cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4590183
title: Data from: Topographic Wetness Index as a proxy for soil moisture: the importance of flow-routing algorithm and grid resolution

description: The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1 - 30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modelling methods and 5200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest R2 (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture. Riihimäki, Kemppinen, Kopecký & Luoto (Preprint). Topographic Wetness Index as a proxy for soil moisture: the importance of flow-routing algorithm and grid resolution. Zenodo. These are the data from Riihimäki & Kemppinen et al. (Preprint).
description: HR and JK were funded by the Doctoral Programme in Geosciences at the University of Helsinki. JK was also funded by the Arctic Interactions at the University of Oulu and Academy of Finland (project 318930, Profi 4). MK was funded by the Czech Academy of Sciences (project RVO 67985939). The field research was funded by the Academy of Finland (project 286950).
DOI: 10.5676/eum_saf_cm/toa_gerb/v002
title: CM SAF TOA Radiation GERB/SEVIRI Data Record - Edition 2

description: The TOA Radiation GERB/SEVIRI record from CM SAF provides estimates of the Top Of Atmosphere broadband radiative fluxes derived 
     from the Geostationary Earth Radiation Budget (GERB) and the Spinning Enhance Visible and InfraRed Imager (SEVIRI) 
     sensors onboard the Meteosat Second Generation satellites. 
     The outgoing radiation is reported in terms of TOA Reflected Solar (TRS) and TOA Emitted Thermal (TET) fluxes.      
     This second edition is the improved and extended follow-up of the first edition of the record available from CM SAF under DOI 10.5676/EUM_SAF_CM/TOA_GERB/V001. 
     The main improvements are:
     (1) In addition to the all sky fluxes, the edition 2.0 also provides an estimate of the corresponding clear sky fluxes.
     (2) A correction is applied for the GERB and SEVIRI sensor ageing.
     (3) Data from the MSG1, MSG2 and MSG3 satellites have been homogenized. 
     (4) Data from the backup MSG satellite has been used to reduce the gaps in the data record.  
     (5) The temporal coverage is extended to 1st Feb. 2004 - 30th April 2015. 
      
     The original GERB level 2.0 High Resolution (HR) and SEVIRI level 1.5 observations have been processed to estimate solar and thermal fluxes 
     in hourly boxes on the GERB HR grid.
     From the hourly values, the daily mean, the monthly mean and the monthly mean diurnal cycle are estimated. 
     Finally, the data is regridded on a regular latitude-longitude grid covering 70°N - 70° and 70°W-70°E, with a spatial resolution of 0.1°. 
     Being based on 15' geostationary observations, the data record is very well suited to study the diurnal cycle of atmospheric radiation, e.g. the effect of convection on the OLR. 
     The spatial resolution of the data, 0.1°, allows to study the effect on the radiation budget of processes taking place at fine spatial scale, e.g. valley fog.  
     Combining the all-sky and clear-sky allows quantifying the cloud radiative effects at high spatial and temporal resolution.
DOI: 10.5281/zenodo.6385096
title: Methane emission and 13C data - Mycklemossen

description: Picarro data from automatic chamber system Temperature and PAR data from automatic chamber system
DOI: 10.5281/zenodo.6970428
title: Zion

description: # Zion #This is a research work illustrating the switchable photovoltaic effect in WS2 nanotube, and prototyping a "four in one" artificial machine vision. #Codes can be found in ZION to Matrix #Source data can be found in SOURCE DATA #Enjoy!
description: Beijing Institute of Technology, Liangxiang University Town Fangshan Beijing
DOI: 10.5676/eum_saf_cm/toa_met/v001
title: Top of Atmosphere Radiation MVIRI/SEVIRI Data Record

