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367 results for “gap filling”
Homogenized, gap-filled, daily air temperature data for Saddle, 1986 - ongoing.
As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily air temperature at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months. These interruptions have led to missing or faulty data at times and inconsistent data gap-filling. Additionally, a mixture of mechanical hygrothermograph chart and temperature sensors with electronic data loggers have been used since the inception of the Saddle station to measure and record air temperature. Thus, a close inspection of potential influence from instrument turnover and relevant notes from research staff is required for a quality, daily air temperature time series for Saddle. Here we present a quality-controlled, gap-filled, daily time series of maximum, average, minimum, and diurnal air temperatures that accounts for instrument turnover at the Saddle. Methods follow those used to gap-fill long-term daily air temperature at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily air temperature datasets. Metadata for this data package centralizes the most complete station history for Saddle air temperature and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses.
Gap-filled Meteorological Data for the Virginia Coast Reserve LTER - 1989-2022
This is a gap-filled dataset of the VCR-LTER meteorological sensor data on a daily level. It includes the csv of the generated data, as well as the R script used to generate it.
Gap-filled daily precipitation at the 15 long-term NPP sites at Jornada Basin LTER, 1980-ongoing
This package contains gap-filled daily precipitation values for the 15 NPP sites at Jornada Basin LTER in southern New Mexico, USA. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. Gap-filled daily precipitation was calculated for the period from 1980 to 2020 at each site using the closest rain gauges that provided a minimum resolution of daily precipitation data. The Methods section and attached documents describe this process in detail. The rain gauges used are described, with respect to their relationship to NPP sites, in the attached "daily_gapfill_ppt_gauge_usage.csv" file. Although automated weather stations became operational at all NPP sites in 2013 (except P-SMAL, in 2017), updates to this data package are ongoing and are intended to gap-fill any missing or invalid data from the weather stations.
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>
Northern Italy gap-filled MODIS Land Surface Temperature 1km daily
<p>Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.</p> <p>The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.</p> <ul> <li>Spatial resolution: 1 km</li> <li>Temporal resolution: 1 day</li> <li>Temporal extent: 2014-2018</li> <li>Units: Celsius</li> <li>Aggregation method: average</li> <li>Format: stored as a <a href="https://grass.osgeo.org/">GRASS GIS</a> 8+ project</li> <li>Software used: GRASS GIS 8.4.0</li> </ul> <p>Reference:<br><br>Metz, M.; Andreo, V.; Neteler, M. <em>A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data</em>. Remote Sens. 2017, 9, 1333. <a href="https://doi.org/10.3390/rs9121333">https://doi.org/10.3390/rs9121333</a></p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod09a1v006/">https://lpdaac.usgs.gov/products/mod09a1v006/</a></p> <p>Data provided by:</p> <p>mundialis GmbH & Co. KG<br>Koelnstrasse 99<br>53111 Bonn, Germany<br><a href="https://www.mundialis.de">https://www.mundialis.de</a></p>
Machine learning methods for gap-filling in greenhouse gas emissions databases
<p>Datasets for use with code related to "Machine learning methods for gap-filling in greenhouse gas emissions databases" manuscript submitted to the Journal of Industrial Ecology. Code for using the datasets can be found at <a href="https://github.com/luke-scot/ml-ghg-databases">https://github.com/luke-scot/ml-ghg-databases</a>.</p>
Half-hourly gap-filled Northern Hemisphere lake and reservoir carbon flux and micrometeorology, 2006 - 2015
This archive accompanies the manuscript New insights into diel to interannual variation in carbon emissions from lakes and reservoirs We synthesize 171 site-months (and 3,832 site-hours) of high-frequency flux measurements to quantify the magnitudes and temporal variability of direct CO2 fluxes from 13 lakes and reservoirs in the Northern Hemisphere (NH). Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. These data were collected based on a workshop and open call for eddy covariance observations over lakes organized by Ankur Desai (UW-Madison), Timo Vesala (U Helsinki), and Malgorzata Golub (DKIT).
