single file: perhaps one data table. Today, many research projects generate many files, often created by multiple collaborators, and often valuable for secondary use. Experiments in genomics can generate multiple raw files per biological sample, plus layers of processed data.<\/p>\n\n\n\n<p>Research Data Management is the process of providing appropriate labeling, storage, and access for data at all stages of a research project. It is not a single activity but a continuous discipline that spans the entire life of a research project: and often extends well beyond it.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.editage.us\/blog\/funding-sources-for-early-career-researchers\/\">Funding agencies<\/a> now require a data management or data sharing plan to be submitted with <a href=\"https:\/\/www.editage.us\/blog\/grant-application-challenges\/\">grant applications<\/a>. Many academic journals also require the submission of relevant data alongside manuscripts to promote open access and reproducibility. Early and attentive management at each step of the data lifecycle will ensure the discoverability and longevity of your research.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Why_Should_Researchers_Care\"><\/span>Why Should Researchers Care?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Beyond compliance, good RDM makes practical sense:<\/p>\n\n\n\n<ul><li>Organized, well-documented data is simply easier to analyze<\/li><li>You can find your own files when you need them: sometimes years later<\/li><li>You avoid drowning in irrelevant or duplicate data<\/li><li>You protect against data loss from accidents, equipment failure, or staff turnover<\/li><li>You get credit for your data and avoid accusations of misconduct<\/li><li>You enable other researchers to build on your work, amplifying your impact<\/li><\/ul>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"The_Research_Data_Lifecycle\"><\/span><a id=\"_Toc231838214\">The Research Data Lifecycle<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Understanding RDM starts with understanding the data lifecycle: the sequence of stages your data passes through from creation to long-term use. The lifecycle is typically represented as a cycle rather than a straight line, because data created in one project frequently becomes the raw material for future research.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Lifecycle Stage<\/strong><\/td><td><strong>What Happens<\/strong><\/td><td><strong>Key RDM Activities<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Plan<\/td><td><a href=\"https:\/\/researcher.life\/blog\/article\/what-is-research-design-types-examples\/\">Research design<\/a>, DMP creation<\/td><td>Defining data needs, legal\/ethical review, storage planning<\/td><\/tr><tr><td>Collect<\/td><td>Data generation or acquisition<\/td><td>Naming conventions, quality control, format selection<\/td><\/tr><tr><td>Process &amp; Analyze<\/td><td><a href=\"https:\/\/www.editage.com\/insights\/5-common-pitfalls-in-data-cleaning-that-biomedical-researchers-need-to-know\">Cleaning<\/a>, transforming, analyzing<\/td><td>Version control, documentation, code management<\/td><\/tr><tr><td>Store<\/td><td>Securing data during the project<\/td><td>Backup implementation, access controls, encryption<\/td><\/tr><tr><td>Share &amp; Publish<\/td><td>Making data available<\/td><td>Repository selection, licensing, persistent identifiers<\/td><\/tr><tr><td>Preserve &amp; Reuse<\/td><td>Long-term archiving<\/td><td>Format migration, metadata maintenance, enabling secondary use<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"The_FAIR_Principles_The_Foundation_of_Good_RDM\"><\/span><a id=\"_Toc231838215\">The FAIR Principles: The Foundation of Good RDM<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>FAIR refers to the findability, accessibility, interoperability, and reuse of digital assets. Every step in research data management is closely connected to FAIR.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"What_Each_Principle_Means_in_Practice\"><\/span>What Each Principle Means in Practice<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul><li>Findable: Metadata and data should be easy to locate for both humans and computers. This means rich metadata, clear naming, and the use of persistent identifiers like DOIs.<\/li><li>Accessible: There should be clarity on how data can be retrieved: including what authentication or authorization is required. \u201cAccessible\u201d does not necessarily mean \u201copen to everyone\u201d; it means the access conditions are clearly defined.<\/li><li>Interoperable: Data should be structured in a way that enables it to work with other datasets, tools, and workflows. This is primarily achieved through the use of standard formats and shared vocabularies.<\/li><li>Reusable: Metadata and data should be sufficiently well-described so that others can reproduce, replicate, or build upon the work in different settings.