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Figure 4 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 4 SEM photos of larval H.longicornis from a colony started with specimens from Queensland, Australia (Accession # RML58949). a dorsal full body b dorsal capitulum c ventral full body d ventral capitulum.
Figure 3 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 3 SEM photos of nymphal H.longicornis from a colony started with specimens from Jeju-teukbyeoljachido, Republic of Korea (Accession # RML48803). a dorsal full body b dorsal capitulum c ventral full body d ventral capitulum.
Figure 1 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 1 SEM photos of female H.longicornis from a colony started with specimens from Jeju-teukbyeoljachido, Republic of Korea (Accession # RML48803) a dorsal full body b dorsal capitulum c ventral full body d ventral capitulum.
Figure 1 from: Abram PK, Talamas EJ, Acheampong S, Mason PG, Gariepy TD (2019) First detection of the samurai wasp, Trissolcus japonicus (Ashmead) (Hymenoptera, Scelionidae), in Canada. Journal of Hymenoptera Research 68: 29-36. https://doi.org/10.3897/jhr.68.32203
Figure 1 - Trissolcus japonicus female (FSCA 00033107) from Chilliwack, British Columbia: A head, anterior view, cs: clypeal setae B head and mesosoma, anterolateral view, ats: postacetabular sulcus, eps: episternal foveae, mpit: mesopleural pit C head, mesosoma, metasoma, dorsal view. Scale bars in millimeters.
Data for paper "Using synthetic semiochemicals to train canines to detect bark beetle-infested trees" in Ann For Sci
<p><strong>ESM_0</strong> Photo. Entrainment of semiochemicals with Porapak <sup>® </sup>Q plug from cylinders used in stimuli delivery in dog training platform. (DOCX)</p> <p><strong>ESM_1</strong> Fig. Educational scent platform. (PDF)<br> <strong>ESM_2 </strong>Fig. Training platform stimuli layout and decline in response to no<br> target scent. (PDF)<br> <strong>ESM_3 </strong>Table. Evaluation of the dog detection performance as number of<br> indications with decreasing amounts of scent molecules over time. (PDF)</p> <p><strong>ESM_4_V1</strong> Video. Educational scent platform in operation. (AVI)<br> <strong>ESM_4_V2</strong> Video.<em> </em>Placement of cotton scent pad and the location of the scent by dog on a pine (a non-host tree of the beetle). (AVI)<br> <strong>ESM_4_V3</strong> Video. The search, GPS tracking, and location of natural attacks.<em> </em>(AVI)<br> <strong>ESM_4_V4</strong> Video. The search, location of two adjacent natural attacks, and rewarding. (AVI)</p> <p>The dog detection allows timely removal by sanitation logging of first beetle-attacked trees before offspring emergence, preventing local beetle increases. Detection dogs rapidly learned responding to synthetic bark beetle pheromone components, with known chemical titres, allowing search training during winter in laboratory and field. Dogs trained on synthetics detected naturally attacked trees in summer at a distance of >100 m.</p>
DeepFruits: A Fruit Detection System Using Deep Neural Networks
<p>This is the dataset associated with MDPI Sensors paper entitled "DeepFruits: A Fruit Detection System Using Deep Neural Networks".</p> <p>This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.</p>
Raw metagenomic data from Early Detection Rapid Response samples collected in Alaska in 2017
<p>In response to the threat of introductions of non-native forest insects, the Early Detection and Rapid Response (EDRR) program in Alaska monitors for arrivals of non-native insects, an effort that is limited by the time required to process samples using morphological methods. We compared conventional methods of processing EDRR traps with metabarcoding methods for processing the same samples. </p> <p>We deployed Lindgren funnel traps at three points of entry in Alaska using standard EDRR methods and the trap samples were later processed using routine sorting and identification based on morphology. The samples were then processed using High Throughput Sequencing (HTS) metabarcoding methods. In three samples bycatch was included and in three samples non-native species were added.</p> <p>This dataset includes all of the raw FASTQ files obtained from HTS sequencing. </p> <p>Complete specimen and occurrence data are available via an Arctos (<a href="https://arctosdb.org/">https://arctosdb.org/</a>) archive at <a href="https://arctos.database.museum/archive/2017_edrr_ngs_test_records">https://arctos.database.museum/archive/2017_edrr_ngs_test_records</a>. Sequence data have been deposited in in the NCBI Sequence Read Archive under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/PRJNA542936">PRJNA542936</a>. Complete sample data are provided in the file 2017_EDRR_STDP_sample_data.csv.</p>
Portable Roadway High Water Detection System for Driver Safety and Infrastructure Assessment
<p>Corresponding data set for Tran-SET Project No. 18SATSA05. Abstract of the final report is stated below for reference:</p> <p>"The state of Texas, local governments, and community groups are adopting several initiatives to address this problem. The main goal of this project is to develop an approach to enhance prediction and detection of roadway flooding and help efforts to prevent driving into flood roadways. The motivation of this work was initiated by the alarming number of vehicle related flood fatalities in Texas, mostly at low-water crossings. Texas leads the nation in the number of flood fatalities and about 77% of flood fatalities in Texas are motor vehicle-related. This percentage is much higher than the national percentage reported in several previous studies. Motor vehicle-related flood fatalities in Texas are generally correlated with monthly climate, topography, and population density. For example, about half of the fatalities caused by Hurricane Harvey were transportation-related. The presence of numerous low water crossings throughout Texas contribute to the higher recurrence rates of floods that pose a danger to vehicles. The research team has been challenged to identify