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ICARIA: spatially distributed climate projections from statistical downscaling
<p><strong>ICARIA </strong>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, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a <strong>100x100m resolution grid, </strong>which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes.</p> <p>For further details, find here a brief of the <strong>methodology </strong>followed:<strong> </strong></p> <p><strong>----- </strong></p> <p><em>The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required.</em></p> <p><em>For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach.</em></p> <p><em>For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”.</em></p> <p><em>For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs.</em></p> <p><em>This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future.</em></p> <p>-----</p> <p>The dataset shared here includes information for the three case studies tackled in ICARIA: <strong>Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR)</strong>. The information provided covers data and outcomes by 10 models belonging to CMIP6. 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><strong>Table 1</strong>.<em> Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI).</em></p> <div> <div> <table> <tbody> <tr> <td> <p><strong>CMIP6 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>ACCESS-CM2</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>Bi, D. et al (2020)</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</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>CanESM5</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>CMCC-ESM2</p> </td> <td> <p>1,000º x 1,000º</p> </td> <td> <p>Centro Mediterraneo sui Cambiamenti Climatici (CMCC).</p> </td> <td> <p>Cherchi et al, 2018</p> </td> </tr> <tr> <td> <p>CNRM-ESM2-1</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>EC-EARTH3</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>MPI-ESM1-2-HR</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>Müller et al., (2018)</p> </td> </tr> <tr> <td> <p>MRI-ESM2-0</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>NorESM2-MM</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>UKESM1-0-LL</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> </tbody> </table> <p>The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been <strong>spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model</strong> considering diverse adjustments and topographic corrections. The results presented here are the<strong> median of the 10 models used, obtained for each of the 4 SSP</strong>s and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections.</p> <strong>Table 2.</strong> <em>Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Thermal indicators</strong></p> </td> </tr> <tr> <td> <p>TX90 / TX10</p> </td> <td> <p>Warm/cold days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>90 / 10%</p> </td> </tr> <tr> <td> <p>HD</p> </td> <td> <p>Heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>EHD</p> </td> <td> <p>Extreme heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 35 °C</p> </td> </tr> <tr> <td> <p>TR</p> </td> <td> <p>Tropical nights</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 20 °C</p> </td> </tr> <tr> <td> <p>EQ</p> </td> <td> <p>Equatorial nights</p> </td> <td> <p>AEMet 2020, ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 25 °C</p> </td> </tr> <tr> <td> <p>IN</p> </td> <td> <p>Infernal nights</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>FD</p> </td> <td> <p>Frost days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>< 0 °C</p> </td> </tr> <tr> <td> <p>Max consec</p> </td> <td> <p>Max spell length for above thermal indicators</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>nd</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Nº events</p> </td> <td> <p>Number of above thermal indicators events</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>ne</p> </td> <td> <p>> 3 days</p> </td> </tr> <tr> <td> <p>TXm</p> </td> <td> <p>Mean maximum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TNm</p> </td> <td> <p>Mean minimum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TM</p> </td> <td> <p>Mean temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TA</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>HWle</p> </td> <td> <p>Heatwave length</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWim/HWix</p> </td> <td> <p>Mean and maximum heatwave intensity</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWf</p> </td> <td> <p>Heatwave frequency</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>ne</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWd</p> </td> <td> <p>Heatwave days</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HI - P90</p> </td> <td> <p>Heat Index (percentile 90)</p> </td> <td> <p>NWS (1994)</p> </td> <td> <p>TX, RH</p> </td> <td> <p>°C</p> </td> <td> <p>TX>27 °C, HR> 40%</p> </td> </tr> <tr> <td> <p>UTCI</p> </td> <td> <p>Universal Thermal Climate Index</p> </td> <td> <p>Bröde et al. (2012)</p> </td> <td> <p>TA<br>RH, W</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>UHI</p> </td> <td> <p>Isla de calor (BCN) anual y estacional</p> </td> <td> <p>AMB, Metrobs 2015</p> </td> <td> <p>T</p> </td> <td> <p>°C</p> </td> <td> <p>TM1-TM2 > 0 °C</p> </td> </tr> <tr> <td> <p><strong>Precipitation indicators</strong></p> </td> </tr> <tr> <td> <p>R20</p> </td> <td> <p>Number of heavy precipitation days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>20 mm</p> </td> </tr> <tr> <td> <p>R50, R100</p> </td> <td> <p>Days with extreme heavy rain</p> </td> <td> <p>AMB et al. (2017)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>50mm</p> <p>>100mm</p> </td> </tr> <tr> <td> <p>Ra</p> </td> <td> <p>Yearly and seasonal rainfall relative change</p> </td> <td> <p>ICARIA</p> </td> <td> <p>P</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>IDF - CCF</p> </td> <td> <p>IDF Curves - Climate Change Factor</p> </td> <td> <p>Arnbjerg-Nielsen (2012)</p> </td> <td> <p>P</p> </td> <td> <p>-</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Forest fire indicators</strong></p> </td> </tr> <tr> <td> <p>Mean FWI</p> </td> <td> <p>Mean Canadian FWI in fire season</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>.