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.
42
datasets available to search
ShareScore release 0.9.0
Dataset results
42 results for “Chemical reactions”
Chemical reactors with enhanced heat transfer for conducting simultaneous endothermic and exothermic reactions
<p><strong>Chemical reactors with enhanced heat transfer for conducting simultaneous endothermic and exothermic reactions</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>Many chemical processes utilize catalysts to enhance chemical conversion behavior. A catalyst promotes the rate of chemical conversion but does not affect the energy transformations which occur during the reaction. Often catalytic processes are conducted within tubes which are packed with a suitable catalytic substance. The process gas flows within the tube and contacts the catalytic packing where reaction proceeds. The tube is placed within a hot environment such as a furnace such that the energy for the process can be supplied through the tube wall via conduction. The mechanism for heat transfer with this arrangement is rather tortuous as heat must first be transferred through the outer boundary layer of the tube, conducted through the often-heavy gauge wall of the tube and then pass through the inner boundary layer into the process gas. The process gas is raised in temperature and this energy can be utilized by the process for chemical reaction. The process engineer is often caused to compromise between the pressure drop within the tube reactor with the overall heat transfer and catalytic effectiveness. The inner heat transfer coefficient can be effectively increased by raising the superficial velocity of the process gas. The higher gas velocity therefore improves the thermal effectiveness of the system. However, higher gas velocities increase the system's pressure drop and results in increased compressor sizes and associated operating costs.</p> <p>Streamwise distance (meters), Heat flux (watts per square meter)</p> <p>0 27306.2</p> <p>0.00025 124496</p> <p>0.0005 117728</p> <p>0.00075 106208</p> <p>0.001 94912</p> <p>0.00125 85088</p> <p>0.0015 76704</p> <p>0.00175 69488</p> <p>0.002 63200</p> <p>0.00225 57712</p> <p>0.0025 52832</p> <p>0.00275 48528</p> <p>0.003 44704</p> <p>0.00325 41264</p> <p>0.0035 38144</p> <p>0.00375 35344</p> <p>0.004 32912</p> <p>0.00425 30720</p> <p>0.0045 28688</p> <p>0.00475 26832</p> <p>0.005 25184</p> <p>0.00525 23664</p> <p>0.0055 22272</p> <p>0.00575 21056</p> <p>0.006 19952</p> <p>0.00625 18912</p> <p>0.0065 17936</p> <p>0.00675 17072</p> <p>0.007 16288</p> <p>0.00725 15568</p> <p>0.0075 14864</p> <p>0.00775 14240</p> <p>0.008 13728</p> <p>0.00825 13216</p> <p>0.0085 12752</p> <p>0.00875 12304</p> <p>0.009 11840</p> <p>0.00925 11472</p> <p>0.0095 11168</p> <p>0.00975 10832</p> <p>0.01 10448</p> <p>0.01025 10128</p> <p>0.0105 9952</p> <p>0.01075 9776</p> <p>0.011 9472</p> <p>0.01125 9232</p> <p>0.0115 9136</p> <p>0.01175 8832</p> <p>0.012 8640</p> <p>0.01225 8688</p> <p>0.0125 8576</p> <p>0.01275 