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Text-fig. 5. Anacardiaceae (a–m), Burseraceae (n–q). Scale bars = 1 cm. a–f: Pentoperculum sp. a–c: USNM PAL 772360. a: Lateral view of endocarp, apex up; three germination valves visible, the central clearly displaying the bipartite nature of the valve, reflected light, palladium coated. b: Apical view displaying six locules, with two preserved germination valves at the lower left, reflected light, palladium coated. c: Basal view of the endocarp, the locules suggested by swellings; note point of attachment, micro-CT scan surface rendering. d–f: Pentoperculum sp. USNM PAL 772359, reflected light, palladium coated. d: Lateral view of a probable 6-loculed endocarp, apex up; a single intact germination valve in the center, displaying the central lineation that divides it in two. e: Apical view; two bi-partite germination valves are visible, indicated by arrows to the middle cleavage line of two of the valves. f: Basal view, the locules suggested by the undulations in the margin. g–i: Indet. Spondioideae. USNM PAL 772358, reflected light, palladium coated. g: Lateral view of multi-locular endocarp, apex up. h: Apical view showing finely punctuate surface and peripheral locule cavities. i: Basal view. j–m: Cf. Pleiogynium USNM PAL 772357. j: Lateral view of the multi-locular endocarp, apex up; note intact germination valve on left and exposed locule facing the viewer, micro-CT scan surface rendering. k: Lateral view, rotated about 30° from (j), showing three exposed locules, reflected light, palladium coated; note bipartite locule lining at center. l: Apical view of the multilocular endocarp; the locule with intact germination valve at the upper right, reflected light, palladium coated. Arrows to each locule. m: Basal view showing central point of attachment and prominent radiating ridges aligned with the locules, micro-CT scan surface rendering. n–q: Canarium, USNM PAL 772361. Scale bar = 1 cm. n: Lateral view of endocarp directly facing one germination valve flanked by two strong ridges; apex up; specimen coated in sodium nitrate and photographed by R. A. Scott. o: Lateral view facing one of the three pronounced ridges, flanked to the left and right by two germination valves; apex up. p: Apical view displaying the three strong ridges, arching over the apex and flanking three deep embayments, covered with germination valves. q: Basal view, the three ridges being less pronounced. o–q: Reflected light, palladium coated. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 5. Anacardiaceae (a–m), Burseraceae (n–q). Scale bars = 1 cm. a–f: Pentoperculum sp. a–c: USNM PAL 772360. a: Lateral view of endocarp, apex up; three germination valves visible, the central clearly displaying the bipartite nature of the valve, reflected light, palladium coated. b: Apical view displaying six locules, with two preserved germination valves at the lower left, reflected light, palladium coated. c: Basal view of the endocarp, the locules suggested by swellings; note point of attachment, micro-CT scan surface rendering. d–f: Pentoperculum sp. USNM PAL 772359, reflected light, palladium coated. d: Lateral view of a probable 6-loculed endocarp, apex up; a single intact germination valve in the center, displaying the central lineation that divides it in two. e: Apical view; two bi-partite germination valves are visible, indicated by arrows to the middle cleavage line of two of the valves. f: Basal view, the locules suggested by the undulations in the margin. g–i: Indet. Spondioideae. USNM PAL 772358, reflected light, palladium coated. g: Lateral view of multi-locular endocarp, apex up. h: Apical view showing finely punctuate surface and peripheral locule cavities. i: Basal view. j–m: Cf. Pleiogynium USNM PAL 772357. j: Lateral view of the multi-locular endocarp, apex up; note intact germination valve on left and exposed locule facing the viewer, micro-CT scan surface rendering. k: Lateral view, rotated about 30° from (j), showing three exposed locules, reflected light, palladium coated; note bipartite locule lining at center. l: Apical view of the multilocular endocarp; the locule with intact germination valve at the upper right, reflected light, palladium coated. Arrows to each locule. m: Basal view showing central point of attachment and prominent radiating ridges aligned with the locules, micro-CT scan surface rendering. n–q: Canarium, USNM PAL 772361. Scale bar = 1 cm. n: Lateral view of endocarp directly facing one germination valve flanked by two strong ridges; apex up; specimen coated in sodium nitrate and photographed by R. A. Scott. o: Lateral view facing one of the three pronounced ridges, flanked to the left and right by two germination valves; apex up. p: Apical view displaying the three strong ridges, arching over the apex and flanking three deep embayments, covered with germination valves. q: Basal view, the three ridges being less pronounced. o–q: Reflected light, palladium coated.
