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27 results for “case building”
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
Building a data curation pipeline for complex diseases: the case of Major Depression - Supplementary Material
<p>This entry contains the data generated by the study "Building a data curation pipeline for complex diseases: the case of Major Depression".</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Bioexcel building blocks test cases
<p>Bioexcel building blocks test cases for Lysozyme and Pyruvate Kinase executed in Workstations, OpenNebula VMs and MareNostrum4 HPC.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</p>
Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."
<p>This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022).</p> <p>Further information regarding the source, study period, and horizontal resolution is available in the attached text file. </p> <p> </p> <p><strong><em>References</em></strong></p> <p>Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., <strong>2022</strong>. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>.</p> <p>Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., <strong>2021</strong>. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>.</p> <p>Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., <strong>2021</strong>. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.</p>
Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses
<p>This archive includes the data used (e.g. Time use survey (UK-TUS) data), model files (idf files for running EnergyPlus) and codes for analysis in the paper (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.enbuild.2024.114668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.enbuild.2024.114668</a>).</p> <p>Files in this archive should include:</p> <ul> <li>Time use survey data analysis</li> </ul> <p>o Main dataset: TUS_activity.zip</p> <p>o Code: TUS_clustering_code.zip</p> <p>o Output: InternalHeatProfile.zip</p> <ul> <li>Building energy modeling </li> </ul> <p>o Main dataset (run in EnergPlus 9.4): IDFfiles.zip</p> <p>o Output: Eplus_output.zip</p> <ul> <li>PostProcess analysis</li> </ul> <p>o Code: QF_analysis_code.zip</p> <p>o Output: QF_output.zip</p> <p> </p> <p>Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.</p>
Building better conservation media for primates and people: A case study of orangutan rescue and rehabilitation YouTube videos
<p>1. Conservation organizations rely on social/internet media platforms to raise awareness and fundraise. Social media is a double-edged sword: it can be a wide-reaching and effective tool for education and fundraising, but can also have counter-productive impacts on public views toward wildlife and understanding of wildlife conservation.</p> <p>2. For example, depicting humans interacting with wildlife in media may increase video popularity, but animals shown in anthropogenic contexts are also viewed as appealing pets. We are interested in understanding whether this is true for social media posts (YouTube videos) by orangutan rescue and rehabilitation organizations, which rely on social media for fundraising and awareness-raising. Our goal is to provide data and recommendations to guide these organizations in building media with positive conservation impact while minimizing potential negative effects.</p> <p>3. Using YouTube analytics and sentiment analysis of comments on 118 videos, we ask how viewer responses to videos vary with 1) the amount of human-orangutan interaction depicted, 2) the ages of the orangutans featured, and 3) the mention of threats to orangutans.</p> <p>4. Videos with longer human-orangutan interaction time were viewed more, but comments on them were significantly more likely to be negative toward Indonesian/Malaysian people. Comments on orangutan rescue/rehabilitation videos were more likely to be categorized as negative for orangutan conservation compared to videos about orangutans generally, and within these, so were comments on videos featuring infant and juvenile orangutans.</p> <p>5. Based on our findings, we recommend that orangutan rescue and rehabilitation organizations feature adult and mixed age groups of orangutans rather than infants and juveniles, minimize the amount of human-orangutan interaction shown, and talk about conservation threats to orangutans in their videos. We also recommend that, as a precaution, other primate rescue and rehabilitation groups also abide by these suggestions.</p>
Data and code accompanying "'Safe spaces' and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023
<p>Data and code accompanying "‘Safe spaces’ and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023</p>
Building better conservation media for primates and people: A case study of orangutan rescue and rehabilitation YouTube videos
Open the record for dataset details and reuse information.
