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A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth.
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
Text-fig. 9. Fossil wood localities 4 and 5 close to the palaeopan on the north flank of the Muaredzi Gorge. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 9. Fossil wood localities 4 and 5 close to the palaeopan on the north flank of the Muaredzi Gorge. Image modified from Google Earth.
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth.
Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
<p>The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O<sub>2</sub>) concentrations increased from ~10<sup>−5</sup> times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O<sub>2</sub> mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O<sub>2</sub> and H<sub>2</sub>O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O<sub>2</sub> concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O<sub>2</sub> concentrations of <1% PAL.</p>
Data for "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure"
<p>This file is the dataset used in the article "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure", Nat. Commun. 13, 7211 (2022). https://doi.org/10.1038/s41467-022-34789-2</p>
Data from: Effects of topography and Earth's rotation on the oblique interaction of internal solitary-like waves in the Andaman Sea
<p>This is a modeled data set from the paper:</p> <p>Shimizu, K., and K. Nakayama (2017), Effects of topography and Earth's rotation on the oblique interaction of internal solitary-like waves in the Andaman Sea, Journal of Geophysical Research - Oceans, 122, doi:10.1002/2017JC012888</p> <p>This data set includes results of MITgcm internal solitary-like wave (ISW) simulations in the Andaman Sea, and data derived from them. The model was run at two horizontal resolutions (0.005 and 0.0025 degree resolution) and three cases for the Coriolis effects (Full, Traditional, and No Coriolis). Please refer to the paper for the methods and data used to produce this data set.</p>
The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation
<p>The model code and necessary data: NESMv3_gmd.tar.gz.</p> <p>The model manual :Using NESM v3 model.pdf</p> <p>The reference: Reference.tar.gz</p>
Concentrations of the rare earth elements (REE) and Thorium-232 (232Th) in glacial dust from the northern Gulf of Alaska region
<p>Concentrations of the rare earth elements (REE) and Thorium-232 (232Th) are presented for filtered air (dust) samples collected from the northern Gulf of Alaska region, including from Middleton Island (AK)(59.4214 N, 146.3493 W) and the Copper River delta (60.4324 N, 145.0954 W). Size-fractionated samples were collected in November 2019, using a Tisch Volumetric Flow Controlled (VFC) high volume sampler (Tisch Environmental, TE-5170V- BL) outfitted with a Cascade impactor. The six size fractions collected ranged from <0.49 micrometers (um) to >7.2 um in diameter. This sampler technology is discussed in greater detail in Morton et al, 2013. Samples were filtered with acid-washed Whatman 41 (W41) cellulose fiber filters. Additional bulk dust samples were collected in October 2012, using a Thermo Partisol Plus 2025 using Teflon filters. Samples were fully digested using concentrated nitric and hydrofluoric acids, following the approach of Morton et al, 2013. Samples were analyzed using a Thermofisher iCAP inductively coupled plasma mass spectrometer (ICP-MS) in KED mode, with He as a collision cell gas, adapted from the approach of Trommetter et al (2020). Concentrations were determined from standard curves using a REE ICP-MS standard from High-Purity Standards (that also contained 232Th). Three internal standards (Ge, In, and Bi) were added to both samples and standards, to correct for short-term variability in the instrument response and to evaluate stability of mass response during the ICP-MS run. Concentration estimates for the REE and 232Th were blank-corrected using full-process blanks that included filters deployed during times when there was no known dust deposition. Most of the full-process blank concentrations were 100 times or more smaller than the concentrations of our lowest standard (with the exception of Ce, the concentration of which was ~seven times smaller than our lowest standard. This means that our blank concentrations were very low but also not quantified extremely accurately. Our best estimates are that the full-process blanks, including filters, ranged from 0.02 picograms per square centimeter (pg cm-2) for Eu, Tb, and Ho, to 2 pg cm-2 for Ce. These blank concentrations were in all cases 40 times or more smaller than our lowest REE sample concentration for the <0.49 um size fraction with the smallest amount of dust, and ~3 orders of magnitude smaller than the signal of the largest samples. The REE data are also presented in a double-normalized format that first normalizes to concentrations of Post Archean Australian Shale and then normalizes to the mean REE concentration. The normalization approach is slightly modified from that of Serno et al, 2014.</p>
The rare earth element distribution in marine carbonates as a potential proxy for seawater pH on early earth
