EMPIAR-10491 - Apoferritin tilt series and frame series used to benchmark the resolution of both data types in M [multiple data sets in TIFF format]

EMPIAR-10491

Apoferritin tilt series and frame series used to benchmark the resolution of both data types in M [multiple data sets in TIFF format]

Publication:

Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.7 Å inside cells

Tegunov D, Xue L, Dienemann C, Cramer P, Mahamid J

bioRxiv (2020)

DOI: 10.1101/2020.06.05.136341

Related EMDB entry:

EMD-11603

Deposited:

2020-08-07

Released:

2020-12-02

Last modified:

2020-12-02

Imageset size:

39.60 GB

Imageset DOI:

10.6019/EMPIAR-10491

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
tilt series

Image Sets

1. Unaligned frame series of apoferritin

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

127

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.834, 0.834)

Details:

Dose = 0.88 e-/A^2/frame
Gain reference needs flipping along the Y axis

Files:

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

2. Unaligned tilt movies of apoferritin and corresponding MDOC metadata

Category:

tilt series

Image format:

TIFF

No. of images or tilt series:

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.834, 0.834)

Details:

Dose = 0.88 e-/A^2/frame
Gain reference needs flipping along the Y axis

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

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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)