Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Data file for: Three-Dimensional Electrical Imaging Across the Cona Woka Rift and Yalaxiangbo Dome in Southern Tibetan Plateau

The magnetotellurics data were used to study  the lithospheric electrical  structure across the Cona Woka rift and Yalaxiangbo dome in the southern Tibetan plateau, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences.  The data file of CN6.dat was generated by the Matlab code EM3DVP. You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.

Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes

Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of: Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes Jina Ko1, Martin Wilkovitsch2, Juhyun Oh1, Rainer Kohler1, Evangelia Bolli1,3, Mikael J. Pittet1,3,4,5, Claudio Vinegoni1, David B. Sykes6,7, Hannes Mikula2, Ralph Weissleder1,8*, Jonathan C. T. Carlson1,7* 1 Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114  2 Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria  3 Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland 4 Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland 5 AGORA Cancer Center, Lausanne, Switzerland 6 Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA 7 Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA 8 Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115

opencc-by-4.0Jun 2021View details →

MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)

This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference. Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code> Also see <a href="https://github.com/bioasp/merrin-covert">https://github.com/bioasp/merrin-covert</a> The Docker image can be executed as follows: <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>. The image can be imported using the command <code>docker load</code> with the image file provided in this record: <pre><code>docker load -i image.tar.gz</code></pre> or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>: <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>

Dataset: Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry

This is supplementary data for the article currently in review for The Cryosphere, titled "Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry". <a href="https://doi.org/10.5194/tc-2022-14">See the preprint here</a>

opencc-by-4.0Jul 2022View details →

Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.

This database provides as polygons the contours of 900 juniper shrubs (Juniperus communis L. and Juniperus sabina L.) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation).

DECIMER Image classifier dataset

Images dataset divided into train (10905114 images), validation (2115528 images) and test (544946 images) folders containing a balanced number of images for two classes (chemical structures and non-chemical structures). The chemical structures were generated using RanDepict to random picked compounds from the ChEMBL30 database and the COCONUT database. The non-chemical structures were generated using Python or they were retrieved from several public datasets: COCO dataset, MIT Places-205 dataset, Visual Genome dataset, Google Open labeled Images,  MMU-OCR-21 (kaggle), HandWritten_Character (kaggle), CoronaHack -Chest X-Ray-dataset (kaggle), PANDAS Augmented Images (kaggle), Bacterial_Colony (kaggle), Ceylon Epigraphy Periods (kaggle), Chinese Calligraphy Styles by Calligraphers (kaggle), Graphs Dataset (kaggle), Function_Graphs Polynomial (kaggle), sketches (kaggle), Person Face Sketches (kaggle), Art Pictograms (kaggle), Russian handwritten letters (kaggle), Handwritten Russian Letters (kaggle), Covid-19 Misinformation Tweets Labeled Dataset (kaggle) and grapheme-imgs-224x224 (kaggle). This data was used to build a CNN classification model using as a base model EfficienNetB0 and fine tuning it. The model is available on <a href="https://github.com/Iagea/CNN_chem_not_chem">Github</a>.

opencc-by-4.0Jul 2022View details →

Contact Endoscopy – Narrow Band Imaging (CE-NBI) Data Set for Laryngeal Lesion Assessment

The endoscopic examination of subepithelial vascular variations of vocal folds can provide complementary diagnostic information for clinicians regarding the development of benign and malignant laryngeal lesions. As one novel technique, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) can provide real-time and enhanced visualization of these vascular structures. Several studies have addressed the concern of subjective evaluation of CE-NBI images, resulting in the development of multiple computer-based solutions.  We introduce the CE-NBI data set, the first publicly available data set with enhanced and magnified visualization of vocal fold subepithelial blood vessels. It comprises 11144 images of 210 adult patients with benign and malignant lesions in the vocal fold. Image annotations include as following for all images of every patient:  <ul> <li> Diagnosed laryngeal histopathology label.  </li> </ul> <ul> <li> Lesion type benign-malignant label.  </li> <li> Leukoplakia diagnosis label.  </li> </ul> The dataset consists of two main categories: benign and malignant images. In each category, the images of every patient are ordered according to the laryngeal histopathology class. Additionally, one Excel file is provided to map the image files of each patient to three image labels and image dimensions.  This data has successfully been used to perform clinical evaluations as well as design and develop multiple Machine Learning (ML)-based algorithms for laryngeal cancer assessment.

