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1,026 results for “kinetics”
Fast kinetics data of binding of azide to myoglobin, training data -- MetBio practicals
<p>These data files were acquired during the course of the 3rd <a href="http://frenchbic.cnrs.fr/">FrenchBIC</a> <a href="http://frenchbic.cnrs.fr/2021/01/25/3rd-frenchbic-summer-school-on-methods-for-studying-metals-in-biology/">MetBio summer school</a> taking place in and around Marseille. The data were acquired during the “stopped flow” practicals.</p> <p>The data correspond to the reaction of Myoglobin with various concentrations of azide, triggered using a stopped-flow apparatus and followed by UV/Visible spectroscopy.</p> <p>The concentrations of azide can be deduced from the names of the files and are expressed in millimolar. The concentrations of myoglobin are variable but always much lower than that of azide.</p> <p>The purpose of this dataset is to be used as training data for analysing multiwavelength kinetic data. It will be the subject of a data analysis tutorial using the free software <a href="https://bip.cnrs.fr/groups/bip06/software/">QSoas</a>, to be published <a href="https://vince-debian.blogspot.com/">there</a>.</p> <p>With the exception of the <code>WTV-Azide-0.5mm_3.txt</code>, all the files are in a “matrix” format, in which the first column gives the time and each column after the first corresponds to the absorbances over time of a single wavelength.</p> <p>The <code>WTV-Azide-0.5mm_3.txt</code> uses a different format, in which each line corresponds to a <em>wavelength</em> <em>time</em> <em>absorbance</em> triplet.</p> <p>Useful background reading:</p> <ul> <li> <p>Coletta <em>et al</em>, <strong>1996</strong>, DOI: 10.1111/j.1432-1033.1996.00049.x</p> </li> <li> <p>De Sanctis <em>et al</em>, <strong>2007</strong>, DOI: 10.1529/biophysj.106.098764</p> </li> </ul>
Kinetic features dictate sensorimotor alignment in the superior colliculus.
<p>Tetrode recordings dataset from González-Rueda et al. “Kinetic features dictate sensorimotor alignment in the superior colliculus”, Nature 2024 (<em>https://doi.org/10.1038/s41586-024-07619-2)</em></p> <p>This subset of data corresponds to 5 C57BL6 male adult mice recorded both freely moving during an open field foraging task ("Open field") and head restrained during two visual stimulation tasks ("Visual Stimulation"). Neurons were recorded in both conditions across the superior colliculus using tetrodes in order to determine the sensorimotor alignment between static/kinetic visual features and egocentric head rotations. Detailled explanation of data organisation and content can be found in the file Guide.pdf.</p>
Dataset: Horizon Kinetics SPAC Active ETF (SPAQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Kinetics Medical ETF (MEDX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Kinetics of Dehydrogenation of n-Heptane over Ga-Pt Supported Catalytically Active Liquid Metal Solutions (SCALMS)
<p>This dataset contains all data for the published figures of DOI10.1039/d3re00490b.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Data, plotting scripts, and figures for "A Projective Method for Solving the Single-Group Space-Time Neutron Kinetics Equations with Precursor Advection"
<p>Contains all the files necessary for figure reproduction.</p>
Source data for the kinetic assays in publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase"
<p>Datasets of enzyme kinetics related to the publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase", published in <em>Nature Communications</em> with DOI: https://doi.org/10.1038/s41467-024-50933-6 </p> <p>Individual files contain raw kinetic reaction data of the mycobacterial IMPDHs (wild type and mutant forms <em>Mycobacterium smegmatis</em> or wild type <em>Mycobacterium tuberculosis</em>) as a function of IMP, NAD+, GTP, ATP, ppGpp and Mg2+ concentration.</p> <p>Individual data sets are presented as time data points of the absorbance at 340 nm in an Excel file with a linked Graphpad graphical link. Detailed experimental conditions are available in the related publication.</p>
Strategies to control humidity sensitivity of azobenzene isomerisation kinetics in polymer thin films
<p>This is the full dataset for the manuscript "Strategies to control humidity sensitivity of azobenzene isomerisation kinetics in polymer thin films", submitted to the journal "Communications Materials".</p> <p>All the results presented in the manuscript is based on the data included in this dataset. All the raw data and analysed data is included, excluding final figures in the manuscript, which were composed from the data within.</p> <p>The data includes multiple experiments with different methods and materials. The data is sorted from the top down all the way down to single experiments. The dataset includes "README.txt" files that provide additional information relevant at each level, for example for raw data they provide information on the experimental settings and for analysed data the provide analysis methods used.</p>
Kinetic Insights into Glycerol Electrooxidation on Nickel: Current-Dependent Product Distribution and Reaction Mechanism
