Find research datasets worth reusing

opencc-by-4.0Jan 2020View details →

Synthetic images of cell nuclei in widefield microscopy

The images were generated by <a href="http://www.cs.tut.fi/sgn/csb/simcep/tool.html">SIMCEP</a>, a widefield fluorescence microscopy biological images simulator. The dataset is used to demonstrate the execution of image analysis workflows with BIAFLOWS on a local machine from a jupyter notebook.

opencc-by-4.0Dec 2020View details →

Cloud Condensation Nuclei number concentrations over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract Cloud Condensation Nuclei (CCN) are a subclass of atmospheric aerosol particles, which can be activated to cloud droplets at a certain supersaturation, with respect to water. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as CCN. It was found that CCN are relevant for the Earth’s radiation budget, by affecting cloud albedo and lifetime. When giving a number concentration of CCN, also the supersaturation at which it was measured has to be given. With additional information on particle number size distribution, the hypothetical diameter of particle activation (critical diameter) was derived. Further, the particle hygroscopicity parameter (kappa) was calculated using the critical diameter. Values of kappa can be a proxy for bulk chemical composition of the sampled CCN population. Our dataset gives CCN number concentrations measured by a CCN counter (type CCN-100 by DMT, Boulder, US) operated at five different levels of supersaturation (0.15%, 0.2%, 0.3%, 0.5%, 1%) during the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project. Temporal coverage is from December 20, 2016 to March 19, 2017. We give 5-minute averaged and quality controlled CCN number concentrations, critical diameter and kappa values. Dataset contents <ul> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS100.csv, data file, comma-separated values</li> <li>data_file_header_number_concentration.txt, metadata, text</li> <li>data_file_header_critical_diameter.txt, metadata, text</li> <li>data_file_header_hygroscopicity_parameter.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> The files listed above contain Cloud Condensation Nuclei (CCN) number concentration (N_CCN), critical diameter (D_crit) and particle hygroscopicity parameter (KAPPA) values for the Antarctic Circumnavigation Expedition from in-situ measurements. Each file contains only N_CCN, D_crit or KAPPA values for one of the five measured levels of supersaturation (SS), e.g., N_CCN at SS=0.15% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv or N_CCN at SS=0.2% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv etc. In addition, for each N_CCN value the respective temperature of the CCNCs measurement column (T_col) is given. Values are from 1 Hz measurements and averaged to represent 5-minute intervals. For every given value of CCN number concentration, the respective supersaturation level is given, although files only contain values for one level only. Additionally, longitude and latitude for the ship’s position at the start time of the averaging period are given. For latitude and longitude nan values are given, in cases where positioning data was not available for the given time period. There are no nan values for CCN number concentration included, in a way that only quality assured data is given. Change log v1.1 - data files updated <ul> <li>change dataset title to specify ACE cruise</li> <li>change time resolution to 5 minutes</li> <li>addition of critical diameter data</li> <li>addition of hygroscopicity parameter data</li> <li>create separate data_file_headers</li> <li>add change log</li> </ul> v1.0 - initial release of dataset

opencc-by-4.0Nov 2021View details →

Microscope-Cockpit find nuclei code and microscope simulation configuration

This file contains instructions for setting up a simulated microscope environment using Microscope-Cockpit and Python-Microscope. This environment includes a large tiled image of which segments are returned to simulate stage movement and different colour channels returned to simulate changing an emission filter. This simulated microscope is then used to test the findNuclei script showing the ease of extending Cockpit functionality with Python libraries, Python-openCV is used in this case.

opencc-by-sa-4.0May 2018View details →

Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging

This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors. We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer. We also provide a step by step pseudo code for creating the atlas.

opencc-by-4.0Jul 2019View details →

Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'

