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9,882 results for “Scale”
A fMRI dataset in response to large-scale short natural dynamic facial expression videos
<p>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large<span>-scale</span> natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.</p>
The downside of large-scale events: (un)sustainable consumption & production in São João festivities
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
MRBIGR: a versatile toolbox for genetic causal inference from population-scale multi-omics data
<p>MRBIGR is a multifunctional toolkit for pre-GWAS, GWAS and post-GWAS of both traditional and multi-omics data. MRBIGR provides all the components needed to build a complete GWAS pipeline, and integrates with rich post-GWAS analysis tools such as QTL annotation and haplotype analysis. In particular, Mendelian randomization (MR) analysis, MR-based network construction, module identification and gene ontology analysis are proposed for further genetic regulation studies. Additionally, it also produces rich plots for visualization of the analysis results and other formatted data.</p> <p>This dataset is used to generate images in MRBIGR papers and can also serve as an example to demonstrate how to use MRBIGR.</p>
CRISP: Critical Path Analysis of Large-Scale Microservice Architectures (Artifact)
<p>This zip includes the original artifact used in the <a href="https://www.usenix.org/system/files/atc22-zhang-zhizhou.pdf">CRISP</a> paper.</p> <p>The appendix in the original paper contains the details on the setup and execution of the artifact. There are three components in this artifact: top-down analysis, bottom-up analysis, and anomaly detection.</p> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed.</li> <li>All trace is sanitized consistently. The same service or endpoint will have identical mapping across three directories. (i.e., service 1 represents the same service in all traces). However, the mapping is <strong>inconsistent</strong> with <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>, so please do not mix the traces between the two artifacts. </li> <li>To preserve privacy, the start time of each trace has been randomly shifted. As a result, the start and end times in the traces do not reflect the actual collection times, and users should not attempt to infer when the traces were gathered.</li> <li>Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.</li> </ul> <h3>Top-down analysis</h3> <p>Simply run <code>bash top-down.sh</code> should be sufficient. Note the result looks slightly different since this artifact contains a subset of traces from the original paper.</p> <h3>bottom-up analysis</h3> <p><code>bash bottom-up.sh</code> should generate the figure 13-20 from the original paper. Note instead of having 1m+ traces in the original paper, this artifact contains a subset of 100k traces. So generated figure may look slightly different.</p> <h3>Anomaly detection</h3> <p>This part contains the original repo of TraceAnomaly. The dataset contains the original trace of <strong>Service 3</strong> in <strong>Table 2</strong> from the original paper.</p> <p>To run the experiment, first please refer to the appendix for the setup.</p> <p>Then please run the following:</p> <ul> <li><code>bash preprocess.sh</code> to generate the matrices that are used in the training</li> <li>within <code>TraceAnomaly</code> repo <ul> <li>run <code>bash run-training-WG.sh</code> to run the training using the whole graph. Correspond to <strong>Service 3</strong> row, column <strong>TA</strong> in <strong>Table 2</strong>.</li> <li>run <code>bash run-training-CS.sh</code> to run the training using the critical path only. Correspond to <strong>Service 3</strong> row, column <strong>CRISP</strong> in <strong>Table 2</strong>.</li> </ul> </li> <li>run <code>python3.6 parse-rnvp.py -i path_to_rnvp_file</code> to check the result</li> </ul> <p>Note</p> <ul> <li>The traces among <code>train</code>, <code>normal</code>, and <code>abnormal</code> are split randomly from normal traces. The error generation part can be found in lines 137-144 in <code>preprocess.py</code>. It is also described in Section 7.3 Methodology in the original paper. For real error traces, please refer to <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>.</li> </ul> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <pre>@inproceedings{zhang2022crisp, title={$\{$CRISP$\}$: Critical path analysis of $\{$Large-Scale$\}$ microservice architectures}, author={Zhang, Zhizhou and Ramanathan, Murali Krishna and Raj, Prithvi and Parwal, Abhishek and Sherwood, Timothy and Chabbi, Milind}, booktitle={2022 USENIX Annual Technical Conference (USENIX ATC 22)}, pages={655--672}, year={2022} }</pre> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)
<p>This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation. </p> <p>The dataset includes:</p> <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel. </p> <p>The dataset used for rock parameter statisitcs includes:</p> <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Large-scale metagenomic analysis of oral microbiomes reveals markers for autism spectrum disorders, MetaPhlAn 3 profiles
Open the record for dataset details and reuse information.