8432</p> <p>0.013 8320</p> <p>0.01325 8192</p> <p>0.0135 8016</p> <p>0.01375 7904</p> <p>0.014 7840</p> <p>0.01425 7728</p> <p>0.0145 7648</p> <p>0.01475 7552</p> <p>0.015 7488</p> <p>0.01525 7440</p> <p>0.0155 7360</p> <p>0.01575 7312</p> <p>0.016 7248</p> <p>0.01625 7184</p> <p>0.0165 7104</p> <p>0.01675 7024</p> <p>0.017 6992</p> <p>0.01725 7008</p> <p>0.0175 7008</p> <p>0.01775 6928</p> <p>0.018 6880</p> <p>0.01825 6880</p> <p>0.0185 6800</p> <p>0.01875 6720</p> <p>0.019 6720</p> <p>0.01925 6704</p> <p>0.0195 6640</p> <p>0.01975 6592</p> <p>0.02 6624</p> <p>0.02025 6592</p> <p>0.0205 6496</p> <p>0.02075 6464</p> <p>0.021 6512</p> <p>0.02125 6528</p> <p>0.0215 6448</p> <p>0.02175 6384</p> <p>0.022 6384</p> <p>0.02225 6320</p> <p>0.0225 6272</p> <p>0.02275 6288</p> <p>0.023 6272</p> <p>0.02325 6240</p> <p>0.0235 6192</p> <p>0.02375 6224</p> <p>0.024 6224</p> <p>0.02425 6160</p> <p>0.0245 6112</p> <p>0.02475 6112</p> <p>0.025 6080</p> <p>0.02525 5984</p> <p>0.0255 5968</p> <p>0.02575 6016</p> <p>0.026 6016</p> <p>0.02625 5936</p> <p>0.0265 5888</p> <p>0.02675 5904</p> <p>0.027 5888</p> <p>0.02725 5824</p> <p>0.0275 5776</p> <p>0.02775 5760</p> <p>0.028 5696</p> <p>0.02825 5568</p> <p>0.0285 5552</p> <p>0.02875 5584</p> <p>0.029 5472</p> <p>0.02925 5344</p> <p>0.0295 5280</p> <p>0.02975 5280</p> <p>0.03 5280</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>
Catalytic reactors with enhanced chemical conversion behavior for conducting simultaneous endothermic and exothermic reactions
<p><strong>Catalytic reactors with enhanced chemical conversion behavior for conducting simultaneous endothermic and exothermic reactions</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>It has been taught methods which may be used to transform a monolithic structure into a co-current or countercurrent flow heat exchanger. The monolith is transformed by cutting or grinding the uppermost section of diving walls from rows of channels contained in the honeycomb. The top end of the newly formed groove is then sealed with suitable cement. The depth of the sealant is such that an opening still exists in the side wall of the structure. A manifold is attached to this inlet. A similar exercise is performed at the opposing end to produce an outlet section. Hot gas is passed through the inlet whilst cold coolant is passed through the open end. Efficient heat transfer occurs between the two streams. However, the possibility of using such an arrangement for coupling endothermic and exothermic catalytic processes on opposing sides of each dividing wall is not taught. It has been taught a method to efficiently transfer energy through a divider by contacting a catalyst to the wall and performing an exothermic reaction there. The energy is conducted through the wall and used to heat a gas stream on the opposing side of the wall. An apparatus is described where multiple layers are formed with alternating hot and cold channels to produce a gas heater. However, the method does not discuss the possibility of utilizing this concept for thermally coupling endothermic and exothermic reactions within a monolith reactor.