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1,454 results for “large scale”
Figure 3 in Off the scale: a new species of fishscale gecko (Squamata: Gekkonidae: Geckolepis) with exceptionally large scales
Figure 3 Specimens of Geckolepis megalepis sp. nov. in life. (A) A specimen observed by A. Anker (photograph used with permission); (B) a specimen observed by FG, and (C) a specimen photographed after scale loss, with inset indicating the transparent 'tear zone' at the base of a scale. None of the photographed animals were collected, but their attribution to G. megalepis is clear on the basis of the large size of their scales. Note that the tails of all three specimens are regenerated.
Large-scale semantic indexing of Spanish biomedical literature using contrastive transfer learning
Open the record for dataset details and reuse information.
cigChannel: A large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation
<h2>Assorted channel subset of the cigChannel dataset (V1.0)</h2> <h2>Expansion package of folded and faulted structures</h2> <div> <p>This version includes the last 100 samples (400 samples in total) of the assorted channel subset. Each sample features folded and faulted structures. Explanations of the uploaded zip files are listed below: </p> </div> <ul> <li>Assorted_Channel_Ip_xx-xx.zip: Seismic impedance volumes of sample No.xx to No.xx.</li> <li>Assorted_Channel_Label_xx-xx.zip: Binary-class label volumes of sample No.xx to No.xx, where the value 0 represents the background (non-channel areas) and 1 represents the channel.</li> <li>Assorted_Channel_Seismic_xx-xx.zip: Seismic (amplitude) volumes of sample No.xx to No.xx.</li> </ul> <h3>Portal to the assorted channel subset (main package, the first 300 samples):</h3> <ul> <li>Assorted channel subset: <a href="https://doi.org/10.5281/zenodo.11044512" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11044512</a></li> </ul> <h3>Portals to the other subsets:</h3> <ul> <li>Submarine canyon subset: <a href="https://doi.org/10.5281/zenodo.11079950" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11079950</a></li> <li>Meandering channel subset: <a href="https://doi.org/10.5281/zenodo.11078794" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11078794</a></li> <li>Tributary channel network subset: <a href="https://doi.org/10.5281/zenodo.11073030" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11073030</a></li> </ul>
Output files for Degassing of CO2 triggers large-scale loss of helium from magma oceans
<p>This upload includes output files for the paper Degassing of CO2 triggers large-scale loss of helium from magma oceans.</p>
Data set for analyzing scaling and criticality in large-scale social systems regulated by global factors
<ul><li><strong>Research information</strong>:<ul><li>Title: Scaling and criticality in large-scale social systems regulated by global factors</li><li>Abstract: How do individuals in large-scale social systems behave in response to global factors and what kinds of macroscopic dynamics will emerge as consequences? Here we conduct a naturalistic observation of the daily consumer behaviours of 190,000 individuals from January 2018 to December 2022, extracting the long-term consumption dynamics of about 6000 types of commodities from approximately 2.2 million purchase orders. The consumption dynamics is subdivided into trend, seasonal, and random components, whose dynamic processes across scales are analyzed by a renormalization group. we discover that the coronavirus pandemic, an exogenetic factor acting on the social system, regulates the scaling and criticality of consumption dynamics. On a large time scale, the long-term dynamics of the system, regardless of arising from trend, seasonal, or random individual behaviours, is pushed towards a boundary between independent and correlated phases as the pandemic erupts. On a smaller time scale, short-term consumption dynamics exhibits more diversities in response to the pandemic. While the trend and random behaviours of individuals are driven to quasi-criticality and exhibit scale-invariance as the pandemic breaks out, seasonal behaviours are more robust against regulations. Overall, our study provides insights into the regulation mechanisms of global factors on the dynamics of large-scale and heterogeneous social systems.</li></ul></li><li><strong>Data set description</strong>:<ul><li>RawData: A csv file for approximately 2.2 million times purchase orders made by around 190,000 individuals before, during, and after the outbreak of coronavirus disease 2019 (COVID-19). </li><li>ConsumptionDynamics: A csv file that describes the daily evolution of the consumption frequencies for approximately 6,000 types of commodities. Each row denotes an extracted time series that measures the number of times a commodity is purchased each day. Each column denotes a single day.</li><li>Note that the names of commodities in current data set are writen in Chinese. </li></ul></li></ul><p> </p><p> </p>
PM100: A Job Power Consumption Dataset of a Large-Scale HPC System
<p>The dataset is a collection of jobs extracted from the job_table data of M100 (<a href="https://doi.org/10.5281/zenodo.7588815">https://doi.org/10.5281/zenodo.7588815</a>), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. </p><p>The dataset is stored as a parquet file, where each entry contains the information on a job execution. </p><p>The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: <a href="https://github.com/francescoantici/PM100-data/">https://github.com/francescoantici/PM100-data/</a>.</p>
