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1,465 results for “resilience”
Infrastructure Climate Resilience Assessment Data Starter Kit for Tanzania
<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, 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>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 (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> 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>
Infrastructure Climate Resilience Assessment Data Starter Kit for Ghana
<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, 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>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 (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> 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>
Genomic and environmental influences on resilience in a cold‐water fish near the edge of its range
<p>Small, isolated populations present a challenge for conservation. The dueling effects of selection and drift in a limited pool of genetic diversity make the responses of small populations to environmental perturbations erratic and difficult to predict. This is particularly true at the edge of a species range, where populations often persist at the limits of their environmental tolerances. Populations of cisco, <i>Coregonus artedi</i>, in inland lakes have experienced numerous extirpations along the southern edge of their range in recent decades, which are thought to result from environmental degradation and loss of cold, well-oxygenated habitat as lakes warm. Yet, cisco extirpations do not show a clear latitudinal pattern, suggesting that local environmental factors and potentially local adaptation may influence resilience. Here, we used genomic tools to investigate the nature of this pattern of resilience. We used restriction site-associated DNA capture (Rapture) sequencing to survey genomic diversity and differentiation in southern inland lake cisco populations and compared the frequency of deleterious mutations that potentially influence fitness across lakes. We also examined haplotype diversity in a region of the major histocompatibility complex involved in stress and immune system response. We correlated these metrics to spatial and environmental factors including latitude, lake size, and measures of oxythermal habitat and found significant relationships between genetic metrics and broad and local factors. High levels of genetic differentiation among populations were punctuated by a phylogeographic break and residual patterns of isolation-by-distance. Although the prevalence of deleterious mutations and inbreeding coefficients was significantly correlated with latitude, neutral and non-neutral genetic diversity were most strongly correlated with lake surface area. Notably, differences among lakes in the availability of estimated oxythermal habitat left no clear population genomic signature. Our results shed light on the complex dynamics influencing these isolated populations and provide valuable information for their conservation.</p>
Amazon Rainforest Resilience Data
<p>Dataset and code to accompany 'Pronounced loss of Amazon rainforest resilience since the early 2000s'.</p>
Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'
<p>Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'</p> <p>Contact: C. Scott Watson. c.s.watson@leeds.ac.uk</p> <p>DRR_greenspace<br> DRR_greenspace_polygon.shp - classified potential DRR greenspace (minimum 100 m2)<br> DRR_greenspace_zone_points.shp - classified potential DRR greenspace aggregated to zones<br> DRR_greenspace_zone_polygons_top10.shp - Top 10 maximum capacitated analysis of classified potential DRR greenspace aggregated to zones.<br> <br> Land_cover<br> rf_1986_mode_clipped.tif - 1986 land cover classification<br> rf_2020_mode_clipped.tif - 2020 land cover classification<br> landcover_classes.PNG - land cover classes<br> accuracy_assessment_points_1986 - 1986 land cover accuracy assessment points<br> accuracy_assessment_points_2020 - 2020 land cover accuracy assessment points<br> modified_urban_growth_scenario.shp - hazard-modified urban growth scenario</p> <p> </p>
An event-based precipitation dataset with life cycle evolution using resilient algorithms
<p>The dataset covers eastern Asia at a temporal range of April to June 2016-2020. We identified initial rain clusters (RCs) from the Global Precipitation Measurement 2ADPR dataset and Mesoscale Convective Systems (MCSs) from the Himawari-8 Advanced Himawari Image gridded product. Based on the contours of the initial RCs and MCSs, we then carried out a series of resilient processes, including filtration, segmentation, and consolidation, to obtain the final RCs. The final RCs had a one-to-one correspondence with the relevant MCS. We extracted the RC area, central location, average radar reflectivity profile, average droplet size distribution profile and other precipitation information from the final RCs and retrieved the life cycle evolution of the MCS area, location, and cloud-top brightness temperature from the corresponding MCSs and tracking algorithms. This dataset facilitates studies of the life cycle evolution of precipitation and provides a good foundation for convection parameterizations in precipitation simulations.</p>
Biogeomorphic modeling to assess the resilience of tidal-marsh restoration to sea level rise and sediment supply - Supporting code and data
