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Fig. 10 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 10. Kotatea aurantiaca gen. et comb. nov. (NIWA 54766), SEMs of sclerites. A. Base interior. B. Polyps (in situ).
Fig. 18 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 18. Selected preserved specimens of Kotatea lobata gen. et sp. nov. Note that NIWA 142995, NIWA 101740 and NIWA 108960 contain additional fragments that are not depicted. * = holotype.
Fig. 9 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 9. Kotatea aurantiaca gen. et comb. nov. (NIWA 54766), SEMs of sclerites. A. Collaret and points. B. Distal points. C. Tentacles. D. Polyp neck. E. Polyp mound. F. Lobe surface. G. Lobe interior. H. Base surface.
Fig. 6 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 6. Kotatea amicispongia gen. et sp. nov., holotype (NIWA 156312), SEMs of sclerites. A. Base surface. B. Base interior. C. Polyps (in situ).
Fig. 17 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 17. Kotatea kurakootingotingo gen. et sp. nov., holotype (NIWA 101538), SEMs of sclerites. A. Base surface. B. Base interior. C. Polyps (in situ).
Fig. 8 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 8. Selected preserved specimens of Kotatea aurantiaca gen. et comb. nov. Note that most specimen lots include small additional fragments that are not depicted and NIWA 101181 comprises a total of 67 similar colonies, all of which were examined.
Fig. 5 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 5. Kotatea amicispongia gen. et sp. nov., holotype (NIWA 156312), SEMs of sclerites. A. Collaret and points. B. Distal points. C. Tentacles. D. Polyp neck. E. Polyp mound. F. Lobe surface. G. Lobe interior.
Fig. 4 in Dead man's fingers point to new taxa: two new genera of New Zealand soft corals (Anthozoa, Octocorallia) and a revision of Alcyonium aurantiacum Quoy & Gaimard, 1833
Fig. 4. In situ photographs of species of Kotatea gen. nov. usually possessing robust lobes. A. K. kurakootingotingo gen. et sp. nov., paratype (MAGNT C013955), Manawatāwhi/Three Kings Islands, photo by Coral Reef Research Foundation. B. K. kurakootingotingo gen. et sp. nov., holotype (NIWA 101538) with polyps retracted, Manawatāwhi/Three Kings Islands, photo by NIWA. C. K. lobata gen. et sp. nov., holotype (NIWA 101313), Houhora Harbour, photo by NIWA. D. K. lobata gen. et sp. nov. (paratype NIWA 101268 / MAGNT C013956), Mokohinau Islands, photo by Coral Reef Research Foundation. E–F. K. lobata gen. et sp. nov. exposed at low tide underneath rock overhangs, large colony (paratype, AK 120774) at Muriwai Beach (E) and small colony (uncollected specimen) at Whatipu Beach (F), photos by Wilma Blom, Auckland War Memorial Museum. Scale bars = ~3 cm.
Ethylene (C2H4) point-sources detected by the IASI infrared satellite instrument (2008-2020).
<p>This dataset includes the super-sampled IASI 0.01° × 0.01° C<sub>2</sub>H<sub>4</sub> HRI dataset in GeoTIFF format and the catalogue of the identified and categorized C<sub>2</sub>H<sub>4</sub> point-sources in kml format (C2H4_HRI_pointsources.zip). It also includes the super-sampled IASI C<sub>2</sub>H<sub>4</sub> total columns used to calculate the emission fluxes from the analyzed point-sources (C2H4_column_pointsources.zip) and the source data needed to reproduce the figures (C2H4_SourceData.zip). The code to calculate the C<sub>2</sub>H<sub>4</sub> HRI from IASI spectra and to retrieve the C<sub>2</sub>H<sub>4</sub> total columns is provided (C2H4_codes.zip), along with the artificial neural network used for the retrievals, instructions and example data. The codes of the oversampling, wind rotation and supersampling 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>
3DHD CityScenes: High-Definition Maps in High-Density Point Clouds
<p><strong>Overview</strong></p> <p>3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.</p> <p>Our corresponding paper (published at ITSC 2022) is available <a href="https://www.researchgate.net/publication/364309881_3DHD_CityScenes_High-Definition_Maps_in_High-Density_Point_Clouds">here</a>.<br> Further, we have applied 3DHD CityScenes to map deviation detection <a href="https://www.researchgate.net/publication/368983255_DNN-Based_Map_Deviation_Detection_in_LiDAR_Point_Clouds">here</a>. </p> <p>Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:</p> <ul> <li>Python tools to read, generate, and visualize the dataset,</li> <li>3DHDNet deep learning pipeline (training, inference, evaluation) for<br> map deviation detection and 3D object detection.</li> </ul> <p>The DevKit is available here:</p> <p><a href="https://github.com/volkswagen/3DHD_devkit">https://github.com/volkswagen/3DHD_devkit</a>.</p> <p>The dataset and DevKit have been created by <a href="https://de.linkedin.com/in/christopher-plachetka-42b325115">Christopher Plachetka</a> as project lead during his PhD period at Volkswagen Group, Germany.</p> <p>When using our dataset, you are welcome to cite:</p> <pre><code>@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}</code></pre> <p><strong>Acknowledgements </strong></p> <p>We thank the following interns for their exceptional contributions to our work.</p> <ul> <li><a href="https://www.linkedin.com/in/sertolli/">Benjamin Sertolli</a>: Major contributions to our DevKit during his master thesis</li> <li>Niels Maier: Measurement campaign for data collection and data preparation</li> </ul> <p>The European large-scale project Hi-Drive (<a href="http://www.Hi-Drive.eu">www.Hi-Drive.eu</a>) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.</p> <p><strong>The Dataset</strong></p> <p>After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.</p> <p>1. Dataset</p> <p>This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.</p> <p>During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.</p> <p>To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.