description: The CM  SAF Top of Atmosphere Radiation MVIRI/SEVIRI Data Record provides a homogeneous
      satellite-based climatology of the TOA Reflected Solar (TRS) and Emitted Thermal (TET) radiation in all-sky conditions. The continuous
      monitoring of these components of the Earth Radiation Budget is of prime importance to study climate variability and change. The MVIRI and
      SEVIRI instruments on board the METEOSAT First and Second Generation satellites are combined to generate a long
      Thematic Climate Data Record covering a period of time of 32 years, from 1 February 1983 to 30 April 2015. The TOA radiation products are provided
      as daily mean, monthly mean and monthly averages of the hourly integrated values (diurnal cycle). The data is provided on a regular grid at a
      spatial resolution of 0.05°x0.05° and covers the region between ±70° longitude and ±70° latitude. In
      addition to covering a long time period, the MVIRI/SEVIRI data record is also featured by excellent spatial and temporal samplings.
DOI: 10.5281/zenodo.1246561
title: Modeling Compliance Specifications In Linear Temporal Logic, Event Processing Language And Property Specification Patterns

description: Experimental material & data
DOI: 10.5281/zenodo.7008103
title: Radiance and Cloud Optical Thickness from Large Eddy Simulations over the Sulu Sea

description: This repository contains the data files to accompany the paper "Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network". Please cite the paper as follows: Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-45, in review, 2022. The 6 HDF5 files were generated using a tool called EaR<sup>3</sup>T developed by Hong Chen using Large Eddy Simulations over the Sulu Sea (Yamaguchi et al., 2019). Each hdf5 file contains 6 fields: cot_inp_3d: COT Input: column integrated COT directly from LES data; rad_mca_1d: MCARaTS 1D Radiance: radiance calculated from COT Input using MCARaTS in IPA mode; rad_mca_3d: MCARaTS 3D Radiance: radiance calculated from COT Input using MCARaTS in 3D mode; rad_ret_1d: Radiance from Input COT: radiance calculated from COT Input using a pre-calculated COT vs Radiance relationship; cot_ret_1d: COT from MCARaTS 1D Radiance: COT obtained from MCARaTS 1D Radiance using a pre-calculated COT vs Radiance relationship; cot_ret_3d: COT from MCARaTS 3D Radiance: COT obtained from MCARaTS 3D Radiance using a pre-calculated COT vs Radiance relationship.
DOI: 10.5281/zenodo.891007
title: On The Understandability Of Temporal Properties Formalized In Linear Temporal Logic, Property Specification Patterns And Event Processing Language

description: Experimental material & data
DOI: 10.6084/m9.figshare.7406441.v1
title: Data and supporting R script for: Tissue Tropism and Transmission Ecology Predict Virulence of Human RNA Viruses

description: Ecological trait data for 214 human RNA virus species collected via literature search and supporting R script for classification tree and random forest analyses. <br>Data describes known virulence and fatalities, transmission routes, tissue tropisms, and host range for each virus species. Data was compiled via structured literature searches following Woolhouse & Brierley (2018). Full data dictionary is provided in-file.
DOI: 10.6084/m9.figshare.7860989
title: Suppl Table 1.docx

description: Supplemental Table 1 of paper titled "Attenuated Dentin Matrix Protein 1 Enhance Fibroblast Growth Factor 23 in Carvaria in Primary Hyperparathyroidism Model"<br>
DOI: 10.13026/c2rs98
title: MedNLI — A Natural Language Inference Dataset For The Clinical Domain
DOI: 10.18739/a2251fm3s
title: Gr1kmTM: Greenland 1 kilometer Tide Model, 2021

