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2,904 results for “Solute”
Design of solution V1.0
<p>This report (deliverable) named D6.1 Design of solution is deliverable of WP6 within InterFlex<br> H2020 (project co-founded by the European Commission). CEZ Distribuce as WP6 leader aims<br> to perform demonstrations of innovative smart grid solutions in order to increase DER hosting<br> capacity, integrate EV charging stations and home energy storage systems more effectively<br> and thus cover important challenges of European DSOs.<br> The use cases demonstration is performed under the coordination and management of CEZ<br> Distribuce. Partners involved in the WP6 are Austrian Institute of Technology (AIT), CEZ<br> Solarni, Fronius, Schneider Electric and Siemens.<br> As it is stated in GA in Description of Work (DoW), D6_1 Design of solutions contains detail<br> design of technical solutions of all WP6 use cases, results of customer’s recruitment, lab<br> tests results and KPIs definition.<br> This report describes in detail the design of solutions for WP6 that will be implemented in<br> next stage of the project and then reported in D6.2 Implementation of solution (deliverable<br> due date 12/2018).<br> After the evaluation phase of the project, WP6 results will be reported in D6.3Demonstration<br> activities results (due date 12/2019). This final deliverable of WP6 will also contain CBA and<br> SRA of demonstrated</p>
Nonlinear periodic and supernonlinear periodic ion-acoustic wave solutions for the mKdV equation
<p>(a) - (b) Nonlinear periodic ion-acoustic wave solutions and (c) Supernonlinear periodic ion-acoustic wave solution of the mKdV equation.</p>
Supernonlinear periodic ion-acoustic wave solution for the modified Gardner equation
<p>Small-amplitude supernonlinear periodic ion-acoustic wave solution (SPIAWs) for the modified Gardner equation in nonextensive plasmas. Here, SPIAW corresponds to supernonlinear periodic trajectory which encloses three fixed points with one separatrix.</p>
Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance
<div>This data corresponds to the following paper:</div> <div> </div> <div>Title: Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance</div> <div>Journal: Analytical Chemistry</div> <div>Authors: Josh P. Peters, Charbel Assaf, Farhad Haj Mohamad, Eric Beitz, Sanjay Tiwari, Konrad Aden, Jan-Bernd Hövener and Andrey N. Pravdivtsev</div> <div> </div> <div>The data is organized with respect to the subfigures in figure 2 to 6.</div> <div>A description of acquisition parameters is provided in the "Acquisition parameters.xlsx" file for each dataset.</div> <div> </div> <div>Figure 2:</div> <div>- Overview of pyruvate metabolism in yeast cells using hyperpolarized and 13C labeled [1-13C]pyruvate. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included. </div> <div> </div> <div>Figure 3:</div> <div>- Overview of pyruvate metabolism in yeast cells using co-hyperpolarized [1-13C] and [2-13C]pyruvate at an n.a. of 13C. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 4:</div> <div>- Overview of fumarate metabolism in yeast cells using hyperpolarized and 13C labeled [1,4-13C2]fumarate</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 5:</div> <div>- The fitted conversion exchange rate constants as a function of the yeast concentration. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 6:</div> <div>- Metabolic data from pyruvate metabolism in yeast depending on the position of the yeast in the NMR tube. We </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <p> </p>
Figure 1 in Solution structure of the first RRM domain of human spliceosomal protein SF3b49
Figure 1. – Sampling locality of the present specimen of Lutjanus madras.
Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode
<p>Characterisation dataset for “<span>Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode</span>”, DOI:xx. Data for main and supporting figures provided as *.xlsx and *.txt files.</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FutureMARES report "D6.3, Climate ready strategies for nature-based solutions"
<p>Shapefiles created for the report "D6.3, Climate ready strategies for nature-based solutions", a deliverable report from the Horizon Europe project FutureMARES</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across six European case study areas between 2026 - 2069, and across the five scenarios considered in the FutureMARES project (Global Sustainability RCP2.6, National Enterprise RCP8.5, World Market RCP8.5, Status Quo RCP2.6 and Status Quo RCP8.5). </p> <p>Filenames refer to the case study area and scenario each set of shapefiles belong to. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in the relevant chapter in the report "D6.3, Climate ready strategies for nature-based solutions".</p>
Phytoplankton Dataset: Supporting HAB monitoring based on cost-effective open-hardware solutions
<p>ASTRAL Phytoplankton monitoring dataset which has supported technology validation based on images from single-specie phytoplankton sample and prototype deployment within IMTA labs</p>
Microplastic Dataset: Supporting microplastic monitoring based on cost-effective open-hardware solutions
<p>ASTRAL Dataset related to microplastic monitoring based on the open-hardware initiative PlanktoScope</p>
Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network
<p>Tautomerization plays a critical role in many chemical and biological processes, impacting the molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solutions and complicating drug discovery. Predicting these tautomeric ratios and identifying the predominant species rapidly and accurately is crucial for computational drug discovery. In this study, we introduce sPhysNet-Taut, a deep learning model fine-tuned with experimental data leveraging the Siamese network. This model predicts tautomer ratios in aqueous solution using MMFF94-optimized geometries directly. On an experimental test set, sPhysNet-Taut surpasses all other methods, achieving state-of-the-art performance with an RMSE of 1.9 kcal/mol on the 100-tautomers set and an RMSE of 1.0 kcal/mol on the SAMPL2 challenge, and providing the best ranking power for tautomer pairs. Additionally, our results demonstrate that fine-tuning on experimental data significantly improves model performance compared to training from scratch. This work not only provides a useful deep learning model for predicting tautomer ratios, but also provides a protocol for modeling pairwise data. To facilitate user-friendliness, we developed a readily accessible tool to predict stable tautomeric states in aqueous solutions, enumerating all possible tautomeric states and ranking them using the sPhysNet-Taut model.</p>
Simulation trajectories for the article "Simulated Solute Tempering 2"
