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2,904 results for “Solute”
Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services"
<p>Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services" (Qiu et al. 2025), including source data, R scripts, and output results.</p>
Model weights for a Weather4cast 2021 Challenge Stage 1 solution
<p>This repository contains the pre-trained model weights for the TensorFlow/Keras models used in the <a href="https://www.iarai.ac.at/weather4cast/2021-competition/challenge/">Weather4cast 2021 Challenge Stage 1</a> by the team "antfugue". The model code can be found in <a href="https://github.com/jleinonen/weather4cast-stage1">https://github.com/jleinonen/weather4cast-stage1</a> along with instructions on where to extract the weights.</p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
A database solutions for the type two assembly line balancing problems
<p> Assembly Line Balancing Problems have a significant impact on performance of manufacturing systems, specially for the cases of mass production. These problems are widely cited and treated in the literature. </p> <p>One from the most important variants of those problems is the “Task Restrictions Assembly Line Balancing Problem” of type 2. For this problem, a set of tasks need to be affected to a predefined number of stations m from the way that minimises the cycle time and respects a set of constraints related to precedence and compatibility between tasks (Triki et al., 2016).</p> <p>For this variant we suggest an innovative speed and effective approach based on the hybridisation of two powerful tools: the ant colony optimisation and the genetic algorithm. The effectiveness of this approach is evaluated through a set of instances collected from the literature (Thomas, 1990; Triki et al., 2016) .</p> <p>This document presents the best generated solutions for those problems. </p> <p> </p> <p> </p>
Nature-Based Solutions Tools Catalogue
<p>The Adaptive Cities Through Integrated Nature-Based Solutions (ACT on NBS), EIT Climate KIC project made an inventory and assessment of NBS tools for climate resilient cities. The objectives of the catalogue are: 1) to increase organizational accessibility to data on NBS and climate resilience tools that have been developed and used in Europe and worldwide; 2) that different end-users become aware of several tools that already exist to plan, design, and implement NBS and make use of them to address specific environmental challenges and adaptation measures in their cities, neighborhoods or even regions.</p> <p>The NBS tools catalogue contains 70 tools collected. For this research, a “tool” is understood to be either a methodology, software, catalogue, repository, e-platform, guideline or handbook. The tools were identified through interviews with EU municipalities and workshops organized by ACT on NBS, and by additional desk research.</p> <p>The desk study entailed a combination of reviewing the websites from international organizations and EU granted projects related to cities dealing with NBSs, ecosystem services (ESs), green infrastructure, urban resilience and climate change, and reviewing peer-reviewed scientific journals, reports and grey literature.</p> <p>The latter search was implemented through Google search, Google scholar and Scopus in August 2020. This implies that tools that were developed and published before that date were collected. The search strategy was implemented using combinations of search terms such as: nature-based solutions, NBS, ecosystem-based adaptation, green infrastructure, climate adaptation, climate resilience, ecosystem services, climate hazards, urban biodiversity, urban nature, water and land management “AND” urban areas, cities “AND” tools, software, methodology, catalogue, repository, platform, handbook and guideline.</p> <p>For the assessment, tools were labeled on their descriptive characteristics and potential fields of application. The use of pre-defined indicators was chosen to support the characterization of the tools and to allow for their comparison. The categories and indicators were formulated based on the current literature and refined through expert judgements. The indicators were also, in some cases, further adapted through an iterative process of tools’ analysis.</p> <p>For more information on the NBS tools analysis please read our academic publication on “Nature-Based Solutions Tools for Planning Urban Climate Adaptation: State of the Art”.</p> <p><strong><a href="https://doi.org/10.3390/su13116381">https://doi.org/10.3390/su13116381</a></strong> </p>
Primary data: Signal enhancement of hyperpolarized 15N sites in solution — increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures
<p>Primary data for DOI: 10.1002/nbm.4787</p> <p>NMR in Biomedicine. 2022;e4787</p> <p>Title: Signal enhancement of hyperpolarized 15N sites in solution—increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures</p> <p>Authors: Ayelet Gamliel, David Shaul, J. Moshe Gomori, Rachel Katz-Brull</p> <p>Description:</p> <p>These primary datasets contain data presented in the above publication and consist of:</p> <p>1. 15N-NMR spectra in solutions</p> <p>2. 13C polarization buildup data in solid-state</p> <p>3. 13C microwave profiles in solid state</p> <p>Please consult the Archive Guide.</p>
Data for patient-specific solution of the electrocorticography forward problem in deforming brain
<p>This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.</p>
Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution - dataset
<p>Dataset for "Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution"</p>
Dataset: Swarms in Central Utah – event detection lists, arrival picks, relocations & moment tensor solutions
