cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6881258
title: Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine

description: This dataset contains soil erosion maps in China at 1 km resolution from 2010 to 2021. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).
DOI: 10.5281/zenodo.1405495
title: Rcs-Image Dataset

description: Note: There is a single zip file of the project available under the name "RCS Image Dataset.zip "

A synthetic dataset for object detection, semantic segmentation and depth recognition was created using images from a virtual environment in Unreal Engine. This dataset that could be used to retrain neural networks on virtual aerial images, for object detection, segmentation and depth planning.

There is ground truth for full pixel level semantic segmentation and object detction by way of bounding box coordinates in .exif files. The main  labels present in the dataset are train truck and gas cylinders  The size of the data is currently more than 10GB, with 1000 aerial images, from 100 different waypoints at 10 different heights. For each raw image, there is also an associated depth image, a segmented image, object ground truths with bounding box, and the metadata .exif file.

If you found this dataset useful for your reserach, please cite as,

<pre>@article{smyth2018virtual,
  title={A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation},
  author={Smyth, David L and Fennell, James and Abinesh, Sai and Karimi, Nazli B and Glavin, Frank G and Ullah, Ihsan and Drury, Brett and Madden, Michael G},
  journal={arXiv preprint arXiv:1806.04497},
  year={2018}
}</pre>
description: Note: There is a single zip file of the project available under the name "RCS Image Dataset.zip "
description: {"references": ["Smyth et al, (2018 ). Smyth, D. L., Fennell, J., Abinesh, S., Karimi, N. B., Glavin, F. G., Ullah, I., ... & Madden, M. G. (2018). A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation. arXiv preprint arXiv:1806.04497."]}
DOI: 10.5281/zenodo.1489129
title: Improving Open Source Face Detection By Combining An Adapted Cascade Classification Pipeline And Active Learning

description: The <em><strong>EAVISE Open Source Face Detection Dataset</strong></em> consists of several items that were used to generate the improved frontal face detection model using LBP features and AdaBoost for OpenCV3.2.


	The annotations of the FDDB dataset, converted to the OpenCV format for doing a correct evaluation.
	The final trained model (IterativeHardPositives+ model) which is included in the OpenCV 3.2 framework.
DOI: 10.5281/zenodo.3673882
title: Data for "Characterization of composition and sources of atmospheric submicron particles in Xi'an, China during summer using an aerosol chemical speciation monitor"

description: The dataset used for the study of Li et al. (2020).
DOI: 10.5281/zenodo.4068329
title: Differences in single positive formal thought disorder symptoms between closely matched acute patients with schizophrenia and mania

description: Data related to the manuscript "<strong>Differences in single positive formal thought disorder symptoms between closely matched acute patients with schizophrenia and mania"</strong>
DOI: 10.5281/zenodo.4462258
title: Cr[CH(SiMe3)2]3/SiO2 catalysts for ethene polymerization: the correlation at amolecular level between the chromium loading and the microstructure of the produced polymer

description: <strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis Files are in <strong>txt</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>opj</strong>, and <strong>m</strong> format Information on <strong>origin of the data</strong>: EPR spectroscopic measurements in <strong>spc</strong> and <strong>par</strong> formats EPR spectroscopic simulation and analyses in<strong> m</strong> format UV-vis and IR spectroscopic measurements and analyses; and polymer analyses in <strong>opj</strong> format Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker. FT-IR spectroscopic measurements were generated by Vertex70 produced by Bruker equipped with an MCT detector. UV-Vis-NIR spectroscopic measurements were generated by Cary5000 spectrophotometer produced by Varian equipped with a reflectance sphere. <strong>If t</strong> Files in <strong>PARACAT_WP4_20201229_01_EPR</strong> folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in spc/par formats; files in m format were used to process the data of the same name and saved the results in txt format. <strong>Information on</strong>: specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>GC</strong> – Gas Chromatography, <strong>GPC</strong> – Gel Permeation Chromatography definitions of variables: <strong>Magnetic field, Wavenumber, Pressure</strong> units of measurement: <strong>Gauss (G), milli Tesla (mT), cm<sup>-1</sup>, milli bar (mbar)</strong> abbreviations: <strong>IR/EPR/UV </strong>are data relate to measurements of IR/EPR/UV-Vis spectroscopy. abbreviation: <strong>CO/C2H4</strong> are data relate to methods with CO/ethene dosage. abbreviation: <strong>polym</strong> is data relates to polymer analysis conducted by GC and GPC
description: The spc/par files can be opened on proprietary EPR softwares from Bruker or MatLab (version R2020a used by the authors) with EasySpin toolbox (version 6.0.0-dev.26 used by the authors) The simulation/analysis of EPR measurements can be opened and run on MatLab (version R2020a used by the authors) with EasySpin toolbox (version 6.0.0-dev.26 used by the authors) The opj files can be opened on Origin/OriginPro/Origin Viewer If it is not possible to make the code and software openly available on Zenodo: 1. MatLab can be purchased/downloaded from: https://www.mathworks.com/products/matlab.html 2. Origin/OriginPro/Origin Viewer can be purchased/downloaded from: https://www.originlab.com/ 3. EasySpin toolbox can be downloaded from: https://easyspin.org/
DOI: 10.5281/zenodo.4521304
title: Monitoring of tides in coastal wetlands with the 'Mini Buoy': applications for mangrove restoration

