Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,467
datasets available to search
ShareScore release 0.9.0
Dataset results
6,467 results for “process”
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
ZTF processed light curves
<p><strong><em>ZTF_all.csv</em></strong>: Catalog of the ~ 7 million ZTF periods measured</p> <p><em>source_id</em>: gaia EDR3 source ID</p> <p><em>Prot_all</em>: measured period from the entire light curve in days</p> <p><em>Prot_1</em>: measured period from the first season light curve in days</p> <p><em>Prot_2</em>: measured period from the second season light curve in days</p> <p><em>Prot_3</em>: measured period from the third season light curve in days</p> <p><em>Prot_4</em>: measured period from the fourth season light curve in days</p> <p><em>maxpower_all</em>: normalized maximum LS power from the entire light curve</p> <p><em>maxpower_1</em>: normalized maximum LS power from the first season light curve</p> <p><em>maxpower_2</em>: normalized maximum LS power from the second season light curve</p> <p><em>maxpower_3</em>: normalized maximum LS power from the third season light curve</p> <p><em>maxpower_4</em>: normalized maximum LS power from the fourth season light curve</p> <p><em>ra</em>: right ascension from gaia eDR3 in deg</p> <p><em>dec</em>: declination from gaia eDR3 in deg</p> <p><em>bprp</em>: bprp from gaia eDR3</p> <p><em>abs_G</em>: absolute magnitude from gaia eDR3</p> <p><em>parallax</em>: parallax from gaia eDR3</p> <p><em>ruwe</em>: ruwe number from gaia eDR3</p> <p> </p> <p><em><strong>source_id.csv</strong></em>: source_id to ztf file name </p> <p><em>source_id</em>: gaia eDR3 source ID</p> <p><em>filename</em>: file that contains the light curve associated with the source_id</p> <p> </p> <p><em><strong>ZTFlc.zip</strong></em>: processed light curves</p> <p>Contain multiple .tgz files with processed light curves with the naming convention of <source_id>.pkl. One can use the source_id.csv file to find the .tgz file that contains the light curve with a specific gaia source_id.</p> <p>For each light curve pickle file, it contains the following columns:</p> <p><em>lctime</em>: processed light curve times</p> <p><em>lcmag</em>: processed normalized magnitudes</p> <p><em>orglctime</em>: original light curve times</p> <p><em>orglcmag</em>: original light curve magnitudes </p> <p> </p> <p>pandas version used to save the .pkl files: 1.4.2</p>
Processed data for the study on "Observation of site-selective chemical bond changes via ultrafast chemical shifts"
<p>This repository contains the underlying data for the paper entitled "Observation of site-selective chemical bond changes via ultrafast chemical shifts". The uploaded data are preprocessed data measured at the LCLS SLAC facility. Each individual file contains the measurements at a set delay time and spectrometer setting on a shot-by-shot basis.</p> <p>The files contain the following keys:</p> <pre>['Ebeam_energy_MeV', 'Electron_orbit_probe_x', 'Electron_orbit_probe_y', 'Electron_orbit_pump_x', 'Electron_orbit_pump_y', 'Fiducial', 'Fit_quality', 'Gas_monitor_mJ', 'Group', 'N_electrons', 'Photon_energy_FEL_eV', 'Probe_energy_xtcav', 'Probe_pulse_energy_mJ', 'Probe_width_fs', 'Pump_energy_xtcav', 'Pump_pulse_energy_mJ', 'Pump_width_fs', 'TOF_CO_yield', 'TOF_Ne_yield', 'Time_delay_XTCAV']</pre> <p>Description of the individual entries is found in the supplementary materials of the publication. The following table assigns the time delay and measure electron kinetic energy for each dataset.