</p> <p>Streamwise distance (meter), Heterogeneous reaction rate along the length of the reactor (mole per square meter per second)</p> <p>0 1.88478</p> <p>0.00025 1.92635</p> <p>0.0005 2.0243</p> <p>0.00075 2.14121</p> <p>0.001 2.26285</p> <p>0.00125 2.38391</p> <p>0.0015 2.5016</p> <p>0.00175 2.61386</p> <p>0.002 2.71933</p> <p>0.00225 2.81695</p> <p>0.0025 2.9061</p> <p>0.00275 2.98659</p> <p>0.003 3.05831</p> <p>0.00325 3.12146</p> <p>0.0035 3.17647</p> <p>0.00375 3.22385</p> <p>0.004 3.26403</p> <p>0.00425 3.29778</p> <p>0.0045 3.32579</p> <p>0.00475 3.34828</p> <p>0.005 3.36585</p> <p>0.00525 3.37923</p> <p>0.0055 3.38901</p> <p>0.00575 3.39569</p> <p>0.006 3.39964</p> <p>0.00625 3.40142</p> <p>0.0065 3.40139</p> <p>0.00675 3.39974</p> <p>0.007 3.39685</p> <p>0.00725 3.39304</p> <p>0.0075 3.38846</p> <p>0.00775 3.38332</p> <p>0.008 3.37786</p> <p>0.00825 3.37216</p> <p>0.0085 3.36657</p> <p>0.00875 3.36135</p> <p>0.009 3.35646</p> <p>0.00925 3.35185</p> <p>0.0095 3.34767</p> <p>0.00975 3.34403</p> <p>0.01 3.3408</p> <p>0.01025 3.33803</p> <p>0.0105 3.33572</p> <p>0.01075 3.33405</p> <p>0.011 3.33301</p> <p>0.01125 3.33242</p> <p>0.0115 3.33227</p> <p>0.01175 3.33256</p> <p>0.012 3.33329</p> <p>0.01225 3.33451</p> <p>0.0125 3.33619</p> <p>0.01275 3.33825</p> <p>0.013 3.34069</p> <p>0.01325 3.34347</p> <p>0.0135 3.34647</p> <p>0.01375 3.34978</p> <p>0.014 3.35347</p> <p>0.01425 3.35729</p> <p>0.0145 3.36123</p> <p>0.01475 3.36531</p> <p>0.015 3.36946</p> <p>0.01525 3.37365</p> <p>0.0155 3.37777</p> <p>0.01575 3.38195</p> <p>0.016 3.38636</p> <p>0.01625 3.39101</p> <p>0.0165 3.3956</p> <p>0.01675 3.40005</p> <p>0.017 3.40456</p> <p>0.01725 3.40915</p> <p>0.0175 3.41362</p> <p>0.01775 3.41806</p> <p>0.018 3.42267</p> <p>0.01825 3.42714</p> <p>0.0185 3.43145</p> <p>0.01875 3.43582</p> <p>0.019 3.44022</p> <p>0.01925 3.44451</p> <p>0.0195 3.44872</p> <p>0.01975 3.45302</p> <p>0.02 3.45739</p> <p>0.02025 3.46153</p> <p>0.0205 3.46548</p> <p>0.02075 3.4695</p> <p>0.021 3.47355</p> <p>0.02125 3.47744</p> <p>0.0215 3.4811</p> <p>0.02175 3.48467</p> <p>0.022 3.48808</p> <p>0.02225 3.49137</p> <p>0.0225 3.49463</p> <p>0.02275 3.49784</p> <p>0.023 3.50085</p> <p>0.02325 3.50358</p> <p>0.0235 3.50617</p> <p>0.02375 3.50872</p> <p>0.024 3.51116</p> <p>0.02425 3.51353</p> <p>0.0245 3.51592</p> <p>0.02475 3.51817</p> <p>0.025 3.52004</p> <p>0.02525 3.52161</p> <p>0.0255 3.52305</p> <p>0.02575 3.52439</p> <p>0.026 3.52547</p> <p>0.02625 3.52614</p> <p>0.0265 3.52658</p> <p>0.02675 3.5265</p> <p>0.027 3.52593</p> <p>0.02725 3.5252</p> <p>0.0275 3.52428</p> <p>0.02775 3.52285</p> <p>0.028 3.52091</p> <p>0.02825 3.51866</p> <p>0.0285 3.51596</p> <p>0.02875 3.51277</p> <p>0.029 3.50899</p> <p>0.02925 3.5048</p> <p>0.0295 3.4992</p> <p>0.02975 3.4806</p> <p>0.03 3.46548</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>
An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm.
