Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
387
datasets available to search
ShareScore release 0.7.1
Dataset results
387 results for “Climatic Adaptation”
Interim Data and Results for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Interim exposure data and results for the published case study. External raw data is available <a href="https://zenodo.org/records/14260630">here</a> and the remaining raw data is generated via code. This is too large to share on Zenodo. There are stochastic aspects in going from raw exposure to interim exposure data and interim exposure data to results, which is why we make the specific interim exposure data and results of the published study available here. Other interim data is not stochastic and can be reproduced following the code reproduction instructions (see https://github.com/abpoll/j40_gc). Please see the underlying study for more details about the methods. The data here can be generally reproduced (not bit-wise) following the code and instructions at this repository: https://zenodo.org/records/14261361.</p> <p> </p> <p>The interim exposure data is named "exp" and the results data is named "results."</p>
Literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process (as part of Adaptation AGORA project)
<p>This dataset is the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This database was used to conduct a literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process. It contains 123 papers retrived from Web of Science Databse in June 2023. <span>We used a keyword search to identify and select articles that fell within the scope of our research, with each article containing at least one keyword related to climate change adaptation solutions, climate change, co-production, citizen and stakeholder involvement and factors (enablers and barriers). </span></p> <p><span>We divided the coding framework into four main sections:</span></p> <ul> <li> <p><span>Section 1 collected basic information about the paper (i.e., date, journal, authors, type of study and methods for data collection). </span></p> </li> <li> <p><span>Section 2 sought to better understand the adaptation initiatives treated in the paper. Here, we analysed 5 variables (the adaptation solutions type, sectors, benefits, scale, and location). </span></p> </li> <li> <p><span>Section 3 collected characteristics of the climate change adaptation co-production process, including the definition of co-production, the type of the co-production process, its outputs, and the methods used to engage stakeholders. </span></p> </li> <li> <p><span>Section 4 described the factors that enable or hinder the co-production process and their influence on different aspects of the process. After naming and defining each driver, we recorded the main type of factor, its impact, origin, and spatial and temporal scale of influence; the stakeholders who were responsible for and influenced by the factor, and the impacts on the various steps and outcomes of the co-production process.</span></p> <span> </span></li> </ul>
Population genomics of a forest fungus reveals high gene flow and climate adaptation signatures
Genome sequencing of spatially distributed individuals sheds light on how evolution structures genetic variation. Populations of <i>Phellopilus nigrolimitatus</i>, a red-listed wood-inhabiting fungus associated with old-growth coniferous forests, have decreased in size over the last century due to a loss of suitable habitats. We assessed the population genetic structure and investigated local adaptation in<i> P. nigrolimitatus</i>, by establishing a reference genome and genotyping 327 individuals sampled from 24 locations in Northern Europe by RAD sequencing. We revealed a shallow population genetic structure, indicating large historical population sizes and high levels of gene flow. Despite this weak sub-structuring, two genetic groups were recognized; a western group distributed mostly in Norway and an eastern group covering most of Finland, Poland and Russia. This sub-structuring may reflect co-immigration with the main host, Norway spruce (<i>Picea abies</i>), into Northern Europe after the last ice age. We found evidence of low levels of genetic diversity in southwestern Finland, which has a long history of intensive forestry and urbanization. Numerous loci were significantly associated with one or more environmental factors, indicating adaptation to specific environments. These loci clustered into two groups with different associations with temperature and precipitation. Overall, our findings indicate that the current population genetic structure of P. nigrolimitatus results from a combination of gene flow, genetic drift and selection. The acquisition of similar knowledge especially over broad geographic scales, linking signatures of adaptive genetic variation to evolutionary processes and environmental variation, for other fungal species will undoubtedly be useful for assessment of the combined effects of habitat fragmentation and climate change on fungi strongly bound to old-growth forests. --
Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"
<p>This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.</p> <ul> <li><strong>data.zip </strong>contains raw, intermediate, and final datasets</li> <li><strong>outputs.zip </strong>contains output tables and figures </li> </ul> <p>All replication code for the paper is available on a public Github repository, accessible <a href="https://github.com/ClimateImpactLab/carleton_mortality_2022">here</a>.<br><br>The manuscript and supplementary information are available at the QJE, <a href="https://doi.org/10.1093/qje/qjac020">here</a>.</p>
CRISI-ADAPT II: free downscaled climate projection layers
