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9,882 results for “Scale”
Additional Material for Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
<p>This upload contains the replication package for the study "Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture" submitted for review on the ECSA 2024 Industry Track.<br><br>It contains the following:<br>Focus group-plan.pdf - The plan for the focus group discussion created by the authors.<br>Focus group slides.pdf - The slides used during the focus group discussion.<br>SonarQube measurement results.xlsx - Detailed results of the SonarQube analysis for each microservice in the researched system.</p>
Dataset for the validation of the Delirium Observation Screening Scale in long-term care facilities in Flanders
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The SpinBus architecture: Scaling spin qubits with electron shuttling
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Replication Package for "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"
<p># Replication Package for the Paper: "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>This replication package includes the raw data, questionnaire answers, and a Python notebook to reproduce the results detailed in the paper "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>## Repository Structure<br>1. **Results:** Contains Excel files with the responses from the 2 human experts and the 5 models and the review of the 3 human reviewers.<br>2. **Tables:** Contains the full Wilcoxon Test Results for H01 and H02 as well as the Anderson Darling test for normality and the Spearmans' Rho.</p> <p>## Replication Process<br>To replicate the results of our study, open the provided Python Notebook in Google Colab and follow the instructions to reproduce the results seamlessly.</p> <p># Instructions for Use<br>To utilize this replicability package, refer to the steps outlined in the notebook file.</p> <p># Remarks<br>If you encounter any issues or have any questions, please get in touch with the paper's authors. We will be glad to assist you!</p>
The derived data in manuscript Oblique impact adjacent to Chang'E-5 landing site: Fine-scale analysis and implication on the provenance of returned samples
<p>This website contains the derived data in the <em>manuscript <span>Oblique impact adjacent to Chang’E-5 landing site: </span><span>F</span><span>ine-scale analysis and implication on the provenance of returned samples </span></em>by <span>Wenhui Wu</span><span>, </span><span>Zhaopeng Chen</span><span>, Xin Ren</span><span>, Dawei Liu</span><span>,</span><span> </span><span>Xingguo Zeng, Yuan Chen, Wangli Chen, Wei Yan, Bin Liu,Xiaoxia Zhang, Jianjun Liu</span> for <em><span>Journal of Geophysical Research: Planets</span></em></p>
Harmonic scale factors of fundamental transitions for dispersion-corrected quantum chemical methods
<p>These two datasets contain quantum-chemical calculations, experimental data, and scripts for obtaining scale factors for harmonic vibrational frequencies for fundamental transitions. </p> <p>The first dataset (<span><a href="../api/records/11174948/draft/files/scaling_quantum_chemistry.tar.xz/content" target="_blank" rel="noopener noreferrer">scaling_quantum_chemistry.tar.xz</a></span>) contains quantum-chemical calculations for the training dataset of various molecules and three exemplary molecules: pyrene, protonated LeuEnk, and doubly protonated gramicidin S. The second dataset (<span><a href="../api/records/11174948/draft/files/scaling_fitting.zip/content" target="_blank" rel="noopener noreferrer">scaling_fitting.zip</a></span>) contains the data and scripts for fitting the scale factors. The training set comprises 441 molecules of various sizes (from diatomics to C60). </p>
Data set for: Pore-scale simulation of salt precipitation during CO2 injection into saline aquifers, Water Resources Research
<p>This dataset includes output data used for analyses presented in the paper titled: Pore-scale simulation of salt precipitation during CO2 injection into saline aquifers and submitted to Water Resources Research.</p>
Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish
<p>This dataset is associated to the following publication:</p> <p>Vilcot M, Faure N, Andrews KR, Bowen BW, Leprieur F, Manel S. (2024) <strong>Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish</strong>. <em>Molecular Ecology</em> <strong>33</strong>, e17423. (doi:<a href="https://doi.org/10.1111/mec.17423">10.1111/mec.17423</a>)</p> <p> </p> <h2><strong>Data: </strong></h2> <ul> <li> <p>"Report_DEtel22-6705_SNP_2_ordered_Bowen.csv": SNP data from Dart Sequencing on <em>Etelis coruscans</em>, from Andrews et al. (2020) samples</p> </li> <li>"PA_Mat_GaspObis.RDS": fish species presence data, gathered from an updated version of Albouy et al. (2019)</li> <li> <p>"metadata_samples_full.csv": all <em>Etelis coruscans</em> sample information, from Andrews et al. (2020) and the SEAMOUNTS project</p> </li> <li> <p>"metadata_samples.csv": <em>Etelis coruscans</em> sample information, only for samples that have passed the genetic filtering and were used for subsequent analyses </p> </li> <li> <p>"metadata_stations.csv": sampling station information</p> </li> <li> <p>"Taxonomy_Fishbase.csv": species taxonomic information, downloaded with rfishbase::load_taxa() </p> </li> <li> <p>"traits_Luiz_et_al_2013.csv": species trait information from Luiz et al. (2013) </p> </li> </ul> <h2><strong>Related dataset</strong><strong>: </strong></h2> <p><em>Etelis coruscans </em>SNP data on samples from the SEAMOUNTS project are available at <a href="https://doi.org/10.5281/zenodo.11201065">https://doi.org/10.5281/zenodo.11201065</a></p> <h2><strong>Scripts: </strong></h2> <p>Scripts used to reproduce the analyses and figures of the final article are available at <a href="https://github.com/mvilcot/etelis_SGDCs">https://github.com/mvilcot/etelis_SGDCs</a> </p> <p> </p> <h2><strong>References: </strong></h2> <p>Albouy, C., Archambault, P., Appeltans, W., Araújo, M. B., Beauchesne, D., Cazelles, K., Cirtwill, A. R., Fortin, M.-J., Galiana, N., Leroux, S. J., Pellissier, L., Poisot, T., Stouffer, D. B., Wood, S. A., & Gravel, D. (2019). The marine fish food web is globally connected. Nature Ecology & Evolution, 3(8), Article 8. <a href="https://doi.org/10.1038/s41559-019-0950-y" target="_blank" rel="noopener">https://doi.org/10.1038/s41559-019-0950-y</a> </p> <p>Andrews, K. R., Copus, J. M., Wilcox, C., Williams, A. J., Newman, S. J., Wakefield, C. B., & Bowen, B. W. (2020). Range-Wide population structure of 3 deepwater Eteline snappers across the Indo-Pacific Basin. Journal of Heredity, 111(5), 471‑485. <a href="https://doi.org/10.1093/jhered/esaa029" target="_blank" rel="noopener">https://doi.org/10.1093/jhered/esaa029</a> </p> <p>Luiz, O. J., Allen, A. P., Robertson, D. R., Floeter, S. R., Kulbicki, M., Vigliola, L., Becheler, R., & Madin, J. S. (2013). Adult and larval traits as determinants of geographic range size among tropical reef fishes. Proceedings of the National Academy of Sciences, 110(41), 16498‑16502. <a href="https://doi.org/10.1073/pnas.1304074110" target="_blank" rel="noopener">https://doi.org/10.1073/pnas.1304074110</a> </p> <p>Boettiger, C., Lang, D. T., & Wainwright, P. C. (2012). rfishbase: Exploring, manipulating and visualizing FishBase data from R. Journal of Fish Biology, 81(6), 2030‑2039. <a href="https://doi.org/10.1111/j.1095-8649.2012.03464.x" target="_blank" rel="noopener">https://doi.org/10.1111/j.1095-8649.2012.03464.x</a></p> <p> </p>
Data and code for Article "High-Performance Large-Scale-Integration Organic Phototransistors based on Photovoltaic Nanocells"
<p>The data for figures in the article “High-Performance Large-Scale-Integration Organic Phototransistors based on Photovoltaic Nanocells”. The article <span>DOI: 10.1038/s41565-024-01707-0</span></p>
FIGURE 3. Protosticta samtsensis, all scale bars 0.5 in Description of Protosticta khasia sp. nov. and Yunnanosticta siangi sp. nov., with new records of P. samtsensis Gurung & Phan, 2023 from Northeast India (Odonata: Zygoptera: Platystictidae)
FIGURE 3. Protosticta samtsensis, all scale bars 0.5 mm. Male (IBC-BR443): (a) anterior view of head; (b) dorsal view of prothorax; (c) lateral view of thorax; (d–e) caudal appendages: (d) dorsal view; (e) lateral view; male (IBC-BR502): (f) anterior view of head; (g) dorsal view of prothorax; (h) lateral view of thorax; (i–j) caudal appendages: (i) dorsal view; (j) lateral view; male (IBC-BR503): (k) anterior view of head; (l) dorsal view of prothorax; (m) lateral view of thorax; (n–o) caudal appendages: (n) dorsal view; (o) lateral view.
