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61,240 results for “two”
A Stitch in Time: Combining More than Two Decades of Mooring Data from the Central Oregon Shelf
<p>The highly biologically productive northern California Current, which includes the Oregon continental shelf, is an archetypal eastern boundary region with summertime upwelling driven by prevailing equatorward winds and wintertime downwelling driven by prevailing poleward winds. Between 1960 and 1990, monitoring programs and process studies conducted off the central Oregon coast advanced the understanding of many oceanographic processes, including coastal trapped waves, seasonal upwelling and downwelling in eastern boundary upwelling systems, and seasonal variability of coastal currents. Starting in 1997, the U.S. Global Ocean Ecosystems Dynamics – Long Term Observational Program (GLOBEC-LTOP) continued those monitoring and process study efforts by conducting routine CTD (Conductivity, Temperature, and Depth) and biological sampling survey cruises along the Newport Hydrographic Line (NHL; 44.652°N, 124.1 – 124.65°W), located west of Newport, Oregon. Additionally, GLOBEC-LTOP maintained a mooring slightly south of the NHL, nominally at 44.64°N, 124.30°W, on the 81-meter isobath. This location is referred to as NH-10, as it is located 10 nautical miles or 18.5 km west of Newport. A mooring was first deployed at NH-10 in August 1997. This subsurface mooring collected water column velocity data using an upward-looking acoustic Doppler current profiler. A second mooring with a surface expression was deployed at NH-10 starting in April 1999. This mooring included velocity, temperature and conductivity measurements throughout the water column as well as meteorological measurements. GLOBEC-LTOP and the Oregon State University (OSU) National Oceanographic Partnership Program (NOPP) provided funding for the NH-10 moorings from August 1997 to December 2004. Since June 2006, the NH-10 site has been occupied by a series of moorings operated and maintained by OSU with funding from the Oregon Coastal Ocean Observing System (OrCOOS), the Northwest Association of Networked Ocean Observing Systems (NANOOS), the Center for Coastal Margin Observation & Prediction (CMOP), and most recently the Ocean Observatories Initiative (OOI). While the objectives of these programs differed, each program contributed to long-term observing efforts with moorings routinely measuring meteorological and physical oceanographic variables. This article provides a brief description of each of the six programs, their associated moorings at NH-10, and our efforts to combine over twenty years of temperature, practical salinity, and velocity data into one coherent, hourly averaged, quality-controlled data set. Additionally, the data set includes best-fit seasonal cycles calculated at a daily temporal resolution for each variable using harmonic analysis with a three-harmonic fit to the observations.</p>
Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".
<p><strong>Publication: </strong>Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.</p> <p> </p> <p><strong>Abstract: </strong> Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.</p> <p> </p> <p><strong>This record contains two separate mass-spectrometry metabolomics data sets associated with this study:</strong></p> <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> <p>For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.</p> <p> </p> <p><strong>Data sets in other repositories associated with the same study:</strong></p> <p>Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories:</p> <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul> <p> </p>
Data for: Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality
<p>Data for publication: T. Begusic and G. A. Blake, Two-dimensional infrared-Raman spectroscopy as a probe of water’s tetrahedrality (2022).</p> <p>Contains raw data, processed data, and processing and plotting scripts for the results presented in the manuscript. See README files enclosed in the dataset for details about the files and directories. Main results were produced with codes available at https://github.com/tbegusic/i-pi and https://github.com/tbegusic/encorr.</p>
Reconstruction of prokaryotic genomes from ten termite gut metagenomes using two distinct workflows: SnakeMAGs and ATLAS.