<p>CRISI-ADAPT II project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision making of the related stakeholders. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas.</p> <p>For further details, find here a brief of the methodology followed:</p> <p> </p> <p><strong> Methodology</strong></p> <p>Information provided by 10 models belonging to CMIP6 have been included. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: </p> <p><em>Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. </em></p> <table> <tbody> <tr> <td> <p><strong>CMPI6 MODELS</strong> </p> </td> <td> <p><strong>Resolution</strong> </p> </td> <td> <p><strong>Responsible Centre</strong> </p> </td> <td> <p><strong>References</strong> </p> </td> </tr> <tr> <td> <p><strong>BCC-CSM2-MR</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China. </p> </td> <td> <p>Wu, T. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CanESM5</strong> </p> </td> <td> <p>2,812º x 2,790º </p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </p> </td> <td> <p>Swart, N.C. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CNRM-ESM2-1</strong> </p> </td> <td> <p>1,406º x 1,401º </p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </p> </td> <td> <p>Seferian, R. (2019) </p> </td> </tr> <tr> <td> <p><strong>EC-EARTH3</strong> </p> </td> <td> <p>0,703º x 0,702º </p> </td> <td> <p>EC-EARTH Consortium </p> </td> <td> <p>EC-Earth Consortium. (2019) </p> </td> </tr> <tr> <td> <p><strong>GFDL-ESM4</strong> </p> </td> <td> <p>1,250º x 1,000º </p> </td> <td> <p>National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. </p> </td> <td> <p>Krasting, J.P. et al. (2018) </p> </td> </tr> <tr> <td> <p><strong>MPI-ESM1-2-HR</strong> </p> </td> <td> <p>0,938º x 0,935º </p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany. </p> </td> <td> <p>Von Storch, J. et al. (2017) </p> </td> </tr> <tr> <td> <p><strong>MRI-ESM2-0</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Meteorological Research Institute (MRI), Japan. </p> </td> <td> <p>Yukimoto, S. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>UKESM1-0-LL</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Uk Met Office, Hadley Centre, United Kingdom </p> </td> <td> <p>Good, P. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>NorESM2-MM</strong> </p> </td> <td> <p>1,250º x 0,942º </p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway. </p> </td> <td> <p>Bentsen, M. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>ACCESS-ESM1-5</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia </p> </td> <td> <p>Ziehn, T. et al. (2019)</p> </td> </tr> </tbody> </table> <p>Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo <em>et al.</em> 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. </p> <p>In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: </p> <ol> <li> <p>Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. </p> </li> <li>For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. </li> <li> <p>The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. </p> </li> <li>For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. </li> <li> <p>Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. </p> </li> <li>Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. </li> <li> <p>Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013). </p> </li> </ol>
Climate change alters sexual signaling in a desert-adapted frog
<p>Climate change is altering species' habitats, phenology, and behavior. Although sexual behaviors impact population persistence and fitness, climate change's effects on sexual signals are understudied. Climate change can directly alter temperature-dependent sexual signals, cause changes in body size or condition that affect signal production, or alter the selective landscape of sexual signals. We tested whether temperature-dependent mating calls of Mexican spadefoot toads (<em>Spea multiplicata</em>) had changed in concert with climate in the Southwestern U.S.A. across 22 years. We document increasing air temperatures, decreasing rainfall, and changing seasonal patterns of temperature and rainfall in the spadefoots' habitat. Despite increasing air temperatures, spadefoots' ephemeral breeding ponds have been getting colder at most elevations, and male calls have been slowing as a result. However, temperature-standardized call characters have become faster and male condition has increased, possibly due to changes in the selective environment. Thus, climate change might generate rapid, complex changes in sexual signals with important evolutionary consequences.</p>
Data from: Local adaptation to seasonal cues at the fronts of two parallel, climate-induced butterfly range expansions
<p>Climate change allows species to expand polewards, but non-changing environmental features may limit expansions. Daylength is unaffected by climate and drives life cycle timing in many animals and plants. Because daylength varies over latitudes, poleward-expanding populations must adapt to new daylength conditions. We studied local adaptation to daylength in the butterfly <em>Lasiommata megera</em>, which is expanding northwards along several routes in Europe. Using common garden laboratory experiments with controlled daylengths, we compared diapause induction between populations from the southern-Swedish core range and recently established marginal populations from two independent expansion fronts in Sweden. Caterpillars from the northern populations entered diapause in clearly longer daylengths than those from southern populations, with the exception of caterpillars from one geographically isolated population. The northern populations have repeatedly and rapidly adapted to their local daylengths, indicating that the common use of daylength as seasonal cue need not strongly limit climate-induced insect range expansions.</p>
From common gardens to candidate genes: Exploring local adaptation to climate in red spruce