description: The Greenland 1 kilometer Tide Model (Gr1kmTM) is a barotropic ocean tide model on a 1 km x 1 km polar stereographic grid, developed using the Regional Ocean Modeling System (ROMS). Gr1kmTM consists of spatial grids of complex amplitude coefficients for sea surface height and depth-integrated currents (“volume transports”) for 8 principal tidal constituents: 4 semidiurnal (M2, S2, K2, N2) and 4 diurnal (K1, O1, P1, Q1). See the Methods section for a description of model development. The Gr1kmTM gridded files are in binary format (see Gr1kmTM_FileFormat.pdf) and are designed to be used with the open-source software packages: the Matlab “Tide Model Driver” (TMD) toolbox, the Python “pyTMD” package, and the FORTRAN “OSU Tidal Prediction Software” (OTPS). These packages allow the user to browse the model for different constituents, and to make tidal predictions for any time at any location within the model domain. Please also check the ESR Polar Tide Model webpage (https://www.esr.org/research/polar-tide-models/) and our Arctic Data Center Portal (https://arcticdata.io/catalog/portals/ArcticTides) for other Arctic-region barotropic tide models and more information on software packages.
DOI: 10.5281/zenodo.5915150
title: Large surface-rupture gaps and low surface fault slip of the 2021 Mw 7.4 Maduo earthquake along a low-activity strike-slip fault, Tibetan Plateau

description: In this data set, Text S1 describes methods of (i) field investigation, UAV image collection and interpretation and (ii) horizontal and vertical displacement measurements. Figure S3 shows pre-event topographic expressions of the Maduo earthquake fault. Tables S1 and S2 provide measurement results of horizontal and vertical displacements, respectively. Datasets 1-4 provide the UAV flight swath, interpreted surface ruptures and secondary cracks, horizontal displacements and vertical displacements.
DOI: 10.6084/m9.figshare.12330740
title: Brightfield, fluorescence and phase WSI

description: This dataset includes the MATLAB Demo code of automatic distortion correction and 3D design files for building up the reported WSI system.
DOI: 10.18735/4e78-1311
title: Open Knee(s): Virtual Biomechanical Representations of the Knee Joint

description: This site contains magnetic resonance imaging (MRI) and joint mechanical characterization data for 8 human cadaveric knees that included male and female, young and elderly, and healthy and osteoarthritic donors. Imaging was performed on a 3T magnet and included general purpose imaging (3D T1-weighted without fat suppression, isotropic, 0.5 mm resolution); cartilage imaging (3D T1-weighted with fat suppression, 0.35 mm sagittal plane resolution, 0.7 mm out of plane resolution); and connective tissue imaging (proton density, turbo spin echo in sagittal, axial, and coronal planes, 0.35 mm in plane and 2.8 mm out of plane resolution). Joint mechanical testing was performed using a 6 degree of freedom (DoF) parallel robot and the tibiofemoral joint for each specimen underwent a series of laxity and combined loading tests at various flexion angles. Patellofemoral contact mechanics were also obtained for a series of quadriceps loading at several flexion angles, for each specimen.
DOI: 10.5281/zenodo.4024660
title: DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."

description: The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( https://doi.org/10.1073/pnas.2023757118 ) See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html. The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)). We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e<sup>-2</sup> with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m<sup>2</sup> centered on the array. We set the initial velocity to 1800 m.sec <sup>-1</sup>. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output). Timeseries of physical quantities can be found here https://doi.org/10.5281/zenodo.3701520 Spatial observations acquired during the same period can be found here https://doi.org/10.5281/zenodo.3971815 The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (https://www.liglab.fr/). You can find more information on the method and seismic dataset used in this paper here: https://zenodo.org/deposit/5645545
DOI: 10.5281/zenodo.4313345
title: Data associated with "Multicolor structured illumination microscopy and quantitative control of polychromatic coherent light with a digital micromirror device"

description: Data associated with "Multicolor structured illumination microscopy and quantitative control of polychromatic coherent light with a digital micromirror device", https://doi.org/10.1101/2020.07.27.223941 Data analysis code is available at https://github.com/QI2lab/mcSIM
DOI: 10.5281/zenodo.6541954
title: Diazotroph Elemental Use Efficiency Model Output

description: Dataset containing model output from the PISCES QUOTA model for three experiments performed to explore the impact of global change on the niche and physiology of two explicit marine diazotrophs. The model included diazotroph specific thermal performance curves for growth and elemental use efficiencies (both iron and phosphorus). The experiments were run using the IPCC high emissions RCP8.5 scenario. The explicit diazotroph model for PISCES QUOTA can be found here: https://github.com/lewiswrightson/PISCES-QUOTA-P6Z
description: Other funding sources: U.S. National Science Foundation grants OCE 1851222 and OCE 2149837
DOI: 10.5281/zenodo.6863855
title: LILBID mass spectrum of water