RIBuild: Survey of historic building stock - with case studies on renovation
<p>Database prepared in Excel including four elements, as background information for RIBuild Deliverable D1.1 about the historic building stock:</p> <p>Historic buildings stock energy consumption (1)</p> <p>Historic building stock description (2)</p> <p>Building construction elements (3)</p> <p>Case studies (4)</p> <p>Element (1)-(3) are referring to the historic building stock in RIBuild partner countries in general, while element (4) contains examples of carried out renovation projects, involving internal insulation of a historic building.</p> <p>If available, the case study sheets contain information about the floor area, present use, the building envelope (thickness, materials), renovation history, pre- and post-energy usage and renovation cost. Further, information about typical defects and the main driving forces for the renovation project, planning or design tools used, whether the goal with the renovation was achieved and the satisfaction of the users.</p> <p>Overview of data files to be found in 'RIBuild data WP1' as part of this dataset.</p>
RIBuild: Measurements at case buildings with internal insulation (DK, LV, IT)
<p>Dataset consisting of measurement data at specific locations in Danish, Latvain and Italian cases; one file for each measuring point. Data consists mainly of temperature and relative humidity, but in some cases also heat flow or volumetric water content, or wind speed, wind direction and solar radiation.</p> <p>This dataset together with data from German case buildings (separate data set) form the basis for recommendations concerning the use of different internal insulation systems, reported in RIBuild deliverable D3.2. Further, the dataset was intended as input for validation of simulations performed with RIBuild web tool.</p> <p>Overview of data files to be found in ’RIBuild data WP3_DK LV IT case buildings’ as part of this dataset.</p> <p>Details about case buildings are described in RIBuild deliverable D3.2 available at www.ribuild.eu.</p> <p> </p>
FIGURE 3 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 3. Multidimensional scaling (MDS) plot of 75% similarity in species composition and abundance of symbiotic copepods amongst various sampling periods.
FIGURE 4 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 4. Principal component analysis (PCA) of square root-transformed data from five functional categories of copepods (see text for descriptions.) defined based on behaviour (endo-/ectoparasitic or benthic) and the structure of the feeding appendages (mandibles vs. siphon) at various sampling periods. PC1 accounted for 77.2% of the variability, and PC2 accounted for 13.7%.
FIGURE 2 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 2. Relationships between copepod infection and the amount of resources (Symbiodinium densities and surface areas) provided by host corals. A–E: Mean densities of symbiotic copepods (A: Siphonostomatoida, B: Cyclopoida, C: Harpacticoida, D: all copepods) among 480 Pocillopora damicornis colonies with varying Symbiodinium densities. E–H: Relationship between mean densities of symbiotic copepods (E: Siphonostomatoida, F: Cyclopoida, G: Harpacticoida, H: all copepods) and surface areas of host corals.
FIGURE 1 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 1. Variation in seawater temperature and Symbiodinium density (mean ± SE) observed in Pocillopora damicornis colonies of Nanwan Bay, Southern Taiwan between July 2007 and November 2008. Lowercase and uppercase letters (a, b, c, and d) refer to the results of Tukey's post-hoc comparisons of monthly temperature and Symbiodinium density means, respectively, as a significant effect of time was detected in the overall ANOVA models (p <0.05 for both parameters).
Influence of Project Planning on Performance of Housing Construction Projects in Rwanda: case of Silver Back Mall and Makuza Building Projects in Kigali City
<p><span>The study investigated the </span><span>influence of project planning on performance of Housing Construction Projects in Rwanda. </span><span>The study has the specific objectives of</span><span> determining the influence of project scope planning on performance of housing construction project in Kigali City;</span><span> t</span><span>o establish the influence of project resource planning on performances of housing construction project;</span><span> and t</span><span>o analyze the influence of project risk planning on</span><span> performance of </span><span>housing construction projects performances of housing construction project. <span>The descriptive research design and </span></span><span>correlative approach were used. </span><span>Target population was 161 staff in the housing construction and </span><span>sample size was 161 respondents selected using census survey sampling. Data collection methods were questionnaire and documentary analysis.<span> </span>Methods of analysis were <span>descriptive statistic method; correlation coefficient and </span></span><span>multiple linear regression analysis models.</span><span><span> </span>The findings revealed that correlations between project planning factors and the performance of housing construction projects are highly significant (Sig. = 0.000) and strongly positive. In this model, the R-value is 0.948, indicating a strong positive linear relationship between the predictors and the dependent variable. This means that the independent variables collectively explain 94.8% of the variation in the dependent variable. In this model, the R-Square value is 0.899, which means that approximately 89.9% of the variation in the Performance of housing construction projects can be explained by the predictors included in the model. According to the findings on <span>Ha1 stated that “there is a significant influence of project scope planning on the performance of housing construction projects in Kigali City”. In the ANOVA table, the "Regression" row provides this information where the "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). This indicates that the regression model (which includes </span>project scope planning<span>) is statistically significant. The Ha2 stated that “</span>t<span>here is a significant influence of project resource planning on the performance of housing construction projects in Kigali City”. Similarly, we look at the significance of the predictor "</span>project resource planning<span>" in the regression model. The "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). The Ha3 said that “there is a significant influence of project risk planning on the performance of housing construction projects in Kigali City”. Again, we examine the significance of the predictor "</span>project risk planning<span>" in the regression model. The "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). Therefore, based on the analysis, </span>project risk planning <span>also has a significant influence on the performance of housing construction projects in Kigali City.</span> <span> </span></span></p> <p><span> </span></p> <p><strong><span>Key words: </span></strong><em><span>project planning; performance of Housing Construction Projects; </span></em><em><span>project scope planning; project resource planning; project risk planning</span></em></p>
Effect of Project Management on Success of Mergers and Acquisitions of Companies in Rwanda: case of Investment Finance / Commercial Buildings Project of KCB and Rural Sector Support Project of BPR (2020-2023).
