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81 results for “climate change scenarios”
Data Storage Report. RODBreak - Wave run-up, overtopping and damage in rubble-mound breakwaters under oblique extreme wave conditions due to climate change scenarios
<p>Wave breaking / run-up / overtopping and their impact on the stability of rubble-mound breakwaters (both at trunk and roundhead) are not adequately characterized yet for climate change scenarios. The same happens with the influence of high-incidence angles on such phenomena.</p> <p>To study these phenomena a stretch of a rubble-mound breakwater (head and part of the adjoining trunk, with a slope of 1(V):2(H)) was built in the wave basin of the LUH, The trunk of the breakwater was 7.5 m long and the head had the same cross section as the exposed part of breakwater. The model was 9.0 m long, 0.82 m high and 3.0 m wide. The angle between the longitudinal axis of the breakwater and the tank wall was 70º. Two types of armour elements (rock and Antifer cubes) were tested.</p> <p>60 tests were carried out in this experiment to assess, under extreme wave conditions (wave steepness of 0.055) with different incidence wave angles (from 40º to 90º), the structure behaviour in what concerns wave run-up, wave overtopping and damage progression of the armour layer.</p> <p>The report describes the data collected in those tests as well as how such data is stored.</p>
Figure 7 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 7. Map of potential invasion range of S. woodiana in Europe under the RCP 8.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 6 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 6. Map of potential invasion range of S. woodiana in Europe under the RCP 4.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 4 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 4. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 8.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 5 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 5. Map of potential invasion range of S. woodiana in Europe under the recent climate conditions: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 3 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 3. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 4.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 1 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 1. Map of records of S. woodiana in Europe obtained from GBIF database and published sources (Vikhrev et al., 2024).
Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios
<p>This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"</p>
Morphed extreme weather data for Vantaa and Sodankylä under RCP climate change scenarios by 2030, 2050 and 2080
<p>Morphed extreme weather data for 2 Finnish locations: Vantaa and Sodankylä. Created for "Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios" publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>). Used climate change scenarios are RPC2.6, RCP4.5 and RCP8.5. Data is created for 2030, 2050 and 2080 and includes 6 extreme weather scenarios: </p> <ul> <li>W1 - Winter with high heating demand</li> <li>W2 - Winter with low heating demand</li> <li>W3 - Winter with the coldest individual day by average temperature</li> <li>S1 - Summer with the lowest cooling demand</li> <li>S2 - Summer with the highest heating demand</li> <li>S3 - Summer with the warmest individual day by average temperature</li> </ul> <p>Selected years and the procedure for their selection are described in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>.</p> <p>Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.</p> <p>Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (<a href="https://www.ilmatieteenlaitos.fi/energialaskenta-try2020">https://www.ilmatieteenlaitos.fi/energialaskenta-try2020</a>) for which the climate change data is presented by Ruosteenoja et al. (2016).</p> <p>The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software <a href="https://github.com/japulk/Weather-Morphing-Tool">https://github.com/japulk/Weather-Morphing-Tool</a> For additional information please refer to <a href="https://doi.org/10.1016/j.energy.2024.131636">original article</a> or contact the authors.</p> <p> </p>
Climate change scenarios forecast increased drought exposure for terrestrial vertebrates in the contiguous United States
Open the record for dataset details and reuse information.
Reptile diversity patterns under climate and land use change scenarios in a subtropical montane landscape in Mexico
Open the record for dataset details and reuse information.
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: I - Trends in air temperature, precipitation, and cloudiness
These data contain estiamtes of changes in climate from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 3.
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: II - Change in net primary production (NPP)
This data has npp values from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 4.
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: III - Decadal net primary productivity (NPP) and heterotrophic respiration
These data contain NPP, NEP, and RH values from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. V This file contains data for Figure 5.
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: IV - Relationship between selected carbon pools and fluxes
These data contain NPP, NEP, RH, soil C, soil N, and ecosystem carbon changes from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 6.
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: V - Change in summer albedo by climate scenario
These data contain albedo estimates from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 7.
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model
<p>Model code (R) to accompany the 2023 NOAA report "<strong>Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model</strong>"</p>
Resources for "Enterprise's strategies to improve financial capital under a climate change scenario – evidence of the leading country"
<p>The dataset and code deposited here are resources used for analysis in the study titled "Enterprise’s strategies to improve financial capital under a climate change scenario – evidence of the leading country"</p>
Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)
<p>The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively).</p> <p>The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms). </p> <p>Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95<sup>th</sup> percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5<sup>th</sup> percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).</p> <p> </p>
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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.