cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6826325
title: A high-resolution earthquake catalog for the Tony Creek dual Microseismic Experiment (ToC2ME)

description: Location/detection method: In this study, we revisit the continuous recordings that are acquired by 69 three-component nodes at a Hydraulic Fracturing (HF) site in Alberta, Canada, taking advantage of a machine learning-based seismic detection and location workflow (LOC-FLOW). This workflow integrates four key steps to generate earthquake catalogs, including phase picking using neural network (PhaseNet), rapid association of seismic phases (REAL), seismic location (e.g., VELEST) as well as relocation (e.g., hypoDD). The catalog contains 21,619 earthquakes with magnitudes down to -2 and achieves a high location accuracy at meter scale. The improved earthquake locations allow us to better delineate the fractures and faults activated during the HF. Our results further reveal the complexity of the triggering mechanism of HF-induced earthquakes according to the statistical analysis of the frequency-magnitude distribution of earthquakes. Our study suggests that multiple factors, including pore fluid pressure due to fluid injection and perturbation in the stress field caused by earthquakes, can jointly control the spatiotemporal variations of regional seismicity during the multi-stage HF operation.
DOI: 10.5281/zenodo.2605399
title: Dataset on production, processing and trading activities in Ghana's cocoa value chain

description: This covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets
DOI: 10.5281/zenodo.3691740
title: EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 2 and 3

description: EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters. The current data set is a sample of the data generated within the second and third project years (2018, 2019), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the first (2017) year of EOMORES have also be submitted. The following is included:<br> - Estonia lakes and coast: in situ data 2019<br> - Finland: links to repositories of EO data<br> - Italy CNR: sample of EO data 2018 and 2019<br> - Italy Trasimeno: sample of WISPstation in situ spectral data and water quality parameters data<br> - Lithuania Curonian Lagoon: sample of WISPstation in situ spectral data and satellite based water quality maps<br> - Netherlands Lake Markermeer: sample of EO data 2018 and 2019 (Chl, Secchi, SPM)<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: links to relevant repositories of in situ data http://eomores-h2020.eu
description: The dataset of the first year can be found at DOI: 10.5281/zenodo.1462850
DOI: 10.5281/zenodo.894742
title: Supplementary Online Data For Characterizing The Hologenome Of L. Pustulata And Tracing Genomic Footprints Of Lichenization

description: Processed data and scripts, as well as the the figures and tables for <em>Characterizing the hologenome of L. pustulata and tracing genomic footprints of lichenization</em>. The <em>zip</em> file contains three subfolders for <em>Chapter 3</em>, <em>Chapter 4</em>, <em>Chapter 5</em> of <em>Characterizing the hologenome of L. pustulata and tracing genomic footprints of lichenization</em>. Each subfolder comes with its own <em>README.md</em> further describing its contents.

Furthermore the read mappings against <em>Lasallia pustulata</em> individually, as well as the ones used for the <em>anvi´o</em> visualization, which include <em>L. pustulata</em>, <em>Trebouxia sp.</em> as well as the 499 bacterial scaffolds are given. The <em>Trinity</em> RNAseq assemblies (guided and unguided) are given too.<br>
DOI: 10.5281/zenodo.1217140
title: Macroporous Polymer Monoliths As A Low-Cost Bioanalytical Platform For Biopharmaceutical Glycoproteins

description: This is data generated during my Marie Curie project. It includes the results for the synthesis of the monomers and polymerised high internal phase emulsions (polyHIPEs).

In addition, supplementary data form a publication generated from the co-supervision of a PhD student during the course of the fellowship is also included.
DOI: 10.5281/zenodo.4054808
title: Biocatalytic Nanoparticles for the Stabilization of Degassed Single Electron Transfer Living Radical Pickering Emulsion Polymerizations

description: Raw data for the figures in the main article in Nat. Commun.
DOI: 10.5281/zenodo.4495513
title: Model outputs in the northern shelf of the South China Sea

description: The data used in the figure of manuscript are available in Figure.rar file. The 201706XX_Chla_budget_2.mat files are the model outputs of the chlorophyll budget terms after process.
DOI: 10.5281/zenodo.4425741
title: Training data from Reinforced SciNet

