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518 results for “cycling data”
Chemical data accompanying the manuscript "Chromium cycling in redox-stratified basins challenges δ53Cr paleoredox proxy applications" in Geophysical research Letters
<p>Water column and sediment chromium concentration and stable isotope data and ancillary metal data from Lake Cadagno, Switzerland. These data accompany a manuscript by the same authors in Geophysical Research Letters (doi: 10.1029/2022GL099154).</p> <p> </p> <p>The associated CTD data are available in the following Zenodo dataset: Sepúlveda Steiner, O., Carlino, C., Haizmann, E., Roman, S., Wüest, A., & Bouffard, D. (2022). Lake Cadagno 2017 CTD and water quality monitoring [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.7127882">http://doi.org/10.5281/zenodo.7127882</a></p>
The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage - Data
<p>Postprocessed data set used for RECCAP2 Southern Ocean chapter:</p><p>Hauck, Gregor, et al.: The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage</p><p>The raw data is available at: Müller, Jens Daniel. (2023). RECCAP2-ocean data collection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7990823</p><p>Scripts for plotting are available at https://github.com/RECCAP2-ocean/Southern-Ocean and a frozen version of the scripts is deposited at:</p><p>Judith Hauck, Luke Gregor, Cara Nissen, Lavinia Patara, Mark Hague, & Precious Mongwe. (2023). The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage - Scripts. Zenodo. https://doi.org/10.5281/zenodo.10076121</p><p> </p>
Nitrogen Cycling and Environmental Data in Riparian Soils across Biomes
This dataset compiles soil nitrogen cycle data from riparian soils, sourced from peer-reviewed studies published between 1980 and 2023. The selection process was based on three inclusion criteria: (1) studies measuring in-situ net nitrification rates in the top soil layer using the incubating bag technique, (2) studies reporting net nitrification rates from laboratory incubations without altering the initial nitrogen pool, and (3) studies providing field data on soil nitrogen concentrations, moisture, and temperature. The final dataset (D1) includes data from 174 riparian sites across four continents, with the majority of sites (86%) located in North America and Europe, while only 13 were located in the Southern hemisphere. For each site, we gathered data on net nitrification rates and key soil physicochemical properties, including bulk density, depth, moisture (expressed as water-filled pore space, WFPS), temperature, and ammonium and nitrate concentrations. The dataset includes 734 observations from 99 field sites and 120 observations from 45 laboratory-incubated sites. All publications from which data were used are list in dataset 2 (D2). This comprehensive dataset offers valuable insights into nitrogen dynamics in riparian soils, supporting further research into soil nitrogen cycling across diverse biomes and environmental conditions.
Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)
<p>Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation:</p> <p>Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. <em>J. Climate, </em><strong>30</strong>, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.</p> <p> </p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Data for Lake Mendota Phosphorus Cycling Model
There is an opportunity to advance both prediction accuracy and scientific discovery for phosphorus cycling in Lake Mendota (Wisconsin, USA). Twenty years of phosphorus measurements show patterns at seasonal to decadal scales, suggesting a variety of drivers control lake phosphorus dynamics. Our objectives are to produce a phosphorus budget for Lake Mendota and to accurately predict summertime epilimnetic phosphorus using a simple and adaptable modeling approach. We combined ecological knowledge with machine learning in the emerging paradigm, theory-guided data science (TGDS). A mass balance model (PROCESS) accounted for most of the observed pattern in lake phosphorus. However, inclusion of machine learning (RNN) and an ecological principle (PGRNN) to constrain its output improved summertime phosphorus predictions and accounted for long term changes missed by the mass balance model. TGDS indicated additional processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve our mass balance modeling approach.
Manure Cycling Interview Data
Exploring the potential for nutrient circularity in the beef production system requires an understanding of current practices. Manure nutrients produced in feedlots are an ample source of fertilizer for phosphorus deficient crop and hay lands. However, it is unclear how far manure nutrients are travelling from feedlots, what crops they’re being applied to, and whether those grains are in turn integrated into the feedlot operations. The purpose of these interviews was to ascertain the above information from feedlot managers. In addition, we sought contextual information (provenance of cattle, cattle weights/ages, manure treatment, regulations/guidelines, processing facility destination, barriers, suggestions for improvements). To answer our question about potential manure nutrient circularity, we focused and report here the elements pertaining to feed/grain provenance, crops manure was applied to, and export distance for manure.
