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13,017 results for “Positionning”
Most rotifer species have positive responses in abundance caused by the increase in nitrogen, phosphorus and chlorophyll-a in reservoirs using TITAN analysis
<p>Some zooplanktonic species change their abundances according to the primary productivity increases in freshwater lentic environments. Here, we aimed to i) evaluate the concentration threshold of variables related to eutrophication (nitrogen, phosphorus<br> and chlorophyll-a concentrations) that alter the frequency of occurrence and relative abundance of rotifer species, and ii) analyze which Rotifera species are related positively or negatively to the increase in these variables. The rotifer community<br> structure was studied in fifteen reservoirs in La Plata River Basin, the second largest in South America, relating its abundance with nitrogen, phosphorus and chlorophyll-a values using the Threshold Indicator Rate Analysis (TITAN). Seventy-one rotifer<br> species were registered in the reservoirs, and six species were considered as indicators of changes in their frequency of occurrence and relative abundance with points of change of 1.118 µg.L -1 , 22.44 µg.L -1 and 3.89 µg.L -1 of the nitrogen, phosphorus and<br> chlorophyll-a concentrations, respectively. Species with positive responses to the increase in nutrients were Keratella tropica, Plationus patulus, Filinia terminalis and Synchaeta oblonga, and negative responses Conochilus unicornis and Synchaeta stylata,<br> typical of oligotrophic reservoirs. The only species that presented the same response for all variable concentrations was Keratella tropica, which is a very common species in South America. Our results reinforce the assumption that some rotifer species are good<br> indicators to the variables related to the trophic level in reservoirs. The script and study data are attached to this database.</p>
Data from: Mitochondrial phylogenetics position a new Afrotropical termite species into its own subfamily, the Engelitermitinae (Blattodea: Termitidae)
<p>While new species of termites are described every year, the description of species distant from every known termite species is rare. In this paper, we describe one such species, <em>Engelitermes zambo</em><strong> sp. n.</strong>, an African Termitidae belonging to an entirely new lineage of termites for which we create a new subfamily, Engelitermitinae<strong> subfam. n.</strong> The subfamily status of Engelitermitinae was supported by termite phylogenetic trees, including sequences from the four existing samples of <em>E. zambo </em><strong>sp. n.</strong>, which, albeit with low bootstrap supports, placed <em>Engelitermes</em> <strong>gen. n.</strong> on a long branch sister to <em>Forficulitermes</em>, the two of which formed the sister group of a clade comprising Cubitermitinae, Nasutitermitinae, Syntermitinae, and all other Termitinae. The sister relationship between <em>Engelitermes </em><strong>gen. n.</strong> and<em> Forficulitermes</em> is further supported by the similar gut structure of their workers. In contrast, the soldiers of <em>Engelitermes </em><strong>gen. n.</strong> resemble those of <em>Cephalotermes</em>. Our phylogenetic analyses, including all clades of Termitinae, call for a global taxonomic revision of Termitinae subfamily names. Finally, our study highlights that new unique termite lineages are still awaiting to be described.</p>
MS2DeepScore model trained on allGNPS positive ionmode dataset
<p>MS2DeepScore Siamese neural network, trained on cleaned and curated data that was extracted from GNPS on 01/02/2023.</p>
VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning
<p>This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on <a href="https://github.com/AaltoVision/vio-gnss-dataset">GitHub</a>.</p> <p>The upload contains two datasets (version 1.0.0):</p> <ul> <li>urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) <ul> <li>City streets</li> <li>A building is passed through on two occasions which makes the GNSS location signal unavailable at times.</li> <li>RTK Fix is acquired at times</li> </ul> </li> <li>suburban_nature (10.6 GB, ~19 minutes) <ul> <li>The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation</li> <li>The RTK solution is only Float or None most of the route.</li> </ul> </li> </ul> <p>Details on collecting the data:</p> <ul> <li>Software <ul> <li>The data was collected using <a href="https://github.com/AaltoVision/vio-gnss-recorder">this</a> open-source recorder. <ul> <li>Can be easily replayed using <a href="https://github.com/SpectacularAI/sdk-examples">SpectacularAI's SDK</a> (sdk-examples/python/oak/vio_replay.py)</li> </ul> </li> <li>Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.</li> <li><strong>Necessary files to implement SLAM are included</strong> in the dataset.</li> <li>Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.</li> </ul> </li> <li>Hardware <ul> <li>OAK-D stereo depth + color camera (Luxonis)</li> <li>C099-F9P GNSS module (u-blox)</li> <li>ANN-MB-00 high precision GNSS antenna (u-blox)</li> </ul> </li> </ul>
Survival of juvenile Florida Scrub-Jays is positively correlated with month and negatively correlated with male breeder death