<p>## FILE DESCRIPTION<br>--------------<br>### Figure 1<br>- Fig1a.txt : Cyclic voltammetry of a Ni-based electrode in 0.1 M LiOH and 50 mM glycerol, measured at 5 mV s^-1. <br>- Fig1b.txt : IS spectra obtained at 1.50 V vs RHE in 0.1 M LiOH and 0.1 M LiOH + 50 mM glycerol.<br>- Fig1c.txt : Chronopotentiometric curves at 1 mA cm^-2, 3 mA cm^-2, 5 mA cm^-2 during 2 hours in 0.1 M LiOH + 50 mM glycerol.</p> <p>### Figure 2<br>- Fig2a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1 mA cm^-2.<br>- Fig2b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 3 mA cm^-2.<br>- Fig2c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 5 mA cm^-2.<br>- Fig2d.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 10 mA cm^-2.<br>- Fig2e.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 1 mA cm^-2.<br>- Fig2f.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 3 mA cm^-2.<br>- Fig2g.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 5 mA cm^-2.<br>- Fig2h.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 10 mA cm^-2.</p> <p>### Figure 3<br>- Fig3a.txt : Rate constant comparison at varying current densities applied for the formation of formate, glycolate, glycerate, tartronate and oxalate with its error bar.</p> <p>### Figure 4<br>- Fig4a.txt : Differential optical density (m∆O.D) taken at the maximum absorption peak as a function of potential applied in a solution of 0.1 M LiOH and LiOH 0.1 M + 50 mM glycerol in different regions (capacitive, NiOOH formation and GEOR and OER).<br>- Fig4b.txt : Rate law plot for glycerol and LiOH considering current density as a function of the normalized differential absorption (m∆O.D).</p> <p>### Figure S3<br>- FigS3.txt : X-ray diffraction (XRD) analysis </p> <p><br>### Figure S4<br>- FigS4a.txt : Cyclic Voltammetries in different electrolytes.</p> <p>### Figure S5<br>- FigS5a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.53 V vs RHE.<br>- FigS5b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.62 V vs RHE.<br>- FigS5c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.78 V vs RHE.</p> <p>### Figure S6<br>- FigS6a.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 1 mA cm^-2.<br>- FigS6b.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 3 mA cm^-2.<br>- FigS6c.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 5 mA cm^-2.</p> <p>### Figure S7<br>- FigS7a.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH.<br>- FigS7b.txt : Chronoamperometry measurement without glycerol.<br>- FigS7c.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8d.txt : Chronoamperometry measurement with glycerol.</p> <p>### Figure S8<br>- FigS8a.txt : Differential UV-Vis spectra of pre-catalytic (species formed in capacitive and NiOOH formation region) in 0.1 M LiOH.<br>- FigS8b.txt : Differential UV-Vis spectra of catalytic species (formed in OER and GEOR region) in 0.1 M LiOH.<br>- FigS8c.txt : Differential UV-Vis spectra of pre-catalytic species in 0.1 M LiOH + 50 mM glycerol. <br>- FigS8d.txt : Differential UV-Vis spectra of catalytic species in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8e.txt : Steady state J-V curve with the onset for OER (Oxygen Evolution Reaction) and GEOR (Glycerol Electrooxidation Reaction) indicated.</p> <p>### Figure S9<br>- FigS9.txt : Rate law plot for glycerol and LiOH considering current density (j) as a function of the normalized differential absorption (m∆O.D).</p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Data set: Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "<span>Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance</span>", published in ChemSusChem (<span>2021</span><span>, </span><span>14 (21)</span><span>, 4820-4835, DOI: <span>10.1002/cssc.202101498</span></span>).</p> <p><span>The performance of rechargeable magnesium batteries is strongly dependent on the choice of electrolyte. The desolvation of multivalent cations usually goes along with high energy barriers, which can have a crucial impact on the plating reaction. This can lead to significantly higher overpotentials for magnesium deposition compared to magnesium dissolution. In this work we combine experimental measurements with DFT calculations and continuum modeling to analyze magnesium deposition in various solvents. Jointly, these methods provide a better understanding of the electrode reactions and especially the magnesium deposition mechanism. Thereby, a kinetic model for electrochemical reactions at metal electrodes is developed, which explicitly couples desolvation to electron transfer and, furthermore, qualitatively takes into account effects of the electrochemical double layer. The influence of different solvents on the battery performance is studied for<br>the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate electrolyte salt. It becomes apparent that not necessarily a whole solvent molecule must be stripped from the</span> <span>solvated magnesium cation before the first reduction step can take place. For magnesium reduction it seems to be sufficient to have one coordination site available, so that the magnesium cation is able to get closer to the electrode surface. Thereby, the initial desolvation of the magnesium cation determines the deposition reaction for mono-, tri- and tetraglyme, whereas the influence of the desolvation on the plating reaction is minor for diglyme and<br>tetrahydrofuran. Overall, we can give a clear recommendation for diglyme to be applied as solvent in magnesium electrolytes</span>.<br><br></p>
Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".
