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1,024 results for “Edge”
Data Tables for the CARMA Extragalactic Database for Galaxy Evolution (CARMA EDGE)
<p>Data tables accompanying the Python package <a href="https://github.com/tonywong94/edge_pydb">edge_pydb</a> which provides access to spatially resolved measurements of CO and optical emission from the CARMA EDGE sample of 125 nearby galaxies. A full description can be found in the article by <a href="https://doi.org/10.3847/1538-4365/ad20c9" target="_blank" rel="noopener">Wong et al. (2024)</a>.</p>
3d Transition Metal K-edge XANES Dataset for Machine Learning Models
<p><strong>Data</strong><br><br>This dataset contains machine learning data for K-edge X-ray Absorption Near-Edge Structure (XANES) prediction models for eight 3d transition metals (Ti -Cu).</p> <ul> <li><strong>features_and_spectra:</strong> Material features (X) and corresponding XAS spectra (y) for each dataset split: training (train), validation (val), and test.</li> <li><strong> material_id_and_site:</strong> Material identifiers and site indices (according to <a href="https://github.com/AI-multimodal/Lightshow">Lightshow</a>) for each dataset split. </li> </ul> <p><strong>Funding</strong><br><br>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, at Brookhaven National Laboratory under Contract No. DE-SC0012704 and by Brookhaven National Laboratory (BNL), Laboratory Directed Research and Development (LDRD) grant no. 24-004.</p> <p> </p>
Research data supporting "Observation of a Topological Edge State Stabilized by Dissipation"
<div> <p>This repository contains the data presented in the manuscript titled "Observation of a Topological Edge State Stabilized by Dissipation" by H. Wetter et al., Phys. Rev. Lett. 131, 083801 (2023). The files contain the final data sets relevant to reproduce all plots shown in the paper. Data types are CSV, TIF, SVG, TXT, PNG. No licensed software is required for opening and reading the files.</p> </div>
Radiative Transfer Edge-on Protoplanetary Disk Images
<p>Dataset used to train a Convolutional Autoencoder model to generate synthetic images of edge-on protoplanetary disks. The work is described in "A machine learning framework to predict images of edge-on protoplanetary disks", Telkamps et al. 2022, submitted to AAS. This image dataset was created using the radiative transfer (RT) modeling code MCFOST (Pinte et al. 2006; Pinte et al. 2009). </p>
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</p>
Edge length of land use classes aggregated from Corine 2012 in 100 - 5000 meter radius areas around Landklif study plots
<p>Based on CORINE land cover data 2012, we aggregated the original land use classes into 8 classes: urban, agriculture, grassland, broad-leaved forest, coniferous forest, mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). Based on this new land use classification, we calculated the edge length of landuse patches (total length of edges between different habitat in meter) for 100 - 5000 meters (100-1000 meters with 100 meter interval, 1000 - 5000 meter with 500 meter interval) buffer area around LandKlif plots.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Edge length of land use classes aggregated from Corine 2018 in 100 - 5000 meter radius areas around Landklif study plots
<p><span>Based on CORINE land cover data 2018, we aggregated the original land use classes into 8 classes: urban, agriculture, grassland, broad-leaved forest, coniferous forest, mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). Based on this new land use classification, we calculated the edge length of landuse patches (total length of edges between different habitat in meter) for 100 - 5000 meters (100-1000 meters with 100 meter interval, 1000 - 5000 meter with 500 meter interval) buffer area around LandKlif plots</span>.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
SPICES – Sea ice edge maps from the Fram Strait
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. Thisalgorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include:<br> (1) pre-processing of the raw SAR data, <br> (2) calculation of texture features, <br> (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, <br> (4) expert re-classification of the image into the pre-calculated classes, <br> (5) training of the SVM using input from the previous step, and <br> (6) classifying the full image into the reduced number of classes using the trained SVM. <br> To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p> <p> </p> <p> </p>
SPICES – Sea ice edge maps from the Kara Sea
