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23,670 results for “Site”
Project Information Model resulting from the on-site survey
<p>The On-site Analysis and Verification Service (ODAVS) is a service developed within the Encore project. It allows users to check any constructability issues regarding renovation projects of residential buildings by means of surveys facilitated by a mixed reality tool. </p> <p>This dataset includes two IFC files of the renovation studies developed by Univpm and JEA (both partners of Encore project) at JEA experimental building in Caceres, and assessed on-site on 2021 December 14th and 15th. The dataset also includes an XML file containing the list of 33 URLS pointing to audio files previously published on a different Zenodo dataset [1]. Note that the access to the Zenodo dataset [1] is restricted. The interested users must ask the dataset authors for permission to access the dataset itself. In the XML file, for each comment, the GUID of the IFC object referred by the audio comment itself is given.</p> <p>References</p> <p>[1] "Pictures and Videos Collected During ODAVS Activity", by Carbonari A. and Vaccarini M., DOI 10.5281/zenodo.6531860, URL: https://doi.org/10.5281/zenodo.6531860</p> <p> </p>
Brewer Global Iradiance (GI) total ozone data at two Norwegian sites (2000 to 2020)
<p>Total column ozone (TCO) derived from Global Irradiance (GI) measurements from the Brewer instruments B042 in Oslo (Norway) and B104 in Andøya (Norway) from 01-01-2000 to 31-12-2020.</p> <p>The data consist of daily values averaged +/-2 hours around local noon.</p> <p>GI calibrations where performed with a clear sky direct sun (DS) measurements in 06-2001, 08-2014, 08-2016, 08-2018, and 08-2019 at Andøya, and in 08-2005, 06-2019, and 08-2019 at Oslo. The data has been calibrated with standard lamp measurements and have been homogenized with DS measurements as a function of clouds and solar zenith angle.</p> <p>The method, calibration, and homogenization is described by Bernet et al. (2022) (Appendix A).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research</p> <p>Funded by the Swiss National Science Foundation and the Norwegian Environment Agency</p> <p>Bernet, L., Svendby, T., Hansen, G., Orsolini, Y., Dahlback, A., Goutail, F., Pazmiño, A., Petkov, B., and Kylling, A., Total ozone trends at three northern high-latitude stations, 2022.</p>
Positions for "First insights into migration routes and nonbreeding sites used by Red-rumped Swallows (Cecropis daurica rufula) breeding in the Iberian Peninsula"
<p><strong>Abstract</strong></p> <p>Using EURING data and geolocation, we describe migration routes and nonbreeding range of Red-rumped Swallows breeding in the Western Palearctic. One bird ringed in southern Spain and recovered in southern Morocco indicates southwestern migration; geolocator data from five birds from central and eastern Iberian Peninsula confirm migration to various nonbreeding sites in sub-Saharan west Africa between Senegal/Mauritania and Ghana. Two swallows showed non-breeding site itinerancy by using more than one nonbreeding site per season. Despite wide ranges in departure for autumn (August- October) and spring migration (February-March), all birds arrived at nonbreeding and breeding sites within ±1-week from each other.</p> <p><strong>Zusammenfassung</strong></p> <p>Erste Einblicke in Zugrouten und Überwinterungsgebiete von Rötelschwalben (<em>Cecropis daurica rufula</em>) der Iberischen Halbinsel.<br> In dieser Studie beschreiben wir Zugrouten und Überwinterungsgebiete westpaläarktischer Rötelschwalben basierend auf EURING- und Geolokations-Daten. Eine Rötelschwalbe, die in Südspanien beringt und im südlichen Marokko wiedergefunden wurde, spricht für einen südwestlichen Zug. Geolokalisation von fünf Vögeln der zentralen und östlichen Iberischen Halbinsel zeigen Überwinterungsorte im sub-Saharischen Westafrika zwischen Senegal/Mauretanien und Ghana. Zwei der getrackten Rötelschwalben nutzten mehrere Überwinterungsplätze pro Saison. Trotz der großen Schwankungsbreite der Abzugszeiten im Herbst (August-Oktober) und im Frühjahr (Februar-März) erreichten die getrackten Vögel ihre Nichtbrut- bzw. Brutplätze innerhalb von 1–2 Wochen.</p>
