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1,026 results for “kinetics”
Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices
<p>This dataset corresponds to the following manuscript: </p> <p>Zendrini, M., Dubrovskii, V., Rudra, A., Dede, D., Fontcuberta i Morral, A., Piazza, V. “Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices” <em>ACS Applied Nano Materials 7,16 (2024):</em> 19065–19074</p> <p>DOI: <a href="http://doi.org/10.1021/acsanm.4c02765">doi.org/10.1021/acsanm.4c02765</a></p> <p>The dataset contains raw SEM images in .tif format for all the arrays of nanowires and nanomembranes discussed in the paper. The dataset also contains the AFM scans in .xyz format for all the arrays of nanowires and nanomembranes. The data for the morphological analysis are extracted from the SEM images and the AFM scans and they are collected in two separate .txt files for NWs and NMs.</p>
Dataset for Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics"</strong></p><p>published in ChemElectroChem</p><p>Specifically, the following measurements are provided:</p><ul><li>Scanning electrochemical cell microscopy (SECCM). Cyclic voltammetry (E, i) data for each location across the sample. 5 cycles.</li><li>Chronoamperometry (i, t) for the anodization process.</li><li>Atomic Force Microscopy (AFM) topography.</li><li>Raman microscopy</li><li>X-ray photoelectron spectroscopy (XPS)</li><li>Scanning electron microscopy (SEM)</li></ul>
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
Supplementary Data to Kinetic oxygen isotope fractionation between water and aqueous OH- during hydroxylation of CO2
<p>In this dataset, we provide analytical data to <strong>Kinetic oxygen isotope fractionation between water and aqueous OH<sup>-</sup> during hydroxylation of CO<sub>2</sub></strong> by Bajnai and Herwartz (2021). Also deposited here is the R code that was used to generate the figures in the manuscript.</p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Patient reported outcome measures, load-induced blood marker kinetics, and ambulatory knee load in patients with medial compartment knee osteoarthritis
<p>The goal of this study was (i) to quantify the mechanoresponse of this array of potential blood markers for joint pathology (COMP, MMP-1, MMP-3, MMP-9, CPII, C2C, C2C/CPII, ADAMTS-4, PRG-4, IL-6 and resistin) to a walking stress test in patients with knee OA and to determine the correlation (ii) among the kinetics of these blood markers, (iii) with accumulated knee load during the walking stress, and (iv) with patient reported osteoarthritis outcome and QoL.</p> <p>The dataset includes 24 patients with knee osteoarthritis scheduled to receive high tibial osteotomy. All participants completed questionnaires, and a walking stress test with six blood samples analyzed using enzyme-linked immunosorbent assays for cartilage oligomeric matrix protein (COMP), matrix metalloproteinases (MMP)-1, -3, and -9, epitope resulting from cleavage of type II collagen by collagenases (C2C), type II procollagen (CPII), interleukin (IL)-6, proteoglycan (PRG)-4, A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-4, and resistin, and gait analysis. Joint load was computed from gait analysis data and musculoskeletal modelling in AnyBody Modeling System (AnyBody Technology A/S). Discrete loading parameters were extracted for each step using an inhouse algorithm written in Matlab.</p> <p>The detailed experimental protocol of the umbrella study has been described in Mündermann A, Vach W, Pagenstert G, Egloff C, Nüesch C. Assessing in vivo articular cartilage mechanosensitivity as outcome of high tibial osteotomy in patients with medial compartment osteoarthritis: Experimental protocol. Osteoarthr Cartil Open. 2020 Feb 24;2(2):100043. doi: 10.1016/j.ocarto.2020.100043. PMID: 36474590; PMCID: PMC9718245. The study is registered on clinicaltrials.gov (identifier NCT02622204). The method for computing joint loading has been described in detail in De Pieri E, Nüesch C, Pagenstert G, Viehweger E, Egloff C, Mündermann A. High tibial osteotomy effectively redistributes compressive knee loads during walking. J Orthop Res. 2022 Jun 22. doi: 10.1002/jor.25403. Epub ahead of print. PMID: 35730475.</p>
Silica solubility and dissolution kinetics at high saline geothermal conditions
