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556 results for “infrastructure”
Results from Performance Evaluation and Testing of Virtual Infrastructure Managers
<p>NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to so-called Virtual Network Functions (VNFs). We deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. In this data set we report the results of a performance evaluation we have realized of three open source VIMs, namely OpenStack, Nomad and OpenVIM; both considering stock and the tuned versions. The VIMs and the performance evaluation tools that we employ are provided openly and can be downloaded from our repositories (<a href="https://github.com/superfluidity/openvim4unikernels">https://github.com/superfluidity/openvim4unikernels</a> and <a href="https://github.com/netgroup/vim-tuning-and-eval-tools">https://github.com/netgroup/vim-tuning-and-eval-tools</a>).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 3. ICT-based infrastructure and its four layers Source: adapted after Skouby et al., 2014
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Code and data for "Current fossil fuel infrastructure does not yet commit us to 1.5°C warming"
<p>This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming".</p> <p>See enclosed README file for dependencies and how to run.</p>
Evaluation of Envision Rating System for Underground Transportation Infrastructure
<p>Credit-by-credit evaluation of the Envision rating system for representative underground transportation projects. Related paper presented at the 2019 International Conference for Sustainable Infrastructure.</p>
Towards a cashless society - Examining the Impact of Digital Infrastructure on mPayment Transactions (A Cross-Region Analysis).xlsx
<p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381). </em></p>
Assessing digital infrastructure in internet use: A comparative study of South East Asia and the Balkan Region
<p>Data used for assessing digital infrastructure in internet use - comparison of South East Asia and Balkan Region. Data on mobile cellular, fixed broadband, GDP, Key global ICT indicators . Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381)</p>
A description of the self-deployed-cloud-based ICT infrastructure we use in the SteeleLab
<p>A description of how we manage the data in our group, including</p> <ul> <li>How we get the data off our measurement computers to our Synology Rackstation NAS</li> <li>How we make the data available our NAS to the the containers in our JupyterHub server</li> <li>How we share, maintain, and collaboratively edit our data analysis code / notebooks that run in our JupyterHub </li> </ul>
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure
<p>Dataset and Python code complementing the publication </p> <p>Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 6107. https://doi.org/10.3390/rs14236107</p> <ul> <li><strong>AROSICS.zip</strong> contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m].</li> <li><strong>CC_DistancePointClouds.zip</strong> contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation.</li> <li><strong>CC_PointCloudProcessing.zip</strong> contains the point clouds at post-processing levels II-IV.</li> <li><strong>ODM_Orthomosaics.zip</strong> contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap).</li> <li><strong>ODM_PointClouds.zip</strong> contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap).</li> <li><strong>PointCloudStatistics.zip</strong> contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets.</li> <li><strong>Python_ChangeDetection.zip</strong> contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is <strong>CC_DistancePointClouds.zip.</strong></li> <li><strong>Subsets.zip </strong>contains shapefiles of the two subsets.</li> </ul>
The impact of small-scale green infrastructure on the affective wellbeing associated with urban sites
<p>The database contains participants' reported affective perceptions of 18 images of street images with different levels of green coverage.</p>
A Policy and Infrastructure Evaluation Model of Commodity Flows through Inland Waterway Ports (Dataset)
<p>The purpose of this project is to guide strategic investment into port capacity through the development of a policy and infrastructure evaluation model of inland waterway commodity flows. A multi-stage stochastic optimization model will be developed to evaluate tradeoffs in strategic, long-term port infrastructure investment with mid-term capacity expansion decisions and provision of complementary highway infrastructure made by public and private stakeholders, and shorter-term operational practices made by shippers and carriers. This work builds on prior MarTREC projects which developed a Multi-Commodity Assignment Problem to estimate annual commodity flows through inland waterway ports from truck Global Positioning System (GPS), marine Automatic Identification System (AIS), and the Lock Performance Management System (LPMS). The proposed project will explore critical extensions of the assignment model: 1) disaggregation of the temporal scope to reflect monthly seasonality among commodities, 2) incorporation of uncertainty related to observed vehicle and vessel movement data, and 3) inclusion of transportation costs. With these extensions the team expects to increase the accuracy and resolution of the commodity-based port throughput estimates and to allow the model to be used to not only describe the current system but to prescribe policy and project investment strategies for public and private sector transportation decision makers. Calibration and validation of the multi-stage optimization model will be done through two case studies. The regional-based study will use historical truck GPS, marine AIS, and LPMS datasets. The national-based study will use data from the Billion Ton Study led by the US Department of Energy. This will ensure a feasible and realistic base-case on which to compare future policy scenarios. This project aligns with MarTREC’s research focus area in Maritime and Multimodal Logistics Management by modeling commodity flows through ports that serve as critical connections for the multimodal freight supply chain.</p>
Permafrost Thaw and its Impact on Arctic Infrastructure: A Site Selection Bibliography
<p>Project Bibliography for DRP Task 1.2.1. Cited sources were used in the site selection process. </p>
Are there good ethical reasons why for profit publishers should no longer exist under the conditions of digital infrastructures? And what does this have to do with ethics as a reflexive discipline?
