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zenodo44/100

Maps of the detailed spatially and temporally attributed emission for area of Legerova and Sokolska (TURBAN-D18)

<h3>Basic information</h3> This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see <a href="../records/10982836">https://zenodo.org/records/10982836</a>). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected: <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> For more detailed description of the experiments see the TURBAN project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>. <h3>General organisation, variables and file nomenclature</h3> Each selected epizode (s1-s6) has three subfolders; input files in ASCII (output-ascii) or GeoTiff (output-gis) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (output-png). Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable vsrc_PM10 is the concentration of volume source emissions (VSRC) of the PM10, vsrc_PM25 is the concentration of PM2.5, vsrc_NO is the concentration of NO and vsrc_NO2 is the concentration of NO2. Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019. <h3>Emissions (see section 2.4.3 in Resler et al., 2024)</h3> The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources. Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP). The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river. Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024). <h3>Acknowledgements</h3> The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic. <h3>Literature</h3> Note that some sources are available only in Czech language. Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. <a href="https://doi.org/10.5194/egusphere-2023-2740">https://doi.org/10.5194/egusphere-2023-2740</a> Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území aglomerace CZ01 Praha. Partially available at: <a href="https://www.atem.cz/neevidovane_zdroje.php">https://www.atem.cz/neevidovane_zdroje.php</a> Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: <a href="https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf">https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf</a> Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality. EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: <a href="https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources">https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources</a> van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: <a href="https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf">https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf</a> EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: <a href="https://data.europa.eu/doi/10.2800/293657">https://data.europa.eu/doi/10.2800/293657</a> OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: <a href="https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en">https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en</a>

opencc-by-4.0Apr 2024View details →

zenodo44/100

Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"

This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. Please refer to the README.md file for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility.  Ethics Approval This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.

opencc-by-4.0Apr 2024View details →

zenodo44/100

Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"

This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa. ---------------------- [ReadMe.txt] ReadMe text file. [FigS1.png] This figure is a supplementary figure which appeared as "Figure S1" in the main text. Caption: Figure S1.  Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images. [FigS2.png] This figure is a supplementary figure which appeared as "Figure S2" in the main text. Caption: Figure S2.  Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control. [Experimental_datasheet.xlsx] This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results. [movie/SSS_X_x15.mp4] These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition. ---------------------- For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone". If you have any questions, please send an e-mail to: r-noguchi@env.sc.niigata-u.ac.jp or flugel555@gmail.com . (R. Noguchi)

opencc-by-4.0Jul 2024View details →

zenodo44/100

Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon

Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa Spatial autocorrelation, the relationship between nearby samples of a spatial random variable, is often overlooked in machine learning models, leading to biased results. This study investigates various methods to account for spa- tial autocorrelation when predicting soil organic carbon (SOC) using random forest models. Five models incorporating spatial structure were compared against baseline models that did not have any added spatial components. Cross-validation showed slight improvements in accuracy for models consid- ering spatial autocorrelation, while Shapley Additive Explanations confirmed the importance of spatial variables. However, no decrease in spatial autocor- relation of residuals was observed. Raster-based models exhibited enhanced prediction detail, but high-resolution validation data availability limited thor- ough validation. The findings emphasize the value of incorporating spatial autocorrelation for improved SOC prediction in machine learning models. Considerations such as the distribution of predictions and computational complexity should help guide the selection of suitable approaches for specific spatial modelling tasks.

opencc-by-4.0Nov 2024View details →

zenodo44/100

"Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction." - Data

This repository provides access to the data used in Grisot, G & Herrmann, J. B. (2024) "Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction" It contains the following datasets: <ul> <li><a href="https://zenodo.org/api/records/14235844/draft/files/all_entities.csv/content" target="_blank" rel="noopener noreferrer">all_entities.csv</a>: the spatial entities lists used in the paper (see Grisot, G & Herrmann, J. B., 2023)</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/corpus_books_aggr_sent_norm.csv/content" target="_blank" rel="noopener noreferrer">corpus_books_aggr_sent_norm.csv</a>: a corpus of N=184 Swiss literary narrative texts written in German between 1822 and 1940 by 69 Swiss authors, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/heidi_clean_aggr_sent.csv/content" target="_blank" rel="noopener noreferrer">heidi_clean_aggr_sent.csv</a>: the 1880 digitised edition of the novel Heidi, as available from E-Rara, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/sentiart.csv/content" target="_blank" rel="noopener noreferrer">sentiart.csv</a>: the sentiment lexcon SentiArt (Jacobs, 2019)</li> </ul>

opencc-by-4.0Nov 2024View details →

zenodo44/100

Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution

Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc). Dataset properties: <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> Output datasets:      <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc <ul> <li><data_type> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.