<p>This dataset contains solubility data for silica as a function of time, temperature and salinity. The dataset supports Chapter 2 in the deliverable “Report on mineral solubility and precipitation at high salinities, DOI: https://doi.org/10.48440/gfz.4.8.2023.001 from the H2020 project REFLECT.</p> <p>The silica material used as solid substrate for the dissolution studies was pro analysis sea sand (purified by acid washing and calcinated for analysis) from Merck. The sand grain size (125-250 µm) included in the experiments was obtained by sieving the material. The sieved powder was washed with tap water to remove fine grains from the samples, and dried prior to experiments. The BET surface area of the sand was measured to 0.69 m<sup>2</sup>/g and the weighted mean particle size distribution (PSD) was 118 µm. The crystallographic structure was determined by X-ray diffraction analysis (XRD) and this analysis showed that the sample contained mainly low-quartz (minimum 95% w/w) with a few unidentified impurities. SEM/EDS maps of the silica powder showed essentially pure silica with minor Al impurity. Some grains or regions are enriched in Al and K, suggesting some aluminium silicate. Some minor spots rich in Ti, Fe and Cr were also detected.</p> <p>The experiments conducted to study silica solubility at equilibrium conditions were performed at five different temperatures (100, 125, 150, 175 and 200°C) and four salinities (NaCl concentrations 50.9, 103.6, 215.7 and 338.1 g/kg H<sub>2</sub>O). The columns containing SiO<sub>2</sub> and NaCl solutions where isolated for a reaction time of six days before fluid sampling (Table1 “Silica solubility at high saline geothermal conditions”).</p> <p>The experiments conducted to study silica solubility kinetics were performed for different time periods (from 1 hour up to 144 hours) to study solubility as a function of time. These tests were conducted at 200°C with NaCl concentration 50.92 g/kg and 338.09 g/kg H<sub>2</sub>O (Table2 “Silica solubility kinetics at high saline geothermal conditions”).</p> <p>The experimental setup consists of packed static columns. Maximum four columns (length 40 cm, i.d. 10.22 mm, stainless steel SS316) packed with the material to study can be placed in parallel within the setup. Porous metal frits (HC276) are placed at the outlet and inlet of the columns to prevent entrainment of the material. Approximately 50 g of dried SiO<sub>2</sub> powder is required to fill a column completely and the pore volume was measured gravimetrically to be approximately 15 ml. Two Gilson 307 high performance liquid chromatography (HPLC) pumps are included in the setup. One for filling and displacing column pore fluid and one for diluting the fluid prior to sampling, preventing precipitation of dissolved silica due to depressurization and cooling. Pressure was maintained by a dome loaded backpressure regulator (BPR) from CoreLab at the column outlet and liquid samples were collected using a fraction collector (Gilson FC203B). The setup of columns and inlet/outlet valves was placed in a heating cabinet (Memmert). The columns were thermally insulated to prevent instabilities in temperature and hence pressure when opening the heating cabinet during sampling.</p> <p>The columns are flooded with degassed NaCl fluid at a low flow rate and pressurized initially to 25 bars while temperature is increased slowly to the desired level. The time of start is noted, the brine pump is shut off, and the individual columns isolated by closing inlet and outlet valves. After a period (hours, days, or weeks) samples are withdrawn from the columns and diluted at the mixing point by re-opening the valves and operating both HPLC pumps. A dilution factor of 8.5 is selected to prevent precipitation. For each sampling five samples of 2 ml is collected (totally 10 ml of fluid). The two first samples are considered to contain mainly dead volumes from tubing, fittings and valves and are therefore discharged. The three last samples represent the pore fluid from the column. These samples are analysed for Si and NaCl concentration. The NaCl concentration was analysed to keep control of the dilution step of the sampling process.</p> <p>SiO<sub>2</sub> and NaCl concentrations were analysed using inductively coupled plasma mass spectrometry (ICP-MS) or inductively coupled plasma optical emission spectrometry (ICP-OES). The elements Si and Cl (ICP-MS) or Si and Na (ICP-EOS) were detected.</p> <p>The Si concentration from the analysis was reported as mg/L solution. From this concentration the concentration of SiO<sub>2</sub> in the samples were calculated and reported as mol/kg H<sub>2</sub>O. The conversion from liter solution to kg H<sub>2</sub>O was done using the OLI software for density calculations.</p>
Modulation Engineering: Stimulation Design for Enhanced Kinetic Information from Modulation-Excitation Experiments on Catalytic Systems