<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>
Global transportation infrastructure exposure to the change of precipitation in a warmer world
<p>This repository provides the base data to perform a global transport asset exposure analysis for extreme precipitation under climate change.In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally.</p> <p>For more details, please see:</p> <p>Liu, K., Wang, Q., Wang, M. <em>et al.</em> Global transportation infrastructure exposure to the change of precipitation in a warmer world. <em>Nat Commun</em> <strong>14</strong>, 2541 (2023). https://doi.org/10.1038/s41467-023-38203-3</p>
Drones for Railway Infrastructure Inspection
<p>LMT in collaboration with Latvijas Gaisa Satiksme and Airborne RF performed an operation deployment – Inspection of Railway Infrastructure with Rail Baltica as a use case. With this trial, we enabled 3rd autonomy level of drone flight, the development of a new business case, BVLOS, and remote detection of security threats and C2 only through the cellular network. This is a significant step forwards to increased railway security!</p>
PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
<p>The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.</p>
Energy Savings Related to Green Infrastructure in Valladolid city
<p>Improved green infrastructure results in energy savings through a variety of mechanisms. These include:</p> <ul> <li>Reducing the need to heat buildings by insulating them against the cold</li> <li>Reducing the need to cool buildings by insulating them against the heat</li> <li>Reducing the volume of stormwater entering the sewer system, thus reducing energy consumption in sewage processing</li> </ul> <p>The energy savings resulting from these three mechanisms are estimated by GI-Val <a href="#_ftn1">[</a><a href="https://www.merseyforest.org.uk/gi-val/">https://www.merseyforest.org.uk/gi-val/</a><a href="#_ftn1">]</a> tools 1.1, 1.5 and 2.1 respectively. </p>
Evaluating Institutional Commitments to Open Scholarly Infrastructure: A Review of Open Access Collection Development Policies
<p>Data prepared for the publication "Evaluating Institutional Commitments to Open Scholarly Infrastructure: A Review of Open Access Collection Development Policies."</p> <p><strong>oa-cd-policies.csv</strong></p> <p>Scope: This data represents collection development policies that contain substantial mention of open access.</p> <p>Data collection: The policies were sourced using an Advanced Google Search for "open access" AND "collection development policy" at ".edu" domains.</p> <p>Variables:</p> <ul> <li>institution: Free text, name of the institution.</li> <li>carnegie_class: One of <a href="https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification/">these options</a>; the Carnegie classification of the institution.</li> <li>institution_type: One of public or private; the funding source of the institution.</li> <li>policy_name: Free text; the title of the policy.</li> <li>supplemental_policy: Link to a supplemental open access policy if linked in the collection development policy.</li> <li>cd_policy_link: Link to the policy.</li> <li>infrastructure: TRUE or FALSE; whether the policy includes a commitment to open access scholarly infrastructure development, including open source platforms, locally hosted platforms, consortia, or an institutional repository.</li> <li>excerpt: Free text; text from the policy that mentions infrastructure.</li> </ul> <p><strong>principles-policies.csv</strong></p> <p>Scope: This data represents those collection development policies from oa-cd-policies.csv that contain commitments in line with the <a href="https://openscholarlyinfrastructure.org/">Principles for Open Scholarly Infrastructure</a>.</p> <p>Variables:</p> <ul> <li>institution: Free text, name of the institution.</li> <li>carnegie_class: One of <a href="https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification/">these options</a>; the Carnegie classification of the institution.</li> <li>institution_type: One of public or private; the funding source of the institution.</li> <li>policy_name: Free text; the title of the policy.</li> <li>supplemental_policy: Link to a supplemental open access policy if linked in the collection development policy.</li> <li>cd_policy_link: Link to the policy.</li> <li>principle: One of the three main <a href="https://openscholarlyinfrastructure.org/">Principles</a>.</li> <li>sub_principle: One of the <a href="https://openscholarlyinfrastructure.org/">Sub-Principles</a>.</li> <li>excerpt: Free text; text from the policy that illustrates the sub_principle.</li> </ul>
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>
UAV Surveying - Electrical Tranmission Infrastructure
<p>The data provided in this dataset is from a surveying flight in an electrical transmission infrastructure.</p> <p>The dataset contains information of a 3D LiDAR, a camera, 3 IMUs, drone GPS position and velocity, and RTK position and velocity data, in rosbag format.</p> <p>The LiDAR is an Ouster OS1-128 Rev7, the IMUs are: drone IMU (unknown model), Xsens MTi 630 AHRS and LiDAR internal IMU.</p> <p>The camera has 1280x960p and 145º FOV.</p> <p>The sensor intrinsic and extrinsic are available in the dataset.</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.