Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,152
datasets available to search
ShareScore release 0.9.0
Dataset results
2,152 results for “outputs”
PISM model output data from Garbe et al. (Nature, 2020) publication
<p>This dataset contains the <a href="https://www.pism.io">PISM</a> model output data of the Antarctic Ice Sheet hysteresis simulations published and discussed in</p><p><a href="https://doi.org/10.1038/s41586-020-2727-5">Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. <i>Nature</i><strong> 585</strong>(7826), 2020.</a></p><p>A detailed description of the individual file contents is given in `README.txt` below. The corresponding PISM model code used for these simulations is archived <a href="https://doi.org/10.5281/zenodo.3956431">here</a>.</p><p>In case of questions, feel free to contact me at <a href="mailto:julius.garbe@pik-potsdam.de">julius.garbe@pik-potsdam.de</a>.</p>
Rotating Shallow Water Verification Suite Output
<p>The Rotating_Shallow_Water_Verification_Suite_Output directory contains the output obtained by running a verification suite of shallow water test cases for the barotropic solver of ocean models using two types of spatial discretizations: a mimetic finite volume method based on the TRiSK scheme and a discontinuous Galerkin spectral element method (DGSEM); and a variety of time-stepping methods. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. The output, which is organized into the two directories MPAS_Ocean_Shallow_Water_Output and DGSEM_Rotating_Shallow_Water_Output corresponding to the two spatial discretizations, includes:</p><ul><li>text files containing the exact solution and the numerical error of the various test cases at specific instants of time along with their plots; and </li><li>text files containing the L2 error norm with respect to the exact solution for refinement in both space and time, and the L2 norm of the difference in the numerical solution for refinement only in space and only in time, along with the convergence plots.</li></ul>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Model Outputs for Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry
<p>Includes the model output files used in calculations regarding the research article "Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry". Output files contains: 1) surface Mn concentrations, annual; 2) Mn deposition, monthly; 3) soil Mn map; 4) soil Mn "pseudo" turnover time.</p>
Information content estimation output for specific humidity profiles
<p>Output of information content estimation based on optimal estimation theory <strong>[1]</strong>. Detailed descriptions of the information content estimation performed here can be found in Section 3.3 of <strong>[2]</strong>. The files have been created with the codes published on Github/Zenodo <strong>[3]</strong>.</p> <p>The cryptic file name suffixes _472, _481, _482, _483 and _484 represent different settings of the Neural Network retrieval to estimate the information content for different inputs:</p> <ul> <li>_472: TBs at all frequencies of the microwave radiometers HATPRO and MiRAC-P (instruments are described in <strong>[2]</strong>,<strong>[4]</strong>)</li> <li>_481: TBs only at K-band frequencies (22.24-31.4 GHz)</li> <li>_482: TBs at K- and V-band frequencies (22.24 - 58 GHz)</li> <li>_483: TBs at K- and G-band frequencies (22.24-31.4 GHz, 175.81-190.81 GHz)</li> <li>_484: TBs at K-, G-band and higher frequencies (22.24-31.4 GHz, 175.81-190.81 GHz, 243 GHz, 340 GHz)</li> </ul> <p>Detailed Neural Network settings can also be found in <strong>[2]</strong> and test_purpose.yaml in <strong>[3]</strong>.</p> <p>The file eval_info_content_idx.nc contains indices to consider a subset of the years 2001, 2006, 2011, 2015 of <strong>[5]</strong>, used as evaluation data in <strong>[2]</strong>, for the information content estimation. We did not use the full ERA5 evaluation data set because of computation time.</p> <p><strong>[1]:</strong> Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, no. 2 in Series on atmospheric, oceanic and planetary physics, World Scientific, Singapore, repr edn., ISBN 978-981-02-2740-1, 2008.</p> <p><strong>[2]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p> <p><strong>[4]:</strong> Walbröl, A., Crewell, S., Engelmann, R., Orlandi, E., Griesche, H., Radenz, M., Hofer, J., Althausen, D., Maturilli, M., and Ebell, K.: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC, Scientific Data, 9, 534, https://doi.org/10.1038/s41597-022-01504-1, 2022.</p> <p><strong>[5]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p>
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
Open the record for dataset details and reuse information.
