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163 results for “mortality risk”

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zenodo44/100

Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks

<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes&#39; contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p>&nbsp;</p> <p>Abbreviation for column:</p> <p>X&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Longitude<br> Y&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Latitude<br> LAImax&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Leaf area index max reach<br> Nha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Density per hectar<br> Vha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Volume per hectar<br> NEE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Net ecosystem exchange<br> NPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;net primary production<br> Reco&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Respiration ecosystem<br> GPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Gross primary production<br> Etveg&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration canopy<br> Etsol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration sol<br> TR&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;tree transpiration<br> ETP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;evapotranspiration potentiel<br> BiomassOfReserves&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Biomass of reserve<br> rw&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;ring width<br> dbh&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;diameter at breast heast<br> height&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;height<br> BBday&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Budburst date<br> rFD&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of frost<br> CRIM_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum combined risk index of mortality reach<br> rNSC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of carbon starvation<br> rPLC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of embolism<br> rPLC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of embolism reach<br> CRIM&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;combined risk index of mortality<br> Climate&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Climatic model<br> rNSC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;maximum risk of carbon starvation reach<br> rFD_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of frost&nbsp; reach<br> Scenario_Sylvicol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;null means no silvulcture simulated<br> species&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;species<br> Country&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Country<br> alt_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;altitude of climate simulated<br> grid_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of WATCH<br> grid_eurocordex&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of Eurocordex<br> Pinus_sylvestris&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Fagus_sylvatica&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Quercus_petraea&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Picea_abies&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Pinus_pinaster&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2020View details →
zenodo44/100

Valuation of heat related mortality risk and tick-borne diseases

<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>

opencc-by-4.0Sep 2021View details →
zenodo40/100

Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data

<p>This dataset accompanies the publication, &quot;Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk&quot;, and can be used with the code located at&nbsp;https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>

openmit-licenseFeb 2023View details →
zenodo40/100

Impact of urban heat islands on human mortality risk in European cities

<p>These data contain estimates of temperature-related&nbsp;human mortality, as well as the associated economic assessments,&nbsp;related to&nbsp;urban heat islands&nbsp;for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m&nbsp;resolution.</p> <p>&nbsp;</p> <p>Details of the methodology are provided in the&nbsp;associated paper:</p> <p>Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. <em>Nat Commun</em> <strong>14</strong>, 7438 (2023). <a href="https://doi.org/10.1038/s41467-023-43135-z">https://doi.org/10.1038/s41467-023-43135-z</a></p> <p>And associated core analysis code is available on GitHub at&nbsp;https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209).&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>The content of the files are as follows:</p> <p><strong>spatial_timeseries</strong> zip files: These contain the most unprocessed attributable fraction&nbsp;estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.</p> <p><strong>uhi</strong> csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands&nbsp;for each city. They&nbsp;are identical to Tables S4-S11 in the supplementary materials of the above paper.</p> <p><strong>spatial_maps_time_averaged_diff_from_rural.zip</strong>: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time.&nbsp;</p> <p><strong>data_urbanruralavg_timeseries.nc</strong>: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).</p> <p><strong>avg_diff_from_rural_urbanrural.nc</strong>: The above timeseries file temporally aggregated.</p> <p><strong>simulated_urbanruraldiff_timeseries.zip</strong>: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties&nbsp;in the exposure-response relationships as captured by Monte Carlo simulations.</p> <p><strong>simulated_urbanruraldiff_averaged.zip</strong>: The above simulated timeseries temporally aggregated.</p> <p>&nbsp;</p> <p><strong>Some variables explained:</strong></p> <p>fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)</p> <p>fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)</p> <p>tas = temperature</p> <p>heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)</p> <p>cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)</p> <p>heat = average over days warmer than the age-dependent optimal temperature</p> <p>cold = average over days colder than the age-dependent optimal temperature</p> <p>heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count&nbsp;&ne; total days over period)</p> <p>cold_count = number of days colder than the optimal for the age group</p> <p>rural = rural average</p> <p>imd = land imperviousness</p> <p>popden = population density</p> <p>age groups:&nbsp;</p> <p>20 = 20 to 44<br>45 = 45 to 64<br>65 = 65 to 74<br>75 = 75 to 84<br>85 = 85 and over<br>2085.1 = all above age groups combined, weighted by the local population age structure<br>2085.5 = all above age groups combined, weighted by the&nbsp;2013 European standard population age structure</p> <p>&nbsp;</p> <p>References:</p> <p>De Ridder, K., Lauwaet, D., and Maiheu, B., (2015):&nbsp;UrbClim &ndash; A fast urban boundary layer&nbsp;climate model. Urban Climate, 12, 21&ndash;48. <a href="https://doi.org/10.1016/J.UCLIM.2015.01.001">https://doi.org/10.1016/J.UCLIM.2015.01.001</a>.</p> <p>Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <a href="https://doi.org/10.24381/cds.c6459d3a">https://doi.org/10.24381/cds.c6459d3a</a>.</p> <p>Masselot et al. (2023):&nbsp;Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, <a href="https://doi.org/10.1016/S2542-5196(23)00023-2">https://doi.org/10.1016/S2542-5196(23)00023-2</a>.&nbsp;</p>