description: <strong>Summary:</strong> The results from the training of neural networks in v2 of Reinforced SciNet, published partially in v2 of the paper Operationally meaningful representations of physical systems in neural networks. <strong>File description:</strong> results.txt - The results from the training during<em> reinforcement learning</em>. results_loss.txt - The loss from the training during <em>representation learning</em>. selection.txt - The noise level of latent neurons during <em>representation learning</em>. <strong>Parameters: Reinforcement Learning</strong> Server parameters 21 workers, 2 predictors, 1 trainer each 3M episodes Training parameters glow: 0.1 gamma: 0.01 softmax: 0.5 learning rate: 0.00005 reward clipping: 1.0e-7 Network parameters DPS model:<br> {'env1': [128, 128, 128, 128, 64, 32],<br> 'env2': [128, 128, 128, 128, 64, 32],<br> 'env3': [128, 128, 128, 128, 64, 32]} <strong>Parameters: Representation Learning</strong> Server parameters 21 workers, 2 predictors, 1 trainer each 5M episodes Training parameters learning rate: 0.0001 reward clipping: 1.0e-7 selection discount: 0.04 minimization discount: 0.02 ae discount: 10.0 agent discount: 1. reward rescaling: 10 predicted actions: 1 training data: 200K Network parameters Prediction model:<br> {'env1': [64, 128, 128, 128, 128, 64, 32],<br> 'env2': [64, 128, 128, 128, 128, 64, 32],<br> 'env3': [64, 128, 128, 128, 128, 64, 32]} Encoder model: [128, 128, 64, 32] Decoder model: [32, 64, 128, 128, 128]
DOI: 10.5281/zenodo.4568162
title: Supporting data for 78 LECs in the two candidate landing areas of China's first Mars mission (Tianwen-1)

description: For all 78 LECs (A1-A54 in SUIP and B1-B24 in SCP), (1) the CSFD-based dating plots, (2) the CTX mosaic for their ejecta deposits outline (blue line) and identifiable superposed craters (red circles), and (3) the resulting SCC files (used for dating) for individual LECs exported from ArcGIS with the “Crater Tools”. They have been introduced in the following publication, where more details can be found: Shengli Niu, Feng Zhang, Kaichang Di, Sheng Gou, Zongyu Yue <strong>Layered Ejecta Craters in </strong><strong>the Candidate Landing Areas of </strong><strong>China's First Mars Mission (Tianwen-1)</strong><strong>: Implications for Subsurface Volatile Concentrations</strong> Submitted to Journal of Geophysical Research: Planets, Under Review.
DOI: 10.5281/zenodo.4584135
title: Python repositories using Reinforcement learning

description: This data set contains Reinforcement Learning projects for their analysis with respect to software engineering quality metrics. The projects are extracted from open-source github repositories implemented in python. The selected repositories correspond to the top 20 active repositories (based on their stars)
DOI: 10.5281/zenodo.4587967
title: Enabling single-molecule localization microscopy in turbid food emulsions

description: Supplementary and raw data
DOI: 10.5281/zenodo.50022
title: Hwrt Database Of Handwritten Symbols

description: The HWRT database of handwritten symbols contains on-line data of handwritten symbols such as all alphanumeric characters, arrows, greek characters and mathematical symbols like the integral symbol.

The database can be downloaded in form of bzip2-compressed tar files. Each tar file contains:


	symbols.csv: A CSV file with the rows symbol_id, latex, training_samples, test_samples. The symbol id is an integer, the row latex contains the latex code of the symbol, the rows training_samples and test_samples contain integers with the number of labeled data.
	train-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.
	test-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.


All CSV files use ";" as delimiter and "'" as quotechar. The data is given in YAML format as a list of lists of dictinaries. Each dictionary has the keys "x", "y" and "time". (x,y) are coordinates and time is the UNIX time.

 

About 90% of the data was made available by Daniel Kirsch via github.com/kirel/detexify-data. Thank you very much, Daniel!
DOI: 10.5281/zenodo.3944663
title: Multi-scale optical coherence tomography imaging and visualization of Vermeer's Girl with a Pearl Earring

description: The data sets, software, and figures accompany the publication: "Multi-scale optical coherence tomography imaging and visualization of Vermeer's Girl with a Pearl Earring", Optics Express 28, 26239 (2020) https://doi.org/10.1364/OE.390703 Fig4_largescaleOCT.pdf, high resolution image of figure 4 of the manuscript Fig5_mediumscaleOCT.pdf, high resolution image of figure 5 of the manuscript Fig6_smallscaleOCT.pdf, high resolution image of figure 6 of the manuscript GWPE_DEMO_V5.zip<br> Zip file of a Windows executable of the virtual rendering of the Girl with the Pearl Earring. The readme file describes the operation of the virtual rendering. Stitching_README.txt Describes the stitching software and the accompanying data sets MS-OCT surface data: SurfaceStitchedKwadrant11.npy SurfaceStitchedKwadrant12.npy SurfaceStitchedKwadrant21.npy SurfaceStitchedKwadrant22.npy UpperInterfaceMatrixRawdataKwadrant11.npy MS-OCT thickness data FullResGlazeStratigraphyThicknessKwadrant11.npy FullResGlazeStratigraphyThicknessKwadrant12.npy FullResGlazeStratigraphyThicknessKwadrant21.npy FullResGlazeStratigraphyThicknessKwadrant22.npy MS-OCT Scattering strength data FullResScatteringStrengthKwadrant11.npy FullResScatteringStrengthKwadrant12.npy FullResScatteringStrengthKwadrant21.npy Main.py, DataAnalysisFunctionsRepository.py, and ReadAndCombineKwadrantData.py are Python files for stitching the four quadrants of data together. Runs in Python &gt;3.5. Full stitching image takes more than an hourFullResScatteringStrengthKwadrant22.npy Plot_measure.py and plot_fit.py<br> Analysis of the BRDF measurements of the black underlayer background (RU1) and glaze layer (RU5). Runs in Python &gt; 3.5 Visualization1.ave<br> Video of the multi-scale multi-parameter OCT data set of the Girl Visualization2.mp4<br> Video showing an impression of the interactive demo of the Girl
DOI: 10.5281/zenodo.4057855
title: A center of heavy PM2.5 pollution in the Yangtze River middle basin attributed to regional transport of PM2.5 over China

