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33,754 results for “Enhancers”
Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi
<p>This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision).</p> <p><strong>Experiment description</strong></p> <p>We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model.</p> <p>The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources.</p> <p>In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi.</p> <p><strong>Files</strong></p> <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> <p><strong>Contact</strong></p> <p>Shin-Ya Ogino<br> Japan Agency for Marine-Earth Science and Technology (JAMSTEC)<br> E-mail: ogino-sy@jamstec.go.jp</p>
NanoString dataset for study: Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade
<p>Pre-processed NanoString mRNA abundance data and associated sample sheet for study:</p> <p>Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade</p>
Supplemental material for "Enhanced collisionless laser absorption in strongly magnetized plasmas"
<p>This dataset constitutes supplemental material for the paper titled "Enhanced collisionless laser absorption in strongly magnetized plasmas" by Lili Manzo, Matthew R. Edwards, and Yuan Shi.</p> <p>• figure_data.zip<br> When unzipped, this folder contains subfolders fig1, fig2, …, fig10, each contains data used to generate figures 1,2, …,10 in the paper. The data files are in .txt, .mat, or .dat format, and are intended to be read by MATLAB.</p> <p>The data underlying fig1 and fig3 are generated using the Three-Wave-MATLAB code (https://gitlab.com/seanYuanSHI/three-wave-matlab).</p> <p>The data underlying fig2, fig4, and figs5-10 are raw simulation data or post-processed results of the epoch1d code (https://github.com/Warwick-Plasma/epoch).<br> <br> • figure_programs.zip<br> When unzipped, this folder contains plot_fig1.m, plot_fig2.m, …, plot_fig10.m, which are MATLAB scripts used to plot the corresponding data. Except for plot_fig2.m, which requires MATLAB version 2018 or later, all other scripts can run on MATLAB 2013 or later. The scripts plot the data but does not reproduce the formatting of the figures as shown in the paper.</p> <p>• input.deck<br> This is an example input for the epoch1d code (version 4.17.10) used to generate simulation data in the paper. </p>
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
<p>Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.</p>
NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes
<p>This repository holds all of the raw data generated by my C++ code for a paper "NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes".</p> <p>The C++ code solves the neutron diffusion equation using a novel spatial discretisation called the Virtual Element Method.</p> <p>Alongside the raw data (stored in VTK and HDF5 files) are post-processing python scripts which read the raw data, compute meaningful quantities of interest and generate plots/figures.</p>
Memory reactivation in slow wave sleep enhances relational learning
<p>EEG raw files in Brain Product format (3 files per participant). Sleep EEG recordings from healthy volunters under TMR stimulation. </p> <p>Two conditions: up-going slow oscillation stimulation / down-going oscillation stimulations</p> <p>Two type of sounds: experimental (they hear them before) and control (completely new)</p> <p>Triggers: 100-112 up experimental sounds / >112 up control sounds / 1-12 down experimetal sounds / 12-100 down control</p> <p>Please cite:</p> <p>"Memory reactivation in slow wave sleep enhances relational learning" ( https://doi.org/10.1101/2022.03.29.486197 )</p> <p> </p>
Data_Torrefaction of pulp industry sludge to enhance its fuel characteristics
<p>Recently, under COP26, several countries agreed to phase-out coal from their energy systems. Torrefaction industry can take advantage of this, as the fuel characteristics of the torrefied biomass are comparable to those of coal. However, in terms of economic feasibility, torrefied biomass pellets are not yet competitive with coal without subsidies because of the high price of woody biomass. Thus, there is a need to produce torrefied pellets from low-cost feedstock, and pulp industry sludge is one such feedstock. In that context, this study was focused on torrefaction of pulp industry sludge. Torrefaction experiments were carried out using a continuous reactor, at temperatures 250, 275, and 300 ℃. The heating value of the sludge increased from 19 to 22 MJ/kg after torrefaction at 300 ℃. The fixed carbon content increased from 16 wt.% for dried pulp sludge to 30 wt.% for torrefied pulp sludge. The fuel ratio was in the range of 0.27 to 0.61. The ash content of the pulp sludge is comparable to the agricultural waste, which is around 12 wt.% (dry basis). The cellulose content in the sludge reduced from 35 to 12 wt.% at 300 ℃. The ash related issues such as slagging, fouling and bed agglomeration tendency of the sludge were in the medium range. This study shows that torrefaction treatment can improve the fuel properties of the pulp industry sludge to a level comparable to that of low-rank coal.</p>
