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17,039 results for “Natural”
Scale insects support natural enemies in both landscape trees and shrubs below them
<p>Scale insects are frequently abundant on urban trees. Although scales can worsen tree condition, some tree species tolerate moderate scale densities. Scales are prey for many natural enemies. Therefore, scale-infested trees may conserve natural enemies in their canopies and in nearby plants. We examined if scale-infested oaks—<em>Quercus</em> <em>phellos</em> L.—hosted more natural enemies than scale-uninfested oaks—<em>Q</em>. <em>acutissima</em> Carruth. and <em>Q</em>. <em>lyrata Walter</em> in Raleigh, NC. USA. We also tested if natural enemies were more abundant in holly shrubs (<em>Ilex</em> spp.) planted below scale-infested compared to scale-uninfested oaks. We collected natural enemies from the canopies of both tree types and from holly shrubs planted below these trees. To determine if tree type affected the abundance of natural enemies that passively dispersed to shrubs, we created hanging cup traps to collect arthropods as they fell from trees. To determine if enemies became more abundant on shrubs below scale-infested compared to scale-uninfested trees over short time scales, we collected natural enemies from holly shrubs below each tree type at three to six-day intervals. Scale-infested trees hosted more natural enemies than scale-uninfested trees and shrubs below scale-infested trees hosted more natural enemies than shrubs under scale-uninfested trees. Natural enemy abundance in hanging cup traps did not differ by tree type; however, shrubs underneath scale-infested trees accumulated more natural enemies than shrubs under scale-uninfested trees in six to nine days. Tolerating moderate pest densities in urban trees may support natural enemy communities, and thus biological control services, in shrubs below them.</p>
Fig. 1 in Natural And Artificial Scents Do Not Increase Egg Rejection Rates Of Model Brood Parasitic Eggs By American Robins (Turdus Migratorius)
Fig. 1. Experimental clutches of American Robins, each with a robin-blue (mimetic; left) or a deep-blue (non-mimetic; right) model egg 3D printed in the size, shape, and weight of
Text-fig. 12. Close-up views of silicified stem from Mhengere Fossil Wood Site 2. a: natural longitudinal fracture surface; b: natural cross section (images by MP). in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 12. Close-up views of silicified stem from Mhengere Fossil Wood Site 2. a: natural longitudinal fracture surface; b: natural cross section (images by MP).
Text-fig. 4. Lepidocarpon cone in the process of disaggregating as part of the dispersal strategy of the plants. When preserved isolated, the sporophylls are assigned to the fossil-genus Lepidostrobophyllum. Refigured from Thomas (1981). Grovesend Formation (upper Asrturian – lower Moscovian), Kilmersdon Tip, Radstock Coalfield, UK; Natural History Museum (London) specimen V.60431. in Naming Of Parts: The Use Of Fossil-Taxa In Palaeobotany
Text-fig. 4. Lepidocarpon cone in the process of disaggregating as part of the dispersal strategy of the plants. When preserved isolated, the sporophylls are assigned to the fossil-genus Lepidostrobophyllum. Refigured from Thomas (1981). Grovesend Formation (upper Asrturian – lower Moscovian), Kilmersdon Tip, Radstock Coalfield, UK; Natural History Museum (London) specimen V.60431.
Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right.
Data for the analysis of aquifer-system deformation in the Doñana Natural Space (Spain) using unsupervised cloud-computed InSAR data and wavelet analysis
<p>This are the data necessary to correlate InSAR and hydrogeological information through wavelet analysis, by WaSAR Python script (Jiménez-González & Guardiola-Albert, 2022, http://doi.org/10.5281/zenodo.6334996). The structure and information about the data is the following:</p> <p>PSBAS: Processed Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency (ESA) Sentinel-1 satellites to estimate line-of-sight (LOS) ground motion in the period 2014-2020 in the Doñana area (SW Spain). These images have been processed using the P-SBAS approach (Parallel Small BAseline Subset), which is the parallel computing solution for the SBAS processing chain at the ESA Geohazards Exploitation Platform (GEP) by CNR-IREA.</p> <p>Aggregates deformation: Former InSAR information aggregated in polygons</p> <p>Climate: rainfall and ET information in the Doñana area for the 2014-2020 period. Daily records of evapotranspiration and precipitation have been obtained from the agroclimatic stations belonging to the Junta de Andalucía (https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/).</p> <p>Piezometry: piezometry information in Doñana area for the 2014-2020 period. Groundwater level information was provided by the piezometric networks of the Guadalquivir Hydrographic Confederation and the Geological and Mining Institute of Spain.</p> <p>Pump rates: estimated pumping rate time series in the Matalascañas touristic resort</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers
<p>In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file.</p> <p>Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. </p> <p>Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers.</p> <p>Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. </p> <p>Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.</p>
Mitochondrial influence on sex-specific phenotypes within natural populations
<p>Data and scripts for the manuscript entitled "Mitochondrial influence on sex-specific phenotypes within natural populations" submitted to <em>Evolution</em><em>. </em></p>
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
Supplementary material 1 from: Gruber B, Evans D, Henle K, Bauch B, Schmeller D, Dziock F, Henry P, Lengyel S, Margules C, Dormann C (2012) "Mind the gap!" – How well does Natura 2000 cover species of European interest? Nature Conservation 3: 45-62. https://doi.org/10.3897/natureconservation.3.3732
The table shows the 54 gap species ordered by species group. Please note: The comment column is based on expert opinion of the European Topic Centre, which has the latest version of the Natura2000 data base and also knowledge on confidential sites, which are deleted from the public version of the data base to protect rare species.
