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9 results for “human infrastructure”
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
Data from: Human presence and infrastructure impact wildlife nocturnality differently across an assemblage of mammalian species
<p>Wildlife species may shift towards more nocturnal behavior in areas of higher human influence, but it is unclear how consistent this shift might be. We investigated how humans impact large mammal diel activities in a heavily recreated protected area and an adjacent university-managed forest in southwest British Columbia, Canada. We used camera trap detections of humans and wildlife, along with data on land-use infrastructure (e.g., recreation trails and restricted-access roads), in Bayesian regression models to investigate impacts of human disturbance on wildlife nocturnality. We found moderate evidence that black bears (<em>Ursus americanus</em>) were more nocturnal in response to human detections (mean posterior estimate = 0.35, 90% credible interval = 0.04 to 0.65), but no other clear relationships between wildlife nocturnality and human detections. However, we found evidence that coyotes (<em>Canis latrans</em>) (estimates = 0.81, 95% CI = 0.46 to 1.17) were more nocturnal and snowshoe hares (<em>Lepus americanus</em>) (estimate = -0.87, 95% CI = -1.29 to -0.46) were less nocturnal in areas of higher trail density. We also found that coyotes (estimate = -0.87, 95% CI = -1.29 to -0.46) and cougars (<em>Puma concolor</em>) (estimate = -1.14, 90% CI = -2.16 to -0.12) were less nocturnal in areas of greater road density. Furthermore, coyotes, black-tailed deer (<em>Odocoileus hemionus</em>), and snowshoe hares were moderately more nocturnal in areas near urban-wildland boundaries (estimates and 90% CIs: coyote = -0.29, -0.55 to -0.04, black-tailed deer = -0.25, -0.45 to -0.04, snowshoe hare = -0.24, -0.46 to -0.01). Our findings imply anthropogenic landscape features may influence medium to large-sized mammal diel activities more than direct human presence. While increased nocturnality may be a promising mechanism for human-wildlife coexistence, shifts in temporal activity can also have negative repercussions for wildlife, warranting further research into the causes and consequences of wildlife responses to increasingly human-dominated landscapes.</p>
SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka
<p>The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska.</p> <p>The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another.</p> <p>Each GeoPackage contains the following layers:</p> <ul> <li>ARCADE_WatershedsDB</li> <li>DEC_ContaminatedSitesAK</li> <li>OSM_Point_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements_RRNetwork</li> <li>UiO_MAGT</li> <li>UiO_PermafrostProbability</li> <li>UiO_PermafrostZones</li> </ul> <p>A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal.</p> <p><strong>Download Data</strong></p> <ul> <li><strong>Python Scripts</strong> <ul> <li>01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset.</li> <li>02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets.</li> <li>03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column.</li> <li>04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset.</li> <li>05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in.</li> </ul> </li> <li><strong>GeoPackages</strong> <ul> <li>PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries.</li> <li>PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.</li> </ul> </li> </ul> <p> </p> <ul> </ul> <p> </p>
Data from: Human presence and infrastructure impact wildlife nocturnality differently across an assemblage of mammalian species
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How human infrastructure threatens biodiversity by squeezing sandy coasts - Scripts & Data
<p><strong>Authors</strong>: Eva M. Lansu, Hallie S. Fischman, Christine Angelini, Nadia Hijner, Luc Geelen, Dick Groenendijk, Solveig Höfer, Annemieke M. Kooijman, Max Rietkerk, Sten Tonkens, Sierd de Vries, Martin Wassen, Evaline van Weerlee, Daniël Wille, Valérie Reijers, Tjisse van der Heide</p> <p><strong>Abstract</strong></p> <p>Coastal dunes form valuable ecosystems that provide flood protection, clean drinking water and high biodiversity. Although their functioning critically depends on large-scale habitat zonation arising along sea-to-land gradients, infrastructure development and sea level rise progressively squeeze natural dune ecosystems into a narrow coastal strip. However, it remains unknown how much coastal width is required to support the diverse suite of habitats and species assemblages found in natural, undisturbed dune systems. Here, we investigate plant and habitat diversity in 614 plots along 47 sea-to-land transects in the southeastern USA and the Netherlands. We discover a strong linear relation between habitat diversity and species richness, indicating that diverse habitat assemblages are required for species-rich dunes. Moreover, we find that both plant and habitat diversity non-linearly depend on coastal width, with cumulative plant diversity reaching ~75% of its potential at 800 and 1800 m from the sea in the southeastern USA and the Netherlands, respectively. Alarmingly, dune areas are narrower than these width values along 79% and 66% of southeastern USA and Dutch coastlines, respectively, highlighting that lack of space compromises the ecosystems’ potential biodiversity benefits along the majority of coastlines. Finally, analyses of management measures along the transects reveal that strategic interventions can, at least in part, mitigate biodiversity losses from coastal squeeze. As coastal squeeze is a global phenomenon, our results suggest that it may severely threaten biodiversity in dune areas worldwide. We argue that establishment or expansion of nature reserves may be vital for conserving wide dune systems, and that targeted management measures can help maintain biodiversity where coastal squeeze cannot be alleviated.</p> <p>==============================================</p> <p><strong>Methods</strong></p> <p>The analyses rely on the following freely available datasets:</p> <ul> <li> Lansu et al. (2024) " A global analysis of how human infrastructure squeezes sandy coasts” <em>Nature Communications</em> 15.1 (2024): 432. <a href="https://www.nature.com/articles/s41467-023-44659-0">https://www.nature.com/articles/s41467-023-44659-0</a></li> <li>OpenStreetMap - shoreline: <a href="https://osmdata.openstreetmap.de/data/land-polygons.html">https://osmdata.openstreetmap.de/data/land-polygons.html</a></li> <li>Protected areas: <a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</a></li> </ul> <p> </p> <p> </p>
