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2,837 results for “Climate Data”
Data from: Water use by Swedish boreal forests in a changing climate
The rising levels of atmospheric carbon dioxide concentration ([CO2]) and temperature have the potential to substantially affect the terrestrial water and energy balance by altering the stomatal conductance and transpiration of trees. Many models assume decreases in stomatal conductance and plant water use under rising [CO2], which has been used as a plausible explanation for the positive global trend in river run-off over the past century. Plant water use is, however, also affected by changes in temperature, precipitation and land use, and there is yet no consensus about the contribution of different drivers to temporal trends of evapotranspiration (ET) and river run-off. In this study, we assessed water-use responses to climate change by using both long-term monitoring and experimental data in Swedish boreal forests. Historical trends and patterns in ET of large-scale boreal landscapes were determined using climate and run-off data from the past 50 years, while explicit tree water-use responses to elevated [CO2] and/or air temperature were examined in a whole-tree chamber experiment using mature Norway spruce (Picea abies (L.) Karst.) trees. The results demonstrated that ET estimated from water budgets at the catchment scale increased by 18% over the past 50 years while run-off did not significantly change. The increase in ET was related to increasing precipitation and a steady increase in forest standing biomass over time. The whole-tree chamber experiment showed that Norway spruce trees did not save water under elevated [CO2] and that experimentally elevated air temperature did not increase transpiration as decreased stomatal conductance cancelled the effect of higher vapour pressure deficit in warmed air. Our findings have important implications for projections of future water use of European boreal coniferous forests, indicating that changes in precipitation and standing biomass are more important than the effects of elevated [CO2] or temperature on transpiration rates.
Data from: Microhabitat and body size effects on heat tolerance: implications for responses to climate change (army ants: Formicidae, Ecitoninae)
1. Models that predict organismal and population responses to climate change may be improved by considering ecological factors that affect species thermal tolerance. Species differences in microhabitat use can expose animals to diverse thermal selective environments at a given site and may cause sympatric species to evolve different thermal tolerances. 2. We tested the hypothesis that species differences in body size and microhabitat use (above- vs. below-ground activity) would correspond to differences in thermal tolerance (maximum critical temperatures: CTmax). Thermal buffering effects of soil can reduce exposure to extreme high temperatures for below-ground active species. We predicted larger-bodied individuals and species would have higher CTmax and that species mean CTmax would covary positively with degree of above-ground activity. We used Neotropical army ants (Formicidae: Ecitoninae) as models. Army ants vary in microhabitat use from largely subterranean to largely above-ground active species and are highly size polymorphic. 3. We collected data on above- and below-ground temperatures in habitats used by army ants to test for microhabitat temperature differences, and we conducted CTmax assays for army ant species with varying degrees of surface activity and with different body sizes within and between species. We then tested whether microhabitat use was associated with species differences in CTmax and whether microhabitat was a better predictor of CTmax than body size for species that overlapped in size. 4. Microhabitat use was a highly significant predictor of species' upper thermal tolerance limits, both for raw data and after accounting for the effects of phylogeny. Below-ground species were more thermally sensitive, with lower maximum critical temperatures (CTmax). The smallest workers within each species were the least heat tolerant, but the magnitude of CTmax change with body size was greater in below-ground species. Species-typical microhabitat was a stronger predictor of CTmax than body size for species that overlapped in size. Compared to the soil surface, 10-cm subsoil was a significantly moderated thermal environment for below-ground army ants, while maximum surface raid temperatures sometimes exceeded CTmax for the most thermally sensitive army ant castes. 5. We conclude sympatric species differences in thermal physiology correspond to microhabitat use. These patterns should be accounted for in models of species and community responses to thermal variation and climate change.
Data from: Adaptive limitations of white spruce populations to drought imply vulnerability to climate change in its western range
A cost-effective climate change adaptation strategy for the forestry sector is to move seed sources to more northern and higher elevation planting sites as part of ongoing reforestation programs. This is meant to match locally adapted populations with anticipated environments, but adaptive traits do not always show population differences suitable to mitigate climate change impacts. For white spruce, drought tolerance is a critical adaptive trait to prevent mortality and productivity losses. Here, we use a 40 year old provenance experiment that has been exposed to severe drought periods in 1999 and 2002 to retrospectively investigate drought response and the adaptive capacity of white spruce populations across their boreal range. Relying on dendrochronological analysis under experimentally controlled environments, we evaluate population differences in resistance, resilience and recovery to these extreme events. Results showed evidence for population differentiation in resistance and recovery parameters, but provenances conformed to approximately the same growth rates under drought conditions and had similar resilience metrics. The lack of populations with better growth rates under drought conditions is contrary to expectations for a wide-ranging species with distinct regional climates. Populations from the wettest environments in the northeastern boreal were surprisingly drought tolerant, suggesting that these populations would readily resist water deficits projected for the 2080s, and supporting the view that northeastern Canada will provide a refugium for boreal species under climate change. The findings also suggest that white spruce is sensitive to growth reductions under climate change in the western boreal. The study highlights that population differentiation in adaptive capacity is species and trait specific, and we provide a counter example for drought tolerance traits, where assisted migration prescriptions may be ineffective to mitigate climate change impacts. For resource managers and policy makers, we provide maps where planning for widespread declines of boreal white spruce forests may be unavoidable.
