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1,291 results for “gap”
Biogeography of the world's worst invasive species has spatially-biased knowledge gaps but is predictable
<p>The world's "100 worst invasive species" were listed in 2000. The list is taxonomically diverse and often cited (typically for single-species studies), and its species are frequently reported in global biodiversity databases. We acted on the principle that these notorious species should be well-reported to help answer two questions about global biogeography of invasive species (i.e., not just their invaded ranges): (1) "how are data distributed globally?" and (2) "what predicts diversity?" We collected location data for each of the 100 species from multiple databases; 95 had sufficient data for analyses. For question (1), we mapped global species richness and cumulative occurrences since 2000 in (0.5 degree)<sup>2</sup> grids. For question (2) we compared alternative regression models representing non-exclusive hypotheses for geography (i.e., spatial autocorrelation), sampling effort, climate, and anthropocentric effects.</p> <p>Reported locations of the invasive species were spatially-biased, leaving large gaps on multiple continents. Accordingly, species richness was best explained by both anthropocentric effects not often used in biogeographic models (Government Effectiveness, Voice & Accountability, human population size) and typical natural factors (climate, geography; R<sup>2</sup> = 0.87). Cumulative occurrence was strongly related to anthropocentric effects (R<sup>2</sup> = 0.62). We extract five lessons for invasive species biogeography; foremost is the importance of anthropocentric measures for understanding invasive species diversity patterns and large lacunae in their known global distributions. Despite those knowledge gaps, advanced models here predict well the biogeography of the world's worst invasive species for much of the world.</p>
Selective dynamic band gap tuning in metamaterials using graded photoresponsive resonator arrays
<p>Raw Data for figures:</p> <p>Fig. 2: Dispersion diagrams for non-illuminated (off) and illuminated (on) pillars of different heights (hp). hp1 = 7 mm, hp2 = 9 mm, hp3 = 11 mm, hp4 = 13 mm; p = 0 (1) for purely in- (out-of-plane) behavior</p> <p>Fig. 4: Computed transmission spectrum of a finite structure. a) Numerically simulated transmission spectrum for the considered 8-pillar specimen, both without ("Laser off") and with laser illumination ("Laser on 7th pillar").</p> <p>Fig. 5: Transmission spectrum of the finite structure considered experimentally. a) Measured spectra before (blue) and after (red) illumination of pillar 1. Band gaps are highlighted in light blue and numbered from I to IV; b) Corresponding colour map representing transmission vs. frequency and time (vertical axis) when switching laser illumination on (t = 700 s) and off (t = 2300 s); c) same as a), with illumination of pillar 6; d) same as b), with illumination on pillar 6.</p> <p>Fig. 6: Dynamic modulation of signal frequencies (f1 = 21.5 kHz, f2 = 71.5 kHz) in a graded pillar structure. The different temporal intervals depict tunable suppression and enhancement of specific frequencies through selective pillar illumination.</p>
Kuhlia rupestris, juvenile. © E. Vigneux. Source: Keith et al., 1999. in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Kuhlia rupestris, juvenile. © E. Vigneux. Source: Keith et al., 1999.
