Mimet, Anne · Gourmemon, Damien · Amandine, Vergondy · et al.

14 files · 1.6 GB · shapefile, tiffdeclared

================================================================================ README ================================================================================ Dataset Title: Observation points, movement-proxy data, and high-resolution land cover map for the common blackbird ( Turdus merula ) in Angers, France Version: 1.0 Authors : Mimet, Anne ; Gourmelon Damien ; Oulhen, Thomas ; Vergondy, Amandine Date of biological data collection: Observation points : 15/05/2025 to 06/06/2025 Movement-proxy data : 19/04/2024 to 17/05/2024 -------------------------------------------------------------------------------- DESCRIPTION -------------------------------------------------------------------------------- This dataset contains observed presence/absence of blackbird flights across streets, as well as point observations of the common blackbird across Angers, France, during spring. The data were used to create a connectivity model for the common blackbird in Angers. The 68 point observations provided information on the land cover types used as a possible resource by the common blackbird. The presence/absence of flying blackbirds across 190 streets in Munich was used to derive the resistance of the urban landscape to the movement of common blackbirds in a landscape connectivity model. For the point observations, the presence and absence of the common blackbird was visually and acoustically confirmed after 5 minutes of observations within a radius of 25 m. Movement presence/absence was observed along 50 m street transects. Street transects were observed for 9 minutes, and the presence or absence of common blackbirds crossing this street was recorded. The observation points and movement-proxy data were selected along gradients of greenness and traffic density. -------------------------------------------------------------------------------- FILE LIST -------------------------------------------------------------------------------- 1. movement_proxy.shp Site-level data containing presence and absence of common blackbirds crossing the sampled streets, as well as covariates such as the number of pedestrians passing during the sampling period, geographic information, information on the weather, time of sampling, pseudonomized observer. Number of records: 190 Number of variables: 11 related files: movement_proxy.cpg, movement_proxy.dbf, movement_proxy.prj, movement_proxy.shp, movement_proxy.shx The related files are required because data is stored in a shapefile. For this shapefile to be correctly read by any GIS processing software, all related files need to be saved in the same folder. 2. point_observations.shp Site-level data containing presence and absence of common blackbirds at the observation points. Additionally, geographic and temporary information as well as information on the weather are provided. Number of records: 68 Number of variables: 10 related files: point_observations.cpg, point_observations.dbf, point_observations.prj, point_observations.shp, point_observations.shx The related files are required because data is stored in a shapefile. For this shapefile to be correctly read by any GIS processing software, all related files need to be saved in the same folder. 3. LULC_9Class_Angers.tif and assoociated qlm style file Land use and land cover map at 40 cm resolution for Angers. 3. README.txt This file. -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: movement_proxy.shp -------------------------------------------------------------------------------- StreetCod Site identifier for street transect - these are the same sites as in the file Point_observations.shp (format: [text]) X Northing coordinate of the centre (latitude) (format: [degree]) Y Easting coordinate of the observation point (longitude) (format: [degree]) Obs Id of the observor (2 observors in the dataset) date Date of the observation (format [%d/%m/%y]) Daytime Starting time of observation (format: [text] hour%h%min) Windspeed Windspeed given by the mobile phone application &laquo; Accuweather &raquo; Temperatur Temperature at the time of observation in °C given by the mobile phone application &laquo; Accuweather &raquo; Pedestrian Number of pedestrians combined passing by during the time of observation CarDensity Number of cars counted on a 3-min period FlyBbird Number of common blackbirds Turdus merula that crossed the street transect during the time of observation (9 min). Every crossing event was counted. When the same individual crossed 2 times, it was counted 2 times -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: point_observations.shp -------------------------------------------------------------------------------- StreetCod