Supplementary material 1 from: Schmeller D, Maier A, Evans D, Henle K (2012) National responsibilities for conserving habitats – a freely scalable method. Nature Conservation 3: 21-44. https://doi.org/10.3897/natureconservation.3.3710
The annex does provide a more detailed comparison of the different biogeographic maps. It further gives a list of all forest habitats given in the EU Habitats Direction
Supplementary material 1 from: Schmeller D, Henle K, Loyau A, Besnard A, Henry P (2012) Bird-monitoring in Europe – a first overview of practices, motivations and aims. Nature Conservation 2: 41-57. https://doi.org/10.3897/natureconservation.2.3644
The questionnaire was designed to assess how biodiversity monitoring schemes were carried out and what the motivation was to launch that scheme.
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Changes in behavioural responses to infrastructure affect local and regional connectivity – a simulation study on pond breeding amphibians. Nature Conservation 5: 13-28. https://doi.org/10.3897/natureconservation.5.4611
Full model description following the ODD-template suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4611.app). File format: Adobe PDF document (pdf).:
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Spatial Amphibian Impact Assessment – a management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation 5: 29-52. https://doi.org/10.3897/natureconservation.5.4612
Full model description following the protocol suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4612.app). File format: Adobe PDF document (pdf).:
raw data nature 533:47 Fig.4
<p>Raw data of nature paper Vol. 533:47-51 (2016) Fig.4b is provided. The dataset contains CCD camera images of 12 measurements:</p> <p>Source 1, Th3+ (1021_image54, 2014) 500 images, 4 s exposure time per image</p> <p>Source 1, U3+ (1021_image56, 2014) 500 images, 4 s exposure time per image</p> <p>Source 1, Th2+ (1019_image20, 2014) 500 images, 4 s exposure time per image</p> <p>Source1, U2+ (1019_image21, 2014) 500 images, 4 s exposure time per image</p> <p>Source2, Th3+ (0727_image13, 2015) 500 images, 4 s exposure time per image</p> <p>Source2, U3+ (0727_image14, 2015) 500 images, 4 s exposure time per image</p> <p>Source2, Th2+ (0727_image7, 2015) 500 images, 4 s exposure time per image</p> <p>Source2, U2+ (0727_image8, 2015) 500 images, 4 s exposure time per image</p> <p>Source3, Th3+ (1214_image1, 2015) 2000 images, 1 s exposure time per image</p> <p>Source3, U3+ (1214_image5, 2015) 2000 images, 1 s exposure time per image</p> <p>Source3, Th2+ (1214_image2, 2015) 2000 images, 1 s exposure time per image</p> <p>Source3, U2+ (1214_image4, 2015) 2000 images, 1 s exposure time per image</p> <p>A matlab file for processing of all images is contained in the file folder. Further, each image file folder contains a matlab file used for image post-processing.</p>
Figure 2: Optimization in natural ants collective behavior: foraging and clustering (from [8])-Self-organization and social insects algorithms
<p>On figure 2, two examples of self-organization in natural ants are presented.<br> On the left side, the well-known Deneubourg experiment consists to highlight<br> with a very simple device the ant foraging problem. The ant objectives is<br> to find the optimal way from nest to food source, using pheromone trail deposition.<br> On the right side, cemetery clustering formation are shown at 4<br> successive times: ants form piles of corpses to clean their nests. Each of them<br> has elementary actions, unknowing the whole situation, but dealing only with<br> local information. There is no supervisor to lead the piles formation which<br> emerges from ant interactions.</p>
Figure 6: Erythema values before and after 10 minutes from application 57 -STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES
<p>The values of the erythema are relatively low, which indicates that the<br> product is compatible with the skin and has no sensitizing potential (¯gure6).<br> Having a complex composition of ingredients with varying degrees of un-<br> saturation, with the major organic structure, the product is subjected to de-<br> structive processes at the impact of UV radiation and atmospheric oxygen.<br> As a result this organic substances undergo structural changes, embodied in<br> the initial color change, division of chain, forming oxidizing action groups and<br> oxygen free radicals that continue the destructive process. Because of these<br> features, the product was subjected to accelerated tests of thermal and photo<br> destruction. As a result of these tests, the product's characteristics have not<br> changed signi¯cantly.</p>
Figure 2: Variation of the shear stress depending on the speed rate 54-STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES
<p>The evaluation of photodegradation was carried out through the variation<br> of photoprotective factor of the studied product kept 60 minutes under UV<br> radiation at 65±C. The tests revealed the preservation or even a slight increase<br> in the SPF. The use of screen substances in the formulation of photoprotective<br> products results in undesirable sensory characteristics: visible white ¯lm on the<br> skin surface, unpleasant sensation, dry or oily skin after application.</p>
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.