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Open Access levels of Dutch universities' output 2016-2017 (articles & reviews): green, gold, hybrid and bronze - May 2018
<p>Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.</p> <p>Our previous analysis (<a href="http://doi.org/10.5281/zenodo.1133759">10.5281/zenodo.1133759</a> and <a href="http://doi.org/10.7287/peerj.preprints.3520v1">10.7287/peerj.preprints.3520v1</a>) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels. </p> <p>In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories (IRs) (see <a href="https://unpaywall.org/sources">https://unpaywall.org/sources</a>). This now includes coverage of the IRs from all Dutch universities. </p> <p>Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis. </p> <p>In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands. </p> <p>The following categories are distinguished (description taken from Piwowar at al., 2018, doi: <a href="https://doi.org/10.7717/peerj.4375">10.7717/peerj.4375</a>)</p> <ul> <li><strong>Pure gold</strong>: Published in an open-access journal (as defined by the DOAJ)</li> <li><strong>Hybrid</strong>: Free under an open license in a toll-access journal</li> <li><strong>Bronze</strong>: Free to read on the publisher page, but without a license</li> <li><strong>Green: </strong>Available from an institutional or disciplinary repository (including PubMedCentral)</li> </ul> <p>Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data <a href="https://unpaywall.org/products/simple-query-tool">Simple Query Tool</a>. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared. </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
Dataset: Publication cultures and Dutch research output: a quantitative assessment
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.2643360">Publication cultures and Dutch research output: a quantitative assessment</a></p> <p> </p> <p>On the report:</p> <p>Research into publication cultures commissioned by VSNU and carried out by Utrecht University Library has detailed university output beyond just journal articles, as well as the possibilities to assess open access levels of these other output types. For all four main fields reported on, the use of publication types other than journal articles is indeed substantial. For Social Sciences and Arts & Humanities in particular (with over 40% and over 60% of output respectively not being regular journal articles) looking at journal articles only ignores a significant share of their contribution to research and society. This is not only about books and book chapters, either: book reviews, conference papers, reports, case notes (in law) and all kinds of web publications are also significant parts of university output.</p> <p>Analyzing all these publication forms and especially determining to what extent they are open access is currently not easy. Even combining some the largest citation databases (Web of Science, Scopus and Dimensions) leaves out a lot of non-article content and in some fields even journal articles are only partly covered. Lacking metadata like affiliations and DOIs (either in the original documents or in the scholarly search engines) makes it even harder to analyze open access levels by institution and field. Using repository-harvesting databases like BASE and NARCIS in addition to the main citation databases improves understanding of open access of non-article output, but these routes also have limitations. The report has recommendations for stakeholders, mostly to improve metadata and coverage and apply persistent identifiers.</p>
Regional HYCOM output: absolute and relative wind forcing experiments
<p>Hybrid Coordinate Ocean Model (HYCOM) output from 2 forced regional simulations of the Agulhas Current. The first experiment is forced by absolute winds, the second experiment is forced by relative winds (the wind speed relative to the current speed). Data uploaded here are the sea surface height and surface u and v velocities, for both experiments. This is weekly output from January 1993- December 2013 at 1/10°. Also uploaded is a vertical section of the HYCOM output along the ACT transect in the Agulhas Current (~33.4°S at the coast and extending 300km offshore) for both experiments from 2010- 2013.</p> <p>For more information on the data please refer to "L. Braby, Backeberg, B., Krug M. and Reason C. (in prep), Quantifying the impact of wind-current feedback on mesoscale variability in forced simulation experiments of the Agulhas Current using an eddy tracking algorithm."</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Output of ECHAM with radiation code in single precision
<p>We converted the radiation part of the atmospheric model </p> <p>ECHAM to single precision arithmetic. We analyzed different conversion strategies and finally used a step by step change of all modules, subroutines and functions. We found out that a small code portion still requires double precision arithmetic. We made use of Fortran interfaces to generate code that can be easily changed from double to single precision and vice versa, basically using a simple switch in one module. We compared the output of the single precision version in the CR configuration with observational data and with the original double precision code. The results of both versions are comparable. We extensively tested different parallelization options with respect to the possible performance gain, in both CR and LR configuration. The single precision radiation itself can be about 40\% faster, whereas the speed-up for the whole ECHAM model using the single precision radiation can be about 18\% in the best configuration. We further measured the energy consumption, which could also be reduced by about 17\%.</p> <p>This dataset contains the output data of an ECHAM AMIP experiment 1970-2010 with radiation part of the code in single precision arithmetic (in netcCDF file SPFINALDATA.nc). Corresponding output of a original double precision run is also provided (in netcCDF file DPFINALDATA.nc).</p> <p>The files <em>rad_dp_to_sp.sh</em> and <em>rad_sp_to_dp.sh</em> are conversion shell scripts that perform the necessary changes in the source code.</p> <p> </p>
The Time Variable Ionospheric Electric Field (TiVIE) Model Outputs v 1.0
<p>These are the outputs for the TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.