Pacific Walker Circulation modulated millennial-scale Asian monsoon rainfall variability over past 40 kyr
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Trapping, Remobilization, Ostwald ripening, and Hysteresis in Geological CO2 Storage: Pore-Scale Imaging and Modelling
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Obtaining Continental-Scale, High-Resolution 2-d Ionospheric Flows and Application to Meso-Scale Flow Science
<p>The SuperDARN 2-d velocity vectors using the spherical elementary current systems (SECS) technique. The file names show UT. Each file contains magnetic latitude and longitude, geographic latitude and longitude, northward and eastward velocity in magnetic, northward and eastward velocity in geographic, and the number of echoes in each grid. AACGM is used as the magnetic coordinates.</p>
Data associated with "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales"
<p>The zipped folder contains the processed soil datasets including covariates, soil depths, and SOC concentrations for training the deep learning models described in the paper "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales". </p> <p>"soil_profile/" contains a table including the geolocations of all the soil profiles in this study. "patch_data/" and "point_data/" contain the covariates to feed the models with patch input and point input respectively. "depth/" contains the upper and lower depths of the soil samples. "y/" contains the target variable - SOC concentration of the soil samples. The data files with suffix "_1" is a small subset of their counterparts without "_1" (10 % in sample size) used for model hyperparameters tuning.</p>
Quantifying Winter Forage Resources for Reindeer: Developing a Method to Estimate Ground Lichen Cover and Biomass at a Local Scale
<p>Data and R scripts used in the paper Quantifying Winter Forage Resources for Reindeer: Developing a Method to Estimate Ground Lichen Cover and Biomass at a Local Scale (https://doi.org/10.1016/j.tfp.2024.100768).</p>
Data from: Tree diversity across multiple scales and environmental heterogeneity promote ecosystem multifunctionality in a large temperate forest region
<p><strong>Aim</strong>: Biodiversity across different scales provides multidimensional insurance for ecosystem functioning. Although the effects of biodiversity on ecosystem multifunctionality are well recorded in local communities, they remain poorly understood across scales (from local to larger spatial scales). This study evaluates how multiple attributes of biodiversity maintain ecosystem multifunctionality from local to regional scales, across diverse environmental gradients.</p> <p><strong>Location</strong>: North-eastern China.</p> <p><strong>Time period</strong>: 2017.</p> <p><strong>Major taxa studied</strong>: Woody plants.</p> <p><strong>Methods</strong>: We define multifunctionality using both averaged and modified multiple threshold approaches. Multiple dimensions of biodiversity across varying spatial scales were measured within the framework of Hill‒Chao numbers. Using variance decomposition, linear mixed models, and structural equation modeling, we explored how multiple attributes of tree diversity at varying spatial scales affect multifunctionality, and how these relationships are modulated by environmental drivers.<br>Results: We found that both α- and β-diversity are critical for regional community multifunctionality, while the relationships between species, functional, and phylogenetic diversity and multifunctionality decoupled across spatial scales and thresholds of ecosystem functioning. Phylogenetic β-diversity and species α-diversity are respectively more important for promoting high and moderate threshold multifunctionality (e.g., EMFT90 and EMFT50) in regional communities. Environmental drivers typically have stronger effects than biodiversity on multifunctionality. Soil and climatic conditions had either direct effects on multifunctionality, or indirect ones mediated by species α-diversity. Environmental heterogeneity is important for high threshold multifunctionality, exerting directly and indirectly through phylogenetic β-diversity. Latitude not only directly influences multifunctionality but also modulates it through species α-diversity and phylogenetic β-diversity.</p> <p><strong>Main conclusions</strong>: This study underscores the positive effects of biodiversity on multifunctionality across multiple dimensions. Based on our findings, we conclude that any design of a forested landscape that is aimed at maximizing multifunctionality should consider maintaining high local diversity as well as forest community heterogeneity at varying scales.</p>
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