Figure 4 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 4 - Time table for the eEcoLiDAR project (assuming a start in March 2017). The work plan covers tasks for the NLeSC engineers, the proposed PhD student, and two associated Postdoc projects.
Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.
Figure 2 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 2 - Generic workflow for object-based image analysis (OBIA) of LiDAR point clouds and proposed ecological applications. A workbench (blue) will be developed to handle the data storage, data exploration, and interactive OBIA of the massive LiDAR point clouds. Combined with datasets of bird distributions, climate, and other remote sensing layers (orange), the LiDAR data will be applied to several ecological case studies, e.g. by using species distribution modelling of birds and insect pollinators (green).
Figure 1 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 1 - The vertical and horizontal distribution of plants influences habitat structure and 3D characteristics of vegetation for animals. Illustrated are examples for (a) forests, (b) agricultural and open landscapes, and (c) reedbeds and marshlands. The height, openness and density of vegetation as well as specific habitat features (e.g. tree species, hedges etc.) are key aspects of animal habitat and space use.
Photos de pêcheurs sur la pointe de la crèche
<p>Photos de pêcheurs prises sur la digue de la crèche, à marée basse, le 23 août 2017.</p>
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
<p>Artifacts for the paper titled <strong><em>BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection</em></strong>.</p> <p>This artifact repository contains 3 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Benchmark System</strong></td> </tr> <tr> <td>fse-ob.zip</td> <td>Online Boutique</td> </tr> <tr> <td>fse-ss.zip</td> <td>Sock Shop</td> </tr> <tr> <td>fse-tt.zip</td> <td>Train Ticket</td> </tr> </tbody> </table> <p>Each zip file contains the collected data from the corresponding microservice benchmark systems (e.g., fse-ob.zip contains metrics data collected from the Online Boutique system). </p> <p><strong><strong>Data description</strong></strong></p> <p>To collect the metrics data, we deploy three benchmark microservice systems: Online Boutique, Sock Shop, and Train Ticket, on a Kubernetes cluster consisting of one master node and five worker nodes. Then, we deploy a monitoring system to monitor and collect resource-level and service-level metrics. To generate traffic, we use the load generators supplied by these systems and tailor them to explore all services with a load of 40-50 requests per second. Initially, we operate the applications normally to gather metrics data under normal conditions. Then, we inject faults into the running services. We execute into the designated container using kubectl exec. For CPU hog and memory leak, we use stress-ng to stress the container resource. For network delay and packet loss, we use tc (traffic control) to manipulate the traffic of the container. Specifically, we inject faults into five targeted services of Sock Shop (carts, catalogue, orders, payment, and user), five targeted services of Online Boutique (adservice, cartservice, checkoutservice, currencyservice, and productcatalogue), and five targeted services of Train Ticket (ts-auth-service, ts-order-service, ts-route-service, ts-train-service, ts-travel-service). For each combination of fault type and targeted service, we repeat the operation (i.e., fault injection and metrics data collection) five times, resulting in 100 failure cases for each benchmark microservice system.</p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/baro">https://github.com/phamquiluan/baro</a>.</p>
Datasets for "A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B"
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Kinematic Process and Characteristic of Root-Induced Water Flow Using a Visualizing Point Source Infiltration Experiment
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NEW UZBEKISTAN: A TURNING POINT IN THE DEVELOPMENT OF CIVIL SOCIETY
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CONSIDERATIONS RELATED TO SOME WORDS FROM THE POINT OF VIEW OF MEANING, WEIGHT AND ARTISTRY
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DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
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Investigating the correlation between eye movement characteristics and shooting accuracy of basketball players during 2-Point shots at various exercise intensities
<p><span><span>调查<span>相关性</span></span><a name="_Hlk164072066"></a><a name="_Hlk164076538"></a><span><span>篮球运动员在不同运动强度下两分<span>球投篮</span>时的眼球运动特征和投篮命中率</span><a name="_Hlk168497143"></a><a name="_Hlk168497189"></a><a name="_Hlk168325924"></a><a name="_Hlk168497204"></a></span></span></p>
Supplementary information: Motor point stimulation activates fewer Ia-sensory nerves than peripheral nerve stimulation in human soleus muscle
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Dataset: Inflection Point Acquisition Corp. II (IPXX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Senator Beck Basin Lidar Point Cloud 2017-02-21
<p>This lidar dataset was collected using an airborne Riegl Q1560 dual laser scanner flown aboard a King Air A90 as part of the Airborne Snow Observatory (ASO), a now complete NASA-JPL project. The data acquisition took place on February 21, 2017, over the Senator Beck Basin Study Area in the San Juan Mountains, Colorado as part of the NASA SnowEx campaign (Year 1). Ackroyd et al. (under review) utilized this point cloud for a lidar intensity correction to retrieve snow cover extent and grain size in GIScience and Remote Sensing.</p>
Methanol (CH3OH), acetylene (C2H2), and propylene (C3H6) point-sources detected by the IASI infrared satellite instrument (2007-2023).
