Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,575
datasets available to search
ShareScore release 0.7.1
Dataset results
3,575 results for “2009”
Coral Larval Chamber Experiments 2009_2010
<p>These csv files are the data from coral larval ecology experiments conducted at Carrie Bow Cay, Belize in 2009 and 2010. The metadata file describes the headings for all of the csv files. These data were summarized and analyzed in the paper entitled "The impact of macro algae and cyanobacteria on larval survival and settlement of the scleractinian corals <em>Acropora palmata, A. cervicornis </em>and<em> Pseudodiploria strigosa</em>" that was published in Marine Biology in 2020. </p>
Datos de los contaminantes del aire tomado de la estación de monitoreo las Ferias, Bogotá 2009 - 2020.
<p>Base de datos de los diferentes contaminantes atmosféricos generados por la fuentes fijas y móviles de la ciudad de Bogotá, Colombia.</p>
L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations
<p>This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy).</p> <p>It contains:</p> <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag <strong>fty</strong> in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 M<sub>L</sub> >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag <strong>fty</strong> in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes M<sub>L</sub> >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag <strong>fty</strong> in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> <p>The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database. </p> <p>Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>OT_Date:</strong> date of the origin time in the format YYYY-MM-DD</p> <p><strong>OT_Time:</strong> time of the origin time in the format HH:mm:ss.dcm</p> <p><strong>lat:</strong> hypocenter latitude expressed in degrees </p> <p><strong>lon:</strong> hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep:</strong> hypocenter depth expressed in km </p> <p><strong>ML:</strong> local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)</p> <p> </p> <p><strong>id_dd:</strong> the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset</p> <p><strong>IMPORTANT NOTE about st1 and st2 (below): </strong>the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space</p> <p><strong>st1:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 1 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 1 (FPFIT convention)</li> </ul> <p><strong>dip1: </strong>dip of plane 1</p> <p><strong>rk1: </strong>rake of plane 1</p> <p><strong>st2:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 2 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 2 (FPFIT convention)</li> </ul> <p><strong>dip2: </strong>dip of plane 2</p> <p><strong>rk2: </strong>rake of plane 2</p> <p><strong>fty:</strong> flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP</p> <p><strong>MW:</strong> only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a></p>
The World Loanword Database (WOLD) 2009
<p>Haspelmath, Martin & Tadmor, Uri (eds.) 2009. World Loanword Database. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://wold.clld.org)</p>
Data set from Pouzat and Chaffiol (2009) Journal of Neuroscience Methods 181:119.
<p><span>1</span></p> <p>1</p> <p>1This is the data set of Cockroach first olfactory relay recordings used in Pouzat and Chaffiol (2009) Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR <em>Journal of Neuroscience Methods</em> <strong>181</strong>: 119-1443. These data are also included in the R package STAR. The data are in HDF5 format.</p>
Phlorest phylogeny derived from Gray et al. 2009 'Language phylogenies reveal expansion pulses and pauses in Pacific settlement'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Gray RD, Drummond AJ, & Greenhill SJ 2009. Language phylogenies reveal expansion pulses and pauses in Pacific settlement. Science, 323(5913), 479-483.</p> </blockquote>
Phlorest phylogeny derived from Kitchen et al. 2009 'Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kitchen A, Ehret C, Assefa S & Mulligan CJ. 2009. Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Proceedings of the Royal Society B: Biological Sciences, 270(1668), 2703-2710.</p> </blockquote>
Série de prises de vue d'habitations sur pilotis à Bangkok (Bangkok 2009)
<p>Corpus de photos servant à un projet de recherche sur les textscapes urbains et périurbains (street- et cityscape). </p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2009): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2009. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2009): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2009. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2009): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2009. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2009): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2009. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Data for 'Rapport de données sur les enquêtes menées entre 2009 et 2020'
<p>Data (all_data.csv) and R analyses code for the findings in the ‘Rapport de données sur les enquêtes menées entre 2009 et 2020’. Updated version also includes HTML file descriptive analysis code embedded and the corresponding output.</p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 2: 2005 - 2009) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434376">https://zenodo.org/record/7434376</a></p>
Data layers for FORCOAST service module A3 Limfjorden 2009-2017
<p>The environmental bottom data layers behind the FORCOAST service module A3 is generated by the 3D FlexSem model consisting of a hydrodynamic model coupled to the biogeochemical model ERGOM. The original data is on an unstructured grid (varying size of polygons), but for this purpose the data was interpolated to a structured grid and converted to netcdf files. The data layers are:</p> <p>1) bottom temperature</p> <p>2) bottom salinity</p> <p>3) bottom Chl a</p> <p>4) resuspension of detritus</p> <p>5) bottom oxygen</p> <p>6) bottom detritus</p>
ERA5-Land selected indicators daily aggregates for Africa, 2009
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2009.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect (2009 - 2019): NetCDF format
<p>This dataset is a compilation of particulate inherent optical properties (IOPs) and co-incident high performance liquid chromatography (HPLC) phytoplankton pigment concentrations measured underway on nine Atlantic Meridional Transect (AMT) cruises. The time period of data collection is 2009 - 2019, between Sep-Nov within each year, with measurements collected between approximately 50 degrees South to 50 degrees North. A separate netCDF file is provided for each cruise (AMT 19, and AMT 22-29), including particulate IOPs (absorption, scattering, beam attenuation), pigment concentrations, and associated metadata.</p> <p>A manuscript containing a full description of the dataset, including associated code, will soon be submitted to Earth System Science Data. A Jupytper notebook illustrating data access is provided at: https://github.com/tjor/AMT_ACSpaperplots/blob/main/AMT_DataAccess.ipynb.</p> <p>The data are also released in SeaBASS format: https://seabass.gsfc.nasa.gov/archive/PML/AMT</p>
Trento 1936 - Building 2009
<u>Coordinates</u>: N/A <br><u>Length</u>: 13.38 m<br><u>Width</u>: 8.42 m<br><u>Height</u>: 6.02 m<br><u>Points</u>: 8 <br><u>Vertices</u>: 36 <br><u>Primitives</u>: 12 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12690766/files/building_2009.obj/content">building_2009.obj</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12690766/files/building_2009.glb/content">building_2009.glb</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12690766/files/11574902_metsmods.xml/content">11574902_metsmods.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12690766/files/11574902_metsmods.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12690766/files/11574902_edm.xml/content">11574902_edm.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12690766/files/11574902_edm.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12690766/files/building_2009_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12543681">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12690766">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018
<p>The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements.</p> <p><strong>If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details.</strong></p> <p>The following data sets are included:</p> <p><strong>TM5-4DVar_ACOS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_RT_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_SAT</strong>: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT.</p> <p><strong>TM5-4DVar_IS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements.</p> <p><strong>TM5-4DVar_prior_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_arideast</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_aridwest</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_humid</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region.</p> <p>All data sets have the following <strong>variables</strong>:</p> <p>MonthDate: date (YYYY-MM-DD) of the middle of the individual month</p> <p>Month: MM</p> <p>Year: YYYY</p> <p>NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month.</p> <p>NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.</p> <p> </p> <p>Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013. </p> <p>Metz, E.-M., Vardag, S.N., Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V. K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K., Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S., Deutscher, N.M., Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.</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.