Research Data Life cycle
<p>Research Life cycle Headings & Key Points.</p> <p>1- Planning:</p> <ul> <li>Data management planning (DMPs)</li> <li>Data description and metadata extraction</li> <li>Data documentation</li> <li>Choice of repositories</li> <li>Choices of file formats</li> <li>Data re-use</li> <li>Funders requirements</li> <li>File naming</li> <li>Ethics and Research conduct</li> <li>Funding for RDM activities</li> </ul> <p>2- Managing:</p> <ul> <li>Storage and backup & security</li> <li>Active Metadata collection</li> <li>Tools and software solutions</li> <li>Curation</li> <li>Versioning</li> <li>Provenance</li> </ul> <p>3- Sharing</p> <ul> <li>Data access and Sharing rights</li> <li>Data privacy and GDPR compliance</li> <li>Data ownership, licensing</li> <li>Data Transfer</li> <li>GDPR</li> </ul> <p>4- Preservation and Publication</p> <ul> <li>Citation</li> <li>PrePrint</li> <li>DOI</li> <li>Publishing requirements</li> <li>Long Term Storage</li> <li>Archival and Disposal policies</li> </ul>
MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary</strong></p> <p>The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes.</p> <p>We aim to answer two questions:</p> <p>1. What is the intrinsic spatial resolution of global river dynamics?</p> <p>2. How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes?</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling.</p> <p><strong>Data Products</strong></p> <p>The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).</p> <p><strong>Riv_coast.zip</strong> contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean.</p> <p><strong>· </strong><strong>riv_coast.zip</strong></p> <p><strong> o </strong><strong>cor:</strong> riv_coast_pfaf_ii_COR.shp</p> <p><strong> o </strong><strong>uncor: </strong>riv_coast_pfaf_ii_UNCOR.shp</p> <p><strong> </strong></p> <p><strong>Qout_rivwidth.zip </strong>contains csv files of the aggregate river discharge to the ocean (km<sup>3</sup>/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>Qout_rivwidth.zip: </strong>Qout_pfaf_ii_rivwidth.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_low.zip</strong> contains csv files of the aggregate river storage (km<sup>3</sup>) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_low.zip:</strong> V_pfaf_ii_rivwidth_low.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_nrm.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_nrm.zip: </strong>V_pfaf_ii_rivwidth_nrm.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_hig.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_hig.zip: </strong>V_pfaf_ii_rivwidth_hig.csv</p> <p><strong> </strong></p> <p><strong>Largest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins.</p> <p><strong>· </strong><strong>largest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge contributed by each basin</p> <p><strong> o </strong><strong>riv:</strong> riv_top10_nxx.shp – river reaches that drain the 10 largest basins</p> <p><strong> </strong></p> <p><strong>Smallest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m.</p> <p><strong>· </strong><strong>smallest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge to the ocean from each narrow river reach</p> <p><strong> o </strong><strong>riv: </strong>riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii</p> <p><strong> </strong></p> <p><strong>Global_summary.zip </strong>contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage.</p> <p><strong>· </strong><strong>global_summary.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth: </strong>global summary files for discharge to the ocean (km<sup>3</sup>/yr) under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_low:</strong> global summary files for total river storage (km<sup>3</sup>) for the low residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_nrm:</strong> global summary files for total river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_hig: </strong>global summary files for total river storage (km<sup>3</sup>) for the hig residence time scenario under river width sampling</p> <p><strong> o </strong><strong>cat_small_gl: </strong>cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean</p> <p><strong> </strong></p> <p><strong>Rivwidth_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations.</p> <p><strong>· </strong><strong>Rivwidth_sens.zip</strong></p> <p><strong> o </strong><strong>riv_coast</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_VIC</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_CLSM</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_NOAH</strong></p> <p><strong> o </strong><strong>global_summary_VIC</strong></p> <p><strong> o </strong><strong>global_summary_CLSM</strong></p> <p><strong> o </strong><strong>global_summary_NOAH</strong></p> <p><strong> </strong></p> <p><strong>Cor_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis.</p> <p><strong>· </strong><strong>Cor_sens.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_ENS</strong></p> <p><strong> o </strong><strong>global_summary_ENS</strong></p> <p><strong> </strong></p> <p><strong>Width_val.zip </strong>contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018).</p> <p><strong>· Width_val.zip: </strong>width_validation_pfaf_ii.csv</p> <p> </p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585-588. https://doi.org/10.1126/science.aat0636</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p>
Detrital Carbonate Minerals in Earth's Element Cycles (Data & Scripts)