opencc-by-4.0Oct 2023View details →
dryad40/100

Data from: Spatial modeling of sociodemographic risk for COVID-19 mortality

Open the record for dataset details and reuse information.

publicSep 2024View details →
dryad40/100

Data from: Sex bias in mortality risk changes over the lifespan of bottlenose dolphins

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publicFeb 2025View details →
dryad40/100

Variations in risk-taking behaviour mediate matrix mortality's impact on biodiversity under fragmentation

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publicJul 2024View details →
dryad40/100

Data from: Soilscapes of mortality risk suggest a Goldilocks effect for overwintering ectotherms

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publicOct 2024View details →
zenodo36/100

Knowledge and perceptions of risk factors for maternal mortality among postnatal mothers in Hohoe Municipality of Volta Region, Ghana

<p>This de-identified limited dataset from which the manuscript if spread. It dataset from primary research conducted to assess knowledge of postnatal mothers about clinical risk factors and perceptions about socio-cultural and health system-related factors affecting maternal mortality.</p>

opencc-by-4.0May 2020View details →
dryad36/100

Size-selective mortality induces evolutionary changes in group risk-taking behavior and the circadian system in a fish

<p>1. Intensive and trait-selective mortality of fish and wildlife can cause evolutionary changes in a range of life-history and behavioral traits. These changes might in turn alter the circadian system due to coevolutionary mechanisms or correlated selection responses both at behavioral and molecular levels, with knock-on effects on daily physiological processes and behavioral outputs.</p> <p>2. We examined the evolutionary impact of size-selective harvesting on group risk-taking behavior and the circadian system in a model fish species. We exposed zebrafish (<em>Danio rerio</em>) to either large or small size-selective harvesting relative to a control over five generations, followed by eight generations during which harvesting halted to remove maternal effects.</p> <p>3. Size-selective mortality affected fine-scale timing of behaviors. In particular, small size-selective mortality, typical of specialized fisheries and gape-limited predators targeting smaller size classes, increased group risk-taking behavior during feeding and after simulated predator attacks. Moreover, small size-selective mortality increased early peaks of daily activity as well as extended self-feeding daily activity to the photophase compared to controls. By contrast large size-selective mortality, typical of most wild capture fisheries, only showed an almost significant effect of decreasing group risk-taking behavior during the habituation phase and no clear changes in fine-scale timing of daily behavioral rhythms compared to controls.</p> <p>4. We also found changes in the molecular circadian core clockwork in response to both size selective mortality treatments. These changes disappeared in the clock output pathway because both size-selected lines showed similar transcription profiles. This switch downstream to the molecular circadian core clockwork also resulted in similar overall behavioral rhythms (diurnal swimming and self-feeding in the last hours of darkness) independent of the underlying molecular clock.</p> <p>5. To conclude, our experimental harvest left an asymmetrical evolutionary legacy in group risk-taking behavior and in fine-scale daily behavioral rhythms. Yet, the overall timing of activity showed evolutionary resistance probably maintained by a molecular switch. Our experimental findings suggest that size-selective mortality can have consequences for behavior and physiological processes.</p>