<p>Understanding the marine environment of early Earth is crucial for understanding the evolution of climate and early life. However, the master variable of Archean and Proterozoic seawater, the pH, is poorly constrained, and published ideas about the pH range encompass ~7 pH units from mildly acidic to hyperalkaline. To better infer ancient seawater pH, we examine the possibility of a seawater pH proxy using rare earth elements (REEs) in marine carbonates. The principle is based on increasing concentrations of heavy rare earth elements in solution relative to the light REEs with decreasing pH due to REE complexation and scavenging. We calibrated such an REE pH proxy using pH variability in modern seawater and tested the proxy with ~100 REE measurements from 13 separate carbonate formations. We compared our pH estimates derived from the REE proxy to published pH estimates of Cenozoic and Neoproterozoic seawater that use the established pH proxy of boron isotopes (δ<sup>11</sup>B). REE-pH estimates agree with the Cenozoic and the Ediacaran δ<sup>11</sup>B-pH proxy based on the type of carbonate and boron isotopic composition at corresponding times. The uncertainty in our REE-pH proxy can probably be explained by model assumptions, noise from freshwater influence, siliciclastic input, and diagenesis. This proof-of-concept study demonstrates that the REE-pH method provides pH estimates comparable to boron isotope pH estimates within uncertainties, which potentially could constrain changes in Precambrian seawater pH to better understand the coevolution of life and early Earth's environment.</p>
Data for "The very-high resolution configuration of the EC-Earth global model for HighResMIP"
<p>Model data and plot scripts to reproduce the figures of the manuscript "<em>The very-high resolution configuration of the EC-Earth global model for HighResMIP</em>".</p> <p><strong>Authors</strong></p> <p>Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, Sergi Palomas</p> <p><strong>Abstract</strong></p> <p>We here present the very-high resolution version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ~16 km and an oceanic resolution of 1/12° (~8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ~100-km-grid EC-Earth3P-LR, and the ~25-km-grid EC-Earth3P-HR. The models' biases are evaluated against observations over the period 1980–2014. Compared to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, with improved stormtrack over the North Atlantic, yet worsened stormtrack over the Southern Ocean compared to the lower resolution model versions. Other biases persist with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, and the precipitation excess over the tropical South Atlantic and North Pacific. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at mid-latitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional/local spatial scales, in line with regional climate models.</p>
Auxiliary data for Moustakis et al. 2024 "Temperature overshoot responses to ambitious forestation in an Earth System Model"
<p>The netcdf file "Moustakis_et_al_2024_Data.nc" contains all the key variables presented in the figures of the manuscript of Moustakis et al. 2024: "Temperature overshoot responses to ambitious forestation in an Earth System Model".</p> <p>Please read the README.txt file for more information on the variables included.</p> <p>For any further queries please refer to the corresponding author, Yiannis Moustakis: <br>yiannis.moustakis@geographie.uni-muenchen.de</p> <p> </p>
Dataset: Whole Earth Brands, Inc. (FREE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Whole Earth Brands, Inc. (FREEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Brilliant Earth Group, Inc. (BRLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fabrication, Characterization and Evaluation of an Alginate–Lignin Composite for Rare-Earth Elements Recovery
<p>This dataset contains the raw data for the publication "Fabrication, Characterization and Evaluation of an Alginate–Lignin Composite for Rare-Earth Elements Recovery" by Fila et al, published in Materials. The upload includes raw data of physicochemical characterizations of alginate-based composite, i.e. alginate-lignin, including BET, SEM, TG, XPS and XRD analyses. </p>
Data for "Density deficit of the Earth's core revealed by a multi-megabar primary pressure scale"
<p>This file is the dataset used in the article "Density deficit of the Earth's core revealed by a multi-megabar primary pressure scale", Sci. Adv. 9, eadh8706 (2023). https://doi.org/10.1126/sciadv.adh8706</p>
Reisinger_et_al_2024_Commun_Earth_Environ_dataset
<p>This dataset contains the script for Figure 1 in the perspective paper:</p> <p>Reisinger, A., Cowie, A. L., Geden, O., & Al Khourdajie, A. (2024). Science-based targets miss the mark. Communications Earth & Environment - Nature. <a href="https://doi.org/10.1038/s43247-024-01535-z" rel="nofollow">https://doi.org/10.1038/s43247-024-01535-z</a></p>
Model of the Earth's conductance for the upper 22 km
<p>Model of the Earth's conductance for the upper 22 km obtained by compilation of the models from 1) Grayver, 2021 (https://doi.org/10.1029/2021GC009950) for the conductance of ocean and sediments; 2) global conductivity model from Alekseev et al. 2015 (https://doi.org/10.1186/s40623-015-0272-5); 3) recent 3-D conductivity model of the USA from Munch & Grayver, 2023 ( https://doi.org/10.1016/j.epsl.2022.117939 ).</p> <p>The global conductance distribution (dataset "Conductance") is provied at 6 arc-min (0.1 degree) resolution in geocentric (i.e. spherical) coordinate system in the centres of the corresponding cells. Datasets "theta_c" and "phi_c" stand for co-latitudes and longitudes of the cells' centres correspondingly.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.