Models of the early diagenesis of neodymium and its radiogenic isotope at deep-sea site HH3000, Oregon margin, Northeast Pacific
<p>Outputs of the early diagenetic model for neodymium and its radiogenic isotope at deep sea station HH3000 (3060 m, 43°52'N, 125°38'W) from the Oregon margin, Northeast Pacific.</p> <p>Three types of models are included, and the files are named as following:</p> <p>1. Baseline simulations, "HH3000Nd.baseline.copre_output.xlsx" for the co-precipitation formulation, and "HH3000Nd.baseline.revscan_output.xlsx" for the reversible scavenging formulation.</p> <p>2. Sensitivity tests of silicate dissolution rate: "HH3000Nd.{<em>mineral</em>}.{<em>dissolution rate</em>}_output.xlsx", where {<em>mineral</em>} can be "Basalt", "Plag" (plagioclase), "Cpx" (clinopyroxene) or "Chl" (chlorite), and {<em>dissolution rate</em>} can be "1e0" to "1e5", referring to the order of magnitude reduction of dissolution rate relative to the laboratory-derived rates.</p> <p>3. Sensitivity tests of authigenic clay precipitation rate: “HH3000Nd.basalt.{<em>precipitation rate</em>}.illite_output.xlsx", where "{<em>precipitation rate</em>}" can be "no", "slow", "normal", or "fast".</p> <p>Explanations of the modeled variables in the above files can be found in "model variable note.xlsx".</p>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Measurements in October 2021 using a digital magnetic variation station at the Simeiz-Katsiveli geodynamic test site
<p>Measured by the digital magnetic variation station at the Simeiz-Katsiveli test site during the period October 07–21, 2021.</p>
Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project
<p>Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript:</p> <blockquote> <p>"Harmonized, gap-filled dataset from 20 urban flux tower sites" </p> <p><a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a></p> </blockquote> <p>Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata. </p> <p><strong>Data can be downloaded from the bottom of this page. </strong></p> <table> <tbody> <tr> <td> <p><strong>Sitename</strong></p> </td> <td> <p><strong>City</strong></p> </td> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Observed period</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>AU-Preston</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Aug 2003 – Nov 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>AU-SurreyHills</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Feb 2004 – Jul 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>CA-Sunset</p> </td> <td> <p>Vancouver</p> </td> <td> <p>Canada</p> </td> <td> <p>Jan 2012 – Dec 2016</p> </td> <td> <p>(Christen et al., 2011; Crawford and Christen, 2015)</p> </td> </tr> <tr> <td> <p>FI-Kumpula</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Karsisto et al., 2016)</p> </td> </tr> <tr> <td> <p>FI-Torni</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Järvi et al., 2018; Nordbo et al., 2013)</p> </td> </tr> <tr> <td> <p>FR-Capitole</p> </td> <td> <p>Toulouse</p> </td> <td> <p>France</p> </td> <td> <p>Feb 2004 – Mar 2005</p> </td> <td> <p>(Masson et al., 2008; Goret et al., 2019)</p> </td> </tr> <tr> <td> <p>GR-HECKOR</p> </td> <td> <p>Heraklion</p> </td> <td> <p>Greece</p> </td> <td> <p>Jun 2019 – Jun 2020</p> </td> <td> <p>(Stagakis et al., 2019)</p> </td> </tr> <tr> <td> <p>JP-Yoyogi</p> </td> <td> <p>Tokyo</p> </td> <td> <p>Japan</p> </td> <td> <p>Mar 2016 – Mar 2020</p> </td> <td> <p>(Hirano et al., 2015; Ishidoya et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Jungnang</p> </td> <td> <p>Seoul</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jan 2017 – Apr 2019</p> </td> <td> <p>(Jo et al., n.d.; Hong