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.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “Biogeosciences”
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
JGR_biogeosciences_Supplementary_Information_Field_Data
<p>The dataset is the supplementary material as an Excel file, which includes the field measurements of DOC concentration, CDOM absorption coefficient, remote-sensing reflectance, along with the corresponding coordinates, dates, and times of collection. The study area is the Plum Island Estuary in Massachusetts and the Gulf of Maine (USA).</p>
Data and code associated with Fernández_García et al 2024 Biogeosciences
<p>These files supplement the Manuscript and Supplementary materials presented in the following paper: Palaeoecology of ungulates in northern Iberia during the Late<br>Pleistocene through isotopic analysis of teeth by Monica Fernández-Garcia, Sarah Pederzani, Kate Britton, Lucia Agudo-Pérez, Andrea Cicero, Jeanne Geiling, Joan Daura, Montserrat Sanz and Ana B. Marín-Arroyo under review in the Biogeosciences journal.</p>
Data supplementing article "Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution" under review at the Journal of Geophysical Research - Biogeoscience
<p>These data supplement the article: Du, J. and J. Shen, Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution, under review at the Journal Of Geophysical Research: Biogeoscience</p> <p>contact: Jiabi Du, jiabi@vims.edu</p> <p>Below are descriptions of the data files included here:</p> <p>1. Monthly mean tracer output [1985-2014]</p> <p>-netCDF format results for monthly mean tracer concentrations from different sources (Susquehanna, Potomac, Rappahannock, York, James Rivers, and Coastal Ocean)</p> <p>-grid information are also included</p> <p>2. Matlab Scripts For Plotting.zip:</p> <p>-Matlab scripts used to plot the horizontal map, the vertical profile for the along channel section, the vertical profile for cross-channel sections. The script enables users to define the period and section no to plot. </p> <p>3. tracer influx and outflux ratio at 9 cross-section.xls:</p> <p>-an excel file contains the bottom tracer influx ratio and surface tracer outflux ratio for different rivers at different sections. </p>
Global nutrient cycling by commercially targeted marine fish (Le Mézo et al., 2022, Biogeosciences)
<p>Model outputs and code used for the study "Global nutrient cycling by commercially targeted marine fish" published in <em>Biogeosciences</em> (Le Mézo et al., 2022). </p> <ul> <li>composite4b_cycling_MCV3_PotH_LME_2010_R2_2010_fNPP7_ks24_p50_c7_newFNPP_Online_nruns_31 = model outputs with <ul> <li><strong>composite.maps</strong> contains the 2D fields</li> <li><strong>y200</strong> refers to fields at the pristine state and <strong>yglo</strong> to fields at the global peak catch.</li> <li><strong>dfish</strong> is the fish biomass in wet weight per gram (size class)</li> <li><strong>resp5</strong> is the cycling rate that was used in the paper (defined in the Methods section)</li> </ul> </li> <li><a href="https://zenodo.org/api/files/72a2b98c-e4e5-4282-9ffa-0a7a1717e009/01_Mean_and_uncertainty_commercial_fish.mlx">Main_script.mlx</a> = main code used to compute the nutrient content and cycling of fish and the comparisons with other fields</li> <li><a href="https://zenodo.org/api/files/72a2b98c-e4e5-4282-9ffa-0a7a1717e009/size_bins_width.m">size_bins_width.m</a> = code used to compute the model size bin width</li> <li>data_annual.mat = NO3, PO4, NPP, C export fields</li> <li>composite_MCV3_PotH_LME_2010_R2_2010_fNPP7_ks24_p50_c7_newFNPP_Online_nruns_31.mat is the model outputs with <ul> <li>cyc.composite.maps.yglo.mean.harvest being the catch field at global peak catch</li> </ul> </li> <li>solublefraction_Mahowald2009_360x180.nc is the Fe deposition field</li> <li> Brahneyetal2015_nitrogenandphosphorus2x2annualdep_360x180.nc is the N deposition field</li> <li>mask_LME.mat is the mask of LME areas</li> <li>ocean_topaz_tracers.timmean.BOATS_grid_fed.nc is the modeled dissolved Fe concentrations in seawater by the TOPAZ model</li> <li>zeu_lee_modis_aqua_average_2002-2019.nc is the euphotic depth field used to compute the nutrient concentrations</li> <li><a href="https://zenodo.org/api/files/72a2b98c-e4e5-4282-9ffa-0a7a1717e009/woa05_nitrate_month.nc">woa05_nitrate_month.nc</a> is the NO3 field used to make the spatial interpolations of the Fe:C stoichiometric ratios.</li> <li><a href="https://zenodo.org/api/files/72a2b98c-e4e5-4282-9ffa-0a7a1717e009/mass_50sizes_boats.mat">mass_50sizes_boats.mat</a> is the mass of each size class of the BOATS model</li> <li><a href="https://zenodo.org/api/files/72a2b98c-e4e5-4282-9ffa-0a7a1717e009/Tables%20S2%20and%20S3.xlsx">Tables S2 and S3.xlsx</a> litterature compilation of values for N and P in fish and zooplankton</li> <li>Galbraith et al (2019) SI.pdf is the supplement to Galbraith et al. (2019) in which the data compilation for the Fe content of fish, zooplankton and phytoplankton can be found.</li> </ul>