Transport properties of doped wide band gap layered oxychalcogenide semiconductors Sr2GaO3CuCh, Sr2ScO3CuCh and Sr2InO3CuCh (Ch = S or Se)
<p>The structural, electrical, and optical properties of a series of six layered oxychalcogenides with general formula Sr<sub>2</sub><em>M</em>O<sub>3</sub>Cu<em>Ch</em> where <em>M</em> = Ga, Sc, or In and <em>Ch</em> = S or Se have been investigated. From this set we report the structure and properties of Sr<sub>2</sub>GaO<sub>3</sub>CuSe for the first time, as well as the full structural details of Sr<sub>2</sub>ScO<sub>3</sub>CuSe, which have not previously been available. A systematic study of the suitability of all the Sr<sub>2</sub><em>M</em>O<sub>3</sub>Cu<em>Ch</em> phases as <em>p</em>-type conductors has been carried out, after doping with both sodium and potassium to a nominal composition of <em>A</em><sub>0.05</sub>Sr<sub>1.95</sub><em>M</em>O<sub>3</sub>Cu<em>Ch</em>, (<em>A</em> = Na or K), to increase the hole carrier concentration. Density functional theory calculations were used to determine the electronic band structure and predict the transport properties, while optical properties were determined using UV-vis spectroscopy, and structures confirmed using Rietveld refinement against powder X-ray diffraction data. Room temperature conductivity measurements were carried out on both pristine samples and doped samples, eighteen compositions in total, using four-point probe measurements. We found that the most conductive sample wasK<sub>0.05</sub>Sr<sub>1.95</sub>GaO<sub>3</sub>CuSe, with a measured conductivity of 0.46 Scm<sup>−1</sup>, collected from a sintered pellet. We have also been able to identify a relationship between the conductivity and the geometry of the copper chalcogenide layer within the Sr<sub>2</sub><em>M</em>O<sub>3</sub>Cu<em>Ch</em> series of compounds. As this geometry can be controlled through the material composition, the identification of this structure-property relationship highlights a route to selection and identification of materials with even higher conductivities.</p> <p> </p> <p>This repository contains the computational research data generated for *Transport properties of doped wide band gap layered oxychalcogenide semiconductors Sr2GaO3CuCh, Sr2ScO3CuCh and Sr2InO3CuCh (Ch = S or Se).</p> <p> </p> <p>## Folder Architecture</p> <p>The folders contain the structures relaxed with hybrid functionals (HSE06), the band structure and the uniform DOS and AMSET calculations for each system.</p> <p> </p> <p> </p>
Data associated with 'Closing the stellar labels gap: Stellar label independent evidence for [α/M] information in Gaia BP/RP spectra'
<p>We present a stellar label independent model for Gaia BP/RP (XP) spectra, which does not rely on stellar labels to simulate XP spectra. Here, we provide all of the data required to reproduce <a href="https://github.com/AlexLaroche7/xp_vae">our results</a>. All of the data files should be placed in the data folder. Below we brieflty summarize the contents of each:</p> <p>APOGEE/GAIA XP DATA:</p> <ul> <li>xp_apogee_cat.h5: Contains all Gaia XP data and APOGEE stellar labels used in this work.</li> <li>xp_corrs.npz: Contains Gaia XP covariance matrices for a subset of the test data to compute reconstruction errors.</li> </ul> <p>INTERMEDIATE DATA PRODUCTS:</p> <p><em>(All files below can be reproduced with our codebase. We simply include them to speed up runtime for reproducing our results.)</em></p> <ul> <li>lb23_xp_est_labels.npy: <a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.1494L/abstract">Leung & Bovy (2023)</a> XP coeffficient spectra reconstructions (stellar label dependent implementaton)</li> <li>lb23_xp_est_no_labels.npy: Leung & Bovy (2023) XP coeffficient spectra reconstructions (stellar label independent implementaton)</li> <li>xp_wavelength_space.npy: Observed Gaia XP spectra in wavelength space</li> <li>err_wavelength_space.npy: Observed Gaia XP uncertainties in wavelength space</li> <li>apogee_norm.npz: Means and standard deviations which are used to pre-process Gaia XP data before inputting into our model</li> <li>zhang_stellar_params_xmatch.npz: <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang, Green, Rix (2023)</a> stellar parameter estimates for subset of XP spectra (to compute reconstructions)</li> <li>vae_wavelength_space.npy: Our model XP coefficient reconstructions in wavelength space</li> </ul> <p>If you have any questions please reach out via email: alex.laroche@mail.utoronto.ca</p>
