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10,013 results for “observation”
Raw glider data: 9 months of hydrographic observations in the Gulf of Oman.
<p>68 repeat transects and 2 virtual moorings covering a spring/neap cycle collected by a SeaExplorer glider with T, S, O2, Chl, Optical backscatter, PAR data in the Gulf of Oman. Dataset collected as part of the ONR Global project "Shelf slope dyanmics in the Sea of Oman: How submesoscale processes control food and water security". The glider was deployed from the north shore of Oman into the Gulf of Oman, sampling down to 1000m in the oxygen minimum zone.</p><p> </p><p>Archive contains all of the raw data provided by the SeaExplorer in the manufacturer's standard .csv format. ADCP data collected by the glider are provided alongside the processed dataset: https://zenodo.org/doi/10.5281/zenodo.10075773</p>
Local plot information observed on LandKlif plots during vegetation survey 2019
<p><span>LandKlif local plot information observed on site during vegetation survey 2019, including vegetation height, slope, aspect, proximity to hedge / forest edge / water, intensity of use (only for meadows), and further information on plot habitat.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves_for JGR submission
<p>This folder contains preprocessed data, data processing scripts, Reynolds Averaged Navier Stokes (RANS) simulation scripts, and plotting scripts that produce the results in the manuscript entitled, "Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves," for submission to Journal of Geophysical Research Oceans by Trowbridge, Helfrich, Reeder, Medley, Chang, Jan, Ramp, and Yang.</p> <p> </p>
Vis-NIR Soil Spectral Library of the Hungarian Soil Degradation Observation System
<p>Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can be integrated with wet analysis as an alternative. Therefore, development of national-level soil spectral libraries containing information about all soil types represented in a country is continuously increasing to serve as a basis for calibrated predictive models capable of assessing physical and chemical parameters of soils at multiple spatial scales. In this article, we present a database containing laboratory and visible-near infrared spectral data of legacy soil samples from the Hungarian Soil Degradation Observation System (HSDOS). The published data set includes the following parameters measured in 5,490 soil samples: pH<sub>KCl</sub>, soil organic matter (SOM), calcium carbonate (CaCO<sub>3</sub>), total salt content (TSC), total nitrogen (N<sub>total</sub>), soluble phosphorus (P<sub>2</sub>O<sub>5</sub>-AL), soluble potassium (K<sub>2</sub>O-AL), plasticity index according to Hungarian standard (PLI), soil profile depth and reflectance data between 350 and 2,500 nm wavelength. The presented database can be a complement for further soil related research on continental, national or regional scales to support sustainable soil management.</p> <p>Uploaded CSV file contains variables for general information, soil parameters and reflectance data of spectral bands between 350-2,500 nm. Details about variables can be found in Table 1.</p> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> <td> <p>Method</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>SAMPLE_TDR_ID</p> </td> <td> <p>Original TDR IDs</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SAMPLING_DATE</p> </td> <td> <p>Date of sampling</p> </td> <td> <p>-</p> </td> <td> <p>YYYY-MM-DD</p> </td> </tr> <tr> <td> <p>NORTHING_EOV</p> </td> <td> <p>Northing coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>EASTING_EOV</p> </td> <td> <p>Easting coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>LON_WGS84</p> </td> <td> <p>Longitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>LAT_WGS84</p> </td> <td> <p>Latitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>pH_KCl</p> </td> <td> <p>pH</p> </td> <td> <p>Potentiometer (MSZ–08 0206-2: 1978)<sup>40</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SOM</p> </td> <td> <p>Soil organic matter</p> </td> <td> <p>E4/E6 ratio<sup>41</sup><sup>,</sup><sup>42</sup> (MSZ–08-0452:1980)<sup>43</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>CaCO3</p> </td> <td> <p>Calcium carbonate</p> </td> <td> <p>Scheibler type calcimeter (MSZ–08 