<p><strong><em>SnakeMAGs</em></strong> (Nachida Tadrent, Franck Dedeine, Vincent Hervé (Submitted). <em>SnakeMAGs</em>: a simple, efficient, flexible and scalable workflow to reconstruct prokaryotic genomes from metagenomes<em>.</em> <a href="https://doi.org/10.5281/zenodo.7303463">https://doi.org/10.5281/zenodo.7303463</a>; https://github.com/Nachida08/SnakeMAGs) is a workflow for building MAGs (Metagenome Assembled Genomes) from raw Illumina metagenomic reads. During the test phase of the development of this tool, a comparative analysis with another workflow called ATLAS v2.9.1 (<em>Kieser </em>et al, 2020) was performed. To compare these two workflows, we analyzed ten publicly available termite gut metagenomes (accession numbers: SRR10402454; SRR14739927; SRR8296321; SRR8296327; SRR8296329; SRR8296337; SRR8296343; DRR097505; SRR7466794; SRR7466795) from five different studies : Waidele et al, 2019; Tokuda et al, 2018; Romero Victorica et al, 2020; Moreira et al, 2021; and Calusinska et al, 2020.</p> <p>In this repository, we provide the configuration files that were used to launch each of the workflows (SnakeMAGs_config.yaml and ATLAS_config.yaml), as well as the obtained results, <em>i.e. </em>the MAGs reconstructed from each metagenome and their taxonomic classification.</p>
Data for: The function of stilt roots in the growth strategy of Socratea exorrhiza (Arecaceae) at two neotropical sites
<p>We provide the raw and processed data used in the following study: Goldsmith, G. R., & Zahawi, R. A. (2007). The function of stilt roots in the growth strategy of Socratea exorrhiza (Arecaceae) at two neotropical sites. <em>Revista de Biología Tropical</em>, <em>55</em>(3-4), 787-793.</p> <p><strong>Methods</strong> can be found in the file entitled "README-GoldsmithZahawi-SocrateaData-5Mar23F.txt," while metadata for data columns can be found in the file entitled: "GoldsmithZahawi-SocrateaMetaData-5March2023.csv."</p> <p><strong>Original Published Abstract</strong>: Arboreal palms have developed a variety of structural root modifications and systems to adapt to the<br> harsh abiotic conditions of tropical rain forests. Stilt roots have been proposed to serve a number of functions<br> including the facilitation of rapid vertical growth to the canopy and enhanced mechanical stability. To examine<br> whether stilt roots provide these functions, we compared stilt root characteristics of the neotropical palm tree<br> Socratea exorrhiza on sloped (>20º) and flat locations at two lowland neotropical sites. S. exorrhiza (n=80 trees)<br> did not demonstrate differences in number of roots, vertical stilt root height, root cone circumference, root cone<br> volume, or location of roots as related to slope. However, we found positive relationships between allocation<br> to vertical growth and stilt root architecture including root cone circumference, number of roots, and root cone<br> volume. Accordingly, stilt roots may allow S. exorrhiza to increase height and maintain mechanical stability<br> without having to concurrently invest in increased stem diameter and underground root structure. This strategy<br> likely increases the species ability to rapidly exploit light gaps as compared to non-stilt root palms and may also<br> enhance survival as mature trees approach the theoretical limits of their mechanical stability.</p>
Venkataraman et al. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought
<p>VERSION 1: These supplementary files accompany the manuscript by Venkataraman et al. entitled "Rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought." This includes all raw data in the paper, supplementary data, and instructions for the blood puck feeder.</p> <p>VERSION 2: Supplemental Data Files 16-20 were added on 12/19/2022 to accompany a revision of the original bioRxiv pre-print after peer-review at eLife.</p> <p>VERSION 3: New versions of all files were added on 3/21/2023 to accompany the version of record published in eLife:</p> <p>Krithika Venkataraman , Nadav Shai, Priyanka Lakhiani, Sarah Zylka, Jieqing Zhao, Margaret Herre, Joshua Zeng, Lauren A Neal, Henrik Molina, Li Zhao, Leslie B Vosshall. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought. Elife. 2023 Feb 6;12:e80489. PMID: 36744865 DOI: 10.7554/eLife.80489</p>
Two years of CO2 CO and CH4 from The Cyprus Institute at Nicosia, Cyprus