<p>Simulation trajectories for the article "Simulated Solute Tempering 2".</p> <p>For each set of simulation is provided:</p> <ul> <li>One .pdb file of the system without solvant and hydrogen atoms coordinates.</li> <li>Trajectorie files in .xtc format without solvant and hydrogen atoms coordinates for each of the 4 replicas.</li> <li>DataFrame files in .csv format containing: <ul> <li>Aim Temp (K)</li> <li>E solute scaled (kJ/mole)</li> <li>E solute not scaled (kJ/mole)</li> <li>E solvent (kJ/mole)</li> <li>E solvent-solute (kJ/mole)</li> <li>Step</li> <li>Potential Energy (kJ/mole)</li> <li>Total Energy (kJ/mole)</li> <li>Temperature (K)</li> <li>Speed (ns/day)</li> <li>Time(µs)</li> <li>lambda</li> </ul> </li> </ul> <p>List of files:</p> <pre><code>. ├── CLN025 │ ├── SST2 │ │ ├── CLN025_SST2_F_1.csv │ │ ├── CLN025_SST2_F_1.pdb │ │ ├── CLN025_SST2_F_1.xtc │ │ ├── CLN025_SST2_F_2.csv │ │ ├── CLN025_SST2_F_2.xtc │ │ ├── CLN025_SST2_F_3.csv │ │ ├── CLN025_SST2_F_3.xtc │ │ ├── CLN025_SST2_F_4.csv │ │ ├── CLN025_SST2_F_4.xtc │ │ ├── CLN025_SST2_F_6_rungs_1.csv │ │ ├── CLN025_SST2_F_6_rungs_1.xtc │ │ ├── CLN025_SST2_F_6_rungs_2.csv │ │ ├── CLN025_SST2_F_6_rungs_2.xtc │ │ ├── CLN025_SST2_F_6_rungs_3.csv │ │ ├── CLN025_SST2_F_6_rungs_3.xtc │ │ ├── CLN025_SST2_F_6_rungs_4.csv │ │ ├── CLN025_SST2_F_6_rungs_4.xtc │ │ ├── CLN025_SST2_F_6_rungs.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.csv │ │ ├── CLN025_SST2_F_Tref_350_1.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.xtc │ │ ├── CLN025_SST2_F_Tref_350_2.csv │ │ ├── CLN025_SST2_F_Tref_350_2.xtc │ │ ├── CLN025_SST2_F_Tref_350_3.csv │ │ ├── CLN025_SST2_F_Tref_350_3.xtc │ │ ├── CLN025_SST2_F_Tref_350_4.csv │ │ ├── CLN025_SST2_F_Tref_350_4.xtc │ │ ├── CLN025_SST2_U_1.csv │ │ ├── CLN025_SST2_U_1.pdb │ │ ├── CLN025_SST2_U_1.xtc │ │ ├── CLN025_SST2_U_2.csv │ │ ├── CLN025_SST2_U_2.xtc │ │ ├── CLN025_SST2_U_3.csv │ │ ├── CLN025_SST2_U_3.xtc │ │ ├── CLN025_SST2_U_4.csv │ │ └── CLN025_SST2_U_4.xtc │ └── ST │ ├── CLN025_ST_F_1.csv │ ├── CLN025_ST_F_1.xtc │ ├── CLN025_ST_F_2.csv │ ├── CLN025_ST_F_2.xtc │ ├── CLN025_ST_F_3.csv │ ├── CLN025_ST_F_3.xtc │ ├── CLN025_ST_F_4.csv │ ├── CLN025_ST_F_4.xtc │ ├── CLN025_ST_F.pdb │ ├── CLN025_ST_U_1.csv │ ├── CLN025_ST_U_1.xtc │ ├── CLN025_ST_U_2.csv │ ├── CLN025_ST_U_2.xtc │ ├── CLN025_ST_U_3.csv │ ├── CLN025_ST_U_3.xtc │ ├── CLN025_ST_U_4.csv │ ├── CLN025_ST_U_4.xtc │ └── CLN025_ST_U.pdb ├── PNGase_p97 │ ├── PNGase_p97_F_1.csv │ ├── PNGase_p97_F_1.xtc │ ├── PNGase_p97_F_2.csv │ ├── PNGase_p97_F_2.xtc │ ├── PNGase_p97_F_3.csv │ ├── PNGase_p97_F_3.xtc │ ├── PNGase_p97_F_4.csv │ ├── PNGase_p97_F_4.xtc │ ├── PNGase_p97_F.pdb │ ├── PNGase_p97_U_1.csv │ ├── PNGase_p97_U_1.xtc │ ├── PNGase_p97_U_2.csv │ ├── PNGase_p97_U_2.xtc │ ├── PNGase_p97_U_3.csv │ ├── PNGase_p97_U_3.xtc │ ├── PNGase_p97_U_4.csv │ ├── PNGase_p97_U_4.xtc │ └── PNGase_p97_U.pdb └── TrpCage ├── SST2 │ ├── TrpCage_SST2_F_1.csv │ ├── TrpCage_SST2_F_1.pdb │ ├── TrpCage_SST2_F_1.xtc │ ├── TrpCage_SST2_F_2.csv │ ├── TrpCage_SST2_F_2.xtc │ ├── TrpCage_SST2_F_3.csv │ ├── TrpCage_SST2_F_3.xtc │ ├── TrpCage_SST2_F_4.csv │ ├── TrpCage_SST2_F_4.xtc │ ├── TrpCage_SST2_F_Tref_350_1.csv │ ├── TrpCage_SST2_F_Tref_350_1.pdb │ ├── TrpCage_SST2_F_Tref_350_1.xtc │ ├── TrpCage_SST2_F_Tref_350_2.csv │ ├── TrpCage_SST2_F_Tref_350_2.xtc │ ├── TrpCage_SST2_F_Tref_350_3.csv │ ├── TrpCage_SST2_F_Tref_350_3.xtc │ ├── TrpCage_SST2_F_Tref_350_4.csv │ ├── TrpCage_SST2_F_Tref_350_4.xtc │ ├── TrpCage_SST2_U_1.csv │ ├── TrpCage_SST2_U_1.pdb │ ├── TrpCage_SST2_U_1.xtc │ ├── TrpCage_SST2_U_2.csv │ ├── TrpCage_SST2_U_2.xtc │ ├── TrpCage_SST2_U_3.csv │ ├── TrpCage_SST2_U_3.xtc │ ├── TrpCage_SST2_U_4.csv │ └── TrpCage_SST2_U_4.xtc └── ST ├── TrpCage_ST_F_1.csv ├── TrpCage_ST_F_1.xtc ├── TrpCage_ST_F_2.csv ├── TrpCage_ST_F_2.xtc ├── TrpCage_ST_F_3.csv ├── TrpCage_ST_F_3.xtc ├── TrpCage_ST_F_4.csv ├── TrpCage_ST_F_4.xtc ├── TrpCage_ST_F.pdb ├── TrpCage_ST_U_1.csv ├── TrpCage_ST_U_1.xtc ├── TrpCage_ST_U_2.csv ├── TrpCage_ST_U_2.xtc ├── TrpCage_ST_U_3.csv ├── TrpCage_ST_U_3.xtc ├── TrpCage_ST_U_4.csv ├── TrpCage_ST_U_4.xtc └── TrpCage_ST_U.pdb</code></pre>
Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem
<div><strong>Description. </strong>This is the data used in the experiments presented in the following paper:<br> <div> <ul> <li> <div> <div>N. Arınık, R. Figueiredo, and V. Labatut, “Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem,” <em>Journal of Global Optimization</em>, vol. 86, pp. 355–391, 2023. DOI: <a href="http://doi.org/10.1007/s10898-023-01270-3">10.1007/s10898-023-01270-3</a> ⟨<a href="https://hal.science/hal-03935831">hal-03935831</a>⟩</div> </div> </li> </ul> <p><strong>Source code. </strong>The related source code is available on GitHub: </p> <ul> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/%3C/i%3E%3Ci%3E%3Ci%3ECompNet%3C/i%3E/Sosocc%3C/i%3E">https://github.com/CompNet/Sosocc</a></li> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/CompNet/EnumCC%3C/i%3E">https://github.com/CompNet/EnumCC</a></li> </ul> <div><strong>Citation. </strong>If you use these data, please cite the above reference:</div> <div><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem},</code><br><code> journal = {Journal of Global Optimization},</code><br><code> year = {2023},</code><br><code> volume = {86},</code><br><code> pages = {355-391},</code><br><code> doi = {10.1007/s10898-023-01270-3},</code><br><code>}</code></div> <div> </div> <div><strong>Funding. </strong>This research benefited from the support of Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</div> <div> </div> <div><strong>Further details. </strong>We describe below the structure of the zip file `article_materials.zip`:</div> <div> <ul> <li>Experiments for Dataset 1 <ul> <li>delay_exec_time: all the results and plots regarding the difference of execution times between EnumCC(3) and OneTreeCC() (i.e., EnumCC(3) minus OneTreeCC()), represented on the log-scaled y-axis of the plots. When such difference takes a negative value, this means our proposed method EnumCC(3) runs faster than OneTreeCC().</li> <li>EnumCC_nb-jumps: all the results regarding the number of jumps related to EnumCC(3), i.e. n<sub><em>jump</em></sub>(EnumCC(3))</li> <li>exec_time: all the results regarding the execution times of EnumCC(3) and OneTreeCC().</li> <li>nb-sols: all the results regarding the number of optimals solutions based on EnumCC(3). Note that we show the results of OneTreeCC() only for those with n=50, since both methods run out of the time limit of 12h for several networks with n=50.</li> </ul> </li> <ul> <li>networks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. For complete unweighted signed networks, this model relies on only three parameters: n (number of vertices), l<sub>0</sub> (initial number of modules) and <em>q<sub>m</sub></em> (proportion of misplaced edges, i.e. edges meant to be frustrated by construction). Moreover, we make the assumption that the proportion of misplaced edges is the same inside and between the modules. When it comes to incomplete unweighted signed networks, we introduce two more parameters, which are the density d of the graph and the proportion q neg of the negative edges. The last parameter q<sub>neg</sub> allows to control the ratio of positive to negative edges. For complete unweighted signed networks with d = 1, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40, 45, 50} and <em>q<sub>m</sub></em> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6}. In these networks, the value of <em>q<sub>neg</sub> </em>with the considered parameters is approximately equal to 0.7. For incomplete unweighted signed networks with d <em>∈</em> {0.25, 0.50}, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40}, q<sub>m</sub> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} and <em>q<sub>neg</sub> </em><em>∈</em> {0.3, 0.5, 0.7}. In total, we produce 600 and 1,080 instances for complete and incomplete networks, respectively, which makes a total of 1,680 instances.