cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.159474
title: Experimental Data For The Publication: "Probing The Early Stages Of Shock- Induced Chondritic Meteorite Formation At The Mesoscale"

description: In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management </strong></em>(09/10/14) this data has been made publicly available to complement the open access publication "Probing the early staged of chondritic meteorite formation at the mesoscale using single-bunch X-ray phase-contrast radiography at ESRF". 

There are three data sets. The folder entitled 'DarkFrames' contains the fitted median dark frame radiographs recorded on frames 1 and 2 of the camera. The folder entitled 'Flatfields' contains the mean flatfield radiographs recorded on frames 1 and 2 of the camera. The folder entitled 'Shots' contains the radiographs recorded on static and shock-compressed samples on frames 1 and 2 of the camera.
DOI: 10.5281/zenodo.4055781
title: A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset

description: This is the umbrella archive that contains all the datasets and code associated with: <strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller <em>Mol Syst Biol. (2020) 16: e9798</em><br> https://doi.org/10.15252/msb.20209798 <em>Please cite this article if you re-use any of the data or code.</em> Datasets: Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling 4 cellline experiment: p173: 10.5281/zenodo.3991929 p176: 10.5281/zenodo.3991931 Overexpression experiment: p161: 10.5281/zenodo.3991925 p165: 10.5281/zenodo.3991927<br> Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks 4 cellline experiment: p173: 10.5281/zenodo.3991921 p176: 10.5281/zenodo.3991923 Overexpression experiment: p161: 10.5281/zenodo.4066430 p165: 10.5281/zenodo.3991919<br> Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: 4 cellline experiment: p173/p176: 10.5281/zenodo.3991945 Overexpression experiment: p161/p165: 10.5281/zenodo.3991947<br> Imaging mass cytometry acquisitions: 4 cellline experiment: p173: 10.5281/zenodo.3991937 p176: 10.5281/zenodo.3991939 Overexpression experiment: p161: 10.5281/zenodo.3991933 p165: 10.5281/zenodo.3991935<br> Processed data set: 4 cellline experiment: 10.5281/zenodo.3991942 Overexpression experiment: 10.5281/zenodo.4271917 Code: A snakemake pipeline to reproduce the analyses from this raw data can be found at: http://github.com/BodenmillerGroup/SpheroidPublication A version already containing all the containers can be found at: 10.5281/zenodo.4071861
DOI: 10.5281/zenodo.4563991
title: Survey and Evaluation of Sensors for Overhead Cable Detection using UAVs: Sensor Evaluation Dataset

description: Dataset of sensor readings from various devices in a power line environment as specified in Survey and Evaluation of Sensors for Overhead Cable Detection using UAVs [ICUAS 2021]. Data is stored as ROS bags and compressed using ROS LZ4 compression (uncompressed ~100 GB). ROS bag naming convention is explained by rosbags/README.md, and test descriptions can be found in TestPlan.odt.
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 861111, Drones4Safety
DOI: 10.5281/zenodo.3630302
title: Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?

description: This is a research compendium (RC) for the publication Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques? Code, figures, appendices and the manuscript can be found in the corresponding GitHub repository. This RC is a static snapshot at the time of submission. The GitHub repository holds the latest version and may see changes after the publication was accepted. <strong>Data sources and description</strong> <em>aoi.gpkg</em><strong>:</strong> Area of interest for downloading Sentinel-2 images. <em>Not used in the publication<strong>. </strong></em>Source:<em><strong> </strong></em>Custom. <em>forest_mask.gpkg</em>: A forest/non-forest mask of the Basque Country. <em>Not used in the publication</em>. Source:<em><strong> </strong></em>Custom. <em>hyperspectral.zip: </em>Hyperspectral remote sensing data used to extract reflectance values on the tree level. Source:<em><strong> </strong></em>Custom. <em>plot-locations.gpkg: </em>Spatial location of the plots used in the study. Source:<em><strong> </strong></em>Custom. <em>tree-in-situ-data-corrected.zip: </em>Corrected in-situ data containing defoliation information on the tree level. A correction of the spatial location was applied by the creators of the data. Source:<em><strong> </strong></em>Custom. <em>tree-in-situ-data.zip: </em>First version of in-situ data containing defoliation information on the tree level.<em> Not used in the publication. </em>Source:<em><strong> </strong></em>Custom. <strong>Licenses</strong> All files are licensed under CC BY 4.0.
DOI: 10.5281/zenodo.3733775
title: Free Breathing Lung MRI Dataset at 3T