<\/li><\/ul>\n\n\n\n<p>It is important to know that FAIR is applicable not only to data but also to metadata and relevant infrastructure.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_1_Start_with_Policies_Ethics_and_Legal_Compliance\"><\/span><a id=\"_Toc231838216\">Step 1: Start with Policies, Ethics, and Legal Compliance<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Before collecting a single data point, researchers need to understand the landscape of rules that govern their work.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Applicable_Policies\"><\/span>Applicable Policies<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Countries, umbrella organizations, science foundations, professional societies, institutions, funding bodies, and project boards may all issue policies and guidelines. These inherently reflect best practices, outline legal issues, or offer suggestions for efficiency and resource management. Researchers should check for applicable policies and compliance requirements in their subject area by consulting funder websites, institutional research support offices, or DMP tools.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Ethical_Regulations_and_Legislation\"><\/span>Ethical Regulations and Legislation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Relevant legislation typically pertains to data collection and sharing, designed to safeguard personal data of individuals. For example, within the European Union, individuals possess the right to know which information is collected, processed, and transmitted under the General Data Protection Regulation (GDPR). Ethics committees review proposals for ethical compliance and aim to ensure the rights, safety, and well-being of participants.<\/p>\n\n\n\n<p>Key considerations before starting data collection:<\/p>\n\n\n\n<ul><li>Does this project involve human participants? If so, IRB\/ethics approval is likely required.<\/li><li>Does the data contain personally identifiable information (PII)? If so, GDPR, HIPAA, or local equivalents may apply.<\/li><li>Are there intellectual property considerations: for example, if collaborating with industry partners?<\/li><li>What data retention periods are required by your funder or institution?<\/li><\/ul>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_2_Build_a_Sound_Data_Collection_Strategy\"><\/span><a id=\"_Toc231838217\">Step 2: Build a Sound Data Collection Strategy<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To reach well-founded conclusions, researchers require quality data. Understanding how the research question translates into specific data needs: what data are required, what insights are expected, and in what way: is one of the first steps in conducting research.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Reusing_Existing_Data\"><\/span>Reusing Existing Data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Researchers should first check published data to examine whether it can be integrated into the work. Reusing data can be a tremendous benefit and save significant resources, especially labor and material costs, as well as data retention costs in projects with high data volumes.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Collecting_New_Data\"><\/span>Collecting New Data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>If new original data are required, the guiding principle is to collect as much as required, but no more than necessary. Running a <a href=\"https:\/\/www.editage.com\/insights\/an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers\">sample size calculation<\/a> before collecting data ensures collection of the minimum amount of required data. Data collected beyond requirements need additional resources for administration, processing, and storage.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Ensuring_Data_Quality\"><\/span>Ensuring Data Quality<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Data quality is the degree to which the data at hand can meet their intended purpose while being error-free. The goal of data quality efforts is to assure that the data depicts the real-world entities it measures as comprehensively as possible. Best practices for data quality may vary by discipline. In social sciences, validation through triangulation is common, while in physics, calibration of instruments ensures accuracy.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_3_Organize_Your_Files_and_Folders\"><\/span><a id=\"_Toc231838218\">Step 3: Organize Your Files and Folders<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>A systematic organization of files and working directories is key to efficient filing, navigation, and prompt file retrieval. Using the same filing scheme across projects and teams can simplify and accelerate interactions.