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2,904 results for “Solute”
Reference solutions for "A validated non-linear Kelvin-Helmholtz benchmark for numerical hydrodynamics"
<p>This dataset contains hdf5 files with reference solutions to the validated non-linear Kelvin-Helmholtz benchmark problem described in <a href="https://doi.org/10.1093/mnras/stv2564">Lecoanet et al. (2016)</a>. The hdf5 files include snapshots at t=2, 4, 6, 8 for the Re=10^5 D4096 simulation with Δρ/ρ0=1 (5_4096_2_*.h5), the Re=10^5 D2048 simulation with Δρ/ρ0=0 (5_2048_1_*.h5), and the Re=10^6 D2048 simulation with Δρ/ρ0=0 (6_2048_1_*.h5). The velocity, density, temperature, and dye field are contained in the tasks group. The exact output time and the grid are contained in the scales group.</p>
rOMT processed data: Cerebral amyloid angiopathy is associated with glymphatic transport reduction and time-delayed solute drainage along the neck arteries
<p>This dataset contains the speed map and Péclet map data processed by regularized optimal mass transport method (<a href="https://zenodo.org/record/5809635#.YczuJy2ZNBw">https://zenodo.org/record/5809635#.YczuJy2ZNBw</a>, <a href="https://github.com/xinan-nancy-chen/rOMT">https://github.com/xinan-nancy-chen/rOMT</a>).</p> <p>All files are in nifty format. This dataset contains in total 55 rat cases which are divided by age (3-month, 6-month and 12-month) and status of health ("WT" for wild-type and "CAA" for those who have developed Cerebral Amyloid Angiopathy).</p>
How solution snippets are presented in answers posted on Stack Overflow and how they could be potentially reused.
<p>Software engineering Q&A websites (e.g., Stack Overflow), harness the collective expertise of users to address technical queries. Over time, these platforms evolve into valuable repositories of software engineering knowledge. Such repositories serve as essential resources for developers looking for <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">solutions</abbr></abbr></abbr> to common programming <abbr title="Sentiment effect: -2">problems</abbr>. In Stack Overflow, developers may approach answering questions in various ways. <abbr title="Sentiment effect: 2">Gaining</abbr> insight into how developers formulate their answers on Stack Overflow can enhance knowledge sharing and streamline the process of finding <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">solutions</abbr></abbr></abbr>. Furthermore, such insights could also inform improvements in Generative Artificial Intelligence (GenAI) tools to <abbr title="Sentiment effect: 2">better</abbr> align generated source code for comprehension and understandability, as AI-generated answers are known to include irrelevant information and hallucinations. In this study, we seek to deepen the understanding of how <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">solutions</abbr></abbr></abbr> are presented on Stack Overflow. We conducted an empirical study that investigates programming questions that are answered with a <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">Solution</abbr></abbr></abbr></abbr></abbr> Snippet to understand how a <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">Solution</abbr></abbr></abbr></abbr></abbr> Snippet is presented, and the ways how it should be adapted when it is reused. Our study resulted in two categorizations: 1) eight categories of how <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">Solution</abbr></abbr></abbr></abbr></abbr> Snippets are presented on Stack Overflow answers and 2) five categories of how <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">Solution</abbr></abbr></abbr></abbr></abbr> Snippets could be adapted for reuse. Then, we analyzed these categorizations and discussed the implications. We anticipate that Stack Overflow will remain a valuable resource for the foreseeable future, and the insights revealed in our paper lay the groundwork for <abbr title="Sentiment effect: 2">improving</abbr> program comprehension of <abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1"><abbr title="Sentiment effect: 1">Solution</abbr></abbr></abbr></abbr></abbr> Snippets on Stack Overflow and GenAI tools.</p>
SolProp Dataset for: Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures
<p>The datasets and supplementary materials for the manuscript "Predicting Solubility Limits of Organic Solutes<br> for a Wide Range of Solvents and Temperatures" by Florence H. Vermeire, Yunsie Chung, and William H. Green. <strong>Citations should refer directly to the manuscript (</strong><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Fjacs.2c01768&data=05%7C01%7CFlorence.Vermeire%40ugent.be%7C31f767a36cf547f91aef08da48bb3fcd%7Cd7811cdeecef496c8f91a1786241b99c%7C1%7C0%7C637902266316761522%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=1CKXFXvTtqOGDSBpSw8AeKCt25%2B27CC%2FNU7Ery276D0%3D&reserved=0">https://doi.org/10.1021/jacs.2c01768</a><strong>).</strong></p> <p>The preprint version of of the manuscript is available at: https://doi.org/10.26434/chemrxiv-2022-92hl1</p> <p>The SolProp data collection contains thermodynamic properties related to the dissolution of organic solutes in organic solvents and water. Thermodynamic properties included are solvation free energy at 298K (dGsolv), solvation enthalpy at 298K (dHsolv), Abraham Solute Parameters (E,S,A,B,L), aqueous solid solubility at 298K (logSaq), and solid solubility in organic solvents at various temperatures (logS). The dataset is limited to neutral solutes and solvents.</p> <p>Detailed information can be found in the "ReadMe.txt" file.</p>
Literature database on natural climate solutions implementation
<p>Database of literature reviewed in the publication "What influences the implementation of natural climate solutions? A systematic map and review of the evidence" (Schulte et al., 2021). </p>
Solution Phase Growth and Analysis of Super-thin Zigzag Tin Selenide Nanoribbons
<p>Tin selenide (SnSe), a highly promising layered material, has been garnering particular interest in recent times due to its significant promise for future energy devices. Herein we report a simple solution-phase approach for growing highly crystalline layered SnSe nanoribbons. Polyvinylpyrrolidone (PVP) was used as a templating agent to selectively passivates the (100) and (001) facets of the SnSe nanoribbons resulting in the unique growth of nanoribbons along their <em>b</em>-axis with a defined zigzag edge state along the sidewalls. The SnSe nanoribbons are few layers thick (∼20 layers), with mean widths of ∼40 nm, and achievable length of >1 <em>μ</em>m. Nanoribbons could be produced in relatively high quantities (>150 mg) in a single batch experiment. The PVP coating also offers some resistance to oxidation, with the removal of the PVP seen to lead to the formation of a SnSe/SnO<em><sub>x</sub></em> core-shell structure. The use of non-toxic PVP to replace toxic amines that are typically employed for other 1D forms of SnSe is a significant advantage for sustainable and environmentally friendly applications. Heat transport properties of the SnSe nanoribbons, derived from power-dependent Raman spectroscopy, demonstrate the potential of SnSe nanoribbons as thermoelectric material.</p>
Supplementary material 1 from: Fernandes N, Ferreira EM, Pita R, Mira A, Santos SM (2022) The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 177-196. https://doi.org/10.3897/natureconservation.47.71864
The effect of habitat encroachment by roads on space use and movement patterns of an endangered vole
INNOSETA database 2021: industry solutions
<p>INNOSETA database 2021: industry solutions</p>
INNOSETA database: industry solutions
<p>INNOSETA database: industry solutions</p>
Runoff, suspended sediment, and solute contents data of the Binggou catchment during the spring snowmelt and summer rainfall periods
<p>This is the runoff, suspended sediment, and solute contents data of the Binggou catchment during the spring snowmelt and summer rainfall periods.</p>
Virus Finding Tools: current solutions and limitations - Real Datasets
<p>We assessed the behavior of 9 virus-finding tools using real data. We used six datasets from which we took 3 to 5 samples. In each dataset, at least one virus was present. Confirmation of the virus was performed by RNA-seq for some samples. In the other datasets, the virus was experimentally added to the cellular sample. Only the confirmed species was used to test the reliability of the methods, whereas the others were discarded since no experimental validation was available. Each dataset was picked from GEO and downloaded through the ENA Browser.