description: This is a free breathing lung MRI dataset. Data is acquired with 3D ultra-short te (UTE) radial sequence.
description: Raw data, including trajectory (MRI_Raw_trajm), density compensation (MRI_Raw_dcf2m), and k-space (MRI_Raw_datam), are provided. In addition, a reconstructed image is also provided for reference (MRI_Raw_imoco). Data IO is provided by BART (https://mrirecon.github.io/bart/). Details for image acquisition and reconstruction could be found in the published journal paper (https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27998). Funding information: National Institutes of Health (NIH), grant R01 HL136965.
description: {"references": ["Zhu, X, et al. Magn Reson Med. 2020; 83: 1208\u2013 1221."]}
DOI: 10.5281/zenodo.3836129
title: Cytosolic crowding drives the dynamics of both genome and cytosol in Escherichia coli challenged with sublethal antibiotic treatments

description: Datasets used in the publication:<br> "Cytosolic crowding drives the dynamics of both genome and cytosol in Escherichia coli challenged with sublethal antibiotic treatments". <br> The archives are named according to the treatment administered and contain microscopy images of the chromosomal locus ORI2.<br> 21 fields of view were imaged for 45s at a 9.6 frame rate per second (producing 442 images) every 20 minutes for up to two hours. <br> The number after "Scan" indicates the 20 minutes intervals (1 to 6 meaning 20 to 120 minutes). The number after "Position" indicates the field of view (1 to 21). Each of these folders contains 442 images. <br> <br> Every archive also contains phase contrast images, which are in the folders containing PhC (for Phase Contrast) in the name. Each of these folders contains 21 folders (one for each position) which in turn contain 6 images, taken at the beginning of the aforementioned 45s intervals. Due to space limitations, we could only upload one experiment (out of 3 replicates) from only one of the 3 markers (Ori, Ter and cytoplasmic). The remaining data is available upon reasonable request from the corresponding author of the publication.<br> <br> The Excel files contain data points values and raw measurements for the plots given in Fig. 2, 6A, 6B, 6D and SI11B&C and SI12. The codes used to analyse the data are given at: https://github.com/ver228/bacteria-loci-tracker
DOI: 10.5281/zenodo.3882292
title: Leaf traits

description: Stomatal anatomy, leaf structure and nutrients of tropical rainforest tree species
DOI: 10.5281/zenodo.6375410
title: Early Virtual-Reality-Based Home Rehabilitation after Total Hip Arthroplasty: A Randomized Controlled Trial

description: The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in improving functional outcomes after THA. Subjects were randomized either to an experimental (VRRS; n = 21) or a control group (control; n = 22). All participants were invited to perform a daily home exercise program for rehabilitation after THA with different administration methods—namely, an illustrated booklet for the control group and a tablet with wearable sensors for the VRRS group. The primary outcome was the hip disability (HOOS JR). Secondary outcomes were the level of independence and the degree of global perceived effect of the rehabilitation program (GPE). Outcomes were measured before surgery (T0) and at the 4th (T1), 7th (T2), and 15th (T3) day after surgery. Mixed-model ANOVA showed no significant group effect but a significant effect of time for all variables (<em>p </em>&lt; 0.001); no differences were observed in HOOS JR between VRRS and the control at T0, T1, T2, or T3. Further, no differences in the level of independence were found between VRRS and the control, whereas the GPE was higher at T3 in VRSS compared to the control (4.76 ± 0.43 vs. 3.96 ± 0.65; <em>p </em>&lt; 0.001). Virtual-reality-based home rehabilitation resulted in similar improvements in functional outcomes with a better GPE compared to the traditional rehabilitation program following THA. The application of new technologies could offer novel possibilities for service delivery in rehabilitation.
DOI: 10.5281/zenodo.3894719
title: Single-cell data on lac operon induction by lactose in E. coli

description: Raw data from experiments presented in https://doi.org/10.1101/2020.01.04.894766. Escherichia coli cells are exposed alternatively to glucose and lactose as carbon source, while their growth and lac operon expression are monitored using phase contrast and epifluorescence microscopy. Data from 36 independent experiments are provided: details about conditions are given in Julou_2020_lacInduction_assays.xlsx, and the list of data files in Julou_2020_lacInduction_GL_Preproc_fileList.txt
DOI: 10.5281/zenodo.4498186
title: Is your OCR good enough? A comprehensive assessment of the impact of OCR quality on downstream tasks