<p>Code and data to reproduce figures and analyses of the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Vandenbruwaene, W., Vanlede, J., Belliard, J.-P., Fagherazzi, S., Bouma, T.J., van de Koppel, J., and Temmerman, S.: Biogeomorphic modeling to assess resilience of tidal marsh restoration to sea level rise and sediment supply, Earth Surf. Dynam., submitted.</p> <p>Standard Python dependencies:</p> <ul> <li>GDAL</li> <li>Geopandas</li> <li>Matplotlib</li> <li>NumPy</li> <li>Rasterio</li> <li>SciPy</li> <li>Seaborn</li> <li>Shapely</li> <li>scikit-learn</li> </ul> <p>Third-party Python dependencies:</p> <ul> <li>Centerline (https://github.com/fitodic/centerline)</li> <li>pputils (https://github.com/pprodano/pputils)</li> <li>pysheds (https://github.com/mdbartos/pysheds)</li> </ul> <p>In-house Python dependencies:</p> <ul> <li>Demeter 1.0.5 (https://doi.org/10.5281/zenodo.5205258)</li> <li>OGTools 1.1 (https://doi.org/10.5281/zenodo.3994952)</li> <li>TidalGeoPro 0.1 (https://doi.org/10.5281/zenodo.5205285)</li> </ul>
Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'
<p>Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split.</p> <p>Zipped folders of OrthoFinder results:</p> <p>'Results_Feb16' contains results including all 24 coral species from 7 genera (<em>Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites</em>).</p> <p>'gene_sets_acropora_acuminata_only' contains results including just one species per genera with <em>Acropora acuminata</em>.</p> <p>'gene_sets_acropora_cytherea_only' contains results including just one species per genera with <em>Acropora cytherea</em>.</p> <p>'gene_sets_acropora_digitifera_only' contains results including just one species per genera with <em>Acropora digitifera</em>.</p> <p> </p> <p>Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a></p> <p>Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332</p> <p>Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a></p>
Empirical evidence for recent global shifts in vegetation resilience
<p>Data and codes for the publication:</p> <p>Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. <em>Nat. Clim. Chang.</em> (2022). https://doi.org/10.1038/s41558-022-01352-2</p> <p>Data is described in 'README.txt'.</p>
Dataset of Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities
<p>Data used in the article: </p> <p><strong>Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities</strong></p> <p><strong>Abstract</strong></p> <p>1- Climate models forecast changes in the amounts and distribution of rain, which may affect ecosystems worldwide, especially in drylands where water is already the limiting factor for plant life. Annual plant communities are common in drylands where they can complete their entire life cycle during the rainy period while avoiding the dry season. Moreover, seed dormancy allows them to disperse over time by remaining in the seed bank for long periods. However, the extent to which these communities will be able to tolerate increasing drought is uncertain.</p> <p>2- We performed a five-year rainfall reduction treatment under field conditions and determined its effects on annual plant communities in a Mediterranean gypsum ecosystem. We assessed the taxonomic, functional, and phylogenetic diversity of these communities each year for five years.</p> <p>3-The taxonomic and functional diversity decreased under the rainfall reduction treatment whereas the phylogenetic diversity increased. Moreover, the relative importance of species with drought-resistant functional designs increased in the community assemblages. However, after a rainy season with above average rainfall, all of the diversity values recovered completely even under the rainfall reduction treatment.</p> <p>4- Our results provide important insights into the responses of these plant communities under a climate change scenario, where they indicate high losses of diversity during drought events but rapid recovery in milder years.</p> <p><em>Synthesis</em> Our findings highlight the great resilience of annual plant communities in drylands, which may allow them to tolerate increased drought under the present climate change scenario.</p>
How to Develop Resilience False Information?
<p>In this video, you will </p> <ul> <li> <p>Learn about the problems related to false information; </p> </li> <li> <p>Understand two types of false information: misinformation and disinformation; </p> </li> <li> <p>Develop an awareness of information literacy; </p> </li> <li> <p>Discover some strategies to overcome susceptibility to fake news.</p> </li> </ul>
Appendices/Supplementary materials to Hägele, S., Grosse, E.H. and Ivanov, D. (2023) 'Supply chain resilience: a tertiary study', International Journal of Integrated Supply Management, 16 (1), 52-81. https://doi.org/10.1504/IJISM.2023.127660
<p>This document contains supplementary material to the article:</p> <p><em>Hägele, S., Grosse, E.H. and Ivanov, D. (2023) 'Supply chain resilience: a tertiary study', International Journal of Integrated Supply Management, 16 (1), 52-81. https://doi.org/10.1504/IJISM.2023.127660</em></p>
Connectivity and systemic resilience of the Great Barrier Reef
<p>The text file contains the R code needed to reproduce the GLM with <em>tweedie</em> package as shown in the S3 Table. The zip file contains connectivity networks used in manuscript to obtain Figures 2-5 and S1-S4. The networks are provided in a source-sink format, with different release dates and PLDs provided. The actual data to reproduce the figures has been uploaded as a Supplementary Data File (S1 Data) with the manuscript.</p> <p>If using this data please cite:</p> <p>Hock K, Wolff NH, Ortiz JC, Condie SA, Anthony KRN, Blackwell PG, Mumby PJ (2017) Connectivity and systemic resilience of the Great Barrier Reef. PLoS Biol 15(11): e2003355. https://doi.org/10.1371/journal.pbio.2003355</p>
Leak-resilient enzyme-free nucleic acid dynamical systems through shadow cancellation
