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    Metadata Guidance

    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.

    Prior to the start of a study, PIs and/or key research staff should begin planning for data collection to assure that data is gathered and documented in a consistent manner throughout the project. Part of that preparation includes the identification of the specific data elements to be collected and making decisions regarding the standard(s) associated with them. 

    Metadata is required for all shared datasets and well-constructed metadata will:

    • maintain compliance with the funder’s data sharing policy.
    • assist others in understanding the data, including the method(s) of collection.
    • enable others to identify the data they want and need.
    • communicate data access processes and restrictions and responsibilities for use.

    Click here to access a tool to help you create Metadata for your project.   More information about Metadata components is detailed below. We will continue to add to guidance and tools for creating metadata as it becomes available.

    Data Sharing, it’s all about the Metadata

    The 3 interconnected components of Metadata are:

    1. Data collection involves gathering information from various sources using various methods, such as surveys, interviews, instrument downloads, or manual data entry.

    2. Data Annotation (Metadata) provides information about the context, structure, and attributes of the data and plays a crucial role in both data collection and sharing. It documents the origin, format, and characteristics of the data, making it easier for others to understand and use.

    • Describe items/content for search and discovery purposes and provide important context about the shared data - enabling users to search, browse, sort, and filter information.
    • Explain the organization of the shared data and/or its relationship(s) to other data, including the structure and navigation of folders and files.
    • Define the administrative properties of shared data, which can include elements such as origins/sources, data standards, technical rules, data retention, access rights, and use.

    3. Data sharing refers to the process of making data available to others, either within an organization or to external parties, to collaborate, accelerate research, or foster innovation.

        Common Data Elements and Data Standards

        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. FAIRsharing.org 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: https://doi.org/10.5281/zenodo.8186982

        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.

        3rd Party Resources

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

        Create metadata for your research project - Stanford University

        The Research Data Management Workbook - California Institute of Technology

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