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Folder_Structure\"><\/span>Folder Structure<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Clean working directories have a logical and uniform structure: a standardized folder structure and depth, as well as default folder names. The project folder ideally contains all project files in its logically subdivided subfolders. Using a similar folder structure across projects can facilitate data retrieval and promote standardization while allowing for variations as necessary.<\/p>\n\n\n\n<p>A typical folder hierarchy might look like this:<\/p>\n\n\n\n<p>ProjectName\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 raw_data\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 processed_data\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 analysis\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 manuscripts\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 protocols\/<\/p>\n\n\n\n<p>&nbsp; \u251c\u2500\u2500 code\/<\/p>\n\n\n\n<p>&nbsp; \u2514\u2500\u2500 admin\/<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \u251c\u2500\u2500 DMP\/<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \u2514\u2500\u2500 ethics\/<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"File_Naming_Conventions\"><\/span>File Naming Conventions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Ensure the file name is descriptive, relevant, and allows for easy sorting and filtering. Incorporate dates, version numbers, and project numbers within the file name. A consistent file naming structure might include: [ProjectCode]_[DocumentType]_[Version]_[Date]. For example: TenR_Man_01_MJH_v01_2025-01-01.docx<\/p>\n\n\n\n<p>Key rules for file naming:<\/p>\n\n\n\n<ul><li>Use ISO date format (YYYY-MM-DD) for easy chronological sorting<\/li><li>Avoid spaces: use underscores or hyphens instead<\/li><li>Avoid special characters that may cause issues across operating systems<\/li><li>Be consistent across your entire team<\/li><\/ul>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Version_Control\"><\/span>Version Control<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Have a system in place to track file versions and activities. This will help you identify any changes made to the original file. At any point if you are unsure, you will be able to go back to find the person who made the change and the reasons for it.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_4_Document_Everything_Metadata_and_README_Files\"><\/span><a id=\"_Toc231838219\">Step 4: Document Everything: Metadata and README Files<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Orderly and standardized documentation of both data and its collection method is key to understanding and using any kind of data. Data are usually not self-explanatory: with sufficient documentation, the work remains transparent and reproducible and is less likely to be misinterpreted.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"What_Metadata_Should_Include\"><\/span>What Metadata Should Include<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Metadata comprises information on when data were created, by whom, and with which method. It may also include file sizes, formats, and languages. Common forms include README files, data dictionaries, or computer-readable XML\/JSON files.<\/p>\n\n\n\n<p>A README file should minimally contain:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>README Element<\/strong><\/td><td><strong>Description<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Explanation of data included<\/td><td>What the dataset contains and what it represents<\/td><\/tr><tr><td>Original purpose \/ project affiliation<\/td><td>The <a href=\"https:\/\/www.editage.com\/insights\/how-to-choose-a-research-question\">research question<\/a> and project the data was collected for<\/td><\/tr><tr><td>Author(s) \/ Creator(s)<\/td><td>Who collected or generated the data<\/td><\/tr><tr><td>Date\/period of data creation<\/td><td>When the data was collected or generated<\/td><\/tr><tr><td>Software or hardware requirements<\/td><td>What is needed to open or process the files<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Data_Dictionaries\"><\/span>Data Dictionaries<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>For tabular data especially, a data dictionary is invaluable. It explains what each column or variable represents, the data type, the unit of measurement, and the range of valid values. This is critical for any collaborator: including your future self: who needs to work with the data later.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_5_Store_Data_Securely_Using_the_3-2-1_Rule\"><\/span><a id=\"_Toc231838220\">Step 5: Store Data Securely Using the 3-2-1 Rule<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Storing files on a modern computer is easy. However, securing them over time and in a sustainable way: avoiding data corruption and data loss: requires following some simple principles.