Data repository for " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene"
<p>This is the data presented in the manuscript " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene", <em>Commun Phys</em> <strong>7</strong>, 391 (2024). https://doi.org/10.1038/s42005-024-01887-0</p>
LGHAP v2: Global annual mean 1-km gap-free AOD grids (2000-2023)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 24-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2023. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
Direct evidence for Cooper pairing without a spectral gap in a disordered superconductor above Tc
<p>Supporting data for Bastiaans, KM et al. “Direct evidence for Cooper pairing without a spectral gap in a disordered superconductor above Tc”.</p> <p> </p> <p>The following datafiles are used for the following figures.</p> <p> </p> <p>Fig. 1 A Illustration figure, no data used</p> <p> B Illustration figure, no data used</p> <p> C Illustration figure, no data used</p> <p> D Illustration figure, no data used</p> <p> </p> <p>Fig. 2 A TiN_ALD_2.3K_noise.txt, simulation_qeff.m</p> <p> B TiN_ALD_2.3K_qeff.txt, simulation_qeff.m</p> <p> </p> <p>Fig. 3 A TiN_ALD_2.3K_noise.txt, TiN_ALD_4.0K_noise.txt, TiN_ALD_6.3K_noise.txt, TiN_ALD_7.2K_noise.txt, simulation_qeff.m</p> <p> B TiN_ALD_2.3K_qeff.txt, TiN_sputter_2.3K_qeff.txt, simulation_qeff.m</p> <p> C TiN_ALD_4.0K_qeff.txt, TiN_sputter_4.0K_qeff.txt, simulation_qeff.m</p> <p> D TiN_ALD_6.3K_qeff.txt, TiN_sputter_6.3K_qeff.txt, simulation_qeff.m</p> <p> E TiN_ALD_7.2K_qeff.txt, TiN_sputter_7.2K_qeff.txt, simulation_qeff.m</p> <p> </p> <p>Fig. 4 A TiN_ALD_didv_spectra.txt</p> <p> B TiN_ALD_didv_spectra.txt, TiN_ALD_didv_gap_depth.txt</p> <p> C TiN_ALD_RT.txt</p> <p> D TiN_ALD_qeff_vs_T.txt</p> <p> </p> <p>Fig. S1 A Illustration figure, no data used</p> <p> B TiN_ALD_2.3K_topo_small.txt</p> <p> C TiN_ALD_2.3K_topo_large.txt</p> <p> </p> <p>Fig. S2 TiN_noise_time_traces.txt</p> <p> </p> <p>Fig. S3 TiN_ALD_RT.txt</p> <p> </p> <p>Fig. S4 A TiN_ALD_2.3K_gapmap.txt, TiN_ALD_2.3K_gapmap_inset.txt</p> <p> B TiN_ALD_2.3K_gapmap.txt</p> <p> C TiN_sputter_2.3K_gapmap.txt, TiN_sputter_2.3K_gapmap_inset.txt</p> <p> D TiN_sputter_2.3K_gapmap.txt</p> <p> </p> <p>Fig. S5 A simulation_didv1.txt, T_broadening_fills_gap.ipynb</p> <p> B simulation_didv2.txt, T_broadening_fills_gap.ipynb</p> <p> C TiN_ALD_didv_spectra.txt, T_broadening_fills_gap.ipynb</p> <p> </p> <p>Fig. S6 TiN_sputter_2.3K_noise.txt, TiN_sputter_4.0K_noise.txt, TiN_sputter_6.3K_noise.txt, TiN_sputter_7.2K_noise.txt, simulation_qeff.m</p> <p> </p> <p>Fig. S7 A TiN_sputter_didv_spectra.txt</p> <p> B TiN_sputter_didv_gap_depth.txt</p> <p> C TiN_sputter_RT.txt</p> <p> D TiN_sputter_qeff_vs_T.txt</p> <p> </p> <p>Fig. S8 A TiN_ALD_2.3K_noise.txt, simulation_qeff.m</p> <p> B TiN_ALD_2.3K_qeff.txt, simulation_qeff.m</p> <p> C simulation_S1.txt, simulation_qeff.m</p> <p> D simulation_S2.txt, simulation_qeff.m</p>
GAP interatomic potential for silicon