</p> <pre><code class="language-python">import json json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)</code></pre> <p>2. HD_Map</p> <p>Map items are stored as lists of items in JSON format. In particular, we provide:</p> <ul> <li>traffic signs,</li> <li>traffic lights,</li> <li>pole-like objects,</li> <li>construction site locations,</li> <li>construction site obstacles (point-like such as cones, and line-like such as fences),</li> <li>line-shaped markings (solid, dashed, etc.),</li> <li>polygon-shaped markings (arrows, stop lines, symbols, etc.),</li> <li>lanes (ordinary and temporary),</li> <li>relations between elements (only for construction sites, e.g., sign to lane association).</li> </ul> <p>3. HD_Map_MetaData</p> <p>Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.</p> <p>Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.</p> <p>4. HD_PointCloud_Tiles</p> <p>The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.</p> <ul> <li>x-coordinates: 4 byte integer</li> <li>y-coordinates: 4 byte integer</li> <li>z-coordinates: 4 byte integer</li> <li>intensity of reflected beams: 2 byte unsigned integer</li> <li>ground classification flag: 1 byte unsigned integer</li> </ul> <p>After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.</p> <pre><code class="language-python">import numpy as np import pptk file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['<i4', '<i4', '<i4', '<u2', 'u1'] with open(file_path, "r") as fid: num_points = np.fromfile(fid, count=1, dtype='<u4')[0] # print(num_points) # Init for k, dtype in zip(key_list, type_list): pc_dict[k] = np.zeros([num_points], dtype=dtype) # Read all arrays for k, t in zip(key_list, type_list): pc_dict[k] = np.fromfile(fid, count=num_points, dtype=t) # Unnorm pc_dict['x'] = (pc_dict['x'] / 1000) + 500000 pc_dict['y'] = (pc_dict['y'] / 1000) + 5000000 pc_dict['z'] = (pc_dict['z'] / 1000) pc_dict['intensity'] = pc_dict['intensity'] / 2**16 pc_dict['is_ground'] = pc_dict['is_ground'].astype(np.bool_) fid.close() print(pc_dict) # Visualization # Normalize (due to large UTM values) x_utm = pc_dict['x'] - np.mean(pc_dict['x']) y_utm = pc_dict['y'] - np.mean(pc_dict['y']) z_utm = pc_dict['z'] xyz = np.column_stack((x_utm, y_utm, z_utm)) viewer = pptk.viewer(xyz) viewer.attributes(pc_dict['intensity']) viewer.set(point_size=0.03)</code></pre> <p>5. Trajectories</p> <p>We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.</p> <p>These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.</p> <ul> <li>OP1 – OP5 cover the majority of the map with 5 trajectories.</li> <li>RH1 – RH10 cover the majority of the map with 10 trajectories.</li> </ul> <p>Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here. </p>
Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology
<p class="MsoNormal"><span>Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear.<span> </span>In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.<span> </span></span></p>
"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)
<p>This video is the tenth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022.</p> <p>"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)</p> <p>Bio: Al Edwards has a background in fundamental immunology combined with expertise in biochemical engineering, he is an interdisciplinary researcher focussed on solving current and future healthcare challenges using an engineering science approach that combines a range of fields from biology, biochemistry, chemistry and physics. He works at the interface between academic technology discovery and industrial development and have experience of both fundamental research and the commercialisation of new technology. The two main challenges he currently works on are the development of affordable microfluidics for clinical diagnostics and microbiology, and the engineering science of complex biologic therapeutics such as vaccines. Alexander's research is funded from a wide range of sources, including NIHR , EPSRC, SBRI Healthcare, the Wellcome Trust, Innovate UK and industry</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/21a78Vql8b0</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Lyso over 980 time points during mitosis (ER channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Lysosomes over 980 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Lamp1-Halo during mitosis (only ER channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Ultrafst whole cell imaging data of SiT-Golgi vesicles over 10,000 time points
<p>This dataset is the raw lattice light-sheet microscopy data of SiT-Golgi vesicles over 10,000 consecutive time points, which was used to demonstrate TiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Lyso over 980 time points during mitosis (H2B channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Lysosomes over 980 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Lamp1-Halo during mitosis (only H2B channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Mito over 1000 time points during mitosis (Mito channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Mitochondia over 1000 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Mito-Halo during mitosis (only Mito channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Mito over 1000 time points during mitosis (ER channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Mitochondria over 1000 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Mito-Halo during mitosis (only ER channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Lyso over 980 time points during mitosis (Lyso channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Lysosomes over 980 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Lamp1-Halo during mitosis (only Lyso channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Mito over 1000 time points during mitosis (H2B channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Mitochondia over 1000 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Mito-Halo during mitosis (only H2B channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</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.