description: Is an average OCR quality of 70% enough for my study? What OCR quality should we ask from external suppliers? Should we re-do the OCR of our collections to bring it from 80% to 85%? Libraries and researchers alike face the same dilemma in our times of textual abundance: when is OCR quality good enough? User access, scientific results and the investment of limited resources increasingly depend on answering this question. This project focuses on a comprehensive assessment of the impact of OCR quality in Dutch newspaper, journal and book collections, comparing it with published results for English and French. This is be done via <em>extrinsic evaluation</em>: assessing results from a set of representative downstream tasks, such as text classification or clustering. The ultimate goal of the project is to contribute guidelines detailing when OCR quality is to be considered good enough, in order to inform the development and use of textual collections. The datasets released here are described in this Wiki page. Please refer to the project's repository for more information.
DOI: 10.5281/zenodo.4544398
title: São Paulo dataset at two tunnels for DATAbASE

description: VOC mixing ratios measured @ two traffic tunnels in the Metropolitan Area of São Paulo (MASP), Brazil Samplings @ Jânio Quadros Tunnel (-46° 41' 36.28" W, -23° 35' 15.81 S") carried out during November 4 - 13, 2018 Samplings @ Rodoanel Tunnel (-46° 47' 43.85 W", -23° 27' 38.94 S") carried out during October 26 - November 3, 2018 VOC were collected using sampling canisters and measured by GC-FID. Samples were collected at a constant flow rate (~ 2ml/min) for approximately 8 hours. <br> <br> Contact: thiago.nogueira@iag.usp.br or pamela.dominutti@gmail.com This work was financially supported by the project “Chemical and toxicological SOurce PROfiling of particulate matter in urban air (SOPRO)”, POCI-01-0145-FEDER-029574, funded by FEDER, through Compete 2020 - Programa Operacional Competitividade e Internacionalização (POCI), by national funds (OE), through FCT/MCTES, and by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, São Paulo Research Foundation; Grants no. 18/07848-9).
DOI: 10.5281/zenodo.4636657
title: CBF-PET data and code

description: CBF and PET data and accompanying code
DOI: 10.5281/zenodo.3903074
title: Fe quantification model samples informations -- VRR targets

description: These tables provide informations about the sample set used in the Fe quantification model. This Fe quantification are aimed to refine the iron prediction for Chemcam data. Informations about martian targets on the Vera Rubin ridge are also provided.
DOI: 10.5281/zenodo.4559558
title: Carbon and energy Eddy-covariance fluxes dataset collected at La Guette peatland (23 ha, Loiret, France)

description: Fluxes and energy data measured by Eddy-covariance on La Guette peatland (ec1). Measurements start on 20-01-2017 and are regularly updated with new data. Data include carbon dioxide fluxes (CO2, µmol/m²/s), methane fluxes (CH4, µmol/m²/s), sensible heat fluxes (H, W/m²), latent heat fluxes (LE, W/m²) and evapotranspiration (ETR, mm/h). Zip file contain : metadata file (TOUR_en.json) which describe stations, sensors, variables and process csv file contain time series data for all variables by station Additional information on the measurement can be found in this website : https://data-snot.cnrs.fr/data-access/ We also recommend to contact sno-tourbieres to talk about data acquisition and use : contact.sno-tourbieres@cnrs-orleans.fr
DOI: 10.5281/zenodo.5656281
title: Beirut blast seismic records and Rinex data of CORS-TR DOY217 of 2020

description: These are the seismic data and the Rinex observation data for the DOY 217 (August 4, 2020), the day of the explosion of Beirut Port. The data was used in the results and analysis of a manuscript entitled with "Investigation of the Lithosphere-Atmosphere-Ionosphere Coupling during Beirut Explosion, Lebanon, by Geodetic and Seismological data"
description: {"references": ["Freeshah et al., 2021. Investigation of the Lithosphere-Atmosphere-Ionosphere Coupling during Beirut Explosion, Lebanon, by Geodetic and Seismological data"]}
DOI: 10.5281/zenodo.3598731
title: Supporting data for 2019SW002408: Detection of VLF attenuation in the Earth-ionosphere waveguide caused by X-class solar flares using a global lightning location network

description: This folder contains data from the World Wide Lightning Location Network (WWLLN) used in the paper "<strong>Detection of VLF attenuation in the Earth-ionosphere waveguide caused by X-class solar flares using a global lightning location network</strong>". Contents: 1. readme_2019SW002408.pdf: A detailed explanation of the data files in this folder 2. station_list_20170906-10.csv: A table of WWLLN stations used in this analysis 3. stroke-station_20170906.csv: A list of lightning strokes detected by WWLLN, and the stations detecting each stroke, for the day September 6, 2017. 4. stroke-station_20170910.csv: A list of lightning strokes detected by WWLLN, and the stations detecting each stroke, for the day September 10, 2017.
DOI: 10.5281/zenodo.3765168
title: Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI

description: MRI raw data set of a volunteer examination for the project "Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI". The upload includes the measured k-space for 8 different breathing phases, the respective image reconstructions and the coil sensitivity maps required for the Conjugate Gradient SENSE reconstruction. C++ source code for image reconstruction can be downloaded at https://github.com/expRad/4d_lung.
DOI: 10.5281/zenodo.4290068
title: DeepUWB

description: A dataset for UWB ranging error mitigation in indoor environments, built using Decawave EVB1000 devices and the firmware contiki-uwb. Additional information can be found in the attached file "readme.txt" or in the paper Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge. [ readme.txt ] * Every sample has the following structure: || CIR (157 float values) ||<br> || Error [m] ||<br> || Room (int) ||<br> || Obstacle (10 bool values) ||<br> || Measured Range (UWB) [m] || * Room encoding: 0 -&gt; cross-room measurements<br> 1 -&gt; big room<br> 2 -&gt; medium room<br> 3 -&gt; small room<br> 4 -&gt; outdoor * Obstacle encoding: (1-hot encoding) 1000000000 -&gt; wall<br> 0100000000 -&gt; polystyrene plate<br> 0010000000 -&gt; plastic (trash bin and chair)<br> 0001000000 -&gt; plywood plate<br> 0000100000 -&gt; cardboard box<br> 0000010000 -&gt; LCD TV<br> 0000001000 -&gt; metal plate<br> 0000000100 -&gt; wood door<br> 0000000010 -&gt; glass plate<br> 0000000001 -&gt; metal window * Reading Code: # Import libraries<br> import pandas as pd<br> import numpy as np # Extract dataset<br> dataset = pd.read_pickle('dataset.pkl') # Select specific obstacle configurations<br> ds = np.asarray(dataset.loc[dataset['Objects']=='011111111'][['CIR','Error']]) # Select specific rooms<br> ds = np.asarray(dataset.loc[dataset['Room']==1][['CIR','Error']]) # Select all samples<br> ds = np.asarray(dataset[['CIR','Error']]) # Get X,y for training <br> X = np.vstack(ds[:,0])<br> y = np.array(ds[:,1])
DOI: 10.5281/zenodo.3733776
title: Free Breathing Lung MRI Dataset at 3T

description: This is a free breathing lung MRI dataset. Data is acquired with 3D ultra-short te (UTE) radial sequence.
description: Raw data, including trajectory (MRI_Raw_trajm), density compensation (MRI_Raw_dcf2m), and k-space (MRI_Raw_datam), are provided. In addition, a reconstructed image is also provided for reference (MRI_Raw_imoco). Data IO is provided by BART (https://mrirecon.github.io/bart/). Details for image acquisition and reconstruction could be found in the published journal paper (https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27998). Funding information: National Institutes of Health (NIH), grant R01 HL136965.
description: {"references": ["Zhu, X, et al. Magn Reson Med. 2020; 83: 1208\u2013 1221."]}
DOI: 10.5281/zenodo.6023316
title: Cellpose model for Digital Phase Contrast images

description: <strong>Name: </strong>Cellpose model for Digital Phase Contrast images <strong>Data type: </strong>Cellpose model, trained via transfer learning from ‘cyto’ model. <strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>) <strong>Training Procedure: </strong>Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the Cellpose documentation python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0 The model file (MODEL NAME) in this repository is the result of this training. <strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy
DOI: 10.5281/zenodo.4434462
title: Immunofluorescence staining of a human kidney (#2, peri-tumor area) obtained by MELC

description: 19 marker MELC run in a human peri-tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -&gt; 2<sup>16</sup>).
DOI: 10.5281/zenodo.2812421
title: A Global Database of Historic and Real-time Flood Events based on Social Media

description: Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real time on a global scale using Twitter. The method was developed using 125 million tweets, from which we derived over 10.000 flood events in countries or their first administrative subdivisions across 176 countries in 12 languages in just over four years. Using strict parameters, validation shows that approximately 83% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 70% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on www.globalfloodmonitor.org.
DOI: 10.5281/zenodo.344888
title: 2D Phase Contrast Hela Cells Images With Ground Truth Annotations

description: Original images are from http://www.robots.ox.ac.uk/~vgg/software/cell_detection/. This software is associated with the publication "Learning to Detect Cells Using Non-overlapping Extremal Regions", MICCAI 2012. (DOI: 10.1007/978-3-642-33415-3_43)

Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:


	"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)
	"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)
DOI: 10.1594/pangaea.144767
title: Underway physical oceanography and carbon dioxide measurements during Cap Victor cruise CV2006_05

description: Detailed metadata description of each cruise doi:10.1594/PANGAEA.769638