Raw Data - Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance
<p>The dataset contains raw data that complements the article:</p> <p>Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance</p> <p>Christian Iffelsberger, Daniel Rojas, and Martin Pumera<strong>*</strong></p> <p>https://doi.org/10.1021/acs.jpcc.1c10686</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity
<p># Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity</p> <p>## Author<br> Marieke Scheel, Lund University, Sweden, marieke.scheel@gmail.com</p> <p>## Description<br> Data underlying analysis in:<br> Marieke Scheel, Mats Lindeskog, Benjamin Smith, Susanne Suvanto, Thomas A. M. Pugh<br> Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity<br> Scripts underlying analysis:<br> https://github.com/mariekesche/harvest_driven_canopy_mortality</p> <p>Folder: harvest_checks<br> - NFI_Germany.txt, National Forest Index data from Germany as difference between inventories in 200-2003 and 2011-2013, values are given as fraction, n: number of NFI plots in a grid cell, HARVEST_ALL: clear-cut harvest, HARVEST_PARTIAL: thinning harvest, NATDEAD: natural dead (not harvested)</p> <p>Folder: manag_climfix (S_man,clim)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_co2fix (S_man,CO2)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_ndepfix (S_man,N)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_nofix (S_man)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - closs.txt, biomass loss in kg [C]/m^2 year split into DBH classes<br> - closs_harv.txt, biomass loss due to harvest in kg [C]/m^2 year split into DBH classes<br> - cpool_forest_rel.txt, biomass of forests in kg [C]/m^2 year<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_harv.txt, m^2 of crown/m^2 of ground lost per year due to harvest<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes<br> - diam_dens.txt, total number of trees/m^2 year split into DBH classes<br> - stemloss.txt, number of trees/m^2 year lost in respective cells<br> - stemloss_harv.txt, number of trees/m^2 year lost in respective cells due to harvest</p> <p>Folder: manag_nothin (S_nothin)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year<br> - lai.txt, leaf area index (LAI) for simulated species and plant functional types (PFTs)</p> <p>Folder: PNV (S_PNV)<br> - cflux.txt, Net Primary Production (NPP) values in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: dependencies<br> - gridlist.txt, coordinates of 0.5°x0.5° grid cells that simulations were run on; longitude, latitude, FAO number, size in m^2<br> - landcover_eu.txt, input file LPJ-GUESS model showing changes from natural to forest (harvest); longitude, latitude, year, natural, forest, barren</p> <p>## Dependencies<br> - canopy mortality rates published in "Senf C Pflugmacher D Zhiqiang Y Sebald J Knorn J Neumann M Hostert P and Seidl R 2018 Canopy mortality has doubled in Europe’s temperate forests over the last three decades Nature Communications 9 4978 10.1038/s41467-018-07539-6"<br> - harvest removal rates published in "Ceccherini G Duveiller G Grassi G Lemoine G Avitabile V Pilli R and Cescatti A 2020<br> Abrupt increase in harvested forest area over Europe after 2015 Nature 583 72-77 10.1038/s41586-020-2438-y"<br> - FAO forest removal area data data retrieved from https://www.fao.org/faostat/en/#data/GF (24.07.2021), modified for overview (1st column: Area Code, 2nd column: Year, 3rd column: Area in 1000 ha)</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures
<p><span>Characterisation dataset for “</span><span>Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures”</span><span>, DOI:</span><span>10.1039/d4nh00104d</span><span>. Data for main and supporting figures provided as *.xlsx and *.txt files.</span></p> <p> </p> <p> </p> <p> </p>
Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining"
<p>Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining".</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data (pickles)</li> <li>Discontinue robot data (task performance)</li> <li>EEG data</li> </ol>
El Niño Enhances Snowline Rise and Ice Loss on the Quelccaya Ice Cap, Peru