Site identifier for street transect - this are the same sites as in the file movement_proxy.shp (format: [text]) X Northing coordinate of observation point (latitude) (format: [degree]). Y Easting coordinate of the observation point (longitude) (format: [degree]) Obs Id of the observor (1 in this dataset) date Date of the observation (format [%d/%m/%y]) Daytime Starting time of observation (format: [text] hour%h%min) Windspeed Windspeed given by the mobile phone application &laquo; Accuweather &raquo; Temperatur Temperature at the time of observation in °C given by the mobile phone application &laquo; Accuweather &raquo; AbBlackb Number of common blackbirds Turdus merula that were detected by sight or sound during the 5-min observatin période, over a radius of 25 m. PABlackb Presence-Absence of observed blackbirds derived from the abundance. -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: LULC_9Class_Angers.tif -------------------------------------------------------------------------------- 11 : Buildings < 5m 12 : Buildings 5-10m 13 : Buildings 10-18m 14 : Buildings > 18m 20 : Sealed areas 31 : vegetation < 1m 32 : Vegetation 1-3m 33 : Vegetation > 3m 40 : Farmland 50 : Bare soil 60 : Water -------------------------------------------------------------------------------- METHODS SUMMARY -------------------------------------------------------------------------------- Site Selection for Movement-Proxy Data: - 190 sites selected via stratified random sampling across vegetation cover (computed in a radius of 300m around the points) and traffic density (derived from TomTom navigation data for October 2022) - Observations along 50 m street transects - Detection of presence/absence of common blackbirds crossing the sampled streets Site Selection for Point Observations: - 68 sites selected via stratified random sampling across vegetation cover (computed in a radius of 300m around the points) and traffic density (derived from TomTom navigation data for October 2022) - Visual and acoustic detection of presence/absence of common blackbirds within a 25 m radius LULC map: Derived from land cover information from CoSIA (IGN 2023a), refined with a digital terrain model (IGN 2020) and height model (IGN 2023b) to extract building and vegetation height classes. -------------------------------------------------------------------------------- RELATED PUBLICATIONS -------------------------------------------------------------------------------- Lisa Merkens*, Meret Pundsack*, Anne Mimet*, Damien Gourmelon, Wolfgang W. Weisser. City-specific or generalisable resistances? An urban animal connectivity model performs better when parameterised from two cities. Preprint Submitted to Urban Ecosystems -------------------------------------------------------------------------------- FUNDING -------------------------------------------------------------------------------- Région Pays de la Loire through the PULSAR project VitalConnect (2024_05894 ) -------------------------------------------------------------------------------- LICENSE -------------------------------------------------------------------------------- This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). You are free to: - Share: copy and redistribute the material in any medium or format - Adapt: remix, transform, and build upon the material for any purpose Under the following terms: - Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. Full license text: https://creativecommons.org/licenses/by/4.0/ -------------------------------------------------------------------------------- CITATION -------------------------------------------------------------------------------- If you use this dataset, please cite both the dataset and the associated publication: Dataset: Mimet, A., Gourmelon, D, Oulhen, T., Vergondy, A. (2026) Observation points, movement-proxy data, and high-resolution land cover map for the common blackbird (Turdus merula) in Angers, France [Dataset]. Zenodo. Publication: Lisa Merkens*, Meret Pundsack*, Anne Mimet*, Damien Gourmelon, Wolfgang W. Weisser. City-specific or generalisable resistances? An urban animal connectivity model performs better when parameterised from two cities. Preprint Submitted to Urban Ecosystems -------------------------------------------------------------------------------- CONTACT -------------------------------------------------------------------------------- Anne Mimet Université d'Angers Laboratoire BiodivAG, DEP ENS SCIENCES Biologie, UFR SCIENCES 2 Boulevard de Lavoisier, F-49045 Angers, France Email: anne.mimet@univ-angers.fr ORCID: 0000-0001-9498-436X ================================================================================ END OF README ================================================================================