Pathogen-induced alterations in fine-scale movement behaviour predict impaired reproductive success
<p><strong>This R-Project contains the following code files (in folder R):</strong></p> <p><em>01_movement_data_cleaning: Code to clean the raw ATLAS tracking data</em></p> <p><em>02_nestbox_data_cleaning: Code to clean nest box control sheets</em></p> <p><em>03_pathogen_data_cleaning</em>: <em>Code to clean the laboratory results for blood parasites PCRs</em></p> <p><em>04_ctmm_models</em>: Code to prepare data, applying continuous time movement models (incl. model selection) on movement data. Moreover, track reconstruction is performed</p> <p><em>05_Neural_Network: Code to perform Neural Networks for classification of behavioural states</em></p> <p><em>06_behavioural classification_analyses: Code to analyse Neural Network output</em></p> <p><em>07_HSF: Code for Habitat Selection Functions (HSF)</em></p> <p><em>08_ISSF: : Code for integrated Step Selection Functions (iSSF)</em></p> <p><em>09_breeding_success_HSF: Multivariate model to perform on HSF estimates</em></p> <p><em>10_breeding_success_ISSF: Multivariate model to perform on iSSF estimates</em></p> <p><strong>This repository contains the following data files (in folder data-raw)</strong> :</p> <p><em>Nestboxes_2021: Nest box monitoring sheets 2021</em></p> <p><em><em>Nestboxes_2022: Nest box monitoring sheets 2022</em></em></p> <p><em><em>Nestboxes_2023: Nest box monitoring sheets 2023</em></em></p> <p><em>Starlings_ring: Starling ringing data</em></p> <p><em>pathogen_data</em>: Blood parasite infection data </p> <p> </p>
FIGURE 10 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 10. Phylogenetic consensus tree of the family Ortheziidae based on the Maximum Likelihood analysis of a morphological matrix. Node labels represent Ultrafast Bootstrap values generated in IQTree. Positions of outgroup taxa do not reflect higher-level relationships amongst Coccomorpha.
FIGURE 3 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 3. Barbenigma biza Powell & Miller, sp. nov., adult female paratype. Florida: Brevard Co.: Merritt Island, 4 VII 1988, S. Walker, Stenotaphrum secundatum, FSCA_00073119. a=dome-shaped seta; b=spine; c=tubular duct; d=posterior thoracic spiracle; e=metathoracic leg; f=simple pore; g=ventral seta with blunt, not capitate, apex; h=quadrilocular duct; i=quadrilocular duct cluster; j=spine; k=pore in anal ring; l=anal ring; m=fimbriate seta; n=spine; o=tubular duct; p=dome-shaped seta.
FIGURE 2 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 2. Barbenigma biza Powell & Miller, sp. nov., adult female holotype. Florida: Alachua Co.: Gainesville, 23 III 1999, R. Esser, C. Zamora, Rumex hastatulus, E99-879, FSCA_00073114.
FIGURE 9 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 9. Barbenigma boscus Powell & Miller, sp. nov., adult female holotype. Nebraska: Furnas Co., Arapahoe, 8 VII 2023, D.R. Miller, B.D. Denno, Bromus inermis, E-2023-07304, FSCA_00073131. (A) a=anal ring; b=truncate anal ring seta; c=quadrilocular duct cluster, d=capitate seta. (B) a=capitate setae, b=quadrilocular duct cluster; c=sclerotized left edge of vulva; d=tubular ducts. (C) Barbenigma boscus Powell & Miller, sp. nov., adult female paratype. Nebraska: Furnas Co., Arapahoe, 8 VII 2023, D.R. Miller, B.D. Denno, Bromus inermis, E-2023-07304, FSCA_00073132. a=apical seta on antenna; b=subapical seta on antenna; c=subapical seta on antenna; d=anterior thoracic spiracle pictured on side; e=tubular ducts; f=spine; g=acornshaped and dome-shaped setae in cluster between antennae. (D) Adult female holotype. a=ventral spines; b=marginal fimbriate seta; c=anterior thoracic spiracle.
FIGURE 1 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 1. Barbenigma biza Powell & Miller, sp. nov., adult female. The illustration utilized both the holotype female and a paratype female with the following data: holotype, Florida: Alachua Co.: Gainesville, 23 III 1999, R. Esser, C. Zamora, Rumex hastatulus, E99-879, FSCA_00073114; paratype, Florida: Brevard Co.: Merritt Island, 4 VII 1988, S. Walker, Stenotaphrum secundatum, FSCA_00073118. a=antenna; b=dome-shaped seta; c=fimbriate seta; d=anterior thoracic spiracle; e=simple pore; f=first abdominal spiracle; g=tubular duct; h=metathoracic leg; i=quadrilocular duct cluster; j=spine; k=hair-like seta; l=spine; m=pore in anal ring; n=anal ring; o=spine; p=simple pore; q=tubular duct; r=fimbriate seta.
