Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

High-resolution IMERG satellite precipitation data data (1km) in Iberia Peninsula

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. 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.

opencc-by-4.0Dec 2022View details →

Receptor exocytosis imaged with high temporal resolution for diverse receptor cargos

Cells perceive and interact with their environment in part through the expression, activation, and regulation of receptors on their plasma membrane. These receptors are dynamically trafficked from the plasma membrane in a process called endocytosis and delivered to the plasma membrane via exocytosis. Different receptors take diverse routes through the cell before being delivered via exocytosis. The data in this project focuses on 3 prototypical plasma membrane receptors - the B2 adrenergic receptor, the µ opioid receptor, and the transferrin receptor. Using a pH-sensitive green fluorescent protein variant, we visualized these receptors in cells as they recycled to the plasma membrane. We subsequently hand-labeled a subset of the data in order to build an automated image analysis method that could be used to detect receptor exocytosis across diverse imaging conditions. This repository contains our primary microscopy data from these studies as well as the labeling for use in supervised machine learning. These data support <a href="http://arxiv.org/abs/2106.07623">Evans et al 2021</a> and subsequent publications. Data Collection TIFF image stacks were collected using a Nikon Eclipse TiE Inverted Microscope using TIRF illumination with a solid state 488nm laser through a Nikon 60x/1.49NA TIRF objective and captured using an Andor iXon 897+ EMCCD camera. The camera was windowed to a 300x300 pixel view and images were collected with a 18.5ms exposures (~54Hz). Images were collected across two days, with two coverslips of each condition collected on day 1, and one coverslip collected on day 2. DNA Constructs The 3 cargos imaged in these data are the transferrin receptor (TfR), the B2-adrenergic receptor (B2AR, B2), and the µ opioid receptor (MOR). Constructs encoding these receptors, tagged extracellularly with the ph-sensitive GFP variant Superecliptic pHluorin (SpH, <a href="https://www.cell.com/biophysj/fulltext/S0006-3495(00)76468-X">Sankaranarayanan et al. 2000</a>, have been previously described in <a href="http://www.nature.com/articles/nn1679">Yudowski et al. 2006</a> for B2AR, <a href="https://www.jneurosci.org/content/30/35/11703">Yu et al. 2010</a> for MOR, and <a href="https://www.molbiolcell.org/doi/10.1091/mbc.e08-08-0892">Yudowski et al. 2009</a> for TfR. Cell Culture HEK293 cells were cultured in DMEM High Glucose (Hyclone) supplemented with 10% Heat Inactivated FBS (Gibco). Cells expressing B2 and MOR were stably selected from transient transfection using G418. Cells expressing TfR were transfected 3 days before the experiments presented here using Effectene following manufacturers' instructions. Before imaging, cells were transferred to 25mm diameter #1.5 glass coverslips (Electron Microscopy Sciences). Two days after plating, experiments began. Imaging conditions Cells were imaged in L-15 minimal media supplemented with 1% FBS. For MOR and B2, cells were imaged for 1 minute at ~0.16Hz without perturbation. Then agonist was added (10µM DAMGO for MOR, 10µM isoproterenol for B2) to the media and cells were imaged for 5 minutes to ensure that receptors clustered and internalized. After internalization, cells were bleached with 100% laser power for 1 minute and then imaged at 54Hz to visualize exocytic events. exocytosis was captured for up to 20 minutes after initial treatment, one cell at a time. For TfR, a single frame was taken before bleaching to show receptor expression levels and then cells were bleached and imaged as described above. Data blinding After collection, files were renamed as described in map.md. All metadata files and internalization imaging were separated into the 2 "extras" folders. The exocytosis movies were 'scrambled' to hide cargo identity using the included scrambler.py file. OPP_scramble.log described the mapping of scrambled filenames to the original imaging. Human labeling A subset of the images (22, with roughly equal representation across cargos) were hand labeled for exocytic events. Images were viewed in FIJI <a href="https://www.nature.com/articles/nmeth.2019">Schindelin et al. 2012</a> nad played back at 0.5x. When exocytic events were identified by eye, the playback was paused and the appearance of an event was found through manual advancing of the frames of the movie. The event was labeled using the Cell Counter plugin. Each movie was watched twice to identify as many events as possible. Labeled events are saved a <movie-name>-ZYW-1.xml in this dataset. Data organization All exocytic event movies and any matching human labeling are included in this base directory. All internalization movies and all metadata for all movies are included in the Extras folder for the day that movie was recorded. Coverslip and cargo identity are listed in map.md and the ground truth for cargo identity is in OPP_scramble.log

opencc-by-4.0Dec 2022View details →

High-resolution oil and gas methane emission inventory for the Permian Basin

This dataset consists of a high-resolution (0.01o × 0.01o) 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. 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. Please cite the paper when using the methane inventory dataset: 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. In review, Earth System Science Data journal (2023). Points of Contact at Environmental Defense Fund: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).

opencc-by-4.0Jan 2023View details →

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"

This dataset is associated with the following publication: 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”, Astronomy and Astrophysics, vol. 665, 2022. doi:10.1051/0004-6361/202142493. In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with: <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> 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: - 'ds_sel': contains monthly timeseries of selected intersecting data - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10) - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10) - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10)  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. 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. 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. 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>

opencc-by-4.0Feb 2023View details →

Sample of high-resolution climate dataset based on ML downscaling.