</p> <table> <tbody> <tr> <td>Time delay</td> <td>Ek = 234eV</td> <td>Ek = 220eV</td> <td>Ek = 210eV</td> </tr> <tr> <td>40fs</td> <td>Run_210</td> <td>Run_212</td> <td>Run_213</td> </tr> <tr> <td>20fs</td> <td>Run_231</td> <td>Run_236</td> <td>Run_235</td> </tr> <tr> <td>15fs</td> <td>Run_239</td> <td>Run_242</td> <td>Run_241</td> </tr> <tr> <td>10fs</td> <td>Run_238</td> <td>Run_233</td> <td>Run_234</td> </tr> <tr> <td>5fs</td> <td>Run_228</td> <td>Run_226</td> <td>Run_225</td> </tr> <tr> <td>0</td> <td>Run_221</td> <td>Run_223</td> <td>Run_224</td> </tr> <tr> <td>-5fs</td> <td>Run_217</td> <td>Run_219</td> <td>Run_220</td> </tr> </tbody> </table> <p> </p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Source data for "Lonely individuals process the world in idiosyncratic ways"
<p>The following includes the source data for the manuscript titled "Lonely individuals process the world in idiosyncratic ways".</p>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</p>
Biogeographic distribution and divergent assembly process of cyanobacterial morphotypes in biocrusts across northwestern China
<p><span>The patterns of biogeographical distribution and assembly processes of microbiota are of vital importance for understanding ecological adaptation and functioning maintenance. However, the role of their morphological characteristics in microbial assembly is still poorly ascertained. Here, by integrating high-throughput sequencing and<span> </span>robust extrapolation of traits, we investigated taxonomic and phylogenetic turnovers of various cyanobacterial morphotypes in biocrusts to evaluate the contributions of deterministic and stochastic processes across a large scale of drylands. The results showed that the non-heterocyst filamentous category dominated biocrusts in the arid ecosystem and exhibited strong tolerance against environmental fluctuations. Despite thesignificant distance-decay relationship of <em>β</em>-diversity detected in all categories, both species composition and phylogenetic turnover rates of coccoid cyanobacteria were higher than non-heterocyst filamentous and heterocystous morphotypes. Moreover, the assembly of cyanobacteria was driven by different ecological processes that the entire community and non-heterocyst filamentous morphotype were governed by deterministic processes, while stochasticity prevailed in heterocystous and coccoid cyanobacteria. However, aridity can modulate the balance between determinism and stochasticity and prompt a shifting threshold among morphotypes, generating an alteration in community composition. <a name="_Hlk121763045"></a>Our findings provide a unique perspective to understanding the role of microbial morphology in community assembly, highlighting the differentiation of biogeographic distribution, environmental response, and species preference between cyanobacterial morphotypes with consideration of potential functional consequences.</span></p>
Fault strength and rupture process controlled by fault surface topography
<p>Experimental source data for the study "Fault strength and rupture process controlled by fault surface topography"</p>
Raw and processed data for 'Purification-based quantum error mitigation of pair-correlated electron simulations'
<p>Experimental data for 'Demonstration of purification-based error mitigation for quantum simulation'</p> <p>This directory contains two folders - 'final_data_sets' and 'plots_for_paper'.<br> 'final_data_sets' contains directories with raw experimental data, either in the form of raw shots, or accumulated expectation values. Everything is stored in either json or pickle format. This data was processed to generate the datasets in subfolders of 'plots_for_paper', which also contains notebooks for generating plots in the paper (data processing scripts to appear soon). Note that the python file to generate Fig.3 is in 'plots_for_paper/paper_plot_ring_opening_sixq_10q'.</p> <p>For any further questions, please email teobrien@google.com.</p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
