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1,454 results for “large scale”
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 2
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 2 OF 2 ***</strong></p> <p><strong>Download part 1 here:</strong> <a href="../records/7860223">https://zenodo.org/records/7860223 </a></p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.<br><br>_____________________________<br><br>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
EUV-Induced Hydrogen Desorption As A Step Towards Large-Scale Silicon Quantum Device Patterning
<p><strong>Dataset: </strong>STM, XPS and PEEM raw data, processed data and the codes used for data fitting our <a href="https://doi.org/10.1038/s41467-024-44790-6">published work</a> are all available here.</p> <p><strong>Abstract: </strong>Atomically precise hydrogen desorption lithography using scanning tunnelling microscopy (STM) has enabled the development of single-atom, quantum-electronic devices on a laboratory scale. Scaling up this technology to mass-produce these devices requires bridging the gap between the precision of STM and the processes used in next-generation semiconductor manufacturing. Here, we demonstrate the ability to remove hydrogen from a monohydride Si(001):H surface using extreme ultraviolet (EUV) light. We quantify the desorption characteristics using various techniques, including STM, X-ray photoelectron spectroscopy (XPS), and photoemission electron microscopy (XPEEM). Our results show that desorption is induced by secondary electrons from valence band excitations, consistent with an exactly solvable non-linear differential equation and compatible with the current 13.5 nm (~92 eV) EUV standard for photolithography; the data imply useful exposure times of order minutes for the 300 W sources characteristic of EUV infrastructure. This is an important step towards the EUV patterning of silicon surfaces without traditional resists, by offering the possibility for parallel processing in the fabrication of classical and quantum devices through deterministic doping.</p> <p> </p>
Large-scale annotated dataset for cochlear hair cell detection and classification
<p>Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5 - 15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following <i>in-vivo</i> and <i>in-vitro</i>ototoxic drug application. The dataset includes over 107,000 hair cells which have been manually identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. </p><p>Associated code is provided here: https://github.com/indzhykulianlab/hcat-data</p>
Data for: Characterization of large-scale preferential flow across continental United States
<p>Understanding preferential flow (PF) at large scales is critical for improving land management and groundwater (GW) quality. However, limited knowledge of this process, due to soil surface heterogeneity and observational constraints, hampers progress. In this study, we propose estimating effective PF at remote sensing footprint scale (4 – 9 km) by examining its impact on soil moisture (SM) distribution and shallow GW (SGW) table fluctuations (depth 5 m). Effective PF encompasses macropore, funnel, and finger flow pathways influencing SGW table fluctuations. We compiled daily SGW observations (2019-2021) from 19 continental US (CONUS) sites through USGS. Using inverse modeling in HYDRUS-1D, SGW data, and CHIRPS precipitation data, we inversely estimated soil hydraulic parameters of the dual porosity model (DPM) simulating vertical flow from soil surface to subsurface. Effective PF presence was inferred using three criteria: (1) daily precipitation >= the site-specific average across multiple (calibration) years, (2) daily observed SGW table increase, and (3) daily difference between observed and DPM simulated SGW tables 50% of the site-specific RMSE. Leveraging optimized DPM parameters and associated soil texture, classified PF events, and Soil Moisture Active Passive (SMAP L3E) satellite-based SM, a Random Forest algorithm with 10-fold cross validation predicted large-scale effective PF events. Results indicate seasonal dependence, with spring having the highest occurrence of PF events. The Random Forest model achieved 98% accuracy in predicting large-scale PF events, with SMAP SM and saturated hydraulic conductivity (Ks) among the 4 most impactful variables. Our approach provides a soil hydraulic property, site characteristic, soil texture and remote sensing based generalized tool to analyze large-scale effective PF.</p>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the Max Forest scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the base scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the No LULCC scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm.
