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    Metadata helps researchers understand the content, context, and structure of the dataset. It provides details about variables, units of measurement, data sources, and data collection methods.

    As interdisciplinary research becomes more common, metadata becomes even more critical when datasets from various sources may be combined and analyzed together. It helps researchers from different fields understand and use data from diverse disciplines.

    Metadata can include information about data ownership, licensing, and ethical considerations. Researchers can determine if they have the right to use and share the data based on metadata disclosures and allows them to cite your dataset accurately, giving you credit for your work and contributions to the field.

    Documenting your data at the very beginning of your research project and incorporating changes as your project progresses ensures accuracy and completeness.  Doing so will make the process much easier, as constructing metadata at the end of the project, will be painful and important details may have been lost or forgotten.

    To help others understand your metadata and thus make your data comprehensible and usable, it is good practice to include a README.txt file and/or Data Dictionary with your data. More information about these documents is detailed below. In addition, Stanford University has some excellent information about Metadata and the site is referenced below, as well.

    We will continue to add to guidance and tools for creating metadata as it becomes available.


    Data Standards maintains a registry of terminology artefacts, models/formats, reporting guidelines, and identifier schemas. This link to the search tool displays 60+ data standards that are:
    • recommended by a data policy from a journal, journal publisher, or funder.
    • actively maintained by a representative of the resource.
    • active and ready for use.
    Additional filtering options by subject, domain, species, etc. are available, to narrow down your choices. The FAIRsharing Standards Overview can be found here:

    Common Data Elements

    A Common Data Element (CDE) is a data definition or data element that is commonly used with an agreed-upon standard within a specific domain or across multiple domains and are a recommended component of metadata. The NIH has endorsed CDEs that meet established criteria and the National Library of Medicine maintains the NIH CDE Repository with a search tool that allows users to filter by Institute, data type, keyword, etc.. The use of CDEs contribute to ensure that data is collected, stored, and exchanged consistently and helps to improve data interoperability, facilitate data sharing, and enhance data quality.

    The README File

    The README.txt file is intended as an overview of the data, providing the information needed to make working with (DROs) Digital Research Objects, numerical data, images, spread sheets, etc., easier and increases the accessibility for users and researchers. The following guidelines will help you craft a comprehensive document to assist users. A separate README file is recommended for each distinct dataset. For example, if the same data collection occurs multiple times during your project, a single README file is sufficient for the set. The document may contain any or all of the following information:
    • Keywords: Terms or phrases that describe the subject, domain, and/or content of the data.
    • Persistent Identifiers (PIDs): Unique identifiers, such as: ORCID ids, DOI (Digital Object Identifier), etc.
    • Naming Conventions: Standards used to organize and identify folders and files and for version control.
    • Data Ownership: Details regarding the creator, ownership/source(s), and rights associated with the data.
    • Data Content/Quality: Information on data validation, anomalies, accuracy, precision, and completeness.
    • Time Intervals: Information about the time resolution and frequency of data collection or timestamps indicating when data was collected or recorded.
    Creating a README file at the beginning of your research process, and updating it consistently throughout your research, will help you to compile a final README file when your data is ready for deposit. Publish your README file as a plain text file, avoiding proprietary formats, such as Microsoft Word, whenever possible. The .txt format is recommended due its generic and interoperable properties making it ideal for sharing. If you’ve used (or prefer) a proprietary format, save the document in .txt format prior to sharing.

    The Data Dictionary

    A data dictionary is a structured collection of metadata or information specific to the data elements within your dataset. It helps users understand the context of the data, their attributes, relationships, and definitions. The data dictionary can be part of the README document when the number of data elements is limited, or as a separate document when the data set has a large number of data elements, variables, or requires extensive explanation about the content.  
    • Data Element Name: This is the name of the data element.
    • Definition/Description: Describes the data element, its purpose and its context. e.g., weight in kilos, height in cm
    • Data Type: This defines the type of data that can be stored in a field. E.g., text or numeric, date format
    • Values and Anomalies: Variables used for a particular data element and deviations from standards, norms, or expected results.
    • Data Structure/Groups: A group of data elements that describe a unit in the system and/or relationships between data elements.

    From Stanford University: Creating Metadata for Scientific Research

    Creating metadata manually can be a confusing and time-consuming task.  Stanford University offers information about the process, including existing tools to assist researchers in automating the creation of Metadata.

    Create metadata for your research project - Guides from Stanford University

    We will update this page as we gain more knowledge on this topic.