</li> <li>partitions: folder containing the partitioning results of two methods: EnumCC(3) vs. OneTreeCC(). Note that the results of OneTreeCC() are not shown for space considerations, except for those with n=50.</li> <li>Results</li> </ul> <li>Experiments for Dataset 2</li> <ul> <li>benchmark netwoks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. The optimal solution for a generated network is known by construction. For a given n, d and l<sub>0</sub>, we first create a perfectly structurally balanced (i.e., internally positive and externally negative) signed network with a built-in module structure. The underlying module structure constitutes the optimal partition. Then, in order to take into account different positive to negative ratio values for internal and external edges we generate several signed networks by perturbing the initial signed network without affecting its underlying optimal partition, thanks to its definition of stability range. We generate signed networks with parameter values n <em>∈</em> {30, 40, 50, 60, 70, 90}, d <em>∈</em> {0.25, 1.00} and l<sub>0</sub> <em>∈</em> {2, 4, 6}. In total, we produce 214 and 184 instances for complete and incomplete networks, respectively, which makes a total of 398 instances.</li> <li>benchmark partitions: folder containing the partitioning results of two methods: CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where <em>r<sub>max</sub></em> ∈ {3,4}.</li> <li>results: two `csv` files containing benchmark results between CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where r<sub>max</sub> ∈ {3,4}.</li> </ul> </ul> </div> </div> </div>
Research data for the paper: Adsorption Desalination and Cation Exchange of NaCl and CaCl2-water solutions in LTA zeolites
<p>Adsorption isotherms data of water in LTA zeolites (LTA_Si, NaLTA, CaLTA and NaCaLTA) with 0 Salt, 7 molecs/uc NaCl, 21 molecs/uc NaCl, 7 molecs/uc CaCl2, 21 molecs/uc CaCl2.</p> <p>Water adsorption data in MFI zeolite for force field validation</p> <p>Raw data of Radial Distribution Functions (RDFs) in NaLTA (LTA4A), NaCaLTA (LTA5A), CaLTA (LTACa), distances between structure (Oa), extra framework cations (NaCAT, CaCAT), salt cation (Na, Ca), and water (Ospce)</p> <p> </p>
Extract of the project data from the LIFE KPI webtool. Deliverable 2.5 of the LIFE NatuReef project: Nature-based reef solution for coastal protection and marine biodiversity enhancement. LIFE22-NAT-IT-LIFE-NatuReef/101113742
<p>Key performance indicator, a quantifiable measure of performance over time for a specific objective.</p>
Experimental data generated on the thermal behaviour during the intrusion–extrusion of ZIF-8 with different salt solutions
<p>/* **********<br>/* This work is licensed under a Creative Commons Attribution 4.0 International License.<br>/* **********</p> <p>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union) along with the research on the thermal behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 3.1 (WP3).<br>Underlying data for the publication Bartolomé, L. et al. Tuning Wetting-Dewetting Thermomechanical Energy for Hydrophobic Nanopores via Preferential Intrusion. The Journal of Physical Chemistry Letters 2023, 15, 880-887. https://doi.org/10.1021/acs.jpclett.3c03330. Data related to Figures 1, 3 and 4 in the article.</p> <p>Dataset Identifier: 10.5281/zenodo.13927359 </p> <p>Contact person: Luis Bartolomé (CIC energiGUNE). ORCID: https://orcid.org/0000-0001-9649-1470</p> <p><br>The archive 'JPCL_Tuning.zip' contains 2 folders with 64 files in total.</p>