opencc-zeroOct 2020View details →
zenodo36/100

A Functional Response in Resource Selection Links Multi-Scale Responses of a Large Carnivore to Human Mortality Risk

<p>This repository contains code and data to reproduce results from the manuscript 'A Functional Response in Resource Selection Links Multi-Scale Responses of a Large Carnivore to Human Mortality Risk'.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
dryad36/100

Long-term study shows that increasing body size in response to warmer summers is associated with a higher mortality risk in a long-lived bat species

<p>Change in body size is one of the universal responses to global warming, with most species becoming smaller. While small size in most species corresponds to low individual fitness, small species typically show high population growth rates in cross-species comparisons. It is unclear, there- fore, how climate-induced changes in body size ultimately affect population persistence. Unravelling the relationship between body size, ambient temperature and individual survival is especially important for the conservation of endangered long-lived mammals such as bats. Using an individual-based 24-year dataset from four free-ranging Bechstein'sbat colonies (Myotis bechsteinii), we show for the first time a link between warmer summer temperatures, larger body sizes and increased mortality risk. Our data reveal a crucial time window in June–July, when juveniles grow to larger body sizes in warmer conditions. Body size is also affected by colony size, with larger colonies raising larger offspring. At the same time, larger bats have higher mortality risks throughout their lives. Our results highlight the importance of understanding the link between warmer weather and body size as a fitness-relevant trait for predicting species-specific extinction risks as consequences of global warming.</p>

opencc-zeroDec 2021View details →
dryad36/100

Kidney transplantation waiting times and risk of cardiovascular events and mortality: a retrospective observational cohort study in Taiwan

<p>Objectives: Patients with end-stage renal disease (ESRD) are at a high risk of cardiovascular events (CVEs), and kidney transplantation (KT) has been reported to improve risk of CVEs and survival. As the association of KT timing on long-term survival and clinical outcomes remains unclear, we investigated the association of different KT waiting times on clinical outcomes.</p> <p>Design: Retrospective observational cohort study.</p> <p>Setting: We conducted an observational cohort study using data from the National Health Insurance Research Database in Taiwan. Adult patients who initiated kidney transplantation therapy from 1997 to 2013 were included.</p> <p>Participants: A total of 3562 adult patients who initiated uncomplicated KT therapy were included and categorized into four groups according to KT waiting times after ESRD: Group 1 (&lt;1 year), Group 2 (1–3 years), Group 3 (3–6 years), and Group 4 (&gt;6 years).</p> <p>Primary outcome measure: The main outcome was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke, based on the primary diagnosis in medical records during hospitalization.</p> <p>Results: Compared with Group 1, the adjusted risk of primary outcome events (all-cause death, nonfatal myocardial infarction, or nonfatal stroke) increased by 1.67 times in Group 2 (95% CI: 1.40–2.00; P &lt;0.001), 2.17 times in Group 3 (95% CI: 1.73–2.71; P &lt;0.001), and 3.10 times in Group 4 (95% CI: 2.21–4.35; P &lt;0.001). The rates of primary outcome events were 6.7%, 13.4%, and 14.0% within five years, increasing to 19.5%, 26.3%, and 30.8% within 10 years in Groups 1, 2, and 3, respectively.</p> <p>Conclusions: Our results demonstrate that early KT is associated with superior long-term cardiovascular outcomes compared to late KT in selected ESRD patients receiving uncomplicated KT, suggesting that an early KT could be a better treatment option for ESRD patients who are eligible for transplantation.</p>