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"What_is_the_3-2-1_Backup_Rule\"><\/span>What is the 3-2-1 Backup Rule?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Valuable data should be stored in accordance with the 3-2-1 backup rule: keep three separate instances of the data: the original and two backups: on two distinct storage devices, such as a local copy on a laptop plus network storage, and one offsite backup at a different location.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Backup Layer<\/strong><\/td><td><strong>Example<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Copy 1 (Primary)<\/td><td>Working files on your personal computer or workstation<\/td><\/tr><tr><td>Copy 2 (Local backup)<\/td><td>Institutional network drive, automatically backed up<\/td><\/tr><tr><td>Copy 3 (Offsite backup)<\/td><td>Cloud storage (institutional or commercial) in a different geographic location<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Access_Control_and_Security\"><\/span>Access Control and Security<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Data security is not just about preventing loss: it is also about controlling who can access sensitive data. Key security practices include:<\/p>\n\n\n\n<ul><li>Setting appropriate access permissions for team members<\/li><li>Encrypting sensitive data, especially on portable drives<\/li><li>Using strong authentication (multi-factor authentication where possible)<\/li><li>Reviewing and revoking access when team members leave a project<\/li><\/ul>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Data_Retention\"><\/span>Data Retention<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It is best practice to retain research data over time. Data retention requirements may vary by country, funder, data type, or subject domain, but may require a minimum of 10 years, ranging up to over 25 years. Check your funder\u2019s and institution\u2019s specific requirements: and document them in your DMP.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_6_Develop_a_Data_Management_Plan_DMP\"><\/span><a id=\"_Toc231838221\">Step 6: Develop a Data Management Plan (DMP)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>A DMP essentially outlines data collection, storage, and sharing strategies while describing how privacy, security, and policy compliance are ensured. Usually drafted alongside the project outline, a DMP accompanies the project throughout its lifecycle, maximizing the data\u2019s value and impact.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Core_Questions_a_DMP_Should_Answer\"><\/span>Core Questions a DMP Should Answer<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>DMP Category<\/strong><\/td><td><strong>Key Questions<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Collection<\/td><td>What data will be re-used, collected, or created, and how?<\/td><\/tr><tr><td>Description<\/td><td>What formats and types will be collected? What hardware\/software is required?<\/td><\/tr><tr><td>Standards<\/td><td>How will data be described to enable effective interpretation? Which metadata standards apply?<\/td><\/tr><tr><td>Policies, Legal &amp; Ethics<\/td><td>Which policies and funder requirements apply? How will legal and ethical compliance be met?<\/td><\/tr><tr><td>Storage &amp; Preservation<\/td><td>How will data be stored, secured, and preserved during and after the project?<\/td><\/tr><tr><td>Access<\/td><td>Who needs access during the project, and what authorization rules apply?<\/td><\/tr><tr><td>Sharing &amp; Reuse<\/td><td>How will data be shared, and under what conditions or licenses?<\/td><\/tr><tr><td>Roles &amp; Responsibilities<\/td><td>Who is responsible for each data-related step?<\/td><\/tr><tr><td>Budget<\/td><td>What financial implications arise from data storage, software, or publication?<\/td><\/tr><tr><td>Quality Control<\/td><td>How will data quality be ensured and monitored?<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Tools_to_Help_Create_a_DMP\"><\/span>Tools to Help Create a DMP<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Several free web-based tools can guide researchers through the DMP process and incorporate funder-specific templates:<\/p>\n\n\n\n<ul><li>DMPTool: widely used in the United States, with templates for NIH, NSF, and other funders<\/li><li>DMP Online: commonly used in the UK and Europe<\/li><li>RDMO: supports multiple European funder requirements and provides forms tailored to those requirements<\/li><\/ul>\n\n\n\n<p>A common pitfall is that researchers may create a DMP initially and then fail to regularly review and update it. This oversight can lead to consequences ranging from increased workload to data mismanagement.