effective measures to improve safety at low-water crossings. Therefore, the research team developed an approach that combines data analysis, modeling, and high water detection and communication to improve safety at low-water crossings. Researchers conducted the following tasks: literature review, vehicle-related flood fatality analysis, review of transportation fatalities cause by Hurricane Harvey, and proposed the design of a warning systems at low-water crossings that can detect and predict water depth and velocity at these locations. It was suggested that flood detection and warning can be significantly augment by using sensing technologies that can detect not only the water depth but also the water velocity in combination with a physically-based hydrologic modeling system that can provide forecasts of water depth and velocity at locations of interest. The results of the model and information of the sensors can be used to provide timely and adequate warning to approaching motorists that flooded roadway conditions exist further up the road."</p>
Karst Sinkhole Detecting and Mapping Using Airborne LiDAR
<p>Corresponding data set for Tran-SET Project No. 18GTUNM01. Abstract of the final report is stated below for reference:</p> <p>"The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training.</p> <p>The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise."</p>
Experimental and model-based investigation of the effect of the free- surface flow regime on the detection threshold of warm water inflow
<p>Experimental and numerical injections of warm water into a free surface flow. Different flow regimes were tested and detection thresholds were determined.</p> <p>Experiments were done in an outdoor flume, in June 2016. They were monitored by Fiber-Optic Distributed Temperature Sensing with a double-ended configuration and 20s time resolution. Two flume flow rates Q were tested. For each, a few injection flow rates were done, leading to a total of 10 experiments characterized by an injection ratio R. Only calibrated data are uploaded.</p> <p>Simulations were carried out on HEC-RAS 5.0, a 1D software developed by the US Army Corps of Engineers, and based on the experimental flume geometry. A total of 90 simulations were tested with different flow rates, injection percentage and thermal contrast ∆T between flowing water and injected water.</p> <p>Supplementary data can be found to understand the flume geometry, hydraulic characteristics (Reynolds, Peclet and Froude numbers) and exchanges with the atmosphere for both simulations and experiments.</p>
Supplementary material 4 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Sequences of amplicon sequence variants in FASTA format.
Figure 3 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 3 Phylogenetic tree of HTS sequences generated using qiime phylogeny align-to-tree-mafft-fasttree, accepting default parameters. The graphic was rendered using the Interactive Tree Of Life (Letunic and Bork 2019). An interactive version of this tree is available at https://itol.embl.de/tree/1641591522462921555104654. Colors hightlight major taxonomic groups.
Supplementary material 3 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Amplicon sequence variant table in standard text format
Figure 2 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 2 Comparison of identifications based on morphological and HTS methods. Columns are samples and rows are identifications. White: non-detections. Blue: morphological detections. Red: HTS detections. Purple: detections by both methods.
Supplementary material 2 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
RTL Genomics Data Analysis Methodology
Figures 9-12 in Ultrastructural detection of lipids in the cephalic salivary glands of Apis mellifera and Scaptotrigona postica (Hymenoptera: Apidae) workers
Figures 9-12. Detection of lipid by imidazole osmium (IO) in cells from the cephalic salivary glands of Scaptotrigona postica. (9) Gland cell from newly emerged (NE) and (10) worker working in the brood comb area (CA) showing vesicles containing heterogeneous material (vh) in the apical region. Mitochondria (m) are abundant and variable in electron density and shape. Note the droplets (arrow), smooth endoplasmic reticulum cisterns (rel) and some mitochondria showing IO-positive lipid content inside (ml). (11-12) Glandular cells from forager (FO) workers. (11) Cubic gland cells showing large lipid droplet (lip). (12) Gland cells flattened with drop-shaped lipid vesicles (lip) and heterogeneous vesicles (vh). Note the large alveolar lumen (l) accumulating secretion (s). (c) Cuticle, (m) mitochondrion, (n) nucleus, (s) secretion. Scale bars: 9 = 1 µm, 10, 12 = 2 µm, 11 = 3 µm.
Figure 2 in Are recaptures of banded birds efficient at detecting altitudinal migrations in the Atlantic Forest?
Figure 2. Frequency of altitudinal movements by season (Rainy and Dry) and displacement direction (lower and higher).
Figure 1 in Are recaptures of banded birds efficient at detecting altitudinal migrations in the Atlantic Forest?
Figure 1. Geographic location of the Curucutu region. Altitudinal schematic of Núcleo Curucutu, showing the study locations A. Cota 30; B. Cota 200; C. Cota 400 and D. Cota 700, highlighted by the white rectangle. Model adapted from Malagoli (2013).
Figure 1 in Ecological aspects and molecular detection of Leishmania DNA (Kinetoplastida: Trypanosomatidae) in phlebotomine sand flies (Diptera: Psychodidae) from a rural settlement in the Eastern Amazon, Brazil
Figure 1 Map of the study area and sand flies collection points, Perimetral Norte Rural Seattlement, Pedra Branca Municipality, Amapá State, Brazil.
Fig. 2 in Standard method for detecting Bombyx mori nucleopolyhedrovirus disease-resistant silkworm varieties
Fig. 2. Mulberry leaf size standards of different larval stages for the IIM. (A) Leaf size for second-instar larvae. (B) Leaf size for third-instar larvae. (C) Leaf size for fourth-instar larvae. (D) Leaf size for fifth-instar larvae.
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.