Data Packaging Examples and Best Practices

Best Practices:

EEG data sharing should follow community standards, such as the Brain Imaging Data Structure (BIDS), which aligns files and metadata with FAIR principles to support interoperability and reproducibility. Investigators should convert proprietary formats into open, widely supported ones, such as the European Data Format (EDF), and ensure all accompanying metadata are complete and machine-readable. Metadata files (in .json and .tsv formats) should describe hardware details, such as sampling rate and electrode placement, along with task design, event markers, and de-identified participant information.

EEG-Related Resources:

• The Brain Imaging Data Structure (BIDS) includes an Electroencephalography Extension. This framework provides clear, standardized file and folder organization as well as example datasets.
• Annotation, storage, sharing, and publication of EEG data (Youtube Link) highlights the importance of metadata labeling, annotation, storing, and publishing EEG data.

Best Practices:

Neuroimaging data are typically shared in standardized formats such as DICOM (.dcm) and NIfTI (.nii or .nii.gz). DICOM files include structured headers containing image metadata, while NIfTI files store image data with an optional .json metadata file, often generated during conversion (e.g., using dcm2niix). For sharing and curation, investigators should de-identify data, removing facial features and any “burned-in” text containing protected health information (PHI). When possible, organize files according to the Brain Imaging Data Structure (BIDS) to ensure consistency and interoperability. Common tools for viewing or validating files include ImageJ, MRIcron, and AFNI.

Body MRI data cover multiple anatomical regions, including abdominal, cardiovascular, and musculoskeletal areas, and should be carefully documented to capture acquisition parameters, such as body region, coil type, contrast timing, and patient positioning. DICOM is the standard format for preserving image metadata, but when converting to shareable formats like NIfTI, investigators should retain all relevant sequence parameter and anatomical coverage information. While community standards for body MRI data organization are still developing, investigators should apply consistent folder structures and include sidecar metadata files (e.g., .json and .tsv) describing imaging series, de-identification methods, and protocol details.

MRI, fMRI, and body MRI Resources:

• Neuroimaging DICOM and NIfTI Primer (Data Curation Network) is a joint effort by the Data Curation Network, National Institutes of Mental Health, and National Institute of Neurological Disorders, including a comprehensive Primer for DICOM and NIfTI neuroimaging data.
• The COIBIDAS Report (PDF), developed by the Organization for Human Brain Mapping (OHBM), outlines best practices for fMRI data analysis and sharing. While not a required standard, these community recommendations are widely recognized for promoting neuroimaging research transparency, reliability, and reproducibility.
• For submission to OpenNeuro, follow the guidance and submission steps, and use OpenNeuro’s BIDS Validator to check that the dataset meets community standards.
• For body MRI data, the American College of Radiology’s best practices for data sharing emphasize protecting patient anonymity, curating clinically relevant information to support reuse, and ensuring transparency, compliance, and responsible body MRI data sharing.

Best Practices:

Preparing microscopy data for long-term use and reproducibility involves converting proprietary image files into open formats such as OME-TIFF or the cloud-optimized OME-Zarr, which keep complex image data accessible and reusable. Each data package should include detailed metadata—both the technical acquisition information captured by the microscope and the broader experimental context summarized in a README file. Using clear, descriptive file names and a consistent folder structure to separate raw, standardized, and processed data helps others (and the original study team, in the future) navigate the package easily.

Microscopy Resources:

• Open Microscopy Environment includes free, open tools and metadata specifications to support microscopy data management.
• The Brain Imaging Data Structure (BIDS) now includes a BIDS Formatting Microscopy Extension. This framework provides a clear, standardized way to organize files and folders as well as example datasets.
• IDR Repository Templates for Imaging Metadata is not a HEAL-compliant repository, but it provides useful examples and templates for microscopy data sharing via a repository.

Best Practices:

Ultrasound data is often organized and shared in the Digital Imaging and Communications in Medicine (DICOM) format, which preserves standardized image data and acquisition details. For research applications, DICOM files can be converted to NIfTI to support analysis and visualization. Investigators should keep both the original manufacturer files and standardized exports. Metadata should include details about the imaging device, acquisition settings, and imaging mode, but all Protected Health Information (PHI) must be removed or de-identified. Organizing files in a BIDS-like structure with clear, descriptive names, supports readability and automated processing. Any details not captured in the DICOM header should be added in accompanying .json or .tsv files. Documenting software, processing steps, and data provenance helps maintain transparency and reproducibility.

Ultrasound Resource:

• The Brain Imaging Data Structure (BIDS) is currently developing a BIDS Extension Proposal for Ultrasound. This framework is striving to provide a clear, standardized way to organize files and folders.