description: The Multi-resolution Emission Inventory for China (MEIC) emission sources dataset and PM<sub>2.5</sub> datasets
DOI: 10.5281/zenodo.4301523
title: Results of OLCI-derived Secchi disk depth in lakes across Eastern China

description: <em>Water clarity, represented by Secchi disk depth (Zsd), is the first-order indicator of water quality. This dataset contains Zsd of 86 major large lakes with an area greater than 30 km2 in East China estimated using a random forest regression (RFR)-based Zsd model. Scientists from optical remote sensing and aquatic sciences can use this dataset for their research.</em>
DOI: 10.5281/zenodo.3820685
title: Google Earth trace and GPS coordinates of Mongolian Great Wall

description: Google Earth trace and GPS coordinates of Mongolian Great Wall.
DOI: 10.5281/zenodo.6326109
title: Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)

description: Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.
DOI: 10.5281/zenodo.3265202
title: Dataset - Improved Isolation of Microbiologically Produced (2R,3S)-Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol

description: This dataset in form of an Excel-format contains the data of the journal article “Improved Isolation of Microbiologically Produced Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol”. The dataset shows the values of different experiments, calculations carried out for this publication.
DOI: 10.5281/zenodo.4049842
title: LMD Inconel 718 Spiral 2020-07-31

description: Description of dataset 10.5281/zenodo.4049842 Deposition of Inconel 718 square spiral with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocity = 600 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Spiral size: 60 mm side, 3 mm step<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient The dataset is constituted by:<br> - Melt pool images, in file Deposition_2020_7_31__15_13_17.zip, acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trackData_20200731.csv containing:<br> - t: timestamp in ms. Synchronized with imData_20200731.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imData_20200731.csv containing:<br> - t: timestamp in ms. Synchronized with trackData_20191205.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)
DOI: 10.5281/zenodo.4656144
title: Solar Dataset (10 Minutes Observations)

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset used by Lai et al. (2017). It contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.6319779
title: Hospitality and Tourism Industry from TR-HT

description: Set of data used in the paper <em>The impact of ESG dimensions on firm risk of hospitality and tourism industry.</em> The data were obtained from Thomson Reuters Eikon database (TR_Eikon), we selected those companies whose activity sector was H&T regardless of their country of origin. The data are due in "xls" format and structured in two sheets. - the first sheet contains the selected companies, with the next information: Company Name NAICS Sector Code NAICS Subsector Code NAICS Industry Code NAICS National Industry Name Muestra Final: an indicator variable used to remark that the respective company is used in the study. - The second sheet contains the next information: Company Id: The Company Identificator. A number differentiating each company from the rest. Year: The year of the correspondig register. P_Crisis: Pandemic Crisis. Dummy variable coded 1 if year is 2020, 0 otherwise. F_Crisis Financial Crisis: Dummy variable coded 1 if year is from 2008 to 2012, 0 otherwise Firm_Size: Firm size. Logarithm of total asses Leverage: Leverage. Total debt to total assets ROA: Return on assets. EBITDA divided by total assets B_Gender: Gender diversity. Number of women directors as a percentage of total directors on the board B_Independence: Independence. number of independent directors as a percentage of total directors on the board B_Size: Board size. Number of directors on the board Duality: CEO duality. Dummy variable taking the value 1 if the chairperson of the board is the CEO and 0 otherwise ESG_Score: Evironmental Social and Governance Score. A weighted average relative rating based on reported environmental, social and governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters SOC_Score: Social Pillar Score. A weighted average relative rating based on reported social information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters GOV_Score: Governance Pillar Score. A weighted average relative rating based on reported governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters ENV_Score: Environmental Pillar Score. A weighted average relative rating based on reported environmental information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters Dependent variables D2D: Distance of Default. Merton’s distance to default SD_R: Volatility of the stock returns. Standard deviation of daily stock returns
DOI: 10.5281/zenodo.883133
title: Global Map of Reforestation Potential

description: Global Map of Reforestation Potential
DOI: 10.26093/cds/vizier.22410033
title: Long-term variability in the radio-quiet AGN Ark120