Anonymous Authors

15 files · 2.2 GB · parquet, rar, torchdeclared

Economic losses caused by extreme climate are not confined to the locations where events occur, but can propagate across regions through physical and economic linkages. Yet existing climate-impact assessment methods remain poorly suited to tracing how shocks spread across space and reshape the geography of economic loss. Here we develop a mechanistically informed multi-scale spatiotemporal autoregressive graph neural network model to quantify spatially cascading climate impacts. The model couples scale-specific, physically structured spatiotemporal autoregressive processes through an adaptive gating mechanism, allowing heterogeneous cross-scale interactions to be learned from data. Model estimation is achieved through tailored graph convolutional neural networks that are mathematically equivalent to spatiotemporal autoregressive models, enabling scalability while preserving transparent parameter interpretation. Monte Carlo simulation experiments show that the model accurately recovers true parameters and distinguishes between scale-dependent processes. Applying the framework to extreme precipitation, we find that large-scale upwind-to-downwind cascades driven by atmospheric circulations dominate aggregated economic losses. A one-standard-deviation increase in log extreme precipitation is associated with a 0.19 percentage-point decline in economic growth rate at the large scale, with 62.3% of the loss arising from spatial cascades. These findings highlight the need for transboundary risk governance that incorporates spatial cascading into climate-extremes monitoring and early-warning. Description of the uploaded file Monte Carlo simulation code data_generator_factors.py: Data generation script for multi-scale Monte Carlo simulation experiments. sarnn_model.py:Implementation of the proposed MS-STARGNNs model architecture definition. train.py:Training pipeline script for the MS-STARGNNs model. Data and spatial weights matrices for empirical analysis global_panel_1deg_std.parquet:Standardized Large-scale (1°) datase; global_panel_2km_std.parquet:Standardized Small-scale (2 km) dataset. W_global_2km_knn8.pt:Small-scale spatial weights matrix based on 8-nearest neighbors (KNN8). W_Large-scale:A large-scale spatial weights matrix derived from moisture transport pathways (2005-2021) mapping_1deg_to_2km.parquet: Correspondence file mapping large-scale (1°) grid cells to small-scale (2km) grid cells. ipcc_region_mapping_coarse_8regions.parquet: Mapping file linking Large-scale grid cells to the 8 IPCC AR6 reference regions. ipcc_region_mapping_fine_8regions.parquet: Mapping file linking Small-scale grid cells to the 8 IPCC AR6 reference regions. Code model.py: Core architecture definitions for empirical analysis. Provides the base classes and computational layers engineered to handle real-world geospatial complexities. All subsequent training scripts import modules from this file. STARGNNs.py: Implementation of the single-scale baseline. Serves as a reference point for evaluating the efficacy of cross-scale feature fusion. MS-STARGNNs_fixed.py: Configuration script for the MS-STARGNNs model utilizing fixed autoregressive coefficients. MS-STARGNNs.py: Configuration script for the MS-STARGNNs model utilizing annually varying autoregressive coefficients. MS-STARGNNs_8 regions.py: Executes the MS-STARGNNs model with decoupled regional parameters, loading unique autoregressive weights and βvectors for each IPCC region. Implements null value handling for regions lacking observational data (e.g., Antarctica).

Kavanagh, Jack · Anthony, Patrick

6 files · 1.5 MB · geojson, geopackage, shapefiledeclared

A historical map of the boundaries of the Kazakh Steppe in c. 1848. This map is fully open to fellow researchers and is available in multiple open source formats (SHP, GPKG, GeoJSON).

Ye, Renhao · Shen, Shiyin

69 files · 47 GB · csv, fits, tardeclared

Part 2 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 64-archive subset (RA 137.7-270.0°, Dec 52.9-79.9°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 3 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 180.0-270.0°, Dec 68.7-80.3°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

66 files · 46 GB · csv, fits, tardeclared

Part 4 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 61-archive subset (RA 84.4-285.5°, Dec -41.4-80.4°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 5 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 47.7-93.4°, Dec -67.7--16.7°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

66 files · 46 GB · csv, fits, tardeclared

Part 6 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 61-archive subset (RA 1.2-69.9°, Dec -66.6--54.3°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 7 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 32.7-60.0°, Dec -63.4--48.2°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

64 files · 46 GB · csv, fits, tardeclared

Part 9 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 59-archive subset (RA 60.0-89.9°, Dec -60.1--48.2°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

67 files · 46 GB · csv, fits, tardeclared

Part 8 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 62-archive subset (RA 30.0-89.2°, Dec -61.8--41.8°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 11 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 67.6-90.0°, Dec -48.1--35.7°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

67 files · 46 GB · csv, fits, tardeclared

Part 10 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 62-archive subset (RA 64.3-90.0°, Dec -54.3--41.9°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

46 files · 32 GB · csv, fits, tardeclared

Part 20 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 41-archive subset (RA 34.0-213.7°, Dec -66.5-80.4°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

David, Cédric

19 files · 370 KB · netcdf, parquetdeclared

RAPID comes along with a set of test files based on a synthetic experiment called the Sandbox. More information is available in SANDBOX.md .