Supplementary data for "Upward leaders from instrumented lightning rods competing to connect a downward leader during a lightning attachment process"
<p>High-speed videos of the lightning attachment to lightning rods observed by the Vision Reserch Phantom V12 and V711 cameras. See Instructions file to play the videos.</p>
Establishment of mountain birch (Betula pubescens ssp. tortuosa) on a glacial outwash plain: spatial patterns and decadal processes
<p><span>Most of the Earth's surface has now been modified by humans. In many countries, natural and semi-natural ecosystems mostly occur as islands, isolated by land converted to agriculture and a variety of other land-uses. In this fragmented state, long-distance dispersal may be the only option for species to adapt their ranges in response to changing climate. The order of arrival of species may leave a lasting imprint on community assembly. Although mostly studied at and above the species level, such priority effects also apply at the intraspecific level. We suggest that this may be particularly important in subarctic and arctic ecosystems. </span>Mountain birch (Betula pubescens ssp. tortuosa) is characterised by great intraspecific variation. We explored spatio-temporal patterns of the first two mountain birch generations on a homogeneous, early successional glacial outwash plain in SE Iceland that was the recipient of spatially extensive long-distance dispersal ca. 30 years ago. We evaluated the decadal progress of the young population by remeasuring in 2018, tree density and growth form, plant size, and reproductive effort on 30 transects (150 m2) established in 2008 at four sites on the plain and two adjacent sites ca. 10 km away. All measured variables showed positive increases, but contrary to our predictions of converging dynamics among sites, they had significantly diverged. Thus, two of the sites (only 500 m apart) could not be distinguished in 2008 but by 2018, one of them had much faster growth rates than the other, a higher growth form index reflecting more upright tree stature, greater reproductive effort, and much greater second-generation seedling recruitment. We discuss two hypotheses that may explain the diverging dynamics, site-scale environmental heterogeneity and legacies of intraspecific priority effects. </p>
Process intensification in a chemical reactor with enhanced mass transfer for hydrogen production
<p><strong>Process intensification in a chemical reactor with enhanced mass transfer for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical synthesis is concerned with the construction of complex chemical compounds from simpler ones. A synthesis usually is undertaken for one of three reasons. The first reason is to meet an industrial demand for a product. For example, ammonia is synthesized from nitrogen and hydrogen and is used to make, among other things, ammonium sulfate, employed as a fertilizer; vinyl chloride is made from ethylene and is used in the production of polyvinyl chloride plastic. In general, a vast range of chemical compounds are synthesized for applications. Second, an enormous number of compounds of considerable molecular complexity occur naturally. The syntheses of these natural products have usually been undertaken in the context of the determination of the structures of the compounds; if