Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset

RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.

This data ecosystem, Brain Observatory Storage Service & Database (BossDB), contains several neuro-imaging datasets across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include dense segmentation and meshes.

This dataset includes data and models relevant to benchmarking multimodal biological models. The data has been sourced and curated by a team of experts at CZI and is provided as part of these datasets only when it is not publicly available or requires transformation to support effective model benchmarking.

ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methylation studies. It also provides unmethylated whole-genome amplified (WGA) DNA for H. pylori 26695 and a dam- dcm- double mutant (DM) of E. coli that lacks canonical 5mC and 6mA methylation. These variants, together with their wild-type counterparts, offer value for both training and benchmarking DNA methylation calling models.