The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

The lesson introduces the Brain Imaging Data Structure (BIDS), the community standard for organizing, curating, and sharing neuroimaging and associated data. The session focuses on understanding the BIDS framework, learning its data structure and validation processes.

This session moves from BIDS basics into analysis workflows, focusing on how to turn raw, BIDS-organized data into derivatives using BIDS Apps and containers for reproducible processing. It compares end-to-end pipelines across fMRI and PET (and notes EEG/MEG), explains typical preprocessing choices, and shows how standardized inputs plus containerized tools (Docker/AppTainer) yield consistent, auditable outputs.

The session explains GDPR rules around data sharing for research in Europe, the distinction between law and ethics, and introduces practical solutions for securely sharing sensitive datasets. Researchers have more flexibility than commonly assumed: scientific research is considered a public interest task, so explicit consent for data sharing isn’t legally required, though transparency and informing participants remain ethically important. The talk also introduces publicneuro.eu, a controlled-access platform that enables sharing neuroimaging datasets with open metadata, DOIs, and customizable access restrictions while ensuring GDPR compliance.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session dives into practical PET tooling on BIDS data—showing how to run motion correction, register PET↔MRI, extract time–activity curves, and generate standardized PET-BIDS derivatives with clear QC reports. It introduces modular BIDS Apps (head-motion correction, TAC extraction), a full pipeline (PETPrep), and a PET/MRI defacer, with guidance on parameters, outputs, provenance, and why Dockerized containers are the reliable way to run them at scale.

This session introduces two PET quantification tools—bloodstream for processing arterial blood data and kinfitr for kinetic modeling and quantification—built to work with BIDS/BIDS-derivatives and containers. Bloodstream fuses autosampler and manual measurements (whole blood, plasma, parent fraction) using interpolation or fitted models (incl. hierarchical GAMs) to produce a clean arterial input function (AIF) and whole-blood curves with rich QC reports ready. TAC data (e.g., from PETPrep) and blood (e.g., from bloodstream) can be ingested using kinfitr to run reproducible, GUI-driven analyses: define combined ROIs, calculate weighting factors, estimate blood–tissue delay, choose and chain models (e.g., 2TCM → 1TCM with parameter inheritance), and export parameters/diagnostics. Both are available as Docker apps; workflows emphasize configuration files, reports, and standard outputs to support transparency and reuse.

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

Hierarchical Event Descriptors (HED) fill a major gap in the neuroinformatics standards toolkit, namely the specification of the nature(s) of events and time-limited conditions recorded as having occurred during time series recordings (EEG, MEG, iEEG, fMRI, etc.). Here, the HED Working Group presents an online INCF workshop on the need for, structure of, tools for, and use of HED annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. 

In this lesson, attendees will learn about the data structure standards, specifically the Brain Imaging Data Structure (BIDS), an INCF-endorsed standard for organizing, annotating, and describing data collected during neuroimaging experiments. 

This brief video provides a welcome and short introduction to the outline of the INCF Short Course in Neuroinformatics, held Seattle, Washington in October 2023, in coordination with the West Big Data Hub and the University of Washington. 

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This workshop introduces the IBI, the efforts of the Data Standards and Sharing Working Group,  and keynote lectures on the impact of data standards and sharing on large-scale brain projects, as well as a discussion on prospects and needs for neural data sharing.