</p> </td> <td> <p>June-<br>September</p> </td> </tr> <tr> <td> <p>Very High FWI</p> </td> <td> <p>Very High Canadian FWI</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>nd</p> </td> <td> <p>FWI > 38</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>Table 3</strong>. <em>Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em></p> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Drought indicators</strong></p> </td> </tr> <tr> <td> <p>CDDx</p> </td> <td> <p>Maximum dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>CDDm</p> </td> <td> <p>Mean dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>SPI</p> </td> <td> <p>SPI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>McKee et al. (1993) </p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>SPEI</p> </td> <td> <p>SPEI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>Vicente-Serrano et al. (2010)</p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Oceanic indicators</strong></p> </td> </tr> <tr> <td> <p>SS</p> </td> <td> <p>Storm surge</p> </td> <td> <p>Bryant et al. (2016)</p> </td> <td> <p>MT</p> </td> <td> <p>cm</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>OW</p> </td> <td> <p>Significant/maximum wave height</p> </td> <td> <p>ICARIA</p> </td> <td> <p>WH</p> </td> <td> <p>m</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Wind indicators</p> </td> </tr> <tr> <td> <p>EWG</p> </td> <td> <p>Extreme wind gusts</p> </td> <td> <p>ICARIA</p> </td> <td> <p>W</p> </td> <td> <p>km/h</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> </div> <p> </p> </div> </div>
DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions
<p><span>Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.</span></p>
Dataset for the study Multisensory spatial perception in visually impaired infants
<p>Data from the study "Multisensory spatial perception in visually impaired infants". Data are in textual tab-delimited format.</p> <p> </p> <p>Summary</p> <p>Congenitally blind infants are not only deprived of visual input but also of visual influences on the intact senses. The important role that vision plays in the early development of multisensory spatial perception<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1">1</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2">2</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib3">3</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib4">4</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib5">5</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib6">6</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib7">7</a> (e.g., in crossmodal calibration<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a> and in the formation of multisensory spatial representations of the body and the world<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1"><sup>1</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2"><sup>2</sup></a>) raises the possibility that impairments in spatial perception are at the heart of the wide range of difficulties that visually impaired infants show across spatial,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib11">11</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib12">12</a> motor,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib13">13</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib14">14</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib15">15</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib16">16</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib17">17</a> and social domains.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8"><sup>8</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib18"><sup>18</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib19"><sup>19</sup></a> But investigations of early development are needed to clarify how visually impaired infants’ spatial hearing and touch support their emerging ability to make sense of their body and the outside world. We compared sighted (S) and severely visually impaired (SVI) infants’ responses to auditory and tactile stimuli presented on their hands. No statistically reliable differences in the direction or latency of responses to <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/auditory-stimulation">auditory stimuli</a> emerged, but significant group differences emerged in responses to tactile and audiotactile stimuli. The visually impaired infants showed attenuated audiotactile spatial integration and interference, weighted more tactile than auditory cues when the two were presented in conflict, and showed a more limited influence of representations of the external layout of the body on tactile spatial perception.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib20"><sup>20</sup></a> These findings uncover a distinct phenotype of multisensory spatial perception in early postnatal visual deprivation. Importantly, evidence of audiotactile spatial integration in visually impaired infants, albeit to a lesser degree than in sighted infants, signals the potential of multisensory rehabilitation methods in early development.</p> <p>Orienting responses and reaction times (RT) are reported, based on the scoring of two independent naive raters, for each trial of each subject, group (SVI/S), posture (Uncrossed/Crossed), and sensory condition (Tactile only, Auditory only, Audiotactile congruent, Audiotactile incongruent).