opencc-by-4.0Nov 2024View details →

zenodo44/100

MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"

Corresponding peer-reviewed publication This dataset corresponds to all input and output files that were used in the study reported in: <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. We aim to answer two questions: 1.     What is the intrinsic spatial resolution of global river dynamics? 2.     How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes? Data sources The following sources were used to produce files in this dataset: <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> Software The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling. Data Products The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii). Riv_coast.zip contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean. ·       riv_coast.zip     o      cor: riv_coast_pfaf_ii_COR.shp     o      uncor: riv_coast_pfaf_ii_UNCOR.shp   Qout_rivwidth.zip contains csv files of the aggregate river discharge to the ocean (km3/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       Qout_rivwidth.zip: Qout_pfaf_ii_rivwidth.csv   V_rivwidth_low.zip contains csv files of the aggregate river storage (km3) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_low.zip: V_pfaf_ii_rivwidth_low.csv   V_rivwidth_nrm.zip contains csv files of the aggregate river storage (km3) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_nrm.zip: V_pfaf_ii_rivwidth_nrm.csv   V_rivwidth_hig.zip contains csv files of the aggregate river storage (km3) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_hig.zip: V_pfaf_ii_rivwidth_hig.csv   Largest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins. ·       largest_rivs.zip     o      cat: cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins     o      csv: Q_df_top10.csv – total discharge contributed by each basin     o      riv: riv_top10_nxx.shp – river reaches that drain the 10 largest basins   Smallest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m. ·       smallest_rivs.zip     o      cat: cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii     o      csv: Q_df_top10.csv – total discharge to the ocean from each narrow river reach     o      riv: riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii   Global_summary.zip contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage. ·       global_summary.zip     o      Qout_rivwidth: global summary files for discharge to the ocean (km3/yr) under river width sampling     o      V_rivwidth_low: global summary files for total river storage (km3) for the low residence time scenario under river width sampling     o      V_rivwidth_nrm: global summary files for total river storage (km3) for the normal (medium) residence time scenario under river width sampling     o      V_rivwidth_hig: global summary files for total river storage (km3) for the hig residence time scenario under river width sampling     o      cat_small_gl: cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean   Rivwidth_sens.zip contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations. ·       Rivwidth_sens.zip     o      riv_coast     o      Qout_rivwidth_VIC     o      Qout_rivwidth_CLSM     o      Qout_rivwidth_NOAH     o      V_rivwidth_low_VIC     o      V_rivwidth_nrm_VIC     o      V_rivwidth_hig_VIC     o      V_rivwidth_low_CLSM     o      V_rivwidth_nrm_CLSM     o      V_rivwidth_hig_CLSM     o      V_rivwidth_low_NOAH     o      V_rivwidth_nrm_NOAH     o      V_rivwidth_hig_NOAH     o      global_summary_VIC     o      global_summary_CLSM     o      global_summary_NOAH   Cor_sens.zip contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis. ·       Cor_sens.zip     o      Qout_rivwidth_ENS     o      V_rivwidth_low_ENS     o      V_rivwidth_nrm_ENS     o      V_rivwidth_hig_ENS     o      global_summary_ENS   Width_val.zip contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018). ·       Width_val.zip: width_validation_pfaf_ii.csv   Known bugs in this dataset or the associated manuscript No bugs have been identified at this time.   References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. https://doi.org/10.1126/science.aat0636 Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-024-01421-5 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. https://doi.org/10.1029/2019WR025287 Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

opencc-by-sa-4.0Sep 2024View details →

zenodo44/100

SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data

Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA  is avaiable.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'

<div> <h3>Compendium DOI:</h3> <a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a>  </div> The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication. <div> <h3>Maintainer of this repository:</h3> </div> Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>) <div> <h3>Published paper:</h3> </div> Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. Journal of Paleolithic Archaeology (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a> <div> <h3>Abstract:</h3> </div> High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy. <div> <h3>Keywords:</h3> </div> Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy <div> <h3>Overview of contents and how to reproduce:</h3> </div> Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here. <div> <h3>Licenses:</h3> </div> Code: MIT <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024). Data and intellectual work: Creative Commons Attribution 4.0 International License (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).

