The San Juan River is a major water source for communities in the Four Corners region of the United States (parts of Colorado, Arizona, New Mexico, Utah) and is a vital source of water for the Navajo Nation. The Navajo Nation Environmental Protection Agency (NNEPA) periodically samples surface water on the Navajo Nation and has found that some elements exceed NNEPA surface water standards (the upper limits of an element for consumption or other use of water). Constituents of concern are substances that could be harmful if present in sufficient quantities, and it is important to monitor the concentrations of these substances in the environment. In the San Juan River, constituents of concern include metals detected in river water, such as arsenic, lead, and aluminum. These metals can come from natural sources or can result from anthropogenic (human) activities and can affect the health of people, plants, and animals. The U.S. Geological Survey (USGS) is working with the NNEPA to identify sources of metals and trace elements entering the San Juan River from tributaries in the reach flowing through the Navajo Nation, and to quantify the contribution from each natural and human-caused source. Sediments were collected in sediment traps in 33 ephemeral or perennial channels that flow into the San Juan River. The sediment traps were placed in the apparent thalweg of the channel, and attached to a T-post. Sites were checked every 2 to 3 weeks and sediment traps were collected if material accumulated. If the traps were empty, they were left deployed. The sediment traps filled during storm events. This data release contains sediment electron microscopy back scatter images and energy-dispersive X-ray spectroscopy (EDS) spectra to identify general chemistry, mineralogy, and grain size from sediments mobilized during high-flow events in the tributaries to the San Juan River. Images and EDS spectra from four locations at 33 sample sites and a sample database file are included. The database file includes the name of each site, names of associated images, grain size and rounding, and elements identified in each image. Images are provided in .zip folders by sample location.

including self-captured light field microscopy datasets with lab-assembled LF microscope.

THIS RESOURCE IS NO LONGER IN SERVICE, documented June 5, 2017. It has been merged with Cell Image Library. Database for sharing and mining cellular and subcellular high resolution 2D, 3D and 4D data from light and electron microscopy, including correlated imaging that makes unique and valuable datasets available to the scientific community for visualization, reuse and reanalysis. Techniques range from wide field mosaics taken with multiphoton microscopy to 3D reconstructions of cellular ultrastructure using electron tomography. Contributions from the community are welcome. The CCDB was designed around the process of reconstruction from 2D micrographs, capturing key steps in the process from experiment to analysis. The CCDB refers to the set of images taken from microscope the as the Microscopy Product. The microscopy product refers to a set of related 2D images taken by light (epifluorescence, transmitted light, confocal or multiphoton) or electron microscopy (conventional or high voltage transmission electron microscopy). These image sets may comprise a tilt series, optical section series, through focus series, serial sections, mosaics, time series or a set of survey sections taken in a single microscopy session that are not related in any systematic way. A given set of data may be more than one product, for example, it is possible for a set of images to be both a mosaic and a tilt series. The Microscopy Product ID serves as the accession number for the CCDB. All microscopy products must belong to a project and be stored along with key specimen preparation details. Each project receives a unique Project ID that groups together related microscopy products. Many of the datasets come from published literature, but publication is not a prerequisite for inclusion in the CCDB. Any datasets that are of high quality and interest to the scientific community can be included in the CCDB.

The Electron Microscopy Data Bank (EMDB) is a public repository for electron microscopy density maps of macromolecular complexes and subcellular structures. It covers a variety of techniques, including single-particle analysis, electron tomography, and electron (2D) crystallography. The EMDB map distribution format follows the CCP4 definition, which is widely recognized by software packages used by the structural biology community.

This is a set of databases containing published use of substances which can be applied to rodents in order to contrast specific structures for optical intravital microscopy.

The first dataset contains applied final dosages, calculated for 25g-mice, as well as the orignally published amounts, concentrations and application routes of agents directly applied into the target organism.

The second dataset contains dosages and cell numbers for the external contrastation and subsequent application of cells into the target organism.

Filtering possible for organ system and contrasted structure/cell type in both datasets, substance class and fluorescent detection windows can be filtered in the dataset for direct agent application.

Source publications are listed by DOI.

BioSR is a biological image dataset for super-resolution microscopy, currently including more than 2200 pairs of low-and-high resolution images covering four biology structures (CCPs, ER, MTs, F-actin), nine signal levels (15-600 average photon count), and two upscaling-factors (linear SIM and non-linear SIM). BioSR is now freely available, aiming to provide a high-quality dataset for the community of single bio-image super-resolution algorithm and advanced SIM reconstruction algorithm developers. For more information about BioSR, please see our Nature Methods manuscript, "Evaluation and development of deep neural networks for image super-resolution in optical microscopy" (DOI: 10.1038/s41592-020-01048-5). Update 2022.10.04 Add DataSet of rDL-SRM (Zenodo Link).xlsx file, which includes descriptions and Zenodo links of BioSR+ (data extension of BioSR) and other data used in our Nature Biotechnology paper "Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes" (DOI: 10.1038/s41587-022-01471-3 ).