Gap filled GEBA data
<p>Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of ~1400 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (truds@uio.no).</p>
SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)
<p>The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.</p>
Fig. 2 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 2. Solutan echinoderm Dehmicystis globulus (Dehm, 1934) from the the Lower Devonian Hunsrück Slate, Germany. A. Holotype (SMF-HS 432), almost complete specimen showing the theca and posterior appendage. Note anus in on right side. B. SMF-HS 969, slab containing three individuals. Note that individual 1 has the anus on "left" position. Repository number assigned to a replica of the newly prepared slab. Photographs courtesy of Ulrich Jansen.
Fig. 4 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 4. Camera lucida drawings of solutan echinoderm Dehmicystis ariasi sp. nov. from the Llagarinos Formation, Ludlow, Silurian, northern Central Iberian Zone, Spain; with interpretation of main anatomical parts.
Fig. 3 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 3. Solutan echinoderm Dehmicystis ariasi sp. nov. from the Llagarinos Formation, Ludlow, Silurian, northern Central Iberian Zone, Spain. A. MGM-208S. A1, complete specimen in dorsal aspect preserving proxistele and feeding appendage above the body. A2, counterpart of same specimen. A3, detail of feeding appendage consisting on biserial floor plates and cover plates. Gonopore on single plate, and a possible hydropore close to the feeding appendage attachment. A4, detail of the periproct on right position. Note that the periproct is visible from the inside and was located on dorsal right part of the theca. A5, detail of the appendage preserving the complete proxistele and proximal dististele. Photographs are from latex casts whitened with ammonium chloride sublimated.
Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method
<p><span>GRACE, </span><span>GRACE-FO</span><span>, Data gap, long short-term memory Bayesian convolutional neural network (LSTM-BCNN)</span></p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
FIGURE 7 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 7 | Parurohyal of Ituglanis crispim, paratype, MZUSP 12208, 26.7 mm SL. Dorsal view. Scale bar = 0.2 mm.
FIGURE 10 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 10 | Caudal-fin skeleton of Ituglanis crispim, paratype, MZUSP 126756, 29.7 mm SL (c&s). Lateral view. Abbreviations: epural (EP); hypurapophysis (ha) plus secondary hypurapophysis (has) (HA+HAS); hypural 5 (HU5); presumed hypurals 3 plus 4 (HU3+HU4); hypurals 1 plus 2 plus parhypural (PH+HU1+HU2); preural centrum 1 plus ural centrum 1 (PU1+U1); preural centrum 2 (PU2); uroneural (UR). Scale bar = 0.5 mm.
FIGURE 4 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 4 | Anterior part of skull and related structures of Ituglanis crispim, paratype, MZUSP 126756, 29.7 mm SL (c&s). Ventral view. Abbreviations: mesethmoid (ME); premaxilla (PMX); maxilla (MX); barbular (BA); orbitoshpenoid (OS); autopalatine (AP); and vomer (VO). Some elements are not shown. Scale bar = 0.5 mm.
FIGURE 2 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 2 | Pelvic girdle of Ituglanis crispim, paratype, MZUSP 126756, 29.7 mm SL (c&s), dorsal view. Abbreviations: basipterygium (BP); external process (EP); internal process (IP); pelvic splint (PS). Scale bar = 0.2 mm.
FIGURE 9 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 9 | Radiographs of types specimens of Ituglanis crispim. Lateral views. MZUSP 126756, paratypes, 32.6, 29.4, 29.7 mm SL, respectively (A, C, D, latter now c&s); B. MZUSP 126762, holotype, 28.8 mm SL.
FIGURE 6 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 6 | Hyoid arch of Ituglanis crispim, paratype, MZUSP 126756, 29.7 mm SL (c&s). Lateral view. Abbreviations: anterior ceratohyal (AC); BR1–8, branchiostegal rays 1 to 8; inter-ceratohyal cartilage (IC); posterior ceratohyal (PC); ventral hypohyal (VH). Image from right side, inverted to conform to standard position. Scale bar = 0.5 mm.
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.