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'A possible ~20yr periodicity in long-term optical photometric and spectral variations of the nearby radio-quiet active galactic nucleus Ark 120.' (bibcode: 2019ApJS..241...33L)
DOI: 10.5281/zenodo.4456134
title: Reply to comment on "High-resolution, multi-layer modelling of Singapore's urban climate incorporating local climate zones"

description: This data is for the publication submitted to the Journal of Geophysical Research Atmospheres
DOI: 10.5281/zenodo.53934
title: Boats-Global-Fisheries-Economics-Model-Dataset-Carozza-Et-Al-2016

description: Here we provide the MATLAB functions, fisheries data, model forcing data (net primary production and temperature), and model output required to generate the figures and perform calculations from Carozza et al. (2017) [Carozza DA, Bianchi D, Galbraith ED (2017) Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model. PLoS ONE 12(1): e0169763. doi:10.1371/journal.pone.0169763]. The plot script (plot_figures_PLoSONE_repository.m) is written in MATLAB version R2012a.
DOI: 10.5281/zenodo.4276276
title: Allochthonous and autochthonous halothermotolerant bioanodes from hypersaline sediment and textile wastewater: A promising microbial electrochemical process for energy recovery coupled with real textile wastewater treatment

description: 16 S sequencing data Askri et al
DOI: 10.5281/zenodo.4301522
title: Results of OLCI-derived Secchi disk depth in lakes across Eastern China

description: <em>Water clarity, represented by Secchi disk depth (Zsd), is the first-order indicator of water quality. This dataset contains Zsd of 86 major large lakes with an area greater than 30 km2 in East China estimated using a random forest regression (RFR)-based Zsd model. Scientists from optical remote sensing and aquatic sciences can use this dataset for their research.</em>
DOI: 10.5281/zenodo.5511612
title: WRF simulations of 2017 Europe Heatwave

description: The WRF model (version 4.1.4) domain was configured with a 9 km grid spacing (547x462 grid cells) with 51 vertical levels covering Europe. National Centers provide the initial and boundary conditions for Environmental Prediction Final Operational Model Global Tropospheric Analyses (NCEP FNL) data, which is available at a 6-hourly and 1-degree temporal and spatial resolution, respectively. The simulations started on 2017-06-08 at 00:00 UTC and ended on 2017-06-25 at 00:00 UTC and with 3-hour steps. The following parameterization schemes were used: the Rapid Radiative Transfer model scheme for longwave radiation, Goddard shortwave scheme, Bougeault-Lacarrère for the planetary boundary layer, WRF single moment 6-class scheme as microphysics, Kain-Fritsch as cumulus scheme, and multi-layer Building Energy Parameterization (BEP) as the urban scheme. The simulations are performed using two LSMs, i.e., Noah and Noah-MP. For the LULC, the Moderate Resolution Imaging Spectroradiometer (MODIS) data is used in two ways: i) the default MODIS LULC which has one urban class `Urban and Built-up Lands'; and ii) the MODIS LULC product in which its original urban land cover is replaced by the 10 urban LCZ classes, available from the European LCZ map at 100 m spatial resolution (hereafter denoted as WUDAPT LULC). The data format is NetCDF containing detailed metadata. Two-dimensional variables are included, horizontal wind components at 10 m AGL, atmospheric pressure at the surface, water vapor mixing ratio at 2 m AGL, the temperature at 2 m AGL, land use/ land cover, sensible heat flux, latent heat flux, and ground heat flux, the height of the terrain, and land mask. These WRF simulations are used to analyze the results of the "Modelling Large-Scale Heatwave by Incorporating Enhanced Urban Representation" manuscript submitted to JGR-Atmospheres.
DOI: 10.5281/zenodo.7884663
title: Supplementary Data for GCB-23-0339

description: Supplementary tables S1 to S30 for <strong>"Integrative population genetics and metagenomics reveals urbanization increases pathogen loads and decreases connectivity in a wild bee" </strong>published in <em>Global Change Biology</em>.
DOI: 10.5281/zenodo.7884662
title: Supplementary Data for GCB-23-0339

description: Supplementary tables S1 to S30 for <strong>"Integrative population genetics and metagenomics reveals urbanization increases pathogen loads and decreases connectivity in a wild bee" </strong>published in <em>Global Change Biology</em>.