<p>Two years of carbon dioxide (CO2), carbon monoxide (CO) and methane (CH4) concentration measurements, were performed for the first time in the city of Nicosia, Cyprus from 11/02/2020 to 07/09/2022.</p> <p>The dataset is generated from three Picarro G2401s. Property of LSCE (187) and CYI (1172 &1173). They were consecutively installed at the Cyprus Institute, on top of the NTL building, in Nicosia residential area. The dataset was processed by LSCE at Gif-sur-Yvette in France and calibrated against a World Meteorological Organization (WMO) reference scale. </p> <p>The Eastern Mediterranean and Middle East (EMME) region, with its population of more than 400 million, is identified as one of the primary climate “hot spots” worldwide, experiencing adverse impacts ranging from extreme weather events to poor air quality. Projections show that these phenomena are expected to further exacerbate in the coming decades. At central position, lies Cyprus, an island country that receives long-range transported pollution from various anthropogenic and natural sources.</p> <p> </p>
Simulated shielding performance of 2500 two-layer permutations of the first 50 chemical elements against trapped particles on GTO
<p>The 2Layer.csv file contains ionizing dose results of simulating the shielding performance of all 2500 two-layer permutations of the first 50 chemical elements against trapped particles in Geostationary Transfer Orbit (GTO). The material properties are sourced from the Geant4 material database, with materials referenced by their Geant4 material database names. For elements that are gases under normal conditions, the liquified version from the Geant4 database is used if available, denoted by an "l" prefix (e.g., "G4_lH2"). The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac. The simulated geometry is provided in the file 2Layer.gdml.</p> <p>The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV. The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 2.1e+11 electrons and 8.4e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the two layers being 0.75 g/cm2 in depth. The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.</p> <p>Column A: ID of the material combination<br> Column B: Z-Number of Material 1<br> Column C: Z-Number of Material 2<br> Column D: Name of the first layer material.<br> Column E: Name of the second layer material.<br> Column F: Ionizing dose due to electrons in units of kRad per month.<br> Column G: Statistical error in the electron dose in kRad per month.<br> Column H: Ionizing dose due to protons in kRad per month.<br> Column I: Statistical error in the proton dose in kRad per month.<br> Column J: Total ionizing dose from electrons and protons in kRad per month.<br> Column K: Statistical error in the total ionizing dose in kRad per month.<br> Column L: Rank of the material combination sorted from lowest electron dose to highest.<br> Column M: Rank of the material combination sorted from lowest proton dose to highest.<br> Column N: Rank of the material combination sorted from lowest total ionizing dose to highest.</p> <p>All dose values are rounded according to their statistical uncertainty as reported by GRAS/Geant4.</p>
Failure without tears: Two-step attachment in a climbing cactus
<p>Climbing plants can be extremely adaptable to diverse habitats and capable of colonizing perturbed, unstructured and even moving environments. The timing of the attachment process, whether instantaneous (e.g., a pre-formed hook) or slow (growth process) crucially depends on the environmental context and the evolutionary history of the group concerned. We observed how spines and adhesive roots develop and tested their mechanical strength in the climbing cactus <em>Selenicereus setaceus</em> (Cactaceae) in its natural habitat. Spines are formed on the edges of the triangular cross section of the climbing stem and originate in soft axillary buds (areoles). Roots are formed in the inner hard core of the stem (wood cylinder) and grow via tunnelling through soft tissue, emerging from the outer skin. We measured maximal spine strength and root strength via simple tensile tests using a field measuring Instron device. Spine and root strengths differ, and this has a biological significance for the support of the stem. Our measurements indicate that the measured mean strength of a single spine could theoretically support an average force of 2.8 N. This corresponds to an equivalent