EMPIAR-10172 - CryoET of hemagglutinin single particle with spot-to-plunge time of 100ms [multiple data sets in MRC format]

EMPIAR-10172

CryoET of hemagglutinin single particle with spot-to-plunge time of 100ms [multiple data sets in MRC format]

Publication:

Reducing effects of particle adsorption to the air-water interface in cryoEM

Noble AJ, Wei H, Dandey VP, Zhang Z, Potter CS, Carragher B

BioRxiv (---)

DOI: 10.1101/288340

Related EMDB entry:

EMD-7628

Deposited:

2018-04-03

Released:

2018-04-05

Last modified:

2018-04-05

Imageset size:

21.93 GB

Imageset DOI:

10.6019/EMPIAR-10172

Experimental metadata:

Download xml json

Contains:

tilt series

Image Sets

1. Tilt-series image stacks

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(3712, 4984)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(2.34, 2.34)

Details:

Not frame-aligned.
.mdoc files included.

Files:

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

2. Appion-Protomo tilt-series alignments

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(3712, 4984)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(2.34, 2.34)

Details:

Includes a full tilt-series alignment run for each tilt-series. To view properly, navigate to the protomo_alignments folder in Appion-Protomo.

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

AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM.

Ramírez-Aportela E, Zarrabeitia OL, Fonseca YC, Ceska T, Subramaniam S, Carazo JM, Sorzano COS. (2026)

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)

Helical reconstruction of amyloids in cryoSPARC.

Schaefer JH, O'Neill RT, Donnelly JP, Powers ET, Kelly JW, Lander GC. (2026)

An unattended image-processing pipeline for on-the-fly quality assessment and 3D exploration in cryo-EM.

Marchán Torres D, Conesa P, Garcia A, Iceta M, Broche L, Gragera M, Linares R, Kwong HS, Chichón FJ, Svensson O, Arranz R, Effantin G, Kandiah E, Carazo JM, Sorzano COS. (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)

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

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

CryoWriter: a robotic solution for improved Cryo-EM grid preparation.

Kv C, Ekundayo B, Di Fabrizio M, Mohammed I, Radecke J, Stahlberg H, Kube M. (2026)