All numerical data used in the manuscript “Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided. These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr) Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340  (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> ) For an in-depth understanding of the description below, a study of the above mentioned manuscript is required. (A) Station model results The station results can be found in files with filenames of the form: station $MODEL.nc The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file. In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as: st $STATION $FIELDhour st $STATION $FIELD month where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable st Finokalia CCN02 hour found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN0.2 at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model. st Vavihill DU month Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours) (B) Station observational results There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is station observations.nc while the following fields are contained in there: st $STATION $FIELDhour st $STATION $FIELD month The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch). (C) Station Multi-model Median Monthly averages of the models can be found in the file station MMM.nc The following fields can be found in the file st $STATION $FIELD month median st $STATION$FIELD month quart25 st $STATION$FIELD month quart75 where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively. (D) Global model results In the following single file can be found for each of the models the surface distribution of various fields. results global models year2011.nc They correspond to the annual mean of the year 2011. The resolution of the grid is 1◦ × 1◦. The file contains the following variables: $FIELD $MODEL The $FIELD and $MODEL can be found in Tables 3 and 1, respectively. (E) Global average results In the file surface_ global_average_year2011.nc can be found in 5◦×5◦ resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as: med $FIELD div $FIELD where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results. Tables and details on the fields provided are given in the readme file.

opencc-by-4.0Sep 2020View details →

Dataset for the Intensity Ratio Nuclei Cytoplasm Tool

Pairs of fluorescently stained images of nuclei and cytoplasm. Example input data for the Intensity Ratio Nuclei Cytoplasm Tool.

opencc-by-4.0Jan 2021View details →

Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography

We provide: the tomographic model for dephts 68 to 768 km as text files, where the first column is the longitude, the second is the latitude and the third if the velocity perturbation; the proposed limits for the Paranapanema Block, Luiz Alves Craton and Rio Apa Craton as a csv file (Figure 12 of the paper), where the first column is the name of the feature, the second is the longitude and the third is the latitude; and the abstract for the paper "Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography".

opencc-by-4.0Jan 2022View details →

Dataset: A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities

The dataset accompanying the paper "A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities" (Hviding et al. in prep)

opencc-by-4.0Jun 2022View details →

An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines

Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert. The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. <a href="https://doi.org/10.1016/j.dib.2022.108769">10.1016/j.dib.2022.108769</a> ) Dataset description: Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale) Annotations: 24-bit .png format (RGB) Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it

opencc-by-4.0Jun 2022View details →

3D Nuclei annotations and StarDist 3D model(s) (rat brain)

Name: 3D Nuclei annotations and StarDist3D model(s) (rat brain) Images:  From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/) These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62). Conda environment: A conda environment was created using the yml file  stardist0.8_TF1.15.yml Training : Training was performed using the jupyter notebook 1-Training_notebook.ipynb. Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/) Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/). Validation:  To save metrics in a csv file and compare predictions to the annotations the jupyter notebook 2-QC_notebook.ipynb can be used on the validation folder. Large images: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif ) These images were obtained using the plugin <a href="https://imagej.net/plugins/bigstitcher/">BigSticher </a>on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.

opencc-by-4.0Jun 2024View details →

Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei

Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045).    Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.

opencc-by-4.0Mar 2019View details →

Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign

This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.

opencc-by-4.0Jun 2019View details →

Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"

This repository contains the source data for the manuscript "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements" published in Atmospheric Measurement Techniques. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.

opencc-by-4.0Oct 2022View details →

Data Release of Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations

Dataset of the paper "Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations".   All the necessary code to deal with these data can be found in: https://github.com/IvanMuro/agn_frac_data_release

opencc-by-4.0Jun 2023View details →

Deconvolved STED nanoscopy images of the nuclear phosphatidylinositol 4,5-bisphosphate and nuclear speckle marker SON together with deconvolved confocal images of DAPI stained nuclei in human formalin-fixed paraffin-embedded skin warts sections