This dataset contains light-field microscopy images and converted sub-aperture images. The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.

This data accompanies work from the paper entitled:

Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis.

Waithe D1*,2,, Brown JM3, Reglinski K4,6,7, Diez-Sevilla I⁵, Roberts D⁵, Christian Eggeling1,4,6,8

1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU.
6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany.
7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany.
8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany.

Further details of these datasets can be found in the methods section of the above paper.

Erythroblast DAPI (+glycophorin A): erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone, Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.

Neuroblastoma phalloidin (+DAPI): images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7.

Fibroblast nucleopore: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8.

Eukaryote DAPI: eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9.

C127 DAPI: C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1.

HEK peroxisome All: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9.

Erythroid DAPI All: Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9

COS-7 nucleopore. Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2

COS-7 nucleopore 40x. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11. Average number of cells per image: 31.6.

COS-7 nucleopore 10x. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6

Dataset Annotation

Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (https://www.openmicroscopy.org/omero/). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet. The scripts used to perform this conversion are documented in the repository (https://github.com/dwaithe/amca/scripts/).

This dataset contains the DOIs of the corpus, used for the natural language processing analysis described in the article of the same title. The DOIs all point to articles published in the Microscopy and Microanalysis conference proceeding, spanning 2002 through 2019.

Research data for the manuscript entitled "Coexisting hematite nanoparticles induces and accelerates the transformation of ferrihydrite: pathway and underlying machanisms"

Cell refractive index (RI) was proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used Digital Holographic Microscopy (DHM) to compare RI and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the phase shifts distribution within the reconstructed quantitative phase images (QPIs) was analyzed by the method of bimodality coefficients. The observed correlation of RI and bimodality profile with the cells metastatic potential was validated by real time impedance based-assay and clonogenic tests. We suggest RI and QPIs histograms bimodality analysis to be developed as optical biomarkers useful in label-free detection and quantitative evaluation of cell metastatic potential.

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Advancements in the field of microscopy and imaging have pushed the boundaries of what was once thought possible in many fields of research. New techniques coupled with the application of new technologies allow researchers to probe further and with greater accuracy to answer increasingly complex questions. While these new techniques allow for far greater specificity of observation and increased sensitivity in regard to both resolution and frequency, the amount of data generated is increasing to a point where conventional systems are unable to manage it. At the current time, there is no practical way to analyze, mine, share or interact with large (100+TB) brain image datasets. The development of a national, scalable archival solution and gateway for such datasets is a pressing problem extremely important and central to the NIH mission as in the future there will be a continuous and sustained growth in data scale. To address this issue, we are establishing the BRAIN Imaging Archive. The Archive will encompass the deposition of datasets, the integration of datasets into a searchable web- accessible system, the redistribution of datasets, and a computational enclave to allow researchers to process datasets in-place and share restricted and pre-release datasets. The Archive will, for the first time, provide researchers with a practical way to analyze, mine, share or interact with large (100+TB) image datasets by creating a unique public resource for the BRAIN research community.

Raw microscopy data underlying the publication, structured per experiment (figure). The imaging setting that is varied in the experiments (exposure/dwell time, beam current, landing energy) is indicated by the folder or image name. The images may have a data bar indicating the relevant acquisition settings.

• For more information on the sample preparation and image acquisition, see the Methodology section in the publication.
• The code and data analysis notebooks are included in the accompanying Github repository: https://github.com/hoogenboom-group/Kievits-OSTEM-2023

Data set structure:

• 1_Optimization-OSTEM: contains the images for the landing energy optimization (Figure 2 in the article). Images have a pixel size of 1 nm and where acquired with increments of 500 eV (electron volts). Dwell times are indicated in the folder names. The 'Qualitative' folder contains a subset of these data for ease of plotting Figure 2A.
• 2_Qualitative-comparison: contains qualitative images of rat pancreas and zebrafish larva (Figure 3). Both raw images (with metadata in TIFF header) and cropped images are included (used for publication). Images have a pixel size of 4 nm.
• 3_SNR-comparison-detectors: contains images used for the quantitative comparison of detectors (Figure 4). Contains a folder per detection technique with all the images. The 'Qualitative' folder contains a subset of these data for ease of plotting Figure 4A.
• 4_Image-resolution-detectors: contains raw images and extracted edge widths from FEI Image (Figure 5). The acquisition settings are indicated in the folder names, with one folder per detection technique.
• 5_Current-SNR-relation: contains the images with increasing beam current / fixed dwell time and increasing dwell time / fixed current (Figure 6). The 'Qualitative' folder contains a subset of the data for ease of plotting Figure 6A and 6B.
• 6_Supplementary: contains the supplementary data, used for the background texture analysis. The results in Figure S4 and Table S1 are based on these images.