Dataset for article "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design, Des. 2024, 24, 1, 378–390
<p>Supported dataset for publication "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design Des. 2024, 24, 1, 378–390, https://doi.org/10.1021/acs.cgd.3c01094.</p> <p>The directory Data contains the figures (png, jpg), and vido (AVI) source data files for the manuscript.</p>
Increasing sustainability in palaeoproteomics by optimizing digestion times for large-scale archaeological bone analyses
<p>Palaeoproteomic analysis of skeletal proteomes is used to provide taxonomic identifications for an increasing number of archaeological specimens. The success rate depends on a range of taphonomic factors and differences in the extraction protocols employed. By analyzing 12 archaeological bone specimens from two archaeological sites, we demonstrate that reducing digestion duration from 18 to 3 hours has no measurable impact on the obtained taxonomic identifications. Peptide marker recovery, COL1 sequence coverage, or proteome complexity are also not significantly impacted. Although we observe minor differences in sequence coverage and glutamine deamidation, these are not consistent across our dataset. A 6-fold reduction in digestion time reduces electricity consumption, and therefore CO<sub>2</sub> emission intensities. We furthermore demonstrate that working in 96-well plates further reduces electricity consumption by 60%, in comparison to individual microtubes. Reducing digestion time therefore has no impact on the taxonomic identifications, while reducing the environmental impact of palaeoproteomic projects.</p>
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini.
Synthetic dataset from - Bar et al., Sifting through the haystack - efficiently finding rare behaviors in large-scale datasets, WACV 2025
<p>This is a synthetic dataset emulating pose estimation data, introduced in the associated paper. </p> <p>Briefly, each sample in the dataset is a sequence of 5-keypoints with 9 timesteps. Movement of each keypoint in time is determined by a sinus with some amplitude A and some frequency f, this is loosely inspired by larval zebrafish swimming movement. <br>There are two types of behaviors - a common behavior (aka forming the majority of the samples in the dataset) where the frequency of the sinus is larger than the amplitude, and a rare one where the amplitude is larger than the frequency. <br>We vary the similarity between the rare and common behaviors by relaxing the standard deviation of the gaussian from which we draw these movement parameters (behavior similarity, sd= [0.5, 1.5, 2.5, 5]). We also test different levels of data imbalance, varying the frequency of the rare behavior (rarity=[1.5%,5%,12%,24%]). </p> <p>Thus we created 16 datasets with all possible combinations.</p> <p>The data generation code will become available in our code repository: https://github.com/shir3bar/SiftingTheHaystack</p> <p>The data was used to create a controlled experimental sandbox in which we could test our pipeline for detecting rare behaviors. Sounds interesting? Read our paper and check out the code :)</p> <p> </p> <p> </p>
Digital repository for: Large-scale forest disturbance and associated management shape bird communities in Central European spruce forests
<p>Repository containing R-script and data to reproduce analysis and main figures on the effect of large-scale forest disturbance and associated pre- and post-disturbance management on bird communities in the Harz Mountains, Germany.</p> <p>R-script includes:</p> <ul> <li>indicator species analysis (R package indicspecies; Cáceres & Legendre, 2009)</li> <li>non-metric multidimensional scaling (R package vegan; Oksanen et al., 2016)</li> <li>rarefaction- and extrapolation of Hill numbers (R package iNEXT; Hsieh et al., 2019)</li> <li>multi-species community distance sampling (R package sp Abundance; Doser et al., 2023)</li> </ul> <p>Attached files:</p> <ul> <li><strong>bird_data_Graser_et_al.csv </strong>(row data of bird species point counts per distance category)</li> <li><strong>bird_data_abundance_100_Graser_et_al.csv </strong>(abundance of species per sampling site, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>siteCovs_Graser_et_al.csv</strong> (environmental variables for each sampling point)</li> <li><strong>A_species_matrix_100_new_Graser_et_al.csv</strong> (species-site matrix of <strong>bark-beetle disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>B_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>windthrow disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>C_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle/windthrow