# Big-Data Electron-microscopy for Novel Community Hypotheses: Measuring And Retrieving Knowledge (BENCHMARK)

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $631,212

## Abstract

Project Summary
In an effort to better understand structural organization and anatomy of nervous systems at
unprecedented spatial resolution, recent efforts, including BRAIN Initiative funded projects, have
collected increasingly larger datasets using Electron Microscopy (EM) and X-Ray
Microtomography (XRM). We can now image neural tissue across a range of different scales,
potentially forming the basis for the next generation of brain atlases at submicron and nanometer
resolution. However, there is huge variability in data collection approaches, as well as ongoing
research into evolving imaging technology, experimental protocols, data storage, and post-
processing methods. Different resolutions, contrasts, staining, image corrections, data
compression, machine learning algorithms, and metadata are all being developed. To enable
comparison, meta-analysis, and registration with other datasets and imaging modalities, new
standards for EM and XRM data are required, similar to those pursued in light microscopy,
magnetic resonance imaging, and other domains. In this time period of growth in EM and XRM
imaging, and its increased adoption and utilization for neuroscientific investigations, it is a critical
time to implement standards that ensure interoperability, sustainability, and availability of these
expensive datasets. This will be critical to enable openness, sharing between laboratories, and
reproducible results on these large and expensive datasets. This proposal aims to develop
standards for large scale EM and XRM structural data, as well as standards for annotations and
links to complementary data sources. This will enable validation, sharing, and replication, greatly
amplifying investment in other BRAIN initiative projects in this community. Our team will bring
together a community of researchers into two complementary Working Groups (WGs) for Image
and Experimental Metadata Standards and Annotation Standards. This community of interest will
collaboratively develop standards and disseminate results in conjunction with BRAIN initiative
projects and archives. Finally, this project will build tools to query and retrieve image and
annotation data, including motif discovery, through a community portal and open source tools.
This will allow scientists to reproducibly analyze data, test hypotheses, and share data products
and results with the community. We will emphasize collaboration with existing standards across
communities and the development and integration of software tools supporting the standards to
ensure adoption.

## Key facts

- **NIH application ID:** 10457455
- **Project number:** 5R01MH126684-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** William R Gray Roncal
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $631,212
- **Award type:** 5
- **Project period:** 2021-08-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10457455

## Citation

> US National Institutes of Health, RePORTER application 10457455, Big-Data Electron-microscopy for Novel Community Hypotheses: Measuring And Retrieving Knowledge (BENCHMARK) (5R01MH126684-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10457455. Licensed CC0.

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