0206-2:1978)<sup>40</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>TSC</p> </td> <td> <p>Total salt content</p> </td> <td> <p>EC-TDS electrode (MSZ–08-0206-2:1978)<sup>40</sup></p> </td> <td> <p>w/w %</p> </td> </tr> <tr> <td> <p>TN</p> </td> <td> <p>Total nitrogen</p> </td> <td> <p>Kjeldahl method<sup>44</sup> (ISO 11261:1995)<sup>45</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>P2O5_AL</p> </td> <td> <p>Soluble phosphorus</p> </td> <td> <p>AL extract atomic adsorption spectrophotometry (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>K2O_AL</p> </td> <td> <p>Soluble potassium</p> </td> <td> <p>AL extract, flame photometer (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>PLI</p> </td> <td> <p>Plasticity index according to Hungarian standard</p> </td> <td> <p>Yarn test of Arany (MSZ–08 0205-2:1978)<sup>47</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PROFILE_LEVEL</p> </td> <td> <p>Soil profile depth level</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SPC350:2500</p> </td> <td> <p>spectral reflectance in the range of 350 and 2500 nm</p> </td> <td> <p>ASD FieldSpec 4 spectroradiometer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Table 1. Summary of included attributes and data set structure with laboratory test methods applied on the soil samples.</p> <p> </p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p>Mészáros, J., Kovács, Zs., László, P., Vass-Meyndt, Sz., Koós, S., Pirkó, B., Szűcs-Vásárhelyi, N., Bakacsi, Zs., Laborczi, A., Balog, K., & Pásztor, L. (2024). Vis-NIR soil spectral library of the Hungarian Soil Degradation Observation System. <em>Sci Data</em> <strong>12</strong>, 363 (2025). https://doi.org/10.1038/s41597-025-04667-9</p>
Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations
<p>Updated Jan 17, 2024 to include estimates through calendar year 2024.</p> <p>These files contain an estimate of the Atlantic Meridional Overturning Circulation (AMOC) volume and heat transports, computed using observations of temperature, salinity and subsurface velocity from the Argo array of profiling floats (DOI: 10.17882/42182#116315), and satellite-based observations of sea level from altimetry (DOI: 10.48670/moi-00148 and DOI: 10.48670/moi-00149). The estimates are computed using the techniques of Willis (2010) and Hobbs and Willis (2012). In addition, estimates of wind stress at the surface were estimated from European Center for Medium Range Weather Forecast, ERA5 analysis (DOI: 10.24381/cds.143582cf).</p> <p>Note that in all files, although there are 12 time-steps per year, each time step represents a 3-month average, so the time series is over sampled.</p> <p>The .txt file contains comma separated values of the time series, with 1 header line and the following columns, estimated as in Willis (2010) and Hobbs and Willis (2012): </p> <p>Column 1: Decimal year</p> <p>Column 2: Ekman Volume Transport (Sverdrups)</p> <p>Column 3: Northward Geostrophic Transport (Sverdrups)</p> <p>Column 4: Meridional Overturning Volume Transport (Sverdrups)</p> <p>Column 5: Meridional Overturning Heat Transport (PetaWatts)</p> <p>The file called “trans_Argo_ERA5.nc” contains an estimate of the geostrophic transport as a function of latitude, longitude, depth and time, for the upper 2000 m for latitudes near 41 N in the Atlantic Ocean, estimated as described in Willis (2010). Also included are Ekman Transport and Overturning Transport as functions of time and latitude for this region.</p> <p>The file called “Q_ARGO_obs_dens_2000depth_ERA5.nc” contains estimates of heat transport for these regions based on various assumptions about the temperature of the ocean at depths unmeasured by the Core Argo array (depths below 2000m), estimated as described in Hobbs and Willis (2012). These assumptions are described in the variable “Hpar”.</p> <p> </p> <p>If you use these data please cite:</p> <p>Willis, J. K., and Hobbs, W. R., Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.8170366.</p> <p> </p> <p>References & Acknowledgements:</p> <p>Hobbs, W. R., and J. K. Willis (2012), Midlatitude North Atlantic heat transport: A time series based on satellite and drifter data. J. Geophys. Res., 117, C01008, doi:10.1029/2011JC007039.