<p>Juvenile survival in birds is difficult to estimate, but this vital rate can be an important consideration for management decisions. We estimated juvenile survival of cooperatively breeding Florida Scrub-Jays (<em>Aphelocoma</em> <em>coerulescens</em>) in a landscape degraded by fire suppression and fragmentation using data from marked (n = 325) and unmarked juveniles (n = 1,306) with an integrated hierarchical Bayesian model. To assess the combined analyses, we also analyzed these datasets separately, with a Cormack–Jolly–Seber model (marked) and a young model (unmarked). Our data consisted of monthly censuses of territorial family groups from Florida Scrub-Jay populations in East Central Florida collected over a 22-year period. Juvenile survival was estimated from July when young Florida Scrub-Jays begin developing independence to March when they become first-year individuals and grouped according to the habitat quality class of their natal territory that were based on shrub height (with intermediate shrub heights being optimal and short and tall shrub heights being suboptimal) and the presence of sandy openings (the preferred open having many sandy openings; closed not having enough). Parameter estimates in the combined analysis were intermediate to the separate analyses. Notable differences among the separate analyses were that suboptimal habitat survival was lower in the unmarked analysis, the unmarked analysis showed a linear effect of time not seen in the marked analysis, and there was an effect of male breeder death in the marked but not unmarked analysis. The combined data analysis provided more inference than did either dataset analyzed separately including juveniles in optimal-closed territories unexpectedly had higher survival than those in optimal-open, survival increased through time, and male breeder death had a negative effect on survival. This study suggests that optimal-closed habitat may play an important role in juvenile Florida Scrub-Jay survival perhaps by providing better cover from predators and warrants further investigation for management implications.</p>
Fulldome video about the effects of observer position on celestial observations
<p>Fulldome video that demonstrates effects of the observer position on different kinds of astronomical observations: which stars are visible, the position of the stars, the times that the stars rise and set, and analogous effects for the Sun.</p> <p>Produced in collaboration with CeSDA (Italy) and NOESIS (Greece)</p>
Phylogenetic position and evolution of Mackenziella psocoides: analysis files
<p>This repository contains the necessary files (dataset, parameters and scripts) to repeat the phylogenetic analyses and ancestral body size estimation performed in the paper "Breakaway from a globular body shape: molecular phylogeny reveals the evolutionary history of the enigmatic springtail <em>Mackenziella psocoides".</em> Result files are also provided.</p>
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
A set of positive examples consisting of short-term schedules for testing the acquisition of MiniZinc scheduling models
<p>To test the robustness of schedule model acquisition in a variety of situations, we generated 48,000 instances of schedules with variations in the following five dimensions:</p> <p>1. task description, 2. temporal constraints, 3. resource constraints, 4. the introduction or absence of noisy columns, and 5. the number of tasks and resources in a schedule.</p> <p>The file 'schedule_robustness_dimensions.pl' contains the list of constraints used to generate each table which, thus, need to be acquired.</p> <p>The dimensions are:</p> <p>1. Different ways to describe a task:<br> a. only the start time and duration columns are part of the table, the task duration is a pre-assigned input parameter,<br> b. only the start time and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> c. only the duration and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> d. all three columns are present in the table, the task duration is a pre-assigned input parameter,<br> e. the start time, duration and end time columns are all part of the table, and the task duration is calculated using a formula,<br> f. only the start time and end time columns are part of the table, and the task duration is calculated using a formula,<br> g. only the duration and end time columns are part of the table, and the task duration is calculated using a formula,<br> h. all three columns are present in the table, and the task duration is calculated using a formula.</p> <p>2. Different ways of expressing temporal constraints between task $i$ and its successor $j$<br> a. no temporal constraints at all,<br> b. $\textit{start}_i + \textit{cst} \leq \textit{start}_j$,<br> c. $\textit{start}_i + \textit{cst} \geq \textit{start}_j$,<br> d. $\textit{start}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> e. $\textit{start}_i + \textit{cst} = \textit{start}_j$,<br> f. $\textit{start}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> g. $\textit{start}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> h. $\textit{start}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{end\_time}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> i. $\textit{start}_i + \textit{cst} = \textit{end\_time}_j$,<br> j. $\textit{end\_time}_i + \textit{cst} \leq \textit{start}_j$,<br> k. $\textit{end\_time}_i + \textit{cst} \geq \textit{start}_j$,<br> l. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{end\_time}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> m. $\textit{end\_time}_i + \textit{cst} = \textit{start}_j$,<br> n. $\textit{end\_time}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> o. $\textit{end\_time}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> p. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{end\_time}_i+\textit{cst}_2\geq\textit{end\_time}_j$ ($\textit{cst}_2\neq\textit{cst}_1$),<br> q. $\textit{end\_time}_i + \textit{cst} = \textit{end\_time}_j$.<br> Note that we only generate temporal constraints that mention the start time, i.e. 2b–2m, if the start time attribute is part of the table, i.e. not in the cases 1c or 1g.</p> <p>3. Different ways of expressing resource scheduling constraints:<br> a. no scheduling constraints at all,<br> b. a DISJUNCTIVE constraint for each subset of tasks using the same resource,<br> c. a DIFFN constraint on all tasks, so that there is no overlap between tasks that will be assigned to the same resource,<br> d. a SHIFT constraint that forces the start and end times of each task to be within the same availability period, with no gap between two consecutive availability periods,<br> e. a CALENDAR constraint that forces the start and end time of each task assigned to a given resource $r$ to fall within the same period of availability of the resource $r$,<br> f. a set of DISJUNCTIVE constraints and a SHIFT constraint,<br> g. a DIFFN and a SHIFT constraint,<br> h. a set of DISJUNCTIVE constraints and a CALENDAR constraint,<br> i. a DIFFN and a CALENDAR constraint.<br> The combination of certain temporal and resource constraints may lead to infeasibility. For instance, in a temporal constraint of type 2c, the two corresponding tasks may overlap, which is incompatible with a DISJUNCTIVE constraint between these tasks, i.e. a constraint of type 3b. Therefore, we do not generate scheduling instances that mix the dimensions 2c, 2d, 2e, 2g, 2h, 2i, 2o, 2p, and 2q, with the dimensions 3b, 3c, 3f, 3g, 3h, and 3i. Note that the number of resources generated varies according to the number of tasks, as explained in Item 5.</p> <p>4. Creating noisy columns or not:<br> a. no additional noisy columns,<br> b. three extra columns with random values standing for noise.</p> <p>5. Number of tasks and resources referenced by the schedule:<br> a. 10 tasks and 2 resources,<br> b. 100 tasks and 10 resources,<br> c. 1,000 tasks and 20 resources,<br> d. 10,000 tasks and 100 resources.</p> <p><br> For each valid combination of dimensions, we generated 10 instances of schedules.</p>
FIGURE 37 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 37 Metaleg of male. Lateral view, A) Merodon megavidus, metaleg, B) Merodon avidus, metafemur and metatibia, C) Merodon moenium, metafemur and metatibia, D) Merodon femoratus, metafemur. Scale: 1 mm.
FIGURE 35 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 35 Male genitalia. Surstyle lobe, lateral view, A) Merodon nigritarsis, B) Merodon latifemoris, C) Merodon femoratoides. Scale: 0.2 mm. al–anterior lobe of surstylus, pl–posterior lobe of surstylus.
FIGURE 34 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 34 Part of mesothorax. Lateral view, A) Merodon albifrons, B) Merodon nigritarsis. Scale: 0.25 mm. cxp–posterior part of mid coxa (Marcos-García et al., 2007).
FIGURE 32 Male genitalia. A in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 32 Male genitalia. A) Merodon rutitarsis sp. n., epandrium, lateral view (hump marked with arrow), B) Merodon rutitarsis sp. n., surstyle lobe, ventral view (black setulae marked with arrow), C) Merodon avidus, epandrium, lateral view. Scale: 0.5 mm. al–anterior lobe of surstylus, c–circus, pl–posterior lobe of surstylus.
FIGURE 33 Male genitalia. A in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 33 Male genitalia. A) Merodon rutitarsis sp. n., hypandrium, lateral view, B) Merodon rutitarsis sp. n., hypandrium, ventral view, C) Merodon avidus, apical part of hypandrium (projections behind the ctenidium marked with arrow) (SEM), ventral view. Scale: A–B) 0.2 mm, C) 0.002 mm.
FIGURE 31 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 31 Head of male. A) Merodon rutitarsis sp.n., lateral view, B) Merodon rutitarsis sp. n., dorsal view, C) Merodon avidus, dorsal view. Scale: 1 mm.
FIGURE 30 Hypandrium. A in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 30 Hypandrium. A) Merodon cohurnus sp. n., lateral view, B) Merodon cohurnus sp. n., ventral view, C) Merodon quadraticus, lateral view. Scale: 0.5 mm. l–lingual, lp–lateral projection, s–lateral sclerite of aedeagus, th–subapical thorn on ventral margin.
FIGURE 25 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 25 Head of Merodon obstipus sp. n. A) male, lateral view, B) female, lateral view; C) male, dorsal view, D) female, dorsal view. Scale: 1 mm.
FIGURE 29 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 29 Male genitalia of Merodon cohurnus sp. n. A) epandrium, lateral view, B) surstyle lobe, ventral view. Scale: 0.5 mm. al–anterior lobe of surstylus, c–circus, pl–posterior lobe of surstylus.
FIGURE 13 Male genitalia. Surstyle lobe. Ventral view, A in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 13 Male genitalia. Surstyle lobe. Ventral view, A) Merodon crassifemoris, B) Merodon angustus, C) Merodon femoratus (incision between anterior and posterior lobes of surstylus marked with arrow), D) Merodon testaceus (incision between anterior and posterior lobes of surstylus marked with arrow), E) Merodon quadraticus, F) Merodon taniniensis. Scale: 0.2 mm. al–anterior lobe of surstylus, pl–posterior lobe of surstylus.
FIGURE 9 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 9 Male genitalia, aedeagus. Lateral view, A) Merodon nigritarsis, B) M. avidus. Scale 0.4 mm. s–lateral sclerite of aedeagus.
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.