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1,255 results for “High-resolution”
Supplementary Material for "A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters"
<p>This is a file containing supplementary material for the paper <em>A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters.</em> For each star in target globular clusters, it lists crucial information on the linelist analyzed. In particular:</p> <ol> <li>Star ID.</li> <li>Chemical element.</li> <li>Wavelength.</li> <li>log <em>gf</em></li> <li>Excitation potential.</li> <li>Measured equivalent width with uncertaintiy.</li> </ol>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 2 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 1 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
High-resolution BIOCLIM and ENVIREM grids for Europe in consecutive 100-year bins spanning the last 21,000 years
<p>Here, I provide a dataset of gridded climatic variables at a spatial resolution of 30 arc-seconds for 210 consecutive 100-year bins spanning the period from 21,000 to 0 BP. The dataset includes 19 bioclimatic and 16 ENVIREM variables (described by Title & Bemmels, 2018) commonly used in species distribution modelling. It covers the European continent and adjacent regions within the following boundaries: 32.5°W–70°E and 32.5°N–82.5°N.</p> <p>For each 100-year bin, bioclimatic and ENVIREM variables were calculated based on the downscaled and debiased monthly temperature and precipitation simulations of the Community Climate System Model version 3 (CCSM3; Collins et al., 2006) as provided by the PaleoView software (Fordham et al., 2017). The downscaling procedure was based on the delta-change method (Ramirez Villejas & Jarvis, 2010). As a baseline climatic data, I used monthly temperature and precipitation grids from the CHELSA database for 1940–1989 (Karger et al., 2017).</p> <p>A detailed description of the dataset, including the downscaling method applied, can be found in the Technical specification attached to this dataset.</p>
Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers
<p>Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.</p> <p>Publication <a href="https://www.nature.com/articles/s41597-020-0479-6">https://www.nature.com/articles/s41597-020-0479-6</a></p>
Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers
<p>Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.</p> <p>Publication <a href="https://www.nature.com/articles/s41597-020-0479-6">https://www.nature.com/articles/s41597-020-0479-6</a></p>
A High-Resolution Dataset of Global Urban Fraction for Mesoscale Urban Modelling
<p>Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 and 2021 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4% (2020) and 76.7% (2021). In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset.</p> <p>Note: version 2.0.0 uses WorldCover 2021 v200 dataset for processing of urban fractions, while version 1.0.0 uses WorldCover 2020 v100 dataset.</p> <p>For more information please see here: <a href="https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4">https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4</a></p>
High-resolution IMERG satellite precipitation data data (1km) in Iberia Peninsula
<p>In summary, the SMPD method with the use of surface water balance principle has a solider physical basis than previous downscaling methods. Through introducing SSM as an auxiliary variable, the impact of inherent bias in satellite estimates on the downscaled results can be moderately reduced compared to the conventional statistical method. The validation with rain gauge data highlights the importance of SSM as a fully independent source of information that can be effectively used for downscaling coarse-resolution precipitation at a daily scale, which is rarely conducted in current related studies.</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
High-resolution oil and gas methane emission inventory for the Permian Basin
<p>This dataset consists of a high-resolution (0.01<sup>o</sup> × 0.01<sup>o</sup>) oil and gas methane emission inventory for the Permian Basin, developed at Environmental Defense Fund (<a href="http://www.edf.org">www.edf.org</a>). The Permian Basin in western Texas and southern New Mexico is the largest oil producing basin in the U.S., accounting for more than 40% of national oil production in 2021. It is also the nation's largest methane emitting basin, with recent measurement-based estimates of more than three million metric tons per year. Here, we develop an improved inventory of oil and gas methane emissions for the Permian Basin, based on recent facility-scale measurements and updated oil and gas activity data for the year 2021.</p> <p>Full details for the oil and gas methane emission inventory development and key results can be found in the following journal paper, which is under review at Earth System Science Data journal.</p> <p>Please cite the paper when using the methane inventory dataset:</p> <p>Omara, M., Gautam, R., O'Brien, M.A., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D.R., Chulakadaba, A., Miller, C.C., Franklin, J., Wofsy, S., and Hamburg, S.P. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution. <em>In review</em>, Earth System Science Data journal (2023).</p> <p>Points of Contact at Environmental Defense Fund: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Sample of high-resolution climate dataset based on ML downscaling.
<p>The ClimateByte project created a high-resolution (downscaled) climate dataset for specific regions based on the CINECA MISTRAL observational dataset. AMIGO selected one region from the larger dataset and made that portion openly available to the other members of the EUH4D project for research and non-commercial purposes. Resolution of this dataset is 300m and covers the Adige Valley of Trentino Alto-Adige (Italy)</p>
High-Resolution Daily Precipitation Data over the Philippines (Alcantara and Ahn, 2023)
<p>The high-resolution nationwide 20-year (2001-2020) daily precipitation data over the Philippines generated by Alcantara and Ahn (2023) is a dataset that provides daily precipitation data for the entire country with a spatial resolution of 0.1° x 0.1°. This dataset is created using the Quadruple Collocation (QC) approach, which combines four parent datasets - ERA5, PERSIANN, CHIRPS, and GPM - to produce a more accurate dataset than any of the individual parent datasets. The dataset is available in NetCDF format (version 4), which is a standardized data format commonly used for the exchange of large data files. This format can be read using major programming languages such as R, Python, C++, and others, making it easily accessible for use in various research applications. With its high spatial and temporal resolution, the dataset can be a valuable resource for climate research, hydrological modeling, and other related fields. In the future, data from 2021 up to present will also be added.</p>
Predictive high-resolution mapping of sea floor rock cover for the UK and Ireland
<p>Predicted seabed rock cover using the machine learning algorithm Catboost and marine environmental predictors.</p> <p>Supporting data for T4.1 of Horizon 2020 project FutureMARES.</p>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas
<p>We present the design of a digital phantom that tries to overcome the problems of the current PET digital brain phantoms, particularly for the simulation of simultaneous PET-MRI data sets. We propose a new brain digital brain phantom based on the BigBrain atlas, a free, publicly available tool that provides considerable neuroanatomical insight into the human brain with an ultrahigh-resolution 3D model of a human brain at nearly cellular resolution of 20 micrometers. We used the histology maps, the classified tissue maps and the MRI image of the BigBrain atlas, as well as the Hammersmith atlas and a PET [18F]FDG template as inputs to create an instance of this ultra high-resolution heterogeneous PET-MRI phantom.</p> <p>Full details of this phantom in Medical Physics: "Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas", <a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218.</a></p> <p>You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms </p> <p>Please cite this paper if you use this phantom in your work:</p> <p>Belzunce, M.A. and Reader, A.J. (2020), Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas. Med. Phys., 47: 3356-3362. doi:<a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218</a></p>
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
<p>The products are the first <strong>regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations</strong>. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the <strong>SM-based (Soil-Moisture-based) inversion approach</strong>. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and on their performance assessment can be found in:</p> <p><strong>Dari, J.</strong>, Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. <strong>Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space</strong>. <em>Earth System Science Data, </em>15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.</p> <p> </p> <p><strong>Novelties in v1.1 with respect to v1.0:</strong></p> <p>v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021.</p> <p><strong>Acknowledgements</strong>:</p> <p>ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB).</p> <p>ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).</p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek
<p>This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.</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.