</p> <p>Willis, J. K. (2010), Can in situ floats and satellite altimeters detect long-term changes in Atlantic Ocean overturning?, Geophys. Res. Lett., 37, L06602, doi:10.1029/2010GL042372. http://www.agu.org/pubs/crossref/2010/2010GL042372.shtml</p> <p>This study has been conducted using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00149">https://doi.org/10.48670/moi-00149</a> and <a href="https://doi.org/10.48670/moi-00148">https://doi.org/10.48670/moi-00148</a></p> <p> </p> <p>These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System. “</p> <p>Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. <a href="https://doi.org/10.17882/42182#116315">https://doi.org/10.17882/42182#116315</a><a name="_Hlk188024500"></a></p> <p>Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: 10.24381/cds.143582cf (Accessed on 24-Dec-2022)</p> <p> </p>
Multiple years of Seaglider observations of hydrography, dissolved oxygen, chlorophyll a, and optical backscatter at Station ALOHA
<p><strong>File descriptions:</strong></p> <p>Seaglider missions are identified as GLIDER_MISSION<em> </em>(i.e. sg148_12 is glider 148, mission 12) and each have three files associated. For example:</p> <ol> <li><strong>sg148_12_qc_pass.xlsx</strong> contains only quality controlled (QC flags = 1) core data for an entire mission. Core data may include temperature, conductivity, salinity, potential density anomaly, calibrated dissolved oxygen concentrations, calibrated chlorophyll <em>a</em> concentrations, and the backscattering coefficient due to particles (bbp) at up to three wavelengths (470, either 650 or 660, and 700 nm) and spike flags. Bbp data is corrected with an <em>in situ</em> dark subtraction from near 200 m deep. Associated metadata (datetime, latitude, longitude, depth, dive number, and vertical profile direction) is also included.</li> <li><strong>sg148_12_alldata.nc </strong>contains all data (i.e. all QC flag levels) and associated quality control flags. In addition to core and metadata, factory-only calibrated observations (e.g. dissolved oxygen concentrations, chlorophyll <em>a</em> concentrations, and bbp) are listed. </li> <li><strong>sg148_12_qctests.nc</strong><em> </em>contains all quality control test values (pass: QC = 1, input flag: QC = 2, questionable data QC = 3, bad data: QC = 4). The maximum test QC flag value (e.g. out of range, density inversions, bioflouling, etc.) was passed to the variable QC flag (e.g chla_qcflag or salin_qcflag). . </li> </ol> <p> </p> <p><strong>Dataset description:</strong></p> <p>The SCOPE-ALOHA Seaglider dataset was designed to monitor the spatial and temporal variability of physical and biogeochemical properties around the long term sampling site Station ALOHA (22°45′N, 158°W). Seagliders are autonomous underwater vehicles that take high frequency (up to 0.2 Hz in our dataset), depth-resolved observations over several months and can be used to map large spatial features. The gliders depicted in this study were equipped with sensors to measure temperature, salinity, pressure, dissolved oxygen concentration (O2), chlorophyll a concentration (Chl a) from fluorescence (excitation/emission lambda = 470/695 nm), and the particulate backscattering coefficient (bbp) at three wavelengths (lambda = 470 nm, 700 nm, and either 650 or 660 nm depending upon mission). Vertical profiles down to at least 200 m were collected for all sensors over periods of several months per mission. This dataset comprises 18 missions between 2008 and 2023 centered on Station ALOHA, totaling over 20,000 depth profiles. Chlorophyll <em>a</em> and oxygen concentrations are calibrated with discrete observations. Particulate backscattering coefficients are corrected with an additional dark subtraction. This dataset is an improvement on the raw data files as they are quality controlled, calibrated, and corrected.</p> <p>Raw data files can be found at https://hahana.soest.hawaii.edu/seagliders/index.php.</p> <p>version notes:</p> <p>v1.0 original</p> <p>v1.1 Metaadata tab on xlsx files edited, no change to data</p> <p>v1.2 fixed error: variable qc flags added to alldata.nc files</p> <p>v1.3 Added error estimates and CF_standard_name to alldata.nc files</p> <p><strong>Methods:</strong></p> <p><em><strong>Code for all processing steps is on GitHub </strong></em><strong>(</strong><em><strong>https://github.com/cathygarcia/SeagliderDataprocessing</strong></em><strong>)</strong><em><strong>.