stem length of 2.62 m (mass of 285 g). The measured mean strength of root could theoretically support an average of 13.71 N. This corresponds to a stem length of 12.91 m (mass of 1398 g). We introduce the notion of two-step attachment in climbing plants. In this cactus, the first step deploys hooks to attach to a substrate; this process is instantaneous and is highly adapted for moving environments. The second step involves root growth for more solid attachment to the substrate; this process involves “slow” root growth and adhesion. The advantage of the two-step strategy is that fast hook attachment can steady the plant for the slower root attachment. This strategy is effective in highly variable and heterogeneous environments when climbing plants are faced with constant moving and perturbed environmental conditions in many ecosystems. We discuss how two-step mechanisms are of interest for technical anchoring applications particularly for “soft-bodied” artefacts, which have to deploy hard and stiff materials originating from a soft compliant body and where tasks involving attachment take place in highly unstable and unpredictable environments.</p>
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
<p>This archive contains all the code and data to reproduce the results of the associated manuscript: Piovani <em>et al</em>, "Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories". We provide filtered scRNA-seq matrices, protein fasta files for each specie used to run SAMap and GenERA as well as the R-code used to generate the datasets and the jupyter notebook to generate SAMap results. In addition we provide the final Seurat objects and all analysis results which can be consulted without re-running the code.</p>
Dataset for "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties"
<p>Data of measurements and model output of the publication "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties". NO DOI YET</p> <p>The data consists of micrometeorological data, COS,CO<sub>2</sub> and H2O flux measurements and resistances of two soybean cultivars at an agricultural field in Italy.</p> <p>For additional information, please contact: <a href="mailto:felix.spielmann@uibk.ac.at">Felix.Spielmann@uibk.ac.at</a> or <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>.</p>
Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials
<p>These are the data associated with the paper "Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials" published in <a href="http://doi.org/10.1364/OE.484232"><em>Optics Express</em> <strong>31</strong>, 18509 (2023)</a></p> <ul> <li>gapsFrequenciesAndWidths.h5: each line of the table lists the largest and second largest gap widths and frequencies in GHz for all 5000 samples studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>largest gap frequency (GHz)</td> </tr> <tr> <td>2</td> <td>largest gap width (GHz)</td> </tr> <tr> <td>3</td> <td>second largest gap frequency (GHz)</td> </tr> <tr> <td>4</td> <td>second largest gap width (GHz)</td> </tr> </tbody> </table> </li> <li>DOS.h5: normalized density of states as a function of the frequency (GHz) averaged over all samples for all <span class="math-tex">\(\chi\)</span> values studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>Frequency (GHz)</td> </tr> <tr> <td>2</td> <td>nDOS</td> </tr> </tbody> </table> <p> </p> </li> </ul>
Comparison of occlusal dental wear and degenerative alterations of the temporomandibular joint in two medieval populations from central Europe
<p>This repository contains the radiographs of the mandibular condyles published in the paper titled "Comparison of dental status parameters and degenerative alterations of the temporomandibular joint in central European medieval skeletal finds". The radiographs were obtained with a portable X-ray unit (Kavo Nomad-Pro).</p> <p>Selected CT reconstructions of the condyles are available for a sub-group of individuals.</p> <p>Files naming convention:</p> <p>- Files name start with the burial number (ex. B10; Z98);</p> <p>- R and L stand for "right" and "left";</p> <p>- AP: anteroposterior view; LM: lateromedial view.</p>
Arctic shoreline displacement and validation data for two pilot study areas