Akandil, Cengiz · Plekhanova, Elena · Rietze, Nils · et al.

33 files · 1.2 GB · csv, shapefile, tiffdeclared

This repository contains the processed datasets used to analyse the spatial relationship between artificial light at night (ALAN) and Arctic fire occurrence. The dataset includes cumulative ALAN layers, binary masked ALAN layers, fire polygon shapefiles, and a final analysis table. The cumulative ALAN layers provide aggregate digital number (DN) values representing light intensity. The masked ALAN layers are binary rasters, where values of 1 indicate lit areas and values of 0 indicate unlit areas. Fire scar centroids were generated from the fire polygons and used to calculate the distance from each fire scar to the nearest lit area. The final analysis table contains the distance to the nearest lit area for each fire scar and control points. The fire polygon shapefile includes fire scars for the period 2001-2013.

Karamichali, Ioanna · Stefanidou, Eleni · Nakas, Christos · et al.

22 files · 418 MB · csv, pdf, shapefiledeclared

This deliverable presents the Phase 2 results of the BioProWRAP project, developed under the CLIMAAX framework, and focuses on enriching the initial wildfire‑risk assessment of Phase 1 through the integration of drought risk-an interconnected hazard with potential cascading effects-alongside diverse local datasets and live biodiversity inputs (crowdsourcing observations and eDNA). Phase 2 also enhances public engagement and inclusion through educational workshops and guided excursions in the three high‑risk, high‑value areas identified in Phase 1. The work was carried out by the REMTH team with continued scientific support from UTH, Greece.

Pezzotta, Andrea · Moretti, Chiara · Gambardella, Giosuè · et al.

4 files · 22 MB · fitsdeclared

Description: Includes binned power spectrum Gaussian covariance, to be used with cloelib/cloelike. Produced up to kmax = 0.4 h/Mpc with 100 linearly spaced bins (using the publicly available COMET code). Uses 4 redshift bins, from z=0.9 to z=1.8, with width of Delta_z=(0.2, 0.2, 0.2, 0.3) The synthetic covariance was generated with the following parameters: parameters = { 'H0': 67.0, 'Omega_cdm0': 0.27, 'Omega_b0': 0.049, 'Omega_k0': 0.0, 'mnu': 0.0, 'w0': -1.0, 'wa': 0.0, 'ns': 0.96, 'As': 2.1e-9, 'gamma_MG': 0.545, 'b1': np.array([1.412, 1.769, 2.039, 2.496]), 'b2': np.array([0.695, 0.870, 1.162, 2.010]), 'bG2': np.array([-0.156, -0.299, -0.400, -0.555]), 'bGam3': np.array([0.323, 0.621, 0.827, 1.137]), 'c0': np.array([30.948, 37.116, 36.738, 53.627]), (Mpc^2 units) 'c2': np.array([46.233, 53.071, 48.626, 60.962]), (Mpc^2 units) 'c4': np.array([10.057, 10.385, 8.643, 8.711]), (Mpc^2 units) 'cnlo': np.array([0.0, 0.0, 0.0, 0.0]), 'NP0': np.array([1.056, 1.152, 1.144, 1.309]), 'NP20': np.array([0.0, 0.0, 0.0, 0.0]), 'NP22': np.array([0.0, 0.0, 0.0, 0.0]), 'fout': np.array([0.0, 0.0, 0.0, 0.0]), 'sigmaz': np.array([0.0, 0.0, 0.0, 0.0]) } The power spectrum data vectors assume a number density of [2.042611E-03, 1.02876011E-03, 0.58531983E-03, 0.313402E-03] (h/Mpc)^3. T The synthetic covariance of BAO measurements is taken from here: https://zenodo.org/records/19729182 Cross-terms are set to zero.

Loh, Alan · Girard, Julien N.

17 files · 89 MB · fitsdeclared