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Dataset results
11 results for “Data ownership”
TRA-DE 2022 German OAP survey data on mobility patterns, car ownership and use
<p>Online Access Panel-based survey data collected Jan-May 2022 in Germany within the research project <a href="https://energysufficiency.de/">Energy Sufficiency</a>. N=3422 (+N=510 for city of Flensburg). Data available as coded and labelled version, incl. codebook and method report from data collection contractor (all in German). Supplementary files: questionnaire (for survey implemented as online questionnaire), codebook and method report (German).</p>
Data from: Patterns of rangeland productivity and land ownership: implications for conservation and management
Rangelands cover 40-50% of the earth's terrestrial surface. While often characterized by limited, yet variable resource availability, rangelands are vital for humans, providing numerous ecosystem goods and services. In the conterminous United States (CONUS) the dominant component of rangeland conservation is a network of public rangelands, concentrated in the west. Public rangelands are interspersed with private and tribal rangelands resulting in a complex mosaic of land tenure and management priorities. We quantify ownership patterns of rangeland production at multiple scales across CONUS and find that both total production and average productivity of private rangelands more than doubles that of public and tribal rangelands. At finer scales, private rangelands are consistently more productive than their public counterparts. We also demonstrate an inverse relationship between public rangeland acreage and productivity. While conserving acreage is crucial to rangeland conservation, just as critical are broad scale ecological patterns and processes that sustain ecosystem services. Across CONUS, ownership regimes capture distinct elements of these patterns and services, demonstrated through disparate production dynamics. As ownership determines the range of feasible conservation actions, and the technical and financial resources available to implement them, understanding ownership-production dynamics is critical for effective and sustained conservation of rangeland ecosystem services.
Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape
Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.
Data from: Cultural burden of wild pet ownership causes selective parrot defaunation despite habitat restoration efforts in Costa Rica
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Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape
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Data from: The body beyond the body: expectation of a sensory event is enough to induce ownership over a fake hand
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Data from: Patterns of rangeland productivity and land ownership: implications for conservation and management
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CN, Remote Driving, Remote driving with data ownership focus
<p>Use Case Category: <strong>Remote Driving</strong><br> User Story: <strong>Remote driving with data ownership focus</strong><br> Location: Chinese (CN) trial site</p> <p>According to 3GPP TS 22.186 R16, Remote Driving “enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited, and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group”.</p> <p>User Story: <strong>Remote driving with data ownership focus</strong></p> <p> </p> <p>In the China test site, the control centre plays an important role to remotely control the autonomous subject vehicle (Vehicle A), autonomous test vehicle (Vehicle B) and Pedestrian C to complete the networking performance testes of connected vehicles. The control centre first plans the scheme and then sends the</p> <p>global path information to Vehicle B through RSU. During the experiment, subject vehicle A communicates with RSU via V2I and with test vehicle B via V2V. After obtaining real-time information, local path planning will be carried out to complete the plan. After the test, Vehicle A notifies the control centre and uploads various data to the cloud server.</p>
Total Cost of Ownership of Plug-in Electric Vehicles Calculations Data sheet
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Data from: Forward modeling the rubber hand: illusion of ownership modifies motor-sensory predictions by the brain
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Data from: Body ownership and agency altered by an electromyographically-controlled robotic arm
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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