opencc-by-4.0Jul 2022View details →

Images and catalogs of HST and JWST images in the SMACS-0723 field

This repository is a first-pass reduction of the HST and JWST images of the SMACS-0723 lensing cluster field.   All images have been processed with the <a href="https://github.com/gbrammer/grizli">grizli</a> software pipeline.  Further documentation will be provided by Brammer et al. (in prep).

Touché22-Image-Retrieval-for-Arguments

Data for the <a href="https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2022. This version is lacking the touche22-image-search-archives.zip and touche22-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/</a>

opencc-by-4.0Apr 2022View details →

Exoplanet imaging data challenge, phase 2

Datasets for the second phase of the Exoplanet Imaging Data Challenge (<a href="https://exoplanet-imaging-challenge.github.io/">https://exoplanet-imaging-challenge.github.io</a>).  The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must perform two tasks: provide (1) astrometry of the detected signals, and (2) spectrophotometry of the detected signals. For this phase, we therefore provide with 8 high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in .fits format): (a) image_cube_instxx.fits: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode; (b) parallactic_angles_instxx.fits: the corresponding parallactic angle and airmass variation during the observation sequence; (c) wavelength_vect_instxx.fits: the corresponding wavelength vector for each spectral channel; (d) psf_cube_instxx.fits: the non-coronagraphic (point spread function) multispectral image of the target star; (e) first_guess_astrometry_instxx.fits: a first guess position w.r.t the star of the (2 or 3) injected planetary signals. Each data set has 2 to 3 injected planetary signals in various locations. Each data set has very different observing conditions, from very good to very bad. Additional information and ressources can be found on the <a href="https://exoplanet-imaging-challenge.github.io/">website</a> and dedicated <a href="https://github.com/exoplanet-imaging-challenge/phase2">Github repository.</a> The results of the data challenge must be submitted directly by participants on the <a href="https://eval.ai/web/challenges/challenge-page/1717/overview">EvalAI platform</a>.

Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC -- Downsampled Version

This repository holds downsampled example data for publication: "A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. For full-resolution testing data, please refer to Zenodo repository at DOI: <a href="https://zenodo.org/record/6334810#.YvD6zHZBxD8">10.5281/zenodo.6334810</a>.

Historic manuscript page images with noisy labels

Images of digitised manuscript pages sourced from <a href="https://iiif.biblissima.fr/collections/">https://iiif.biblissima.fr/collections/</a>. This dataset aims to facilitate experiments using existing data/metadata to train computer vision models. In particular, using 'noisy' labels in some capacity. Each image is taken from a page of a manuscript listed on <a href="https://iiif.biblissima.fr/collections/">https://iiif.biblissima.fr/collections/</a>. Each example includes the labels included in the IIIF manifests for these images. The data includes the following columns: <ul> <li>image: an IIIF URL for the image</li> <li>manifest_url: A URL for the IIIF manifest for the image</li> <li>license: for each image</li> <li>label: the text found in the manifest 'label' field.</li> <li>attribution: which institution the image comes from</li> </ul>

Minirhizotron images for RootPainter demo

This dataset consists of 100 minirhizotron images (2340 x 2400 pixels; resolution: 148 px/mm) taken with the manual UHD minirhizotron camera (<a href="https://www.vienna-scientific.com/products/minirhizotron-systems/manual/">VSI-BARTZ MS-190</a>) from Vienna Scientific Instruments. The images were acquired in a grassland field experiment (<a href="https://gepris.dfg.de/gepris/projekt/420444099?language=en">POEM experiment</a>) in which the order of arrival of three plant functional groups (forbs, grasses and legumes) was manipulated. In each plot, two root observation tubes were installed at a 45° angle six months before the start of the experiment (i.e. the first sowing event on April 13, 2021). Along each tube, images are taken at 18 different depths (from 1.4 to 49.5 cm) twice a month from April to September and once a month from October to March. The POEM experiment is funded by the <a href="https://www.dfg.de/">German Research Foundation</a>.