disturbance, underplanted, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>D_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, salvage-unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>E_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, underplanted, salvage-unlogged </strong>sites for rarefaction and extrapolation, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong> F_species_matrix_100_new_Graser_et_al.cs</strong>v (species-site matrix of <strong>mature spruce plantation </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>msHDS_bird_data_management_model_Graser_et_al.rds</strong> (R-data set for multi-species community distance sampling of the effect of different pre- and post-disturbance management groups)</li> <li><strong>msHDS_bird_data_stand_age_model_Graser_et_al.rds </strong>(R-data set for multi-species community distance sampling of the effect of post-disturbance forest succession)</li> </ul> <p>A more detailed description of the data can be found in the README.txt document.</p> <p><span>References:</span></p> <p><span>Cáceres, M. D., & Legendre, P. (2009). </span><span>Associations between species and groups of sites: Indices and statistical inference. <em>Ecology</em>, <em>90</em>(12), 3566–3574. https://doi.org/10.1890/08-1823.1</span></p> <p><span>Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2023). spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>, <em>15</em>(6), 1024–1033. https://doi.org/10.1111/2041-210X.14332</span></p> <p><span>Hsieh, T. C., Ma, K. H., & Chao, A. (2019). <em>iNEXT-package: Interpolation and extrapolation for species diversity</em>. https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.html</span></p> <p><span>Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., Minchin, P. R., Gavin, L., & Henry, H. (2016). Vegan: Community ecology package. R package version 1.17-4. <em>Http://CRAN. R-Project. </em></span><em><span>Org/Package=vegan</span></em><span>.</span></p> <p></p> <p></p>
Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits
<p><span>Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA<sub>GRM</sub>, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPA<sub>GRM</sub> can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPA<sub>GRM(CCT)</sub> to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPA<sub>GRM</sub> maintains well-controlled type I error rates and SPA<sub>GRM(CCT)</sub> can serve as a broadly effective method. Applying SPA<sub>GRM</sub> to 79 longitudinal traits extracted from</span><span> </span><span>UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.</span></p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020
<p>The datasets are for the article 'Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020', including the WRF Initial conditions (horizontal wind at 850 hPa, geopotential height at 850 and 200 hPa) of sensitivity experiments (CTRL, W_WNPSH, W_SAH, W_TUTT, and S_SASM) in July 2020, and the formation records of the simulated TC formation in all experiments.</p>
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse
<p>The Reddit Politosphere is a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. Based on the <a href="https://doi.org/10.5281/zenodo.3608135">Pushshift Reddit Dataset</a>, it is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data. We also release annotated metadata for subreddits and users.</p> <p>Documentation and scripts for easy data access are provided in an associated <a href="https://github.com/valentinhofmann/politosphere">repository</a> on GitHub.</p>
2018 NSF Large Scale Experiment Workshop on Volcanic Blasts
<p><em><strong>A collection of datasets which were recorded at the 2018 NSF Large Scale Experiment Multiblast workshop on volcanic hazards</strong></em>. The workshop aimed to facilitate interdisciplinary collaboration and improve field-scale testing of monitoring methods and models. The workshop had 47 participants from US-based and international institutions. <a href="https://doi.org/10.1029/2018EO109237">Read some more details in this EOS article</a> or a <a href="https://doi.org/10.31223/X55W4F">full manuscript which is currently in review</a>.</p> <p><strong>attention</strong>: This dataset is UNDER CONSTRUCTION. It is close to, but not absolutely complete. We will publish version 1.0 once the accompanying JGR manuscript has been approved for publication.</p> <p>All data is provided in several zip archives, and small files containing metadata and descriptions. Large data chunks are separated into 'pads' (1–4), which refer to the four experiments that were performed. The archives contain a folder structure, which should allow for compatible extractions, so that archives can be downloaded to a common local folder (e.g. using a script) and extracted there without running into file name conflicts.