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4,276 results for “Estimation”
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Estimated life-cycle-based environmental indicators and social indicators for companies and investment funds
<p>The data files represent the 26 estimated life-cycle-based indicators for a sample of companies and funds, obtained using the methodology described in the linked journal article. The files SD1 and SD2 contain the individual values estimated for the fund and company samples. These estimates are based on the methodology described in the linked article. The data herein is the source for producing all figures of the paper. All companies and funds have been anonymized, as the data is sourced from proprietary databases. At the same link, supplementary file SD3 contains the summary statistics and comparison of sustainable funds versus conventional funds sample. The file SD4 contains the data used to create Figure 4. The file SD5 contains sample data to create Figure 5. The file SD6 contains sample data to create Figure 6. Additional more detailed data can be provided upon reasonable request, but cannot be publicly disclosed as it contains data from licenced databases. </p>
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios
<p>Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.</p> <p>All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>pyCIAM_outputs.zarr.zip</strong>: Outputs of the pyCIAM model, using the <a href="https://doi.org/10.5281/zenodo.6449230">SLIIDERS</a> dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (<a href="https://doi.org/10.5281/zenodo.593357">LocalizeSL</a> and <a href="https://doi.org/10.5281/zenodo.6419953">FACTS</a>) and for multiple emissions scenarios and ice sheet models.</li> <li><strong>pyCIAM_outputs_{case}.nc:</strong> A NetCDF version of <code>pyCIAM_outputs</code>, in which the netcdf files are divided up by adaptation "case" to reduce file size.</li> <li><strong>diaz2016_outputs.zarr.zip</strong>: A replication of the results from <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a> - the model upon which pyCIAM was built, using an identical configuration to that of the original model.</li> <li><strong>suboptimal_capital_by_movefactor.zarr.zip</strong>: An analysis of the observed present-day allocation of capital compared to a "rational" allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.</li> </ul> <p><em>Inputs</em></p> <ul> <li><strong>ar5-msl-rel-2005-quantiles.zarr.zip</strong>: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014EF000239">Kopp 2014</a>, <a href="https://www.pnas.org/doi/pdf/10.1073/pnas.1817205116">Bamber 2019</a>, <a href="https://www.geo.umass.edu/climate/papers2/Deconto_Nature_2021.pdf">DeConto 2021</a>, <a href="https://www.ipcc.ch/srocc/">IPCC SROCC</a>. The results contained in <em>pyCIAM_outputs.zarr.zip</em> cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.</li> <li><strong>diaz2016_inputs_raw.zarr.zip</strong>: The coastal inputs used in <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a>, obtained from <a href="https://github.com/delavane/CIAM">GitHub</a> and formatted for use in the Python-based pyCIAM. These are based on the <a href="http://diva.globalclimateforum.org">Dynamic Integrated Vulnerability Assessment (DIVA)</a> dataset.</li> <li><strong>surge-lookup-seg(_adm).zarr.zip</strong>: Pre-computed lookup tables estimating average annual losses from extreme sea levels due to mortality and capital stock damage. This is an intermediate output of pyCIAM and is not necessary to replicate the model results. However, it is more time consuming to produce than the rest of the model and is provided for users who may wish to start from the pre-computed dataset. Two versions are provided - the first contains estimates for each unique intersection of ~50km coastal segment and state/province-level administrative unit (admin-1). This is derived from the characteristics in SLIIDERS. The second is simply estimated on a version of SLIIDERS collapsed over administrative units to vary only over coastal segments. Both are used in the process of running pyCIAM.</li> <li><strong>ypk_2000_2100.zarr.zip</strong>: An intermediate output in creating SLIIDERS that contains country-level projections of GDP, capital stock, and population, based on the Shared Socioeconomic Pathways (SSPs). This is only used in normalizing costs estimated in pyCIAM by country and global GDP to report in Depsky et al. 2023. It is not used in the execution of pyCIAM but is provided to replicate results reported in the manuscript.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>pyCIAM.zip:</strong> Contains the python-CIAM package as well as a notebook-based workflow to replicate the results presented in Depsky et al. 2023. It also contains two master shell scripts (run_example.sh and run_full_replication.sh) to assist in executing a small sample of the pyCIAM model or in fully executing the workflow of Depsky et al. 2023, respectively. This code is consistent with release 1.2.0 in the <a href="https://github.com/ClimateImpactLab/pyCIAM">pyCIAM GitHub repository</a> and is available as version 1.2.0 of the python-CIAM package on PyPI.</li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p><em><strong>1.2</strong></em></p> <ul> <li>Point `data-acquisition.ipynb` to updated Zenodo deposit that fixes the dtype of `subsets` variable in `diaz2016_inputs_raw.zarr.zip` to be bool rather than int8</li> <li>Variable name bugfix in `data-acquisition.ipynb`</li> <li>Add netcdf versions of SLIIDERS and the pyCIAM results to `upload-zenodo.ipynb`</li> <li>Update results in Zenodo record to use SLIIDERS v1.2</li> <li> </li> </ul> <p><em><strong>1.1.1</strong></em></p> <ul> <li>Bugfix to inputs/diaz2016_inputs_raw.zarr.zip to make the `subsets` variable bool instead of int8.</li> </ul> <p><em><strong>1.1.0</strong></em></p> <ul> <li>Version associated with publication of Depsky et al., 2023</li> </ul>