<p>Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".</p> <p>Authors: Morgan Tranter, Maria Wetzel, Marco De Lucia, Michael Kühn</p> <p>Contact: mtranter@gfz-potsdam.de</p> <p>Submitted to Advances in Geosciences (31.06.2021).<br> Special Issue: European Geosciences Union General Assembly 2021, EGU Division Energy, Resources & Environment (ERE)</p> <p>EGU21 Abstract:<br> https://doi.org/10.5194/egusphere-egu21-9832</p> <p>Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</p>
Figure 1 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 1 Mite growth curve of Dermatophagoides pteronyssinus (a) andD. farinae (b) in the semisynthetic culture medium. Data showing the kinetics of the mites' growth in semi-synthetic culture medium are expressed as arithmetic mean ± standard error of triplicates, at each time of growth.
Figure 3 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 3 Kinetics of Der 1 and Der 2 major allergen levels during the growth ofDermatophagoides pteronyssinus (a) andD. farinae (b) in the semi-synthetic culture medium. Data showing the kinetics
Figure 2 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 2 SDS-PAGE IgE-immunoblotting of extracts fromDermatophagoides pteronyssinus (a) and D. farinae (b) mites along their growth. Mw: Molecular weight marker. Numbers on lines indicate
Kinetics and mechanisms of acid-pH weathering of pyroxenes
<p>Weathering of primary silicate minerals under acidic conditions occurs in contexts as varied as acid mine drainage, volcanic environments, soils, stone monuments subjected to acid rain or Geological Carbon Storage (GCS). Considering the abundance of pyroxenes on the Earth crust, knowledge of their weathering kinetics and mechanisms may help to optimize carbonate yield in GCS. Here we report experimental results from the reaction of the clinopyroxenes augite and diopside in acidic solutions. Dissolution at far-from-equilibrium conditions results in the formation of etch pits where crack initiate and propagate by stress corrosion and pressure exerted by swelling of an amorphous, gel-like Si-rich phase, which precipitates despite the undersaturation of the bulk solution and whose formation is highly controlled by the heterogeneity of the mineral surface and the local transport mechanism. These precipitates are commonly localized within deep etch pits and cracks, characterized by a low fluid renewal where high Si-concentrations can be reached locally, so that supersaturation with respect to amorphous silica can occur. Cracks and silica precipitates are most abundant in the case of augite weathered in flow-through experiments. This is related to its faster reaction rate compared to diopside, most likely due to its higher iron content. Finally, in the case of diopside an amorphous magnesium silicate hydrate (M-S-H) precursor forms, which represents an indirect evidence of the high pH conditions prevailing at the diopside-solution interface during dissolution.</p>
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
<p>Visual predators rely on fast-acting optokinetic responses to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pu rsuits. If echoes are processed on a click by click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high resolution bio-logging of wild predator prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50 200 ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo kinetic responses from trained harb our porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro kinetic response times in echolocation are similar to those of tracking responses in vision, suggesting a common neural underpinning.</p>
Dataset for: Photoreactivity of an Exemplary Anthracene Mixture Revealed by NMR Studies, Including a Kinetic Approach
<p>Anthracenes are an important class of acenes. They are being utilized more and more often in chemistry and materials science, due to their unique rigid molecular structure and photoreactivity. In particular, photodimerization can be harnessed for the fabrication of novel photoresponsive materials. Photoreactions between the same anthracenes have been investigated and utilized in various fields, while reactions between varying anthracenes have barely been investigated. Here, Nuclear Magnetic Resonance (NMR) spectroscopy is employed for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B), in the solutions with only A or B, and in the mixture of A and B. Estimated k values, derived from the presented kinetic model, showed that the dimerization of A was 10 times faster in comparison with B when compounds were investigated in separate samples, and 2 times faster when compounds were prepared in the mixture. Notably, the photoreaction in the mixture, apart from AA and BB, also yielded a large amount of the AB mixdimer. The investigation of a mixture with different anthracenes can deliver relative reactivity under the same experimental conditions. This results in a better understanding of the photoisomerization processes, which is essential for the practical utilization of the photodimerization of anthracenes.