Double-Versus Triple-Potential Well Energy Harvesters: Dynamics and Power Output
<div>The present datasets and figures focus on the analysis of BEH and TEH systems where the corresponding depth of the potential well and the width of their characteristics are the same. The efficiency of energy harvesting for TEH and BEH systems assuming similar potential parameters is provided. The basic types of multistable energy harvesters are bistable energy harvesting systems (BEH) and tristable energy harvesting systems (TEH). Providing such parameters allows for reliable formulation of conclusions about the efficiency in both types of systems. These energy harvesting systems are based on permanent magnets and a cantilever beam designed to obtain energy from vibrations. Starting from the bond graphs, we derived the nonlinear equations of motion. Then we followed the bifurcations along the increasing frequency for both configurations. To identify the character of particular solutions, we estimated their corresponding phase portraits, Poincare sections, and Lyapunov exponents. The selected solutions are associated with their voltage output. The results in this numerical study show clearly that the bistable potential is more efficient for energy harvesting provided the corresponding excitation amplitude is large enough. However, the tristable one could work better in the limits of low-level and low-frequency excitations. </div> <div> <h2>Series information</h2> <p>Potential characteristics of energy harvesting systems caused by magnetic field of distributed permanent magnets:</p> <ul> <li>Fig4a_b_V_y1.txt</li> <li>Fig4a_r_V_y1.txt</li> </ul> <p>Potential characteristics of energy harvesting systems caused by magnetic field effect as in the previous case and an additional change in the stiffness of the flexible cantilever beam:</p> <ul> <li>Fig4b_b_V_y1.txt</li> <li>Fig4b_r_V_y1.txt</li> </ul> <p>Series of steady states of the system against frequency for two potential wells. </p> <ul> <li>Fig6a_p005.txt</li> <li>Fig6c_p025.txt</li> <li>Fig6e_p05.txt</li> <li>Fig6g_p085.txt</li> </ul> <p>Series of steady states of the system against frequency for three potential wells</p> <ul> <li>Fig6b_p005.txt</li> <li>Fig6d_p025.txt</li> <li>Fig6f_p05.txt</li> <li>Fig6h_p085.txt</li> </ul> <p>The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding description. The results were obtained for zero initial conditions. ω and x are dimensionless.</p> </div> <div><strong>Figures:</strong></div> <div> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%202.jpg/content" target="_blank" rel="noopener"><br>ig 2.jpg</a> - A graph of bonds representing the dynamics of the tested design solutions of energy harvesting systems.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%203.jpg/content" target="_blank" rel="noopener">Fig 3.jpg</a> - A Lagrangian bond graph, with causality conflicts intentionally introduced.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%204.jpg/content" target="_blank" rel="noopener">Fig 4.jpg</a> - Potential characteristics of energy harvesting systems caused by: (<strong>a</strong>) magnetic field of distributed permanent magnets (Fig. 1); (<strong>b</strong>) magnetic field effect as in previous case and an additional change in stiffness of the flexible cantilever beam (to satisfy equal potential barriers <em>V</em><sub>2</sub> = <em>V</em><sub>3</sub> ) used in further calculations.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%206.jpg/content" target="_blank" rel="noopener">Fig 6.jpg</a> - Bifurcation diagrams (stroboscopic) of steady states of the system against frequency for: (<strong>a</strong>) Two potential wells; (<strong>b</strong>) three potential wells. The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding sub-figures. The results were obtained for zero initial conditions. <em>ω</em> and <em>x</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%207.jpg/content" target="_blank" rel="noopener">Fig 7.jpg</a> - Exemplary solutions showing the geometrical structures of chaotic phase flows and the corresponding Poincaré cross-sections of a BEH. <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and x' are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%208.jpg/content" target="_blank" rel="noopener">Fig 8.jpg</a> - Examples of periodic responses of a BEH system identified for dimensionless mechanical vibration amplitudes: (<strong>a</strong>) <em>p</em> = 0.05; (<strong>b</strong>) <em>p</em> = 0.25; (<strong>c</strong>) <em>p</em> = 0.5; (<strong>d</strong>) <em>p</em> = 0.85. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%209.jpg/content" target="_blank" rel="noopener">Fig 9.jpg</a> - Example solutions showing geometric structures of chaotic phase flows and corresponding Poincaré cross-sections, which were identified for a system with three potential wells (TEH). <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p>Fig 10.jpg – Influence of external load characteristics on periodic induced solutions in a TEH. Trajectory shapes are plotted for selected frequencies ω. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2011.jpg/content" target="_blank" rel="noopener">Fig 11.jpg</a> - Multicolored maps of the values of effective energy harvesting systems (RMS voltage outputs) with the potential: (<strong>a</strong>) two-well (BEH); (<strong>b</strong>) three-well (TEH) for zero initial conditions. <em>ω</em> and <em>p</em> are dimensionless, while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2012.jpg/content" target="_blank" rel="noopener">Fig 12.jpg</a> - Comparison of RMS<strong> </strong>voltage outputs for two and three wells potential systems versus amplitude and frequency. ω is dimensionless while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> </div>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
ERDS alerts based on WRF model output
<p>Heavy rainfall alerts based on WRF model output at 7.5 km resolution produced by ERDS (<a href="https://erds.ithacaweb.org/">https://erds.ithacaweb.org/</a>).</p>
