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394 results for “hazard”
Dataset of the HazardNet: A Thermal Hazard Prediction Framework for Datacenters
<p>This dataset entry showcases a comprehensive collection obtained from the Tier-0 supercomputer, Marconi A2, hosted at CINECA (<a href="https://www.hpc.cineca.it/">https://www.hpc.cineca.it/</a>). The dataset records inlet and outlet temperatures along with power consumption data from 3312 computing nodes, spanning from January 14, 2019, to December 31, 2019. The data is generated through ExaMon, a sophisticated monitoring datacenter infrastructure. The primary objective of this dataset is to support the research and development of HazardNet, an innovative thermal hazard prediction framework tailored specifically for datacenters. HazardNet integrates a comprehensive pipeline of machine-learning models. Researchers and enthusiasts interested in exploring our work further can find the complete set of codes and machine-learning models at our GitHub repository: <a href="https://github.com/MSKazemi/HazardNet">https://github.com/MSKazemi/HazardNet</a></p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
<p>Human development at the foot of the mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris, and when flood control infrastructure that protects communities fills with sediment, it loses capacity. The estimation and management of sediment-laden floods have proven challenging because cycles of wildfire, precipitation, and infrastructure sedimentation are still poorly understood. Efforts to model compound hazards such as post-fire floods are relatively new, and existing models do not consider the role of flood control infrastructure, such as debris retention basins and flood channels, in the development of post-fire floods. Here we present data sources and calibration methods to estimate sediment-laden flood hazards downstream of infrastructure on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in Southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post-fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal whether existing infrastructure is undersized to contain present-day and future overtopping hazards based on current design standards.</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
A standardised climate change hazard vocabulary for heritage
<p>This dataset (.xlsx) is a vocabulary of climate change hazards for heritage. Hazards are the potential occurrences of natural or physical events that may cause damage or loss. Previously there had been no definitive list of climate hazards for heritage that were directly connected to changing climatic processes. This project addresses this gap by linking the created hazards to the Climatic Impact-Drivers (CIDs) produced by the Intergovernmental Panel on Climate Change (IPCC). The vocabulary consists of 52 primary and key related hazards for heritage. It is international in its remit.</p> <p>The list will be published as a vocabulary on <a href="https://www.heritage-standards.org.uk/fish-vocabularies/" target="_blank" rel="noopener">the Forum on Information Standards in Heritage (FISH)</a> where it can be accessed in multiple formats <a href="https://heritagedata.org/live/schemes/38076.html">including linked data</a>. This .xlsx format places the hazards in relationship to each other and in their CID context. Candidate terms can be submitted to the research group Heritage Environmental Risk and Data Analytics <a href="mailto:herada@ucl.ac.uk" target="_blank" rel="noopener noreferrer">herada@ucl.ac.uk</a> (terms submitted to <a href="mailto:Terminologies@HistoricEngland.org.uk">Terminologies@HistoricEngland.org.uk</a> will be directed to the research group for approval). An accompanying <a href="https://historicengland.org.uk/research/results/reports/13-2024?search=13%2F2024&searchType=research+report">Historic England Research Report</a> provides more information, including the methodology of the project (available in both English and Welsh).</p> <p>The authors are interested in hearing from users of the vocabulary, specifically those that link the hazards to observed impacts of climate change on parts of the historic environment. This dataset was produced as part of a funded 6-month project between Historic England and the UCL Institute for Sustainable Heritage on developing a standardised vocabulary of climate change hazards for the historic environment. The Welsh version of the dataset was translated in 2025 by Lingo Soar, in collaboration with Fforest Fawr UNESCO Geopark, and as part of the UK National Commission for UNESCO's Climate Change and UNESCO Heritage project.</p>
Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans
<p>This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size. </p>
First Street Foundation Flood Model Hazard Layers V1.3
<p>Up to 15 different hazard layers are available, representing 3 different time periods (2021, 2036, 2051) and 4-5 different return periods from the 2-year (coastal only) to the 500-year intervals.</p> <p>Data is delivered in GeoTIFF format and at a 3 meter resolution with each pixel representing depth of flooding in centimeters. This high resolution dataset allows you to visualize flood extents at multiple return periods both today and in the future.</p> <p>The hazard inundation layers are emailed through a clickable link that automatically starts the download of the datasets. The Version 1.3 hazards are available for the contiguous United States.</p> <p>You can download a sample of the hazard layers generated from First Street's Flood Model on this page. You can request access to the hazard layers for areas within the contiguous United States on the First Street website<a href="https://firststreet.org/data-access/paid-access/?utm_source=Hazard_Layers&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component"> here</a>. You can find the data dictionary which breaks down the data that is available with each hazard layer purchase<a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Hazard_Layers&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo"> here</a>. If you are also interested in the flood risk statistics, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Hazard_Layers&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>.</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
On the estimation of landslide intensity, hazard and density via data-driven models.
