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Crop rotation and agri-environment schemes determine bumblebee communities via flower resources
<p>The biodiversity (flower cover and bumblebees) and environmental data used in the analyses.</p>
Supplementary material 2 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
List of genera investigated for the analysis including information on generic distribution, coding and references to the literature used :
FIGURE 1 in Inventorizing the modern benthic foraminiferal assemblage from marginal marine environments across the North West coast of Bay of Bengal
FIGURE 1. LOcATION OF THE SAMPLING STATIONS wITH RESPEcT TO EAcH HAbITAT. CO-ORdINATES OF THE SAMPLEd STATIONS HAVE bEEN dETAILEd IN SUPPLEMENTARy TAbLE S1.
Dataset for Measuring turbulent CO2 fluxes with a closed-path gas analyzer in a marine environment (2018)
<p>Dataset for Honkanen et al. (2018). The package contains .csv datafile and .pdf readme file.</p> <p>Included .csv file consists of CO2 flux and additional data measured at the Utö Atmospheric and Marine Research Station (59°46’55” N, 21°21’27” E) during July-October 2017. The file is comma-separated and its headers describe the variable name and the units: see readme file for more information about the columns. Missing values are indicated with -999. For more information, the reader is referred to Honkanen et al. (2018).</p> <p>Honkanen, M., Tuovinen, J.-P., Laurila, T., Mäkelä, T., Hatakka, J., Kielosto, S., and Laakso, L.: Measuring turbulent CO<sub>2</sub> fluxes with a closed-path gas analyzer in marine environment, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-61, in review, 2018.</p>
FIGURE 3 in New annelid species from the deepest known whale-fall environment: Bathykermadeca thanatos sp. nov. (Annelida: Polynoidae)
FIGURE 3. Bathykermadeca thanatos sp. nov., left parapodium of segment 3, holotype (NSMT-Pol H-682). A, general appearance of parapodium, posterior view; B, notochaetae; C, neurochaetae, middle part of bundle. Scale bars: 1 mm (A); 200 µm (B, C).
FIGURE 1 in New annelid species from the deepest known whale-fall environment: Bathykermadeca thanatos sp. nov. (Annelida: Polynoidae)
FIGURE 1. In situ observation of a whale-fall community at a depth of 4974 m in the Nansei-Shoto Trench. A, two whale vertebrae colonized by benthic invertebrates; B, Bathykermadeca thanatos sp. nov., posterior view; C, same, dorsal view.
FIGURE 2 in New annelid species from the deepest known whale-fall environment: Bathykermadeca thanatos sp. nov. (Annelida: Polynoidae)
FIGURE 2. Bathykermadeca thanatos sp. nov. A–B, holotype (NSMT-Pol H-682); C, paratype (NSMT-Pol P-683); D, paratype (JAMSTEC No. 1120033073). A, Live specimen, dorsal view; B, Fixed specimen, prostomium, dorsal view,white arrows indicating the possible ocular area; C, Fixed specimen, posterior end, ventral view; D, Fixed specimen, everter pharynx with papillae and jaws, anterior view. Scale bars = 5 mm (A); 1 mm (B–D).
FIGURE 4 in New annelid species from the deepest known whale-fall environment: Bathykermadeca thanatos sp. nov. (Annelida: Polynoidae)
FIGURE 4. Bathykermadeca thanatos sp. nov., left parapodium of segment 3, holotype (NSMT-Pol H-682). A, notochaetae; B, tip of notochaeta; C, neurochaetae; D, middle part of neurochaeta.