</strong> The steps listed here are a brief summary. </em></p> <p><em>Temperature, Conductivity, Salinity, and Potential Density Anomaly</em></p> <ul> <li>Both temperature and conductivity profiles were lag corrected.</li> <li>Practical salinity was calculated using the Gibbs Seawater Toolbox (gsw_SP_from_C.m), and then converted to absolute salinity (gsw_SA_from_SP.m). </li> <li>Potential density anomaly was calculated with respect to a reference water pressure of 0 db using the Gibbs Seawater Toolbox (gsw_sigma0.m).</li> </ul> <p><em>Dissolved oxygen concentrations</em></p> <ul> <li>Raw optode phase values proceeded through a series of corrections to account for the effects of temperature, salinity, pressure, and time response in addition to sensor drift (Bittig et al., 2018, Barone et al., 2019).</li> <li> Optode phase values were converted to dissolved oxygen concentrations, and re-calibrated using discrete Winkler measurements. </li> </ul> <p><em>Chlorophyll</em> <em>a</em></p> <ul> <li>Factory-calibrated chlorophyll <em>a</em> observations were re-calibrated using discrete measurements of either HPLC chlorophyll <em>a </em>(16 missions) or fluorometric chlorophyll <em>a</em> (2 missions).</li> <li>Daytime chlorophyll <em>a</em> values are not quench corrected, and may be lower than actual values. It is recommended to use nighttime profiles near the surface. </li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul> <p><em>Backscattering coefficient due to particles (bbp)</em></p> <ul> <li>Factory-calibrated bbp values could have a large offset, that was not expected based on natural variability.</li> <li>A mission-specific deep dark correction (1st percentile of bbp at 190-200 m) was subtracted for each bbp dataset. Both the uncorrected and corrected data are available.</li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul> <p> </p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
HomogWS-se: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
<p>Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series.</p>
Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
<p>The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density-dependent relativistic mean-field description of nuclear matter <a href="https://arxiv.org/abs/2201.12552">Malik et al 2022</a>. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and the low-density pure neutron matter EOS obtained from a precise next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral effective field theory ($\chi$EFT). The number of final sample parameters corresponding to the posterior sets is around fourteen thousand. We present five EOSs among them, namely DDBl, DDBm, DDBu1, DDBu2, and DDBx. The DDBl, DDBm, DDBu2 were chosen so that the radius of the 1.4$M_\odot$ star has the lower limit, a medium value, and the upper limit of the 90% CI for the conditional probabilities $P(R|M)$. We have also included DDBu1 that has a slightly lower $R_{1.4}$ than the upper limit but lies completely inside the 90% CI for the conditional probabilities $P(R|M)$. The DDBx is the one that predicts a maximum mass of 2.5$M_\odot$ and has the following nuclear matter properties, $K_0=300$ MeV, $J_{sym,0}=30$ MeV and $L_{sym,0}=39$ MeV.</p> <p>We also release our entire sets of ~14K NS matter EOS. All the EOSs are for NS core and starting baryon density is 0.04 fm$^{-3}$. One needs to add their own choice of crust EOS for the star properties calculation. The uncertainty in star properties for the choice of the different crust has been discussed in Section 2.1 of the manuscript (arxiv: 2201.12552). </p> <pre> To extract the entire sets of ~14K NS matter EOS files, one needs to follow the steps, 1) unzip DDB_EOS_14K.zip ----------------------------Note------------------------------------- All the eos files have three columns baryon density (fm-3), energy density (MeV.fm-3), and pressure (MeV.fm-3). The starting density is 0.04 fm-3, as it is NS core eos. One needs to add their own choice of crust eos in order to calculate NS properties. ---------------------------------------------------------------</pre> <p> </p> <p> </p>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Seasonal to decadal western boundary current variability from sustained ocean observations