description: This repository contains the datasets for the publication: Monitoring of tides in coastal wetlands with the ‘Mini Buoy’: applications for mangrove restoration Balke T, Vovides A, Schwarz C, Chmura GL, Ladd C, Basyuni M
DOI: 10.5281/zenodo.5553567
title: Data for Statistics on Jupiter's Current Sheet with Juno Data: Geometry, Magnetic Fields and Energetic Particles

description: The statistical data of paper "Statistics on Jupiter’s Current Sheet with Juno Data: Geometry, Magnetic Fields and Energetic Particles"
DOI: 10.5281/zenodo.10146599
title: Annual-resolution Sr/Ca records of fossil Porites corals from the northern South China Sea

description: This dataset reports thirteen annual-resolution Sr/Ca records and seven 10-yr resolution Sr/Ca records of a total of 20 fossil Porites corals collected from the northern South China Sea. The ages of the fossil corals were determined by U-Th geochemistry. The Sr/Ca measurements were carried out using an Inductively Coupled Plasma-optical emission spectroscopy (ICP-OES; Agilent 5110). The international coral standard JCp-1 was measured along with the samples, yielding an average value of 8.839 ± 0.017 mmol/mol (1 SD, n = 36).
DOI: 10.5281/zenodo.3576157
title: 10.5281/zenodo.3576157

description: Data used for model
DOI: 10.5281/zenodo.3922055
title: BIRAFFE: Bio-Reactions and Faces for Emotion-based Personalization

description: We present <strong>BIRAFFE</strong>, a dataset consisting of <em><strong>electrocardiogram (ECG)</strong></em>, <em><strong>galvanic skin reaction (GSR)</strong></em> and changes in <em><strong>facial expression</strong></em> signals recorded during affect elicitation by means of <em><strong>audio-visual stimuli</strong></em> (from IADS and IAPS databases) and our two proof-of-concept <em><strong>affective games</strong></em> ("Affective SpaceShooter 2" and "Fred Me Out 2"). All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR, and Creative Live! web camera for face photos (further analyzed by MS Face API). Besides the signals, the dataset consists also of <em><strong>subjects' self-assessment</strong></em> of their affective state after each stimuli (with the use of two widgets: the first one with <em><strong>5 emoticons</strong></em> and the second with <em><strong>valence and arousal</strong></em> dimensions) and <em><strong>"Big Five" personality traits</strong></em> assessment (using NEO-FFI inventory). For detailed description see <em>BIRAFFE-AfCAI2019-paper.pdf</em>.<br> For preview of the files before downloading the whole dataset see <em>sample-SUB1107-[...]</em> files. All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by <strong>citing the paper</strong>:<br> Kutt, K., Drążyk, D., Jemioło, P., Bobek, S., Giżycka, B., Rodriguez-Fernandez, V., & Nalepa, G. J. (2020). "BIRAFFE: Bio-Reactions and Faces for Emotion-based Personalization". In G. J. Nalepa, J. M. Ferrandez, J. Palma, & V. Julian (Eds.), Proceedings of the 3rd Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2019) (CEUR-WS Vol. 2609). http://ceur-ws.org/Vol-2609/ Our research related to the BIRAFFE dataset is summarized in:<br> Kutt, K., Drążyk, D., Bobek, S., & Nalepa, G. J. (2021). "Personality-Based Affective Adaptation Methods for Intelligent Systems". Sensors, 21(1), 163. https://doi.org/10.3390/s21010163
description: The BIRAFFE dataset is part of the work carried out by the AfCAI Research Group in Kraków, Poland. If you want to know more about us, see: https://afcai.re/
description: {"references": ["Kutt, K., Dr\u0105\u017cyk, D., Jemio\u0142o, P., Bobek, S., Gi\u017cycka, B., Rodriguez-Fernandez, V., & Nalepa, G. J. (2020). \"BIRAFFE: Bio-Reactions and Faces for Emotion-based Personalization\". In G. J. Nalepa, J. M. Ferrandez, J. Palma, & V. Julian (Eds.), Proceedings of the 3rd Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2019) (CEUR-WS Vol. 2609). http://ceur-ws.org/Vol-2609/", "Kutt, K., Dr\u0105\u017cyk, D., Bobek, S., & Nalepa, G. J. (2021). \"Personality-Based Affective Adaptation Methods for Intelligent Systems\". Sensors, 21(1), 163. https://doi.org/10.3390/s21010163"]}
DOI: 10.5281/zenodo.1204913
title: Plantvillage Disease Classification Challenge - Color Images