opencc-zeroMay 2022View details →
zenodo36/100

Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk

<p><strong>Paper Title: </strong>Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk</p> <p><strong>Paper:&nbsp;</strong><a href="https://doi.org/10.1007/s13755-024-00332-4">https://doi.org/10.1007/s13755-024-00332-4</a> (<span>full-text view-only version: <a title="URL d'origine&nbsp;: https://rdcu.be/eccmN. Cliquez ou appuyez si vous faites confiance &agrave; ce lien." href="https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Frdcu.be%2FeccmN&amp;data=05%7C02%7Chakima.laribi%40usherbrooke.ca%7C23870f4657634d7a102908dd5b986df8%7C3a5a8744593545f99423b32c3a5de082%7C0%7C0%7C638767432425362186%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&amp;sdata=d9ieen5mU9pPFXGv8hJNF%2Bf5UlJNyhjDuk%2F8MWEKo28%3D&amp;reserved=0" target="_blank" rel="noopener noreferrer">https://rdcu.be/eccmN</a></span>)</p> <p><strong>GitHub Link:&nbsp;</strong><a href="https://github.com/MEDomics-UdeS/POYM" target="_blank" rel="noopener">https://github.com/MEDomics-UdeS/POYM&nbsp;</a></p> <p><strong>Description:</strong></p> <p>This dataset accompanies&nbsp;<a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a> and contains synthetic data generated using the <a href="https://doi.org/10.1038/s41746-023-00771-5" target="_blank" rel="noopener">AVATAR method</a> in partnership with <a href="https://www.octopize.io/" target="_blank" rel="noopener">Octopize</a>.</p> <p><strong>Files:</strong></p> <ul> <li><strong>dataset.csv:</strong> This file contains 248,485 rows and 247 columns, representing 248,485 synthetic visits from 123,646 synthetic patients. Detailed descriptions of each column can be found in <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a>. To preserve patient's privacy, we did not save admission and discharge dates. Consequently, it is not possible to split the dataset temporally as done with the original dataset or to identify admissions with same-day discharge.</li> </ul> <p><strong>Comparison of synthetic and original data: </strong><a href="https://doi.org/10.21203/rs.3.rs-5363467/v1">https://doi.org/10.21203/rs.3.rs-5363467/v1</a></p> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
dryad36/100

The risk faced by the early bat: individual plasticity and mortality costs of the timing of spring departure after hibernation

<p>Hibernation is a widespread adaptation in animals to seasonally changing environmental conditions. In the face of global anthropogenic change, information about plastic adjustments to environmental conditions and associated mortality costs are urgently needed to assess population persistence of hibernating species. Here, we used a five-year data set of 1,047 RFID-tagged individuals from two bat species, Myotis nattereri and Myotis daubentonii that were automatically recorded each time they entered or left a hibernaculum. Because the two species differ in foraging strategy and activity pattern during winter, we expected species–specific responses in the timing of hibernation relative to environmental conditions, as well as different mortality costs of early departure from the hibernaculum in spring. Applying mixed-effects modelling, we were able to disentangle population-level and individual-level plasticity in the timing of departure. To estimate mortality costs of early departure, we used both a capture mark recapture analysis and a novel approach that takes into account individual exposure times to mortality outside the hibernaculum. We found that the timing of departure varied between species as well as among and within individuals, and was plastically adjusted to large-scale weather conditions as measured by the NAO (North Atlantic Oscillation) index. Individuals of M. nattereri, which can exploit milder temperatures for foraging during winter, tuned departure more closely to the NAO index than individuals of M. daubentoniid which do not hunt during winter. Both analytical approaches used to estimate mortality costs showed that early departing individuals were less likely to survive until the subsequent hibernation period than individuals that departed later. Overall, our study demonstrates that individuals of long-lived hibernating bat species have the potential to plastically adjust to changing climatic conditions, although the potential for adjustment differs between species.</p>

opencc-zeroNov 2022View details →
zenodo36/100

Future drought-induced tree mortality risk in Amazon rainforest

<p>ORCHIDEE-CAN-NHA simulation outputs of aboveground biomass carbon gain and carbon loss, forced by four climate models from ISIMIP2b program</p>

opencc-by-4.0Feb 2023View details →
dryad36/100

Data from: Mitochondrial function declines with age within individuals but is not linked to the pattern of growth or mortality risk in zebra finch