Data from: Tracking climate change in a dispersal-limited species: reduced spatial and genetic connectivity in a montane salamander
Tropical montane taxa are often locally adapted to very specific climatic conditions, contributing to their lower dispersal potential across complex landscapes. Climate and landscape features in montane regions affect population genetic structure in predictable ways, yet few empirical studies quantify the effects of both factors in shaping genetic structure of montane-adapted taxa. Here, we considered temporal and spatial variability in climate to explain contemporary genetic differentiation between populations of the montane salamander, Pseudoeurycea leprosa. Specifically, we used ecological niche modelling (ENM) and measured spatial connectivity and gene flow (using both mtDNA and microsatellite markers) across extant populations of P. leprosa in the Trans-Mexican Volcanic Belt (TVB). Our results indicate significant spatial and genetic isolation among populations, but we cannot distinguish between isolation by distance over time or current landscape barriers as mechanisms shaping population genetic divergences. Combining ecological niche modelling, spatial connectivity analyses, and historical and contemporary genetic signatures from different classes of genetic markers allows for inference of historical evolutionary processes and predictions of the impacts future climate change will have on the genetic diversity of montane taxa with low dispersal rates. Pseudoeurycea leprosa is one montane species among many endemic to this region and thus is a case study for the continued persistence of spatially and genetically isolated populations in the highly biodiverse TVB of central Mexico.
Data from: The influence of Late Quaternary climate-change velocity on species endemism
The effects of climate change on biodiversity should depend in part on climate displacement rate (climate-change velocity) and its interaction with species' capacity to migrate. We estimated Late Quaternary glacial-interglacial climate-change velocity by integrating macroclimatic shifts since the Last Glacial Maximum with topoclimatic gradients. Globally, areas with high velocities were associated with marked absences of small-ranged amphibians, mammals and birds. The association between endemism and velocity was weakest in the highly vagile birds and strongest in the weakly dispersing amphibians, linking dispersal ability to extinction risk due to climate change. High velocity was also associated with low endemism at regional scales, especially in wet and aseasonal regions, where conditions otherwise favor high richness. Overall, we show that low-velocity areas are essential refuges for Earth's many small-ranged species.
Data from: Ancient vicariance and climate-driven extinction explain continental-wide disjunctions in Africa: the case of the Rand Flora genus Canarina (Campanulaceae)
Transoceanic distributions have attracted the interest of scientists for centuries. Less attention has been paid to the evolutionary origins of 'continent-wide' disjunctions, in which related taxa are distributed across isolated regions within the same continent. A prime example is the 'Rand Flora' pattern, which shows sister taxa disjunctly distributed in the continental margins of Africa. Here, we explore the evolutionary origins of this pattern using the genus Canarina, with three species: C. canariensis, associated with the Canarian laurisilva, and C. eminii and C. abyssinica, endemic to the Afromontane region in East Africa, as case study. We infer phylogenetic relationships, divergence times and the history of migration events within Canarina using Bayesian inference on a large sample of chloroplast and nuclear sequences. Ecological niche modelling was employed to infer the climatic niche of Canarina through time. Dating was performed with a novel nested approach to solve the problem of using deep time calibration points within a molecular dataset comprising both above-species and population-level sampling. Results show C. abyssinica as sister to a clade formed by disjunct C. eminii and C. canariensis. Miocene divergences were inferred among species, whereas infraspecific divergences fell within the Pleistocene–Holocene periods. Although C. eminii and C. canariensis showed a strong genetic geographic structure, among-population divergences were older in the former than in the latter. Our results suggest that Canarina originated in East Africa and later migrated across North Africa, with vicariance and aridification-driven extinction explaining the 7000 km/7 million year divergence between the Canarian and East African endemics.
Data from: Biodiversity ensures plant-pollinator phenological synchrony against climate change
Climate change has the potential to alter the phenological synchrony between interacting mutualists, such as plants and their pollinators. However, high levels of biodiversity might buffer the negative effects of species-specific phenological shifts and maintain synchrony at the community level, as predicted by the biodiversity insurance hypothesis. Here, we explore how biodiversity might enhance and stabilise phenological synchrony between a valuable crop, apple and its native pollinators. We combine 46 years of data on apple flowering phenology with historical records of bee pollinators over the same period. When the key apple pollinators are considered altogether, we found extensive synchrony between bee activity and apple peak bloom due to complementarity among bee species' activity periods, and also a stable trend over time due to differential responses to warming climate among bee species. A simulation model confirms that high biodiversity levels can ensure plant–pollinator phenological synchrony and thus pollination function.