et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Ochang</p> </td> <td> <p>Ochang</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jun 2015 – Jul 2017</p> </td> <td> <p>(Hong et al., 2019, 2020)</p> </td> </tr> <tr> <td> <p>MX-Escandon</p> </td> <td> <p>Mexico City</p> </td> <td> <p>Mexico</p> </td> <td> <p>Jun 2011 – Sep 2012</p> </td> <td> <p>(Velasco et al., 2011, 2014)</p> </td> </tr> <tr> <td> <p>NL-Amsterdam</p> </td> <td> <p>Amsterdam</p> </td> <td> <p>Netherlands</p> </td> <td> <p>Jan 2019 – Oct 2020</p> </td> <td> <p>(Steeneveld et al., 2020)</p> </td> </tr> <tr> <td> <p>PL-Lipowa</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013; Pawlak et al., 2011)</p> </td> </tr> <tr> <td> <p>PL-Narutowicza</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013, 2006)</p> </td> </tr> <tr> <td> <p>SG-TelokKurau</p> </td> <td> <p>Singapore</p> </td> <td> <p>Singapore</p> </td> <td> <p>Feb 2015 – Feb 2016</p> </td> <td> <p>(Roth et al., 2017)</p> </td> </tr> <tr> <td> <p>UK-KingsCollege</p> </td> <td> <p>London</p> </td> <td> <p>UK</p> </td> <td> <p>Apr 2012 – Jan 2014</p> </td> <td> <p>(Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b)</p> </td> </tr> <tr> <td> <p>UK-Swindon</p> </td> <td> <p>Swindon</p> </td> <td> <p>UK</p> </td> <td> <p>May 2011 – Apr 2013</p> </td> <td> <p>(Ward et al., 2013)</p> </td> </tr> <tr> <td> <p>US-Baltimore</p> </td> <td> <p>Baltimore</p> </td> <td> <p>USA</p> </td> <td> <p>Jan 2002 – Jan 2007</p> </td> <td> <p>(Crawford et al., 2011)</p> </td> </tr> <tr> <td> <p>US-Minneapolis</p> </td> <td> <p>Minneapolis</p> </td> <td> <p>USA</p> </td> <td> <p>Jun 2006 – May 2009</p> </td> <td> <p>(Peters et al., 2011; Menzer and McFadden, 2017)</p> </td> </tr> <tr> <td> <p>US-WestPhoenix</p> </td> <td> <p>Phoenix</p> </td> <td> <p>USA</p> </td> <td> <p>Dec 2011 – Jan 2013</p> </td> <td> <p>(Chow, 2017; Chow et al., 2014)</p> </td> </tr> </tbody> </table> <p>For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>.</p> <p>For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>.</p> <p><strong>Data</strong></p> <p>Two data archives are available on this page.</p> <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> <p><strong>Full collection</strong></p> <p>The full archive includes site folders with:</p> <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> <p><strong>"Obs Only"</strong></p> <p>This archive contains duplicate data from the full collection (observations after QC):</p> <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> <p><strong>Site references</strong></p> <p>Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015.</p> <p>Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017.</p> <p>Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014.</p> <p>Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b.</p> <p>Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015.</p> <p>Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011.</p> <p>Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006.</p> <p>Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013.</p> <p>Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019.</p> <p>Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015.</p> <p>Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020.</p> <p>Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019.</p> <p>Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020.</p> <p>Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018.</p> <p>Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d.</p> <p>Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b.</p> <p>Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008.</p> <p>Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017.</p> <p>Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013.</p> <p>Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011.</p> <p>Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011.</p> <p>Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017.</p> <p>Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019.</p> <p>Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020.</p> <p>Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011.</p> <p>Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013.</p> <p>Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014.</p> <p>Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.</p>
