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Data Collection
Explore these resources to support transparent and reproducible data collection in your research. They provide practical tools and guidance to help implement open science practices from the ground up.
FAIR
This page introduces the FAIR Principles — guidelines for making research data Findable, Accessible, Interoperable, and Reusable — and shows how to apply them to enhance open science and data sharing.PsyToolkit
PsyToolkit is a free online platform for creating and running psychological experiments and surveys. It lets researchers easily collect, store, and share data, supporting transparent and reproducible research.PsychoPy
PsychoPy is an open-source platform for designing and running neuroscience and psychology experiments. It enables researchers to create custom tasks, collect data efficiently, and share experiments to support transparent and reproducible research.Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a widely adopted standard for organizing, labeling, and describing neuroimaging data. It gives researchers a clear, consistent way to structure their files, which reduces confusion and saves time during analysis. By using BIDS, research teams can more easily share data, apply common tools, and reproduce results with greater confidence. This framework supports transparency, improves collaboration, and helps keep neuroscience workflows more efficient and reliable.BIDS Starter Kit
The BIDS Starter Kit is a community-driven collection of guides, examples, and practical tools designed to help researchers adopt the Brain Imaging Data Structure (BIDS) with confidence. It offers clear tutorials, best-practice workflows, and ready-to-use templates for organizing neuroimaging datasets. Whether you're new to BIDS or looking to refine your workflow, the Starter Kit makes it easier to structure data correctly, improve reproducibility, and collaborate effectively across research teams.BIDS Validator
The BIDS Validator is an automated tool that checks whether a neuroimaging dataset follows the Brain Imaging Data Structure (BIDS) standard. By scanning files and metadata for errors, inconsistencies, or missing information, it helps researchers ensure their datasets are well-organized, compliant, and ready for sharing or analysis. Using the Validator streamlines quality control and supports more transparent, reproducible neuroimaging research.BIDS Apps
BIDS Apps are a curated collection of open-source neuroimaging tools designed to run seamlessly on BIDS-formatted datasets. Each app bundles a complete analysis pipeline—packaged for easy use, portability, and reproducibility—so researchers can process data consistently across different computing environments. With BIDS Apps, complex workflows become more accessible, standardized, and shareable, supporting transparent and reproducible neuroimaging research.ezBIDS
ezBIDS is a user-friendly tool that helps researchers convert their neuroimaging data into the Brain Imaging Data Structure (BIDS) format. It guides users through the organization and annotation process step by step, automatically handling much of the file structuring and metadata creation. With ezBIDS, preparing datasets for analysis, sharing, and reproducibility becomes faster, easier, and far less error-prone.NeuroData Without Borders (NWB)
Neurodata Without Borders (NWB) is a community-driven data standard designed to organize, store, and share neurophysiology data in a consistent and transparent way. By providing a unified format and tools for managing complex experimental recordings, NWB helps researchers make their datasets interoperable, reusable, and easier to integrate into open-science workflows. This standard supports greater collaboration, long-term preservation, and more efficient analysis across the neuroscience community.DataLad
DataLad is a flexible data management system that helps researchers organize, track, and structure their datasets while integrating seamlessly with widely used data infrastructures like Git and Git-annex. It enables transparent version control, efficient sharing, and reliable reproduction of complex research workflows. By unifying data, code, and metadata, DataLad supports collaborative, open-science practices and makes it easier to manage research projects from start to finish.Center for Expanded Data Annotation and Retrieval (CEDAR)
CEDAR is a platform designed for creating, managing, and sharing high-quality metadata. It provides structured templates, automated validation, and collaborative tools that help researchers standardize their data descriptions and ensure they meet community and publisher requirements. By simplifying the process of generating clear, consistent metadata, CEDAR enhances the discoverability, reuse, and transparency of research within open-science workflows.