A large-scale artificial forest tree population for sampling and estimation methods simulations
<p>The dataset utilized in the Simulation Study section of the article is generated for a 10 km x 10 km area. This artificial dataset is constructed by populating polygons with trees sourced from the NFI database, representing the stands of the associated Vosges polygon. The aim is to create a sizable dataset that closely resembles a real forest by incorporating authentic information. As the Vosges dataset consists of a set of polygons, the artificial dataset is formed using voronoi polygons, each randomly linked to a Vosges dataset polygon. The Vosges dataset contributes basal area information to the voronoi polygons. These voronoi polygons are then filled with trees from the NFI database, corresponding to the stands of the Vosges polygon. The dataset comprises 1,174,227 trees, each with specified attributes such as circumference at a height of 130cm, top height, volume, species, and status (dead/alive).<br> </p>
A tunable family of CAAC-ruthenium olefin metathesis catalysts modularly derived from a large-scale produced ibuprofen intermediate
<p>Data confirming the structure of the new compounds obtained within the project, published in <br><i><strong>Chem. Sci.</strong></i>, 2023,<strong>14</strong>, 10744-10755</p><p>The research was supported by the National Science Centre, Poland (OPUS grant DEC-2017/27/B/ST5/02563).</p>
Large-scale green grabbing for wind and solar PV development in Brazil
<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2><p>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.</p><p>Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).</p><p>We still provide a repository which at least allows to understand the procedures we used during the analysis.</p><h3>Land tenure data set</h3><p>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.</p><h3>Analysis of land tenure data and park ownership/investment information</h3><p>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.</p><p>The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:</p><p>- 1.1-figures-results-1-wind.R</p><p>- 1.2-figures-results-1-solar.R</p><p>These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:</p><p>- 2-random-sampling-areas.R</p><p>- 3-intersection-parks-land-tenure.R</p><p>- 4-figures-land-tenure.R</p><p>This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:</p><p>- 5-validation.R</p>
Tactician's Web of Large-Scale Formal Knowledge
<p>This dataset contains machine-readable data extracted by <a href="https://coq-tactician.github.io">Tactician</a> from formal knowledge encoded in the <a href="https://coq.inria.fr">Coq Proof Assistant</a>. It includes definitions, theorems, proof terms and tactical proofs presented both as Coq's default textual format and a graph format. The entire known universe of formal knowledge in Coq is encoded as a single interconnected graph of mathematical concepts where all knowledge is faithfully and unambiguously encoded. The dataset is intended for machine learning, analysis and general proof engineering purposes. In addition to exploring this offline dataset, it is also possible to interact directly with Coq in the same data format through <a href="https://coq-tactician.github.io/api">Tactician's API</a>.</p><p>See the README.md file for more information.</p>
Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds
<p>Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds.</p> <p> </p> <p>Please see the README for more information. </p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection
<p>This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.</p>
Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory
<p>Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration.</p> <p>In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics.</p> <p>Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios.</p> <p>The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of <em>Wjet</em> = 8.9 and 12.4 m/s (indicated as “<em>Wjet</em>” in the database files); (ii) equivalent full-scale roughness lengths <em>z0eq</em> = 0.007, 0.020, 0.32 m (“<em>z0eq</em>” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows:</p> <p>The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (<em>z</em>) of the probes were <em>z</em> = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “<em>v_zXXXXmm</em>”. In this notation, '<em>v</em>' designates the velocity component: longitudinal ‘<em>U</em>’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘<em>V</em>’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘<em>W</em>’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial <em>r</em> distances with respect to the jet impingement position in the range <em>r/D</em> (<em>D</em> = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position <em>r/D</em> = 0.8 was adjusted to <em>r/D</em> = 0.75. This modification was necessary due to irregularities on the chamber floor at <em>r/D</em> = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “<em>r/D_distance</em>” in the dataset files. The ceiling height of the testing chamber is <em>H</em> = 3.75 m, which leads to <em>H/D</em> > 1 allowing for a full vertical development of the downburst radial outflow. For every <em>r/D</em> position, each experiment with the same initial condition (i.e., <em>Wjet</em>) was repeated 10 times (“<em>repetition#</em>” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 <em>Wjet</em> × 3 rough surface × 10 repetitions × 10 <em>r/D</em> positions × 11 heights <em>z</em>) of downburst-like outflows were recorded during this set of experimental tests.