Shape from Shading Digital Elevation Model for Oxia Planum Candidate Landing Site
<p>This data set contains the calibrated and map-projected HiRISE image ESP_037558_1985 and the matching Shape from Shading DEM using the method described in Hess et al., (2019a), and in more detail in Hess et al. (2022). The SfS DTM was part of the EPSC abstract Hess et al., (2019b). When using the data please reference Hess et al. (2022) for the method.</p> <p>ESP_037558_1985_30cm_o.cub: Image data in radiances, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>ESP_037558_1985_30cm_DEM.cub: Digital Elevation Model (DEM) with heights in meter, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>Cube files can be converted to other data formats using gdal (https://gdal.org/) or directly loaded in, e.g., ArcGIS or QGIS.</p> <p> </p> <p>Hess, M., Wohlfarth, K., Grumpe, A., Wöhler, C., Ruesch, O., and Wu, B.: ATMOSPHERICALLY COMPENSATED SHAPE FROM SHADING ON THE MARTIAN SURFACE: TOWARDS THE PERFECT DIGITAL TERRAIN MODEL OF MARS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1405–1411, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1405-2019, 2019a.</p> <p>Hess, Marcel. "High Resolution Digital Terrain Model for the Landing Site of the Rosalind Franklin (ExoMars) Rover." Proc. European Planetary Science Congress, EPSC-DPS2019-1533-4, Geneva, Switzerland, 2019b.</p> <p>Hess, M.; Tenthoff, M.; Wohlfarth, K.; Wöhler, C. Atmospheric Correction for High-Resolution Shape from Shading on Mars. <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 158. https://doi.org/10.3390/jimaging8060158</p>
Canopy solar-induces chlorophyll fluorescence measurements at seven sites
<p>This dataset will accompany the paper "Direct validation of TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements reveals shortcomings with the current satellite SIF products" in the Remote Sensing of Environment. This dataset includes the canopy SIF measurements at noon timescale for six sites and a public hourly canopy SIF dataset that accompanies the paper "Mechanistic evidence for tracking the seasonality of photosynthesis with solar induced fluorescence" in the Proceedings of the National Academy of Sciences. All relevant methodological information can be found in the paper: Shanshan, D., Xinjie, L., Jidai, C., Weina, D., and Liangyun, L. in press.</p>
On-site Jomo Propitiation in Chug valley: photographs
<p>Both participants in this ritual are <em>bon-po</em>, practitioners of the traditional religious system in the Chug valley. Jomo Gamdong is the place on the west bank of the Chug river which is considered the <em>pho-bra</em><em>ṅ</em> ‘palace’ of <em>jo-mo u-si</em><em>ṅ bu-khrid</em>, one of the nine daughters of the female deity <em>jo-mo a-ni u-</em><em>ñi</em><em>ṅ</em>, located on the Bhutan-Arunachal border in Jomotsangkha block of Samdrup Jongkhar district in Bhutan. The peak of <em>jo-mo u-si</em><em>ṅ bu-khrid</em> is the westernmost peak in the north of the Chug valley (see figure1). Propitiation of the Jomo takes place on monthly basis at home. Only on rare occasions does the <em>bon-po</em> go to the Jomo Gamdong for the propitiation, typically once in a few years.</p> <p>The <em>phobrang</em> is located in a grove of oak trees starting from the large trees in figure5. The area is a collection of large rocks (see photographs). Each rock has a significance, of which little could be recorded (exceptions are the white female <em>naga</em> in figure2 and the servant of the Jomo in figure3). As the marking on the tree in figure4 shows, at the time of the visit many trees were about to be felled. Although the land is owned by individual households from Jagarbasti village in the Chug valley, the trees were felled by people from the neighbouring village of Lish Gonpatse (whether this is a case of illegal felling and encroachment as a result of the on-going land dispute between the two communities, or whether the owners from Jagarbasti sold the standing trees (with the land?) to the Lish Gonpatse people is not clear. This degradation of the site, in combination with the 2016 death of <em>bon-po</em> Kesang, means that propitiation of the Jomo here has ceased completely.