EMPIAR-10473 - Micrograph frames from 110 internal SEMC/NYSBC test datasets used for Topaz-Denoise model generation & analysis [1000000 micrographs in MRCS format]

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EMPIAR-10473

Micrograph frames from 110 internal SEMC/NYSBC test datasets used for Topaz-Denoise model generation & analysis [1000000 micrographs in MRCS format]

Publication:

Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Bepler T, Kelley K, Noble AJ, Berger B

Nature communications 11 (2020)

PMID: 33060581

DOI: 10.1038/s41467-020-18952-1

Related EMDB entry:

EMD-22058 EMD-22056 EMD-22057 EMD-22059 EMD-22060 EMD-22052 EMD-22053

Deposited:

2019-06-18

Released:

2020-11-27

Last modified:

2021-09-07

Imageset size:

37.30 TB

Imageset DOI:

10.6019/EMPIAR-10473

Experimental metadata:

Download xml json

Contains:

micrographs - single frame

Image Sets

1. Unaligned micrograph frames from 110 internal SEMC/NYSBC test datasets

Category:

micrographs - single frame

Image format:

MRCS

No. of images or tilt series:

1000000

Image size:

(None, None)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(None, None)

Details:

Here are micrograph frames for 110 internal SEMC/NYSBC datasets that we used to generate and analyze numerous different denoising models when writing the Topaz-Denoise software and manuscript. Many of these datasets were used in the pre-trained denoising models included in Topaz. Some frames are in mrcs format, some are in tiff format, and some are zipped with bzip2.

In this EMPIAR entry, you will find an Excel (.xlsx) with sample information, pixelsize, microscope information, and total dose for each dataset.

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Citations

High-resolution ab initio reconstruction enables cryo-EM structure determination of small particles

Kim K, Li H, Clarke OB. (2025)

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.

Krentzel D, Elphick M, Domart MC, Peddie CJ, Laine RF, Shand C, Henriques R, Collinson LM, Jones ML. (2025)

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.

Arora S, Pan SH, Kumar S, Singh SK, Chauhan U, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Chui KT, Gupta BB. (2025)

PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning.

Costa-Gomes B, Greer J, Juraschko N, Parkhurst J, Mirecka J, Famili M, Rangel-Smith C, Strickson O, Lowe A, Basham M, Burnley T. (2025)

Self-supervised learning for generalizable particle picking in cryo-EM micrographs.

Zamanos A, Koromilas P, Bouritsas G, Kastritis PL, Panagakis Y. (2025)

Laser flash melting cryo-EM samples to overcome preferred orientation.

Straub MS, Harder OF, Mowry NJ, Barrass SV, Hruby J, Drabbels M, Lorenz UJ. (2025)

In situ structure determination of conformationally flexible targets using nextPYP.

Liu HF, Zhou Y, Huang Q, Martin J, Bartesaghi A. (2025)

EMICSS: added-value annotations for EMDB entries.

Duraisamy AK, Fonseca N, Kleywegt GJ, Patwardhan A, Morris KL. (2025)

Global conformations of <i>Pichia pastoris</i> complex I are distinguished by the binding of a unique interdomain bridging subunit.

Lee CS, Grba DN, Wright JJ, Ivanov BS, Hirst J. (2025)

Overcoming Preferred Orientation in Cryo-EM With Ultrasonic Excitation During Vitrification

Williams HM, Curtis WA, Haubner M, Hruby J, Drabbels M, Lorenz UJ. (2025)

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