<p>Dataset used in the publication "Modulation Engineering: Stimulation Design for Enhanced Kinetic Information from Modulation-Excitation Experiments on Catalytic Systems" (<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Facscatal.3c00646&data=05%7C01%7CValentijn.DeCoster%40UGent.be%7C2a7a2c81f646405654d308db2f58f8ec%7Cd7811cdeecef496c8f91a1786241b99c%7C1%7C0%7C638155831400735344%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=e%2Fyp50KsegsMtKcY9ijwh3AqUbrsxFLo%2BTXyGmzOLps%3D&reserved=0">https://doi.org/10.1021/acscatal.3c00646</a>).<br> A description document ("Data overview.docx") is included and provides an overview of the dataset.</p>
Data for removal kinetics and breakthrough curves of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns [Dataset]
<p>This dataset describes the transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns. The experiments aimed at providing sustainable treatment options for relevant persistent, mobile and toxic (i.e., PMT substances) linked to vehicular traffic pollution. We assessed removal for 1H-benzotriazole, N'N-diphenylguanidine, and hexamethoxymethyl-melamine (PMT precursor) in batch and column experiments using pyrogenic carbonaceous adsorbents (e.g., GAC and biochar). Data contain kinetics batch experiments and breakthrough curves for the target contaminants.</p>
Silica dissolution and precipitation kinetics in hot geothermal conditions
<p>This dataset report quartz dissolution kinetics as obtained from packed column experiments at different flow rates. Variables were temperature, pressure and NaCl content as incicated in the table. Silica values are reported as mg/L of Si as measured by ICP-OES. Also included in the table is a column describing how data series were treated to extract steady-state values for each flow rate (cf. the report to which the current dataset is related). The column "solubility used" states the solubility used to calculate dissolution (k<sub>+</sub>) and precipitation (k<sub>-</sub>) rate constants along with a column "source" which briefly indicates how this value was obtained. Further details are given in the report.</p> <p>Factors used to get from the raw data to the reported rate constants are also given. Not included in the table, but common for all data points are a quartz BET surface are of 0.6922 m<sup>2</sup>/g, 10 g quartz and a quartz activity assumed to be 1.</p> <p>Note that this dataset contain several measurement points that are not representative. These include points close do equilibrium where kinetic information cannot be reliably obtained and points where it is suspected that a temperature drop during sampling may have caused erroneous results (the Si content actually represents a somewhat lower temperature that was not measured). The reader is referred to the full report for details.</p>
Preliminary Supplementary Information for "Kinetics of Deoxyribose-1-Phosphate Decay in Aqueous Solution"
<p>This is the dataset for our upcoming publication tenatively titled "Kinetics of Deoxyribose-1-Phosphate Decay in Aqueous Solution" and may serve as a preliminary Supplementary Information.</p> <p>We employed high-throughput UV spectroscopy-based monitoring of the apparent conversion of deoxyribosyl nucleoside phosphorolysis to access the kinetics of deoxyribose-1-phosphate hydrolysis in aqueous solution at different pH values and temperatures.</p> <p>Please see the files below for a general description of this entry and the full dataset(s).</p>
Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells
<p><strong>Cell culture and data set</strong></p> <p>Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4<sup>LUC+</sup> cells (or LM2-4), a metastatic variant of the human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7].</p> <ul> </ul> <p><strong>Tumor injections</strong></p> <p>LM2-4<sup>LUC+</sup> cells were orthotopically implanted (10<sup>6</sup> cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice.</p> <p><strong>Tumor measurements</strong></p> <p>Tumor size was measured regularly with calipers to a maximum volume of 2 cm<sup>3</sup>, calculated by the formula </p> <p><span class="math-tex">\(V = \frac{\pi}{6} w^2 L\)</span></p> <p>(ellipsoid) where <em>L</em> is the largest and <em>w</em> is the smallest tumor diameter.</p> <p><strong>Please cite: </strong>Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p> <p> </p> <p>In the file, the columns correspond to:</p> <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm<sup>3</sup>)</li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, <em>10</em>(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800</p> <p>[2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389.</p> <p>[3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217.</p> <p>[4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989</p> <p>[5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021</p> <p>[6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017</p> <p>[7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p>
Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).