description: <br>
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.<br>
<br>
# Data origins<br>
The dataset is originally hosted at PlantVillage Disease Classification Challenge.<br>
We use the modified version in this github repository to do controlled experiments.<br>
We only use the raw color images dataset and delete the unconventional characters in the classes directory name and `.csv` filenames.<br>
<br>
# Directory explanation<br>
The `80-20` direcotry has multiple `.txt` files which contain the training (~80%), validation(~10%) and testing (~10%) datasets instances filenames and the corresponding label indexes. The validation dataset quantity is `5430` in all data separation. In our experiment code (not included in this archive), the validation and testing dataset are merged together.<br>
<br>
# Data usage<br>
## Replicate our experiments<br>
We have used this dataset in writing our paper. The reference information can be seen at https://gitlab.com/huix/leaf-disease-plant-village.<br>
<br>
### Steps<br>
1. `cd` to the direcotry (e.g. `/home/usrname/plantvillage_deeplearning_paper_dataset`) that contains the `color` directory.<br>
2. run `python change_filename_prefix.py --prefix /home/usrname/plantvillage_deeplearning_paper_dataset` to modify the prefix path (which is `/home/h/plantvillage_deeplearning_paper_dataset` in our former generated datasets).<br>
3. Fin. You can use our opens ource codes repository to do the later experiments.<br>
<br>
## Generate your own training/validation/testing datasets<br>
This data separation generating code isn't included in the dataset archive, it is in our open source code. Please see our open source code repository for the detailed information.<br>
If you have any questions, you can contact the author through email.<br>
The email address is a QR code in the archive.
description: https://gitlab.com/huix/leaf-disease-plant-village
description: {"references": ["Hughes, D.P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv e-prints (2015). 1511.08060", "Mohanty, S.P., Hughes, D.P., Salath\u00e9, M.: Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7, 1419 (2016). doi:10.3389/fpls.2016.01419"]}
DOI: 10.5281/zenodo.3607252
title: Photovoltaic system power production

description: The dataset contains data related to the photovoltaic (PV) system installed at Poznan Supercomputing and Networking Center used to power the servers in one of the server rooms. The data come from a 1-month measurement period taken between 03.09.2019-03.10.2019. The PV system consists of 80 PV panels with 20kW peak power. Servers are directly connected to inverters so that they can be supplied directly by the power grid, PV system in the case of sufficient generation or by batteries (75kWh) if energy production is too low. Switching between power sources can be controlled by the user, however, if batteries are discharged below 60%, servers immediately switch to the power grid to extend battery life. During the measurements, only part of the servers was attached to the PV system responsible for ~1kW power usage. Poznan’s coordinates: 52°24'24.91" N 16°55'47.75" E Time period: 03.09.2019-03.10.2019
description: Dataset is provided in CSV format, with the following headers: Date [DD/MM/YY hh:mm] - time of measurements Power [W] - current power production Day_yield [Wh] - energy yield (on a daily basis)
DOI: 10.5281/zenodo.3993798
title: Data production of ''Lunar Titanium and Frequency‐Dependent Microwave Loss Tangent ... LRO Diviner Lunar Radiometers"