</p> <p>Trial is the trial number, condition is the sensory condition, audio and tactile respectively refer to the side of the stimulated hand, response_status reports if the response is defined or undefined, response modality reports if the modality used by subjects to respond/not to respond to stimuli (hand, eye, both hands, no motion), group is if the subject was a sighted (S) or a severely visually impaired (SVI) infant, age_mounth is the age expressed in months, RT_rater1, RT_rater 2 and RT are respectively the RT assigned by the two raters and the merge of the two estimations (for RTs, the mean), the same organization for response_side, and for response_modality (for those variables, when the estimation of the two raters did not agree, the merged classification was set to unknown, that is uncertain/undefined).</p>
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</p>
Spatial and Temporal Patterns in Atmospheric Deposition of Dissolved Organic Carbon
Atmospheric deposition of dissolved organic carbon (DOC) to terrestrial ecosystems is a small, but rarely studied component of the global carbon (C) cycle. Emissions of volatile organic compounds (VOC) and organic particulates are the sources of atmospheric C and deposition represents a major pathway for the removal of organic C from the atmosphere. Here, we evaluate the spatial and temporal patterns of DOC deposition using 70 datasets at least one year in length ranging from 40° south to 66° north latitude. Globally, the median DOC concentration in bulk deposition was 1.7 mg L-1. The DOC concentrations were significantly higher in tropical (< 25°) latitudes compared to temperate (> 25°) latitudes. DOC deposition was significantly higher in the tropics because of both higher DOC concentrations and precipitation. Using the global median or latitudinal specific DOC concentrations leads to a calculated global deposition of 202 or 295 Tg C yr-1 respectively. Many sites exhibited seasonal variability in DOC concentration. At temperate sites, DOC concentrations were higher during the growing season; at tropical sites, DOC concentrations were higher during the dry season. Thirteen of the thirty-four long-term (> 10 years) datasets showed significant declines in DOC concentration over time with the others showing no significant change. Based on the magnitude and timing of the various sources of organic C to the atmosphere, biogenic VOCs likely explain the latitudinal pattern and the seasonal pattern at temperate latitudes while decreases in anthropogenic emissions are the most likely explanation for the declines in DOC concentration.
Ovenbird song recordings from Alberta (Canada) with individual labels and spatial locations, 2015-2016
This dataset includes spatially localized and individually identified Ovenbird songs. We used automated species detection and acoustic localization to localize Ovenbird singing events from microphone arrays in Alberta, Canada (2015-2016). We then hand-annotated songs to individuals based on acoustic characteristics. This dataset includes the manual annotations and annotations from automated individual identification approaches. This data publication pertains to the manuscript [in prep] by Lapp et al on Ovenbird individual identification and provides further details on the study and the individual identification approach.
Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009
Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.
MCR LTER: Coral Reef: Patterns and implications of spatial covariation in herbivore functions on resilience of coral reefs
These data and code were generated in support of the manuscript: Cook DT, Holbrook SJ, and Schmitt RJ, Scientific Reports. In 2017, we collected biological and physical data from 20 sites along the north shore of Moorea, French Polynesia, to investigate spatial patterns in grazing and browsing functions of herbivorous fishes, environmental correlates, and implications for coral resilience. In addition to the data collected at the 20 north shore sites, we conducted a 10-day field experiment to assess the relationship between browsing intensity and potential of reversing a coral-to-macroalgae shift. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2025).
MCR LTER: Coral Reef: Spatial portfolios in coral metapopulations are shaped by spatiotemporal asynchrony in environmental conditions; Data for Srednick et al., 2026 Ecology Letters
Using wavelet analyses of a 19-year coral community timeseries from Moorea, French Polynesia, we quantified timescale-specific population synchrony in four common coral genera and evaluated the predictors of spatial portfolio effects. We detected synchrony within genera associated with synchrony in degree heating days, diurnal temperature range (DTR), and macroalgal cover at different timescales. Synchrony in DTR and macroalgal cover was associated with lower synchrony of Pocillopora and Porites populations, respectively. Population (for three of four genera) and environmental synchrony were stronger within than among habitats across timescales, underscoring the role of habitat-specific conditions in driving spatial synchrony and spatial portfolios. These results describe how the spatial and temporal scales of heterogeneity in environmental and ecological conditions determine synchrony in coral population dynamics and support a spatial portfolio effect, which may buffer coral metapopulations from island-scale collapse. Data in support of analyses for: Spatial portfolios in coral metapopulations are shaped by spatiotemporal asynchrony in environmental conditions. Published in Ecology Letters 2026.