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_7_Choose_Open_and_Standard_File_Formats\"><\/span><a id=\"_Toc231838222\">Step 7: Choose Open and Standard File Formats<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Open and standard file formats facilitate data handling within research groups and beyond, while ensuring that data remain readable and accessible over time. The practical recommendation: retain your original proprietary file if needed for active work, but always produce an open-format copy for archiving.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Recommended_Formats_by_Data_Type\"><\/span>Recommended Formats by Data Type<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Data Type<\/strong><\/td><td><strong>Recommended Open Formats<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Tabular data \/ Statistics<\/td><td>CSV, plain text (UTF-8)<\/td><\/tr><tr><td>Text documents<\/td><td>PDF\/A (archival), TXT, ODT<\/td><\/tr><tr><td>Images \/ Photographs<\/td><td>TIFF, PNG, JPEG 2000<\/td><\/tr><tr><td>Audio<\/td><td>FLAC, BWF (Broadcast Wave)<\/td><\/tr><tr><td>Video<\/td><td>MP4, MKV<\/td><\/tr><tr><td>Containers \/ Archives<\/td><td>ZIP, TAR<\/td><\/tr><tr><td>Scientific sequences (bioinformatics)<\/td><td>FASTA, FASTQ<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Step_8_Share_Your_Data_The_Open_Science_Imperative\"><\/span><a id=\"_Toc231838223\">Step 8: Share Your Data: The Open Science Imperative<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Data publication refers to making research datasets openly accessible for review and reuse. In the context of the reproducibility crisis, data sharing plays a key role in enabling research reproducibility. Research data publication promotes transparency, credibility, long-term accessibility, reproducibility, and collaboration.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"What_Data_Should_be_Shared\"><\/span>What Data Should be Shared?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Publish<\/strong><\/td><td><strong>Do Not Publish<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Unique datasets difficult to recreate<\/td><td>Test or pilot data<\/td><\/tr><tr><td>Data with high relevance to the scientific community<\/td><td>Discarded or erroneous data<\/td><\/tr><tr><td>Data that is anonymized or safely de-identified<\/td><td>Data with no medium- or long-term relevance<\/td><\/tr><tr><td>Complex or expensive-to-generate data<\/td><td>Data containing unresolvable personal identifiers<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"De-identifying_Sensitive_Data\"><\/span>De-identifying Sensitive Data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>When human subject data is involved, sharing requires careful de-identification. This includes removing direct identifiers such as names and addresses, aggregating variables such as grouping ages into ranges, suppressing rare values, or adding noise to geographic data. These practices help balance openness with privacy protections under regulations like HIPAA in the US or GDPR in the EU.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Choosing_a_Data_Repository\"><\/span>Choosing a Data Repository<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Researchers can choose a general-purpose data repository or select a topic-specific repository. Common repositories include:<\/p>\n\n\n\n<ul><li>Zenodo: free, general-purpose, supported by CERN<\/li><li>Dryad: popular in the life and environmental sciences<\/li><li>Figshare: supports a wide variety of file types<\/li><li>Harvard Dataverse: widely used in the social sciences and humanities<\/li><li>Domain-specific repositories: such as NCBI (genomics), ICPSR (social science), or UK Data Archive<\/li><\/ul>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Licenses_and_Persistent_Identifiers\"><\/span>Licenses and Persistent Identifiers<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Researchers should choose an appropriate license: such as a Creative Commons or MIT license: to specify how data can be reused. As a rule of thumb, select a license that is as open as possible and imposes minimal restrictions. Data publications should be assigned a persistent identifier such as a DOI to enhance long-term findability and prevent isolation, in line with the FAIR principles.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Practical_Implementation_Getting_Started\"><\/span><a id=\"_Toc231838224\">Practical Implementation: Getting Started<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Shifting to manage research data digitally can seem like a daunting task, but it doesn\u2019t need to be. Many organizations and institutions provide research data management support, mostly through research services offices or research librarians.