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67 results for “spatial information”
Collection of spatial information and maps of human past and environment in the Uralic languages speaker area
<p>The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a> - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.</p>
Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000
<p>Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.</p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p><a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a></p> <p><strong>Custom code used for digital soil mapping and validation is available on GitHub:</strong></p> <p><a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a></p> <p><strong>Description of the files:</strong></p> <p>The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.</p> <table> <tbody> <tr> <td> <p><strong>SOC property maps</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Year</strong></p> </td> <td> <p><strong>Filename</strong></p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q95.tif</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Integrating pheromonal and spatial information in the amygdalo-hippocampal network. Villafranca-Faus et al. 2021
<p>The local field potential (LFP) of the dorsal hippocampus (CA1) and cortical amygdala (PMCo) of mice, under head-fix recording and inmersed on a virtual environtmernt.</p> <p><strong>Paper Abstract</strong>: <br> Vomeronasal information is critical in mice for territorial behavior. Consequently, learning the territorial spatial structure should incorporate the vomeronasal signals indicating individual identity into the hippocampal cognitive map. In this work we show in mice that navigating a virtual environment induces synchronic activity, with causality in both directionalities, between the vomeronasal amygdala and the dorsal CA1 of the hippocampus in the theta frequency range. The detection of urine stimuli induces synaptic plasticity in the vomeronasal pathway and the dorsal hippocampus, even in animals with experimentally induced anosmia. In the dorsal hippocampus, this plasticity is associated with the overexpression of pAKT and pGSK3β. An amygdalo-entorhino-hippocampal circuit likely underlies this effect of pheromonal information on hippocampal learning. This circuit likely constitutes the neural substrate of territorial behavior in mice, and it allows the integration of social and spatial information.</p>
Data and scripts for: Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification
<p>Data derivates and analysis scripts (in R) used for the paper "Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification", published in Biological Conservation, on analyzing annual spatial overlap of 25 seabird populations across 23 species to assess variability and inform global efforts to improve spatial conservation measures.</p>
Recalibration of the lunar chronology due to spatial cratering-rate variability - Supporting Information
<p>CR_moon.csv: Relative cratering rate shown in Fig.3 and 5.a. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</p> <p>SI_convert_age.m: Matlab code computing model ages of Plutarch and Kirkwood craters using the chronology function presented in this study.</p>
Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
<p><span>Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study. </span></p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
GapAnalysis: An R package to calculate conservation indicators using spatial information
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Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
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Data from: Genomics-informed conservation units reveal spatial variation in climate vulnerability in a migratory bird
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Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems.
<p>The development of efficient sampling protocols for the capture of environmental DNA (eDNA) could greatly help improve accuracy of occupancy monitoring for species that are difficult to detect. However, the process of developing a protocol in situ is complicated for rare species by the fact that animal locations are often unknown. We tested sampling designs in lake and stream systems to determine the most effective eDNA sampling protocols for two rare species: the Sierra Nevada yellow-legged frog (<i>Rana sierrae</i>) and the foothill yellow-legged frog (<i>R. boylii</i>). We varied water volume, spatial sampling, and seasonal timing in lakes and streams; in lakes we also tested multiple filter types. We found that filtering 2 L versus 1 L increased the odds of detection in streams 5.42X (95% CI: 3.2-9.19X) in our protocol, from a probability of 0.51 to 0.85 per technical replicate. Lake sample volumes were limited by filter clogging and we found no effect of volume or filter type. Sampling later in the season increased the odds of detection in streams by 1.96X for every 30 days (95% CI: 1.3 - 2.97X) but there was no effect for lakes. Spatial autocorrelation of the quantity of yellow-legged frog eDNA captured in streams between 100 and 200 m, indicating that sampling at close intervals is important.</p>
The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task
<p>Data for article:</p> <p>The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task</p>
Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems
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Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
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Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
Behavioural and neurophysiological data from experiments on the impact of spatial frequency information to action comprehension
