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45 results for “feature importance”
Summary of the most important features for selected ABs
<p>These data summarizes the relevant findings and the identified limitations (in terms of "Category", "Technology", "Properties", "Limitation", and "Applicability to railway"), coming from the overview of different Alternative Bearers (ABs), carried out in deliverable D21 (AB4Rail project, www.ab4rail.eu).<br> The results have provided an overview of several technologies, each of them showing specific characteristics. The heterogeneous nature of different ABs allows to provide a plethora of available communication technologies to be potentially used by the Adaptable Communication System (ACS) for different railway scenarios. All the selected ABs provide the IP interconnection feature since they are Integrated within OSI reference model.<br> In this way, it collects the planned objectives of deliverable D2.1, expressed as a technological overview of selected ABs, as possible candidates coexisting with Traditional Bearers (TBs) for supporting railway applications.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Towards understanding the importance of time-series features in automated algorithm performance prediction
<p><strong>merged_feature_importance.csv</strong> - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.</p> <p><strong>Catch22.csv</strong> - Catch22 features (raw time-series)</p> <p><strong>Catch22Log.csv</strong> - Catch22 features (log time-series)</p> <p><strong>Catch22Diff.csv</strong> - Catch22 features (differenced time-series)</p> <p><strong>TSFresh.csv</strong> - TSFresh features (raw time-series)</p> <p><strong>TSFreshLog.csv</strong> - TSFresh features (log time-series)</p> <p><strong>TSFreshDiff.csv</strong> - TSFresh features (differenced time-series)</p> <p><strong>mape.csv</strong> - sMAPE performance for all forecasting algoirthms</p>
Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models
<p>This contains:</p> <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> <p>For the paper Using <em>feature importance as exploratory data analysis tool on earth system models.</em></p>
Data from: Bee-mediated pollen transport across five urban landscape features: Buildings are important barriers
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Data from: The importance of using topographic features to predict climate-resilient habitat for migratory forest landbirds: an example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler
<p>Maintaining a functionally-connected network of high-quality habitat is one of the most effective responses to biodiversity loss. However, the spatial distribution of suitable habitat may shift over time in response to climate change. Taxa such as migratory forest landbirds are already undergoing climate-driven range shifts. Therefore, patches of climate-resilient habitat (also known as "climate refugia") are especially valuable from a conservation perspective. Here, we performed maximum entropy (Maxent) species distribution modeling to predict suitable and potentially climate-resilient habitat in Nova Scotia, Canada, for three migratory forest landbirds: Rusty Blackbird (Euphagus carolinus), Olive-sided Flycatcher (Contopus cooperi), and Canada Warbler (Cardellina canadensis). We used a reverse stepwise elimination technique to identify covariates that influence habitat suitability for the target species at broad scales, including abiotic (topographic control of moisture and nutrient accumulation) and biotic (forest characteristics) covariates. As topography should be relatively unaffected by a changing climate and helps regulate the structure and composition of forest habitat, we posit that the inclusion of appropriate topographic features may support the identification of climate-resilient habitat. Of all covariates, Depth to water table was the most important predictor of relative habitat suitability for the Rusty Blackbird and Canada Warbler, with both species showing a strong association with wet areas. Mean canopy height was the most important predictor for the Olive-sided Flycatcher, whereby the species was associated with taller trees. Our models, which comprise the finest scale species distribution models available for these species in this region, further indicated that, for all species, habitat (1) remains relatively abundant and well distributed in Nova Scotia and (2) is often located in wet lowlands (a climate-resilient topographic landform). These findings suggest that opportunities remain to conserve breeding habitat for these species despite changing temperature and precipitation regimes.</p>
FIGURE 4. A–B. Ascodipteron species A in Investigation of taxonomically important morphological features of endoparasitic bat flies of the subfamily Ascodipterinae (Diptera: Streblidae) by scanning electron microscopy
FIGURE 4. A–B. Ascodipteron species A (ex. R. paradoxalophus), Tuyen Province, Vietnam. A. Overview of microvillilike organelles on subdermal surface of neosome. B. Enlargement of A. C– D. Ascodipteron emballonurae (ex. H. pomona), Quang Nam, Vietnam. C. Overview of microvillilike organelles on subdermal surface of neosome. D. Enlargement of C. E–F. Ascodipteron species A (ex. R. paradoxalophus), Tuyen Province, Vietnam. E. Anus, cerci, and genital orifice. F. Cercus, enlargement. Scale in microns.
FIGURE 5. A–D. Ascodipteron species A in Investigation of taxonomically important morphological features of endoparasitic bat flies of the subfamily Ascodipterinae (Diptera: Streblidae) by scanning electron microscopy
FIGURE 5. A–D. Ascodipteron species A (ex. H. pomona), larva (prepupa), Quang Nam Province, Vietnam. A. Ventroposterior aspect. B. Ventral spiracle (enlargement). C. Dorsoposterior aspect. D. Dorsal and ventral spiracles (enlargement). Abbreviations: ao, anal orifice; dsp, dorsal spiracle; vsp, ventral spiracle. Scale in microns.