<p>This is a data set associated with the paper <em>An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. </em>by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33<sup>rd</sup> European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece.</p>
Dataset from Nature Materials paper: Amorphous nickel hydroxide shell tailors local chemical environment on platinum surface for alkaline hydrogen evolution reaction
<p>Dataset of the paper "Amorphous nickel hydroxide shell tailors local chemical environment on platinum surface for alkaline hydrogen evolution reaction" accepted in Nature Materials.</p> <p>- GCGA_inputs.zip: A zip file containing all needed input files to run a grand canonical genetic algorithm (GCGA) global optimization structure search. Note that the script would need modifications to be compatible with later version of the GOCIA package, please following the most updated instructions at https://github.com/zishengz/gocia</p> <p>- GCGA_Ni12OxHy_all_samples.db: An ASE database file containing all unique structures from the GCGA search of Ni12OxHy on a Pt(111) surface. </p> <p>- GM_Ni12O25H13.vasp: The structure of the global minimum structure from GCGA search, which is also the surface structure we focused on in this study, in VASP structure format.</p> <p>- rxn_structures.zip: A zip file containing the structures of reaction intermediates investigated in this work, in VASP structure format</p> <p> </p>
Data from: Size-resolved chemical composition of sub-20 nm particles from methanesulfonic acid reactions with methylamine and ammonia
Open the record for dataset details and reuse information.
Temperature and chemical composition of combustion gases over reaction time in several combustion tests conducted at a lab pilot plant to characterise a SRF prepared for an aluminium scrap pre-heating system (REVaMP project)
<p>Open access to experimental data generated by the REVaMP project (GA 869882, Horizon 2020, European Union) along the research of the combustion of a SRF, prepared from ASR, to be used as alternative fuel in a scrap pre-heater at an aluminium refinery plant.Research pertaining to Task 1.1 (WP1), Deliverable D1. <br> Underlying data for the publication Acha, E.; Lopez-Urionabarrenechea, A.;Delgado, C.; et al. Combustion of a Solid Recovered Fuel (SRF) Produced from the Polymeric Fraction of Automotive Shredder Residue (ASR). Polymers 2021, 13, 3807. https://doi.org/10.3390/polym13213807. Data related to Figures 3, 4, 5 in the article and Figures S3 and S5 of Supplementary materials of the manuscript.</p> <p>Subject: Study of the combustion of a SRF prepared from ASR in a lab-scale pilot plant consisting of a tank reactor and a packed-bed tubular reactor arranged in series, to evaluate the effects on kinetics and thermodynamics of the temperature and the type and flow rate of the oxidiser. Real-time measurements of the temperature of the tank, temperature of the gases and of the concentrations of O<sub>2</sub>, CO<sub>2</sub>, CO, N<sub>2</sub>O, NO<sub>2</sub>, NO, NH<sub>3</sub>, SO<sub>2</sub>, CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>6</sub>H<sub>14</sub>, CH<sub>2</sub>O, HCl and HF in the combustion gases produced in 5 combustion runs performed at different reaction conditions. Useful information for designing the operation conditions of the SRF combustion chamber of the scrap pre-heater and for defining the flue gas cleaning requirements.</p>
Inference and Test Generation Using Program Invariants in Chemical Reaction Networks Artifacts
<p>The artifacts for Inference and Test Generation Using Program Invariants in Chemical Reaction Networks, published at ICSE 2022.</p> <p>The pdf of the paper can be accessed at <a href="https://ieeexplore.ieee.org/document/9794130">IEEEXplore</a>.</p> <p><strong>To cite this work, please use the citation below:</strong></p> <pre>@INPROCEEDINGS{GertenICSE22, author={Gerten, Michael C. and Marsh, Alexis L. and Lathrop, James I. and Cohen, Myra B. and Miner, Andrew S. and Klinge, Titus H.}, booktitle={2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)}, title={Inference and Test Generation Using Program Invariants in Chemical Reaction Networks}, month={May}, year={2022}, pages={1193-1205}, doi={10.1145/3510003.3510176}}</pre> <p>The artifacts are also available on <a href="https://github.com/LavaOps/ICSE-2022-Artifacts">GitHub</a>.</p> <p><strong>This is an updated version of the ChemFlow tool. The update addressed an overflow error when computing gaussian elimination that could result in incorrect invariants with certain model inputs. After verification, all models in this work were not affected by this bug and have the same set of invariants generated by both versions. We have updated the docker file to use the new code as well.</strong></p>
Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface
<p>Data availability for silica measurements</p>
Raw data: The Chemical and Electronic Properties of Stability-Enhanced, Mixed Ir-TiOx Oxygen Evolution Reaction Catalysts
<p>Raw data for the publication:</p> <h4><em>The Chemical and Electronic Properties of Stability-Enhanced, Mixed Ir-TiO<sub>x</sub> Oxygen Evolution Reaction Catalysts</em></h4> <div>Marianne van der Merwe, Raul Garcia-Diez, Leopold Lahn, R. Enggar Wibowo, Johannes Frisch, Mihaela Gorgoi, Wanli Yang, Shigenori Ueda, Regan G. Wilks, Olga Kasian, and Marcus Bär</div> <div>ACS Catalysis <strong>2023</strong> <em>13</em> (23), 15427-15438</div> <p>DOI: 10.1021/acscatal.3c02948</p> <p><strong>Copyright © 2023 The Authors. Published by American Chemical Society</strong>. This publication is licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>.</p>
Dataset for "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model"
<p>This repository contains all the datasets and scripts used to generate the figures in the paper tilted "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model". The data and scripts are organized according to the figure numbers to facilitate ease of use and reproducibility.</p> <p><strong>Directory Structure</strong><br>Each figure has its own dedicated folder. Inside each folder, you will find:</p> <p><strong>Data files: </strong>These files contain the dataset used to generate the corresponding figure.<br><strong>Scripts: </strong>Python or other relevant scripts needed to process the data and create the figure.<br><strong>README.txt: </strong>Each figure folder includes a separate README.txt file that provides detailed instructions on how to run the scripts.<br><strong>How to Use</strong><br>Navigate to a Figure's Folder: Locate the folder corresponding to the figure number you are interested in (e.g., Fig_01, Fig_02, etc.).</p> <p>Read the README.txt: Open the README.txt file in that folder. It contains specific instructions on how to execute the code and generate the figure, along with any necessary setup details.</p> <p><strong>Run the Scripts:</strong> Follow the instructions in the README.txt file to run the script(s) and generate the figure.</p>
Data to support "Physics-based representations for machine learning properties of chemical reactions
<p>4 datasets of reaction data: </p> <p>1. SN2-20 dataset adapted from https://iopscience.iop.org/article/10.1088/2632-2153/aba822/meta</p> <p>2. Proparg-21-TS dataset from https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc00482d</p> <p>3. GDB7-22-TS dataset from https://www.nature.com/articles/s41597-020-0460-4</p> <p>4. Our Hydroform-22-TS dataset of 2,350 structures of reactant and product structures and associated barriers</p> <p>In all cases, there are xyz files of reactant(s) and product(s) structures, and a csv file of associated properties (reaction energies for the first case, e.e. values for the second, and barriers for the third and fourth).</p> <p>For example usage see https://github.com/lcmd-epfl/b2r2-reaction-rep</p>
Chemical reaction networks of glycolonitrile and glycolonitrile-H
<p>Reaction networks of glycolonitrile (HOCH<sub>2</sub>CN) and HOCH<sub>2</sub>CHN radical obtained with the automated reaction discovery program <a href="https://rxnkin.usc.es/index.php/AutoMeKin">AutoMeKin</a></p>
data for "A machine-learned approach to monitor chemical reaction via in-situ infrared spectroscopy"
<p>Source spectral and structural data of the AIMD trajectory and NEB calculation</p> <p>1. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/180-structure-IR.zip">180-structure-IR</a>.zip Source spectral and structural data of the AIMD trajectory for the selected 180 configurations</p> <p>2. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/md-pos-1.xyz?versionId=1a43760b-5cba-4548-85e6-6a45252403fc">md-pos-1.xyz</a> AIMD trajectories</p> <p>3. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-0-5100.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-5101-7500.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-7501-9500.tar.gz</a> Source spectral and structural data of the AIMD trajectory for the extracted 9500 configurations</p> <p>4. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/split-neb-75.zip?versionId=a30f0f34-3ce2-4619-abd3-62e71174069b">split-neb-75.zip</a> Source spectral and structural data of the AIMD trajectory for the CO-CO dimerization reaction</p>
Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry
<p>Q-Chem output files, extracted SMILES, activation energies, and enthalpies of formation for 16,452 B97-D3/def2-mSVP reactions and for 12,001 ωB97X-D3/def2-TZVP reactions. The raw log files are stored in <em>b97d3.tar.gz</em> and <em>wb97xd3.tar.gz</em> for B97-D3/def2-mSVP and ωB97X-D3/def2-TZVP data, respectively. Each archive contains a separate folder for each reaction. Within each folder are three log files for a reaction corresponding to reactant, product, and transition state. Each log file contains the output of a geometry optimization and harmonic vibrational analysis.</p> <p>Atom-mapped SMILES, activation energies, and enthalpies of formation for each reaction are listed in the comma-separated values files <em>b97d3.csv</em> and <em>wb97xd3.csv</em>. The reactions are listed in the same order as the corresponding folders in the archive files.</p> <p>Additional archives containing log files for all successfully optimized transition states are stored in <em>ts_with_dup_b97d3.tar.gz</em> and <em>ts_with_dup_wb97xd3.tar.gz</em>. There are 69,366 B97-D3/def2-mSVP transition states and 24,987 ωB97X-D3/def2-TZVP transition states. These data are better used with caution because they contain many duplicate transition states and the corresponding reactants and products are not known.</p>
Synchronized reagent delivery in double emulsions for triggering chemical reactions and gene expression
<p>Data underlying the figures in the publication “Synchronized reagent delivery in double emulsions for triggering chemical reactions and gene expression”, published in Small Methods.</p> <p>Table of contents:</p> <p>1. Figure 2C; Origin file containing all data and analysis for Figure 2C. Requires Origin Software to open.</p> <p>2. Figure 3; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure 3B and 3C. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>200721 FDG in PVA after prod_FDG sample 6_006.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 PVA shake_002.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 PVA shake_002.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>3. Figure 4BC; Origin file containing all data and analysis for Figure 4B and 4C. Requires Origin Software to open.</p> <p>4. Figure 5; Origin file containing all data and analysis for Figure 5D. Requires Origin Software to open.</p> <p>File Figure 5_201215-new LUVs 500 mM DH5alpha: FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure 5B. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 18h after prod_012.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 18h after prod- 05%SDS_018.fcs</p> <p>5. Figure S2; PDF files, report from DLS instrument.</p> <p>6. Figure S4; Excel file containing the values extracted from microscopy images for Figure S4.</p> <p>7. Figure S5; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure S5. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>200721 FDG in PVA after prod_FDG sample 6_006.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 5 SDS 01 shake_008.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 6 SDS 0001 shake_006.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 5 SDS 01 shake_008.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 6 SDS 0001 shake_006.fcs</p> <p>8. Figure S6; Origin file containing all data and analysis for Figure S6 left-Requires Origin Software to open-, and Excel file containing the values from 96 well plate measurements used for Figure S6 right.</p> <p>9. Figure S7; Excel files containing the values from 96 well plate measurements used for Figure S7 left, and the values extracted from microscopy images for Figure S7 right.</p> <p>10. Figure S9; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure S9. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 3h30min after prod_005.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 18h after prod_012.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 30min after prod_001.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 3h30min after prod - 05%SDS_008.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 18h after prod- 05%SDS_018.fcs</p>
An Integrated Self-Optimizing Programmable Chemical Synthesis and Reaction Engine
Open the record for dataset details and reuse information.
A Programmable Hybrid Digital Chemical Information Processor based on the Belousov-Zhabotinsky Reaction
Open the record for dataset details and reuse information.
Chemical Vapor Deposition Strategy of Fe-N-C Nanotubes for the Oxygen Evolution Reaction
<p>Data associated with the paper to be published with the same title</p>
Chemical Vapor Deposition Strategy towards CuNiNC Films with Catalytic Activity for the Oxygen Evolution Reaction
<p>Data associated with the manuscript submitted for publication with the same title</p>
Chemical reactions of ultracold alkaline-earth-metal diatomic molecules
Open the record for dataset details and reuse information.
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.