IPTA DR2 - GWB analysis MCMC output
<p><strong>IPTA DR2 common red noise, MCMC output</strong><br> </p> <p>These files are the primary output from a Markov chain Monte Carlo (MCMC) sampling process. They are samples from the posterior probability distribution for a particular model described in the companion paper.</p> <p>Each zipped tarball contains four files. The "chain" file has several tab-separated columns, each of which corresponds to a model parameter, except the last four which are metadata. The parameter names (including metadata) are listed in the companion "params" file. The frequencies used in the common red noise models are listed in the "crn_frequencies" file. Additional information is provided in a README file.</p> <p>Each row of the chain file is one sample from the model posterior. The first samples at the beginning of the MCMC are the "burn-in" phase, before the chain has converged to the posterior. We recommend discarding the first ~25% of samples before using them to make inferences.</p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs.<br> A description of the model runs and visualization of model output and analysis can be found in the<br> accompanying manuscript.</p> <p><br> Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential<br> impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean<br> Processes.</p> <p><br> This work was supported by the Regional and Global Model Analysis (RGMA) program of the US<br> Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for<br> this project was provided by the National Science Foundation, under award #173886.<br> </p> <p><strong>River Nutrient Forcing</strong></p> <p>The experiments involved modifying the nutrient concentrations in the river nutrient forcing files.<br> The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual<br> river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point<br> to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient<br> inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters<br> thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans.</p> <p>The experiments were:<br> i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time.<br> <strong>river_nutrients_GNEWS2000_gx1v6.nc</strong><br> ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled.<br> <strong>river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc</strong><br> iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to<br> the river volume discharge contained in <strong>runoff.daitren.iaf.20120419.nc</strong><br> <strong>river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc</strong><br> iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc</strong><br> v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and<br> doubled in concentration.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc</strong></p> <p>River nutrient fluxes are in units of nmol/cm2/s<br> Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in<br> concentration/timing.</p> <p><br> Variables of interest:<br> din_riv_flux: dissolved inorganic nitrogen river flux<br> don_riv_flux: dissolved organic nitrogen river flux</p> <p>Each of the experiments is described in detail in Gibson et al (2022).</p> <p>---------------------------------------------<br> There are multiple versions of most output file types, corresponding to the river nutrient experiments that<br> were conducted.</p> <p><br> Many variables in the output files are <strong>regional averages</strong> where model regions are indicated by a number<br> *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering<br> used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers<br> assigned to regions within the region mask provided in the grid file.</p> <p><strong>Grid File/region masks</strong><br> gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been<br> updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates<br> which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which<br> indicates which grid cells are in the broader regions.</p> <p><br> Variables contained in this file are:<br> Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in<br> Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in</p> <p><br> TLAT: latitude of grid cell<br> TLONG: longitude of grid cell<br> TAREA: Area of grid cell<br> HT: Bathymetry of grid cell</p> <p> </p><table> <tbody> <tr> <td> </td> <td> <p><strong>Arctic_Region (seas)</strong></p> </td> <td> <p><strong>Arctic_Coast_Mask </strong><strong>(coast)</strong></p> </td> </tr> <tr> <td> <p><strong>Bering Sea</strong></p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p><strong>Chukchi Sea</strong></p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p><strong>East Siberian Sea</strong></p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p><strong>Laptev Sea</strong></p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p><strong>Beaufort Sea</strong></p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p><strong>Barents Sea</strong></p> </td> <td> <p>6</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p><strong>Canadian Basin</strong></p> </td> <td> <p>7</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Eurasian Basin</strong></p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Nordic Seas</strong></p> </td> <td> <p>9</p> </td> <td> <p>7</p> </td> </tr> <tr> <td> <p><strong>Labrador Sea</strong></p> </td> <td> <p>10</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p><strong>Kara Sea</strong></p> </td> <td> <p>11</p> </td> <td> <p>9</p> </td> </tr> </tbody> </table> --------------<p></p> <p> </p><p>Model outputs that were analyzed in the manuscript are contained in three different kinds of output file.<br> For each file type a file exists for each river nutrient experiment.</p> <p></p> <p>The following series of files contains variables related to the particulate organic carbon flux to the<br> sediment, demineralization and remineralization rates.