a material is deduced to have a particular structure on the basis of its chemical reactions and physical properties, then the discovery that a compound synthesized by an unambiguous method for this structure is identical to the natural product provides confirmation of the validity of the assigned structure. Third, a synthesis may be carried out to obtain a compound of specific structure that does not occur naturally and has not previously been made. This type of synthesis is performed in order to examine the properties of the compound and thereby test theories of chemical structure and reactivity.</p> <p>Streamwise distance (millimeters), Wall centerline temperature (degrees kelvin)</p> <p>0 501.211</p> <p>0.00025 501.158</p> <p>0.0005 500.999</p> <p>0.00075 500.714</p> <p>0.001 500.254</p> <p>0.00125 499.764</p> <p>0.0015 499.441</p> <p>0.00175 499.3</p> <p>0.002 499.307</p> <p>0.00225 499.312</p> <p>0.0025 499.195</p> <p>0.00275 498.964</p> <p>0.003 498.605</p> <p>0.00325 498.22</p> <p>0.0035 497.936</p> <p>0.00375 497.767</p> <p>0.004 497.696</p> <p>0.00425 497.643</p> <p>0.0045 497.535</p> <p>0.00475 497.367</p> <p>0.005 497.127</p> <p>0.00525 496.875</p> <p>0.0055 496.695</p> <p>0.00575 496.597</p> <p>0.006 496.57</p> <p>0.00625 496.556</p> <p>0.0065 496.505</p> <p>0.00675 496.414</p> <p>0.007 496.275</p> <p>0.00725 496.132</p> <p>0.0075 496.044</p> <p>0.00775 496.017</p> <p>0.008 496.045</p> <p>0.00825 496.079</p> <p>0.0085 496.078</p> <p>0.00875 496.046</p> <p>0.009 495.978</p> <p>0.00925 495.91</p> <p>0.0095 495.887</p> <p>0.00975 495.916</p> <p>0.01 495.987</p> <p>0.01025 496.057</p> <p>0.0105 496.093</p> <p>0.01075 496.099</p> <p>0.011 496.076</p> <p>0.01125 496.056</p> <p>0.0115 496.073</p> <p>0.01175 496.134</p> <p>0.012 496.231</p> <p>0.01225 496.323</p> <p>0.0125 496.38</p> <p>0.01275 496.409</p> <p>0.013 496.413</p> <p>0.01325 496.421</p> <p>0.0135 496.462</p> <p>0.01375 496.541</p> <p>0.014 496.652</p> <p>0.01425 496.755</p> <p>0.0145 496.824</p> <p>0.01475 496.865</p> <p>0.015 496.886</p> <p>0.01525 496.912</p> <p>0.0155 496.967</p> <p>0.01575 497.057</p> <p>0.016 497.176</p> <p>0.01625 497.286</p> <p>0.0165 497.362</p> <p>0.01675 497.414</p> <p>0.017 497.448</p> <p>0.01725 497.486</p> <p>0.0175 497.553</p> <p>0.01775 497.65</p> <p>0.018 497.773</p> <p>0.01825 497.887</p> <p>0.0185 497.968</p> <p>0.01875 498.026</p> <p>0.019 498.069</p> <p>0.01925 498.117</p> <p>0.0195 498.191</p> <p>0.01975 498.292</p> <p>0.02 498.416</p> <p>0.02025 498.53</p> <p>0.0205 498.614</p> <p>0.02075 498.677</p> <p>0.021 498.727</p> <p>0.02125 498.781</p> <p>0.0215 498.859</p> <p>0.02175 498.96</p> <p>0.022 499.08</p> <p>0.02225 499.19</p> <p>0.0225 499.272</p> <p>0.02275 499.334</p> <p>0.023 499.385</p> <p>0.02325 499.44</p> <p>0.0235 499.514</p> <p>0.02375 499.608</p> <p>0.024 499.718</p> <p>0.02425 499.818</p> <p>0.0245 499.892</p> <p>0.02475 499.948</p> <p>0.025 499.992</p> <p>0.02525 500.039</p> <p>0.0255 500.1</p> <p>0.02575 500.178</p> <p>0.026 500.268</p> <p>0.02625 500.349</p> <p>0.0265 500.407</p> <p>0.02675 500.448</p> <p>0.027 500.477</p> <p>0.02725 500.506</p> <p>0.0275 500.545</p> <p>0.02775 500.596</p> <p>0.028 500.656</p> <p>0.02825 500.708</p> <p>0.0285 500.741</p> <p>0.02875 500.757</p> <p>0.029 500.759</p> <p>0.02925 500.755</p> <p>0.0295 500.754</p> <p>0.02975 500.757</p> <p>0.03 500.759</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport
<p><strong>Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Consideration must be given to the rate at which each step in the planned sequence occurs. In many instances, a desired reaction is possible in principle but in practice takes place so slowly as to be ineffective. It is then necessary to investigate whether the rate can be increased to a practicable level by altering the conditions of the reaction, for example, by raising the temperature or by adding an extra species, called a catalyst, that increases the rate without altering the course of the reaction.</p> <p>Streamwise distance (millimeters), Oxidation channel centerline temperature (degrees kelvin)</p> <p>0 376.499</p> <p>0.00025 391.55</p> <p>0.0005 411.347</p> <p>0.00075 432.857</p> <p>0.001 451.349</p> <p>0.00125 465.437</p> <p>0.0015 475.536</p> <p>0.00175 482.591</p> <p>0.002 487.466</p> <p>0.00225 490.805</p> <p>0.0025 493.063</p> <p>0.00275 494.556</p> <p>0.003 495.508</p> <p>0.00325 496.074</p> <p>0.0035 496.366</p> <p>0.00375 496.469</p> <p>0.004 496.424</p> <p>0.00425 496.278</p> <p>0.0045 496.077</p> <p>0.00475 495.832</p> <p>0.005 495.555</p> <p>0.00525 495.26</p> <p>0.0055 494.955</p> <p>0.00575 494.646</p> <p>0.006 494.337</p> <p>0.00625 494.033</p> <p>0.0065 493.735</p> <p>0.00675 493.445</p> <p>0.007 493.164</p> <p>0.00725 492.894</p> <p>0.0075 492.635</p> <p>0.00775 492.386</p> <p>0.008 492.15</p> <p>0.00825 491.925</p> <p>0.0085 491.712</p> <p>0.00875 491.511</p> <p>0.009 491.323</p> <p>0.00925 491.146</p> <p>0.0095 490.98</p> <p>0.00975 490.825</p> <p>0.01 490.682</p> <p>0.01025 490.549</p> <p>0.0105 490.427</p> <p>0.01075 490.314</p> <p>0.011 490.212</p> <p>0.01125 490.119</p> <p>0.0115 490.036</p> <p>0.01175 489.96</p> <p>0.012 489.893</p> <p>0.01225 489.833</p> <p>0.0125 489.781</p> <p>0.01275 489.737</p> <p>0.013 489.699</p> <p>0.01325 489.668</p> <p>0.0135 489.643</p> <p>0.01375 489.624</p> <p>0.014 489.61</p> <p>0.01425 489.603</p> <p>0.0145 489.6</p> <p>0.01475 489.601</p> <p>0.015 489.607</p> <p>0.01525 489.618</p> <p>0.0155 489.632</p> <p>0.01575 489.651</p> <p>0.016 489.673</p> <p>0.01625 489.699</p> <p>0.0165 489.728</p> <p>0.01675 489.76</p> <p>0.017 489.796</p> <p>0.01725 489.834</p> <p>0.0175 489.875</p> <p>0.01775 489.918</p> <p>0.018 489.964</p> <p>0.01825 490.012</p> <p>0.0185 490.063</p> <p>0.01875 490.115</p> <p>0.019 490.169</p> <p>0.01925 490.225</p> <p>0.0195 490.283</p> <p>0.01975 490.343</p> <p>0.02 490.404</p> <p>0.02025 490.467</p> <p>0.0205 490.531</p> <p>0.02075 490.597</p> <p>0.021 490.663</p> <p>0.02125 490.731</p> <p>0.0215 490.8</p> <p>0.02175 490.869</p> <p>0.022 490.94</p> <p>0.02225 491.011</p> <p>0.0225 491.083</p> <p>0.02275 491.155</p> <p>0.023 491.228</p> <p>0.02325 491.301</p> <p>0.0235 491.374</p> <p>0.02375 491.448</p> <p>0.024 491.521</p> <p>0.02425 491.595</p> <p>0.0245 491.668</p> <p>0.02475 491.741</p> <p>0.025 491.813</p> <p>0.02525 491.885</p> <p>0.0255 491.955</p> <p>0.02575 492.025</p> <p>0.026 492.094</p> <p>0.02625 492.161</p> <p>0.0265 492.228</p> <p>0.02675 492.292</p> <p>0.027 492.355</p> <p>0.02725 492.415</p> <p>0.0275 492.474</p> <p>0.02775 492.529</p> <p>0.028 492.583</p> <p>0.02825 492.633</p> <p>0.0285 492.68</p> <p>0.02875 492.724</p> <p>0.029 492.764</p> <p>0.02925 492.801</p> <p>0.0295 492.832</p> <p>0.02975 492.863</p> <p>0.03 492.881</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Process intensification in a chemical reactor with enhanced heat exchange