<p>NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters:</p> <p><code>[mesh]</code><br><code>filename=small_arctic_10km.msh</code></p> <p><code>[simul]</code><br><code>duration=150</code><br><code>time_init=2006-11-15</code><br><code>timestep=900</code></p> <p><code>[dynamics]</code><br><code>compression_factor=13800</code><br><code>C_lab=2675000</code><br><code>nu0=0.301</code><br><code>tan_phi=0.624</code><br><code>substeps=90</code><br><code>time_relaxation_damage=15</code><br><code>use_temperature_dependent_healing=true</code></p> <p><code>[output]</code><br><code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code><br><code>output_per_day=4</code><br><code>variables=M_VT</code><br><code>variables=Concentration</code><br><code>variables=Thickness</code></p> <p><code>[setup]</code><br><code>atmosphere-type=era5</code><br><code>ice-type=topaz_osisaf_icesat</code><br><code>ocean-type=topaz</code><br><code>bathymetry-type=etopo</code><br><code>dynamics-type=bbm</code></p> <p><code>[thermo]</code><br><code>diffusivity_sss=0</code><br><code>diffusivity_sst=0</code><br><code>h_young_max=0.3</code><br><code>newice_type=1</code><br><code>hnull=0.5</code></p> <p><code>[debugging]</code><br><code>check_fields_fast=false</code></p> <p>The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook:</p> <p>https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb</p> <p> </p>
Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0
<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0. </p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>
Rayleigh Sample Outputs
<p>This zip file contains a sample Rayleigh output dataset generated by using the 'benchmark_diagnostics_input' file from the 'input_examples' directory. This is the same dataset that is needed to run the plotting example notebook Diagnostics_Plotting.ipynb, which is found in Rayleigh's post_processing directory. All data and python files necessary to run that notebook, as well as the notebook itself are contained in this zip file.</p> <p>Note that the latest release of the Rayleigh code can be found at <a href="../records/11391213">https://zenodo.org/records/11391213.</a></p> <p>Documentation can be found at <a href="https://rayleigh-documentation.readthedocs.io">https://rayleigh-documentation.readthedocs.io</a>.</p>
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Labor flows and input-output connections in Hungary
<p>Aggregated data on labor flows and input-output connections in Hungary (2015-2017).</p> <p>Related preprint: https://arxiv.org/abs/2405.07071</p> <p>Interactive visualization site: https://vis.csh.ac.at/colocation-suppliers/</p> <p>The related research by Sándor Juhász was supported by the European Union’s Marie Sklodowska-Curie Postdoctoral Fellowship Program (SUPPED, grant number 101062606).</p> <p>The data preparation was done with the help of the Databank of HUN-REN Centre for Economic and Regional Studies. The shared datasets are based on the value-added tax return data files of the Hungarian Central Statistical Office. The calculations and conclusions drawn from them are the sole intellectual property of the authors.</p> <p> </p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16</em> and <em>windout_40m_jan16_sms</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna of the Dahra Djoloff research center
<p>The dataset contains UAV outputs (mosaic , surface model and terrain) and the associated measurements of vegetation( herbaceous and woody) that were made within the research isra station of Dahra Djoloff.</p> <p>Sites</p> <p>The sites were 38 ha-1 plots across the research station. The UAV were collected on the same site at the same date in October 2018(end of the wet season and maximum of the biomass). The sites were the sites of previous studies (Raynal 1964, Ndiaye et al. 2014, Ndiaye et al. 2015). The plots were chosen based on several studies of vegetation dynamics and these plots were judged to be representative of the diversity of vegetation type within the research station.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The plots were mapped using a Dji Spark UAV with the litchi application for the automatic flight. The flight plan was six 100 m transects each separated by 20 m was performed at an altitude of 80 m and at a speed of 5 m.s-1. Images were acquired in autofocus mode (ISO exposure were automatically adjusted) at two-second intervals throughout the flight. The angle of view was 80°. The frontal overlap was about 90% and the side overlap about 80% with 80° angle</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>For the Landscape dataset, 10 squares of 1 m² were sampled; All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The positions of the squared was mark r with a plastic bag on the ground.</p> <p>Tree measurement.</p> <p>For the landscape, we selected 10 trees on the UAV maps. The measurements were made after image analysis in January 2019 and January 2020. The trees were not measured on all the site.</p> <p>The measured variables were the maximum height of the tree (using a clinometer), the diameter of the tree crown in the north-south direction and in the west-east direction. Their tree crown area was calculated assuming that the crown was a circle. The trunk diameters were measured at 0.30 cm in both direction and the circumference were calculated. All woody species were identified at the species and genus levels.