Data for "Simpson's Law and Spectral Cancellation of Climate Feedbacks"
<p>Line-by-line output from the Reference Forward Model (Dudhia 2016) and plotting scripts for the paper "Simpson's Law and Spectral Cancellation of Climate Feedbacks" published in Geophysical Research Letters. </p>
Sampling data accompanying "An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model"
<p>Datasets recording sampling results, accompanying the manuscript <em>An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model, </em>Rothenberg, D. and Wang, C., submitted, GMD. Please see the included README for more details.</p>
Philippines - Open, validated health, climate, environment and socioeconomic data
<p>The <strong>Project Climate Change, Health, and Artificial Intelligence (Project CCHAIN)</strong> dataset is a validated, open-sourced linked dataset containing 20 years (2003-2022) of climate, environmental, socioeconomic, and health dimensions at the barangay (village) level across twelve Philippine cities (Dagupan, Palayan, Navotas, Mandaluyong, Muntinlupa, Legazpi, Iloilo, Mandaue, Tacloban, Zamboanga, Cagayan de Oro, Davao).</p> <p>The full documentation can be accessed <a href="https://thinkingmachines.github.io/project-cchain">here</a>.</p> <p>The tables are designed in a way that users can <strong>choose variables that are most relevant</strong> to their focus city and use case, and <strong>link these variables</strong> to form a single dataset by merging using standard geography codes and calendar dates. This can be done using the provided <a href="https://drive.google.com/drive/folders/123MrMXSVT6149MvJ8hGoDgN1s-YlQoH_">linking notebook</a>, or offline using the user's own code.</p> <p>Here are some tips on how make most use of this dataset:</p> <ul> <li> <p><strong>Focus on one location.</strong> Starting with a detailed analysis of one location allows for a better understanding of the local dynamics, which may differ across locations.</p> </li> <li> <p><strong>Choose one health data source.</strong> Pick one of either a central or local data source. Using two different data health sources is not advised because it will lead to double/overcounting of disease cases.</p> </li> <li> <p><strong>Do not use all variables at once- do a literature review first to identify possible key variables</strong>. to identify possible key variables. More often than not, using all variables is not necessary and may even yield subpar results.</p> </li> <li> <p><strong>Check data availability</strong> on your focus location and make sure they fit the requirements of your study.</p> </li> </ul>
Infrastructure Climate Resilience Assessment Data Starter Kit for French Guiana
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Data for "Millennial modulation of Atlantic Multidecadal Variability in a climate model"
<p>Processed data and Jupyter notebooks for manuscript "Millennial modulation of Atlantic Multidecadal Variability in a climate model" by Joakim Kjellsson and Wonsun Park</p>
Data for "Climate change will reduce inland wetland areas and disrupt their seasonal regimes in North America"
<p>Dataset in matlab format that used to plot figures in the manuscript of Climate change will reduce inland wetland areas and disrupt their seasonal regimes in North America. Using load Figure#.mat in Figure#.m to plot corresponding figure. </p>
Data from: The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976–2100
<p>Recent climatic trends have increased forest fire activity in Canada. This study aimed to evaluate how forest fire conditions might evolve across the Canadian borael forests in the future and to inform discussion about the impact of climate change on fire danger and severity.</p>
Climate Security on social media: raw and processed data from Twitter
<p>This dataset reflects climate security dialogues on Twitter, from January 2014 to May 2023.</p>
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
Fig. 3 in Data from: Climate niche differentiation between two passerines despite ongoing gene flow
Fig. 3 Sexual dimorphism oc the CF and FM duration oc echolocation pulses emitted bv hanging Hipposideros pratti. A-1, B-1, C-1 ss. A-2, B-2, C-2: the distribution oc CF and FM duration oc echolocation pulses recorded crom male and cemale bats. A-3, B-3, C-3: statistical comparison oc these two temporal parameters oc echolocation pulses recorded crom male and cemale bats (see Fig. 2 cor legends)
Fig. 2 in Data from: Climate niche differentiation between two passerines despite ongoing gene flow
Fig. 2 Sexual dimorphism oc the CF, FM bandwidth, and PRR oc echolocation pulses emitted bv hanging Hipposideros pratti. A-1, B-1, C-1 ss. A-2, B-2, C-2: the distribution oc CF, FM bandwidth, and PRR oc echolocation pulses recorded crom male and cemale bats. A-3, B-3, C-3: statistical comparison oc these three pulse parameters recorded crom male and cemale bats. The number within each parenthesis indicates the sample size. Student's t test: *p <0.05; **p <0.01
Fig. 1 in Data from: Climate niche differentiation between two passerines despite ongoing gene flow
Fig. 1 The sonogram (a) and power spectrum (b) oc a representatise echolocation pulse emitted bv a hanging Hipposideros pratti. The CF (kHz), FM bandwidth (kHz), CF duration (ms), and FM duration (ms) oc this echolocation pulse were 60.0, 12.8, 7.5, and 2.7. Clearlv, the second harmonic is the predominant one that contains the highest energv
Infrastructure Climate Resilience Assessment Data Starter Kit for Morocco
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
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.