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135 results for “rapid assessment”
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
Social determinants of Covid-19 infection and death in a rural Indonesia: A rapid healthcare assessment
<p>Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.</p>
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
Figure 2 in A rapid assessment of cave occupancy for Pacific sheath-tailed bats (fanihin ganas, Emballonura semicaudata rotensis) and Mariana swiftlets (chachaguak, Aerodramus bartschi) on Aguiguan, Mariana Islands
Figure 2: Locations of caves surveyed for Pacific sheath-tailed bat and Mariana swiftlet occupancy by the Commonwealth of the Northern Mariana Islands Division of Fish and Wildlife in 2021, with WorldView-2 satellite imagery (dated April 14, 2019) for reference. Surveyed caves are labeled: 1 – East Black Noddy Cave; 2 – Cliff Cave; 3 – Pillar Cave; 4 – Guano Cave; 5 – New Cave Complex; 6 – Southern Cave Complex; 7 – Crevice Cave
Figure 1 in A rapid assessment of cave occupancy for Pacific sheath-tailed bats (fanihin ganas, Emballonura semicaudata rotensis) and Mariana swiftlets (chachaguak, Aerodramus bartschi) on Aguiguan, Mariana Islands
Figure 1: Map depicting the location of the Commonwealth of the Northern Mariana Islands in relation to the Asia-Pacific region and the location of Aguiguan (blue circle) within the archipelago. Sources: Basemaps: Esri, The General Bathymetric Chart of the Oceans, National Oceanic and Atmospheric Administration, National Geographic, DeLorme, HERE, Geonames.org, Garmin, United States Geological Survey, Earthstar Geographics.
Figure 4 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 4 Relationship between sugarcane rust mite density and counting speed of the imprinting technique.
Figure 3 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 3 Within-plant distribution of sugarcane rust mite population based on the imprinting tech- nique (mean ± SEM). The numbers within brackets are the proportions of mite populations within plants. Means across leaves with the same capital letters are not significantly different and means with the same lower letters on a given leaf position are not significantly different (Tukey,P <0.05).
Figure 5 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 5 Physiological parameters of sugarcane canopy (mean±SEM).A=photosynthetic rate, gsw =stomatal conductance,Ci =intercellular CO2, E=transpiration, WUE=water use efficiency.
Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.
Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.
Figure 3. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 3. A & B, the spectacular scenery of mountaineous landscapes forming part of the proposed BR; C, the endemic spiral aloe; D, the near-endemic Lesotho lily; E, the Lesotho red-hot poker; F, locally uncommon fern – bracken; G, endemic Maloti minnow; H, common eland; and I, its presence on rock paintings; J, some of the wetlands in the BNR; K, some of them damaged by diggings of Sloggett's ice rat (Source: K. Kobisi).
Figure 1. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 1. A & B, Lesotho's first proposed Biosphere Reserve, showing the core (Tšehlanyane National Park and Bokong Nature Reserve), buffer and transition zones (Source: T. Leballo).
Figure 2 in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 2. Transect walks covered during the different phases of the biodiversity survey (data collection) in the proposed Biosphere Reserve (Source: T. Leballo).
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>
Water Sanitation & Hygiene Quick Check (WASH Q-Check): A rapid assessment tool for healthcare facilities in India
<p>This is a data sheet of the "<strong>Water Sanitation and Hygiene Quick Check (WASH Q-Check): A rapid assessment tool for healthcare facilities in India</strong><strong> </strong>". </p>
Data from: Utilising citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry
Open the record for dataset details and reuse information.
Data from: Comparison of rapid biodiversity assessment of meiobenthos using MALDI-TOF MS and metabarcoding
<p>Nowadays, most biodiversity assessments involving meiofauna are mainly carried out using very time-consuming, specimen-wise morphological identifications, which demands comprehensive taxonomic knowledge. Animals have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect collected data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species which do not exist in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation [herein log(x + 1)], the number of reads per MOTU reflects relative species abundances in metabarcoding inference. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by specimen identification, but when it comes to sample-wise analyses, metabarcoding outperforms other techniques by far.</p>
Supporting code and data for REBA (Rapid EOO-Based Assessment)
<p>Shared here are code and data to support the manuscript: "Using publicly available data to conduct rapid assessments of extinction risk." Raw data (North American plant occurrence records) were harvested from <a href="https://www.gbif.org/">GBIF</a> using the R package <a href="https://cran.r-project.org/web/packages/rgbif/index.html"><strong>rgbif</strong></a>. Subsequent data manipulation, cleaning, and statistical modeling was conducted using the codebase documented by the project <a href="https://github.com/eveskew/plant_rapid_assessment">GitHub repository</a>, which is also archived here.</p> <p><strong>plant_rapid_assessment-v1.1.0.zip</strong>: An archived version of the GitHub repository files, including most project data, analysis code, and relevant outputs.</p> <p><strong>gbif_data.zip</strong>: This ZIP archive contains the key project data files that are <strong>not</strong> uploaded to the GitHub repository (because of file size considerations). Once decompressed, contains a "gbif_occurrences" subdirectory, which holds all raw GBIF occurrence records for each species queried, and a "gbif_cleaned" subdirectory, which holds the cleaned, derived GBIF occurrence record dataset (for all species). Note that the directory is ~13 GB decompressed.</p>
Data set and analytic codes supporting "Length-biomass equations to allow rapid assessment of semi-aquatic bug biomass in tropical streams"
<p>Body size and biomass data of semi-aquatic bugs (Gerromorpha, Hemiptera) collected in a range of habitat types (oil palm, oil palm with buffer, logged forest, and old-growth forest) in the SAFE Project (Stability of Altered Forest Ecosystems) experimental area, Maliau Basin, and Danum Valley. The sites are located in Sabah, Malaysia. In this study, we developed length-biomass equations to estimate the biomass of semi-aquatic bugs.</p>
Figure 1 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 1 Mites appearances in visual direct count (left) and imprinting techniques (right).
ScienceDex guides
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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.