<p>DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviors. However, strand displacement circuits are susceptible to background noise that disrupts the circuit behavior, commonly known as leaks. The side effects of leaks are particularly severe in circuits with complex dynamical elements (e.g., feedback loops), as their leaks amplify nonlinearly, disrupting the circuit function. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the proposed method introduce a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method's capabilities, the inner workings of its components, and, most importantly, robustness to practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several leak-affected catalytic and autocatalytic dynamical systems of practical importance. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function through simple adjustments to the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work.</p>
When resilience is not enough: 2022 extreme marine heatwave threatens climatic refugia for a habitat-forming Mediterranean octocoral
<p>Climate change is impacting ecosystems worldwide, and the Mediterranean Sea is no exception. Extreme climatic events, such as marine heat waves (MHWs), are increasing in frequency, extent, and intensity during the last decades, which has been associated with an increase in mass mortality events for multiple species. Coralligenous assemblages, where the octocoral <em>Paramuricea clavata</em> lives, are strongly affected by MHWs. The Medes Islands Marine Reserve (NW Mediterranean) was considered a climate refugia for <em>P. clavata</em>, as their populations were showing some resilience to these changing conditions. In this study, we assessed the impacts of the MHWs that occurred between 2016 and 2022 in seven shallow populations of the octocoral <em>P. clavata</em> from a Mediterranean Marine Protected Area. The years that the mortality rates increased significantly were associated with the ones with strong MHWs, 2022 being the one with higher mortalities. In 2022, with 50 MHW days, the proportion of total affected colonies was almost 70%, with a proportion of the injured surface of almost 40%, reaching levels never attained in our study site since the monitoring was started. We also found spatial variability between the monitored populations. Whereas few of them showed low levels of mortality, others lost around 75% of their biomass. The significant impacts documented here raise concerns about the future of shallow <em>P. clavata</em> populations across the Mediterranean, suggesting that the resilience of this species may not be maintained to sustain these populations face the ongoing warming trends.</p>
Electronic Supplementary Material for: The biogeography of population resilience in lowland South America
<p>## Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter</p> <p>This repository holds the code and data for reproducing the analysis in the chapter "The biogeography of population resilience in lowland South America", forthcoming in the Oxford Handbook of Resilience in Climate History. It also contains supplementary information on the statistical modelling undertaken as part of the aforementioned work. </p> <p>The code comprises five principal sections, plus setup:</p> <p>0. Setup & data loading<br>1. Data processing and display<br>2. Radiocarbon analysis<br>3. Statistical modelling<br>4. Output<br>5. Supplementary output</p> <p>The code features an extended version of the p2pPerm function: (https://github.com/philriris/p2pPerm) that was first introduced in Riris and De Souza (2021) (https://doi.org/10.3389/fevo.2021.740629), here called the `resmet` (RESilience METrics) function. </p> <p>In addition, the code is accompanied by three datasets:</p> <p>- A table containing archaeological radiocarbon dates from lowland tropical South America <br>- A shapefile of South American ecoregions, original data available here: http://ecologicalregions.info/data/sa/<br>- A table of domesticated Neotropical plants resolved in Amazonian palaeoecological records, after Iriarte et al. (2020)(https://doi.org/10.1016/j.quascirev.2020.106582)</p> <p>Briefly, the georeferenced radiocarbon data used in the paper have been compiled from a wide range of sources, including Goldberg et al. (2016), Riris & Arroyo-Kalin (2019), Napolitano et al. (2019) Arroyo-Kalin & Riris (2021), De Souza & Riris (2021), and Bird et al. (2022). These sources have been extensively cross-checked for duplicate lab codes, variation in site naming conventions, and reported locations, in order to minimise errors arising from these variables. It does not purport to be error-free, although it is adequate for the current analysis. </p> <p>As well as its use in the production of Figure 1, the ecoregions shapefile has been intersected with the radiocarbon date locations to append this information to `rhdata.csv`. An extended description and rationale for its use can be found in the main text. </p> <p>For the convenience of the end-user, an additional file containing the main results (metrics_regular.csv) is included. The data contained in this table is the subject of section 3 of the code, Statistical Modelling. It forms the basis of the discussion in the chapter. </p> <p>Data cleaning was carried out manually on the raw output of the `resmet` function to remove false positives from the table. These "events" are either: a) statistically significant downturns present in periods where, logically, no humans should be present, e.g. in the Greater Antilles before ~6000 cal BP, or: b) downturns where there are no minima in the summed probability distributions of calibrated radiocarbon dates, returning nonsensical resilience metrics. Removing these data rows introduces