Where possible, employ one or multiple established metadata standards, or schemas, that are widely used within your discipline. Electronic Lab Notebooks offer several advantages over traditional paper notebooks. They are used to document hypotheses, experiments, analyses, and interpretations of experiments. Lab Notebook: For research groups that use them, lab notebooks are often the primary record of the research process.Learn more about protocols.io for HMS/HSDM/HSPH researchers. If you need to maintain protocols, we strongly recommend a tool like protocols.io. Protocol: A protocol describes the procedure(s) or method(s) used in the implementation of a research project or experiment.Data Dictionary: Also known as a codebook, a data dictionary defines and describes the elements of a dataset so that it can be understood and used at a later date.README: A README File is a text file located in a project-related folder that describes the contents and structure of the folder and/or a dataset so that a researcher can locate the information they need.This documentation can be maintained in a variety of forms. Consider using an existing schema or templates to make the process standardized and easier.Įxperimental and data analysis metadata should be stored alongside your research data. Additional Metadata: Most metadata will be collected manually.Technical Metadata: Generated from research instruments and software used.Your metadata will likely come from several sources during your research: This also ensures that the metadata record is complete and accurate. It is easiest and most efficient to record metadata during the research process while the data still are active. Whenever possible, it is best to consult community standards before you begin collecting research data. Many fields within the biomedical science community are developing standards for what metadata to collect across different data types. ![]() Dataset Level Metadata: Information about the objectives of the research project, participating investigators, relevant publications, and funding sources.Analytical Metadata: Information about data analysis methods, including software name and version, quality control parameters, and output file type details.Experimental Metadata: Information about the experimental conditions (e.g., assay type, time points), the experimental protocol, and the equipment used to generate the data.Technical Metadata: Information automatically generated by research instruments and associated software.Reagent Metadata: Information about the clinical samples, biological reagents (e.g., cell lines, antibodies, siRNAs), chemical reagents (e.g., drugs), etc.Types of documentation to capture biomedical data include: It also facilitates long-term archival preservation of the data. Good metadata enables you to understand, use, and share your own data now and in the future and helps other researchers discover, access, use, repurpose, and cite your data in the long term.
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