EMPIAR-10509 - Structure of the Bacterial Ribosome at 2 Å Resolution [multiple data sets in TIFF format]

EMPIAR-10509

Structure of the Bacterial Ribosome at 2 Å Resolution [multiple data sets in TIFF format]

Publication:

Structure of the bacterial ribosome at 2 Å resolution

Watson ZL, Ward FR, Méheust R, Ad O, Schepartz A, Banfield JF, Cate JH

eLife 9 (2020)

PMID: 32924932

DOI: 10.7554/elife.60482

Related EMDB entry:

EMD-22586

Deposited:

2020-09-04

Released:

2020-09-30

Last modified:

2022-02-28

Imageset size:

2.14 TB

Imageset DOI:

10.6019/EMPIAR-10509

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned movies of 70S ribosome complex 1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

3380

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.71, 0.71)

Details:

See details for entire dataset

Files:

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2. Unaligned movies of 70S ribosome complex 2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

3394

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.71, 0.71)

Details:

See details for entire dataset

Files:

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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Citations

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

He L, Bartesaghi A. (2026)

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

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)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)