opencc-by-4.0May 2024View details →

zenodo44/100

Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm

CHARACTERISTICS Planet: Mars Radius: RM = 3389.5 km (volumetric mean planetary radius) Spacecraft: NASA/Mars Atmosphere and Volatile Evolution (MAVEN) Spacecraft coordinates system: Mars Solar Orbital (MSO) equivalent to Sun-State coordinate system: <ul> <li>+XMSO points towards the Sun from the planet’s centre,</li> <li>+ZMSO towards Mars’ North pole and perpendicular to the orbital plane defined as the XMSO–YMSO plane passing through the centre of Mars,</li> <li>YMSO completes the orthogonal system.</li> </ul> Time span: 01/11/2014 to 30/04/2024 (Mars Years MY32 to MY36 included, part of MY37). Total number N of candidate bow shock crossings in the database: N = 20107 ORIGINAL DATASETS USED The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used. METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>.  Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a> These algorithms consist of two consecutive steps:  <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> REMARK ON VERSIONS From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted $\theta_{Bn}$(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that: <ul> <li>45∘<θBn<135∘: quasi-perpendicular shock condition</li> <li>θBn≤45∘ and θBn≥135∘: quasi-parallel shock condition</li> </ul> Uncertainty on these angles is estimated to be ± 5º.  From Version 4 onwards, we also added the solar longitude Ls (in degrees). For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper. VARIABLES DESCRIPTION This database contains the following ASCII variables: <ul> <li>Bow shock times in MAVEN's database (1-s resolution): Tbs</li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius RM (RM = 3389.5 km): XMSO, YMSO, ZMSO and Euclidean distance $R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}$ (in RM)</li> <li>Solar Zenith angle in degrees: SZA = $\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}$ (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface $\theta_{Bn}$ (ThetaBn, in º) <ul> <li>45 < ThetaBn <  135 deg: quasi-&perp; shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath $\longrightarrow$ solar wind, flag = 0.</li> <li>solar wind $\longrightarrow$ sheath, flag = 1.</li> </ul> </li> </ul> WARNING This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 RM (with RM = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind. Current formatting optimised for MATLAB. ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>.      LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license. Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF),                                                              Austrian Academy of Sciences (ÖAW), 2021-09-08 Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.   Contact email:        cyril.simon.wedlund@gmail.com

opencc-by-4.0Nov 2021View details →

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Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin

This dataset and the associated Python notebooks are related to the publication "Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin".

opencc-by-4.0Nov 2021View details →

zenodo44/100

[ITU AI/ML Challenge 2021] Dataset IEEE 802.11ax Spatial Reuse

This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (2021 edition). More information can be found here: <a href="https://challenge.aiforgood.itu.int/">https://challenge.aiforgood.itu.int/</a> and <a href="https://www.upf.edu/web/wnrg/2021-edition">https://www.upf.edu/web/wnrg/2021-edition</a>.  The dataset contains the information of 3.000 IEEE 802.11ax deployments (divided into two different scenarios) at which the Basic Service Set (BSS) of interest applies different possible OBSS/PD thresholds in the context of the Spatial Reuse (SR) operation. In total, 21 OBSS/PD values are considered for each deployment, and some of the deployments include data from different STA locations. The provided files are expected to be used for training Machine Learning (ML) and Federated Learning (FL) algorithms. More specifically, the dataset is divided as follows: <ul> <li>Scenario 1: 1,000 different deployments with 2-6 APs and 1 STA per AP. A minimum distance limitation is applied, so that each AP different from AP_A is located at a minimum distance of 10 meters from that one. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce1.txt">output_11ax_sr_simulations_sce1.txt</a>: contains the output generated by the simulator for the deployments in Scenario 1.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce1.zip">simulator_input_files_sce1.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 1.</li> </ol> </li> <li>Scenario 2: 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce2.txt">output_11ax_sr_simulations_sce2.txt</a>: contains the output generated by the simulator for the deployments in Scenario 2.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce2.zip">simulator_input_files_sce2.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 2.</li> </ol> </li> <li>Scenario 3: 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax, and for up to 20 different locations of different STAs of the BSS of interest ("BSS_A"). Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/output_11ax_sr_simulations_sce3.txt">output_11ax_sr_simulations_sce3.txt</a>: contains the output generated by the simulator for the deployments in Scenario 3.</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_sce3.zip">simulator_input_files_sce3.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 3.</li> </ol> </li> <li>Test: 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. A random OBSS/PD threshold is applied. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/22d1265c-0dea-4bf8-a223-e61c45deb076/output_11ax_sr_simulations_test.txt?versionId=91ccbe70-a821-4601-8db7-1763883dc984">output_11ax_sr_simulations_test.txt</a>: contains the output generated by the simulator for the evaluation deployments. The label (throughput) has been replaced with "0s".</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_test.zip?versionId=b1581c0b-419c-422b-bb2d-4dbf77f05dd2">simulator_input_files_test.zip</a>: contains the input files used by the simulator to simulate the evaluation deployments.</li> </ol> </li> </ul>

opencc-by-4.0Aug 2021View details →

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Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord

Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. Biogeosciences, 16(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll a biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence.   We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)).