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>silicon</strong> [1]. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by recomputing the <strong>Si database of Bartók et al.</strong> [3] at the <strong>PW91</strong> level of theory [4] using the VASP code [5,6,7]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>3-body</strong> (angle_3b) descriptors [5] plus <strong>SOAP-type descriptors</strong> (soap_turbo) [9,10], as implemented in the <strong>TurboGAP</strong> code [11]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and TurboGAP. More details will follow in a scientific<br> publication in due course (bibligraphical data will be added as it becomes available).</p> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>LibAtoms: <a href="https://libatoms.github.io">https://libatoms.github.io</a></li> <li>A.P. Bartók, J. Kermode, N. Bernstein, and G. Csányi. Phys. Rev. X 8, 041048 (2018).</li> <li>J.P. Perdew and Y. Wang. Phys. Rev. B 45, 13244 (1992).</li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).</li> <li>VASP: <a href="http://vasp.at">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>T. Bucko, S. Lebègue, T. Gould, and J.G. Ángyán, J. Phys.: Condens. Matter 28, 045201 (2016).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi">http://turbogap.fi</a></li> </ol>
Code repository that supports the research presented in the paper "The gender gap in higher STEM studies: a Systematic Literature Review"
<p>Resources for the Systematic Literature Review (SLR) carries out as part of PhD thesis about the gender gap in STEM studies in higher education by Sonia Verdugo-Castro and supervised by Alicia García-Holgado and Mª Cruz Sánchez Gómez.</p> <p>The SLR covers papers in WoS and Scopus from 2015 to 2021.</p> <p>All the papers retrieved and the different steps in the SLR selection process are contained and documented in:</p> <ul> <li><a href="https://docs.google.com/spreadsheets/d/1ldml-Mg-oguX9gXayllojBRtZ1kjdD4WRSqYwf1yZ0o/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1ldml-Mg-oguX9gXayllojBRtZ1kjdD4WRSqYwf1yZ0o/edit?usp=sharing</a></li> </ul>
Weak effects of birds, bats and ants on their arthropod prey on pioneering tropical forest gap vegetation
<p>The relative roles of plants competing for resources versus top-down control of vegetation by herbivores, in turn impacted by predators, during early stages of tropical forest succession remain poorly understood. Here we examine the impact of insectivorous birds, bats and ants exclusion on arthropods communities on replicated 5x5 m of pioneering early successional vegetation plots in lowland tropical forest gaps in Papua New Guinea. In plots from which focal taxa of predators were excluded we observed increased biomass of herbivorous and predatory arthropods, and increased density, and decreased diversity of herbivorous insects. However, changes in the biomass of plants, herbivores and arthropod predators were positively correlated or uncorrelated between these three trophic levels and also between individual arthropod orders. Arthropod abundance and biomass correlated strongly with the plant biomass irrespective of the arthropods' trophic position – a signal of bottom-up control. Patterns in herbivore specialization confirm lack of a strong top-down control and were largely unaffected by the exclusion of insectivorous birds, bats and ants. No changes of plant-herbivore interaction networks were detected except for decrease in modularity of the exclosure plots. Our results suggest weak top-down control of herbivores, limited compensation between arthropod and vertebrate predators, and limited intra-guild predation by birds, bats and ants. Possible explanations are strong bottom-up control, a low activity of the higher order predators, especially birds, possibly also bats, in gaps, and continuous influx of herbivores from surrounding mature forest matrix.</p>
Dataset for the paper: "Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance"
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Andres Parra-Puerto, Javier Rubio-Garcia, Matthew Markiewicz, Zhuo Zheng and Anthony Kucernak</p> <p>Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance </p> <p>DOI: https://doi.org/10.1002/celc.202101617 </p> <p>Please cite the above reference if you wish to use this data </p> <p>DOI of this data file is: 10.5281/zenodo.6261512</p>
DevOps: is there a gap between Education and Industry?