<p>Dataset containing results of moment tensor inversions, relocations and event detections for our study "Petersen & Pankow (2023): Small-magnitude seismic swarms in Central Utah: Interactions of regional tectonics, local structures and hydrothermal systems" (<a href="https://doi.org/10.1029/2023GC010867">https://doi.org/10.1029/2023GC010867</a>).<br>Please read the pdf-README file for more information on the dataset.</p>
eHealth solutions in Africa
<p>Information about eHealth solutions and in African countries, mainly from the countries participating in the BETTEReHEALTH project (Ghana, Malawi, Ethiopia, Tunisia). From the BETTEReHEALTH registry: <a href="https://registry.betterehealth.eu/ehealth-solution">https://registry.betterehealth.eu/ehealth-solution</a></p>
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Soil Solution Resin Available Nitrogen
Resin available soil solution nitrogen was measured during seasonal incubations in 2014 and 2015 on all Climate Change Across Seasons Experiment (CCASE) plots. Reference (or control) plots are shared with the collaborating Northern Forest DroughtNet experiment. There are six plots total (each 11 x 14m). Two are warmed 5 degrees C throughout the growing season (Plots 3 and 4). Two others are warmed 5 degrees C in the growing season and have snow removed during winter to induce soil freeze/thaw cycles (Plots 5 and 6). Four kilometers (2.5 mi) of heating cable are buried in the soil to warm these four plots. Two additional plots serve as controls for our experiment (Plots 1 and 2). Analysis and results from these data are presented in Sanders-DeMott 2018. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Sanders-DeMott, R., Sorensen, P.O., Reinmann, A.B. et al. Growing season warming and winter freeze–thaw cycles reduce root nitrogen uptake capacity and increase soil solution nitrogen in a northern forest ecosystem. Biogeochemistry 137, 337–349 (2018). https://doi.org/10.1007/s10533-018-0422-5
Dataset for: Diurnal patterns in solute concentrations measured with in situ UV-Vis sensors: natural fluctuations or artefacts?
<p>This dataset contains high-resolution (10-minute interval) data for nitrate, dissolved organic carbon, precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya. This data was used for the analysis of diurnal patterns in nitrate and dissolved organic carbon concentrations. The zipped folder contains the following files and data:</p> <ul> <li>Calibration.csv: <ul> <li>site = name of measuring site</li> <li>date = date and time of grab sample (yyyy-mm-dd hh:mm:ss)</li> <li>DOC = dissolved organic carbon concentration in grab sample (mg C/L)</li> <li>nitrate = nitrate concenctration in grab sample (mg N/L)</li> </ul> </li> <li>Files with suffix ".ts.csv" (time series data from 1-11-2014 to 31-10-2019; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>nit.raw = nitrate concentration measured by sensor (mg N/L)</li> <li>nit.flag = indication of validity of nitrate measurement (if NA, measurement is valid; for explanation of flags, see supplement of <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021592">Jacobs et al. 2018</a>)</li> <li>nit.proc = processed nitrate concentration (mg N/L)</li> <li>nit.bg = background concentration of nitrate (48-h moving median; mg N/L)</li> <li>nit.patt = deviation from background concentration of nitrate (nit.proc minus nit.bg; mg N/L)</li> <li>doc.raw = dissolved organic carbon concentration measured by sensor (mg C/L)</li> <li>doc.flag = indication of validity of dissolved organic carbon measurement (if NA, measurement is valid)</li> <li>doc.proc = processed dissolved organic carbon concentration (mg C/L)</li> <li>doc.bg = background concentration of dissolved organic carbon (48-h moving median; mg C/L)</li> <li>doc.patt = deviation from background concentration of dissolved organic carbon (doc.proc minus doc.bg; mg C/L)</li> <li>p = precipitation (mm/10 mins)</li> <li>q = discharge (m³/s)</li> <li>sensor = serial number of sensor</li> </ul> </li> <li>Files with suffix ".exp.csv" (data for sensor comparison experiment from 5-9-2017 to 1-12-2017; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>prec = precipitation (mm/10 mins)</li> <li>nit.orig = processed nitrate concentration measured by fixed sensor (mg N/L)</li> <li>nit.bg.orig = background concentration of nitrate measured by fixed sensor (48-h moving median; mg N/L)</li> <li>nit.patt.orig = deviation from background concentration of nitrate measured by fixed sensor (nit.orig minus nit.bg.orig; mg N/L)</li> <li>nit.dup = processed nitrate concentration measured by mobile sensor (mg N/L)</li> <li>nit.bg.dup = background concentration of nitrate measured by mobile sensor (48-h moving median; mg N/L)</li> <li>nit.patt.dup = deviation from background concentration of nitrate measured by mobile sensor (nit.dup minus nit.bg.dup; mg N/L)</li> <li>doc.orig = processed dissolved organic carbon concentration measured by fixed sensor (mg C/L)</li> <li>doc.bg.orig = background concentration of dissolved organic carbonmeasured by fixed sensor (48-h moving median; mg C/L)</li> <li>doc.patt.orig = deviation from background concentration of dissolved organic carbonmeasured by fixed sensor (doc.orig minus doc.bg.orig; mg C/L)</li> <li>doc.dup = processed dissolved organic carbonconcentration measured by mobile sensor (mg C/L)</li> <li>doc.bg.dup = background concentration of dissolved organic carbonmeasured by mobile sensor (48-h moving median; mg C/L)</li> <li>doc.patt.dup = deviation from background concentration of dissolved organic carbon measured by mobile sensor (doc.dup minus doc.bg.dup; mg C/L)</li> <li>set = experimental treatment</li> </ul> </li> </ul>
Figure 5 in Does your preservative preserve? A comparison of the efficacy of some pitfall trap solutions in preserving the internal reproductive organs of dung beetles
Figure 5. Pitfall trap with protective caging and cover placed on-top of a manually constructed soil mound so as to prevent interference from mammals and dilution and/or overspilling from precipitation and surface runoff.