</p> <p>The relevant stanza from the Jomo propitiation ritual (see file), in which the very first offerings are always made to the Jomo, are as follows:</p> <p><strong>Phonetic:</strong> [ʨʰɔtɔː {ŋa} ʨʰɔtɔː, ʨʰɔtɔː {ŋa} ʨʰɔtɔː, kʰamzaŋ {ŋa} ama ʥɔmɔː, jap {la} gawɔː ʑɔkpɔː, makpøn {na} meme ʨʰøjɕi, lúmɔː karmɔː {ŋa}, lúmɔː jáŋzɔm {ma}, lúmɔː manɖɛn {na} ʦʰeriŋ zaŋmɔː, ʦʰɛnla sɛrgi ʥɔːmɔː {ŋa} sagaŋ, ɬabraŋ {ŋa} laʨʰɛn, lúbraŋ {ŋa} lúʨʰuŋ, pʰygɔː {ŋa} gile.</p> <p>ʦʰekjɛt náŋla, jáŋkjɛt náŋla, ʨʰamneː kakɲeː, rimneː kakɲeː, natʥa kakɲeː, rimʥa kakɲeː, siwɔː neːdak, kɔrda ɬa lú ʦɛn sum kakɲeː, ʦʰɛnla sɛrgi ʥɔːmɔː, sɛrkjɛm karpɔː, pʰyjgɔː gile.</p> <p>jaski kɔːra, yønla gonʨʰa, náŋna gile. lemʨʰaŋ karpɔː, pʰyjgo gile. ʨʰɔːto {ŋa} ʨʰɔːtɔː.]</p> <p><strong>Tibetan:</strong> <em>ḥbyo ldog ḥbyo ldog, ḥbyo ldog ḥbyo ldog! kham-zaṅ a-ma jo-mo, yab dgaḥ-bo źog-po (<TM ʑokpo), dmag-dpon mes-mes chos-śi (?), klu-mo dkar-mo, klu-mo dbyaṅ-ḥdzoms, klu-mo sman-dran (?) tshe-riṅ bzaṅ-mo, mtshan ser-gi jo-mo sa-gaṅ (?), lha-braṅ lha-chen, klu-braṅ klu-chuṅ, phul-dgos gi-le (< TSD gi-la-aj)!</em></p> <p><em>tshe-skyed gnaṅ-la, g.yaṅ-skyed gnaṅ-la, cham-nad bkag-ñe (<TSD ɲi), rims-nad bkag-ñe, nat-brgya bkag-ñe, rims-brgya bkag-ñe, sre-ḥu (?) gnas-bdag, skor-da lha klu btsan gsum bkag-ñe, mtshan-la ser-gi jo-mo, ser-skyem dkar-po, phul-dgos gi-le!</em></p> <p><em>g.yas-kyi skor-ra (?), g.yon-la bgon-chas (?), gnaṅ-na gi-le! lem-chaṅ (?) dkar-po, phul-dgos gi-le! ḥbyo-ldog ḥbyo-ldog!</em></p> <p><strong>English:</strong> Hark (and) dispel, hark (and) dispel, hark (and) dispel, hark (and) dispel! The healthy (?) mother Jomo, the father Gawo Zhokpo, the general Meme Choshi, the white female <em>naga</em> (see figure2), the female <em>naga</em> Yangzom, the female <em>naga</em>Mandren Tshering Zangmo, the name is the pit (?) (of) the golden Jomo, the deity residence for the important deities, the <em>naga</em> residence for the less important <em>naga</em>, it has to be offered ok!</p> <p>Provide (with) longevity, provide (with) prosperity, preventing the flu diseases, preventing the infectious diseases, preventing the hundred diseases, preventing the hundred infectious diseases, the host of the smallpox (?), prevent the roaming of the three, the deities, the <em>naga</em> and the <em>tsen</em>, the name (is) the golden Jomo, the white libation offering, it has to be offered ok!</p> <p>Surrounding on the right side (?), the clothing apparel on the left (?), it is provided ok! The white <em>lemchang</em>, it has to be offered ok! Hark (and) dispel, hark (and) dispel!</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Reference site conditions for floating wind arrays: dataset of reference sites
<h1>IEA Task 49: Integrated Design of Floating Wind Arrays</h1> <h3>This dataset contains atmospheric and oceanographic data of 11 locations around the globe of future floating offshore wind farms.<br>Each dataset was used to perform a metocean analysis for preliminary design, published in the Work Package 1 Report of IEA Task 49.