<p><strong>Process intensification in a chemical reactor with enhanced heat exchange</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>A reactor, in chemical engineering, is a device or vessel within which chemical processes are carried out for experimental or manufacturing purposes. Reactors range in size and complexity from small, open kettles fitted with simple stirrers and heaters to large, elaborate vessels equipped with jackets or internal coils for heating or cooling, nozzles or ports for adding and removing materials, sources of ultraviolet radiation or electrical energy, specially designed agitators or scrapers, and strong walls and tight seals to permit operation at high or low pressures. In many cases, these reactors are provided with instruments that measure temperature, pressure, pH, or other properties of the contents. They are constructed of materials selected for strength and resistance to attack by the substances being processed. For certain processes that are particularly adaptable to continuous operation, reactors are devised so that the chemical reaction may continue for weeks or months while starting materials are introduced continuously or intermittently and the finished product is removed in a steady stream or in periodic discharges. Scale of temperature is a methodology of calibrating the physical quantity temperature in metrology. Empirical scales measure temperature in relation to convenient and stable parameters, such as the freezing and boiling point of water. Absolute temperature is based on thermodynamic principles: using the lowest possible temperature as the zero point, and selecting a convenient incremental unit.</p> <p>Streamwise distance (millimeters), Wall centerline temperature (degrees kelvin)</p> <p>0 498.195</p> <p>0.00025 498.22</p> <p>0.0005 498.261</p> <p>0.00075 498.266</p> <p>0.001 498.214</p> <p>0.00125 498.105</p> <p>0.0015 497.946</p> <p>0.00175 497.745</p> <p>0.002 497.508</p> <p>0.00225 497.244</p> <p>0.0025 496.958</p> <p>0.00275 496.655</p> <p>0.003 496.34</p> <p>0.00325 496.017</p> <p>0.0035 495.69</p> <p>0.00375 495.362</p> <p>0.004 495.035</p> <p>0.00425 494.711</p> <p>0.0045 494.392</p> <p>0.00475 494.081</p> <p>0.005 493.776</p> <p>0.00525 493.48</p> <p>0.0055 493.194</p> <p>0.00575 492.917</p> <p>0.006 492.651</p> <p>0.00625 492.395</p> <p>0.0065 492.151</p> <p>0.00675 491.917</p> <p>0.007 491.694</p> <p>0.00725 491.483</p> <p>0.0075 491.283</p> <p>0.00775 491.094</p> <p>0.008 490.917</p> <p>0.00825 490.751</p> <p>0.0085 490.596</p> <p>0.00875 490.451</p> <p>0.009 490.318</p> <p>0.00925 490.194</p> <p>0.0095 490.081</p> <p>0.00975 489.978</p> <p>0.01 489.883</p> <p>0.01025 489.798</p> <p>0.0105 489.721</p> <p>0.01075 489.652</p> <p>0.011 489.591</p> <p>0.01125 489.538</p> <p>0.0115 489.492</p> <p>0.01175 489.453</p> <p>0.012 489.421</p> <p>0.01225 489.395</p> <p>0.0125 489.374</p> <p>0.01275 489.36</p> <p>0.013 489.35</p> <p>0.01325 489.346</p> <p>0.0135 489.347</p> <p>0.01375 489.353</p> <p>0.014 489.362</p> <p>0.01425 489.376</p> <p>0.0145 489.394</p> <p>0.01475 489.415</p> <p>0.015 489.44</p> <p>0.01525 489.469</p> <p>0.0155 489.5</p> <p>0.01575 489.534</p> <p>0.016 489.571</p> <p>0.01625 489.612</p> <p>0.0165 489.655</p> <p>0.01675 489.7</p> <p>0.017 489.748</p> <p>0.01725 489.798</p> <p>0.0175 489.85</p> <p>0.01775 489.904</p> <p>0.018 489.96</p> <p>0.01825 490.017</p> <p>0.0185 490.076</p> <p>0.01875 490.138</p> <p>0.019 490.2</p> <p>0.01925 490.264</p> <p>0.0195 490.33</p> <p>0.01975 490.396</p> <p>0.02 490.464</p> <p>0.02025 490.533</p> <p>0.0205 490.603</p> <p>0.02075 490.674</p> <p>0.021 490.745</p> <p>0.02125 490.818</p> <p>0.0215 490.891</p> <p>0.02175 490.964</p> <p>0.022 491.038</p> <p>0.02225 491.112</p> <p>0.0225 491.187</p> <p>0.02275 491.262</p> <p>0.023 491.336</p> <p>0.02325 491.411</p> <p>0.0235 491.485</p> <p>0.02375 491.56</p> <p>0.024 491.633</p> <p>0.02425 491.706</p> <p>0.0245 491.778</p> <p>0.02475 491.85</p> <p>0.025 491.92</p> <p>0.02525 491.988</p> <p>0.0255 492.056</p> <p>0.02575 492.122</p> <p>0.026 492.187</p> <p>0.02625 492.249</p> <p>0.0265 492.309</p> <p>0.02675 492.367</p> <p>0.027 492.422</p> <p>0.02725 492.474</p> <p>0.0275 492.524</p> <p>0.02775 492.57</p> <p>0.028 492.612</p> <p>0.02825 492.651</p> <p>0.0285 492.686</p> <p>0.02875 492.717</p> <p>0.029 492.742</p> <p>0.02925 492.764</p> <p>0.0295 492.779</p> <p>0.02975 492.789</p> <p>0.03 492.793</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
