Documenting your research data is a prerequisite for data sharing and your own use of your data in the future. Good documentation helps your data be discoverable, understood, and trusted by others. Please view our individual modules for the training “Documenting Your Research Data” to learn documentation best practices by subtopic (e.g., code, tabular data, using documentation standards). Resources in these modules are also available in the Guide for Documenting Research Data.

You can access these modules by logging in Blackboard with JHED and password. You first need to self-enroll with this link before start viewing your first module. 

  • Introduction to Documentation

    While you probably understand that data documentation is important,  you may not have received specific training on this topic during the course of your career. In this module, you will learn what you should document about your data, code, notes/methods, and project as a whole. This module will also expose you to common forms of research documentation, without diving deeply into any one research domain or data type. This module is for researchers who want an overview of what aspects of their research to document and general steps to do so.  Please note, this module is not a prerequisite for the other modules in this series, but viewing it first may improve your familiarity with the topics covered in our more specialized documentation modules.

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  • Metadata and Metadata Standards

    Using metadata standards will help you stay consistent in your documentation and ensures other researchers in your field can understand and re-use your data. This module will define metadata and metadata standards, introduce different types of documentation where standards can be used, provide examples of standards in practice, and show where to find standards for your field. This module is for any researcher who wants to improve the reusability of their data, regardless of file format or discipline. 

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  • Tabular Data

    Tabular data is one of the most widely used ways to structure research data. Without good documentation of the variables/elements and values, it will be difficult to utilize your valuable research. In this module you will learn what to document about your tabular data set and how to create that documentation including discussions around codebooks and data dictionaries. This module is for researchers who work with tabular data and want to improve the reusability of their data.  

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  • Code

    Researchers often write code to acquire, manage and analyze research data. Providing good documentation for your code is important for others to understand and reuse your scripts. In this module you will learn 6 best practices tips to document code well, including giving good variable/function names, writing helpful comments, using a computational notebook, attaching a ReadMe, using a version control system and sharing code in an online repository. This module is for researchers who write code for research and want to improve documenting their scripts, code, and software. 

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  • Medical Data

    Medical and health research have distinct requirements for documentation. Good documentation facilitates collaboration among a broad range of specialized roles, translational research for applying results to treatments, and compliance with regulations for protecting patients. This module is for anyone conducting or supporting research in clinical, biomedical, and public health research, emphasizing planning for sharing data with collaborators and biomedical communities.

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  • Geospatial Data

    Geospatial data is created and used by a wide variety of research disciplines. This module covers what you should document about geospatial data and how to do so, often by using examples from ESRI ArcGIS. It also contains an optional module on “what are geospatial data”, which provides definitions and examples of what the term means. This module is for people who would want to learn how to better document their geospatial data, but don’t know where to start.

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These materials are licensed under a Creative Commons Attribution-NonCommercial–ShareAlike 4.0 International License, attributable to Data Services, Johns Hopkins University.​