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Spatial Variability in Marsh Vulnerability and Coastal Forest Loss in Chesapeake Bay
Sea level rise (SLR) and saltwater intrusion are driving shifts in coastal ecosystems that must migrate to survive. Marsh migration into adjacent uplands is a primary mechanism for sustaining coastal marshes, but potentially limited by natural and anthropogenic barriers. In this study, we focus on the Chesapeake Bay as a case study and combine previous delineations of the marsh-forest boundary and high-resolution topobathymetric data with sea level rise predictions to uniquely assess marsh migration potential on the scale of U.S. Geological Survey HUC10 watersheds. Combining these predictions results in a high-resolution Chesapeake Bay-wide assessment of marsh migration potential through the end of the century. Additionally, we analyze high-resolution land use data within the potential migration area to assess what ecosystems are at risk of loss to marsh via salinization and what potential anthropogenic features exist in the marsh migration corridor. The data consists of 3 files created from analyses conducted during the study: 1) A table summarizing characteristics of the study sites, including elevation and land use, and 2) A zipped Shapefile containing the boundaries of the HUC10 watersheds, 3) A zipped raster (CB_MarshMigrationArea.tif) of elevation categories. Cell values indicate: 1 = area below threshold elevation 2 = area between threshold elevation and 0.5 m of SLR. 3 = area between 0.5 and 1 m of SLR. 4 = area between 1 and 1.5 m of SLR. 5 = area between 1.5 and 2 m of SLR. 6 = area between 2 and 2.5 m of SLR. 7 = area above 2.5 m of SLR. Additionally, uploaded are 10 additional files containing the exact copies of the publicly available data we analyzed to create the above files. To obtain these files from their original sources (i.e. USGS, NOAA, etc) please see the links provided in the Metadata-LO-Letters-dat-V3.rtf file. 1) Points at the marsh-forest boundary 2) Chesapeake Conservancy High-Resolution Land Use 3) Chesapeake Conservancy High-Resolution L
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Open Database of Spatial Room Impulse Responses at Detmold University of Music
<p>This repository contains an open source database of Spatial Room Impulse Responses (SRIR) captured at three different performance spaces of the Detmold University of Music. It includes the following rooms: </p> <ul> <li>Detmold Konzerthaus (medium sized concert hall, ~600 seats).</li> <li>Brahmssaal (small music chamber room, ~100 seats).</li> <li>Detmold Sommertheater (theater, ~300 seats).</li> </ul> <p>The collection contains approximately 600 multichannel RIRs corresponding to several source and receiver configurations. For each room we include measurement positions on stage and at the audience area captured with both an artificial head and an open microphone array compatible with the Spatial Decomposition Method (SDM).</p> <p>The Detmold Konzerthaus holds a large scale Wave Field Synthesis system and a Room Acoustic Enhancement System. SRIRs of an ensemble of focused sources on stage and with conditions of increased artificial reverberation are also included.</p> <p>If you use this dataset for your research, please cite our work:</p> <p>Amengual Gari, S. V.; Sahin, B.; Eddy, D; Kob, M.: <strong>"Open Database of Spatial Room Impulse Responses at Detmold University of Music"</strong>, <em>149th Convention of the Audio Engineering Society, </em>2020.</p> <p> </p> <p>The database is organized in 3 sets:</p> <p><strong>- Set A: </strong></p> <p>Source: Single Source measurements.</p> <p>Receiver: Open Array and Dummy Head.</p> <p>Rooms: BS, DST, KH</p> <p>Special configurations: Artificial reverberation, music stand on stage</p> <p><strong>- Set B: </strong></p> <p>Source: Loudspeaker and WFS orchestra</p> <p>Receiver: Open Array.</p> <p>Rooms: KH</p> <p><strong>- Set C:</strong></p> <p>Source: Loudspeaker orchestra</p> <p>Receiver: Dummy Head and Omni8 array</p> <p>Rooms: KH</p> <p> </p> <p>Further details on the measurement procedure and acoustical analysis of the RIRs can be found in the following publications:</p> <p><strong>Set A</strong></p> <p>Amengual Gari, S. V., Investigations on the Influence of Acoustics on Live Music Performance using Virtual Acoustic Methods, Ph.D. thesis, 2017.</p> <p>Amengual Garí, S. V.; Kob, M: "Investigating the impact of a music stand on stage using spatial impulse responses". 142nd Convention of the Audio Engineering Society, Berlin, May 2017.</p> <p><strong>Set B</strong></p> <p>Amengual Garí, S. V.; Pätynen, J.; Lokki, T.: "Physical and perceptual comparison of real and focused sound sources in a concert hall". Journal of the Audio Engineering Society, vol. 64 (12), pp. 1014-1025, December 2016.</p> <p><strong>Set C</strong></p> <p>Sahin, B., ““Investigation of the Detmold Concert Hall auditorium acoustics by comparing preference ratings and objective measurements.”, M.Sc. Thesis, 2017.</p> <p>Sahin, B., Amengual, S. V., and Kob, M., “Investigating listeners’ preferences in Detmold Concert Hall by comparing sensory evaluation and objective measurements,” Proc. 43th DAGA, Kiel, 2017.<br> </p>
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</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.