description: LILBID mass spectrum of water, shown in "Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers" by Klenner et al (2022), published in Earth and Space Science.
DOI: 10.5522/04/14614071
title: Precompensation of 3D field distortions in remote focus two-photon microscopy

description: Datasets and MATLAB code used to generate figures in <i>Precompensation of 3D field distortions in remote focus microscopy</i>.<br>Contains the raw C-z-stack tiff files. The accompanying bead tracking xml files and a snapshot of the MATLAB code used to generate the figures is included. The current ray model version may be found at https://github.com/SilverLabUCL/SilverLab-Microscope.
DOI: 10.6084/m9.figshare.14460096
title: D01-SupplementalData.xlsx

description: List of subjects/animals used in the experiments and image processing details for each subject/animal
DOI: 10.6084/m9.figshare.6833378
title: Serval roadkill dataset

description: This dataset contains data and R code used in the following publication:<br>Williams, S.T., Collinson, W., Patterson-Abrolat, C., Marneweck, D.G, Swanepoel, L.H. (2019) Using road patrol data to identify factors associated with carnivore roadkill counts. PeerJ. DOI: 10.7717/peerj.6650. Available at https://doi.org/10.7717/peerj.6650.<br>A preprint is also available at https://doi.org/10.7287/peerj.preprints.27114.<br>The main dataset contains records of serval (Leptailurus serval) roadkill on the N3 in South Africa from 01/01/2014-31/12/2017. Data were collected by N3TC, in partnership with the Endangered Wildlife Trust.<br>Associated datasets were used to create predictor variables.<br>The R code was used to generate and manipulate the data, and to fit generalised linear mixed effects models with a negative binomial distribution to identify variables that were associated with serval roadkill counts.<br>For further details see the research paper, or contact the authors at samual.t.williams@gmail.com or lourens.swanepoel.univen@gmail.com.
DOI: 10.17632/9mz3rbyppy.1
title: LPS-Nicotine groups (Marwa-9)

description: LPS-nicotine groups (Marwa-9)
DOI: 10.6084/m9.figshare.13667411
title: Data.xlsx

description: Data from study "Effects of photobiomodulation on the redox state of healthy and cancer cells"
DOI: 10.5683/sp2/z3cake
title: Comtrade Medicinal and Aromatic Plants (MAPs) Statistics of Western Balkan Countries (WBCs), Central and Eastern European Countries (CEECs) and South-Eastern European Countries (SEECs) in a Comparative Perspective

description: These are pharmaceutical plants statistics of Western Balkan Countries (WBCs), Central and Eastern European Countries (CEECs) and South Eastern European Countries (SEECs) that were compiled from the COMTRADE database of the United Nations Statistics Division. In this context, external trade of the commodity groups (i.e. HS 1211 - Plants and Parts of Plants, [including seeds and fruits] of a kind used primarily in perfumery, in pharmacy or for insecticidal, fungicidal or similar purposes, fresh or dried, whether or not cut, crushed or powdered, and SITC.3 292.4 pharmaceutical plants) of WBCs (excluding Kosovo due to missing data), CEECs and SEECs were analyzed in frame of available data in COMTRADE database. This dataset consists of statistical data for the WBCs, CEECs and the SEECs for the period of 2013 to 2017. The COMTRADE Indicators consist of the import and export of MAPs products in the WBCs, CEECs and the SEECs. WBCs consist of Albania, Bosnia & Herzegovina, Kosovo, FYR Macedonia, Montenegro, and Serbia. Central and Eastern Europe Region covers the countries that namely are listed as such: Bulgaria, Croatia, Chekia, Estonia, Hungry, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia (11 EU Member States); and Albania, Bosnia & Herzegovina, Macedonia, Montenegro, and Serbia. South-Eastern Europe Region comprises the countries that namely are listed as follows: Albania, Bosnia & Herzegovina, Bulgaria, Croatia, Greece, Kosovo, FYR Macedonia, Montenegro, and Serbia.
DOI: 10.6084/m9.figshare.19706704
title: Significant Human Modification of the Lower Arkansas River Sediment Budget

