EMPIAR-10313 - Cryo-EM reconstructions of tau filaments in chronic traumatic encephalopathy [multiple data sets in MRCS and MRC formats]

EMPIAR-10313

Cryo-EM reconstructions of tau filaments in chronic traumatic encephalopathy [multiple data sets in MRCS and MRC formats]

Publication:

Novel tau filament fold in chronic traumatic encephalopathy encloses hydrophobic molecules

Falcon B, Zivanov J, Zhang W, Murzin AG, Garringer HJ, Vidal R, Crowther RA, Newell KL, Ghetti B, Goedert M, Scheres SHW

Nature 568 (2019) 420-423

PMID: 30894745

DOI: 10.1038/s41586-019-1026-5

Related EMDB entry:

EMD-0528 EMD-0527

Deposited:

2019-08-20

Released:

2020-02-07

Last modified:

2020-02-07

Imageset size:

1.24 TB

Imageset DOI:

10.6019/EMPIAR-10313

Experimental metadata:

Download xml json

Compressed by PDBj:

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

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Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Raw Movies

Category:

micrographs - multiframe

Image format:

MRCS

No. of images or tilt series:

5420

Image size:

(3838, 3710)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.055, 1.055)

Details:

The dose rate is 1.23 electrons / A^2 / frame.

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2. Aligned Micrographs

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

5420

Image size:

(3838, 3710)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.055, 1.055)

Details:

Aligned and dose-weighted sums. Particle positions are in the particles.star files.

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.

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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)