A global analysis of how human infrastructure squeezes sandy coasts - Scripts & Data
<p><strong>Abstract</strong></p> <p>Coastal ecosystems provide vital services, but human disturbance causes massive losses. Remaining ecosystems are squeezed between rising seas and human infrastructure development. While shoreline retreat is intensively studied, coastal congestion through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide to show that infrastructure occurs at a median distance of 392 meter from sandy shorelines. Moreover, we find that 33% of sandy shores harbour less than 100m of infrastructure-free space, and that 23–30% of this space may be lost by 2100 due to rising sea levels. Further analyses show that population density and gross domestic product explain 35–39% of observed squeeze variation, emphasizing the intensifying pressure imposed as countries develop and populations grow. Encouragingly, we find that nature reserves relieve squeezing by 4–7 times. Yet, at presentonly 16%of world’s sandy shores have a protected status. We therefore advocate the incorporation of nature protection into spatial planning policies.</p> <p>==============================================================================================================</p> <p><strong>Methods</strong></p> <p>The analyses rely on the following freely available datasets:</p> <ul> <li>OpenStreetMap - streets: <a href="https://www.openstreetmap.org/#map=7/52.154/5.295">https://www.openstreetmap.org/#map=7/52.154/5.295</a> </li> <li>OpenStreetMap - shoreline: <a href="https://osmdata.openstreetmap.de/data/land-polygons.html">https://osmdata.openstreetmap.de/data/land-polygons.html</a></li> <li>Global Urban Footprint: <a href="https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/">https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/</a></li> <li>World population: <a href="https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b">https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b</a></li> <li>GDP per capita: <a href="https://data.worldbank.org/indicator/NY.GDP.PCAP.CD">https://data.worldbank.org/indicator/NY.GDP.PCAP.CD</a></li> <li>Protected areas: <a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</a></li> <li>Projected shoreline change: <a href="https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e">https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e</a></li> <li>CoastalDEM: <a href="https://www.climatecentral.org/coastaldem-v2.1">https://www.climatecentral.org/coastaldem-v2.1</a></li> </ul> <p>In addition, we requested the sandy shoreline data from:</p> <ul> <li>Luijendijk et al. (2018) "The State of the World's Beaches", Scientific Reports 8: 6641; <a href="https://www.nature.com/articles/s41598-018-24630-6">https://www.nature.com/articles/s41598-018-24630-6</a></li> </ul> <p>The deposited folder contains 4 subfolders based on the separate analyses presented in the paper. In each subfolder you find a Matlab script to run and accompanying datasets to load. A readme file is included, which further explains the scripts and datasets.</p> <p><strong>Source data file</strong></p> <p>In the data file <em>Source_Data.xlsx</em>, each sheet contains the data for one figure or table of the manuscript.</p>
Do human infrastructures shape nest distribution in the landscape depending on individual personality in a farmland bird of prey?
<p>1. Individuals´ distribution across habitats may depend on their personality. Human activities and infrastructures are critical elements of the landscape that may impact the habitat selection process. However, depending on their personality, individuals may respond differently to these unnatural elements.</p> <p>2. In the present study, we first investigated whether some human infrastructures (buildings, roads and paths) shaped Montagu's harrier nest spatial distribution in the landscape according to female personality (boldness). Second, we tested if the reproductive success of females depended on their boldness and nest location regarding infrastructures.</p> <p>3. Using a long-term (19 years) data set, we calculated the distance from each nest to the nearest infrastructure type and the density of each infrastructure type around the nest. We tested the effects of female boldness (bold vs. shy) and its interaction with egg-laying date on these six metrics.</p> <p>4. Nest location in the landscape depended on female personality and on some human infrastructures: the building density was smaller around nests from shy females than from bold ones. Nest distribution related to other infrastructure metrics did not depend on female boldness. The pattern related to building density is consistent with some habitat choice hypotheses which are discussed. Path density around nests negatively affected reproductive success regardless of female boldness, and late breeders nested further away from paths than early breeders. Human activities on paths (more common later in the season) could lead to disturbance and a decrease in parental care, reducing reproductive success.</p> <p>5. Increasing human presence in farmlands implies a need to better understand its impact on population composition, in terms of personality. Our results suggest that individual behavioural differences should be taken into account in studies assessing the effects of human disturbance on animal populations, to propose more appropriate conservation measures.</p>
Do human infrastructures shape nest distribution in the landscape depending on individual personality in a farmland bird of prey?
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How digital infrastructures empower humanities researcher(s): The PARTHENOS Training Suite
<p>1<sup>st</sup> Project Presentation</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.