errors to the variable Cumulative, which counts the cumulative number of downturns detected by the `permTest` function in `rcarbon`. The file version of the output in this repository should be considered authoritative for present purposes, as these counting errors in Cumulative have been manually fixed too. </p> <p>### References</p> <p>- Arroyo-Kalin, M. and Riris, P. 2021. Did pre-Columbian populations of the Amazonian biome reach carrying capacity during the Late Holocene? *Phil. Trans. R. Soc. B* 376: 20190715 http://doi.org/10.1098/rstb.2019.0715</p> <p>- Bird, D., Miranda, L., Vander Linden, M., Robinson, E., Bocinsky, R.K., Nicholson, C., Capriles, J.M., Finley, J.B., Gayo, E.M., Gil, A. and d’Alpoim Guedes, J. 2022. p3k14c, a synthetic global database of archaeological radiocarbon dates. *Scientific Data* 9: 1-19. https://doi.org/10.1038/s41597-022-01118-7</p> <p>- De Souza, J.G. & Riris, P. 2021. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. *Journal of Archaeological Science*. 125: 105293. https://doi.org/10.1016/j.jas.2020.105293</p> <p>- Goldberg, A., Mychajliw, A.M. and Hadly, E.A. 2016. Post-invasion demography of prehistoric humans in South America. *Nature* 532: 232-235. https://doi.org/10.1038/nature17176 </p> <p>- Iriarte, J., Elliott, S., Maezumi, S.Y., Alves, D., Gonda, R., Robinson, M., de Souza, J.G., Watling, J. and Handley, J. 2020. The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. _Quaternary Science Reviews_ 248: 106582. https://doi.org/10.1016/j.quascirev.2020.106582 </p> <p>- Napolitano MF, DiNapoli RJ, Stone JH, Levin MJ, Jew NP, Lane BG, O’Connor JT, Fitzpatrick SM. 2019. Reevaluating human colonization of the Caribbean using chronometric hygiene and Bayesian modeling. _Science Advances_. 5: eaar7806. https://doi.org/10.1126/sciadv.aar7806</p> <p>- Riris, P. and Arroyo-Kalin, M. 2019. Widespread population decline in South America correlates with mid-Holocene climate change. *Scientific Reports* 9: 6850. https://doi.org/10.1038/s41598-019-43086-w</p> <p>- Riris, P. and De Souza, J.G. 2021. Formal tests for resistance-resilience in archaeological time series. _Frontiers in Ecology and Evolution_, 9. https://doi.org/10.3389/fevo.2021.740629</p> <p> </p>
Data and code for high-resolution climate-resilient corridor mapping in southwestern Costa Rica (beta)
<p>High-resolution mapping and validation of potential climate-resilient corridors in southwestern Costa Rica. A fully documented, complete version of this repository will be archived with a DOI upon manuscript publication.</p>
Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Original dataset on Forest Resilience Indicators and their compliance with Open data criteria)
<p>This dataset represents the original analysis on which my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor". </p> <ul> <li>The first sheet contains the coding on information systems on <strong>bird species occurrence</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The second sheet contains the coding on information systems on <strong>tree species distribution</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The third sheet contains the coding on information systems on <strong>soil water conditions</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fourth sheet contains the coding on information systems on <strong>canopy cover</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fifth sheet contains the coding on information systems on <strong>carbon sequestration</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The sixth sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The seventh sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages, ranked from highest to lowest compliance</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The last sheet gives <strong>information on the coding</strong>. </li> </ul>
Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors: Dataset
<p>This dataset is linked to the paper “Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors" submitted to Science of The Total Environment.</p>
Trophic state resilience to hurricane disturbance of Lake Yojoa, Honduras (DATA)
<p><strong>Datasets for manuscript entitled "Trophic state resilience to hurricane disturbance of Lake Yojoa, Honduras"</strong></p> <p><strong>Abstract:</strong> Cyclones are a poorly described disturbance in tropical lakes, with the potential to alter ecosystems and compromise the services they provide. In November 2020, Hurricanes Eta and Iota made landfall near the Nicaragua-Honduras border, inundating the region with a large amount of late-season precipitation. To understand the impact of these storms on Lake Yojoa, Honduras, we compared 2020 and 2021 conditions using continuous (every 16 days) data collected from five pelagic locations. The storms resulted in increased Secchi depth and decreased algal abundance in December 2020, and January and February 2021, and lower-than-average accumulation of hypolimnetic nutrients from the onset of stratification (April 2021) until mixus in November 2021. Despite the reduced hypolimnetic nutrient concentrations, epilimnetic nutrient concentrations returned to (and in some cases exceeded) pre-hurricane levels following annual water column turnover in 2021. This response suggests that Lake Yojoa’s trophic state had only an ephemeral response to the disturbance imposed by the two hurricanes, likely due to internal input of sediment derived nutrients. These aseasonal storms acted as a large-scale experiment that resulted in nutrient dilution and demonstrated the resilience of Lake Yojoa’s trophic state to temporary nutrient reductions.</p>
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.