Interplay of correlations and Majorana mode from local solution perspective
<p>Raw numerical data used to produce figures 2-14 in the article Interplay of correlations and Majorana mode from local solution perspective </p>
Serious games for Serious Energy Solutions project dataset
<p>This dataset includes the chat transcripts from two iterations of playing the Serious Game New Shores: A game for Democracy with a more diverse (reflecting the public) and a less diverse group (reflecting policymakers). It also includes the results for all players in the 10 rounds of the two game iterations and the final reports produced by the serious game for each iteration. The aim of the project is to investigate the impact of diversity on decision-making for sustainability using a serious game as a research tool.</p>
Hard Rod Fluid Analytical Solution in .npy (For Operator learning)
<p>Hard rod fluid with diameter of 1.0 between 2 walls with distance L+2d=10.0,<br>under different chemical potential (mu) and external potential (V_ext(x)), with a sampling distance dz=0.1.</p> <p>Data are stored as numpy structured ndarrays, each npy file store one type of data. <br>The ndarrays has the shape of (n_sample,n_grid), with 4 fields:<br>the position "z", <br>the density profile "rho", <br>the local chemical potential "muloc"<br>the one-body direct correlation function "c1".<br>Where muloc(z)=mu-V_ext(z)</p> <p>In Operator Learning, I've tried the mapping of rho to c1 as well as muloc to rho.</p> <p>dataload.py contains some functions used for loading data<br>data_model_train.py contains the read-in function for model-training data.</p> <p>data_4groups.npy saves 8000 data, with 2000 in each group, generated from the following external potentials:<br>group 1: V_ext=0 <br>group 2: V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3)<br>group 3: V_binary[z] = -mg*z<br>group 4: V_binary[z] = -u0*(abs(z/z0))^a0<br>with mu, epsilon, a, mg, u0, z0, a0 are adjustable constants of the potential </p> <p>data_mixed23.npy saves 2000 data, generated from the following external potentials:<br> V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3) -mg*z</p> <p>Folders are taped in .zip files.</p> <p>Folder "~/R_data_generator" stores the generator of the data, analytical solution of 1D hard rod fluid.<br>Folder "~/Rdata" contains same data in .Rdata file, as R matrixs<br>Folder "~/R2py" contains the script to generate .npy file from .Rdata file</p>
Data and simulation scripts for the manuscript "Patchy charge distribution affects the pH in protein solutions during dialysis."
<p>A zip archive containing the following data:</p> <ul> <li>README.md file describing in detail how to navigate the archive</li> <li>LICENSE.md file describing the license under which the data can be used or reproduced</li> <li>python scripts required to reproduce the plots presented in the manuscript</li> <li>processed simulation results used by the plotting scripts</li> <li>python scripts required to run the simulations in order to reproduce the results</li> </ul> <p> </p> <p> </p>
Figure 1-3. Blepharotoma spp. habitus. 1 in South American Melolonthinae (Coleoptera: Scarabaeidae) classification and nomenclature: some problems and solutions
Figure 1-3. Blepharotoma spp. habitus. 1) B. sp. 2) B. angustata lectotype. 3) B. schencklingi.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.