CS2900 - Multi-dimensional Data Processing Slidecasts Royal Holloway, University of London
<p>Slidecasts of CS2900 (Multi-dimensional Data Processing - but essentially Linear Algebra for Computer Scientists) 2018/2019. For undergraduate CS students.</p>
Landsat 8 and SRTM Data Processed for Soil Classification (Yuri Coelho's Thesis)
<p>This dataset was processed for the Senior Thesis of Yuri Coelho.</p> <p>The dataset is composed by the processed rasters that are used in the Senior Thesis. The objective of the dataset is to classify satellite data into Soil Classes.</p>
Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects
<p><strong>Introduction</strong></p> <p>Seaweed samples can be divided into two groups:</p> <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> <p>Seaweed samples will always have the following information-tracks:</p> <ol> <li><strong>Sample Code: </strong>A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li><strong>Seaweed Processing Code:</strong> The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li><strong>Batch Number:</strong> A code describing details about the harvest and origin of the seaweed.</li> <li><strong>Sample Overview:</strong> An Excel file describing all details about the sample: first <strong>sample code</strong>, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>
Processing of haptic texture information over sequential exploration movements
<p>Where textures are defined by repetitive small spatial structures, exploration covering a greater extent will lead to signal repetition. We investigated how sensory estimates derived from these signals are integrated. In Experiment 1 participants stroked with the index finger one to eight times across two virtual gratings. Half of the participants discriminated according to ridge amplitude, the other half according to ridge spatial period. In both tasks just noticeable differences (JNDs) decreased with an increasing number of strokes. Those gains from additional exploration were over 3 times smaller than predicted for optimal observers who have access to equally reliable, and therefore equally weighted estimates for the entire exploration. We assume that the sequential nature of the exploration leads to memory decay of sensory estimates. Thus, participants compare an overall estimate of the first stimulus, which is affected by memory decay, to stroke-specific estimates during the exploration of the second stimulus. This was tested in Experiments 2 & 3. The spatial period of one stroke across either the first or second of two sequentially presented gratings was slightly discrepant from periods in all other strokes. This allowed calculating weights of stroke-specific estimates in the overall percept. As predicted, weights were approximately equal for all strokes in the first stimulus, while weights decreased during the exploration of the second stimulus. A quantitative Kalman filter model of our assumptions was consistent with the data. Hence, our results support an optimal integration model for sequential information given that memory decay affects comparison processes.</p>
ScienceDex guides
Understand access before you commit
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.