<p>This is the dataset obtained from a survey with 99 respondents about the teaching or use of DevOps technological practices in Higher-Educatoin or Industry, respectively.</p> <p>Questions are tabbled in the first line, and results of this study are published in the following Reference:</p>
Data from: The megaherbivore gap after the non-avian dinosaur extinctions modified trait evolution and diversification of tropical palms
<p><span>The Cretaceous–Paleogene (K-Pg) extinction of the non-avian dinosaurs (66 Ma) led to a 25 million year gap of megaherbivores (>1000 kg) before the evolution of megaherbivorous mammals in the Late Eocene (40 Ma). The botanical consequences of this 'Paleocene megaherbivore gap' (PMHG) remain poorly explored. We hypothesize that the absence of megaherbivores should result in changes in the diversification and trait evolution of associated plant lineages. We used phylogenetic time- and trait-dependent diversification models with palms (Arecaceae) and show that the PMHG was characterized by speciation slowdowns, decreased evolution of armature, and increased evolution of megafaunal (</span>≥<span>4cm) fruits. This suggests that the absence of browsing by megaherbivores during the PMHG may have led to a loss of defence traits, but absence of megaherbivorous seed dispersers did not lead to a loss of megafaunal fruits. Instead, increases in PMHG fruit sizes may be explained by Late Paleocene rising temperatures, rainforest expansion, and the subsequent radiation of seed dispersing birds and mammals. We show that the profound impact of the PMHG on plant diversification can be detected even with the overwriting of adaptations by the subsequent Late Eocene opening-up of megaherbivore-associated ecological opportunities. Our study provides a quantitative, comparative framework to assess </span>diversification and adaptation during one of the most enigmatic periods in angiosperm history.</p>
Snow pit dataset from "Comparison of snowpack structure in gaps and under the canopy in a humid boreal forest"
<p>This original dataset contains snow pit measurements collected at Montmorency Forest (47.29°N, 71.17°W) from 22 November, 2018 to 6 June, 2019. The study site is a balsam fir – whit birch stand on a 12° slope of north-east aspect. The dataset is described in the publication “<strong><em>Comparison of snowpack structure in gaps and under the canopy in a humid boreal forest</em></strong>” from Bouchard et al., 2022. In this dataset you can find:</p> <p> </p> <ul> <li>inside forest gaps: <ul> <li>26 snow height measurements (26 data points)</li> <li>26 snowpack stratigraphy (380 data points)</li> <li>26 snow temperature profiles (427 data points)</li> <li>26 snow density profiles (803 data points)</li> <li>2 snow specific surface area (SSA) profiles (97 data points)</li> </ul> </li> </ul> <p> </p> <ul> <li>under the canopy: <ul> <li>26 snow height measurements (26 data points)</li> <li>26 snowpack stratigraphy (227 data points)</li> <li>26 snow temperature profiles (325 data points)</li> <li>26 snow density profiles (623 data points)</li> <li>2 snow SSA profiles (89 data points)</li> </ul> </li> </ul> <p> </p> <p>For density measurements, the height value corresponds to the center of the 3-cm thick box cutter. For the SSA, the value is measured optically at the top of the sample. This value is representative of the top 1 cm of the snow sample, as this is the typical e-folding depth of 1310 nm radiation in snow. Grain type codes for the snowpack stratigraphy corresponds to the <em>International Classification for Seasonal Snow </em>(Fierz et al., 2009):</p> <p> </p> <ul> <li>PP: Precipitation particle</li> <li>DF: Decomposed and Fragmented precipitation particles</li> <li>RG: Rounded Grains</li> <li>FC: Faceted Crystals</li> <li>DH: Depth Hoar</li> <li>MFpc: Melt Forms – rounded polycrystals</li> <li>MFcl: Melt Forms – clustered rounded grains</li> <li>MFcr: Melt Forms – melt-freeze crusts</li> <li>IF: Ice Formations</li> </ul>
Urban biotic homogenization: approaches and knowledge gaps
