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IRIS: ICESat-2 River Surface Slope
<p><strong>ICESat-2 River Surface Slope (IRIS)</strong></p> <p>When using this data please cite<strong> </strong><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023.</p> <p>A detailed description of the methodology and validation is published in <em>Scherer D., Schwatke C., Dettmering D., Seitz F. </em>: <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>. Water Resources Research, <a href="http://doi.org/10.1029/2022WR032842">10.1029/2022WR032842</a>, 2022.</p> <p><strong>1. Summary</strong><br>The unique multibeam lidar altimeter of ICESat-2 is used to measure reach-scale water surface slope (WSS) every time the spacecraft’s orbit crosses a reach. The method of deriving WSS from simultaneous ICESat-2 ATL13 (<em>Jasinski et al., 2021</em>) observations is described in detail and validated in <em>Scherer et al. </em>(2022). In this ICESat-2 River Surface Slope (IRIS) dataset, we provide the minimum, average, and maximum slope derived with three different approaches (across, along, and combined) per reach. Additionally, we give the standard deviation and epochs of the derived WSS data. The reaches are defined by the SWOT River Database (SWORD, <em>Altenau et al., 2021</em>).</p> <p>An interactive map is available at <a href="https://dahiti.dgfi.tum.de/en/products/water-surface-slope/.">DAHITI</a>.</p> <p><strong>2. Version History</strong></p> <p>IRIS <strong>v0</strong>: Only includes the reaches studied in Scherer et al. (2022).<br>Based on ICESat-2 ATL13 v5, Cycle 1-13 (October 2018 to October 2021) and SWORD Version v1.</p> <p>IRIS <strong>v1</strong>: Global coverage (limited by ICESat-2 data availability and cloud cover).<br>Based on ICESat-2 ATL13 v5, Cycle 1-16 (October 2018 to August 2022) and SWORD Version v2.</p> <p>IRIS <strong>v2</strong>: Global coverage with 6,083 additional reaches and 92,347 more observations compared to v1.<br>Based on ICESat-2 ATL13 <strong>v6</strong>, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v15.</p> <p>IRIS <strong>v2.1</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and <strong>SWORD Version v16</strong>.</p> <p>IRIS <strong>v2.2</strong>: 3,251 additional reaches and 58,862 new observations compared to v2.1.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>20</strong> (October 2018 to <strong>August</strong> 2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.3</strong>: 1,595 additional reaches and 32,590 new observations compared to v2.2.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>21</strong> (October 2018 to <strong>October </strong>2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.6</strong>: 2,755 additional reaches and 362,136 new observations compared to v2.3.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>23</strong> (October 2018 to <strong>May 2024</strong>) and SWORD Version v16.</p> <p>IRIS <strong>v2.9</strong>: 2,485 additional reaches and 184,549 new observations compared to v2.6.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>24</strong> (October 2018 to <strong>August 2024</strong>) and SWORD Version v16.<br>Fixed some broken geometries in the gpkg data.</p> <p>IRIS <strong>v3.0</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and <strong>SWORD Version</strong> <strong>v17</strong>.</p> <p>IRIS <strong>v3.2</strong>: 1,370 additional reaches and 210,951 new observations compared to v3.0.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>26</strong> (October 2018 to <strong>December 2024</strong>) and SWORD Version v17.</p> <p><strong>3. Data Format and Variable Description</strong></p> <p>From Version 2.6, <strong>IRIS is also available as GeoPackage</strong>.<br>The IRIS data is stored in a single NetCDF4 file which is structured in a single group containing the following variables:<br><strong><em>reach_id</em></strong>:<br>The SWORD reach identifier [-]<br><strong><em>lon</em></strong>:<br>Approx. centroid longitude of the SWORD reach [degrees east]<br><strong><em>lat</em></strong>:<br>Approx. centroid latitude of the SWORD reach [degrees north]<br><strong><em>across_flag, along_flag, combined_flag:</em></strong><br>Flags indicating whether ICESat-2 [across/along/combined] slope is available (1) for the reach or not (0) [-]<br><strong><em>avg_across_slope, avg_along_slope, avg_combined_slope:</em></strong><br>Average (median) ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>min_across_slope, min_along_slope, min_combined_slope:</em></strong><br>Minimum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>max_across_slope, max_along_slope, max_combined_slope:</em></strong><br>Maximum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>std_across_slope, std_along_slope, std_combined_slope:</em></strong><br>ICESat-2 [across/along/combined] slope standard deviation for the reach [mm/km]<br><strong><em>n_across_slope, n_along_slope, n_combined_slope:</em></strong><br>Number of days with ICESat-2 [across/along/combined] slope observations for the reach [-]<br><strong><em>min_date_across_slope, min_date_along_slope:, min_date_combined_slope:</em></strong><br>First date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]<br><strong><em>max_date_across_slope, max_date_along_slope:, max_date_combined_slope:</em></strong><br>Latest