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"A_Realistic_Starting_Sequence\"><\/span>A Realistic Starting Sequence<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4>Before the project:<\/h4>\n\n\n\n<ul><li>Review funder and institutional data policies<\/li><li>Seek ethics approval if required<\/li><li>Draft your DMP<\/li><li>Set up your folder structure and file naming convention<\/li><\/ul>\n\n\n\n<h4>During the project:<\/h4>\n\n\n\n<ul><li>Apply naming conventions consistently<\/li><li>Maintain metadata and documentation as you go<\/li><li>Back up data regularly using the 3-2-1 rule<\/li><li>Use version control for code and evolving data files<\/li><\/ul>\n\n\n\n<h4>At the end of the project:<\/h4>\n\n\n\n<ul><li>Prepare data and metadata for publication<\/li><li>Convert files to open formats<\/li><li>Choose a repository and deposit your data<\/li><li>Assign a DOI or other persistent identifier<\/li><li>Update your DMP to reflect what was actually done<\/li><\/ul>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Choosing_a_Digital_Tool_Common_Approaches\"><\/span>Choosing a Digital Tool: Common Approaches<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Approach<\/strong><\/td><td><strong>Pros<\/strong><\/td><td><strong>Cons<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Paper notebooks<\/td><td>Zero cost, no setup<\/td><td>Extremely difficult to find, share, or back up data<\/td><\/tr><tr><td>Shared server folders<\/td><td>Familiar, low cost<\/td><td>Easily loses control with multiple users; hard to search<\/td><\/tr><tr><td>Cloud storage (generic)<\/td><td>Accessible, low cost<\/td><td>Limited structure; depends on individuals to follow conventions<\/td><\/tr><tr><td>Electronic Lab Notebook (ELN)<\/td><td>Structured, searchable, version-controlled<\/td><td>Takes time to set up; may have licensing costs<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Communicating_Change_Within_Your_Lab\"><\/span>Communicating Change Within Your Lab<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Communication is key when changing existing practices within the lab. Make sure everyone understands why these changes are happening. This could mean involving lab members in decision making, listening to feedback from those who handle data day-to-day, and providing context and background on why switching to digital data management is important. If only some lab members follow the new practices, the benefits will be drastically reduced.<\/p>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Continuous_Self-Monitoring\"><\/span><a id=\"_Toc231838225\">Continuous Self-Monitoring<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Continuous self-monitoring involves setting regular intervals at which to evaluate progress within and beyond projects in order to spot potential problems and ascertain the overall effectiveness of the work. By performing such self-monitoring, researchers can potentially mitigate the risk of getting lost in detail and better visualize goals.<\/p>\n\n\n\n<p>Regular review allows mapping of progress against strategies, schedules, budgets, and other metrics to keep work on track and allow for corrective actions. This can help tackle issues that might be minor today but can compound over time and require excessive resources in the long run.<\/p>\n\n\n\n<p>Suggested checkpoints:<\/p>\n\n\n\n<ul><li>At project initiation: confirm DMP is complete and the team is aligned<\/li><li>At each major milestone: review whether data practices are being followed<\/li><li>Annually: review storage needs, access permissions, and policy changes<\/li><li>At project close: complete repository deposit and final documentation update<\/li><\/ul>\n\n\n\n<h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><a id=\"_Toc231838226\">Frequently<\/a> Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Do_I_need_a_Data_Management_Plan_even_if_my_funder_doesnt_require_one\"><\/span>Do I need a Data Management Plan even if my funder doesn\u2019t require one?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Yes. A DMP is useful regardless of funder requirements because it forces you to think through data needs proactively, prevents costly corrections later, and ensures your team is aligned. Many researchers who draft DMPs voluntarily report that the process itself surfaces problems they hadn\u2019t anticipated.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Whats_the_difference_between_a_data_repository_and_cloud_storage_like_Google_Drive_or_Dropbox\"><\/span>What\u2019s the difference between a data repository and cloud storage like Google Drive or Dropbox?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Commercial cloud storage services are designed for active file access and sharing, not for long-term preservation or discoverability. A data repository assigns your dataset a persistent identifier (such as a DOI), indexes it for search, and ensures it remains accessible according to defined standards, sometimes for decades. Cloud storage accounts can be closed, reorganized, or have their terms changed without notice.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"How_do_I_handle_data_that_belongs_to_multiple_collaborators_or_institutions\"><\/span>How do I handle data that belongs to multiple collaborators or institutions?