<p>Earth surface conditions, including climate and sea level, are largely controlled by the cycling of carbon and biogeochemically coupled elements. However, most elemental budgets cannot be consentaneously balanced for the present state. Here, we investigate the possible role of riverine carbonate minerals in biogeochemical cycles. We derive individual river basin export fluxes, the global export flux to the ocean and its reduction by human influence, utilizing state-of-the-art regression techniques and published global-scale datasets. Results point to a significance of riverine detrital carbonates for the global mass balances of carbon, calcium, alkalinity and strontium, which might help solving this long-standing problem. </p> <p>[Plain Language summary from: Müller et al. 2022, Detrital Carbonates in Earth's Element Cycles, GBC, <a href="https://doi.org/10.1002/essoar.10508409.1">https://doi.org/10.1002/essoar.10508409.1</a> ].</p> <p>Here data and scripts on which these investigations are based can be accessed.</p> <p> </p> <p>Funding:<br> This work was carried out under the umbrella of the Netherlands Earth System Science Centre (NESSC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 847504. Funding was also provided by BMBF-project PALMOD (Ref 01LP1506C) through the German Federal Ministry of Education and Research (BMBF) as Research for Sustainability inititative (FONA). AS thanks the European Research Council for Consolidator Grant 771497.</p>
Test data for concise cycle test - CCT008
<p>Project: Hybrid-BioVGE</p> <p>The Hybrid – BioVGE project is proposed with the primary objective to develop, design and demonstrate a highly integrated solar/biomass hybrid air conditioning system for space cooling and heating of residential and commercial buildings that is affordable, operating with improved efficiency and with a strong market potential.</p> <p>Project details at https://hybrid-biovge.inegi.up.pt/index.asp</p> <p>File Description: Test data for CCT008 from day 1 to day 8</p> <p>Work task: WT 7.2</p> <p>Responsible: OST, Switzerland</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Supporting data of: Hydrography and food distribution during a tidal cycle above a cold-water coral mound
<p>This file contains the raw data and data analyses scripts to:</p> <p>Hydrography and food distribution during a tidal cycle above a cold-water coral mound</p> <p>Evert de Froe, Sandra R. Maier, Henriette G. Horn, George A. Wolff, Sabena Blackbird, Christian Mohn, Mads Schultz, Anna-Selma van der Kaaden, Chiu H. Cheng, Evi Wubben, Britt van Haastregt, Eva Friis Moller, Marc Lavaleye, Karline Soetaert, Gert-Jan Reichart, Dick van Oevelen.</p> <p>Deep Sea Research Part I: Oceanographic Research Papers, 2022,<br> ISSN 0967-0637,<br> https://doi.org/10.1016/j.dsr.2022.103854.<br> <strong>Abstract: </strong>Cold-water corals (CWCs) are important ecosystem engineers in the deep sea that provide habitat for numerous species and can form large coral mounds. These mounds influence surrounding currents and induce distinct hydrodynamic features, such as internal waves and episodic downwelling events that accelerate transport of organic matter towards the mounds, supplying the corals with food. To date, research on organic matter distribution at coral mounds has focussed either on seasonal timescales or has provided single point snapshots. Data on food distribution at the timescale of a diurnal tidal cycle is currently limited. Here, we integrate physical, biogeochemical, and biological data throughout the water column and along a transect on the south-eastern slope of Rockall Bank, Northeast Atlantic Ocean. This transect consisted of 24-hour sampling stations at four locations: Bank, Upper slope, Lower slope, and the Oreo coral mound. We investigated how the organic matter distribution in the water column along the transect is affected by tidal activity. Repeated CTD casts indicated that the water column above Oreo mound was more dynamic than above other stations in multiple ways. First, the bottom water showed high variability in physical parameters and nutrient concentrations, possibly due to the interaction of the tide with the mound topography. Second, in the surface water a diurnal tidal wave replenished nutrients in the photic zone, supporting new primary production. Third, above the coral mound an internal wave (200 m amplitude) was recorded at 400 m depth after the turning of the barotropic tide. After this wave passed, high quality organic matter was recorded in bottom waters on the mound coinciding with shallow water physical characteristics such as high oxygen concentration and high temperature. Trophic markers in the benthic community suggest feeding on a variety of food sources, including phytodetritus and zooplankton. We suggest that there are three transport mechanisms that supply food to the CWC ecosystem. First, small phytodetritus particles are transported downwards to the seafloor by advection from internal waves, supplying high quality organic matter to the CWC reef community. Second, the shoaling of deeper nutrient-rich water into the surface water layer above the coral mound could stimulate diatom growth, which form fast-sinking aggregates. Third, evidence from lipid analysis indicates that zooplankton faecal pellets also enhance supply of organic matter to the reef communities. This study is the first to report organic matter quality and composition over a tidal cycle at a coral mound and provides evidence that fresh high-quality organic matter is transported towards a coral reef during a tidal cycle.</p> <p> </p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Research Data Life Cycle
<p>Visual description of an ideal research data life cycle, including traditional elements (dark blue), data reuse elements (light blue) and dissemination/sharing/publication elements (light red).</p> <p>Inspired by: Ruegg et al, Completing the data life cycle: using information management in macrosystems ecology research, Front Ecol Environ 2014; 12(1): 24–30, doi:10.1890/120375</p>
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load
<p>The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.</p>
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
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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.