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.
814
datasets available to search
ShareScore release 0.9.0
Dataset results
814 results for “Routing”
Fig. 4 in Molecular Identification, Fatty Acid Profile and Trace Elements in a Stranded Fin Whale in Sabah (Borneo, Malaysia): Implications on Migration Routes and Trophic Ecology of Southern Fin Whales.
Fig. 4. (a) Concentrations of trace elements (Mean ± SD) in the skin and blubber of the southern fin whale recorded in the present study compared to (b) the concentrations of trace elements in the skin of southern right whales (Eubalaena australis) extracted from the results of Martino et al. (2013).
Fig. 3 in Molecular Identification, Fatty Acid Profile and Trace Elements in a Stranded Fin Whale in Sabah (Borneo, Malaysia): Implications on Migration Routes and Trophic Ecology of Southern Fin Whales.
Fig. 3. Comparison of the percentages of fatty acid profiles for (a) SFA, (b) MUFA and (c) PUFA in the tissues of adult male (M) and female (F) southern humpback whales during the early and late migrations extracted from the results of Waugh et al. (2012), epipelagic and mesopelagic (i.e., average) fish in the South China Sea (SCS) extracted from the supplementary data of Wang et al. (2019) and the southern fin whale in the present study.
Fig. 3 in Multiple evolutionary routes of the single polar capsule in Thelohanellus species (Myxozoa; Myxobolidae)
Fig. 3. Ancestral polar capsule number reconstruction for myxobolid species based on the preferred ML tree (Fig. 2). Pie charts show the marginal probability of the ancestral state at each node. Green circle represents species with single polar capsule, black circle represents species with two equal polar capsules, white circle represents species with two unequal polar capsules. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Multiple evolutionary routes of the single polar capsule in Thelohanellus species (Myxozoa; Myxobolidae)
Fig. 2. SSU rDNA based maximum likelihood tree of selected myxobolid species. The table containing detail information of taxa is shown behind taxa names; Bootstrap supports and posterior probabilities are given beside the nodes, respectively. Asterisks represent values = 100/1.00. Dashes represent values <60/0.60.
Fig. 1 in Multiple evolutionary routes of the single polar capsule in Thelohanellus species (Myxozoa; Myxobolidae)
Fig. 1. Photomicrograph of fresh spores of Thelohanellus kitauei and Thelohanellus wuhanensis. A - the spore of T. kitauei with two polar capsules (arrow); B, C - the spores of T. wuhanensis with two polar capsules (arrows). Scale bars indicate 20 μm for A, B and C.
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm
<p>Figure 6 shows some of the best solutions searched by the proposed method. In this figure, the horizontal axis represents the x-axis with increasing positive values to the right and the vertical axis represents the y-axis with increasing positive values upward. </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 7. Application main form which includes route, bus stops and GA parameters
<p>For this study, the school bus routes within the Ankara Province were used as case studies. The school bus routes were recorded by using the Android application and instantaneous GPS monitoring method. The home address of each student was taken as a stopping point. At the end of each route, the distances between the beginning and end points were recorded. After transferring the obtained route data into the database, the ill-adapted points were eliminated. By means of the developed application, the existing school bus routes are dynamically optimized using GA. It was developed both as a mobile and desktop application. Using Android- based mobile software, the information regarding GPS locations of bus stops and school buses is transferred to the server on a real-time basis. Using the desktop software, where GAs are run, these coordinates are shown on a Google map, and the most suitable route is produced and sent to the school bus via server. This method provides the opportunity to dynamically reflect certain factors such as a different initial point for the school bus, some students being absent from the school on a particular day, and eventual changes on the existing roads on the route production process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</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 10. Obtained experimental results
<p>The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.</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>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
A map describing routes used in goose monitoring at Meise Botanic Garden
<p>The geese living in Meise Botanic Garden were surveyed between October 11, 2011 - July 10, 2017. Each day that the geese were monitored one route was chosen at random. This map describes those routes</p> <p><strong>routemap.png</strong></p> <p>A map of the survey routes within the Botanic Garden. CC BY-SA</p> <p>The background map is derived from the OpenStreetMap see <a href="https://www.openstreetmap.org/copyright">https://www.openstreetmap.org/copyright</a></p>
Comparing the route-choice behavior of pedestrians around obstacles in a virtual experiment and a field study
<p>There is a great controversy about the application of virtual experiment in pedestrian routing behavior research. We conducted field observations and virtual experiment to study the route choice behavior of pedestrians. The route choice behavior around obstacles are compared qualitatively. The results shows that distance to the exit routes as well as the density around the exits show great influence on the pedestrians' route choice behavior while the speed of frontal pedestrians shows no obvious impact on the route choice. Pedestrians prefer to choose local closer exit or the exit with less occupants. The results of logistic regression show that the similar results can be obtained in virtual experiment and field observation qualitatively. This work can verify the validity of the virtual experiment for studying route choice behavior of pedestrians, which is of great importance for the application of virtual experiment.</p> <p>The files are trajectories of the recorded videos in the field observation.</p>
Figure 1 in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 1 Geographic location of the A. aegypti populations included in this study and gene-flow models evaluated. In both graphics, circles indicate the populations and arrows represent the gene flow between populations. A) Scale at American continent level (N = 2,996 specimens from six locations: Mexico – North America (M-NA), Venezuela (VZ), Peru (PE), Brazilian Amazon (BrAm), southeastern Brazil (SEBr), and Colombia (CO). B) Scale at South America level (N = 1,083 specimens from six locations: Venezuela, Peru, Brazilian Amazon (5 locations; Brazilian Amazon (BrAM), Manaus (MAO), Belém (BL). Boa Vista (BV), Rio Branco (RB), Porto Velho (PV)), Southeastern Brazil (SEBr) and Colombia (2 locations; Sucre (S), Quindio (Q)).
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H found of the ND4 gene for the A. aegypti populations in the American continent. The blue horizontal bars above the branches reflect the 95% CI for the branch supports. The color bars (blue, green, and red) on the tree terminals indicate which haplotypes are exclusive for a specific population. The dotted lines on the right side of the tree and numbers I or II indicate to what clade each of the terminals belong. H1-Col (Colombia (Sucre and Quindio), Venezuela, Peru, M-NA, Brasil (MA-O, RBPV, SEBr, BE-L)), H4 (Venezuela, M-NA, Brasil (MA-O, RBPV, BEL)), H3 (Venezuela, M-NA, Brazil (RBPV, SEBr, BE-L)), H2-Col (Colombia (Sucre), Venezuela, Peru, M-NA, Brazil (RBPV, SEBr, BE-L)), H13 (M-NA, Brazil (SEBr)), H8 (Venezuela, Brazil (SEBr)).
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.