description: This dataset includes the sediment data, MKARNS dredging summary, wing-dike and bar areas, USGS aggregates sand mining data, and suspended sediment and sand deficit in the Lower Arkansas River.
DOI: 10.34740/kaggle/dsv/1955934
title: Deep Shape From Template Dataset(DSfTD)
DOI: 10.5281/zenodo.5702416
title: Topoisomerase II phylogenies

description: Topoisomerase II phylogenies
DOI: 10.5281/zenodo.5702415
title: Topoisomerase II phylogenies

description: Topoisomerase II phylogenies
DOI: 10.5281/zenodo.7081315
title: Topoisomerase II phylogenies

description: Topoisomerase II phylogenies
DOI: 10.5281/zenodo.7081266
title: Drone aerial imagery of a macroalgal-covered coastline in Kobbefjord (SW Greenland) in 2018 and 2019

description: Aerial images of inner Kobbefjord (SW Greenland) were acquired on two occasions in 2018 (2018-09-07 15:06; 2018-09-07 16:00) representing different tidal levels and on one occasion in 2019 (2019-08-26). Images were acquired using a DJI Mavic Air drone. The imagery was acquired in connection with the “Greenland Ecosystem Monitoring Program” “Nuuk Basis” monitoring of marine flora (http://g-e-m.dk/). This drone survey was carried out with the primary goal of documenting and quantifying the area distribution of the tidal vegetation (dominated by the brown macroalga <em>Ascophyllum nodosum</em>) and shallow subtidal vegetation at the monitoring site. Images were processed using Agisoft Metashape Professional to produce high resolution orthomosaics from each UAV survey.
DOI: 10.5281/zenodo.7081265
title: Drone aerial imagery of a macroalgal-covered coastline in Kobbefjord (SW Greenland) in 2018 and 2019

description: Aerial images of inner Kobbefjord (SW Greenland) were acquired on two occasions in 2018 (2018-09-07 15:06; 2018-09-07 16:00) representing different tidal levels and on one occasion in 2019 (2019-08-26). Images were acquired using a DJI Mavic Air drone. The imagery was acquired in connection with the “Greenland Ecosystem Monitoring Program” “Nuuk Basis” monitoring of marine flora (http://g-e-m.dk/). This drone survey was carried out with the primary goal of documenting and quantifying the area distribution of the tidal vegetation (dominated by the brown macroalga <em>Ascophyllum nodosum</em>) and shallow subtidal vegetation at the monitoring site. Images were processed using Agisoft Metashape Professional to produce high resolution orthomosaics from each UAV survey.
DOI: 10.21227/yvxd-mf03
title: Medium observation under jamming attacks in VANETs

description: Three synthetic and validated datasets correspond to three typical vehicular communication scenarios. Each subdataset comprises approximately $15000$ to $28000$ samples that are MAC observations in each CCHI of all vehicles in the network during the travel on the road segment of $2km$. Each sample includes six most concerned features: number of successful received packets $l_s$, number of failed transmission on the medium $l_c$, number of error received packets $l_e$, number of packet transmitted by the given vehicle $l_{tx}$, time step $t_i$ and estimated number of neighbor vehicles $n_{nb}$. Besides, there is other information such as node id of the observing vehicles, system time, idle time computed in slot time according to the standard IEEE 802.11p, and monitoring of medium state.
DOI: 10.6084/m9.figshare.19519375
title: Primary production and dark C fixation.xlsx

description: Dataset provides values for primary production and dark carbon fixation along with the various environmental parameters for the Arabian Sea (the north-western Indian Ocean) sampled during the Indian winter monsoon season.