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. This algorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include: (1) pre-processing of the raw SAR data, (2) calculation of texture features, (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, (4) expert re-classification of the image into the pre-calculated classes, (5) training of the SVM using input from the previous step, and (6) classifying the full image into the reduced number of classes using the trained SVM. To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p>
Mobile Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</p>
XRS carbon K-edge speciation mapping of an Eocene (ca. 53 Mya) ant entrapped in amber from Oise, France
<p>XRS carbon K-edge speciation mapping of an Eocene (ca. 53 Mya) ant entrapped in amber from Oise, France</p>
EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress
<p>Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"<em>Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam</em>" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"<em>A statistical framework for analysing weld residual stresses for structural integrity assessment</em>" Nadri et al. (2008)</a>.</p> <p>Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.</p>
Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population - Supporting Material
<p>Supporting material for the study:</p> <p><strong>"Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population."</strong></p> <p><strong>Authors:</strong></p> <p>Cathleen Petit-Cailleux1, Hendrik Davi1, François Lefèvre1, Joseph Garrigue<strong>2</strong>, Jean-André Magdalou<strong>2</strong>, Christophe Hurson<strong>2,3</strong><strong>, </strong>Elodie Magnanou<strong>2,4</strong>, and Sylvie Oddou-Muratorio1.</p> <p> </p> <p>Adresses</p> <p>1INRA, UR 629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, France</p> <p><strong>2</strong>Réserve Naturelle Nationale de la Forêt de la Massane, France</p> <p><strong>3</strong>Fédération des Réserves Naturelles Catalanes, Prades, France</p> <p><strong>4</strong>Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins, BIOM, F-66650 Banyuls-sur-Mer, France</p> <p><strong>ORCID:</strong></p> <p>Cathleen Petit-Cailleux: <a href="https://orcid.org/0000-0001-7714-6583">https://orcid.org/0000-0001-7714-6583</a></p> <p>François Lefèvre : <a href="https://orcid.org/0000-0003-2242-7251">https://orcid.org/0000-0003-2242-7251</a></p> <p>Sylvie Oddou-Muratorio <a href="https://orcid.org/0000-0003-2374-8313">https://orcid.org/0000-0003-2374-8313</a></p> <p> </p> <p>-------------</p> <p>Raw data of the Table_Massane_moratlity_trees.csv and climate can be obtained from Joseph Garrigue, Jean-André Magdalou and Christophe Hurson.</p> <p>The inventories files and daily climate are the input dataset to run CASTANEA models.</p> <p>All details are provided in the article.</p>
Empirical measurements of function placements and executions in a mixed cloud-edge cluster
<p>Empirical measurements used for the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
Calculated O K-edge XAS Spectra of Niobium Oxide Phases using Bethe-Salpeter equation
<p>X-ray absorption spectra (XAS) were calculated for the oxygen K-edge for 20 different phases of Niobium oxides (NbO<sub>x</sub>) using the Bethe-Salpeter equation as implemented within the OCEAN code. Fifteen of the NbO<sub>x</sub> phases are amorphous, in which nine are stoichiometric Nb<sub>2</sub>O<sub>5</sub>, and slightly off-stoichiometric containing a variety of different defects. The other five structures are different crystalline phases (with spacegroup): NbO (Pm3m), NbO<sub>2</sub> (P4<sub>2</sub>/mnm), N-Nb<sub>2</sub>O<sub>5</sub> (C<sub>2</sub>/m), M-Nb<sub>2</sub>O<sub>5</sub> (I4/mmm), and B-Nb<sub>2</sub>O<sub>5</sub> (C<sub>2</sub>/c). The data is given in terms of four different folders: (1) VASP POSCARs for amorphous structures (called amorph_POSCARs), (2) VASP POSCARs for crystalline structures (called crystalline_nboxide_structures), (3) OCEAN outputs for amorphous structures (called amorph_U4), and (4) OCEAN outputs for crystalline structures (called cryst_U4). The 'amorph_U4' folder contains subfolders for each structure, and within each subfolder is a 'Results' folder containing the OCEAN input and output files, including individual XAS spectra for each individual oxygen atom in the structure. The organization of the 'cryst_U4' folder structure is the same as the 'amorph_U4' folder.</p> <p>To calculate the XAS spectra within the OCEAN code, we first performed DFT (using Quantum Espresso) with an energy cut-off of 92 Ryd., and a simplified Hubbard U of 4 eV was applied to the Nb <em>d</em> orbitals. The `O-high' and `Nb-sp' pseudopotentials from PseudoDojo were used. For the BSE, the electron orbitals were down-sampled onto real space grids chosen to match 1 grid point per 1 a.u., the k-point meshes were chosen to exceed 1 grid point per 0.16 a.u.<sup>-1</sup>, and the number of conduction bands was set to 0.126 times the unit cell volume (in a.u.<sup>3</sup>). <br> For the screening, electron orbitals were calculated on k-point meshes exceeding 1 grid point per 0.56 a.u.<sup>-1</sup>, and the number of conduction bands was set to 0.437 times the unit cell volume (in a.u.<sup>3</sup>).<br> A Lorentzian core-hole broadening of 0.07 eV was included, and additional Gaussian broadening of 0.5 eV was applied.</p>