Tables of results for lower mantle grain size and viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are shown in Figs. 7-10 and Figs. S4-S5 in Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates".</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>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
Code and data to "Statistical learning and topkriging improve spatio-temporal low-flow estimation"
<p>This data and software supports the manuscript "Statistical learning and topkriging improve spatio-temporal low-flow estimation" (https:://doi.org/<span>10.1029/2024WR038329</span>).</p> <p>The dataset consists of:</p> <ul> <li>all produced predictions of the models (data/predictions.RDS and data/predictions_csv/*)</li> <li>observational data (data/observations.csv)</li> <li>additional catchment data (data/catchment_data.csv) used for presenting the figures</li> <li>state boundaries of Austria as a shape file (data/boundaries.*)</li> <li>partial predictions of a model-based boosting approach (data/partial_predictions.csv)</li> <li>Example output of number of EOF, due to long computational time (data/number_eofs.RDS)</li> <li>IDs of near natural catchments (data/ids_low_flow.csv)</li> </ul> <p>Additionally, the code is provided to:</p> <ul> <li>Compute the number of EOFs (functions/number_eofs.R)</li> <li>Produce all the figures and tables in the paper (scripts/plotting_results.R)</li> </ul>
Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
<p>Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our <a href="https://www.mdpi.com/2072-4292/13/21/4302">original</a> publication for more details.</p> <p>0: Uncertain</p> <p>1: No Data</p> <p>2: Background class</p> <p>3: Unchanged coconut plantation</p> <p>4: Damaged coconut plantation</p> <p>5: New coconut plantation</p> <p> </p> <p> </p> <p> </p>
Wflow SBM streamflow estimates for CAMELS data set
<p>The dataset contains 3 simulated timeseries at the basin outlet for the CAMELS dataset created with the wflow_sbm model at various spatial resolutions (3km, 1km, 200m). The data set includes the calculated objective functions NSE, KGE 2009, KGE 2012, and KGE NP.</p>
Observation based gridded annual runoff estimates over Victoria, Australia
<p>The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012. The methodology extended the R package <em>rtop</em> to allow <em>top-kriging</em> with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf</p> <p> </p>
Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers
<p>Anonymised dataset analysed in the study "Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers". </p>
Data from: "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success"
<p>Secondary datasets used for analysis in "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success". Raw GPS relocation data are not publicly available due to potential ethical implications but are available from the corresponding author (Nathan Hooven, nathan.d.hooven@gmail.com) upon reasonable request. Datasets include:</p> <p>elk_days_part.csv: generated movement metrics and days from parturition for all cow elk for which reproductive success was confirmed</p> <p>elk_np.csv: generated movement metrics for non-parturient cow elk</p> <p>elk_unknowns.csv: generated movement metrics for cows with unknown reproductive status, but were confirmed pregnant in mid-winter</p> <p>elk_thisyear.csv: generated movement metrics for 2020 Vectronic cows monitored in 2021</p> <p>Part_dates.csv: Confirmed and predicted dates of parturition for all elk in training and testing sets</p> <p>all_prob_summary_parturient.csv: Confirmed and predicted dates of parturition and differences for confirmed successful elk</p> <p>Decision rules 1.xlsx: Spreadsheet with classification accuracy based upon varying decision rules</p> <p>Decision rules 2.csv: Plottable summary of classification accuracy based upon varying decision rules</p> <p> </p>
Dataset In-vivo estimation of axonal morphology from MRI and EEG data
<p>This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.874023/full">https://www.frontiersin.org/articles/10.3389/fnins.2022.874023</a></p> <p>The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts.</p> <p>The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT)</p> <p>The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r)</p> <p>-------------------------------------------------------------------------<br> CONTENT:</p> <p>This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat</p> <p>The codes used in the analysis of this data are available on our online repository: <a href="https://github.com/LREN-physics/AxonalMorphology">https://github.com/LREN-physics/AxonalMorphology</a>.</p> <p>-------------------------------------------------------------------------<br> AUTHORS:</p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
Water content in the upper 1 meter of the Martian regolith, estimated from FREND experiment data