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
<p><strong>License + Attribution</strong></p> <p>This dataset is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>. Commercial usage is not permitted. If you use this dataset or the code in a scientific publication, please cite the following <a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.html">paper</a>:</p> <blockquote> <p>@inproceedings{FischerECCV2018,<br> author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},<br> title = "{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}",<br> booktitle = {European Conference on Computer Vision},<br> year = {2018},<br> month = {September},<br> pages = {339--357}<br> }</p> </blockquote> <p>This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).</p> <p>More information can be found on the Personal Robotic Lab's website: <a href="https://www.imperial.ac.uk/personal-robotics/software/">https://www.imperial.ac.uk/personal-robotics/software/</a>.</p> <p><strong>Overview</strong></p> <p>The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix <em>_glasses</em>), and 2) One with natural appearances (no eyetracking glasses are worn; suffix <em>_noglasses</em>). The <em>_noglasses</em> images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the <em>_glasses</em> images.</p> <p>There is code accompanying this dataset: <a href="https://github.com/Tobias-Fischer/rt_gene">https://github.com/Tobias-Fischer/rt_gene</a>. Please use the issue tracker in the code respository if you have questions regarding the dataset.</p> <p><strong>Subjects / 3-Fold evaluation</strong></p> <p>15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used).</p> <p>We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups):</p> <ol> <li>'s001', 's002', 's008', 's010'</li> <li>'s003', 's004', 's007', 's009'</li> <li>'s005', 's006', 's011', 's012', 's013'</li> </ol> <p>The validation set consists of sessions 's014', 's015' and 's016'.</p> <p>While the MATLAB script (<em>prepare_dataset.m</em>; see code repository) creates train and test images for each subject, all images were used for the evaluation (see <em>evaluate_model.py</em>).</p> <p><strong>Labeled dataset (sXYZ_glasses)</strong></p> <p>The file for each subject contains the following information:</p> <ul> <li>label_combined.txt This is the main file containing labels. The formatting is as follows:<br> seq_number, [head pose: right(pos) / left(neg), up (pos) / down(neg)], [gaze: right(pos) / left(neg), up(pos) / down(neg)], timestamp</li> <li>label_headpose.txt This file contains more detail about the head pose of the subject.<br> seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp</li> <li>kinect2_calibration.yaml<br> The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"original" folder <ul> <li>The face_before_inpainting folder contains the face with a large margin to the left and right.</li> <li>The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder.</li> <li>The overlay folder contains images where the mask was overlaid on the face_before_inpainting images.</li> <li>The face folder contains the face image extracted using MTCNN with a tighter margin.</li> <li>The left and right folders contain the left and right eye image areas.</li> </ul> </li> <li>The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting).</li> <li>"inpainted" folder <ul> <li>The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting.</li> <li>Then, the images contained in the face, left and right folders were extracted using MTCNN as above.</li> </ul> </li> </ul> <p><strong>Unlabeled dataset (sXYZ_noglasses)</strong></p> <ul> <li>kinect2_calibration.yaml<br> This file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"face" folder<br> This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).</li> </ul>
Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer: TerrainSkin Video
<p><strong><em>Terrain</em>Skin:4DFlow</strong> is a three-hour durational response to a three-screen dance video installation, <em>Terrain</em>Skin, currently screening in the Dance Interactive Space in Firkin Crane, Cork. <em>Terrain</em>Skin explores the symbiotic relationship between body-mind and environment. This live response through the mediums of movement, video and visual art, allows insight into an evolving creative process, a process of becoming, a process of construction, a process of re-configuration, a process of transition from site-specific environments to a performance installation environment. This live response aims to dissolve the boundaries between performer and audience, through creating a shared immersive interactive space of heightened kinaesthetic and energetic awareness.</p> <p>Mairéad Vaughan – Movement<br> Carolyn Collier – Visual Art<br> Aoife Desmond – Visual Art<br> Dara O’Brien – Video</p> <p>This video, and another <strong><em>Terrain</em>Skin4DFlow Performance</strong> (<a href="http://doi.org/10.5281/zenodo.2613568">http://doi.org/10.5281/zenodo.2613568</a>), form part of a PhD thesis held in the University of Limerick Institutional Repository (ULIR):</p> <p>Vaughan, M. (2016) <em>'Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer'</em>, University of Limerick (PhD Thesis). Available from: <a href="https://ulir.ul.ie/handle/10344/5217">https://ulir.ul.ie/handle/10344/5217</a>.</p> <p>The research was funded by the Arts Council of Ireland.</p>
Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer: TerrainSkin:4DFlow Performance
<p><strong><em>Terrain</em>Skin:4DFlow</strong> is a three-hour durational response to a three-screen dance video installation, <em>Terrain</em>Skin, currently screening in the Dance Interactive Space in Firkin Crane, Cork. <em>Terrain</em>Skin explores the symbiotic relationship between body-mind and environment. This live response through the mediums of movement, video and visual art, allows insight into an evolving creative process, a process of becoming, a process of construction, a process of re-configuration, a process of transition from site-specific environments to a performance installation environment. This live response aims to dissolve the boundaries between performer and audience, through creating a shared immersive interactive space of heightened kinaesthetic and energetic awareness.</p> <p>Mairéad Vaughan – Movement<br> Carolyn Collier – Visual Art<br> Aoife Desmond – Visual Art<br> Dara O’Brien – Video</p> <p>This video, and another <strong><em>Terrain</em>Skin Video</strong> (<a href="http://doi.org/10.5281/zenodo.2613559">http://doi.org/10.5281/zenodo.2613559</a>), form part of a PhD thesis held in the University of Limerick Institutional Repository (ULIR):</p> <p>Vaughan, M. (2016) <em>'Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer'</em>, University of Limerick (PhD Thesis). Available from: <a href="https://ulir.ul.ie/handle/10344/5217">https://ulir.ul.ie/handle/10344/5217</a>.</p> <p>The research was funded by the Arts Council of Ireland.</p>
Diurnal regulation of photosynthetic light absorption, electron transport and carbon fixation in two contrasting oceanic environments
<p>Dataset accompanying the publication Schuback N & Tortell PD, Diurnal regulation of photosynthetic light absorption, electron transport and carbon fixation in two contrasting oceanic environments, Biogeosciences, 2019. https://doi.org/10.5194/bg-2018-524.</p>
Biochar NET-2-U. Negative Emission Technologies are nice to the environment.
<p>Stable Isotope Data of the citizen science project "Biochar NET-2-U".</p>
Biochar NET-2-U-2. Negative Emission Technologies are nice to the environment part two.
<p>Stable Isotope Data of the citizen science project "Biochar NET-2-U-2".</p>
Figure 1 in Social Life in Arid Environments: The Case Study of Cataglyphis Ants
Figure 1 Cataglyphis ants show several adaptations to heat and desiccation. (a) C. rosenhaueri forager from southern Spain, whose long legs isolate its body from the hot ground surface. (b) Silver appearance of C. bombycina is produced by hairs that reflect sunrays. (c) Species of the C. bicolor group have a modified petiole for raising the abdomen, which increases maneuverability and speed. (d) During colony fission, C. hispanica workers are transported to a new nest location by a few individuals. Photo credits: (a–c) Fernando Amor, (d) Teresa Cerdá.
Improvement of the Power Consumption of Docker Container Environments by evaluation and adaptation of logging techniques
<pre>Container technologies are becoming increasingly important in cloud computing, data centers, and software development. As a result, the number of computing units increases significantly, leading to an increase in power consumption. If the energy consumption of a computing instance is changed minimally by an adapted configuration, this can have a large impact on entire container environments. This paper uses the example of docker logging drivers to show what these effects could be in terms of CPU consumption, reachability, power consumption and power costs.</pre>
Data for the manuscript: TOWARDS AN INTEGRATED SOIL-PLANT-ATMOSPHERE MODELING ENVIRONMENT: IMPLEMENTATION OF A DYNAMIC PLANT UPTAKE MODULE FOR THE HYDRUS MODEL
<p>Data used in the manuscript for the theoretical and experimental validation, and for the Global Sensitivity Analysis. For a thorough description, please refer to the manuscript.</p>
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
FIGURE 35 in On the spider genus Cryptachaea from Peru, Bolivia and cave environments in Brazil: a new species, additional descriptions and new records (Araneae, Theridiidae)
FIGURE 35. Distribution of Cryptachaea species: Star, Cryptachaea dea (Buckup & Marques, 2006); Square, Cryptachaea schneirlai (Levi, 1959).
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.