<p>Arctic shoreline displacement data for two pilot study areas are supporting information for the paper <em>Nylén, Calle-Navarro and Gonzales-Inca: Arctic shoreline displacement with open satellite imagery and data fusion – Pilot study 1984–2022</em><em>. </em>The two study areas are:</p> <ul> <li>Tanafjorden: a low-arctic meso-tidal fjord coast in mainland Norway</li> <li>Ny-Ålesund: a high-arctic micro-tidal glaciated coast in north-western Svalbard</li> </ul> <p>The study areas are 2500 km² each.</p> <p>The dataset includes following files:</p> <ul> <li><em>Calculating_coastal_landcover_timeseries_summaries.R</em>: code for summarizing the coastal land cover time-series in the R software.</li> <li><em>Calculating_shoreline_timeseries.R</em>: code for calculating the shoreline by fitting and smoothing a polyline to the land cover raster in the R software.</li> <li><em>X_timeseries.tif</em>: a multiband GeoTIFF raster file, with each band describing coastal land cover during one of the eight time-steps (1984–1988, 1989–1993, 1994–1998, 1999–2003, 2004–2008, 2009–2013, 2014–2018 and 2019–2022).</li> <li><em>X_summary.tif</em>: a multiband GeoTIFF raster file, with bands that summarize the time-series from different viewpoints. These summary variables are: probability of belonging to the land class, long-term trend (between 1984-2003 and 2004-2022), change intensity, first time-step in water class, last time-step in water class, first time-step in land class and last time-step in land class.</li> <li><em>X_shoreline.geojson</em>: a GeoJSON vector file, consisting of polylines for the shoreline during each time-step. The attributes of the polyline layer describe the time-step and the total length of the shoreline.</li> <li><em>NyAlesund_timeseries_REDUCED.tif</em>: a reduced time-series for the Ny-Ålesund study area, including only the time-steps with adequate number of observations (i.e., excluding 1984–1988, 1994–1998 and 2004–2008).</li> <li><em>X_reference_shoreline.geojson</em>: a GeoJSON vector file including the manually digitized (scale 1/5000) reference shoreline corresponding to the time-step 2019–2022.</li> <li><em>X_validationpoints.geojson</em>: a GeoJSON vector file including 2000 random points (within 2 km from the reference shoreline) that have been manually classified into water and land. The classification corresponds to the time-step 2019–2022.</li> </ul>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p>
Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States
<p><strong>Description</strong></p> <p>This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request. </p> <p> </p> <p><strong>Abstract</strong></p> <p>Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.</p> <p> </p>
Data of the publication "Absence of Localization in Two-Dimensional Clifford Circuits"
<p>We analyze a Floquet circuit with random Clifford gates in one and two spatial dimensions. By using<br> random graphs and methods from percolation theory, we prove in the two-dimensional (2D) setting that<br> some local operators grow at a ballistic rate, which implies the absence of localization. In contrast, the<br> one-dimensional model displays a strong form of localization, characterized by the emergence of left- and<br> right-blocking walls in random locations. We provide additional insights by complementing our analytical results with numerical simulations of operator spreading and entanglement growth, which show the<br> absence (presence) of localization in two dimensions (one dimension). Furthermore, we unveil how the<br> spectral form factor of the Floquet unitary in 2D circuits behaves like that of quasifree fermions with<br> chaotic single-particle dynamics, with an exponential ramp that persists up to times scaling linearly with<br> the size of the system. Our work sheds light on the nature of disordered Floquet Clifford dynamics and<br> their relationship to fully chaotic quantum dynamics.</p>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Dynamic traffic noise levels (LAeq) in the city of Tartu for two times of the day: 04:00 and 16:00
<p>Animations of the dynamic traffic noise levels (LAeq) in the city of Tartu, Estonia, for two times of the day: (a) 04:00 (sparse traffic) and (b) 16:00 (dense traffic). Blue circles represent the location of vehicles. Colours on receiver points represent noise levels according to legend (c).</p>
Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies
<p>Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies</p> <p><strong>Abstract</strong></p> <p>Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Morphology contains the following files:</p> <p>wing_morpho.R<br> R scripts for data transformation of wing shape</p> <p>genital_morpho.R<br> R scripts for data transformation of genital morphology</p> <p><br> Models_used.R:<br> R scripts used to produce the statistical analyses.</p> <p>genital_morpho_master_with_pca.txt<br> Phenotypic data for genital morphology</p> <p>wing_contemporary_morpho_master_with_pca.txt<br> Phenotypic data for contemporary wing patterns</p> <p>wing_historic_morpho_master_with_pca.txt<br> Phenotypic data for wing patterns from museum samples</p> <p>The text files contains the following information:</p> <p>ID = Individual ID<br> genotyped_allopatric = was the individual genotyped<br> latitude<br> longitude<br> DATE = Date of collection<br> DAY = Day of collection<br> MONTH = Month of collection<br> YEAR = Year of collection<br> SPOT = Collection site<br> boxplotID = ID to reproduce boxplot order as used in the paper<br> colory = color code to plot<br> cycle = year cycle (2018/19 or 2020/21)<br> yeartype = even or odd year<br> genital_x_LM1 = linear measure of genital landmark 1 along the x axis<br> genital_y_LM1 = linear measure of genital landmark 1 along the y axis<br> genital_x_LM2 = linear measure of genital landmark 2 along the x axis <br> genital_y_LM2 = linear measure of genital landmark 2 along the y axis <br> genital_x_LM3 = linear measure of genital landmark 3 along the x axis <br> genital_y_LM3 = linear measure of genital landmark 3 along the y axis <br> genital_x_LM4 = linear measure of genital landmark 4 along the x axis <br> genital_y_LM4 = linear measure of genital landmark 4 along the y axis <br> genital_x_LM5 = linear measure of genital landmark 5 along the x axis <br> genital_y_LM5 = linear measure of genital landmark 5 along the y axis <br> v_t1 = length relationship between v and t1<br> v_t2 = length relationship between v and t2 <br> v_t3 = length relationship between v and t3 <br> t3_t1 = length relationship between t3_t1 <br> t3_t2 = length relationship between t3_t2 <br> t2_t1 = length relationship between t2_t1 <br> v_tg = length relationship between v and tg <br> PC1.x = PC1 axis for unprojected morphospace<br> PC2.x = PC2 axis for unprojected morphospace <br> PC3.x = PC3 axis for unprojected morphospace <br> PC4.x = PC4 axis for unprojected morphospace <br> PC5.x = PC5 axis for unprojected morphospace <br> PC6.x = PC6 axis for unprojected morphospace <br> PC7.x = PC7 axis for unprojected morphospace <br> PC1.y = PC1 axis for projected morphospace <br> PC2.y = PC2 axis for projected morphospace <br> PC3.y = PC3 axis for projected morphospace <br> PC4.y = PC4 axis for projected morphospace <br> PC5.y = PC5 axis for projected morphospace <br> PC6.y = PC6 axis for projected morphospace <br> PC7.y = PC7 axis for projected morphospace</p> <p> </p> <p><br> wing_ProcCoord1 = Procrustes coordinate 1<br> wing_ProcCoord2 = Procrustes coordinate 2<br> wing_ProcCoord3 = Procrustes coordinate 3<br> wing_ProcCoord4 = Procrustes coordinate 4<br> wing_ProcCoord5 = Procrustes coordinate 5<br> wing_ProcCoord6 = Procrustes coordinate 6<br> wing_ProcCoord7 = Procrustes coordinate 7<br> wing_ProcCoord8 = Procrustes coordinate 8<br> wing_ProcCoord9 = Procrustes coordinate 9<br> wing_ProcCoord10 = Procrustes coordinate 10<br> wing_ProcCoord11 = Procrustes coordinate 11<br> wing_ProcCoord12 = Procrustes coordinate 12<br> wing_ProcCoord13 = Procrustes coordinate 13<br> wing_ProcCoord14 = Procrustes coordinate 14<br> wing_ProcCoord15 = Procrustes coordinate 15<br> wing_ProcCoord16 = Procrustes coordinate 16<br> wing_ProcCoord17 = Procrustes coordinate 17<br> wing_ProcCoord18 = Procrustes coordinate 18<br> wing_ProcCoord19 = Procrustes coordinate 19<br> wing_ProcCoord20 = Procrustes coordinate 20<br> wing_ProcCoord21 = Procrustes coordinate 21<br> wing_ProcCoord22 = Procrustes coordinate 22<br> wing_ProcCoord23 = Procrustes coordinate 23<br> wing_ProcCoord24 = Procrustes coordinate 24<br> wing_ProcCoord25 = Procrustes coordinate 25<br> wing_ProcCoord26 = Procrustes coordinate 26<br> wing_ProcCoord27 = Procrustes coordinate 27<br> wing_ProcCoord28 = Procrustes coordinate 28<br> wing_ProcCoord29 = Procrustes coordinate 29<br> wing_ProcCoord30 = Procrustes coordinate 30<br> wing_ProcCoord31 = Procrustes coordinate 31<br> wing_ProcCoord32 = Procrustes coordinate 32<br> wing_ProcCoord33 = Procrustes coordinate 