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9,882 results for “Scale”
JetClass: A Large-Scale Dataset for Deep Learning in Jet Physics
<p>JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper <a href="https://arxiv.org/abs/2202.03772">Particle Transformer for Jet Tagging</a>. An interface to use the dataset is provided in <a href="https://github.com/jet-universe/particle_transformer">https://github.com/jet-universe/particle_transformer</a>.</p>
A Large Scale Fabrication of Graphene Based Nano-electromechanical Contact Switches With Ultra-low Pull-in Voltage [Dataset]
<p>Original data set for the 'A Large Scale Fabrication of Graphene Based Nano-electromechanical Contact<br> Switches With Ultra-low Pull-in Voltage' is uploaded.</p>
Evaluating CO2 breakthrough in a shaly caprock material: a multi-scale experimental approach
<p>These are image datasets analysed in the publication "Evaluating CO2 breakthrough in a shaly a caprock material: a multi-scale experimental approach" by Stavropoulou and Laloui in 2022 in Scientific Reports, DOI: https://doi.org/10.1038/s41598-022-14793-8</p> <ol> <li>File 1 contains the 3D reconstructed x-ray tomography volumes analysed in the paper in bin 2. Note that image registration has to be performed before further analysis for accounting rigid body deformations (rotation and translation) that may have taken place between the scans during cell transport. All registrations have been performed using as reference scan 01.<br> </li> <li>FIle 2 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.</li> </ol>
Supplement to Pandemic-Scale Phylogenomics Reveals A Landscape of SARS-CoV2 Recombination
<p>Extended Data S1-S2 and Tables S6-S9 corresponding to the publication entitled Pandemic-Scale Phylogenomics Reveals A Landscape of SARS-CoV2 Recombination. Data S1 contains the phylogeny analyzed in the aforementioned study, and Data S2 contains a table of all samples considered to be descendant of a recombinant node in the study, as well as the relevant recombinant node ID. Tables S6-S9 are acknowledgments tables corresponding to GISAID (Table S6), China National Center for Bioinformation (Table S7), COVID-19 Genomics UK (COG-UK) (Table S8), and National Center for Biotechnology Information database (Table S9) on which the study was based.</p>
Broad-scale patterns of geographic avoidance between species emerge in the absence of fine-scale mechanisms of coexistence
<p>Aim: The need to forecast range shifts under future climate change has motivated an increasing interest in better understanding the role of biotic interactions in driving diversity patterns. The contribution of biotic interactions to shaping broad-scale species distributions is however, still debated, partly due to the difficulty of detecting their effects. We aim to test whether spatial exclusion between potentially competing species can be detected at the species range scale, and whether this pattern relates to fine-scale mechanisms of coexistence.</p> <p>Location: Western Palearctic</p> <p>Time period: Anthropocene</p> <p>Taxa: bats (Chiroptera)</p> <p>Methods: We develop and evaluate a measure of geographic avoidance that uses outputs of species distribution models to quantify geographic exclusion patterns expected if interspecific competition affects broad-scale distributions. We apply the measure to 10 Palearctic bat species belonging to four morphologically similar cryptic groups in which competition is likely to occur. We compare outputs to null models based on pairs of virtual species and to expectations based on ecological similarity and fine-scale coexistence mechanisms. We project changes in range suitability under climate change taking into account effects of geographic avoidance.</p> <p>Results: Values of geographic avoidance were above null expectations for two cryptic species pairs, suggesting that interspecific competition could have contributed to shaping their broad-scale distributions. These two pairs showed highest levels of ecological similarity and no trophic or habitat partitioning. Considering the role of competition modified predictions of future range suitability.</p> <p>Conclusions: Our results support the role of interspecific competition in limiting the geographic ranges of morphologically similar species in the absence of fine-scale mechanisms of coexistence. This study highlights the importance of incorporating biotic interactions into predictive models of range shifts under climate change, and the need for further integration of community ecology with species distribution models to understand the role of competition in ecology and biogeography.</p>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Data accompanying "A new brittle rheology and numerical framework for large-scale sea-ice models"
<p>Data accompanying "A new brittle rheology and numerical framework for<br> large-scale sea-ice models" by E. Olason et al, accepted for publication in<br> Journal of Advances in Modelling Earth Systems (2022).</p> <p>Files:<br> * CS2SMOS.tar.bz2: Contains Cryosat2/SMOS data, post-precessed and used to<br> produce figures comparing modelled thickness to observations.<br> * deformation_maps_demo.ipynb: An example jupyter notebook to read pairs.npz<br> * OlasonEtAl_BBM.tar.bz2: Thickness fields from the MEB run used to produce<br> figure 1 (netCDF).<br> * OlasonEtAl_MEB.tar.bz2: Thickness fields from the BBM run used to produce<br> figure 8 (netCDF).<br> * OlasonEtAl_mEVP.tar.bz2: Thickness fields from the mEVP run used to produce<br> figure 8 (netCDF).<br> * pairs.npz: Displacement pairs derived from the model's Lagrangian mesh used<br> to produce figures 3, 4, and 5 (numpy data file).<br> * Winter2006_7_BBM.nc.bz2: Thickness, concentration, and velocity fields from<br> the BBM run for the winter 2006-7 widely used in the paper (netCDF).<br> * Winter2006_7_mEVP.nc.bz2: Thickness, concentration, and velocity fields from<br> the mEVP run for the winter 2006-7 used for comparison in the paper<br> (netCDF).</p> <p> </p>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?