The collection and analysis of formalin-fixed paraffin-embedded (FFPE) human skin sections was approved by the local ethics-committee at the Department of Pathology, University of Cologne, Germany. Written informed consentwas obtained from all patients in accordance with the Declaration of Helsinki. For biopsy materials from archival paraffin blocks of human skin, an informed consent was obtained from all the subjects and ethical approval obtained from the Ethics Committee at the University of Cologne. Surgically removed human FFPE skin biopsies were sectioned into 4 µm sections. Sections were dewaxed, and indirectly immunofluorescently labeled against nuclear phosphatidylinositol 4,5-bisphosphate (nPI(4,5)P2) using  5 µg/mL rabbit primary polyclonal antibody (Echelon Biosciences Inc. Z-A045, clone 2C11). The primary antibody against nPI(4,5)P2 was recognized by the goat secondary antibody conjugated with Abberrior Star 635P (Abberior 2-0002-007-5). Sections were indirectly immunofluorescently labeled against nuclear speckle marker SON using  1 µg/mL rabbit primary polyclonal antibody (Abcam ab121759). The primary antibody against SON was recognized by the goat secondary antibody conjugated with Abberrior Star 580 (Abberrior ST580-1002). Sections were co-stained by DAPI 1:1000 in PBS for 5 min. Imaging of nPI(4,5)P2-635P channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 7% 633 nm laser; 775 Notch filter; 50% 775 nm STED laser; 30% 3D STED; HyD 639-698 nm, photon-counting mode, gain 100, gating 0.3-10 ns. Imaging of SON-580 channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 10% 585 nm laser; 775 Notch filter; 80% 775 nm STED laser, 30% 3D STED; Hybrid detector (HyD) 589-616 nm, photon-counting mode, gain 100, gating 0.4-10 ns. Z-stacks of STED images were deconvolved using Huygens Professional 22.10 software (Scientific Imaging B.V.). Data sets were processed using Workflow Processor. The workflow consisted of selecting images, setting up the microscopy and deconvolution parameters and saving deconvolved images as 8-bit TIFF single files for individual channels (which were later used for the quantitative analyses; see below). Microscopy parameters were optimized and set as follows. Sampling intervals were ≤20 nm in X and Y and  ≤20 nm in Z. Numerical aperture was 1.4; refractive indexes of the lens immersion oil was 1.518 and of the embedding media 1.458; objective quality was good, coverslip position was 0 µm and imaging direction was downward. For nPI(4,5)P2-635P STED channel the backprojected pinhole was 216 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 633 and 651 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 25, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 5.1. For SON-580 STED channel the backprojected pinhole was 195 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 585 and 602 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 20, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 4.

opencc-by-4.0Apr 2024View details →

The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6

GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view.  Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.

opencc-by-4.0Mar 2022View details →

Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.

This is a data set that contains labelled HeLa cell images, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949) Details of the imaging, preparation and segmentation have been published in: <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. J. Imaging 2019, 5(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, 2020;  <a href="https://doi.org/10.1371/journal.pone.0230605">https://doi.org/10.1371/journal.pone.0230605</a></li> <li> Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells, J. Imaging 2021, 7(6), 93; <a href="https://doi.org/10.3390/jimaging7060093">https://doi.org/10.3390/jimaging7060093</a> </li> </ul> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul>

opencc-by-4.0Sep 2022View details →

Tracking the photomineralization mechanism in irradiated lab-generated and field-collected brown carbon samples and its effect on cloud condensation nuclei abilities

Data set of the data presented in figures and tables in our manuscript on the photomineralization of brown carbon samples: ammonium sulfate-methylglyoxal solutions, Suwannee River fulvic acid isolates, firewood smoke and ambient aerosols from Padua, Italy.

opencc-by-nc-nd-4.0May 2024View details →

Data for: Solvent effects in hyperpolarization of 15N nuclei in [15N3]metronidazole and [15N3]nimorazole antibiotics via SABRE-SHEATH

Raw 15N and 1H NMR spectra for the article which is under revision at the time posting this dataset. These results are also available as preprint at https://doi.org/10.26434/chemrxiv-2024-6pg8b 15N NMR spectra were acquired using SpinSolve Expert Software (Magritek) 1H NMR spectra were acquired using TopSpin (Bruker)