Supplementary Information to: "Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman"
<p>This dataset contains supplementary information required to understand and reproduce the study detailed in our manuscript titled "<em>Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman</em>" which was submitted for publication to Palaeogeography, Palaeoclimatology, Palaeoecology.</p>
Edge infrastructure traces
<p>These Edge infrastructure traces consist of bandwidth and latency between devices along with the execution time of the microservices of a video processing application on the different devices. These traces were collected between 2022-10-24 and 2022-11-09.</p> <p>The execution times are related to a set of microservices, including video encoding and framing, along with the training and inference model for road sign classification.<br>The microservices' descriptions are provided in the following research paper:<br>N. Mehran, Z. N. Samani, D. Kimovski, and R. Prodan, "Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum," 2022 IEEE International Conference on Cluster Computing (CLUSTER), 2022, pp. 58-70, DOI: 10.1109/CLUSTER51413.2022.00021.</p> <p><br>Devices are as follows:<br>- D01 machine has a twelve-core AMD Ryzen Threadripper 2920X processor with 32GB memory;<br>- D02 machine has an eight-core Intel Core(TM) i7-7700 processor with 16GB memory;<br>- Nvidia Jetson Nano machine has a four-core ARM Cortex-A72 processor with 4GB memory;<br>- Raspberry Pi 4 has a four-core ARM Cortex A57 processor with 4GB of memory.</p> <p>Moreover, the dataset provides the network traces regarding the bandwidth and latency between the Edge devices. We provided the size of the transmitted messages between the devices for the throughput measurements. For the network latency, the information related to the round trip time is calculated by ICMP message request and reply.</p> <p>The traces include five parts of data as follows:</p> <p>BW-MessageSize.csv<br>- Timestamp: Date and time in CET<br>- Source device: the throughput from which we are checking; D02 machine<br>- Destination device: the throughput to which we are checking; D01 or D02 machine<br>- Message size (MB): the message size transmitted between the devices<br>- BW (Mbps): the maximum achievable bandwidth<br>*** "-1" means "iperf3: error - unable to connect to server: Connection refused"</p> <p> </p> <p>JetsonNano-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: the Nvidia Jetson Nano single-board computer<br>- Microservice: includes encoding, framing, training, or inference of Dockerized microservices<br>- Execution Time (seconds)</p> <p><br>Large-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: the highest performance machine (D01 machine) in the C3 testbed<br>- Microservice: includes encoding, framing, training, or inference (containers)<br>- Execution time (seconds)</p> <p> </p> <p><br>Latency.csv<br>- Timestamp: Date and time in CET<br>- Source device: the latency from which we are checking; D02 machine<br>- Destination device: the latency to which we were checking; D01 or D02 machine<br>- Minimum latency (ms) of the round-trip times among four transmitted messages<br>- Average latency (ms) of the round-trip times among four transmitted messages<br>- Maximum latency (ms) of the round-trip times among four transmitted messages<br>- Mean standard deviation of the round-trip times among four transmitted messages</p> <p> </p> <p><br>RPi4-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: Raspberry Pi v4 single-board computer<br>- Microservice: includes encoding, framing, training, or inference (containers)<br>- Execution time (seconds)</p> <p> </p> <p>For more information regarding the testbed, please refer to the C3 website at https://c3.itec.aau.at/.</p> <p><br>Authors:<br>Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, Josef Hammer, Radu Prodan<br>Institute of Information Technology, Alpen-Adria-Universitaet Klagenfurt, Austria</p> <p><br> </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.