description: These are the image data of "Lunar Titanium and Frequency‐Dependent Microwave Loss Tangent as Constrained by the Chang'E‐2 MRM and LRO Diviner Lunar Radiometers".
DOI: 10.5281/zenodo.4265028
title: Low-Latitude Whistler-Mode and Higher-Latitude Z-mode Emission at Jupiter Observed by Juno

description: This is the supporting data set for the paper by the same title published in AGU JGR Space Physics, 10.1029/2020JA028742 <strong>Abstract</strong> Whistler-mode chorus emission is important in the scattering and acceleration of electrons and filling of the radiation belts at Jupiter. In this work whistler mode magnetic intensity levels at Jupiter are comprehensively binned and parameterized. The frequency range of whistler mode under study extends from the proton cyclotron frequency, f<sub>cH</sub>, to f<sub>ceq</sub>/2, where f<sub>ceq</sub> is the cyclotron frequency mapped to the magnetic equator. Parametric dependence of magnetic plasma wave intensity is obtained versus frequency, latitude, and M-shell, as determined using a current magnetic field model based on Juno data. The results extend similar analyses of Jupiter whistler-mode emission obtained by the Galileo spacecraft, particularly on the nightside, and provide better coverage in latitude. Peaks in whistler-mode emission occur near M ~ 8-9, similar to previous studies, with average peak intensities approaching 10<sup>-2</sup> nT<sup>2</sup>, as also found by Galileo on the dayside. Auroral hiss and probably Z-mode are observed at higher latitudes. Jovian chorus emissions near an equatorial source region are more broad-banded than terrestrial chorus, and are coincident with a broad-banded electron distribution of free energy and strong electron scattering to large pitch angles. Intense whistler mode within a young plasma injection region is also observed, similar to injections in Saturn's magnetosphere. Future study of wave particle interactions within the chorus source region will be important. Possible Z-mode emission at significant intensity levels is observed in Jupiter’s inner magnetosphere, more intense, but not unlike Z-mode observed at Saturn. <strong>Key Points</strong> Jupiter whistler-mode & Z-mode intensity is binned in f, M, Magnetic latitude, and MLT Chorus emission coincident with electron scattering in source region Narrowband Z-mode emission likely observed at higher frequencies
DOI: 10.5281/zenodo.4415600
title: Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons

description: The dataset includes all data for "Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons" by Zou et al. from Guangzhou Institute of Geochemistry.
DOI: 10.5281/zenodo.4443822
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The <strong>Ocular Toxoplasmosis (OT) Fundus Images Dataset</strong> contains a set of eye images collected at the <em>Hospital de Clínicas</em> medical center from Asunción, Paraguay. The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience. Dataset structure: <pre><code>+-- Ocular_Toxoplasmosis_Data | +-- masks | +-- images | +-- classification | +-- healthy | +-- non_healthy | +-- dataset_labels.csv</code></pre> The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The classification folder contains the <strong>healthy</strong> images and <strong>non healthy</strong> images. The non healthy set contains images with both active and inactive lesions caused by ocular toxoplasmosis. Finally, it also includes a <strong>csv</strong> file with the labels for each image.
DOI: 10.5281/zenodo.4899075
title: The crustal anisotropy of west Ordos block and its geodynamic implications

description: The data packet includes the raw receiver functions of all stations in the paper.
DOI: 10.5281/zenodo.4916850
title: Aflatoxin results data

description: Prof. Michael Wandayi Okoth Prof. Wambui Kogi-Makau Dr.Ing. Patrick Kuloba
DOI: 10.5281/zenodo.5068200
title: HSI - Datasets

