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Fig. 3 in Capture of Zeugodacus cucurbitae (Diptera: Tephritidae) in traps baited with torula yeast solution versus cucumber volatile plugs
Fig. 3. Captures of melon flies, Zeugodacus cucurbitae, in Multilure traps baited with torula yeast borax solution or cucumber volatile plugs over a 6 wk period at the high-capture site (D). Points represent means (± 1 SE) of 10 traps per lure type.
BRAIN Journal-Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical- Chemical System-Figure 1. Two compartments containing solution separated by a membrane.
<p>In fact, this equation will be found first if one consults physical or chemical textbooks for<br> diffusion. Also one may be able to find already-existing diffusion simulators to see vivid images of<br> the process.</p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 4. The solution of C6 found by CEACO
<p>Furthermore, the GA has not been able to find the best solutions in thirteen of the fourteen examples. Therefore, it is the weakest algorithm among the five presented algorithms. However, SS_ACO has been able to find better solutions than the GA and has come up with the best solution in 12 examples. Among remaining five algorithms, PSO has failed in improving the solutions in 10 examples and has come up with solutions similar to the ones found by GA. From the comparison between GAPSO and CEACO, it can be seen that GAPSO in six examples has been able to find better solutions than the proposed algorithm. However, the CEACO has found better solutions than this algorithm for one example. For example, the solution of C6 is shown in Figure 4 which is the best found solution until now by other algorithms. </p>
USP5 zinc-finger ubiquitin binding domain with L-cysteine(residue195)-glutathione disulfide structure solution
<p>USP5 zinc-finger ubiquitin binding domain with L-cysteine(residue195)-glutathione disulfide structure solution to 1.6 Å resolution</p>
Complete numerical solutions for "Inference of ecological and social drivers of human brain-size evolution" by Mauricio González-Forero and Andy Gardner
<p>This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.</p> <p>/1RatioForm/ -> solutions for power competence.<br> /2DiffForm/ -> solutions for exponential competence.</p> <p>/1RatioForm/1BenchmarkFromSimpleInitialGuess -> solution for the step 1 of initialization (section 5 of the SI).<br> /1RatioForm/2Benchmark/ -> solution for the step 2 of the initialization (section 5 of the SI).<br> /1RatioForm/3AdditiveCoop/ -> solutions across the P parameter combinations for additive cooperation.<br> /1RatioForm/4MultCoop/ -> solutions across the P parameter combinations for multiplicative cooperation.<br> /1RatioForm/5SubMultCoop/ -> solutions across the P parameter combinations for submultiplicative cooperation.</p> <p>The contents of /2DiffForm/ are analogous.</p> <p>The P vector is written in these files in the order (etas,etac,etaC,etag), where<br> etas -> P1<br> etac -> P3<br> etaC -> P2<br> etag -> P4</p> <p>The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).</p> <p>The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.</p> <p>The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.</p> <p>Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.</p> <p>The terminal folders contain the solutions and have the following files:<br> brainNashDeep.m -> the master file launching the iteration of best responses.<br> brainMainDeep.m -> the file launching one iteration solving the optimal control problem to find best response.<br> brainMainTestRunDeep.m -> runs a test to check if there are infeasibility warnings.<br> brainContinuous.m -> specifies the dynamic constraints.<br> brainEndpoint.m -> specifies the terminal constraints.<br> parameters.m -> specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br> getSolution.m -> extracts solution.<br> plots.m -> plots solutions over best response iterations.<br> brainPlot.m -> plots solution of a given best response iteration.<br> guessDeep.mat -> initial guess and resident used.<br> solutionNashDeep.mat -> solutions over best response iterations.<br> solutionDeep.mat -> solution of last best response iteration.</p> <p> </p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution
<p>When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution
<p>VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Data for a dairy farm microgrid solution
<p><strong>Load data </strong>(Farm_load_kW_data.txt)</p> <p>A pre-determined hourly data series of electricity consumption (one year). The consumption was considered independent from the microgrid operating state – network connected or islanded operation. Load was not controlled in order to obtain longer islanded operation capability, nor to minimize power exchange with the network in normal state. The dairy farm case was with about 180 cows and corresponding electricity consumption of approximately 261 MWh/a. The farm data series was created based on data from similar size farms. Daily consumption profile was based on diurnal consumption data of a large cowhouse in a winter day, and the variation from day to day throughout the year was approximated by creating sliding data series based on monthly electricity consumption. The dataset was then suitably scaled for the specified annual consumption.</p> <p><strong>PV generation data </strong>(PV_pu_data.txt)</p> <p>An hourly PV production data series for one year was created for a specific location (in Finland) based on MERRA-2 time series data on radiation [1] and air temperature [2]. The daily average radiation and temperature were scaled to match monthly values from PVGIS database [3,4]. PV panel generation (in per units) was calculated considering location and temperature, and selected panel tilt given by PVGIS ‘optimal inclination angle’. The PV generation data series was then scaled appropriately for the selected PV capacity in the case study.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly, Time- Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Huld, T., Müller, R. & Gambardella, A. A new solar radition database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012).</li> <li>European Communities (2012), PVGIS interactive application. Available: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#.</li> </ol>
Data for article: Understanding the Visible-light Photocatalytic Activity of GaN:ZnO Solid Solution: the Role of Rh2-yCryO3 Cocatalyst and Charge Carrier Lifetimes Over Tens of Seconds