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>For each plot, we had</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> </ul> <p>All the different geotiff can directly be download.</p> <p>Data are in a zip file that contains the shapefile with the position and table with the field measurements.</p> <p>The shapefile “Herbaceous.shp” contain the positions of the squared sample but also of squared that contains only soil (squared cut before the flight).</p> <p>The CSV “Herbaceous-landscape.csv” contains the measurement of Aboveground biomass. (FM fresh mass and DM dry mass). Both are in g (g.m-²). The biomass was available for 346 squared.</p> <p>The shapefile “tree.shp” contains the positions of the tree. Here the shapefile contains the positions of all the tree preselected on the map. Only a selection of theses tree was measured on the field.</p> <p>The file “Tree-landscape.csv” contains the tree measurements with the species, the height (in m), the trunk circumference (TC) in cm and the area of the crown(area) in m². The tree measurements were available for 240 trees.</p> <p> </p><p>reference</p> <p></p> <p>Ndiaye, O., A. T. Diop, L. E. Akpo, and M. Diène. 2014. Dynamique de la teneur en carbone et en azote des sols dans les systèmes d’exploitation du Ferlo: cas du CRZ de Dahra. Journal of Applied Biosciences <strong>83</strong>:7554-7569.</p> <p>Ndiaye, O., A. T. Diop, M. Diène, and L. E. Akpo. 2015. Étude comparée de la végétation de 1964 et 2011 en milieu pâturé: Cas du CRZ de Dahra. Journal of Applied Biosciences <strong>88</strong>:8235–8248.</p> <p>Raynal, J. 1964. Etude botanique de pâturages du Centre de Recherches Zootechniques de Dahra-Djoloff (Sénégal).</p> <p> </p>
UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna across Senegal
<p>This dataset contains UAV outputs (mosaic, surface and terrain model) and field measurement of vegetation that were made in northern and Eastern Senegal.</p> <p>Sites</p> <p>National gradient measurements</p> <p>For the national gradients, the measurements were made on 45 different plots in two different field campaign. One in the Northern part at the end of September 2020 and the other in South eastern part of Senegal in middle of October. The selection of the site was a combination of accessibility (not far from the road) and diversity of vegetation. The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1 for the Northern Part and ranging 759 mm.y-1 to 1246 mm y-1 for the south eastern part.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The UAV was an Anafi of Parrot with PIX4D capture application using the double gird flight plan in a square generally of 100m*100m; The height of the flight was 80m with an overlap of 80% at low speed with 80° angle °. The flights were made at any time during the day.</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>3 squares of 1 m² were sampled. All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The height of 5 herbaceous individuals selected randomly were measured. We recorded the species composition with percentage of cover of each species. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. The positions of the squared was mark with a wood triangle painted on the ground.</p> <p>Tree measurement.</p> <p>Four trees were measured on the field. It was the four woody individuals the closest to the first square of herbaceous measurements were made in each direction (Northwest, North east, South West, South East).</p> <p>The distance to the first square of each tree were measured using a telemeter. The height was also measured with a laser telemeter. The circumference at 0.30cm and 1.3 cm were measured. The diameter of the tree crown in the north-south direction and in the west-east direction were measured to the crow area calculated assuming that the crown was a circle.</p> <p>The species were recorded. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>The data are organized in two separated folders for each dataset.</p> <p>Each dataset folders contains four folders:</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements.</li> </ul> <p>The shapefile” national-shape.shp" contains the positions of both tree and herbaceous samples. In some case it was hard to position the squared or the tree. The position and the shape of the object are not well defined.</p> <p>The file “tree-national.xlsx” contains the information on the tree measurement. The ID that contains the site and the positions of the trees, the distance from the squared in m that indicate the distance of the tree to the biomass square. The height H (in m), the trunk circumference at 1.30m (TC1.3) and at 0.3m(TC0.3) in cmand the area of crown (Area). The species is also described.</p> <p>The file “ herbacous_national.xlsx” contains the information on the herbaceous layer.</p> <p>For each square, the height of the herbaceous layer (H), Fresh mass (FM), Dry matter content (DMC) and dry Mass (DM) are presented; The last columns of the file are the different species with the percentage of cover in each case.</p> <p> </p>
Example Output from A PamChip Asssay
<p>This document contains an example dataset obtained after processing the PamChip assay on BioNavigator.w</p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></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.