opencc-by-4.0Oct 2021View details →

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Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps. License Creative Commons CC-BY Disclaimer Despite our best efforts to validate the data, some information may be incorrect. Description of the dataset Typologies of UA <ul> <li>Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li>Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li>Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li>Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li>Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li>Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> Land use typologies <ul> <li>Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li>Vineyard: grape vines grown in order to produce wine or table grape.</li> <li>Olive groves: olive trees grown in order to produce olive oil or table olives.</li> <li>Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li>Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li>Nurseries: ornamental plants, trees, flowers.</li> </ul> Credit Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846 https://www.mdpi.com/2071-1050/11/7/1846

opencc-by-4.0Dec 2021View details →

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Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021

This data set, described in detail in <a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al. (2022)</a>, contains Newport Hydrographic Line station data; gridded, cross-shelf hydrographic sections; and derived monthly climatologies for temperature, practical salinity, potential density, spiciness, and dissolved oxygen. It consists of CSV (Comma Separated Values) files (newport_hydrographic_line_station_data.zip) that contain CTD observations collected at the seven hydrographic stations located 1, 3, 5, 10, 15, 20 and 25 nautical miles west of Newport, Oregon between March 1997 and July 2021. Additionally, the data set contains three NetCDF files that follow CF (Climate and Forecast) metadata conventions: newport_hydrographic_line_gridded_sections.nc contains observations gridded to a 0.01o x 1 dbar longitude - pressure grid to create cross-shelf hydrographic sections for each of the five variables for each cruise. newport_hydrographic_line_gridded_section_climatologies.nc contains climatological hydrographic sections, calculated using harmonic analysis over the 24-year period March 1997 to February 2021 and reported here for the middle of each month, and newport_hydrographic_line_gridded_section_coefficients.nc contains the associated linear regression model coefficients for all five variables. From the regression coefficients, users can construct seasonal cycles at any location in the gridded section with a temporal resolution that best suits their specific needs. Finally, this data set includes example MATLAB and R scripts that show how to read the data files, plot cross-shelf hydrographic sections, and calculate daily and monthly climatologies using the regression coefficients.

opencc-by-4.0Jan 2022View details →

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Spatially resolved metabolic composition in seeds of common bean: comparison of the low phytic acid mutant and the wild type

Common bean (Phaseolus vulgaris L.) seeds are a good source of energy, are rich in proteins and carbohydrates, minerals and vitamins (such as Fe, Zn, B-vitamin), and bioactive compounds, such as polyphenols. However, the presence of some antinutritional compounds, such as phytic acid (PA), which decreases mineral bioavailability, can limit the nutritional value of common beans. Therefore, genotypes with low PA concentrations in common beans have been generated. The increased bioavailability of Fe from LPA mutant seeds compared to the wild type common beans was shown in a stable Fe-isotope absorption study in Swiss women, indicating that the seeds of LPA common bean could be used to help remedy the Fe malnutrition in women. Within this TNA project, we spatially resolved molecular composition in LPA mutant and wild-type common beans, particularly the distribution of PA. In total, three replicates of each genotype were analyzed with MeV-SIMS at RBI. Positive and negative modes were operated for analysis of the samples and of the standard (PA). Best spectra were obtained in negative mode, in which three distinct peaks were observed in the standard (PA): 63 m/z: PO2-, 79 m/z: PO3- and 97 m/z: H2PO4-.

opencc-by-4.0Feb 2022View details →

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Data set for the article "Tides, topography, and seagrass cover controls on the spatial distribution of Pinna nobilis on a coastal lagoon tidal flat"

Data set includes: coordinates of the GNSS points (reference system WGS84 UTM33N); density of P. nobilis and cover of C.nodosa detected in the orthophoto in the 25m2 cells; tidal levels measured (and, for comparison, simulated with the hydrodynamic model) corrected with respect to the IGM datum; number of emersions and flood duration for different levels of the tidal flat; statistics. The first Excel sheet includes a detailed description of the data.

opencc-by-4.0May 2021View details →

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Indicators and socio-spatial vulnerability index

This table contains all variables used to compute the socio-spatial vulnerability index and the values of this index, at the dristrict scale, on the coastal zone of Bangladesh (16 districts).

opencc-by-4.0Feb 2022View details →

zenodo44/100

Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month; dD = number of decade): <code>ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif</code> Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Decadal Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7427010">https://zenodo.org/record/7427010</a>

opencc-by-sa-4.0Feb 2022View details →

zenodo44/100

Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment

The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. Data deposit in preparation for article: Effect of spatial input data quality on the uncertainty of the SWAT model, submitted 2022 Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn Uuemaa contact: alexander.kmoch@ut.ee

opencc-by-4.0Mar 2022View details →

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