</p> <p>We employed the following datasets: GEO GSE74949 (Samples: SRR2932830, SRR2932844, SRR2932845, SRR2932846, SRR2932847 - Human Papillomavirus 16), GEO GSE65486 (Samples: SRR1946683, SRR1946684, SRR1946685, SRR1946686, SRR1946687 - Hepatitis B Virus), GEO GSE61141 (Samples: SRR1565938, SRR1565939, SRR1565940, SRR1565941, SRR1565942 - Human Rhinovirus 16), GEO GSE59717 (Samples: SRR1523654, SRR1523655, SRR1523656, SRR1523657, SRR1523668 - Human alphaherpesvirus 1), GEO GSE148729 (Samples: SRR11550056, SRR11550033 - Severe Acute Respiratory Syndrome Coronavirus 2), GEO GSE84346 (Samples: SRR3898707, SRR3898708, SRR3898709 - Hepatitis C virus), ENA PRJNA257197 (Samples: SRR1553464 - Zaire Ebolavirus)</p> <p>The tools employed in our benchmarking are VirusFinder, VirusSeq, VirTect, viGEN, VirDetect, DAMIAN, Metamap, Kraken2, Centrifuge. DAMIAN could not be used for single-ended samples (GEO GSE84346).</p> <p>For each sample, we provide a ".tar.bz2" archive. Inside each archive, there is one folder for each analysis, as described in our manuscript. Due to large file size, FASTQ and intermediate analysis files are not included.</p>
[Research Data] Mining Relevant Solutions for Programming Tasks from Search Engine Results
<p>[Abstract]</p> <p>Software development is a knowledge-intensive activity. Official documentation for developers may not be sufficient for all developer needs. Searching for information on the Internet is a usual practice, but finding really useful information may be challenging, because the best solutions are not always among the first ranked pages. So, developers have to read and discard irrelevant pages, that is, pages that do not have code examples or that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results that remove irrelevant pages. The approach works as follows: a query related to the programming task is prepared, and given as an input to a search engine. The returned pages pass through an automatic filter to select relevant pages. We evaluated the top-20 pages returned by the Google search engine, for 10 different queries, and observed that only 31\% of the evaluated pages are relevant to developers. Then, we proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google’s search engine has been used as a baseline, and our results have shown that Google’s search engine returns a reasonable number of irrelevant pages for developers, and we could find an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customized web search filter for development content.</p> <p>[Contents of Research Data.rar file]</p> <p>The Research Data.rar file has a folder called Research Data that contains 3 folders internally, with the names: “01 – Source Code”, “02 - Data” and “03 – Preprocessing rules”. The folder “01 – Source Code” contains the JAVA source code of the implementations of the proposed approaches. The folder “02 - Data” contains the data of the evaluations carried out in the work, which are in the folders “01 - Evaluation results of pages returned by Google” and “02 - Results of approaches comparisons”. The folder “01 - Evaluation results of pages returned by Google” has the evaluations carried out on the first 20 pages returned by Google, following the criteria defined in the work, for the 10 queries considered in the evaluation. The folder “02 - Results of approaches comparisons” contains the results of the evaluation of the proposed approaches, for the 10 queries considered in the evaluation. In this evaluation, the number of pages given as input for the approaches was increased from 3 to 20 pages, for each number of pages a folder was generated with the results. In addition to the results of the Precision, Recall and F-Measure metrics that are in the file named Results Approaches.txt, other files were generated for analysis. For example, the Instances_without_outliers.txt file shows which pages were filtered out after applying the outlier page removal filter. The Selected Pages Approach 4.txt file, on the other hand, shows which pages were filtered after applying the filters of the GORCUO approach. The folder “03 - Preprocessing rules” has a file called Rules.java. In this file, there is the commented JAVA source code, from the implementation of the rules created in the pre-processing stage of the proposed approach.</p>
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
<p>Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in increasing the flexibility and reducing the computational cost of seismic modeling and inversion. We propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy wavefield solutions. In this letter, we present the relevant dataset to the paper. </p>
