EMPIAR-10445 - Movies of DPS in 260 nm gold foil holes, which eliminate specimen movement [1357 multi-frame micrographs composed of 36 frames each in MRCS format]

EMPIAR-10445

Movies of DPS in 260 nm gold foil holes, which eliminate specimen movement [1357 multi-frame micrographs composed of 36 frames each in MRCS format]

Publication:

Cryo-EM with sub-1 Å specimen movement

Naydenova K, Jia P, Russo CJ

Science (New York, N.Y.) 370 (2020) 223-226

PMID: 33033219

DOI: 10.1126/science.abb7927

Related EMDB entry:

EMD-11210

Deposited:

2020-06-16

Released:

2020-10-21

Last modified:

2022-01-31

Imageset size:

691.83 GB

Imageset DOI:

10.6019/EMPIAR-10445

Experimental metadata:

Download xml json

Compressed by PDBj:

Image formats and pixel types shown below are those of the original, non-compressed files.

MRCS movies (data/*.mrcs) in this entry were compressed by bzip2 (pbzip2)
by EMPIAR Japan to save space and bandwidth.

Some programs (e.g. RELION >= 4.0.1 and CryoSPARC) can process compressed movies without explicit
decompression. For other programs, movies have to be decompressed beforehand. In this case,
you might want to convert decompressed MRC movies into TIFF to save your local storage.

Original size:   1,600,783,693 kilobytes
Compressed size:   725,437,448 kilobytes

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multi-frame micrographs of DPS in 260 nm hole supports

Category:

micrographs - multiframe

Image format:

MRCS

No. of images or tilt series:

1357

Image size:

(4096, 4096)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(0.646, 0.646)

Details:

The beam shifts (X and Y), as reported by EPU, for each micrograph are listed in the attached .dat file.

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Citations

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Yu C, Xu Z, Zeng Q, Wan X, El-Messiry H, Zhang F, Han R. (2026)

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Poudel B, Gyawali R, Dhakal A, Cheng J, Xu D. (2026)

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Dual site targeting of the bacterial 70S ribosome by tetracyclines.

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

See all citations

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[2026-07-15]

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