<p>Dataset underlying the publication 'Understanding the Visible-light Photocatalytic Activity of GaN:ZnO Solid Solution: the Role of Rh<sub>2-y</sub>Cr<sub>y</sub>O<sub>3</sub> Cocatalyst and Charge Carrier Lifetimes Over Tens of Seconds', published in Chemical Science (doi: 10.1039/C8SC02348D)</p>
bin3C - Cluster report and CheckM result for bin3C solution of the real human gut microbiome
<p>Supplementary data table S3 from the manuscript</p> <p>bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</p> <p>A real human gut microbiome was deconvoluted using bin3C. Subsequently, bin3C produced a report detailing per-cluster statistics for the entire solution. The largest 296 clusters were then analyzed with CheckM and joined to the report.</p>
bin3C - CheckM result for bin3C solution of the simulated community at full depth
<p>Supplementary data table S2 from the manuscript</p> <p><strong>bin3C :</strong><strong> Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</strong></p> <p>CheckM validation result for the simulated community at full shotgun and Hi-C depth of coverage.</p>
Inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs"
<p>This file contains the main inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs".</p>
Dataset From: Unexpectedly Large Decay Lengths of Double Layer Forces in Solutions of Symmetric, Multivalent Electrolytes
<p>The dataset for the publication "Unexpectedly Large Decay Lengths of Double Layer Forces in Solutions of Symmetric, Multivalent Electrolytes". DOI: 10.1021/acs.jpcb.8b12246.</p> <p>Files containing data have .dat extension and are in text format.</p>
One-dimensional hydrodynamic solution data of wavelet-based adaptive finite volume and discontinuous Galerkin shallow water solvers
<p>Raw data for a series of idealised, one-dimensional hydrodynamic test cases:</p> <ul> <li>dambreakwet (SWASHES 4.1.1)</li> <li>dambreakdry (SWASHES 4.1.2)</li> <li>dambreakmanning (SWASHES 4.1.3)</li> <li>dambreakupslope, dambreakdownslope (<a href="http://doi.org/10.1061/(ASCE)HY.1943-7900.0000494">Kesserwani and Liang 2011</a>)</li> <li>dambreakonehump (<a href="https://doi.org/10.1080/19942060.2011.11015393">Ozmen-Cagatay and Kocaman 2011</a>)</li> <li>lakeatrest (<a href="https://doi.org/10.29007/vm3q">Kesserwani et al. 2018</a>)</li> <li>parabolicbowlswashes (SWASHES 4.2.1)</li> <li>parabolicbowlliangmarche (<a href="https://doi.org/10.1016/j.advwatres.2009.02.010">Liang and Marche 2009</a>)</li> <li>steadysubcritical (SWASHES 3.1.3)</li> <li>steadysupercritical (<a href="https://doi.org/10.2166/hydro.2015.039">Haleem et al. 2015</a>)</li> <li>steadytranscriticalshockless (SWASHES 4.1.4)</li> <li>steadytranscriticalshock (SWASHES 4.1.5) </li> </ul> <p>SWASHES refers to <a href="https://doi.org/10.1002/fld.3741">Delestre et al. 2013</a> </p>
iNSPiRe FP7 - Retrofit solutions database
<p>Energy, economic and environmental performance of a set of residential and office building retrofit measures.</p> <p>A simulation-based database organized in excel sheets collects information on the energy performance, installation and actual costs and environmental impact of different renovation packages applied to the envelope and to the HVAC system of residential and office buildings belonging to different climates and construction periods.</p> <p>The research leading to these results has received funding from the European Community's Seventh 455 Framework Programme (FP7/2007-2013) under grant agreement n° 314461.</p>
FAIRsFAIR Data of Survey on Semantics and interoperability solutions
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The work package "WP2 FAIR Practices: Semantics, Interoperability, and Services" will produce three reports on FAIR requirements for persistence and interoperability to identify domain-specific standards and practices in use. These will review and document commonalities and possible gaps regarding semantic interoperability, and the use of metadata and persistent identifiers across infrastructures. They will also look into differences in terms of standards, vocabularies and ontologies. The collected information will be updated during the course of the project in cooperation with other tasks and EOSC projects.</p> <p>This survey was done to complement and validate the information from desk research for the first of these reports. It was aimed at data managers and data support experts. We hoped to get information about tools and services we might have missed, but also some reflections on the thinking around identifiers and ontologies and other semantic artefacts. The information was also collected to support preparing workshops on semantics and interoperability that are forthcoming in the project, as well as the work on software and services. The survey covers questions about metadata, use of persistent identifiers, use of semantic artefacts and handling research software.</p> <p>The survey was conducted as a joint effort with WP3, FAIR Policy and Practice and its open consultation, and was disseminated on the fairsfair.eu web pages, social media channels and via email lists. We received 66 answers during the period the survey was open, that is between 15 July to 2 October 2019.</p>
Figure 6 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 6. Example of a spatially-explicit soil sampling design, based on five nested triangles. Dots with numbers represent soil core samples, distributed between thee base poles (A, B, C). Distances between the samples follow a decimal logarithmic scale. The largest triangle edge length is 10 meters. Note that some samples are directly next to each other. A more extensive design based on triangles or rectangles can be found in Saraeva et al. (2015) or Widenfalk et al. (2015), respectively.
Figure 4 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 4. Ecosystem processes that can be affected by different functional groups of Collembola. Soil-dwelling Collembola such as Onychiuridae are associated with roots and involved in rhizosphere processes. Many litter-dwelling Isotomidae and Tomoceridae are associated with decomposition processes by grazing and transformation of litter. Most of Hypogastruridae and Neanuridae and litter-dwelling Entomobryidae are largely microbivores and impact directly microbial communities in the litter. Surface-dwelling Symphypleona, Entomobryidae and Paronellidae affect microbial biofilms aboveground. Collembola, especially large surface-dwelling species, support various groups of terrestrial invertebrate predators. Summarised from Potapov et al. (2020, 2016) and Rusek (1998).
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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.