EMPIAR-12284 - Mouse TMEM63b in DDM-CHS micelle [multiple data sets in TIFF format]

EMPIAR-12284

Mouse TMEM63b in DDM-CHS micelle [multiple data sets in TIFF format]

Publication:

Mouse TMEM63b in DDM-CHS micelle

Miyata Y, Takahashi K, Lee Y, Sultan C.S., Kuribayashi R, Takahashi M, Hata K, Bamba T, Izumi Y, Liu K, Uemura T, Nomura N, Iwata S, Nagata S, Segawa K, Nishizawa T

(---)

Related EMDB entry:

EMD-60252

Deposited:

2024-09-03

Released:

2025-06-19

Last modified:

2025-06-19

Imageset size:

995.67 GB

Imageset DOI:

10.6019/EMPIAR-12284

Experimental metadata:

Download xml json

Deposited via PDBj

Contains:

micrographs - multiframe

Image Sets

1. Imageset_1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

2212

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

- 105k (0.83 A/px)
- K3, CDS, Counted SuperResolution - bin2
- 7.6 e/px/sec x 4.6 sec (about 50 e/A^2)
- 49 frames (discard first frame)
- defocus -0.8 ~ -1.6 um
- single shot/hole (beam diameter = 1.1 um)
- Please use gain.(mrc|tiff) as a gain reference without flipping or rotation.

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2. Imageset_2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

1856

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

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)

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)

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)