<p><span>Local adaptation to climate is common in plant species and has been studied in a range of contexts, from improving crop yields to predicting population maladaptation to future conditions. The genomic era has brought new tools to study this process, which was historically explored through common garden experiments. </span></p> <p><span>In this study, we combine genomic methods and common gardens to investigate local adaptation in red spruce and identify environmental gradients and loci involved in climate adaptation. We first use climate transfer functions to estimate the impact of climate change on seedling performance in three common gardens. We then explore the use of multivariate gene-environment association (GEA) methods to identify genes underlying climate adaptation, with particular attention to the implications of conducting genome scans with and without correction for neutral population structure.</span></p> <p><span>This integrative approach uncovered phenotypic evidence of local adaptation to climate and identified a set of putatively adaptive genes, some of which are involved in three main adaptive pathways found in other temperate and boreal coniferous species: drought tolerance, cold hardiness, and phenology. These putatively adaptive genes segregated into two "modules" associated with different environmental gradients.</span></p> <p><span>This study nicely exemplifies the multivariate dimension of adaptation to climate in trees. </span></p>
Data from: Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards
<p>Multivariate adaptation to climatic shifts may be limited by trait integration that causes genetic variation to be low in the direction of selection. However, strong episodes of selection induced by extreme climatic pressures may facilitate future population-wide responses if selection reduces trait integration and increases adaptive potential (i.e., evolvability). We explain this counter-intuitive framework for extreme climatic events in which directional selection leads to increased evolvability and exemplify its use in a case study. We tested this hypothesis in two populations of the lizard <em>Anolis scriptus</em> that experienced hurricane-induced selection on limb traits. We surveyed populations immediately before and after the hurricane as well as the offspring of post-hurricane survivors, allowing us to estimate both selection and response to selection on key functional traits: forelimb length, hindlimb length, and toepad area. Direct selection was parallel in both islands and strong in several limb traits. Even though overall limb integration did not change after the hurricane, both populations showed a non-significant tendency toward increased evolvability after the hurricane despite the direction of selection not being aligned with the axis of most variance (i.e., body size). The population with comparably lower between-limb integration showed a less constrained response to selection. Hurricane-induced selection, not aligned with the pattern of high trait correlations, likely conflicts with selection occurring during normal ecological conditions that favor functional coordination between limb traits, and would likely need to be very strong and more persistent to elicit a greater change in trait integration and evolvability. Future tests of this hypothesis should use G-matrices in a variety of wild organisms experiencing selection due to extreme climatic events. </p>
Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia (The genome assembly and annotion fiile)
<p>The genome assembly and annotion fiile used in the manuscript: <strong>Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia</strong></p>
Central America climate adaptation corridor analysis
<p>This repository contains data and code used for a regional-scale analysis of potential climate adaptation corridors and their ecological characteristics and conservation status in Central America. This repository is associated with a manuscript "Mapping climate adaptation corridors for biodiversity - A regional-scale case study in Central America" by McCullough et al. At the time that this repository was archived, the paper had not yet been accepted at PLOS One, but hopefully it is by the time anyone is reading this. The readme inside the repository provides an overview of subdirectories and R scripts. This is an updated version of a beta version repository that had less documentation and did not contain some of the larger GIS files. This research was supported by the International Conservation Fund of Canada, the Bobolink Foundation, the BAND Foundation, and the Gordon and Betty Moore Foundation.</p>
Documents presenting past climate change adaptations (2008 - 2020)
<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 show the references of the selected documents (n = 60) through the systematic review from which the adaptation responses for data analysis were extracted.</p>
Fig 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 1: Relative gene expression in tambaqui juveniles farmed in two Brazilian regions: Northern (Balbina; BA) and Southeast (Brumado; BRU). Different letters represent statistical differences between populations. The graphs show expression of A) hif-1α (p = 0.137), B) hsp-70 (p = 0.465), C) mstn (p = 0.907), D) ube3a (p = 0.205), E) ras (p = 0.041), F) cry-1 (p = 0.001), G) per-1 (p = 0.001), H) ogt (p = 0.001) and I) acly (p = 0.025).
Fig 3 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 3: IBR analyses of relative gene expression in Balbina (BA) and Brumado (BRU) populations. The IBR values are 42.7 (Balbina) and 6.79 (Brumado).
Fig 2 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 2: Heatmap of relative expression in Balbina (BA) and Brumado (BRU) populations. The colour scale ranges from blue (low transcript levels) to red (high transcript levels).