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)

opencc-by-4.0Feb 2023View details →

High-Resolution Daily Precipitation Data over the Philippines (Alcantara and Ahn, 2023)

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.

opencc-by-4.0Feb 2023View details →

Predictive high-resolution mapping of sea floor rock cover for the UK and Ireland

Predicted seabed rock cover using the machine learning algorithm Catboost and marine environmental predictors. Supporting data for T4.1 of Horizon 2020 project FutureMARES.

CoastSeg: Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.

Region: 298 coastal counties of the conterminous USA Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a>

CoastSeg: Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.

Region: 2001 coastal provinces or regions of the world Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a> Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json

REPUBLIC PageXML ground truth handwritten resolutions States General

Using annotation software provided through the Transkribus Platform we annotated scans, concerning mostly 17th century handwritten documents from the National Archive of the Netherlands, with their textual transcriptions. The resulting ground truth was used to train a machine learning model yielding very accurate results. The ground truth was made available as an open access dataset.

opencc-by-4.0Apr 2023View details →

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset

Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows:    The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R2) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.

CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format.

CoastSeg: global 30-m shoreline in 5x5 degree chunks Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a> Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson

opencc-by-4.0Apr 2023View details →

CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format. Version 2.

Datapackage for national high-resolution conservation prioritisation of boreal forests

This data package concerns the following work: Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen, National high-resolution conservation prioritisation of boreal forests, Forest Ecology and Management, Volume 541, 2023, 121079 ISSN 0378-1127 <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a> 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). This data package includes 3 folders (see details of the data in the article): 1)    Data folder     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.         (1)    bir = birch, obl = other broad leaved tree, st = forest site type     b)    Other data layers:         i)    condition_layer.img where the magnitude of the penalty is defined         ii)    ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not         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.         iv)    similarity matrix 2)    Input folder     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)         i)    .spp file for list of input features for the analysis         ii)    .dat file for the analysis settings         iii)    .bat file to run the analysis in command line         iv)    conditionlayer.txt to define the used condition file in the analysis         v)    groups file to define the use of the condition layer         vi)    interact file to define the interactions between feature layers in connectivity calculations 3)    Output folder     a)    includes folder for each analysis version. Each folder includes         i)    rank file in .img format which is the actual spatial priority ranking result         ii)    wrscr file which describes the weighted range size corrected richness of all input features         iii)    curves file: the performance of each input feature within the cell removal         iv)    jpg picture of the result 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. See Moilanen et al. (2014) for more technical information on the input and output files. Overview of the data 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. 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. 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) 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. 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. 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. These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning. Literature Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI. 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. 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 http://hdl.handle.net/10138/308426. 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. 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. 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. 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> 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. 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. 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. Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393. 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. 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. Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0. 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. 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.

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.

The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a> - FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m). - FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1 - FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1 Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.

Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space

GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.   * Start of the season Day of the Year (SOSD) * Maximun of the Season Day of the Year (MAXD) * End of the Season Day of the Year (EOSD) * Start of the season Value (SOSV) * Maximun of the Season Value (MAXV) * End of the Season Value (EOSV)   These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package.

Geospatial, biophysical and socioeconomic data for the Athens municipality on a zipcode resolution

A collated dataset from various remote sensing, local and national sources.

opencc-by-4.0Jun 2023View details →

Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"

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". The different files contain: <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: GCM-RCM matrices for the three heat metrics.</li> </ul>

opencc-by-4.0May 2018View details →

High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas

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. 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> You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms  Please cite this paper if you use this phantom in your work: 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>

High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes

A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356. The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023). Data description Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes. To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover. Land surface category (LSC) These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories. Discrete LSC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 11 </td> <td> Tree (leaf-on) </td> </tr> <tr> <td> 12 </td> <td> Shrubland (leaf-on) </td> </tr> <tr> <td> 13 </td> <td> Grassland </td> </tr> <tr> <td> 14 </td> <td> Woody vegetation (leaf-off) </td> </tr> <tr> <td> 15 </td> <td> Wilted herbaceous vegetation </td> </tr> <tr> <td> 21 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 22 </td> <td> Water </td> </tr> <tr> <td> 24 </td> <td> Built-up </td> </tr> </tbody> </table> In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype. Land use land cover After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system. The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions. Discrete LC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 10 </td> <td> Tree cover </td> </tr> <tr> <td> 20 </td> <td> Shrubland </td> </tr> <tr> <td> 30 </td> <td> Grassland </td> </tr> <tr> <td> 40 </td> <td> Cropland </td> </tr> <tr> <td> 50 </td> <td> Built-up </td> </tr> <tr> <td> 60 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 80 </td> <td> Permanent water bodies </td> </tr> <tr> <td> 90 </td> <td> Herbaceous wetland </td> </tr> </tbody> </table> Land use land cover change Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change. Files The zip files contain the following data: <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “tile-year-month.tif”. References Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963                                                                   Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. Zanaga, D., et al.., 2021. ESA WorldCover 10 m 2020 v100. <a href="https://doi.org/10.5281/zenodo.5571936 ">https://doi.org/10.5281/zenodo.5571936 </a>