description: Labeled <strong>HSI</strong> datasets consisting of files in <strong>.hdr</strong> and <strong>.bin</strong> format and also as <strong>.mat</strong> files for Matlab. plastics dataset: Seven classes of different plastics consisting of phenolic resin (PF), polyamide (PA), polyvinylchloride (PVC), polyethylene (PE), polymethylmethacrylate (PMMA), polyester resins (UP) and polystyrene (PS). realWaste dataset: Four classes of different plastics (PA, PP, PS, ABS) and printed curcuit boards (PCB's).
DOI: 10.5281/zenodo.5176102
title: Jupiter magnetosphere simulation data - average

description: MHD simulation data of Jupiter's magnetosphere
DOI: 10.5281/zenodo.5587753
title: Rapeseed Map Example data

description: This dataset provides 30m-grid rapeseed map example data. *** The data file is in “.tif" format *** Temporal Resolution: Yearly *** Pixel size: 30 m *** Projection information: EPSG: 4326
description: beijing normal university
DOI: 10.5281/zenodo.5709687
title: Elevational variability and controls on temperature sensitivity of soil organic matter decomposition in alpine forests

description: All data for ECS21-0520 "Elevational variability and controls on temperature sensitivity of soil organic matter decomposition in alpine forests"
DOI: 10.5281/zenodo.5732281
title: Vineyard Dataset

description: Vineyard Dataset for training the DIscriminator and the FC Layer
DOI: 10.5281/zenodo.6602519
title: PCM Experiments

description: Project: Hybrid-BioVGE The Hybrid – BioVGE project is proposed with the primary objective to develop, design and demonstrate a highly integrated solar/biomass hybrid air conditioning system for space cooling and heating of residential and commercial buildings that is affordable, operating with improved efficiency and with a strong market potential. Project details at https://hybrid-biovge.inegi.up.pt/index.asp File Description: Experiments on Phase Change Materials.
DOI: 10.5281/zenodo.6881257
title: Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine

description: This dataset contains soil erosion maps in China at 500m resolution from 2010 to 2020. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).
description: Code reference https://github.com/HaixuHe/Soil-china
DOI: 10.5281/zenodo.7096938
title: MHD Model of Ganymede's Magnetosphere: Predicted OCFB and magnetic footprint surface locations for Juno's flyby

description: This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish coordinates for the predicted location of the open-closed-field line-boundary (OCFB) on Ganymede's surface. Additionally we provide coordinates of Juno's magnetic footprint, namely the surface locations that connect to Juno's trajectory through magnetic field lines. For the surface locations we use a western longitude planetographic coordinate system where 0° longitude is in direction of the y-axis and 90° in direction of the x-axis of the cartesian GPhiO system. The GPhiO system is defined by the primary direction<br> z parallel to Jupiter’s rotation axis, the secondary direction y is pointing towards Jupiter barycenter<br> and x completes the right-handed system approximately in direction of plasma flow. <strong>Duling2022_JunoGanymede_modeled_surface_OCFB.txt</strong> Columns: Longitude [°]<br> Northern OCFB latitude [°]<br> Southern OCFB latitude [°] <strong>Duling2022_JunoGanymede_modeled_magnetic_footprint.txt</strong> Columns: Spacecraft time [UTC]<br> Magnetic footprint longitude [°]<br> Magnetic footprint latitude [°]<br> Length of field line between Juno and surface [radii]<br> Length of field line between Juno and surface [km]<br> r coordinate of Juno [radii]<br> Latitude of Juno [°]<br> Longitude of Juno [°]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km] <strong>Duling2022_JunoGanymede_surface_map.png</strong> A plot that visualizes the data of this repository.
DOI: 10.5281/zenodo.7513533
title: Global Monthly Gross Primary Production

description: This is global monthly gross primary production from 2001-2016 derived from P model using pyrealm python package. Unit for GPP is gC/m2 day (monthly daily mean value). Detailed description of data can be found in Shen et al. 2023. For detailed description of P model please refer to Wang et al. 2017, Cai and Prentice 2020 and Stocker et al. 2020.
DOI: 10.5281/zenodo.7695976
title: Data collected from Vietnam

description: This dataset contains qualitative and quantitative data that has been collected in Vietnam, the first page is the summary of all the information.
DOI: 10.57760/sciencedb.07073
title: cstr解析测试
title: cstr解析测试

description: cstr解析测试cstr解析测试
description: cstr解析测试cstr解析测试