</h3> <div> <div> <div><a name="_msocom_1"></a></div> </div> </div> <table> <tbody> <tr> <td><strong>4COffshore ID</strong></td> <td><strong>Name</strong></td> <td><strong>Latitude [deg]</strong></td> <td><strong>Longitude [deg]</strong></td> <td><strong>Water Depth [m] (GEBCO)</strong></td> <td><strong>Distance from shore [m]</strong></td> <td><strong>Country</strong></td> <td><strong>Dataset curated by</strong></td> </tr> <tr> <td>IT95 </td> <td>Hannibal </td> <td> <div>37.842</div> </td> <td> <div>12.0722</div> </td> <td> <div> -353</div> </td> <td> <div> 35</div> </td> <td>Italy </td> <td>RSE</td> </tr> <tr> <td>US0W </td> <td> Humboldt</td> <td> <div> 40.928</div> </td> <td> <div>-124.708</div> </td> <td> <div> -707</div> </td> <td> <div> 43.8</div> </td> <td>USA</td> <td>NREL</td> </tr> <tr> <td>KR0R </td> <td>Ulsan </td> <td> <div> 35.449</div> </td> <td> <div> 129.949</div> </td> <td> <div> -188</div> </td> <td> <div> 32</div> </td> <td>South Korea </td> <td>UOU</td> </tr> <tr> <td>IE34 </td> <td>Moneypoint One</td> <td> <div> 52.519</div> </td> <td> <div>-10.276</div> </td> <td> <div> -1<a>02</a> </div> </td> <td> <div> 23.4</div> </td> <td>Ireland </td> <td>GDG</td> </tr> <tr> <td>UK6L </td> <td>Havbredey </td> <td> <div>58.862</div> </td> <td> <div> <div> <p>-5.54</p> </div> </div> </td> <td> <div> <div> <p> -91</p> </div> </div> </td> <td> <div> <div> <p> 41.6</p> </div> </div> </td> <td>Scotland </td> <td>DHI</td> </tr> <tr> <td>JP06 </td> <td>Fukushima</td> <td> <div>37.311</div> </td> <td> <div>141.251</div> </td> <td> <div> -90</div> </td> <td> <div> 19.4</div> </td> <td>Japan </td> <td>AIT</td> </tr> <tr> <td>NO44 </td> <td>Utsira nord<strong>*</strong></td> <td> <div>59.276</div> </td> <td> <div>4.541</div> </td> <td> <div> -273</div> </td> <td> <div> 42.4</div> </td> <td>Norway </td> <td>4subsea / UiS,UiB*</td> </tr> <tr> <td>USZ3 </td> <td>Gulf of Maine</td> <td> <div>43.25</div> </td> <td> <div>-69.5</div> </td> <td> <div> -148</div> </td> <td> <div> 138</div> </td> <td>USA </td> <td>NREL</td> </tr> <tr> <td>KR88 </td> <td>Geomundo<strong>**</strong></td> <td> <div>34.039</div> </td> <td> <div>126.901</div> </td> <td> <div> -70</div> </td> <td> <div> 47</div> </td> <td>South Korea </td> <td>IAE**</td> </tr> <tr> <td>FR87 </td> <td>Sud de la Bretagne II</td> <td> <div>47.325</div> </td> <td> <div>-3.659</div> </td> <td> <div> -94</div> </td> <td> <div> 30.7</div> </td> <td>France </td> <td>UiB</td> </tr> <tr> <td>NO66</td> <td>Sørlige Nordsjø II<strong>***</strong></td> <td> <div> 56.78 </div> </td> <td> <div> 4.92 </div> </td> <td> <div> -60</div> </td> <td> <div> 180</div> </td> <td>Norway</td> <td>4subsea / UiS,UiB***</td> </tr> </tbody> </table> <p>* Suplementary dataset published at: https://doi.org/10.5281/zenodo.10048048</p> <p>** Dataset is confidential, for details of usage reach out to the contact person.</p> <p>*** Suplementary dataset published at: https://doi.org/10.5281/zenodo.7057407</p>
Exploration of historical mining site - Akersberg silver mines (St. Hanshaugen, Oslo, Norway, 04/11/2024)
<p>Exploration of the historical mining site of Akesrberg (St. Hanshaugen, Oslo, Norway, 04/11/2024)</p> <p>- Main ore minerals: pyrite, sphalerite, galena, chalcopyrite, argentite, stembergite</p> <p>- Provisional References:</p> <ul> <li>https://www.researchgate.net/publication/330971315_Akersberg_gruver_Akersberg_Silver_Mines_pages_168-174_in_Arnesen_R_Gjemte_og_glemte_steder_-_urban_utforsking_i_Oslo_og_omradet_rundt_In_Norwegian</li> <li>https://foreninger.uio.no/ngf/ngt/pdfs/NGT_71_2_121-128.pdf</li> <li>https://www.mindat.org/loc-37117.html</li> <li>https://no.wikipedia.org/wiki/Akersberg_gruver</li> </ul>
Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)
<p>Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)</p> <p>- Main ore minerals: pyrite, chalcopyrite, siderite, aragonite</p> <p>- Provisional References:</p> <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>
Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C
