Macquarie University
13 files

DeepSlice Source Data and Ground Truth datasets

posted on 2023-09-20, 06:08 authored by Simon McMullanSimon McMullan, Harry Carey

Hi! Welcome to the DeepSlice data repository!

This repository contains:

  1. Source data used to generate the graphs shown in Figures 1, 2 & 3 of Carey et al. (2023).
  2. Histological images and alignment data used to guide the development of, and assess the performance of, the DeepSlice algorithm.
  3. Code you can use to replicate the DeepSlice alignment files

Sources data are organised in Excel spreadsheets, with data corresponding to each panel itemised in separate worksheets (Figure 1.xlsx, Figure 2.xlsx, Figure 3.xlsx).

Histological images corresponding to Ground Truth datasets are saved in .Zip files.

The following zip repositories are included:


Source DOI








β amyloid

provided by Yates et al. 10.3389/fninf.2019.00075





The GLT1a, PcP2, CamKII, Myelin & Pitx3 datasets are made available from eBrains via CC4.0 license.

Calb1 dataset is courtesy of the Allen Institute for Brain Science. Allen Mouse Brain Atlas dataset# 71717640 Available from Allen Institute for Brain Science (2011).

The β amyloid dataset is courtesy of Yates et al.

Alignment metadata, generated by human or DeepSlice, are provided as .xml and .csv files in corresponding Zip archives. To view human- or DeepSlice-generated alignments save image files and .xml files to the same directory and load in QuickNII ( The DeepSlice source code, and tools used to calculate differences in alignments, are available at

We hope you find this useful, either as a way of validating or replicating our work or as a means to quantify the performance of other alignment algorithms. Don't forget to cite our paper if you do!

Harry Carey, Michael Pegios, Lewis Martin, Chris Saleeba, Anita J. Turner, Nicholas Everett, Maja A. Puchades, Jan G. Bjaalie & Simon McMullan (2023) DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas, Nature Communications, DOI:10.1038/s41467-023-41645-4


Research Project ID

Project ID : 231089747

Q/A Log

  • Institutional review completed
  • FAIR assessment completed

FAIR Self Assessment Summary

This text has been generated from a tool that has been adapted from the ARDC FAIR Assessment Tool Findable -------- Does the dataset have any identifiers assigned? Global Is the dataset identifier included in all metadata records/files describing the data? Yes How is the data described with metadata? Comprehensively (see suggestion) using a recognised formal machine-readable metadata schema What type of repository or registry is the metadata record in? Data is in one place but discoverable through several registries Accessible ---------- How accessible is the data? Publicly accessible Is the data available online without requiring specialised protocols or tools once access has been approved? Standard web service API (e.g. OGC) Will the metadata record be available even if the data is no longer available? Yes Interoperable ------------- What (file) format(s) is the data available in? In a structured, open standard, machine-readable format What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements? Standardised vocabularies/ontologies/schema without global identifiers How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)? The metadata record includes URI links to related metadata, data and definitions Reusable -------- Which of the following best describes the license/usage rights attached to the data? Standard machine-readable license (e.g. Creative Commons) How much provenance information has been captured to facilitate data reuse? Fully recorded in a machine-readable format

FAIR Self Assessment Rating

  • 5 Stars

Data Sensitivity

  • General