<p>Mitochondrial dysfunction is considered a highly conserved hallmark of ageing. However, most studies in both model and non-model organisms are cross-sectional in design; therefore, little is known, at the individual level, on how mitochondrial function changes with age, its link to early developmental conditions or its relationship with survival. Here we manipulated the postnatal growth in zebra finches (<em>Taeniopygia guttata</em>) via dietary modification that induced accelerated growth without changing adult body size. In the same individuals, we examined blood cells mitochondrial functioning (mainly erythrocytes) when they were young (ca 36 weeks) and again in mid-aged (ca 91 weeks) adulthood. Mitochondrial function was strongly influenced by age but not by postnatal growth conditions. Across all groups, within individual <em>ROUTINE</em> respiration, <em>OXPHOS</em> and <em>OXPHOS</em> coupling efficiency significantly declined with age, while <em>LEAK</em> respiration increased. However, we found no link between mitochondrial function and the probability of survival into relatively old age (ca 4 years). Our results suggests that the association between accelerated growth and reduced longevity, evident in this as in other species, is not attributable to age-related changes in any of the measured mitochondrial function traits.</p>

opencc-zeroMar 2023View details →
dryad36/100

When death comes: Linking predator-prey activity patterns to timing of mortality to understand predation risk

<p>The assumption that activity and foraging are risky for prey underlies many predator-prey theories and has led to the use of predator-prey activity overlap as a proxy of predation risk. However, the simultaneous measures of prey and predator activity along with timing of predation required to test this assumption have not been available. Here, we used accelerometry data on snowshoe hares (<em>Lepus</em> <em>americanus</em>) and Canada lynx (<em>Lynx canadensis)</em> to determine activity patterns of prey and predators and match these to precise timing of predation. Surprisingly, we found that lynx kills of hares were as likely to occur during the day when hares were inactive as at night when hares were active. We also found that activity rates of hares were not related to the chance of predation at daily and weekly scales, whereas lynx activity rates positively affected the diel pattern of lynx predation on hares and their weekly kill rates of hares. Our findings suggest that predator-prey diel activity overlap may not always be a good proxy of predation risk, and highlight a need for examining the link between predation and spatiotemporal behavior of predator and prey to improve our understanding of how predator-prey behavioral interactions drive predation risk.</p>

opencc-zeroDec 2022View details →
zenodo36/100

Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India

<p>This Zenodo resource contains the data used to perform analysis in the article &quot;Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India&quot;.</p> <p>Data</p> <p>The data is organized in the form of tables.</p> <p>hypothesis-test-data</p> <p>This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.</p> <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> <p>lasso-covid19India</p> <p>This table contains data used for analysis on cases throughout India.</p> <p>Columns from COVID-19 India data</p> <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>lasso-KA+TN-bulletin</p> <p>This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.</p> <p>Data from Media Bulletin</p> <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> <p>Calculated Data</p> <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>Code</p> <p>The code is available at this <a href="https://github.com/harishpb26/Sex-disaggregated-Analysis-of-Risk-Factors-of-COVID-19-Mortality-Rates-in-India">Github Repository</a>.</p>

opencc-by-4.0May 2023View details →
ClinicalTrials.gov36/100

A Study of Sotatercept in Participants With PAH WHO FC III or FC IV at High Risk of Mortality (MK-7962-006/ZENITH)

ClinicalTrials.gov study NCT04896008. IPD Sharing: YES. Countries: 12. Publications: 1.

controlledIPD-YESFeb 2026View details →

ScienceDex guides

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record