date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]</p> <p><strong>4. References</strong></p> <p><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023<br><em>Scherer D., Schwatke C., Dettmering D., Seitz F. (2022): <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>, Water Resources Research, https://doi.org/10.1029/2022WR032842</em><br><em>Jasinski M., Stoll J., Hancock D., Robbins J., Nattala J., Morison J., Jones B., Ondrusek M., Pavelsky T.M., Parrish C. and the ICESat-2-Science-Team (2021). <strong>ATLAS/ICESat-2 L3A Inland Water Surface Height</strong>, Version 5. [Dataset]</em><br><em>Altenau E.H., Pavelsky T.M., Durand, M.T., Yang X., Frasson, R.P.d.M., Bendezu, L. (2021): <strong>SWOT River Database (SWORD)</strong> [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898569</em></p>
GRWSE-global river water surface elevation from sentinel-3
<p>This dataset includes time series of Water Surface Elevation (WSE) of large rivers at over 3000 virtual stations. The WSE time series were created using Sentinel-3A and Sentinel-3B altimetry data. </p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></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>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</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>
FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling
<p>The Flux data kit is an effort to expand upon the existing work by Ukkola et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukkola et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of the following files, containing different versions of the same data and site meta information.</p> <ul> <li><code>FLUXDATAKIT_LSM.tar.gz</code> file contains compressed NetCDF files compatible with the ALMA scheme for land surface modelling. </li> <li><code>FLUXDATAKIT_FLUXNET.tar.gz</code> file contains data in a CSV format according to the FLUXNET specifications.</li> <li><code>rsofun_driver_data_v3.3.rds</code> file is a compressed serialized R file containing data formatted for use with the {rsofun} R package.</li> <li><code><a href="../api/records/11370417/draft/files/fdk_site_info.csv/content" target="_blank" rel="noopener noreferrer">fdk_site_info.csv</a></code> contains site meta information in tabular form</li> <li><a href="../api/records/11370417/draft/files/fdk_site_fullyearsequence.csv/content" target="_blank" rel="noopener noreferrer"><code>fdk_site_fullyearsequence.csv</code></a> contains information about complete sequences of good-quality data by site (see also <a href="https://geco-bern.github.io/FluxDataKit/articles/04_data_use.html">here</a>).</li> </ul> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects
<p><strong>Study Sample and Ethics Statement</strong></p> <p>The sample consisted of 71 university students, average age 20.38 years (<em>SD</em> = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki.</p> <p>In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71.</p> <p><strong>Measurement Setup</strong></p> <p>BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (<a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">Bjegojević et al., 2020</a>). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and the gain was set to 1000.</p> <p>ECG signals were recorded during relaxation in a sitting position and data were recorded during 2 min long intervals. More information is available in the article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>].</p> <p><strong>Dataset, Code, and Feature Extraction Instructions</strong></p> <ol> <li><a href="https://zenodo.org/record/5736849/files/analysisECG.R?download=1">analysisECG.R</a>, function with analysis procedures written in <a href="https://www.r-project.org/">R programming language</a></li> <li><a href="https://zenodo.org/record/5736849/files/anec12919-sup-0001-supinfo.pdf?download=1">anec12919-sup-0001-supinfo.pdf</a>, detailed ECG processing and feature extraction procedure (also available as <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fanec.12919&file=anec12919-sup-0001-Supinfo.docx">supplementary material</a> for article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>])</li> <li><a href="https://zenodo.org/record/5736849/files/ecg_70.txt?download=1">ecg_70.txt</a>, .txt data file, text format</li> <li><a href="https://zenodo.org/record/5736849/files/mainECG.R?download=1">mainECG.R</a>, a main program written in R programming language</li> <li><a href="https://zenodo.org/record/5736849/files/R-studio-version-info.txt?download=1">R-studio-version-info.txt</a>, the version of <a href="https://www.rstudio.com/">R Studio</a> where the code was tested</li> <li><a href="https://zenodo.org/record/5736849/files/R-version-info.txt?download=1">R-version-info.txt</a><a href="https://zenodo.org/api/files/aa8d999e-1b08-44b5-883f-0540afe8feb8/R-version-info.txt"> </a>, the version of R programming language where the code was tested</li> </ol> <p>For ECG-based feature extraction, we used the following R packages:</p> <ol> <li><strong>signal</strong> - Signal Processing Functions (signal developers (2014). <em>signal: Signal processing</em>. <a href="http://r-forge.r-project.org/projects/signal/">http://r-forge.r-project.org/projects/signal/</a>)</li> <li><strong>pracma</strong> - Practical Numerical Math Functions ( Borchers, H. W. (2019). <em>Package ‘pracma’: Practical numerical math functions</em>. R package version, 2(1). <a href="https://CRAN.R-project.org/package=pracma">https://CRAN.R-project.org/package=pracma</a>)</li> </ol> <p>Please, note that the results of personality trait tests are not available in the current dataset. We are planning to open them in our future research. For more information and planned availability in open access, please, contact the corresponding author of [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>] by e-mail (<a href="mailto:nadica.miljkovic@etf.bg.ac.rs">nadica.miljkovic@etf.bg.ac.rs</a>).