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Establish a data governance agreement at the start of the project. This should clearly state who owns the data, who has access rights, how it can be shared or published, and what happens to the data if the collaboration ends or a team member moves to another institution. This is particularly important when collaborating across countries with different legal frameworks.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"My_lab_generates_very_large_datasets_terabytes_of_imaging_or_sequencing_data_Does_RDM_still_apply\"><\/span>My lab generates very large datasets (terabytes of imaging or sequencing data). Does RDM still apply?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>RDM applies especially to large datasets, which are harder to manage retroactively. For high-volume data, it is worth planning storage infrastructure and costs explicitly in your DMP. Some funders allow research data storage costs to be included in grant budgets. Domain-specific repositories often have infrastructure designed for large scientific datasets.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Is_anonymized_data_always_safe_to_share\"><\/span>Is anonymized data always safe to share?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Not necessarily. Re-identification risks are real, especially with genomic data, rare disease data, or small geographic populations. Even data that has been processed to remove obvious identifiers can sometimes be cross-referenced with publicly available information to re-identify individuals. For sensitive data, consult your institution\u2019s data protection officer or legal team before sharing, and consider whether controlled-access sharing is more appropriate.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"How_long_should_I_keep_my_research_data_after_a_project_ends\"><\/span>How long should I keep my research data after a project ends?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This varies by discipline, country, funder, and data type. A common minimum is 10 years, but some funders, institutions, or regulatory frameworks require longer retention, in some cases 25 years or more. <a href=\"https:\/\/www.editage.com\/insights\/a-young-researchers-guide-to-a-clinical-trial\">Clinical trial<\/a> data often carries extended retention requirements. Check your specific funder\u2019s policy and document the required retention period in your DMP.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"What_happens_to_research_data_when_a_PhD_student_or_postdoc_leaves_the_lab\"><\/span>What happens to research data when a PhD student or postdoc leaves the lab?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This is a common and underappreciated risk. Before a lab member departs, ensure that all data is properly documented, stored in a shared institutional location (not on a personal device), and that at least one remaining team member can access and understand it. Creating a \u201cknowledge transfer file\u201d or offboarding checklist is a best practice that many institutions now recommend.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Can_I_use_AI_tools_to_help_with_data_documentation_or_organization\"><\/span>Can I use AI tools to help with data documentation or organization?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>AI tools can assist with tasks like generating README templates, drafting data dictionaries, or suggesting metadata fields: but the researcher remains responsible for the accuracy and completeness of all documentation. AI-generated metadata should always be reviewed against the actual data. Be cautious about uploading sensitive or confidential datasets to third-party AI services, as this may violate data governance agreements or privacy regulations.<\/p>\n","protected":false},"excerpt":{"rendered":"Contents Glossary of Key Terms Key Takeaways What Is Research Data Management? The Research Data Lifecycle The FAIR Principles: The Foundation of Good RDM Step 1: Start with Policies, Ethics, and Legal Compliance Step 2: Build a Sound Data Collection Strategy Step 3: Organize Your Files and Folders Step 4: Document Everything: Metadata and README [&hellip;]","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"","_ayudawp_aiss_summary_provider":"","_ayudawp_aiss_summary_hash":""},"categories":[14],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Research Data Management: How to Make a Data Management Plan (DMP) - Educational Articles For Researchers, Students And Authors - Editage Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Data Management: How to Make a Data Management Plan (DMP) - Educational Articles For Researchers, Students And Authors - Editage Blog\" \/>\n<meta property=\"og:description\" content=\"Contents Glossary of Key Terms Key Takeaways What Is Research Data Management? 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