<p>The geographic prediction of landslide occurrence is undertaken by assessing whether a slope may be stable or unstable. In other words, current practices treat slopes where a single landslide occurred in the same way as slopes where many landslides occurred. At the slope scale, this procedure inevitably underestimates the effect of multiple landslides.<br> Here we model the number of landslides per slope instead. Then, thanks to the close relation that the number of failures shows with respect to landslide size, we convert the estimated number of landslides into estimated landslide areas. Ultimately, we also estimate the expected proportion of a slope affected by landslides. This framework is more informative than the stable/unstable paradigm and may help landslide risk mitigation strategies.</p>
Prosociality as response to slow- and fast-onset climate hazards
<p>This repository contains the information videos used in the treatments, as well as the data and code that replicates tables and figures for the following paper:<br> <strong>Title:</strong> Prosociality as response to slow- and fast-onset climate hazards<br> <strong>Authors:</strong> Ivo Steimanis<sup>1</sup> & Björn Vollan<sup>1,*</sup><br> <strong>Affiliations:</strong> <sup>1</sup> Department of Economics, Philipps University Marburg, 35032 Marburg, Germany<br> <strong>*Correspondence to:</strong> Björn Vollan <a href="mailto:bjoern.vollan@wiwi.uni-marburg.de">bjoern.vollan@wiwi.uni-marburg.de</a><br> <strong>ORCID:</strong> Steimanis: 0000-0002-8550-4675; Vollan: 0000-0002-5592-4185<br> <strong>Classification:</strong> Social Sciences, Economic Sciences<br> <strong>Keywords:</strong> climate hazards, prosociality, in-group favoritism, antisociality</p> <p> </p>
Thermal demagnetization data of Risica et al. (Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala)
<p>Thermal demagnetization data (repository data) of Risica et al. "Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala".</p>
Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards
<p>Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards (Advances in Statistical Climatology, Meteorology and Oceanography, May 2022)</p>
Ungulates mitigate the effects of drought and shrub encroachment on the fire hazard of Mediterranean oak woodlands
<p>Dataset included: Shrub density; Shrub biomass; Fuel load of <em>Cistus ladanifer</em>; Fuel load herbs; Fuel load litter</p>
Ensemble of global landslide hazard from PHELS
<p>Daily global landslide hazard from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for different hydrological predictor variables alongside global landslide susceptibility estimates: A 7-day antecedent rainfall index (ARI7), daily rainfall, daily root-zone soil moisture (rzmc, 0-100cm depth) and the combination of rainfall&rzmc. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events. For all four hydrological predictor variable (combinations) deterministic hazard estimations are provided. Please note the different order of magnitude in the hazard values when using one or two hydrological predictor variables. For rainfall&rzmc results of ensemble simulations (100 members) are additionally provided, more specifically the ensemble average and standard deviation. The latter is a measure for the uncertainty of the estimated hazard. Details can be found in Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M. (2023): Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling (NHESS, https://doi.org/10.5194/egusphere-2023-869). The global landslide susceptibility estimation is described in Felsberg et al. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling (NHESS, https://doi.org/10.5194/nhess-22-3063-2022)</p>
Number of fishers adaptations with increasing single hazard exposure
<p>Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. Here we calculated the number of the different types of fishers’ responses inside each of the corresponding climate change hazard exposure levels (i.e. percentiles) from four selected hazards: sea surface temperature rate of change, accumulated intensity of marine heatwaves, sea level rise of sea surges, and frequency of tropical storms. The magnitude of exposure was calculated using the following percentiles: 25th percentile (low exposure), 50th (medium), 75th (high), and 90th (hotspot)</p>
FIGURE 5 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California
FIGURE 5. (A) Change in relative prevalence of southern species of heterobranchs at Hazard Canyon Reef, with yearly mean of the Multivariate ENSO Index, v.2 (MEI), 1999–2021. Relative prevalence of southern species calculated as the proportion of southern species found each year out of the total number of southern species (n = 23) found throughout the entire study, minus the same proportion calculated each year for northern species (also n = 23). (B) Bimonthly values of the MEI. Positive values are shown in red, clusters of which indicate El Niño events of differing strength, and negatives values in blue, indicating La Niña events of varying strength. Values between 0.5 and –0.5 are considered ENSO neutral.
FIGURE 4 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California
FIGURE 4. Seasonal variation in abundance and egg-laying activity of 10 of the 15 most abundant heterobranchs at Hazard Canyon Reef, 1999–2021. Values shown are means ± 1 SE of monthly number of individuals per hour per observer (n = 8, 21, 3, 15 for winter, spring, summer, fall, respectively). Black bars at top of graphs indicate egg masses were observed at least once in a given season.
FIGURE 1 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California
FIGURE 1. Map showing location of Hazard Canyon Reef between Morro Bay and Point Buchon, San Luis Obispo County, California.
FIGURE 2 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California
FIGURE 2. Hazard Canyon Reef. (A) Study site (outlined in white), looking northwest, 23 November 2011, -0.33 m tide, with a building ocean swell. The crevice forming the slender extension of the study area visible here is about 25 m long. (B) At northwestern-most part of study area, 26 May 2017, tide level at -0.46 m.
FIGURE 6 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California
FIGURE 6. Yearly change in abundance of the most abundant nudibranchs at Hazard Canyon, 2005–2021. (A) three northern species, and (B) three southern taxa. Values shown are yearly means +- 1 SE of monthly number of individuals per hour per observer; note separate, logarithmic axis in (B) for Okenia rosacea. In (B) Doriopsilla spp. = D. albopunctata and D. fulva, which combined were considered D. albopunctata until delineated by Hoover et al. (2015).
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
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.