<p> </p> <p>Cross-transect velocity time series for HR-XBT transects IX21, PX30, and PX40 in support of: <a href="http://doi.org/10.1029/2022GL097834">Chandler et al. (2022). Seasonal to decadal western boundary current variability from sustained ocean observations.</a> </p> <p> </p> <p>Each netcdf file includes the following variables:</p> <ul> <li>time</li> <li>longitude</li> <li>latitude</li> <li>depth</li> <li>vel</li> <li>gvel_LNM</li> <li>long_for_vel_err</li> <li>lat_for_vel_err</li> <li>vel_err</li> <li>wbc_transport</li> </ul> <p> </p> <p>See also <a href="https://github.com/mlchandler/wbc_sustained_obs">https://github.com/mlchandler/wbc_sustained_obs</a></p>
Sample dataset: ovitrap sticks pictures and observed egg count
<p>This dataset consists of 300 ovitrap sticks pictures that contain at least one <em>Aedes aegypty</em> egg. The pictures were taken with an iPhone 7 mobile phone and were used to assess the performance of a mosquito egg counter algorithm and application (<a href="https://ovitrap-monitor.netlify.app/">https://ovitrap-monitor.netlify.app/</a>). The ovitraps are part of a weekly surveillance program carried out in the city of Córdoba (Argentina) by the Health Ministry authorities. This dataset is a smaple obtained between December 2021 and March 2022. We also include a text file with the code of the ovitraps and the number of eggs counted by a technician under magnifying glasses (i.e., observed counts).</p>
Dataset for Observations of gravity wave refraction and its causes and consequences
<p>Dataset for the publication submitted to Journal of Geophysical Research: Atmospheres. The title of the publication is:</p> <p>Observations of gravity wave refraction and its causes and consequences</p>
Observation of confinement induced resonances in a 3D lattice
<p>Data set relative to the publication "Observation of confinement induced resonances in a 3D optical lattice", D.Capecchi, C.Cantillano, M.J. Mark, F. Meinert, A.Schindewolf, M. Landini, A. Saenz, F. Revuelta, H.-C. Naegerl (2022), arxiv:2209.12504</p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
How does Mg2+(aq.) interact with ATP(aq.)? Observations through the lens of liquid-jet photoelectron spectroscopy - data
<p>Dataset pertaining to the article "How does Mg2+(aq) interact with ATP(aq)? Biomolecular Structure through the Lens of Liquid-Jet Photoemission Spectroscopy", published in Journal of the American Chemical Society (<a href="https://doi.org/10.1021/jacs.4c03174" target="_blank" rel="noopener">doi: 10.1021/jacs.4c03174</a>). Here, we arrive at new information on the interaction of ATP with Mg under physiological conditions by interpreting photoelectron spectra and intermolecular Coulombic decay from a liquid microjet.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). For ATP spectra, the ADP overview spectrum, and ADP/Mg2+ Mg 2s spectra, a binding energy correction shifting the liquid 1b1 feature to 11.33 eV is applied.<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to adenosine phosphate PES measurements:<br>atp-mg.h5 - ATP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>adp-mg.h5 - ADP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>amp-mg.h5 - AMP photoemission spectra in the presence of Mg2+ cations (a single concentration)<br>atp-adp-amp.h5 - ATP, ADP, AMP photoemission without Mg admixture<br>mg-only.h5 - Mg 2s core level spectra without ATP<br>tham-only.h5 - VB band measured with only THAM (tris(hydroxymethyl)aminomethane), used as buffer for pH stabilization<br>atp-icd.h5 - ATP photoemission spectra in the presence of Mg2+ cations, kinetic energy range of ICD features (publication is based on the last three entries).<br><br></p> <p>Numeric representations of the traces shown in the article's figures:<br>Figure_3-data.txt<br>Figure_5a-Mg2p.txt<br>Figure_5a-Mg2s.txt<br>Figure_5a-Mgonly.txt<br>Figure_5a-P2p.txt<br>Figure_5a-P2s.txt<br>Figure_5b.txt<br>Figure_5c.txt<br>Figure_6-ADP.txt<br>Figure_6-AMP.txt<br>Figure_6-ATP.txt<br>Figure_8a-data.txt<br>Figure_S2-Tris.txt<br>Figure_S2-Tris_with_Mg2+.txt<br>Figure_S4-data.txt.</p> <p>Version history<br>3: updated to reflect changes in Figure numbering between ArXiv-post and version published in JACS, additional Figure 5-data added<br>2: NeXus-data added<br>1: initial upload</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we are curious to learn about it.</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.