<h1><span><br></span></h1> <p><span>Background: the study aimed to assess the impact of project management on the success of mergers and acquisitions in Rwandan companies, focusing on the Investment Finance/Commercial Buildings project of KCB and the Rural Sector Support Project of BPR. The specific objectives included examining the effects of project timeline management, project budget management, project human resource management, project quality planning management, and project risk assessment on the success of mergers and acquisitions. <span> </span>Methods: The research utilized descriptive and correlative research designs, employing a mixed approach of qualitative and quantitative methods. Data were collected from a population of 237 individuals, with a sample size of 149 selected through stratified and purposive sampling. Statistical Package for the Social Sciences (SPSS) version 23.0 was used for data processing and analysis, employing descriptive statistics, correlation, and multiple linear regression analysis. Findings: The correlation analysis revealed significant and positive relationships between the independent variables (X1, X2, X3, X4, and X5) and the dependent variable </span><span>(Y - success of mergers and acquisitions). Effective timeline management (X1) demonstrated a significant positive correlation (Pearson Correlation = 0.476), emphasizing its importance for project success. Project budget management (X2) also exhibited a significant positive correlation (Pearson Correlation = 0.583), highlighting the value of budget management. Efficient human resource management (X3) showed a significant positive correlation (Pearson Correlation = 0.522), underscoring the role of human resources in project success. Effective quality planning management (X4) displayed a significant positive correlation (Pearson Correlation = 0.621), emphasizing the importance of quality planning. Thorough project risk assessment (X5) had a significant positive correlation (Pearson Correlation = 0.671), stressing the role of risk assessment in achieving success. Conclusion: all independent variables (X1, X2, X3, X4, and X5) were found to be positively and significantly correlated with the success of merger and acquisition projects. These results underscore the crucial role of project management factors, including timeline management, budget management, human resource management, quality planning management, and risk assessment, in determining the success of mergers and acquisitions in Rwandan companies. Recommendations: Based on the findings, it is recommended that companies engaged in mergers and acquisitions in Rwanda prioritize effective project management strategies, focusing on timeline management, budget management, human resource management, quality planning management, and risk assessment. Implementing robust project management practices can enhance the likelihood of successful outcomes in the context of mergers and acquisitions. Additionally, organizations are encouraged to invest in training and development programs to build the necessary skills and competencies in project management for their personnel involved in such strategic initiatives.</span></p>
Figure 2 in Contrasting modes of handling moss for feeding and case-building by the caddisfly Scelotrichia willcairnsi (Insecta: Trichoptera)
Figure 2. Scelotrichia willcairnsi sp. nov., male genitalia: (A) dorsal view; (B) ventral view; (C) lateral view. Scale bar50.1 mm.
Figure 1 in Contrasting modes of handling moss for feeding and case-building by the caddisfly Scelotrichia willcairnsi (Insecta: Trichoptera)
Figure 1. (A) Scelotrichia willcairnsi sp. nov., cased final instar larvae and pupae among moss, Platyhypnidium muelleri; (B) S. willcairnsi sp. nov., mature larva in case made of fragments of moss leaf; (C) enlargement of small section of case showing moss leaf fragments; (D) sample of larval gut contents showing ingested moss leaf fragments; (E) adult male caddisfly. Scale bars: (B,E)51 mm; (C,D)550 mm.
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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.