33<br> wing_ProcCoord34 = Procrustes coordinate 34<br> wing_ProcCoord35 = Procrustes coordinate 35<br> wing_ProcCoord36 = Procrustes coordinate 36<br> wing_ProcCoord37 = Procrustes coordinate 37<br> wing_ProcCoord38 = Procrustes coordinate 38<br> wing_ProcCoord39 = Procrustes coordinate 39<br> wing_ProcCoord40 = Procrustes coordinate 40<br> wing_ProcCoord41 = Procrustes coordinate 41<br> wing_ProcCoord42 = Procrustes coordinate 42<br> wing_ProcCoord43 = Procrustes coordinate 43<br> wing_ProcCoord44 = Procrustes coordinate 44<br> wing_ProcCoord45 = Procrustes coordinate 45<br> wing_ProcCoord46 = Procrustes coordinate 46<br> wing_ProcCoord47 = Procrustes coordinate 47<br> wing_ProcCoord48 = Procrustes coordinate 48<br> wing_ProcCoord49 = Procrustes coordinate 49<br> wing_ProcCoord50 = Procrustes coordinate 50<br> wing_ProcCoord51 = Procrustes coordinate 51<br> wing_ProcCoord52 = Procrustes coordinate 52<br> wing_ProcCoord53 = Procrustes coordinate 53<br> wing_ProcCoord54 = Procrustes coordinate 54<br> PC1.x = PC1 unprojected<br> PC2.x = PC2 unprojected<br> PC3.x = PC3 unprojected<br> PC4.x = PC4 unprojected<br> PC5.x = PC5 unprojected<br> PC6.x = PC6 unprojected<br> PC7.x = PC7 unprojected<br> PC8.x = PC8 unprojected<br> PC9.x = PC9 unprojected<br> PC10.x = PC10 unprojected<br> PC11.x = PC11 unprojected<br> PC12.x = PC12 unprojected<br> PC13.x = PC13 unprojected<br> PC14.x = PC14 unprojected<br> PC15.x = PC15 unprojected<br> PC16.x = PC16 unprojected<br> PC17.x = PC17 unprojected<br> PC18.x = PC18 unprojected<br> PC19.x = PC19 unprojected<br> PC20.x = PC20 unprojected<br> PC21.x = PC21 unprojected<br> PC22.x = PC22 unprojected<br> PC23.x = PC23 unprojected<br> PC24.x = PC24 unprojected<br> PC25.x = PC25 unprojected<br> PC26.x = PC26 unprojected<br> PC27.x = PC27 unprojected<br> PC28.x = PC28 unprojected<br> PC29.x = PC29 unprojected<br> PC30.x = PC30 unprojected<br> PC31.x = PC31 unprojected<br> PC32.x = PC32 unprojected<br> PC33.x = PC33 unprojected<br> PC34.x = PC34 unprojected<br> PC35.x = PC35 unprojected<br> PC36 = PC36 unprojected<br> PC37 = PC37 unprojected<br> PC38 = PC38 unprojected<br> PC39 = PC39 unprojected<br> PC40 = PC40 unprojected<br> PC41 = PC41 unprojected<br> PC42 = PC42 unprojected<br> PC43 = PC43 unprojected<br> PC44 = PC44 unprojected<br> PC45 = PC45 unprojected<br> PC46 = PC46 unprojected<br> PC47 = PC47 unprojected<br> PC48 = PC48 unprojected<br> PC49 = PC49 unprojected<br> PC50 = PC50 unprojected<br> PC51 = PC51 unprojected<br> PC52 = PC52 unprojected<br> PC53 = PC53 unprojected<br> PC54 = PC54 unprojected<br> PC1.y = PC1 projected<br> PC2.y = PC2 projected<br> PC3.y = PC3 projected<br> PC4.y = PC4 projected<br> PC5.y = PC5 projected<br> PC6.y = PC6 projected<br> PC7.y = PC7 projected<br> PC8.y = PC8 projected<br> PC9.y = PC9 projected<br> PC10.y = PC10 projected<br> PC11.y = PC11 projected<br> PC12.y = PC12 projected<br> PC13.y = PC13 projected<br> PC14.y = PC14 projected<br> PC15.y = PC15 projected<br> PC16.y = PC16 projected<br> PC17.y = PC17 projected<br> PC18.y = PC18 projected<br> PC19.y = PC19 projected<br> PC20.y = PC20 projected<br> PC21.y = PC21 projected<br> PC22.y = PC22 projected<br> PC23.y = PC23 projected<br> PC24.y = PC24 projected<br> PC25.y = PC25 projected<br> PC26.y = PC26 projected<br> PC27.y = PC27 projected<br> PC28.y = PC28 projected<br> PC29.y = PC29 projected<br> PC30.y = PC30 projected<br> PC31.y = PC31 projected<br> PC32.y = PC32 projected<br> PC33.y = PC33 projected<br> PC34.y = PC34 projected<br> PC35.y = PC35 projected</p> <p> </p> <p> </p> <p> </p> <p>Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795):</p> <p>all_euryale_calls.vcf.gz<br> The unfiltered VCF file</p> <p>euryale_V2.sh<br> Shell script for the genomic data analysis</p> <p>introgress.R<br> R script for running Introgress</p> <p>introgress_all_east2.txt<br> Output of Introgress for the Eastern contact zone</p> <p>introgress_all_west2.txt<br> Output of Introgress for the Western contact zone</p> <p>Admixture_output.txt<br> Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID</p> <p>Outliers2BombyxMori.txt<br> BLAST summary of outlier regions against Bombyx Mori</p> <p>Outliers2ManjolaJurtina.txt<br> BLAST summary of outlier regions against Manjola jurtina</p> <p>Outliers2ParargeAegeria.txt<br> BLAST summary of outlier regions against Pararge aegeria</p> <p> </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.