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9,882 results for “scales”
Disentangling direct and indirect drivers of farmland biodiversity at landscape scale
<p><span>To stop the ongoing decline of farmland biodiversity there are increasing claims for a paradigm shift in agriculture, namely from conserving and restoring farmland biodiversity at field scale (α-diversity) to doing it at landscape scale (γ-diversity). However, knowledge on factors driving farmland γ-diversity is currently limited. Here, we quantified farmland γ-diversity in 123 landscapes and analysed direct and indirect effects of abiotic and land-use factors shaping it using structural equation models. The direction and strength of effects of factors shaping γ-diversity were only partially consistent with what is known about factors shaping α-diversity, and indirect effects were often stronger than direct effects or even opposite. Thus, relationships between factors shaping α-diversity cannot simply be up-scaled to γ-diversity, and also indirect effects should no longer be neglected. Finally, we show that local mitigation measures benefit farmland γ-diversity at landscape scale and are therefore a useful tool for designing biodiversity-friendly landscapes. </span></p>
The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain
<p>A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org</p>
Hydraulic scale model experiments on the two-dimensional run-up and overtopping of solitary waves at a vertical wall and a dam-like structure
<p>This dataset includes the experimental data, which were generated during the study on the run-up and overtopping of solitary waves at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
DongTing: A Large-scale Dataset for Anomaly Detection of the Linux Kernel
<p>DongTing is the first large-scale dataset dedicated to Linux kernel anomaly detection. The dataset covers Linux kernels released in the last five years and includes a total of 18,966 well-labeled normal and attack sequences. The entire dataset is 85 GB in size (after decompression). The attack data covers 26 major kernel releases and contains a total of 12,116 system call sequences collected from running 17,855 bug-triggering programs. The normal data comes from 6,850 normal programs in four kernel regression test suites. We maintain the dataset and source code in Zenodo and Github, respectively, and back up the dataset and code in Baidu netdisk.</p> <h3><strong>Dataset</strong></h3> <p>The dataset is stored at <a href="http://doi.org/10.5281/zenodo.6627050">http://doi.org/10.5281/zenodo.6627050</a></p> <ul> <li>The data includes `abnormal_data`, `normal_data`, `models`, `npz` and baseline data, with a total volume of nearly 87 GB (including 85 GB for abnormal data and normal data, it's after decompression files size).</li> <li>The `Abnormal_data` directory contains 12,116 files containing system call sequence for 26 kernel releases, and the `Normal_data` directory contains 6,850 files containing system call sequences collected from four regression test suites. All of which are raw sequences.</li> <li>CNN/RNN, LSTM, and Wavenet (three sets of hyperparameters per model) machine learning models are selected, the ECOD model (without hyperparameters) was also chosen for the evaluation of DT. DT_abnormal, DT_normal, ADFA-LD, and PLAID are used for training respectively. The results of DT training models are stored in the directory `Models-DongTing`, and the results of ADFA-LD and PLAID training models are stored in the directory `Models-Comparison`.</li> <li>The directory `npz `stores the encoded dataset of DongTing, ADFA-LD, and PLAID (sequence length varies from 8 to 4495), according to syscall_64.tbl in Linux kernel 5.17, including the training set, validation set, and test set.</li> <li>The file `Baseline.xlsx` contains all the information about DongTing dataset, which can be used in training machine learning models. For example, the whole dataset is randomly divided into three sets with the ratio of 80%:10%:10% (training: validation: test). The implementation of dataset division can be found in the source code.</li> </ul> <h3><strong>Source Code</strong></h3> <p><br>The source code for dataset development is stored at <a href="https://github.com/HNUSystemsLab/DongTing">https://github.com/HNUSystemsLab/DongTing</a> and the following is a brief introduction.</p> <ul> <li>The source code contains three folders, i.e., `Source Code Files`, `Documents` and `DB`, where `Documents `stores the detailed documents related to development, `DB` stores samples data, and `Source Code Files` stores the source code related to the development of our dataset.</li> <li>The detailed description about the source code can be found in `Documents/Documentation.pdf`. The document consists of four parts: environment requirements, database, program structure and working steps, model training and evaluation (including training and evaluation). It details the preparation of the environment, data import method, functional description of each file in the source code directory, how model training and evaluation work and other related contents.