<p>This dataset includes the super-sampled IASI 0.01° × 0.01° CH3OH, C2H2, C3H6, and SO2 HRI dataset in GeoTIFF format and the catalogue of identified and categorized point-sources of CH3OH, C2H2, and C3H6 in kml format. The oversampling, wind rotation and supersampling prorcedure and codes are available in the paper of Clarisse <em>et al.</em> (2019) at <a href="https://doi.org/10.5194/amt-12-5457-2019">https://doi.org/10.5194/amt-12-5457-2019</a>.</p>
Presence points of infested trees by Xyleutes ceramica in 10 teak plantations in Thailand
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InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset
<h2>Background</h2> <p>This resource contains Indoor Lodz University of Technology Point Cloud Dataset (<strong>InLUT3D</strong>) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of <strong>321</strong> scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.</p> <h2>Train/test split</h2> <p>The datset's authors impose the following train-test split:</p> <table> <tbody> <tr> <td><strong>Split</strong></td> <td><strong>Setup range<br></strong></td> </tr> <tr> <td>train</td> <td>from <em>setup_0 </em>to <em>setup_300</em></td> </tr> <tr> <td>test</td> <td>from <em>setup_301 </em>to <em>setup_320</em></td> </tr> </tbody> </table> <p>The corresponding setups' objects are recommended for splits for the classificcation task.</p> <h2>Available categories</h2> <p>The points are divided into <strong>18 </strong>distinct categories outlined in the <em>label.yaml</em> file along with their respective codes and colors. Among categories you will find:</p> <ul> <li>ceiling,</li> <li>floor,</li> <li>wall,</li> <li>stairs,</li> <li>column,</li> <li>chair,</li> <li>sofa,</li> <li>table,</li> <li>storage,</li> <li>door,</li> <li>window,</li> <li>plant,</li> <li>dish,</li> <li>wallmounted,</li> <li>device,</li> <li>radiator,</li> <li>lighting,</li> <li>other.</li> </ul> <h2>Challenges</h2> <p>Several challenges are intrinsic to the presented dataset:</p> <ol> <li>Extremely non-uniform categories distribution across the dataset.</li> <li>Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.</li> <li>Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).</li> <li>High point density throughout the dataset.</li> </ol> <h2>Dataset structure</h2> <p>The structure of the dataset is the following:</p> <p>inlut3d.tar.gz/<br>├─ setup_0/<br>│ ├─ projection.jpg<br>│ ├─ segmentation.jpg<br>│ ├─ setup_0.pts<br>├─ setup_1/<br>│ ├─ projection.jpg<br>│ ├─ segmentation.jpg<br>│ ├─ setup_1.pts<br>...</p> <table> <tbody> <tr> <td><strong>projection.jpg</strong></td> <td>A file containing a spherical projection of a corresponding PTS file.</td> </tr> <tr> <td><strong>segmentation.jpg</strong></td> <td>A file with objects marked with unique colours.</td> </tr> <tr> <td><strong>setup_x.pts</strong></td> <td>A file with point cloud int the textual PTS format.</td> </tr> </tbody> </table> <h2>Point characteristic</h2> <p>Each PTS file contains 8 columns:</p> <table> <tbody> <tr> <td><strong>Column ID</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>X Cartesian coordinate</td> </tr> <tr> <td>2</td> <td>Y Cartesian coordinate</td> </tr> <tr> <td>3</td> <td>Z Cartesian Coordinate</td> </tr> <tr> <td>4</td> <td>Red colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>5</td> <td>Green colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>6</td> <td>Blue colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>7</td> <td>Category code</td> </tr> <tr> <td>8</td> <td>Instance ID</td> </tr> </tbody> </table> <p> </p>
Zero-Point-Energy Driven Isotopic Exchange of the [H3O]^- anion Probed by Mid-Infrared Action Spectroscopy
<p>Dataset associated with doi: 10.1021/jacs.4c05543</p>
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.