Terahertz optoacoustic detection of aqueous salt solutions
<p>We calculate time-domain THz-OA signal intensities of NaCl solutions with different concentration (0.5, 0.75, 1, 1.5, 2, 3 mol/L).</p>
Modeling the Organic Carbon Oxidation and Redox Sequence under the Partial- Equilibrium Approach: a Discussion by means of a Semi-Analytical Solution
<p>Filename: Analytical_solution.xls<br> File format: Excel<br> Description: It contains the calculation of the analytical model, programmed by means of excel. The sheet "advection" contains the calculation of the advection model and the sheet "diffusion" that of the diffusion model. The sheet "Phreeqc results" contains the results of the Phreeqc batch model (see Batch.pqi). Input data are highlighted in yellow.</p> <p>Filename: Batch.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the calculation of the batch model by Phreeqc. It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: PEA_om_adv.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the advection model by Phreeqc using the PEA (Partial Equilibrium Assumption). It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: PEA_om_dif.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the diffusion model by Phreeqc using the PEA (Partial Equilibrium Assumption). It requires the Phreeqc database file Phreeqc.dat</p> <p>Filename: KIN_om_adv.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the advection model by Phreeqc using the fully kinetic approach. It requires the Phreeqc database file PhreeqcKIN.dat</p> <p>Filename: KIN_om_dif.pqi<br> File format: Phreeqc<br> Description: It contains the input file for the numerical calculation of the diffusion model by Phreeqc using the fully kinetic approach. It requires the Phreeqc database file PhreeqcKIN.dat</p> <p>Filename: phreeqc.dat<br> File format: Phreeqc database<br> Description: It contains thermodynamic data for the batch model (see Batch.pqi) and the PEA models (see PEA_om_adv.pqi and PEA_om_dif.pqi). It is the default thermodynamic database of Phreeqc, dat), from which we removed ammonium to avoid the unrealistic reduction of NO3- and N2 to NH4+</p> <p>Filename: phreeqcKIN.dat<br> File format: Phreeqc database<br> Description: It contains thermodynamic data for the fully kinetic models (see KIN_om_adv.pqi and KIN_om_dif.pqi). It is the default thermodynamic database of Phreeqc, dat), from which we removed the relevant equilibrium redox reaction, so that they can be treated kinetically.</p> <p> </p> <p> </p>
FIWARE-enabled smart solution for the optimal management and operation of raw-water supply hydraulic works
<p>This database includes the data used to produce the results for the following article:</p> <p>Kossieris, P., Pantazis, Bellos, V., C., Makropoulos, C., 2022. FIWARE-enabled smart solution for the optimal management and operation of raw-water supply hydraulic works. 7th IAHR Europe Congress, September 7th – 9th, 2022, Athens, Greece (accepted paper for oral presentation, in press). </p>
Quadris - 43802 solutions
<p>This contains the 'unique/' directory in which Quadris solutions are stored. See my Github for further details.</p>
Privacy Challenges and Solutions in Smart Health: A Systematic Literature Review Extraction Forms
<p>Data extraction forms for a Systematic Literature Review on Privacy Challenges and Solutions in Smart Health</p>
Spectral characterization of a supercontinuum source based on self-phase modulation in an aqueous K$_{2}$ZnCl$_{4}$ salt solution
<p>Spectral data and Matlab scripts for analysis, associated with the article "Spectral characterization of a supercontinuum source based on self-phase modulation in an aqueous K$_{2}$ZnCl$_{4}$ salt solution", submitted to Applied Optics.</p>
MM-MD and QM/MM-MD trajectories of para-nitrophenolate derivatives in aqueous solution
<p>Classical MD (GAFF/TIP3P) performed of four para-nitrophenolate derivatives. All simulated in water for 600000 steps of 2 fs.</p> <p>QM/MM-CPMD (BLYP/TIP3P) simulation of para-nitrophenolate for 44000 steps of 10 a.u.</p> <p>QM/MM-BOMD (BLYP/TIP3P) simulation of three para-nitrophenolate derivatives for 44000 steps of 10 a.u.</p> <p>Input files, output files and trajectories are provided for all of the above.</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.