</li> </ul> <p>We additionally maintain the dataset and source code on Baidu.com <a href="https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds">https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds</a> to facilitate the access from China.</p> <p> </p> <h3>Tips: </h3> <p>If you find DongTing useful for your research, please cite the article as "DongTing: A large-scale dataset for anomaly detection of the Linux kernel".</p> <blockquote> <p><br>@article{DUAN2023111745,<br>title = {DongTing: A large-scale dataset for anomaly detection of the Linux kernel},<br>journal = {Journal of Systems and Software},<br>volume = {203},<br>pages = {111745},<br>year = {2023},<br>issn = {0164-1212},<br>doi = {https://doi.org/10.1016/j.jss.2023.111745},<br>url = {https://www.sciencedirect.com/science/article/pii/S0164121223001401},<br>author = {Guoyun Duan and Yuanzhi Fu and Minjie Cai and Hao Chen and Jianhua Sun}<br>}<br><br></p> </blockquote>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of spatiotemporal covariates that could potentially explain pronghorn (Antilocapra americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem (50.0757<sup>o</sup> N, −108.7526<sup>o</sup> W). Known-fate data were collected from 170 adult female pronghorn monitored with GPS collars from 2003-2011, which were used to construct a time-to-event (TTE) dataset with a daily timescale and an annual recurrent origin of 11 November. Seasonal risk periods (winter, spring, summer, autumn) were defined by median migration dates of collared pronghorn. We linked this TTE dataset with spatiotemporal covariates that were extracted and collated from pronghorn seasonal activity areas (estimated using 95% minimum convex polygons) to form a final dataset. Specifically, average fence and road densities (km/km2), average snow water equivalent (SWE; kg/m2), and maximum decadal normalized difference vegetation index (NDVI) were considered as predictors. We tested for these main effects of spatiotemporal risk covariates as well as the hypotheses that pronghorn mortality risk from roads or fences could be intensified during severe winter weather (i.e., interactions: SWE*road density and SWE*fence density). We also compare an analogous frequentist implementation to estimate model-averaged risk coefficients. Ultimately, the study aimed to develop the first broad-scale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.</p> <p> </p>
Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"
<p>This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar". </p>
Environmental DNA reveals fine-scale habitat associations for sedentary and resident marine species across a coastal mosaic of soft and hard-bottom habitats
<p>Accurate knowledge on spatiotemporal distributions of marine species and their association with surrounding habitats is crucial to inform adaptive management actions responding to coastal degradation across the globe. Here, we investigate the potential use of environmental DNA (eDNA) to detect species-habitat associations in a patchy coastal area of the Baltic Sea. We directly compare species-specific qPCR analysis of eDNA with baited remote underwater video systems (BRUVS), two non-invasive methods widely used to monitor marine habitats. Four focal species (cod Gadus morhua, flounder Platichthys flesus, plaice Pleuronectes platessa and goldsinny wrasse Ctenolabrus rupestris) were selected based on contrasting habitat associations (reef- vs. sand-associated species), as well as differential levels of mobility and residency, to investigate whether these factors affected the detection of species-habitat associations from eDNA. To this end, a species-specific qPCR assay for goldsinny wrasse is developed and made available herein. In addition, potential correlations between eDNA signals and abundance counts (MaxN) from videos were assessed. Results from Bayesian multi-level models revealed strong evidence for a sand association for sedentary flounder (98% posterior probability) and a reef association for highly resident wrasse (99% posterior probability) using eDNA, in agreement with BRUVS. However, contrary to BRUVS, eDNA sampling did not detect habitat associations for cod or plaice. We found a positive correlation between eDNA detection and MaxN for wrasse (posterior probability 95%), but not for the remaining species and explanatory power of all relationships was generally limited. Our results indicate that eDNA sampling can detect species-habitat associations on a fine spatial scale, yet this ability likely depends on the mobility and residency of the target organism, with associations for sedentary or resident species most likely to be detected. Combined sampling with conventional non-invasive methods is advised to improve detection of habitat associations for mobile and transient species, or for species with low eDNA concentrations. </p>
WaterBench-Iowa: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting
<p>WaterBench-Iowa is a comprehensive benchmark dataset for streamflow forecasting. It follows FAIR data principles that are prepared with a focus on convenience for utilizing in data-driven and machine learning studies and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies. This dataset has a high temporal and spatial resolution with rich metadata and relational information, which can be used for varieties of deep learning and machine learning research. To some extent, WaterBench makes up for the lack of a unified benchmark in earth science research. We highly encourage researchers to use the WaterBench for deep learning research in hydrology.</p>
Datasets for ``Dissipative magnetic structures and scales in small-scale dynamos''
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Dissipative magnetic structures and scales in small-scale dynamos" by A. Brandenburg, I. Rogachevskii, and J. Schober. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood: Additional data
<p>Contains data used in the following publication:</p> <p>Felix Neff, Jonas Hagge, Rafael Achury, Didem Ambarlı, Christian Ammer, Peter Schall, Sebastian Seibold, Michael Staab, Wolfgang W. Weisser, Martin M. Gossner (2022). <em>Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood. </em>Functional Ecology. <a href="https://doi.org/10.1111/1365-2435.14186">https://doi.org/10.1111/1365-2435.14186</a></p> <p>These are complementary data, which are needed to reproduce the analyses. Most data are archived in the Biodiversity Exploratories Information System (<a href="https://doi.org/10.17616/R32P9Q">https://doi.org/10.17616/R32P9Q</a>).</p> <p>The following data are included:</p> <ul> <li><strong>BELongDead_Subplots_Normal.csv</strong>: List of <em>normal</em> subplots within the BELongDead projects (only these were included in the analyses)</li> <li><strong>Body_length.csv</strong>: Body length data for study species assembled from Freude et al. (1965-1998)</li> <li><strong>Lightness_completion.csv</strong>: Colour lightness recorded from measured individuals and photos from coleonet.de (Lompe, 2002)</li> <li><strong>Name_standardisation.csv</strong>: Dataset used to standardise taxonomic names from different sources</li> <li><strong>Saproxylic_species_sub.csv</strong>: List of saproxylic species (according to Schmidl & Bussler (2004)), which were recorded in the project</li> <li><strong>Similar_Species.csv</strong>: List of similar species for species with missing traits. Based on these, missing traits were estimated</li> </ul> <p><strong>References</strong></p> <p>Freude, H., Harde, K. W., & Lohse, G. A. (1965–1998). <em>Die Käfer Mitteleuropas Band 1-15</em>. Goecke und Evers.</p> <p>Lompe, A. (2002). <em>Käfer Europas</em>. <a href="http://coleonet.de/">http://coleonet.de/</a></p> <p>Schmidl, J., & Bussler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. <em>Naturschutz und Landschaftsplanung</em>, <em>36</em>(7), 202–218.</p>
Deep Learning based Urban Morphology for City-scale Environmental Modeling
<p>The WRF simulations were performed using the Weather Research and Forecasting (WRF) model, version 4.2.1. The three nested domains are centered over Chicago, USA, with a spatial resolution of 9, 3, and 1 km for the outermost, middle, and innermost domains. The model was implemented with 42 pressure levels, with the first model level located at 21.2 m and the first 1 km vertical height containing 11 model levels. The initial and boundary conditions are taken from the National Centers for Environmental Prediction (NCEP) Final Reanalysis dataset at 1 degree spatial and 6-hourly temporal resolution.</p><p>The physics components include the WRF single moment 6 class for microphysics, Dudhia for shortwave, the Rapid Radiative Transfer Model for longwave radiation parameterizations, Bougeault for the planetary boundary layer, Noah for the land surface model, Building Environment Parametrization (BEP) for the urban model, and Grell for the cumulus scheme (only for the outermost domain of 9 km spatial resolution). The LCZs of Chicago, USA, are generated using the crowd-sourcing method. The training dataset, created manually, is obtained from the WUDAPT portal, and random forest classification is applied to Landsat 8 imagery to derive the LCZs for the desired region. The simulations are performed from 1/Jul/2018 00:00 to 7/Jul/2018 06:00, where the first 6 hours are discarded as spin-up time.</p><p>The Digital Synthetic City (DSC) of Chicago, USA, uses satellite imagery and global-scale population and elevation data as input to the automatic method for producing a statistically similar and synthetic city-scale 3D urban model as output.</p><p>The Control simulations use National Land Cover Database land use/land cover with NUDAPT parameters, the three default WRF urban classes, and corresponding UCPs; the WUDAPT uses the MODIS classes with additional urban LCZs and UCPs from Brousse et al. (2016), and the DSC uses the WUDAPT classes with UCPs generated from DSC method.</p><p>The dataset contains:</p><p>1. Output from DSC in Shapefile.</p><p>2. WRF model output for the third domain (1 km) spatial resolution domain for (a) NUDAPT or Control (b) WUDAPT or LCZs (c) DSC</p>