Datasets for "Update on our action plan for equity, inclusion, and diversity in publishing at JGR: Biogeosciences"
<p>Datasets supporting "Update on our action plan for equity, inclusion, and diversity in publishing at JGR: Biogeosciences." These files provide summary data on submissions to AGU journals between 2018 and 2022. They also include decision data for manuscripts submitted to AGU journals with a decision made between 2018 and 2021. Summary data are presented in compliance with AGU's Privacy Policy, https://www.agu.org/Privacy-Policy</p>
Dataset for the manuscript "Barium stable isotopes as a fingerprint of biological cycling in the Amazon River Basin" submitted to Biogeosciences Discussions
<p>This file contains the dataset of the manuscript submitted to Biogeosciences Discussions entitled "Barium stable isotopes as a fingerprint of biological cycling in the Amazon River Basin" by Charbonnier et al. It consists in three spreadsheets: (1) dissolved species concentration and dissolved Ba isotope data on river dissolved load; (2) main characteristics and Ba isotope data of river sediments; (3) compilation of major element and Ba and Li concentration in rocks (compilation made from the GEOROC database) and in the dissolved load of river draining single rock types (compilation made from previously published data and new data).</p>
Data and Software accompanying "Slope-aspect induced climate differences influence how water is exchanged between the land and atmosphere", submitted to JGR: Biogeosciences August 2020
<p>This is a collection of all of the data files and python notebooks used in creating the manuscript "Slope-aspect induced climate differences influence how water is exchanged between the land and atmosphere", submitted to JGR: Biogeosciences August 2020. </p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
Datasets from "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks". JGR Biogeosciences.
<p>This repository contains the datasets linked to "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks" (Journal of Geophysical Research: Biogeosciences, <a href="https://doi.org/10.1029/2024JG008047">https://doi.org/10.1029/2024JG008047</a>). This includes:</p> <ul> <li>The images and annotation* text files (in YOLO format) used for the detection of siliceous microfossils. </li> <li>The models and training results for the trainings presented in the main text and supporting information.</li> </ul> <p>*Note that while annotations were attributed to 14 general categories, only twelve of these were used during training (<em>i.e. </em>Pennate, Centric, Silicoflagellate, Centric_debris, Spore, Other_Biomin, Cocco, Silicoflagellate_debris, Undetermined_silica, Chateoceros_Bacteriastrum, Calcispheres, Foraminifer), and all were pooled into a single "Microfossil" category for the purpose of the training.</p>
Dataset for Paul et al., "Revisiting the applicability and constraints of molybdenum and uranium-based paleo redox proxies: comparing two contrasting sill fjords", Biogeosciences, 2023
<p>The datafile contains the solid phase data for Gullmar Fjord (GF-117) and Koljö Fjord (KF-43), presented in Figures 3, 4 and 6 in the publication:</p> <ol> <li>pore water data (SO<sub>4</sub><sup>2-</sup>, ΣH<sub>2</sub>S, Fe, Mn, Mo, and U)</li> <li>solid-phase carbon and nitrogen data (TOC, TN, C/N, OC<sub>terr</sub> and OC<sub>phyt</sub>)</li> <li>solid-phase sequential extraction data (Mo, U, Fe, Mn, Al, Ca, and S)</li> <li>Mo and U-Enrichment Factors (EF)</li> </ol>
Dataset for Rastogi et al., 2018 Biogeosciences. https://doi.org/10.5194/bg-2018-85
<p>This repository contains all data for the manuscript by Rastogi et al., titled "Ecosystem fluxes of carbonyl sulfide in an old-growth forest: temporal dynamics and responses to diffuse radiation and heat waves". This manuscript has been accepted for publication in the Biogeosciences. </p>
Biogeosciences paper data for Hasanyar et al. "How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?"