<h1>Overview</h1> <p>This dataset is derived from the <a href="https://quijote-simulations.readthedocs.io/en/latest/bsq.html">Big Sobol Sequence (BSQ) of the Quijote simulations</a>, a collection of N-body simulations designed for machine learning applications. Each simulation consists of a point cloud (points in space, with 3D coordinates attached to them) generated under a varying set of cosmological parameters. Each point represents a simulated galaxy and is accompanied by associated properties such as velocity and mass. The cardinality of each point cloud is 5000 points. The dataset is split into 11,200 simulations in the training set, 608 in the validation set, and 576 in the test set. </p> <h1>File structure</h1> <p>The dataset is provided in <a href="https://www.tensorflow.org/tutorials/load_data/tfrecord">TFRecord format</a>. The training simulations are split across 50 TFRecord files following the naming convention <code>halos_train_<i>.tfrecord</code>. The validation and test sets are provided in <code>halos_val_1.tfrecord</code> and <code>halos_test_1.tfrecord</code>, respectively.</p> <p>Each dataset can be loaded using TensorFlow as shown in the code example below:</p> <div> <blockquote> <div>import tensorflow as tf<br><br>files = tf.io.gfile.glob(f"halos*train*.tfrecord") # replace 'train' with 'val' or 'test'</div> <div>dataset = tf.data.TFRecordDataset(files)</div> </blockquote> </div> <h2>TFRecord structure</h2> <p>Each record (corresponding to a point cloud) in a TFRecord file contains the following feature fields:</p> <ul> <li><code><strong>"x"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Position along x axis</li> <li><code><strong>"y"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Position along y axis</li> <li><code><strong>"z"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Position along z axis</li> <li><code><strong>"v_x"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Velocity along x axis</li> <li><code><strong>"v_y"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Velocity along y axis</li> <li><code><strong>"v_z"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Velocity along z axis</li> <li><code><strong>"J_x"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Angular momentum along x axis</li> <li><code><strong>"J_y"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Angular momentum along y axis</li> <li><code><strong>"J_z"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Angular momentum along z axis</li> <li><code><strong>"M200c"</strong></code>: <em>(tensor of shape [5000], dtype=float)</em> - Virial mass</li> <li><code><strong>"Omega_m"</strong></code>: <em>(float)</em> - Matter density</li> <li><code><strong>"Omega_b"</strong></code>: <em>(float)</em> - Baryon density</li> <li><code><strong>"h"</strong></code>: <em>(float)</em> - Hubble parameter</li> <li><code><strong>"n_s"</strong></code>: <em>(float)</em> - Density perturbation spectral index</li> <li><code><strong>"sigma_8"</strong></code>: <em>(float)</em> - RMS matter fluctuation amplitude on a scale of 8 Mpc/h</li> <li><code><strong>"tpcf"</strong></code>: <em>(tensor of shape [24], dtype=float)</em> - Two-point correlation function</li> </ul> <p> </p>
AlleNoise - large-scale text classification benchmark dataset with real-world label noise
<div> <div> <div> <div> <p><span>AlleNoise</span><span> is a benchmark dataset for large-scale multi-class text classification with real-world label noise. It consists of e-commerce product titles from Allegro.com with corresponding category labels. The noise distribution comes from actual users of a major e-commerce marketplace, so it realistically reflects the semantics of human mistakes. In addition to the noisy labels, we provide human-verified clean labels and a meaningful, hierarchical taxonomy of categories. Code and data is available at https://github.com/allegro/AlleNoise.<br></span></p> </div> </div> </div> </div>
Data from: On the importance of scale in evolutionary quantitative genetics
<p>The informed use of scales and units in evolutionary quantitative genetics is often neglected, and naïve standardizations can cause misinterpretations of empirical results. A potentially influential example of such neglect can be found in the recent book by Stevan J. Arnold (2023. Evolutionary Quantitative Genetics Oxford University Press). There, Arnold championed the use of heritability over mean-scaled genetic variance as a measure of evolutionary potential arguing that mean-scaled genetic variances are correlated with trait means while heritabilities are not. Here, we show that Arnold's empirical result is an artifact of ignoring the units in which traits are measured. More importantly, Arnold's argument mistakenly assumes that the goal of mean scaling is to remove the relationship between mean and variance. In our view, mean scaling is useful because it puts traits with different units on a common scale that makes evolutionary changes, or their potential, readily interpretable and comparable in terms of proportions of the mean.</p>
WildAvatar: Web-scale In-the-wild Video Dataset for 3D Avatar Creation
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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.