</p> <p>Several teams collaborated to come up with this dataset. Below we list the teams from which data was used and is part of the current version of the dataset. More data may be published in the future and added in a later version. The teams collaborated to varying degrees for different tasks.</p> <p><strong>Teams</strong> in <em>alphabetical</em> order:</p> <ul> <li>Baylor<br> Baylor University<br> Lead by Kenneth Befus</li> <li>BYU<br> Brigham Young University<br> Lead by Neilsen<br> Contributors: TODO</li> <li>INGV<br> Istituto Nazionale di Geofisica e Vulcanologia, Rome<br> Lead by Taddeucci<br> Contributors: Ricci</li> <li>LDEO<br> Lamont Doherty Earth Observatory, Columbia University<br> Lead by Lev, Oppenheimer</li> <li>MTU<br> Michigan Tech University<br> Lead by Waite<br> Contributors: TODO</li> <li>UB<br> University at Buffalo<br> Lead by Sonder, Valentine<br> Contributors: David Hyman, Kayley DiemKaye, Norman Yu</li> <li>UCSB<br> University of California Santa Barbara<br> Lead by Matoza<br> Contributors: Sean Maher, Richard Sanderson</li> <li>UMKC<br> University of Missouri, Kansas City<br> Lead by Graettinger<br> Contributors: Kadie Bennis</li> <li>Yamagata<br> Yamagata University<br> Lead by Kae Tsunematsu</li> </ul> <p><strong>Parts of This Dataset</strong></p> <ul> <li><em>Coordinates & Positions:</em><br> Lead by the UB team.<br> Coordinates and Locations of Blast Charges, Sensors etc.</li> <li> <p><em>Elevation Data of Craters:</em><br> Lead by the UMKC and LDEO teams (Graettinger, Lev).<br> Elevation data were created from photographs taken right after charge detonations. The 3D-data was derived in a standard photogrammetry software (Metashape™). This data was then rasterized and imported into ArcGIS™, and is provided here. Fine adjustments were made to better match reference locations of the available site coordinate system.</p> </li> <li> <p><em>Ejecta Data:</em><br> Lead by the UMKC team.<br> Spatial distribution of ejected material.<br> The <code>.csv</code> files contain the same information as the Excel sheet, but do not contain any graphs.</p> </li> <li> <p><em>Airborne Pressure Data:</em><br> Lead by the BYU team.<br> Archive files: <code>buy_pad[i].zip</code>.<br> Data is arranged in four zip-archives, one for each blast sequence ("Pad"). Each file contains time and pressure arrays and some metadata of one microphone channel. Individual sensor locations are in the <code>positions.zip</code>.</p> <ul> <li>Format: Matlab <code>.mat</code></li> <li>File name patterns after unpacking:<br> <code>data/BYU Acoustics/Data/Aligned with Infra peaks/Pad [i]/TimeSyncPad[i]Ch[k].mat</code><br> <code>[i]</code>: Pad number (1 ... 4)<br> <code>[k]</code>: Channel number.</li> </ul> </li> <li> <p><em>Seismo-Acoustic Data:</em><br> Lead by two teams, UCSB and MTU, who deployed horizontally distributed (UCSB) and vertically distributed (MTU) seismometer stations, and infrasound sensors. MTU also provided a geophone chain. Some of the UCSB sensors were combined with the rapid BYU provided microphones to record a very wide frequency spectrum in ground and atmosphere.</p> <ul> <li> <p>UCSB data structure:<br> Data archives are provided in sensor groups for all experiments (pads). Archive file names are <code>ucsb_[sensor_type]_[sensor_gid].zip</code>. <code>[sensor_type]</code> is one of <code>inf</code> or <code>seis</code>. <code>[sensor_gid]</code> is an identifier for the sensor group (may also be a single sensor) the archive contains. E.g. <code>inf_nyi1</code> contains infrasound data of sensors <code>NYI1.1</code>, <code>NYI1.2</code> and <code>NYI1.3</code>. Use the preview window above to look into the archives. The <code>position</code> archive contains the sensor locations.</p> </li> <li> <p>MTU data structure:<br> Data archives (<code>mtu_seis-infr_pad[i].zip</code>) are organized in 'pads' 1 ... 4 and contain all sensors (infrasound, seismometer, geophones).</p> </li> </ul> </li> <li> <p><em>Video Material:</em><br> No leading team here. Cameras were contributed teams by INGV, LDEO, Yamagata, UB.<br> A drone was deployed for a map-view. Six or more cameras for each pad. Read the <code>video_readme.pdf</code> for details about camera locations and types.</p> </li> </ul> <p><strong>Changes</strong></p> <ul> <li>v0.5:<br> Added the <code>drone2</code> videos from Baylor. (All video zips were updated!)</li> <li>v0.4:<br> Added analysis pack 1 (<code>multiblast_analysis-pack1.zip</code>) code that produced figures and tables of the ms in review. This code is also available on <a href="https://gitlab.com/isonder/2018_blasts">gitlab.com/isonder/2018_blasts</a>.</li> <li>v0.3:<br> Added <code>mtu_seis_infr_metadata.zip</code>. Metadata for the MTU dataset.</li> <li>v0.2:<br> Second batch of main data. >90% complete, I guess.</li> <li>v0.1:<br> First batch of main data.</li> </ul>
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.