<p>The proposed dataset is related to the following publication:<br> Hasanyar, M., Romary, T., Wang, S., Flipo, N. (2023) How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow? Biogeosciences</p> <p>It consists in command files for the c-rive0.32 software available here<br> Shuaitao Wang, Lauriane Vilmin, Masihullah Hasanyar, & Nicolas Flipo. (2023). C-RIVE (0.32). Zenodo. https://doi.org/10.5281/zenodo.7849609</p> <p><br> To run the model :<br> 1. Compile crive0.32<br> 2. Copy the executable in the current directory<br> 3. In a terminal launch<br> > ./crive0.32 ./sensitivity.COMM test.log</p> <p>The “sensitivity.COMM” holds the settings for the C-RIVE simulation related to the paper mentioned in the front head of the current file<br> The information on different parameters of “sensitivity.COMM” are included in “layers”, “meteo” and “param_bio” folders.<br> The layer folder holds the information about the initial conditions of the model (Table 2 in the article).<br> The “meteo” folder holds the meteorological information and finally the “param_bio” folder holds the information on physiology of bacteria, phytoplankton and other model species.</p> <p><br> The output files are written in $HOME/OUTPUT_CRIVE. It is possible to change it directely in sensitivity.COMM, second entry 'OUTPUT_FOLDER'</p>
Supporting Data for Tanioka and Matsumoto (2020), A meta-analysis on environmental drivers of marine phytoplankton C:N:P, Biogeosciences
<p>This dataset contains files used to make figures for Tanioka, T., & Matsumoto, K. (2020). A meta-analysis on environmental drivers of marine phytoplankton C:N:P. <em>Biogeosciences</em>, <em>17</em>(11), 2939–2954. <a href="https://doi.org/10.5194/bg-17-2939-2020">https://doi.org/10.5194/bg-17-2939-2020</a></p> <p><strong>Files uploaded:</strong></p> <p><strong>Bibliography</strong></p> <ol> <li>AppendixS1.pdf : list of 104 papers used in the main meta-analysis.</li> </ol> <p><strong>Excel spreadsheets</strong>:</p> <ol> <li>New_data_190211a.xlsx: C:N and C:P dataset</li> <li>res_all_190211a_P.csv: Effect sizes calculated for each P experiment</li> <li>res_all_190211a_N.csv: Effect sizes calculated for each N experiment</li> <li>res_all_190211a_Fe.csv: Effect sizes calculated for each Fe experiment</li> <li>res_all_190211a_I.csv: Effect sizes calculated for each I experiment</li> <li>res_all_190211a_I.csv: Effect sizes calculated for each T experiment</li> <li>META_BIiblio_articles1st_k4899.xlsx : list of 4899 in the first round of data collection/screening (see Fig. 1 in the main text)</li> <li>META_Biblio_articles2nd_k948.xlsx : list of 948 papers in the second round of data collection/screening (see Fig. 1 in the main text)</li> <li>META_Biblio_articles3rd_k196.xlsx : list of 196 papers in the third round of data collection/screening (see Fig. 1 in the main text)</li> </ol> <p><strong>R scripts:</strong></p> <ol> <li>analysis_190211a_test_P.R: script to conduct meta-analysis on P experiments</li> <li>analysis_190211a_test_N.R: script to conduct meta-analysis on N experiments</li> <li>analysis_190211a_test_F.R: script to conduct meta-analysis on Fe experiments</li> <li>analysis_190211a_test_I.R: script to conduct meta-analysis on I experiments</li> <li>analysis_190211a_test_T.R: script to conduct meta-analysis on T experiments</li> <li>functions_eff_logrr.R: function file to calculate ln(RR) (used in “analysis_190211a_test_X.R”)</li> <li>functions_scalc.R function file to calculate s-factor (used in “analysis_190211a_test_X.R”)</li> </ol>
Dataset_2020_Biogeosciences_Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems
<p>Dataset associated to the publication: Erktan, A., Rillig, M.C., Carminati, A., Jousset, A., Scheu, S. (2020) Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems, Biogeosciences, accepted the 27/08/2020.</p>
Dastaset for: "Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P. and Ojeda-Zujar, J. (2016). Modelling interannual variation in the spring and autumn land surface phenology of the European forest, Biogeosciences, 13
<p>Dastaset for: "Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P. and Ojeda-Zujar, J. (2016). Modelling interannual variation in the spring and autumn land surface phenology of the European forest, Biogeosciences, 13</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.