<p>This dataset contains a raw map of neutron suppression and a smoothed map of water content in the upper meter of the Martian subsurface derived from FREND instrument data, between latitudes 50° north and south. FREND is a high resolution neutron telescope onboard ExoMars’ TGO orbiter (see Mitrofanov, I. et al. Fine Resolution Epithermal Neutron Detector (FREND) Onboard the ExoMars Trace Gas Orbiter. Space Sci Rev 214, 86 (2018). <a href="https://doi.org/10.1007/s11214-018-0522-5">https://doi.org/10.1007/s11214-018-0522-5</a>).</p> <p>Neutron Suppression is defined as ratio between neutron count rate (counts per second) at each pixel and a reference count rate (average count rate over Solis Planum area between -115° and -65° longitude and -55° and -15° latitude). It is a dimensionless parameter, showing how much the neutron flux is suppressed relative to the reference Solis Planum area, known to be one of the driest on Mars with water equivalent hydrogen (WEH) estimated as 2.78 weight percent (wt%, see Boynton, W.V. et al. Concentration of H, Si, Cl, K, Fe, and Th in the low- and mid-latitude regions of Mars. Journal of Geophysical Research 112, (2007) E12S99. <a href="https://doi.org/10.1029/2007JE002887">https://doi.org/10.1029/2007JE002887</a>). Statistical errors are given in each pixel. Map of neutron suppression can be used to locate regions of high or low water content.</p> <p>Map of water is derived from the neutron suppression map in the following way: neutron suppression map is smoothed with a gaussian 12° (full width half maximum) filter, considering statistical errors in each pixel. Transition from smoothed neutron suppression values to WEH values is done through numerical modeling of all physical process from neutron emission from the Martian regolith to detection of neutrons by FREND instrument, depending on the WEH content in the regolith. Map of water contains three values for each pixel: minimum, mean and maximum WEH in wt%: mean values are derived from neutron suppression map, maximum and minimum values correspond to plus and minus error of the neutron suppression. These three values represent the range of WEH in each pixel within 1 error margin of neutron suppression values.</p>
Spacecraft Pose Estimation Dataset (SPEED)
<p>The SPEED dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>"Pose Estimation challenge" </strong>in collaboration<strong> with Stanford Universitiy's Space Rendezvous Lab (SLAB)</strong>. It features images and poses of the Tango spacecraft (PRISMA mission), 12000 of them generated by SLAB's Optical Simulator using a high fidelity texture model and 300 images from the TRON facility, using a physical mock-up model of Tango.</p> <p>The goal of the competition was estimate the relative pose (distance and orientation) from pixel images only.</p> <ul> <li>Detailed information about the original competition can be found at <a href="https://kelvins.esa.int/satellite-pose-estimation-challenge/">https://kelvins.esa.int/satellite-pose-estimation-challenge/</a></li> <li>A follow-up competition with a larger and improved dataset <strong>(SPEED+)</strong> is available on Zenodo as well: <a href="https://zenodo.org/record/5588480">https://zenodo.org/record/5588480</a></li> </ul> <p>A publication about the results of the pose estimation challenge has been published as</p> <ul> <li>Kisantal, Mate, et al. "Satellite pose estimation challenge: Dataset, competition design, and results." <em>IEEE Transactions on Aerospace and Electronic Systems</em> 56.5 (2020): 4083-4098.</li> </ul>
PIANO-AUG - Piano Multipitch Estimation with Augmentation Dataset
<p>This dataset includes a 29 minutes long piano recording with a total of 4584 notes. The recording includes sections of single notes as well as groups of simultaneously sounding notes (intervals, chords), which ascend chromatically within the MIDI pitch range [21,92].</p> <p>In particular, the segments cover single notes, intervals (two-note groups), as well as common three- voiced and four-voiced chords. It must be noted that we do not investigate chord inversions here, all chords are used in their root position.</p> <p>The audio is created by rendering the MIDI file using the “Grand Piano” plugin in Ableton Live 8 with default settings except from the “Reverb Amount” being set to zero.</p> <p>The <a href="https://github.com/spotify/pedalboard">pedalboard</a> python library (0.4.1) can be used to create multiple augmented versions of the unprocessed piano recording. In particular, we create pairs of mild (-) and heavy (+) augmentations for each of the compression (comp), gain (gain), low-pass filter (lpf), and reverb (rev) effects using <a href="https://github.com/jakobabesser/piano_aug/blob/main/create_augmented_versions.py">this Python script</a></p> <p>Further details are provided at <a href="https://github.com/jakobabesser/piano_aug">https://github.com/jakobabesser/piano_aug</a>.</p>
SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
<p><a href="http://changhoonhahn.github.io/SEDflow">SEDflow</a> is an accelerated Bayesian SED modeling method that uses the <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H/abstract">Hahn et al. (2022a)</a> PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions of galaxy properties from optical photometry. SEDflow is<span class="math-tex">\(10^5\times\)</span> faster than conventional Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors. This repository includes all of the data used to train, validate, and test SEDflow.</p> <p>This repository also includes a value-added catalog with detailed physical properties of 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/). The properties are inferred from optical photometry in the <em>u, g, r, i, z</em> bands using SEDflow. For more details on this catalog and SEDflow see the <a href="http://changhoonhahn.github.io/SEDflow">documentation</a> and Hahn & Melchior (2022). </p> <p>For each galaxy, the catalog provides posteriors of: </p> <ul> <li>log_mstar: log10 of stellar mass</li> <li>log_sfr_1gyr: log10 of average star formation rate over 1Gyr</li> <li>log_z_mw: log10 of mass-weighted metallicity</li> <li>beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions</li> <li>fburst: fraction of stellar mass formed by a starburst event</li> <li>tburst: time of the starburst event</li> <li>log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions</li> <li>tau_bc: birth cloud optical depth</li> <li>tau_ism: diffuse dust optical depth</li> <li>n_dust: Calzetti (2001) dust index</li> </ul>
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</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.