<p>Understanding object-directed actions performed by others is central to everyday life. This ability is thought to rely on the interaction between the dorsal action observation network (AON) and a ventral object recognition pathway. On this view, the AON would encode action kinematics, and the ventral pathway, the most likely intention afforded by the objects. However, experimental evidence supporting this model is still scarce. Here, we aimed to disentangle the contribution of dorsal vs. ventral pathways to action comprehension by exploiting their differential tuning to lowspatial frequencies (LSFs) and high-spatial frequencies (HSFs). We filtered naturalistic action images to contain only LSF or HSF and measured behavioral performance and corticospinal excitability (CSE) using transcranial magnetic stimulation (TMS). Actions were embedded in congruent or incongruent scenarios as defined by the compatibility between grips and intentions afforded by the contextual objects. Behaviorally, participants were better at discriminating congruent actions in intact than LSF images. This effect was reversed for incongruent actions, with better performance for LSF than intact and HSF. These modulations were mirrored at the neurophysiological level, with greater CSE facilitation for congruent than incongruent actions for HSF and the opposite pattern for LSF images. Finally, only for LSF did we observe CSE modulations according to grip kinematics. While results point to differential dorsal (LSF) and ventral (HSF) contributions to action comprehension for grip and context encoding, respectively, the negative congruency effect for LSF images suggests that object processing may influence action perception not only through ventral-to-dorsal connections, but also through a dorsal-to-dorsal route involved in predictive processing.</p>
Data from: Phylogenetically informed spatial planning is required to conserve the mammalian tree of life
In the face of the current extinction crisis and severely limited conservation resources, safeguarding the tree of life is increasingly recognized as a high priority. We conducted a first systematic global assessment of the conservation of phylogenetic diversity (PD) that uses realistic area targets and highlights the key areas for conservation of the mammalian tree of life. Our approach offers a substantially more effective conservation solution than one focused on species. In many locations, priorities for PD differ substantially from those of a species-based approach that ignores evolutionary relationships. This discrepancy increases rapidly as the amount of land available for conservation declines, as does the relative benefit for mammal conservation (for the same area protected). This benefit is equivalent to an additional 5900 Myr of distinct mammalian evolution captured simply through a better informed choice of priority areas. Our study uses area targets for PD to generate more realistic conservation scenarios, and tests the impact of phylogenetic uncertainty when selecting areas to represent diversity across a phylogeny. It demonstrates the opportunity of using rapidly growing phylogenetic information in conservation planning and the readiness for a new generation of conservation planning applications that explicitly consider the heritage of the tree of life's biodiversity.
Data from: Spatial dynamics of habitat use informs reintroduction efforts in the presence of an invasive predator
1. Islands experience major impacts from introduced species, especially nocturnal predators. The Brown Treesnake (Boiga irregularis) extirpated almost the entire native avifauna on Guam shortly after introduction. Reintroductions from neighboring islands can restore bird communities but will take place in heavily managed units where snake populations can be controlled. Yet reintroductions often proceed without relevant biological information such as nocturnal habitat and space use when nocturnal predators are present. To guide efforts, we studied diurnal and nocturnal habitat and space use by the avian frugivore community on the nearby island of Saipan where the snake is absent. 2. Using radio telemetry and a Bayesian framework, we first compared data typically accessible to resource managers (diurnal home range [DHR] and habitat selection) and which often forms the basis of reintroduction plans. We contrast this with data on nocturnal habitat use that is often not available but relevant when nocturnal predation threat is high. 3. DHR size varied within and among species with Micronesian Starlings (Aplonis opaca) having DHRs at least 45 ha larger than other species. For all species, night roost locations were generally spatially clustered within a DHR and covered a smaller spatial extent. Two species had higher probabilities of roosting outside DHR boundaries but, when outside, roosts were often within 200 m of the DHR boundary. All species selected forested habitats during day and night, with some species choosing native forest over non-native habitat. 4. Synthesis and applications If individuals roosted randomly throughout DHR's, landscape-level suppression of B. irregularis might be the only viable option for reintroductions of some species. However, individuals of all species showed clustered roosting and high site fidelity. If site fidelity is common, then individuals that roost within fenced areas where snake populations are severely reduced or eliminated will experience high survivorship even if DHRs extend into surrounding unprotected matrix. There was large overlap in habitat selection during day and night indicating managed areas should be composed of native forest with non-native forests of secondary importance. In systems with nocturnal predators, understanding variability in diurnal and nocturnal habitat use could lead to better informed decisions.
Spatial Informatics Group (SIG)_Ethiopia - DEMO ONLY - informing fieldwork for NFI
This project was created to re-asess forest and forest change over the subplots of the old NFI sampling units.
Supporting information for Spatial scales of the velocity shear layer and Kelvin-Helmholtz waves on the magnetopause: First statistical results
<p>Kelvin-Helmholtz (KH) waves observed by ARTEMIS P1 from 2015 to 2020. From the first column to the last column is: probe, date, time interval of KH waves, the location of KH waves in GSM coordinate, and the dominate period, phase speed, and wavelength of KH waves.</p>
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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.