<p>DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?</p> <p>Journal of Water Process Engineering, Volume 35, June 2020, 101235</p> <p>https://doi.org/10.1016/j.jwpe.2020.101235</p> <p> </p>
Fig. 4. Female genitalia, scale bars 0.25 in On Anchonidium Bedel, 1884 sensu stricto, with descriptions of two new species from the Iberian peninsula (Coleoptera, Curculionidae: Molytinae)
Fig. 4. Female genitalia, scale bars 0.25 mm. A. spathiferum sp. nov., Serra da Estrela: (A) Ventrite VIII. (D) Spermatheca. (G) Gonocoxite of ovipositor. (L-M) Sclerotized bursal atrium (dorso-ventral and lateral view). A. braunerti sp. nov., Serra do Monchique: (B) Ventrite VIII. (E) Partly broken spermatheca. (ovipositor and bursal atrium are missing). A. unguiculare, France, St.-Coulomb: (C) Ventrite VIII. (F) Spermatheca. (H) Gonocoxite of ovipositor. (J-K) Sclerotized bursal atrium (dorso-ventral and lateral view) A. spathiferum sp. nov.: (I) Dissected female genitalia. Abbreviations: te = tergite VIII; ve = ventrite VIII; ov = ovipositor; in = intesticals; bu = strongly sclerotized bursal atrium; sp = spermatheca. A. caucasicum: (N-O) Sclerotized bursal atrium. Caulomorphus lederi: (P-Q) Sclerotized bursal atrium. ►
Data and code for Scale-invariant random geometry from mating of trees: a numerical study
<p>This entry contains the source code, simulation data and data analysis notebooks for the paper</p> <p>T. Budd, A. Castro, "Scale-invariant random geometry from mating of trees: a numerical study." <a href="https://arxiv.org/abs/2207.05355">arXiv:2207.05355</a>.</p> <p>This work is part of START-UP 2018 programme with project number 740.018.017, which is financed by the Dutch Research Council (NWO).</p>
Rare and declining bee species are key to consistent pollination of wildflowers and crops across large spatial scales
<p>Biodiversity promotes ecosystem function in experiments, but it remains uncertain how biodiversity loss affects function in larger-scale natural ecosystems, where rare and declining species which are likely to be lost and function needs to be maintained across space and time. Here we explore the importance of rare and declining bee species to the pollination of three wildflowers and three crops using large-scale (72 sites across 5,000 km2), multi-year datasets. Half (82/164) bee species were rare or declining, but these species provided ~15% of overall pollination. To determine the number of species important to ecosystem function, we used two methods of 'scaling up', both of which have previously been used for biodiversity-function analysis. First, we summed bee species' contributions to pollination across space and time and then found the minimum set of species needed to provide a threshold level of function across all sites; according to this method, effectively no rare and declining bee species were important to pollination. Second, we account for the "insurance value" of biodiversity by finding the minimum set of bee species needed to simultaneously provide a threshold level of function at each site in each year. The second method leads to the conclusion that 25 rare and eight declining bee species (36% and 53% of all rare and declining bee species, respectively) are important. Our findings provide some of the strongest evidence yet for the importance of rare and declining species, thereby providing a more direct link between real-world biodiversity loss and ecosystem function.</p>
The use of GRDC gauging stations for calibrating large-scale hydrological models
<p>The Global Runoff Data Centre provides time series of observed discharges that are very valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology which, though investigated several times, has not been satisfactorily solved. Grid-based hydrological models need to fit the reported station location to the river network depending on the resolution, to compare simulated discharge with observed discharge. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the wrong basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcseconds) river network, and we provide source codes and high- and low-resolution watershed boundaries.</p> <p>Same as release on Github: https://github.com/iiasa/CWATM_grdc_calibration_stations/releases/tag/V1.0</p>
Geoelectrical and hydro-chemical monitoring of karst formation at the laboratory scale