</p> <p>The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>).</p> <p>DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “<em>z0eq</em>”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the <em>U – ln(z)</em> chart, employing the logarithmic law-of-the-wall (dependent on roughness length <em>z0</em> and friction velocity <em>u*</em>), the equivalent roughness lengths for the three surfaces were determined: <em>z0eq</em> = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively.</p> <p>In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (<em>U</em>, <em>V</em>, <em>W</em>) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order.</p> <p>Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.</p>
Using mobile device built-in microphones to monitor bats: A new opportunity for large-scale participatory science initiatives
<p>Here we present audio data collected in the study titled "Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives", now accepted for publication in Biodiversity and Conservation (DOI: 10.1007/s10531-024-02818-9).</p> <p>Citizen science has become a crucial tool in biodiversity monitoring, often facilitated by the diffusion of mobile devices, such as smartphones and tablets. High costs of professional equipment often limit large-scale monitoring, particularly in bat monitoring programmes based on acoustic surveys. Here we present the potential of using mobile devices for bat monitoring, allowing for large-scale, volunteer-based monitoring programmes. We initially compared mobile devices' performance with a professional bat detector for recording low-frequency bat calls. We then conducted a citizen science pilot study to test the method's feasibility in a real-world setting, recording echolocation and social calls of nine European bat species. We found high similarity in spectrogram quality between calls recorded by mobile devices and professional bat detectors. However, differences in sound quality and effectiveness among mobile device brands and models were found. The citizen science pilot study tested 35 mobile device models, all of which effectively recorded bats. This study suggests that mobile devices could be an accessible, no-cost tool for large-scale bat monitoring. Incorporating mobile devices into existing monitoring networks or creating new dedicated programmes could not only enhance data collection, but also boost public knowledge and awareness about bats, ultimately promoting informed decision-making and better conservation strategies.</p> <p>To facilitate a comprehensive evaluation of the acoustic data quality achievable through the described method, we are providing access to all bat recordings collected via mobile devices during the study. These have been compiled into the compressed file named "mobile_device_recs.zip".</p> <p>For those seeking a quicker review, we recommend downloading the "Appendix_S6_recs.zip" file, which contains selected examples of echolocation or social calls from the nine bat species recorded during our research. Spectrograms of these example recordings are displayed in Appendix S6 of the published study.</p>
Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"
<p>Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".</p>
A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration
Open the record for dataset details and reuse information.
Large-scale simulation of thermal conductivity in CaSiO3 perovskite with neuroevolution potential
<p>The dataset contains the thermal conductivity of CaSiO3 perovskit, MgSiO3 perovskite, periclase, as well as the heat flux across the core-mantle boundary.</p>
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Source data of the MultiSTAAR manuscript "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies".
<p>This dataset serves as the source data for Figures 2-3 and Extended Data Figures 1-2 of the MultiSTAAR manuscript titled "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies". MultiSTAAR is a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies.<br><br><strong>Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and ncRNA multi-trait analysis of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerids (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Figure 3.</strong> TOPMed genetic region (2-kb sliding window) unconditional multi-trait analysis results of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Extended Data Figure 1.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of fasting glucose (FG) and fasting insulin (FI) using TOPMed data (<em>n</em> = 21,731).<br><br><strong>Extended Data Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of C-reactive protein (CRP), interleukin 6 (IL-6), lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, and lipoprotein-associated phospholipase A2 (Lp-PLA2) mass using TOPMed data (<em>n</em> = 9,380).</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.