4 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file

This is a 4 image lysozyme datasets derived from https://doi.org/10.5281/zenodo.3352357.  The specific 4 images are able to be processed by the software package DIALS using commands in the linked dataset above.  The images were rounded to integer and compressed to save file space using this script: <pre><code class="language-python">import shutil, h5py import numpy as np shutil.copyfile('../lyso009a_0087.JF07T32V01_master.h5', 'lyso009a_0087.JF07T32V01_master_4img.h5') h5 = h5py.File('lyso009a_0087.JF07T32V01_master_4img.h5', 'r+') data = h5["entry/data/data"][()] del h5["entry/data/data"] h = h5["entry/data"] subset = data[5:9].astype(np.int32) h.create_dataset("data", subset.shape, subset.dtype, subset, compression="gzip", compression_opts=9) h5.close()</code></pre> The .expt file was created by dials.import and is useful for regression testing in DIALS.

HER2 overexpression in gastroesophageal adenocarcinoma from immunohistochemstry imaging.

HER2 overexpression in gastroesophageal adenocarcinoma from immunohistochemstry imaging. Primary dataset used in Predicting the HER2 status in esophageal cancer from tissue microarrays using convolutional neural networks [1]. This dataset is composed by HER2 immunohistochemistry images of gastroesophageal adenocarcinoma patients. We provide a train-test split of .jpg image files of individual tissue cores, which were labeled with an immunohistochemstry score (ranging from 0 to 3) and a HER2 overexpression status (positive or negative).   Detailed information Train split comes from a multi-spot tissue microarray (TMA) with 165 tumor cases and a single-spot TMA with 428 tumor cases, as described elsewhere [2]. Test split comes from an independent single-spot TMA with 307 tumor cases as the test dataset. The test set consisted of tumor cases that occurred at a later time point compared to the training set cases. This dataset construction strategy mimics how such a model would be developed and deployed in a clinical routine. Coincidentally, the test set does not include tumor cases with an IHC score of 1. The multi-spot TMA was composed of eight tissue cores (1.2 mm diameter) of each tumor - four cores punched on the tumor margin and four in the tumor center. To construct the single-spot TMA, one tissue core per patient from the tumor center was punched. The cores were transferred to TMA receiver blocks. Each TMA block contained 72 tissue cores. Subsequently, 4 µm-thick sections from the TMA blocks were prepared and transferred to an adhesive-coated slide system (Instrumedics Inc., Hackensack, NJ). We used a HER2 antibody (Ventana clone 4B5, Roche Diagnostics, Rotkreuz, Switzerland) on the automated Ventana/Roche slide stainer to perform immunohistochemistry (IHC) on the TMA slides. HER2 expression in carcinoma cells was assessed according to staining criteria listed in the Supplemental Table 1 of [1]. Scores 0 and 1 indicated negative HER2 status, and score 3 indicated positive HER2 status. Immunohistochemical expression evaluation was assessed manually by two pathologists according to [3]. Discrepant results, which occurred only in a small number of samples, were resolved by consensus review. Spots with a score of 2 were analyzed by fluorescence in situ hybridization (ISH) to resolve the HER2 status. The ISH analysis evaluated the HER2 gene amplification status using the Zytolight SPEC ERBB2/CEN 17 Dual Probe Kit (Zytomed Systems GmbH, Germany) according to the manufacturer's protocol. A fluorescence microscope (DM5500, Leica, Wetzlar, Germany) with a 63× objective was used for scanning the tumor tissue for amplification hotspots. We counted the signals in randomly chosen areas of homogeneously distributed signals. Twenty tumor cells were evaluated by counting green HER2 and orange centromere-17 (CEN17) signals. The reading strategy followed the recommendations of HER2/CEN17 ratio ≥ 2.0 or HER2 signals ≥ 6.0 for HER2 positive and a HER2/CEN17 ratio < 2.0 for HER2 negative samples. Slides were digitised with a slide scanner (NanoZoomer S360, Hamamatsu Photonics, Japan) with 40-times magnification and used QuPath's [4] TMA dearrayer to slice the digitized slides into individual images.   [1] Pisula JI, Datta RR, Boerner-Valdez L, Avemarg JR, Jung JO, Plum P, Loeser H, Lohneis P, Meuschke M, dos Santos DP, Gebauer F, et al. Predicting the HER2 status in esophageal cancer from tissue microarrays using convolutional neural networks. bioRxiv: https://www.biorxiv.org/content/10.1101/2022.05.13.491769v1 [2] Plum PS, Gebauer F, Krämer M, Alakus H, Berlth F, Chon SH, Schiffmann L, Zander T, Büttner R, Hölscher AH, Bruns CJ. HER2/neu (ERBB2) expression and gene amplification correlates with better survival in esophageal adenocarcinoma. BMC cancer. 2019 Dec;19(1):1-9. [3] Lordick F, Al-Batran SE, Dietel M, Gaiser T, Hofheinz RD, Kirchner T, Kreipe HH, Lorenzen S, Möhler M, Quaas A, Röcken C. HER2 testing in gastric cancer: results of a German expert meeting. Journal of cancer research and clinical oncology. 2017 May;143(5):835-41. [4] Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, James JA. QuPath: Open source software for digital pathology image analysis. Scientific reports. 2017 Dec 4;7(1):1-7.

Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset

Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.

opencc-by-4.0Sep 2022View details →

Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results

This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images. The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016).

opencc-by-4.0Sep 2022View details →

hiPSC 3D immunofluorescence images, test data set 2x2, 10Z

Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal 3 Channels were imaged: - C01: DAPI, nuclear stain - C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog - C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1   This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.   The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless).

opencc-by-4.0Sep 2022View details →

Correlating NanoSIMS images with ultra-high resolution EM images in Look@NanoSIMS

Supplement of the study by Spataro et al. (2022) describes how to perform correlative image analysis in Look@NanoSIMS. This repository contains files used as an example in that analysis. * Rat7SNR_21.tif = ultra-high resolution EM image (4096 x 3072 pixels) * Spataro-Sept-2018_3.im.zip = zipped raw data produced by NanoSIMS 50L (256 x 256 pixels) * Spataro-Sept-2018_3.zip = zipped folder containing data generated by Look@NanoSIMS To start the analysis, download all files to a folder on your computer (preferably in the same folder) and unzip the file Spataro-Sept-2018_3.zip. The latter step will create a folder Spataro-Sept-2018_3 containing files generated by Look@NanoSIMS when analysing data in Spataro-Sept-2018_3.im.zip and Rat7SNR_21.tif. The files include information about the alignment of individual planes (xyalign.mat), coordinates of the pairs of reference points in the EM and NanoSIMS images (points_10x.mat), regions of interest defined for the resampled (cells_10x.mat) and original NanoSIMS data (cells_1x.mat), and Look@NanoSIMS preferences saved for the resampled (prefs_10x.mat) and original (prefs_1x.mat and prefs.mat) NanoSIMS data. You can use these files to reproduce the analysis described in the Supplement of Spataro et al. (2022). Reference: S. Spataro, B. Maco, S. Escrig, L. Jensen, L. Polerecky, G. Knott, A. Meibom, and B. L. Schneider (2022). Alpha-synuclein-induced changes to neuronal metabolism revealed by stable isotope labeling and ultra-high-resolution imaging.