<p>El Niño Enhances Snowline Rise on the Quelccaya Ice Cap, Peru (in-review)</p> <p>Kara A. Lamantia, Laura J. Larocca, Lonnie G. Thompson, Bryan Mark</p> <p>Exported results from automated snow cover area detection on the Quelccaya Ice Cap (QIC). Further calculated results are detailed in the supplementary documentation in the draft manuscript. See READ ME.txt file for details</p> <p>Sample Code available for Landsat 8 imagery here at the following URL: https://code.<br>earthengine.google.com/cfcbd0780ff3f09b0698035cd6dd678a</p>
Data for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Extinction spectra of arrays of aluminum nanoparticles with diameters between 40 - 100 nm.</p> <p>Circular dichroism spectra of Tol-BINAP films on Al nanoparticle arrays before and after annealing of the films.</p> <p>Electromagnetic simulations of phase, electric (Eenh) field and magnetic (Henh) field enhancements as well as optical chirality density (Cenh) enhancement around flat aluminum hexagonal pyramid at specified wavelength. The simulations were performed with FDTD using Ansys Lumerical.</p>
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation"
<p>Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation".</p> <p><strong>Versions of dataset:</strong></p> <p><strong><span>V1: </span></strong><span>First dataset regarding the data used in the article.</span></p> <p><strong><span>V2:</span></strong><span> The dataset </span><span>was newly reorganized</span><span>, containing the </span><span>data,</span><span> that </span><span>were used</span><span> for the published article. </span><span>More information can be found</span><span> in the README file.</span></p> <p><strong>Article abstract</strong></p> <p>The urgent need for sustainable and innovative approaches to mitigate the increasing levels of atmospheric CO<sub>2</sub> necessitates the development of efficient methods for its removal. In this study, we focus on the new, innovative approach for synthesis and functionalization of metal-organic framework (MOF) ZIF-8 in one step at room temperature to enhance its capacity for CO<sub>2</sub> capture. Specifically, we investigated the impact of four amino-compounds, namely tetraethylenepentamine (TEPA), hexadecylamine (HDA), <a title="Learn more about ethanolamine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemical-engineering/ethanolamine">ethanolamine</a> (ELA), and cyclopropylamine (CPA), on the <a title="Learn more about chemical structure from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/structure-composition">chemical structure</a>, size, surface area and porosity, and CO<sub>2</sub> capturing of ZIF-8 powder. By varying concentrations of the amino-compounds, we examined their influence on the ZIF-8 properties. Our findings demonstrate that each amino-compound and its respective concentration exhibit distinct effects on the characteristics of ZIF-8. Notably, the ZIF-8 sample functionalized with the highest presented concentration of TEPA exhibited significant improvement in CO<sub>2</sub> trapping efficiency, with a 33.3% enhancement. Moreover, least concentrated samples with added HDA or CPA demonstrated notable improvements with enhancements of 46.6% and 18.6%, respectively. These results highlight the potential of simple synthesis and functionalization techniques for MOFs in enhancing their CO<sub>2</sub> capture capabilities. The findings from this study offer new opportunities for the development of strategies to mitigate CO<sub>2</sub> emissions using MOFs.</p>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods - Dataset & Code
<p>This object contains the dataset and python code used for the paper:</p> <p>S. Brenner and R. Sablatnig. On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods. Accepted for OAGM Workshop 2019<strong>, </strong>Steyr, Austria.</p> <p>The dataset is a modified subset of the UCL Multispectral Processed Images of Parchment Damage Dataset (<a href="http://dx.doi.org/10.14324/000.ds.1469099">10.14324/000.ds.1469099</a>). The accompanying code documents how the modified version was created and how the evaluations described in the paper were performed.</p>
Supplementary files for "Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework"
<p>DFT optimised structures for the paper: Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework. See the paper for additional information on the computational setup.</p> <p>Files are named HP or LP for high-pressure and low-pressure phases, followed by the pressure as calculated by DFT. The HP structure was optimized in two different space groups and are labelled accordingly. The DFT optimized structure without volume restrictions is found in opt-vol.POSCAR</p> <p> </p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.