General acquisition parameters

Pixel size = 1 nm (unless stated otherwise)

Beam current = 0.4nA (unless stated otherwise)

BSD, SE Landing energy = 1.5keV (typically)

OSTEM landing energy = 4keV (typically)

ADF-STEM landing energy = 25keV

Dataset Overview

This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data.

Contents

1. Raw Data: Original microscopy images in CZI format

   • It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes.

2. Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/).

3. Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization.

   • Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing).

4. Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights.

   • Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs.

5. Data Transfer Agreement: Documentation regarding data sharing policies and agreements.

Workflow Overview

• Deskewing: Corrects image distortions caused during acquisition.

• Deconvolution: Enhances image clarity and sharpness.

• Downsampling: Reduces resolution for efficient processing and sharing.

• Conversion: CZI to Zarr or TIFF format for optimized storage and computational use.

Data Access & Usage

• The dataset, including raw and processed files, is hosted on Zenodo.

• Users are encouraged to download downsampled versions for testing before using full-resolution data.

• Processing scripts enable reproducibility and customization for different research applications.

• Data transfer policies are outlined in the included Data Transfer Agreement.

https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice

Raw data and rules to participate in an opinion article on open microscopy data crowdsourced over Twitter.

Light-sheet light-field fluorescence microscopy database

This repository is related to the paper ‘QCM: real-time quantitative quality control of single-molecule localization microscopy data’ by MAILFERT et al. , Biophys Journal, 2025, doi.org/10.1016/j.bpj.2025.02.018 This dataset contains: The QCM software The raw data used to publish the article Data analyzed by QCM. Although single molecule localization microscopy (SMLM) is a powerful and informative imaging technique, it remains very time-consuming, both in terms of data acquisition, post-processing analysis and interpretation of results. All these steps require a high level of expertise on the part of the experimenters to generate reliable SMLM observations. This has led to the development of Quality Control Maps (QCM), the main features of which are: an analysis speed, which, thanks to a graphical processing unit (GPU), exceeds the performance of comparable algorithms, real-time calculation of image quality indicators, and a processing mode without parameter adjustment. Accordingly, QCM provides instant feedback at launch and during acquisition of SMLM data, enabling experimenters to assess the accuracy and robustness of SMLM data acquisition. Licence CC BY-NC-SA 4.0

Light sheet microscopy is a powerful technique for high-speed 3D imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are memory and performance-optimized. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution, and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel ra..., The light sheet, 2-photon, and phase images were collected with homemade light sheet, 2-photon, and oblique illumination "phase" microscopes. The widefield and confocal images were collected with Andor BC43 Benchtop Confocal Microscope (Oxford Instruments). The dataset has been processed with PetaKit5D (https://github.com/abcucberkeley/PetaKit5D)., , # Data for "Image processing tools for petabyte-scale light sheet microscopy data (Part 1/2)"

The image data is organized for the figures in the paper "Image processing tools for petabyte-scale light sheet microscopy data" (https://doi.org/10.1101/2023.12.31.573734):

Description of the data and file structure

Schema of data archive

20211003_Aang_largeFOV_11h_stepAndSettle.zip
├── 20211003_Aang_largeFOV_11h_stepAndSettle
│  ├── flatfieldCorrection
│  │  └── averaged
│  │    ├── 488.tif
│  │    └── 560.tif
│  ├── PSF
│  │  ├── 488_NA0p4_sig0p1_highSN.tif
│  │  └── 560_NA0p4_sig0p1_highSN.tif
│  └── run01
│    ├── ImageList_run01.csv
│    ├── Scan_Iter_0000_000*_000x_00*y_000z_000*t_Settings.txt
│    ├── Scan_Iter_0000_000*_CamA_ch0_CAM1_stack0000_488nm_0000000msec_0155431219msecAbs_000x_00*y_000z_000*t.tif
│    └── Scan_Iter_0000_000*_CamB_ch0_CAM1_stack0000_488nm_0000000msec_0155431219msecAbs_00...

Contains tracks data and metadata in SQL. Can be installed in a local MySQL database for querying.

The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets.Sample: Mouse choroid plexusDataset ID: jrc_choroid-plexus-2EM Data DOI: 10.25378/janelia.13123427EM voxel size (nm): 8.0 x 8.0 x 8.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: s3://janelia-cosem-datasets/jrc_choroid-plexus-2/jrc_choroid-plexus-2.zarr/recon-1/labels/inference/segmentations/Visualization Website: https://openorganelle.janelia.org/datasets/jrc_choroid-plexus-2Publication: Heinrich et al., 2020Segmented Organelles: Endo (M625k), ER (M650k), Mito (M875k), Nucleus (M1100k), PM (M975k), Vesicle (M800k)Included in Dataset: predictions, segmentations