<p>Urbanization is restructuring ecosystems at an unprecedented pace, with complex and profound consequences for life on earth. One of the hypothesized trajectories of urban ecosystems and species communities is biotic homogenization, possibly leading to very similar species assemblages in cities across the globe. Urbanization can, however, also have the opposite effect: biotic diversification, with cities – at least on the local scale – becoming biologically more diverse, mainly as a consequence of high species introduction rates and habitat diversification. Applying the hierarchy-of-hypotheses approach, we systematically map and structure the comprehensive body of literature on the urban biotic homogenization (UBH) hypothesis, comprising 225 individual studies (i.e. tests of the hypothesis) retrieved from 145 publications. The UBH hypothesis is studied at multiple levels with a multitude of approaches and underlying assumptions. We show that UBH is generally used with two very different connotations: about half of the studies investigated a potential increase in community similarity across cities, whereas the other half investigated biotic homogenization within cities, the latter being supported more frequently. We also found strong research biases: (1) a taxonomic bias towards birds and plants; (2) a bias towards small and medium distances (<5000 km) in comparisons across cities; (3) a dominance of studies substituting space for time vs. true temporal studies; (4) a strong focus on terrestrial vs. aquatic systems; (5) more extraurban (including periurban) areas than natural or rural ecosystems for comparison to urban systems; (6) a bias towards taxonomic vs. functional, phylogenetic and temporal homogenization; and (7) more studies undertaken in Europe and North America than in other continents. The overall level of empirical support for the UBH hypothesis was mixed, with 55% of the studies reporting supporting evidence. Results significantly differed when a natural/nature reserve, an extraurban or rural/agricultural area served as reference to infer biotic homogenization, with homogenization being detected least frequently when urban systems were compared to agricultural, i.e. other anthropogenically influenced, study sites. We provide an evidence map and a bibliographic network, and identify key references on UBH with the goal to enhance accessibility and orientation for future research on this topic.</p>
Range-wide habitat use of the Harpy Eagle indicates four major tropical forest gaps in the Key Biodiversity Area network
<p>Quantifying habitat use is important for understanding how animals meet their requirements for survival and provides information for conservation planning. Currently, assessments of range-wide habitat use that delimit species distributions are incomplete for many taxa. The Harpy Eagle (Harpia harpyja) is a raptor of conservation concern, widely distributed across Neotropical lowland forests, that currently faces threats from habitat loss and fragmentation. Here, we use penalized logistic regression to identify species-habitat associations and predict habitat suitability based on a new International Union for the Conservation of Nature range metric, termed Area of Habitat. From the species-habitat model, we performed a gap analysis to identify areas of high habitat suitability in regions with limited coverage in the Key Biodiversity Area (KBA) network. Range-wide habitat use indicated that Harpy Eagles prefer areas of 70-75% evergreen forest cover, low elevation, and high vegetation species richness. Conversely, Harpy Eagles avoid areas of >10% cultivated landcover and mosaic forest, and topographically complex areas. Our species-habitat model identified a large continuous area of potential habitat across the pan-Amazonia region, and a habitat corridor from the Chocó-Darién ecoregion of Colombia running north along the Caribbean coast of Central America. Little habitat was predicted across the Atlantic Forest biome, which is now severely degraded. The current KBA network covered 18% of medium to high Harpy Eagle habitat exceeding a target biodiversity area representation of 10%, based on species range size. Four major areas of high suitability habitat lacking coverage in the KBA network were identified in north and west Colombia, western Guyana, and north-west Brazil. We recommend these multiple gaps of habitat as new KBAs for strengthening the current KBA network. Modelled area of habitat estimates as described here are a useful tool for large-scale conservation planning and can be readily applied to many taxa.</p>
A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies
<p>This repository contains the data, codes and files required to reproduce the results of the manuscript "A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies" submitted to the Journal of Advances in Modeling Earth Systems (JAMES).</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
VIIRS gap-free satellite data for Chla in Chesapeake Bay from 2011 to 2018
<p>The uploaded data are daily and 7-day blended VIIRS satellite Chla data for the Chesapeake Bay. </p> <p>Data are used by the publication "Chlorophyll-a in Chesapeake Bay based on VIIRS satellite data: spatiotemporal variability and prediction with machine learning" submitted to Ocean Modelling</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.