<p>Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".</p>
A unifying framework for mean-field theories of asymmetric kinetic Ising systems [Dataset]
<p>Datasets for reproducing the results in the article Aguilera, M., Moosavi, S.A. & Shimazaki, H. A unifying framework for mean-field theories of asymmetric kinetic Ising systems. <em>Nature Communications</em> <strong>12, </strong>1197 (2021). https://doi.org/10.1038/s41467-021-20890-5. Results can be reproduced using the code repository of the article https://github.com/MiguelAguilera/kinetic-Plefka-expansions</p> <p>The main dataset contains simulations of an asymmetric, kinetic Sherrington-Kirkpatrick (SK) model around the equivalent of a ferromagnetic phase transition in the equilibrium SK model. External fields <span class="math-tex">\(H_i\)</span> are sampled from independent uniform distributions <span class="math-tex">\(\mathcal{U}(-\beta H_0, \beta H_0)\)</span> with <span class="math-tex">\(H_0=0.5\)</span>, whereas coupling terms <span class="math-tex">\(J_{ij}\)</span> are sampled from independent Gaussian distributions <span class="math-tex">\(\mathcal{N}(\beta \frac{J_0}{N},\beta^2 \frac{J_\sigma^2}{N})\)</span>, with <span class="math-tex">\(J_0=1, J_\sigma = 0.1\)</span> where <span class="math-tex">\(\beta\)</span> is a scaling parameter (i.e., an inverse temperature).</p> <p>To study the non-stationary transient dynamics of the model, we start from <span class="math-tex">\(\mathbf s_0 = \mathbf 1\)</span> (all elements set to 1 at <span class="math-tex">\(t=0\)</span>) and recursively update its state for <span class="math-tex">\(T=128\)</span> steps. We repeated this stochastic simulation for <span class="math-tex">\(10^6\)</span> trials for 21 values of <span class="math-tex">\(\beta\)</span> in the range <span class="math-tex">\([0.7\beta_c, 1.3\beta_c]\)</span>, except for the reconstruction of the phase transition where we used <span class="math-tex">\(R=10^5\)</span> and 201 values of <span class="math-tex">\(\beta\)</span> in the same range.<br> <br> Each file is stored in: 'data-H0-0.5-J0-1.0-Js-0.1-N-512-R-1000000-beta-[beta_ref].npz', where [beta_ref] contains the normalized value of <span class="math-tex">\(\beta/\beta_C\)</span> between 0.7 and 1.3.<br> <br> Furthermore, data in the folders 'forward.zip', 'inverse.zip' and 'reconstruction.zip' contain files to reproduce the results of the paper above. These files show the results of solving the forward Ising problem, the inverse Ising problem, and the reconstruction of the phase transition combining forward and inverse problems.</p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.
<p>This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST):</p> <p>AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html</p> <p>NOAA-OISST: https://www.ncdc.noaa.gov/oisst</p> <p>From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived. </p> <p>Then the fields are then temporally smoothed using a running average of 12 months. Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall).</p> <p>Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc).</p> <p>Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc).</p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends</p>
A CO2 valorization plant to produce light hydrocarbons: kinetic model, process design and life cycle assessment
<p>Supplementary material: Reaction indexes, Conservation equations, boundary conditions and used coefficients. Additional experimental results, Experimental data fitting, Stream properties and composition of the CO2 plant, Life Cycle Assessment indicators, assumptions and data input </p>
[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu
<p>## "Spatial scales of kinetic energy in the Arctic Ocean"</p> <p>Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9).</p> <p>## Description</p> <p>This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9.</p> <p>1) Jupyter notebook scripts for visualization<br>the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly.</p> <p>2) Matlab scripts for plots<br>All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.</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.