FIGURE 6 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 6. Barbenigma biza Powell & Miller, sp. nov., second instar?, Florida: Orange Co.: Maitland, 5 XI 1969, H.M. VanPelt, in soil (lawn), FSCA_00073127. The anal ring enlargement utilized a second specimen with the following data: Florida: Marion Co.: Belleview, 15 IX 1966, H. Myers, in soil, DPI #125664, FSCA_00073129. a=acorn-shaped seta; b=tubular duct set in circular area; c=posterior thoracic spiracle; d=tubular duct; e=simple pore; f=hair-like seta; g=anal ring viewed from venter; h=looking straight down at anal ring; i=tubular duct; j=simple pore; k=hair-like seta; l=dome-shaped seta.
FIGURE 8 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 8. Barbenigma boscus Powell & Miller, sp. nov., adult female holotype. Nebraska: Furnas Co., Arapahoe, 8 VII 2023, D.R. Miller, B.D. Denno, Bromus inermis, E-2023-07304, FSCA_00073131. a=antenna; b=dome-shaped setae; c=simple pore; d=fimbriate seta; e=spine; f=posterior thoracic spiracle; g=ventral seta; h=tubular duct; i=metathoracic leg; j=ventral setae with capitate apex; k=quadrilocular duct; l=quadrilocular duct cluster; m=spine, n=tubular duct, o=anal ring, p=tubular duct; q=spine; r=fimbriate setae; s=simple pore; t=unequal-sided structure.
FIGURE 5 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 5. Barbenigma biza Powell & Miller, sp. nov., third instar?, Florida: Broward: Lighthouse Point, 17 VI 1974, F. Donaldson, Stenotaphrum secundatum, FSCA_00073125. a=acorn-shaped seta; b=prothoracic leg; c=alveolar derm surrounding the anterior thoracic spiracle; d=tubular duct; e=second abdominal spiracle; f=alveolar derm with spine and fimbriate seta; g=alveolar derm with fimbriate and hair-like setae; h=edge of anal ring; i=anal ring; j=fimbriate seta; k=alveolar derm with setae and patches of microtrichia; l=tubular duct; m=spine; n=fimbriate seta; o=acorn-shaped seta.
FIGURE 7 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 7. Barbenigma biza Powell & Miller, sp. nov., first instar?, Florida: Alachua Co.: Gainesville, 23 III 1999, R. Esser, C. Zamora, Rumex hastatulus, E99-879, FSCA_00073115. a=acorn-shaped setae; b=tubular duct on alveolar derm nodule; c=alveolar derm with anterior thoracic spiracle; d=tubular duct; e=simple pore; f=anal ring (specimen rolled), showing part of venter and part of dorsum; g=simple pore; h=hair-like seta; i=alveolar derm with marginal hair-like seta; j=tubular duct; k=dome-shaped seta.
FIGURE 4 in Barbenigma Powell & Miller, a bizarre new genus and two new species of scale insects (Hemiptera: Coccomorpha: Ortheziidae) from the United States
FIGURE 4. (A) Barbenigma biza Powell & Miller, sp. nov., holotype adult female, Florida: Alachua Co.: Gainesville, 23 III 1999, R. Esser, C. Zamora, Rumex hastatulus, E99-879, FSCA_00073114. a= tubular duct; b=quadrilocular duct cluster; c=ventral hair-like seta; d=fimbriate seta; e=spine; f=fimbriate seta. (B) Barbenigma biza Powell & Miller, sp. nov., adult female paratype. Florida: Brevard Co.: Merritt Island, 4 VII 1988, S. Walker, Stenotaphrum secundatum, FSCA_00073119. a=quadrilocular duct cluster; b=ventral hair-like seta; c=sclerotized right edge of vulva; d=tubular duct; e=spine. (C) Barbenigma biza Powell & Miller, sp. nov., adult female paratype. Florida: Palm Beach Co.: West Palm Beach, 15 XI 1965, R.S. Mullin, N. Bezona, Stenotaphrum secundatum, 124942, FSCA_00073122. a=acorn-shaped seta between antennae. (D) Barbenigma biza Powell & Miller, sp. nov., second-instar. Orange Co., Maitland, 5 XI 1969, in soil (lawn), coll. H.M. VanPelt, DPI# 125664, FSCA_00073126. a=tubular ducts set in raised circular area; b=tubular ducts.
FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting
<div>Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.</div> <div> </div> <div>This dataset comprises three folders: the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.</div> <div> </div> <div>FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.</div>
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.