<br> bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc</p> <p><br> Variables contained in these are:<br> POCTOSED_AVG* : Particulate organic carbon flux to sediment<br> PONTOSED_AVG* : Particulate organic nitrogen flux to sediment<br> SEDDENITRIF_AVG* : Sediment denitrification rate<br> POC_PROD_AVG* : Production of Particulate organic carbon<br> POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell<br> DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate<br> DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_P_LIM_AVG* : Diatom phosphorous limitation<br> DIAT_FE_LIM_AVG* : Diatom iron limitation<br> DIAT_LIGHT_LIM_AVG*: Diatom light limitation<br> SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation<br> SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation<br> SP_FE_LIM_AVG* : Small phytoplankton iron limitation<br> SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation<br> Where * represents the coastal region number.<br> ----------------------------------------</p> <p><br> The following series of files contains primary production for the small and large phytoplankton groups<br> and the zooplankton biomass.</p> <p>Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc</p> <p>Regional seas averages – based on regions marked in the Arctic_Region variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc</p> <p>Variables contained in these files are:<br> TAREA_SUM* – total area of the region<br> PPSP_REGSUM* – sum of primary production by small phytoplankton in a region<br> PPDIAT_REGSUM*– sum of primary production by diatoms in a region<br> ZOOC_AVG*– sum of zooplankton biomass in a region</p> <p>----------------------------------------<br> The following series of files contains ice associated variables and mixed layer nutrients</p> <p>bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc</p> <p>HI_REGAVG* : Regional averaged ice depth<br> HS_REGAVG* : Regional averaged snow depth<br> ICEAREA_REGSUM* : Regional sum ice area<br> ICEVOL_REGSUM*: Regional sum volume area<br> MLAM_REGAVG* : Regional average ammonium concentration in mixed layer<br> MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer<br> PP_REGAVG* : Regional average primary production (ice algae)<br> PP_REGSUM* : Regional total primary production (ice algae)<br> TAREA_SUM* : Total area of region<br> TIME : time</p>
Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery
<p>The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a></p> </li> </ul>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
Outputs of the Jupyter Notebook - Tree crown detection using DeepForest
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a></p> </li> <li> <p>Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a></p> </li> <li> <p>Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a></p> </li> <li> <p>Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a></p> </li> </ul> <p><em>Modelling publications</em></p> <ul> <li> <p>Ben G Weinstein, Sergio Marconi, Mélaine Aubry-Kientz, Gregoire Vincent, Henry Senyondo, and Ethan P White. Deepforest: a python package for rgb deep learning tree crown delineation. <em>Methods in Ecology and Evolution</em>, 11:1743–1751, 2020. URL: <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472</a>, <a href="https://doi.org/https://doi.org/10.1111/2041-210X.13472">doi:https://doi.org/10.1111/2041-210X.13472</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. <em>Remote Sensing</em>, 2019. URL: <a href="https://www.mdpi.com/2072-4292/11/11/1309">https://www.mdpi.com/2072-4292/11/11/1309</a>, <a href="https://doi.org/10.3390/rs11111309">doi:10.3390/rs11111309</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. <em>Ecological Informatics</em>, 56:101061, 2020. URL: <a href="https://www.sciencedirect.com/science/article/pii/S157495412030011X">https://www.sciencedirect.com/science/article/pii/S157495412030011X</a>, <a href="https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101061">doi:https://doi.org/10.1016/j.ecoinf.2020.101061</a>.</p> </li> </ul>
Outputs of the Jupyter Notebook - SEVIRI Level 1.5
<p>The dataset contains the outputs of the notebook "SEVIRI Level 1.5" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a>, 18/01/22 (latest revision)</p> </li> </ul> <p><em>Dataset originator/creator</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset authors</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Martin Wooster, Jiangping He, Weidong Xu, and Alessio Lattanzio. Frp - product user manual. URL: <a href="https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq">https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq</a> (visited on 2021-11-18).</p> </li> <li> <p>MJ Wooster, G Roberts, PH Freeborn, W Xu, Y Govaerts, R Beeby, J He, A Lattanzio, D Fisher, and R Mullen. Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis. <em>Atmospheric Chemistry and Physics</em>, 15(22):13217–13239, 2015.</p> </li> </ul>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Effect of live cribwall on slope stability - modelling outputs
<p>These datasets contain outputs from a novel live cribwall model. The model assess the effect of a live cribwall on slope stability over time. The dataset contains Factor of Safety records under different plant cover and climate change scenarios. The model is still unpublished. For more detail, please get in touch aol3@gcu.ac.uk </p>
Outputs of the Jupyter Notebook - Tree crown delineation using detectreeRGB
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge, <a href="https://github.com/shmh40">@shmh40</a></li> <li>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge <a href="https://github.com/shmh40">@shmh40</a></li> <li>James G. C. Ball (contributor), University of Cambridge <a href="https://github.com/PatBall1">@PatBall1</a></li> <li>David A. Coomes (contributor), University of Cambridge</li> <li>Toby Jackson (contributor), University of Cambridge</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.