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6 results for “data harmonisation”
A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals
<p>The datasets included with this archive supplement the journal article:</p> <p>Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. <em>Remote Sens. 2019</em>, <strong>11</strong>, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>.</p> <p>The archive includes a README file with further explanations.</p>
SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach
<p><strong>BE SURE TO DOWNLOAD 2023.02</strong></p> <p>See the additional notes for updates on the products. </p> <p>Fluxes calculated using the standardized approach:</p> <p> \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\).</p> <p>We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences.</p> <p>The netCDF files contain the following data (<strong>note that only bold names have been updated in v2023</strong>): </p> <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and <em>kw</em> from all wind products (5). </li> <li>fgco2_global:<strong> </strong>the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and <em>kw</em> from all wind products (6)</li> <li><strong>sol:</strong> \(K_0\) is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li><strong>kw:</strong> \(k_w\) is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled<em>: </em>\(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler<em>: </em>scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of <em>spco2_SOCOM_unfilled</em></li> <li><strong>fco2atm: </strong>\(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li><strong>ice: </strong>\([ice]\) is the ice fraction from the OISST product</li> <li><strong>area_ocean:</strong><em> </em>the surface area of the ocean including the fractional area of the coastal regions</li> <li><strong>seafrac: </strong>the fraction of a pixel that is ocean</li> </ul> <p><strong><em>Units are listed in the metadata of each of the netCDF variables. </em></strong></p>
Appendices to the Roadmap for action for the project More Welfare: towards new risk assessment methodologies and harmonised animal welfare data in the EU
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Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data
<p>This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files:</p> <p>1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for:</p> <p>a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). </p> <p>b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest.</p> <p>The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022).</p> <p>2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022).</p> <p>3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). </p> <p>4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution.</p> <p>5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format)</p> <p>For further details, please refer to:</p> <p>Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data.</p> <p>Other references:</p> <p>Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900.</p> <p>Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.</p> <p> </p> <p> </p>
Data from: Harmonised projections of future forest resources in Europe
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