The map viewers of the SPARC Portal ( https://sparc.science ) provide interactive, modular, and continually updated visualizations of nerve-organ anatomy and function. SPARC data and knowledge are registered onto two-dimensional flatmaps, and three-dimensional anatomical organ and whole-body scaffolds. Flatmaps are zoomable maps built from a range of sources and portray anatomy and nerve knowledge of a given species. S caffold maps are three-dimensional geometric models able to represent the spatial distribution of connectivity knowledge, observed data, and computational models. These maps, and their annotations, are published as SPARC datasets and they are available for public viewing using the map viewers on the SPARC Portal ( https://sparc.science/apps/maps ). The map viewers provide visual interfaces for exploring data and tools in the context of knowledge about neural connectivity. Visualization on the viewer can be customized using different settings. Search can be conducted on both flatmaps and scaffolds to find and highlight specific features. The maps sidebar provides the user with a way to navigate the data. Selecting a teardrop marker in the maps will open the sidebar automatically and launch a search for data relevant to the selected anatomical feature. The sidebar includes a mini gallery for each dataset to enable the user to quickly visually page through the content of each dataset and, where appropriate, launch the associated viewer. Data and literature sources and other relevant information of a network and connectivity can be explored comprehensively, based on standardized annotations. Multiple maps can be visualized simultaneously using a split-pane display system. Users can dynamically create permalinks to specific views, allowing knowledge and corresponding visualization to be shared and cited. Authenticated portal users can create new annotations on existing maps by drawing directly on the maps and adding associated literature and/or data evidence in support of their annotations. Development of the map viewers is on-going, and new features continue to be deployed to the SPARC Portal to support the growing collection of data and knowledge available there. This work is supported by the NIH SPARC program under award number OT3OD025347. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

The integrated maps viewer of the SPARC Portal ( https://sparc.science ) provides interactive, modular, and continually-updated visualizations of nerve-organ anatomy and function. SPARC data and knowledge is registered onto two-dimensional flatmaps, and three-dimensional anatomical organ and whole-body scaffolds. Flatmaps are zoomable maps built from a range of sources and portray anatomy and nerve knowledge of a given species, and scaffold maps are geometric models able to represent the spatial distribution of connectivity knowledge, observed data, and computational models. These maps, and their annotations, are published as SPARC datasets and they are available for public viewing using the map viewers on the SPARC Portal ( https://sparc.science/maps ).The map viewers provide visual interfaces for exploring data and tools in the context of knowledge about neural connectivity. Visualization on the viewer can be customized using different settings. Search can be conducted on both the flatmap and scaffold maps to find and highlight specific features. Data and literature sources and other relevant information of a network and connectivity can be explored comprehensively based on standardized annotations. Multiple maps can be visualized simultaneously using a split-pane display system. Users are able to dynamically create permalinks to specific views, allowing knowledge and corresponding visualization to be shared and cited. Development of the map viewers are on-going and new features continue to be deployed to the SPARC Portal. Subject-specific vagus nerve exploration on the flatmaps and whole-body scaffolds is a key feature planned to be available later this year. This work is supported by the NIH SPARC program under award number OT3OD025347. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The December 2022 release of the SPARC Portal ( https://sparc.science ) included the first significant update to the anatomical connectivity flatmaps since the portal first launched. These flatmaps provide an interactive and visual map for the display and exploration of the autonomic nervous system of Human, rat, mouse, pig, and cat ( https://sparc.science/maps ). In addition to improved anatomical ‘cartoons’ for all species and adding a female Human map, these maps, for the first time, automatically render the connectivity knowledge directly retrieved from the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN; https://sparc.science/resources/6eg3VpJbwQR4B84CjrvmyD ). SCKAN contains explicit knowledge about CNS-ANS-end organ circuitry derived from SPARC data and scientific literature, in a form that supports computational reasoning. Each flatmap consists of a manually drawn base layer for the species-specific anatomical cartoon and a layer of manually drawn tracts for the large nerves. By annotating these drawings with standard reference SPARC vocabularies consistent with SCKAN usage, software tools are then able to semantically map connectivity circuits retrieved from SCKAN to the visual representation on each species’ flatmap. Beyond the interactive visual rendering of the circuitry on the SPARC Portal, the semantic consistency between flatmaps and SCKAN powers further user interface components on the SPARC Portal to surface additional knowledge for each rendered connection. For example, comprehensive links to the literature and/or data supporting a connectivity statement can be retrieved from SCKAN and provided to Portal users. Now that tools are in place to support this automated workflow to generate the flatmaps from SCKAN knowledge, we are continuing to improve the SPARC Portal to visualise new knowledge as it becomes available. NIH Common Fund, NIH Office of the Director, Awards OT3OD025349 and OT2 OD030541 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps, and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

The VPH/Physiome project is developing tools and model databases for computational physiology based on three primary model encoding standards: CellML, SBML and FieldML. For the modelling community these standards are the equivalent of the DICOM standard for the clinical imaging community and it is important that the tools adhere to these standards to ensure that models from different groups can be curated, annotated, reused and combined. This chapter discusses the development and use of the VPH/Physiome standards, tools and databases, and also discusses the minimum information standards and ontology-based metadata standards that are complementary to the markup language standards. Data standards are not as well developed as the model encoding standards (with the DICOM standard for medical image encoding being the outstanding exception) but one new data standard being developed as part of the VPH/Physiome suite is BioSignalML and this is described here also. The PMR2 (Physiome Model Repository 2) database for CellML and FieldML files is also described, together with the Application Programming Interfaces (APIs) that facilitate access to the models from the visualization (cmgui and GIMIAS) or computational (OpenCMISS, OpenCell/OpenCOR and other) software.

The multitude of biosignal file formats used in research has hampered the easy exchange of biosignals and their use with physiological modelling software. We describe an abstract data model that accommodates the diversity of formats, along with a software implementation which links biosignal data into the Semantic Web, using existing data formats. Initial application of our work is to sleep study research.

The domain specific nature of biosignal storage formats, along with the lack of support for metadata in generalpurpose biosignal libraries, has hampered the easy interchange of biosignals between disciplines and their integration with physiological modelling software. Extensible Biosignal Metadata (XBM) is introduced as a standard framework to facilitate the sharing of information between and within research groups for both experimentalists and modellers; to help establish more web-accessible biosignal repositories; and, by using semantic web technologies, to result in the discovery of knowledge by automated reasoning systems.