Figure 2. L in Does your preservative preserve? A comparison of the efficacy of some pitfall trap solutions in preserving the internal reproductive organs of dung beetles
Figure 2. L. militaris (female) after 28 days of submergence in 4% PBF showing the well preserved ovary and oocytes.
Figure 3 in Does your preservative preserve? A comparison of the efficacy of some pitfall trap solutions in preserving the internal reproductive organs of dung beetles
Figure 3. Evaporation rates of the eight preservatives in the riparian vine thicket environment. Water is also shown for comparison. The dotted line represents the critical volume. PG = propylene glycol, w vinegar = white vinegar.
Figure 4 in Does your preservative preserve? A comparison of the efficacy of some pitfall trap solutions in preserving the internal reproductive organs of dung beetles
Figure 4. Evaporation rates of the eight preservatives in the low open woodland environment. Water is also shown for comparison. The dotted line represents the critical volume. PG = propylene glycol, w vinegar = white vinegar.
Generalized model-based solutions to false positive error in species detection/non-detection data: DataS5.
<p>Data/code associated with empirical case study (Gray fox relative abundance estimation/prediction) in article "Generalized model-based solutions to false positive error in species detection/non-detection data" [doi pending].</p>
Computational Implementation of "Uncoupling electrokinetic flow solutions", published in Mathematical Geosciences
<p>This dataset includes Python and Mathematica scripts used to generate figures, and images used in the Mathematical Geosciences (MG) manuscript "Uncoupling Electrokinetic Flow Solutions" by Kuhlman and Malama (2020).</p> <p>Python scripts implementing eigenvalue uncoupling approach for differential equations governing 1D cylindrically symmetric electrokinetic flow problem (i.e., flow to a pumping well).</p> <ol> <li>mpmath python script (recombine-expint.py) implementing Theis "type curve" solution for an infinite domain (Figures 1-3 in MG manuscript)</li> <li>fipy python script (compare-via-fipy.py) and plotting script (plot_fipy_results.py) showing a finite-volume fully coupled solution for a similar finite domain for comparison against eigenvalue uncoupling approach (Figure 4 in MG manuscript). Also includes two shell scripts for driving python scripts for a variety of inputs.</li> </ol> <p>mathematica script (periodic-1D-steady-state-type-1.nb) for solving the algebra associated with the governing equations and plotting figures for analytical solution of periodically driven 1D solution (i.e., laboratory sinusoidal streaming potential and electroosmosis; Figures 4-9 in MG manuscript).</p> <p> </p>
Data for "First Principle Calculation on Pressure Dependent Yielding in Solute Strengthened Aluminium Alloys"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "First principle calculations of pressure dependent yielding in solute strengthened aluminium alloys". The details of the DFT calculations are written in the article.</p> <p>The two different file-name conventions are explained below.</p> <p>OUTCAR_Al_R1_HSP<br> OUTCAR_X_HSP_POS</p> <p>Al-files represent the pure aluminium models, showing the dislocation energies at various hydrostatic pressures. X (Cu, Si, Mg) represents the solute specie in the full model showing the configurational energy. R1 is the radius of the relaxed region. HSP represents the superimposed hydrostatic pressure, which is given by (-560+HSP*80) MPa. POS is the atomic index in the OUTCAR files, where the solute is substituted.</p>
Data: "Solute transport in bounded porous media (...)" by Sole-Mari et al. (2020)
<p>Data corresponding to the results of the Monte Carlo set of simulations described in: "Solute transport in bounded porous media characterized by Generalized Sub-Gaussian log-conductivity distributions". Please send questions to guillem.sole.mari@outlook.com.</p>
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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.