<p>Dataset pertaining to the article "Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C", submitted to Physical Chemistry Chemical Physics. Here, we demonstrate how liquid-jet photoemission spectroscopy can be systematically used for chemical analysis, probing tautomeric forms and deprotonation sites in aqueous vitamin C. We also present a fast and reliable computational protocol to model the spectra.</p> <p> </p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p><br>The following files are provided:</p> <p><strong>Photoemission data pertaining to vitamin C PES measurements:</strong></p> <p>vitamin-C.h5</p> <p> </p> <p><strong>Computational data for Figures 3, 5, and 6: binding energy values (plain text) from which the spectra were modeled, optimized molecular geometries (XYZ coordinates, distances in angstroms) used in the calculations, and a sample input for the binding energy calculation. All data for each figure are packed in a zip file.</strong></p> <p>COMPUTATIONAL-DATA-Fig3.zip<br>COMPUTATIONAL-DATA-Fig5.zip<br>COMPUTATIONAL-DATA-Fig6.zip<br><br><br></p> <p><strong>Numeric representations of the traces shown in the article's figures (space-separated or comma-separated ascii-files):</strong></p> <p><strong>Figure 3:</strong><br>EXPT.dat<br>TAUTOMER-A.dat<br>TAUTOMER-B.dat<br>C2.dat<br>C3.dat<br>C5.dat<br>C6.dat<br>C2+C3.dat<br>C2+C5.dat<br>C2+C6.dat<br>C3+C5.dat<br>C3+C6.dat<br>C5+C6.dat</p> <p><strong>Figure 5:</strong><br>EXPT.dat<br>TAUTOMER-A.dat<br>C3.dat<br>C2+C3.dat</p> <p><strong>Figure 6:</strong><br>EXPT.dat<br>C3.dat<br>C3-UFF-ensemble.dat<br>C3-Bondi-single.dat<br>C3-Bondi-ensemble.dat<br>TAUTOMER-A.dat<br>TAUTOMER-A-ensemble.dat<br>C2+C3.dat<br>C2+C3-ensemble.dat</p> <p><strong>Figure S1:</strong><br>EXPT.dat<br>EXPT-850eV.dat</p> <p> </p> <p>Version history:<br>4: Data from version 2 reuploaded<br>3: Experimental NeXus-data added<br>2: Computational data and figure traces added<br>1: Initial upload</p> <p>Contact person for questions regarding this data set: Lukas Tomanik, tomanikl@vscht.cz . If you use these data for your scientific work we are curious to learn about it.</p>
Heritage Sites in Bani Walid, Libya
<p>A dataset of 211 heritage sites in and around the city of Bani Walid, Libya used for the EAMENA Machine Learning Automated Change Detection Case Study.</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>
Training images for the MADE site
<p>Five training images that represent spatial hydraulic conductivity [log10 (cm/s)] of the MAcro Dispersion Experiment (MADE) site in Mississippi, USA. The .mat file contains five matrices each of them representing a training image called:</p> <p>TIlog10K_chanStrebelleRoyane = highly conductive channels in an homogeneous matrix [Strebelle, 2002; Ronayne et al., 2010; Linde et al., 2015].</p> <p>TIlog10K_herten = model based on a mapping study at the Herten site in Germany [Bayer et al., 2011; Comunian et al., 2011; Linde et al., 2015b] featuring representative alluvial deposit structures.</p> <p>TIlog10K_lito = model based on lithological borehole data collected at the MADE site [Bianchi and Zheng, 2016].</p> <p>TIlog10K_multiG = multi-Gaussian field adapted from Bianchi et al., [2011].</p> <p>TIlog10K_outc = model based on a mapping study of a MADE outcrop [Rehfeldt et al., 1992; Linde et al., 2015].</p>
Rakhterā (रखतेरा or Rakhetrā, Ashoknagar). Site of the Bhiyāṃdāṃt caves and images of Ādinātha and other deities.
<p>Rakhterā (रखतेरा or Rakhetrā, Ashoknagar). Site of the Bhiyāṃdāṃt caves and images of Ādinātha and other deities.</p>
Replication package for "An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites" (EMSE 2018)
<p>Replication package for the paper:</p> <blockquote> <p>F. Calefato, F. Lanubile, and N. Novielli (2018) “<a href="http://collab.di.uniba.it/fabio/wp-content/uploads/sites/5/2018/07/EMSE-D-17-00159_R3.compressed.pdf">An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites</a>.” Empirical Software Engineering Journal, DOI: <a href="http://dx.doi.org/10.1007/s10664-018-9642-5">10.1007/s10664-018-9642-5</a>.</p> </blockquote>
Malwa site survey : Raisen District, Raisen Tahsīl, village finds
<p>Malwa site survey : Raisen District, Raisen Tahsīl, village finds, based on surveys conducted by Department of Archaeology and Museums, Madhya Pradesh.</p>
ScienceDex guides
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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.