FIGURE 3. A–B in Investigation of taxonomically important morphological features of endoparasitic bat flies of the subfamily Ascodipterinae (Diptera: Streblidae) by scanning electron microscopy
FIGURE 3. A–B. Ascodipteron emballonurae (ex. H. pomona), Quang Nam Province, Vietnam. A. Genital aperture, lateral view, arrows indicate spiracles. B. Genital aperture, lateral view, enlargement. C–D. Ascodipteron species A (ex. R. paradoxalophus), Tuyen Province, Vietnam. C. Genital aperture (arrow), lateral view. D. Genital aperture, lateral view, enlargement. E–F. Ascodipteron emballonurae (ex. H. pomona), spiracles, Quang Nam, Vietnam. E. Dorsal view. F. Lateral view. Abbreviations: sp5, sp6, sp7, respective numbered terminal spiracles. Scale in microns.
FIGURE 2. A–E in Investigation of taxonomically important morphological features of endoparasitic bat flies of the subfamily Ascodipterinae (Diptera: Streblidae) by scanning electron microscopy
FIGURE 2. A–E. Undescribed genus of Ascodipterinae (ex. R. affinis), Tuyen Province, Vietnam (A–B, D–E), and (ex. R. macrotis) Guangxi Province, China (C). A. Head and thorax, oblique lateral view. B. Labial theca, dorsal view. C. Labial theca, dorsal view (image by light microscopy). D. Labial theca, anterior view. E. Labial theca, anteroventral view. Abbreviation: lg, labial gutter. Scale in microns.
FIGURE 1. A–C. Ascodipteron species A in Investigation of taxonomically important morphological features of endoparasitic bat flies of the subfamily Ascodipterinae (Diptera: Streblidae) by scanning electron microscopy
FIGURE 1. A–C. Ascodipteron species A (ex. R. paradoxalophus), Tuyen Province, Vietnam. A. Head and thorax, dorsal view. Uppermost arrow indicates striations of underlying muscles of dorsal cheliceral blades. B. Head, lateral view. Arrows indicate striations of underlying muscles of dorsal and ventral cheliceral blades. C. Head, anterior view. Arrow indicates striations of ventral cheliceral blades. Abbreviations: ant, antenna; fr, frons; g, gena; lt, labial theca; lv, lateral vertex; sc, scutum. Scale in microns.
Data from: The importance of using topographic features to predict climate-resilient habitat for migratory forest landbirds: an example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler
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IMPORTANT FEATURES OF THE FORMATION OF ECOLOGICAL CULTURE IN STUDENTS
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Figure 8 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 8 - Chrysopodes (Chrysopodes) divisus, third instar A Thorax, dorsal B Abdominal segments A1 to A5, dorsal C Abdominal segments A6 to A10, dorsal. Abbreviations: A4, A6, A8, A10 abdominal segments A2R1 double row of submedian setae (SMS) on anterior fold of second abdominal segment A2R2 double/triple row of SMS on posterior fold of second abdominal segment A3R1 double row of SMS on anterior fold of third abdominal segment A3R2 double/triple row of SMS on posterior fold of third abdominal segment sp spiracle (on anterior subsegment of mesothorax) T1LT prothoracic lateral tubercle T1Sc1 first primary prothoracic sclerite T2Sc3 third primary mesothoracic sclerite T3R1 row of long, sturdy, thorny setae on raised posterior fold of metathorax.
Figure 6 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 6 - Chrysopodes (Chrysopodes) divisus, second instar A Head, dorsal B Head, lateral C Head and thorax, dorsal D Abdominal segments A1 to A5, dorsal E Habitus, lateral F Abdominal segments A6 to A10, dorsal.
Figure 7 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 7 - Chrysopodes (Chrysopodes) divisus, third instar A Habitus, lateral B Habitus, ventral C Head, ventral D Head, lateral. Abbreviations: ge genal marking st stemmata T3R1 row of long, sturdy, thorny setae on raised posterior fold of metathorax.
Figure 25 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 25 - Chrysopodes (Chrysopodes) spinellus, third instar A Habitus, lateral B Habitus, ventral C Head, ventral D Head, lateral. Abbreviations: ge genal marking st stemmata T3R1 row of long, sturdy, thorny setae on raised posterior fold of metathorax.
Figure 4 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 4 - Head and thorax, dorsal, third instar A Chrysopodes (Chrysopodes) divisus B Chrysopodes (Chrysopodes) fumosus C Chrysopodes (Chrysopodes) geayi D Chrysopodes (Chrysopodes) lineafrons E Chrysopodes (Chrysopodes) spinellus. Abbreviations: epi-l epicranial marking, lateral arm epi-m epicranial marking, mesal arm fr frontal marking post postfrontal marking T1Sc1, T1Sc2 first and second primary prothoracic sclerites.
Figure 3 from: Tauber C, Silva P, Albuquerque G, Tauber M (2013) Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys. ZooKeys 262: 39-92. https://doi.org/10.3897/zookeys.262.4119
Figure 3 - Head and thorax, dorsal, second instar A Chrysopodes (Chrysopodes) divisus B Chrysopodes (Chrysopodes) fumosus C Chrysopodes (Chrysopodes) geayi D Chrysopodes (Chrysopodes) lineafrons E Chrysopodes (Chrysopodes) spinellus. Abbreviations: epi-l epicranial marking, lateral arm epi-m epicranial marking, mesal arm fr frontal marking post postfrontal marking T1Sc1, T1Sc2 first and second primary prothoracic sclerites T2Sc3 third primary mesothoracic sclerite T3R1 metathoracic row of robust, thorny setae.
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.