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The study employs 400 MHz 1H-NMR spectroscopy for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B) in the solutions with only A or B and in the mixture of A and B. Experiments were performed using different concentrations and oxygenation level of the sample. CD2Cl2 was used as a solvent. </p> <p> </p> <p><strong>Given data include eight 1H-NMR experiments processed by TopSpin 4.0.7 software of:</strong></p> <ol> <li> 4.5 mM of A (File: 1_A_concentration I.fid.zip)</li> <li> 2.25 mM of A (File: 2_A_concentration II.fid.zip)</li> <li> 2.25 mM of A with oxygen (File: 3_A_concentration II_Oxygen.fid.zip)</li> <li> 4.5 mM of B (File: 4_B_concentration I.fid.zip)</li> <li> 2.25 mM of B (File: 5_B_concentration II.fid.zip)</li> <li> 2.25 mM of B with oxygen (File: 6_B_concentration II_Oxygen.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:1.3 (File: 7_Mixture_ratio I.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:2.3 (File: 8_Mixture_ratio II.fid.zip)</li> </ol> <p>-------------------------------------------------------------</p>
Data from: Fluid-kinetic model of a propulsive magnetic nozzle
<p># Data from: Fluid-kinetic model of a propulsive magnetic nozzle</p> <p> </p> <p>- Authors: Mario Merino, Judit Nuez, Eduardo Ahedo</p> <p>- Contact email: mario.merino@uc3m.es</p> <p>- Date: 2021-10-08</p> <p>- Keywords: magnetic nozzle, plasma propulsion, electrodeless plasma thrusters, kinetic model, collisionless electron cooling, magnetic thrust</p> <p>- Version: 1.0.0</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.5557592</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p>## Abstract</p> <p> </p> <p>This dataset contains the magnetic nozzle fluid-kinetic simulation results used to prepare:</p> <p> </p> <p>_[Mario Merino, Judit Nuez, Eduardo Ahedo, "Fluid-kinetic model of a propulsive magnetic nozzle", Plasma Sources Science and Technology](https://doi.org/10.1088/1361-6595/ac2a0b)._</p> <p> </p> <p>## Dataset description</p> <p> </p> <p>The simulations have been prepared combining two open source codes:</p> <p>[Akiles](10.5281/zenodo.1098432) and [Fumagno](10.5281/zenodo.593787).</p> <p>The model and the simulation cases are explained in the accompanying paper (https://doi.org/10.1088/1361-6595/ac2a0b).</p> <p> </p> <p>## Data files</p> <p> </p> <p>The datafiles are in standard Matlab .mat format. A recent version of [Matlab](https://www.mathworks.com/products/matlab.html) (2018a or newer) is needed to read these files .</p> <p> </p> <p>Datafiles are subdivided into two groups (1D and 2D).</p> <p> </p> <p>In the 1D group, simulations for the first part of the paper are contained. These are simulations along a single (1D) magnetic line. There are 7 files:</p> <p>1. line_J0.mat</p> <p>2. line_phiinfty5.mat</p> <p>3. line_phiinfty6.mat</p> <p>4. line_phiinfty7.mat</p> <p>5. line_phiinfty8.mat</p> <p>6. line_phiinfty9.mat</p> <p>7. line_phiinfty10.mat </p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* h: a vector containing the value of B (magnetic field strength) at each point in the magnetic line</p> <p>* phi: a vector containing the value of phi (electric potential) at each point in the magnetic line</p> <p>* electrons: a structure with all the moments and all the properties of the electrons</p> <p>* ions: a structure with all the moments and all the properties of the ions</p> <p> </p> <p>In the 2D group, simulations for the second part of the paper are contained. These are 2D simulations. A total of 5 files exist, corresponding to each simulation case in the paper:</p> <p>1. F.mat</p> <p>2. PHID.mat</p> <p>3. PHII.mat</p> <p>4. TD.mat</p> <p>5. TI.mat</p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* Z,R: position of points</p> <p>* B,ALPHA,KAPPA: magnetic field strength, angle, and curvature. B_B0 is B normalized with the upstream value on each line.</p> <p>* PHI, EZ, ER: electric potential and field components</p> <p>* J, J0: current density, and the integral current in the magnetic nozzle</p> <p>* N, N1, N2, N4: density of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TE, TE1, TE2, TE4: average temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPARE, TPARE1, TPARE2, TPARE4: parallel temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPERE, TPERE1, TPERE2, TPERE4: perpendicular temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* UE, UE1, UI: velocity of electrons, free electrons, ions</p> <p> </p> <p>## Citation</p> <p> </p> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p> </p> <p>The preferred means of citation is to reference the publication associated to this dataset, of DOI 10.1088/1361-6595/ac2a0b.</p> <p> </p> <p>Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.5557592.</p> <p> </p> <p>## Acknowledgments</p> <p> </p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p> </p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466). </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.