<p>We propose a dataset of two Estaillades limestone core samples subjected to acid percolation at two different rates.</p> <p>We monitored:</p> <ul> <li><strong>Permeability </strong>from high-frequency pressure gradient acquisition. Facing noise, we filtered this permeability with a Savitsky-Golay filter. Then, we resampled permeability values to compare them at specific times when we performed other measurements.</li> <li><strong>pH</strong> at the inlet and outlet.</li> <li><strong>Calcium </strong>concentration at the outlet.</li> <li><strong>Porosity</strong> was initially measured with petrophysical measurements and then calculated from calcium concentration during percolation experiments. We resampled the values to compare them at specific times when we performed other measurements.</li> <li><strong>Water electrical conductivity</strong> at the inlet and outlet.</li> <li><strong>Rock sample electrical conductivity</strong>, from which we calculated the formation factor. Rock sample electrical conductivity was high-frequency acquired. We thus resampled the values to compare them at specific times when we performed other measurements.</li> </ul> <p>We also present the pore size distribution of both samples before and after the percolation experiments. The distribution comes from the mean <strong>chord lengths distribution</strong> generated from tomography imaging.</p> <p>In addition to this dataset, we display a picture of the mesh of the sample and the electrodes generated with EIDORS software and with which we computed the geometric factor.</p>
Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment
<p>Supplementary Material of the article "Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment":</p> <p>- Supplementary Table 1: Raw environmental data: sea surface temperature (°C), salinity, dissolved oxygen (ml.l<sup>-1</sup>), pH, phosphate (mM), nitrite (mM), nitrate (mM), ammonia (mM), chl-a, b, c and pheopigments (mg/m<sup>3</sup>). </p> <p>- Supplementary Table 2: Cell abundance (cell.mL<sup>-1</sup>) of <em>Synechococcus</em> spp. (SYN) and picoeukaryotes (PEUK), and abundance of Virus-like particles for each sample and their respective anomalies, after normalization.</p>
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
<p>Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer</p>
Video Examples from: Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods
<p>This video accompanies our paper: Popp, C. and Murphy, D.T., "Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods", held at the 2022 AES International Conference on Audio for Virtual and Augmented Reality.</p> <p>Room-scale Virtual Reality (VR) presents sound designers with new challenges to tell stories with audio in games with player-driven narratives. These challenges arise from the player moving in and interacting with the virtual environment. The paper performs a small scoping review of VR/non-VR games and associated literature to identify issues and solutions to the placement of speech-based audio using practice-based research methods. The review leads to the proposition of design guidelines and strategies for their implementation. The paper advocates that each instance of speech-based audio should be short, interactive, and complemented by non-speech audio. Furthermore, each instance’s spatial, interactive, visual, aural, and narrative representation should be considered in combination. The paper also suggests that 3D-binaural audio informed by physics can aid storytelling and make virtual environments player-responsive.</p> <p>The paper is part of the research project SuperCharging Audio Storytelling: 3D Audio in Virtual Reality, funded by UK Arts and Humanities Research Council (AHRC) XR Stories Creative Industries Cluster project, grant no. AH/S002839/1. More information about the project can be found at <a href="https://xrstories.co.uk/project/supercharging-audio-storytelling-3d-audio-in-virtual-reality/">XR Stories</a>. Firelight Technologies Pty Ltd and Unity Technologies kindly provided non-commercial licenses for research purposes as part of this research project.</p>
Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"
<p>Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".</p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
Results of ultrasound measurements of sea-ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019
<p>This dataset details the results of testing sea ice cores collected during the SCALE 2019 Winter Cruise using ultrasound techniques. </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.