Simulations in Support of Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1) Paper
<p>Simulations done in support of manuscript entitled: Importance of Ice Nucleation and Precipitation on Climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)</p>
Datasets for "Scaling of the Hosking integral in decaying magnetically-dominated turbulence"
<pre>This directory contains an index.html file with links to the run directories with secondary data for the figures and a Mathematica notebook for make the figures for the paper "Scaling of the Hosking integral in decaying magnetically-dominated turbulence" by H. Zhou, R. Sharma, and A. Brandenburg. If anything turns out to be incomplete, please email hongzhe.zhou@sjtu.edu.cn and brandenb@nordita.org. </pre>
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
<p>This dataset is provided for the paper "Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data".</p> <p>Please refer to the readme in the zipped file for additional documentation.</p> <p>The zipped file contains two CSV files for every country in Africa obtained by queries "[country name]" and "[country name + people]".</p>
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418).</p> <p>Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided.</p> <p>This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.</p> <p> </p>
Dataset for "Imaging of Small-Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results from Laboratory Experiments"
<p>The codes for Monte-Carlo simulation, scripts used to calculate the misfit kernels, and the processed data of the laboratory experiment.</p>
Scale insects support natural enemies in both landscape trees and shrubs below them
<p>Scale insects are frequently abundant on urban trees. Although scales can worsen tree condition, some tree species tolerate moderate scale densities. Scales are prey for many natural enemies. Therefore, scale-infested trees may conserve natural enemies in their canopies and in nearby plants. We examined if scale-infested oaks—<em>Quercus</em> <em>phellos</em> L.—hosted more natural enemies than scale-uninfested oaks—<em>Q</em>. <em>acutissima</em> Carruth. and <em>Q</em>. <em>lyrata Walter</em> in Raleigh, NC. USA. We also tested if natural enemies were more abundant in holly shrubs (<em>Ilex</em> spp.) planted below scale-infested compared to scale-uninfested oaks. We collected natural enemies from the canopies of both tree types and from holly shrubs planted below these trees. To determine if tree type affected the abundance of natural enemies that passively dispersed to shrubs, we created hanging cup traps to collect arthropods as they fell from trees. To determine if enemies became more abundant on shrubs below scale-infested compared to scale-uninfested trees over short time scales, we collected natural enemies from holly shrubs below each tree type at three to six-day intervals. Scale-infested trees hosted more natural enemies than scale-uninfested trees and shrubs below scale-infested trees hosted more natural enemies than shrubs under scale-uninfested trees. Natural enemy abundance in hanging cup traps did not differ by tree type; however, shrubs underneath scale-infested trees accumulated more natural enemies than shrubs under scale-uninfested trees in six to nine days. Tolerating moderate pest densities in urban trees may support natural enemy communities, and thus biological control services, in shrubs below them.</p>
Text-fig. 3. Upper Cretaceous to Oligocene/Miocene Sciadopityspollenites taxa, all scale bars in LM and SEM overview images 10 µm, scale bars in SEM detailed images 2 µm. a–f: cf. Sciadopityspollenites serratus from Vilui basin (Siberia), a – LM image, equatorial view, b – SEM equatorial overview with leptoma, c – detail SEM of leptoma? and echinate verrucae, d – LM image equatorial view, e – SEM of distal polar view, f – SEM detailed view of verrucate, echinate perforate sexine sculpturing; g–i: Sciadopityspollenites serratus from Bayerhof Maar (Germany), g – LM image of proximal polar side, h – SEM overview of distal polar side, i – SEM detail of verrucate, echinate, perforate sculpturing. in The Occurrence Of Pollen Of Sciadopityaceae Luerss. Through Time
Text-fig. 3. Upper Cretaceous to Oligocene/Miocene Sciadopityspollenites taxa, all scale bars in LM and SEM overview images 10 µm, scale bars in SEM detailed images 2 µm. a–f: cf. Sciadopityspollenites serratus from Vilui basin (Siberia), a – LM image, equatorial view, b – SEM equatorial overview with leptoma, c – detail SEM of leptoma? and echinate verrucae, d – LM image equatorial view, e – SEM of distal polar view, f – SEM detailed view of verrucate, echinate perforate sexine sculpturing; g–i: Sciadopityspollenites serratus from Bayerhof Maar (Germany), g – LM image of proximal polar side, h – SEM overview of distal polar side, i – SEM detail of verrucate, echinate, perforate sculpturing.
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.