Data files for "Quantifying the global climate feedback from energy-based adaptation"
<p>Data files for "Quantifying the global climate feedback from energy-based adaptation".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.</p> <p>Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p> <p> </p> <p><strong>Attribution:</strong></p> <p><br>Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources: </p> <p>IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved.</p> <p>IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.</p> <p> </p>
Climate adaptation and genetic differentiation in the mosquito species Culex tarsalis
<p>The increasing prevalence of vector-borne diseases around the world highlights the pressing need for an in-depth exploration of the genetic and environmental factors that shape the adaptability and widespread distribution of mosquito populations. This research focuses on <em>Culex tarsalis</em>, a principal vector for various viral diseases including West Nile Virus (WNV). Through the development of a new reference genome and the examination of Restriction-Site Associated DNA sequencing (RAD-seq) data from over 300 individuals and 28 locations, we demonstrate that variables such as temperature, evaporation rates, and the density of vegetation significantly impact the genetic makeup of <em>Cx. tarsalis</em> populations. Among the alleles most strongly associated with environmental factors is a nonsynonymous mutation in a key gene related to circadian rhythms. These results offer new insights into the mechanisms of spread and adaptation in a key North American vector species, which is poised to become a growing health threat to both humans and animals in the face of ongoing climate change.</p>
Dataset for digital twins for managing bridge climate change adaptation
<p><span>This is the dataset for embedding in the novel digital twin driven by</span><span> BIM technology to manage the climate change adaptation measures for the bridges. A 6D BIM model has been established and embeded with change adaptation measures, timeline schedule, climate change adaptation cost estimation, and carbon emission estimation.</span></p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
Large scale experiments to improve monopile scour protection design adapted to climate change
<p>Offshore wind farms contribute significantly to contemporary renewable energy production. By installing these offshore structures, new technical design challenges arise, such as foundation optimisation. Present LCoE (Levelized Cost of Electricity) of offshore wind turbines amounts up to 170 Euro/MWh (Crown Estate, 2015), but the ambition is to reduce this by 2020 to 90 Euro/MWh (EY, 2015). Offshore wind turbine foundation costs are 20 % of the total costs in the case of a monopile (NREL, 2014). An important part of those costs is related to the foundation's scour protection. Therefore, optimisations in the design of the scour protection are indispensable.<br>Another promising track to reduce the costs of offshore wind turbines is their lifetime extension. Recent studies (Crown Estate, 2015) show that a 5 year lifetime extension can reduce the cost per kWh by 6 %. To check the feasibility of a lifetime extension, it will be necessary to diagnose or inspect the conditions of several core parts of the turbines, notably its foundation and scour protection. Therefore, more fundamental insight into the (longer term damage) behaviour of the scour protection around the monopile is needed.<br>Beside the interest in design optimisation and lifetime extension, the influence of climate change needs to be investigated in more detail. Climate change will increase the design storm conditions and influence the scour protection stability. Therefore, research towards a risk-based design will help to evaluate the functionality of scour protection already installed and improve the design of future scour protections adapted to climate change.<br>Based on the above motivations, the main research objective is to establish a basic benchmark dataset on the stability of scour protection around monopile foundations to serve as a basis for model tests in other flumes in the future (rather than to carry out a traditional sensitivity study with a fine resolution for all governing parameters). <br>To achieve the goals, scour researchers from several institutions are set to start work on a collaborative project at HR Wallingford's Fast Flow Facility (FFF) as part of PROTEUS, an EU-funded Hydralab+ project. Hydralab+ which is funded by the EU's Horizon 2020 Research and Innovation Programme brings together facilities and researchers in experimental hydraulic and hydrodynamics.<br>The research project aims to improve the design of scour protection around offshore wind turbine monopiles, as well as future-proofing them against the impacts of climate change. <br>PROTEUS, which stands for the 'PRotection of Offshore wind Turbine monopilEs against Scouring' will facilitate the conducting of a series of large scale experiments over a seven week period in the FFF flume at HR Wallingford's UK physical modelling facilities.<br>Partners involved in PROTEUS are: Department of Civil Engineering at Ghent University, HR Wallingford (UK), the Ludwig-Franzius Institute for Hydraulic, Estuarine and Coastal Engineering at the University of Hannover, the Faculty of Engineering at the University of Porto, the Geotechnics division of the Belgian Department of Mobility and Public Works, and International Marine and Dredging Consultants (IMDC nv).</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.