</p> <p><strong>Citing Instruction</strong></p> <p>If you find these signals and code useful for your own research or teaching class, please cite relevant dataset and supporting publications:</p> <ol> <li> <p>Boljanić, T., Miljković, N., Lazarević, L. B., Knežević, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. <em>Annals of Noninvasive Electrocardiology</em>, 00, e12919. <a href="https://doi.org/10.1111/anec.12919">https://doi.org/10.1111/anec.12919</a></p> </li> <li> <p>Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). <a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation</a>. <em>XXIVI Scientific Conference on Empirical Studies in Psychology</em>, p. 17-19.</p> </li> <li> <p>Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.5599239">https://doi.org/10.5281/zenodo.5599239</a></p> </li> </ol>
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>
Dataset supporting the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020)"
<p>Dataset corresponding to figure 3 of the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020), DOI: <a href="https://doi.org/10.1021/acs.jpclett.0c01747">10.1021/acs.jpclett.0c01747</a>"</p> <p>List of files:<br> There are two folders corresponding to brominated and debrominated structures:</p> <ul> <li>.siesta files: STM simulated images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> </ul>
Geothermal Surface Manifestation photographic dataset
<p>This is a zip file of Geothermal Surface Manifestation (GSM) photographic dataset. It contains 5,000 images, 500 images each type of eight types of GSMs. The eight types of GSMs are warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and none GSM. These GSM images are used for training and testing a GoogLeNet model to perform recognition of GSMs. The overall accuracy of this model reaches about 90%.</p> <p>It includes eight folders after unzipping:<br> 1.WS-Warm spring, <br> 2.HS-Hot spring, <br> 3.FO-Fountain, <br> 4.FU-Fumarole, <br> 5.MP-Mud pot, <br> 6.HA-Hydrothermal alteration, <br> 7.CL-Crater lake,<br> 8.NG-No geothermal</p> <p>It is a benchmark dataset for a manuscript titled "Recognition of surface geothermal manifestations: a comparison of machine learning and deep learning" .</p>
Dataset supporting the paper "Inducing open-shell character in porphyrins through surface-assisted phenalenyl π-extension. J. Am. Chem. Soc 142, 18109 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "<em>Inducing open-shell character in porphyrins through surface-assisted phenalenyl π-extension. J. Am. Chem. Soc 142, 18109 (2020)</em>" DOI: <a href="https://doi.org/10.1021/jacs.0c07781">10.1021/jacs.0c07781</a>.</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Dataset supporting the paper "Doublet-Singlet-Doublet Transition in a Single Organic Molecule Magnet On-Surface Constructed with up to 3 Aluminum Atoms. Nano Letters 21, 8317 (2021)"
<p>Dataset corresponding to theoretical calculations in the paper "Doublet-Singlet-Doublet Transition in a Single Organic Molecule Magnet On-Surface Constructed with up to 3 Aluminum Atoms" Nano Letters 21, 8317 (2021), <a href="https://doi.org/10.1021/acs.nanolett.1c02881">https://doi.org/10.1021/acs.nanolett.1c02881</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR and POSCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.
<p>Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here. </p> <p> </p> <p>The data is further presented and discussed in a manuscript: </p> <p>Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó<span>, </span>Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022</p> <p> </p> <p>Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene. <br> Positive values indicate oceanic outgassing, thus sea to air flux.</p> <p> </p> <p>Definitions of acronyms, site abbreviations, or other project-specific designations:<br> deg = degree <br> SW = seawater concentration</p> <p>ATM= atmosphere</p> <p>SAT = saturation</p> <p>flux= air-sea flux in (micro)umol_m^(2)_d^(-1)<br> nM = nano Molar seawater concentration defined as nmol dm^(-3)</p> <p>LAT, LONG = Latitude, Longitude. (negative indicates west and south)</p> <p>The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY_HH:MM</p> <p>Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data.</p> <p>Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"</p> <p> </p> <p>Negative values correspond to readings below the blank and detection limit. <br> They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.</p> <p> </p> <p>Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.</p> <p> </p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
ScienceDex guides
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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.