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103 results for “Spatial processes”
Data from: Comparison of spatial and temporal genetic differentiation in a harmful dinoflagellate species emphasises impact of local processes
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Data from: Ant diversity in Neotropical savannas: hierarchical processes acting at multiple spatial scales
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Physical processes determine spatial structure in water temperature and residence time on a wide reef flat
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Environmental filtering and spatial processes shape the beta diversity of liana communities in a valley savanna in Southwest China
<p><b><span>Questions:</span></b> Lianas contribute substantially to diversity and function of ecosystems. What is the relative importance of environmental filtering and spatial processes on structuring liana beta diversity at taxonomic, functional and phylogenetic levels? Is there any synergy between these drivers (environmental factors and spatial distance) on shaping three dimensions of beta diversity in a savanna liana community?</p> <p><b><span>Location:</span></b> A dry-hot valley savanna in Yunnan Province, southwest China.</p> <p><b><span>Methods: </span></b>We established 30 20×20 m plots in the savanna to collect data on the distribution of 22 liana species, 19 functional traits, and plot-level soil nutrients, elevation, and slope. The relative contributions of these environmental factors and spatial distance to liana taxonomic, functional and phylogenetic dissimilarity were analyzed using multiple regression on distance matrices. We also tested which environmental factors influence the beta diversity of liana community using permutational multivariate analysis of variance.</p> <p><b><span>Results: </span></b>Both environmental and spatial distances were significantly correlated with taxonomic, functional and phylogenetic dissimilarity. Spatial distance explained more variation in taxonomic beta diversity than environmental factors. But for both nearest neighbor functional and phylogenetic distance <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, environment explained a relatively higher variation than space did. Moreover, the proportion explained by environmental variables was ranked in decreasing order as follows: functional <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, phylogenetic <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, and taxonomic beta diversity. We found soil pH had the highest contribution to taxonomic and functional beta diversity, while soil total nitrogen contributed most to phylogenetic beta diversity.</p> <p><b><span>Conclusions: </span></b>This study revealed that liana taxonomic, functional and phylogenetic beta diversity in the study hot-dry savanna ecosystem are affected and maintained by both environmental filtering and spatial processes. Moreover, the functional and phylogenetic diversities were more strongly subject to environmental filtering. Our study provides the information on mechanisms underlying liana diversity maintenance in savanna, which are necessary to inform conservation management in this vulnerable ecosystem. </p>
The spatial landscape of lung pathology during COVID-19 progression - processed IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
Data from: Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability
The kidney podocyte's function depends on finger-like projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics can result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling, lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration by dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology.
Data from: Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales
Elucidating patterns of population structure for species with complex life histories, and disentangling the processes driving such patterns, remains a significant analytical challenge. Humpback whale (Megaptera novaeangliae) populations display complex genetic structures that have not been fully resolved at all spatial scales. We generated a data set of nuclear markers for 3,575 samples spanning the seven breeding stocks and substocks found in the South Atlantic and western and northern Indian Oceans. For the total sample, and males and females separately, we assessed genetic diversity, tested for genetic differentiation between putative populations and isolation by distance, estimated the number of genetic clusters without a priori population information, and estimated rates of gene flow using maximum likelihood and Bayesian approaches. At the ocean basin scale, structure is governed by geographic distance (IBD p<0.05) and female fidelity to breeding areas, in line with current understanding of the drivers of broad-scale population structure. Consistent with previous studies, the Arabian Sea breeding stock was highly genetically differentiated (FST 0.034-0.161; p<0.01 for all comparisons). However, the breeding stock boundary between west South Africa and east Africa was more porous than expected based on genetic differentiation, cluster, and gene flow analyses. Instances of male-fidelity to breeding areas and relatively high rates of dispersal for females were also observed between the three substocks in the western Indian Ocean. This mismatch between demographic units and current management boundaries may have ramifications for assessments of the status and continued protections of populations still in recovery from commercial whaling.
The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing.
<p>The data set Kubicek et al._JECP_DataSet.sav containts the data of the paper from Kubicek, C., Jovanovic, B., & Schwarzer, G. (2017). The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing. Journal of Experimental Child Psychology, 158, 64–76.</p> <p> </p>
Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.
Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.
Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).
Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
Data from: The role of spatial averaging scale in leaf-to-canopy scaling of non-linear processes in homogeneous canopies
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Data from: Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability
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Data from: Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales
Open the record for dataset details and reuse information.
Environmental filtering and spatial processes shape the beta diversity of liana communities in a valley savanna in Southwest China
Open the record for dataset details and reuse information.
OCO-2 Level 2 spatially ordered geolocated retrievals screened using the A-band Preprocessor, Retrospective Processing V10r (OCO2_L2_ABand) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.
OCO-2 Level 2 spatially ordered geolocated retrievals screened using the A-band Preprocessor, Retrospective Processing V11r (OCO2_L2_ABand) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.
OCO-2 Level 2 spatially ordered geolocated retrievals screened using the IMAP-DOAS Preprocessor (IDP), Retrospective Processing V11r (OCO2_L2_IMAPDOAS) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-3 Level 2 spatially ordered geolocated retrievals screened using the IMAP-DOAS Preprocessor (IDP), Retrospective Processing V11r (OCO3_L2_IMAPDOAS) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
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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.