Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,994
datasets available to search
ShareScore release 0.9.0
Dataset results
1,994 results for “Mortality;”
Fig. 5 in Mortality Of Amphibians On The Roads Of Lviv Region (Ukraine): Trend For The Last Decade
Fig. 5. The distribution of investigated sites under the traffic influence on amphibians on the surveyed roads of Lviv Region (green circle — low level impact, yellow — middle, red — high, white — no amphibian mortality).
Fig. 3 in Mortality Of Juvenile So-Iuy Mullet, Liza Haematocheilus (Teleostei, Mugilidae), In The Sea Of Azov Associated With Metacercaria (Digenea)
Fig. 3.Relationship between the fish total length and the intensity of Diplostomum spp. in juveniles of L. haematocheilus.* Referred to a significant level of 95 %.
Fig. 2 in Mortality Of Juvenile So-Iuy Mullet, Liza Haematocheilus (Teleostei, Mugilidae), In The Sea Of Azov Associated With Metacercaria (Digenea)
Fig. 2. Frequency distribution of T. imbutiforme split following the pre-mortality and post-mortality events. Solid line with solid point and dashed line with open square = the observed and predicted pre-mortality frequency distribution; dashed line with solid triangle = the fitted post-mortality frequency distribution; solid double line with crosses and symbols = the estimated percentage survival of fish with that number of parasites.
Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"
<p>This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.</p> <ul> <li><strong>data.zip </strong>contains raw, intermediate, and final datasets</li> <li><strong>outputs.zip </strong>contains output tables and figures </li> </ul> <p>All replication code for the paper is available on a public Github repository, accessible <a href="https://github.com/ClimateImpactLab/carleton_mortality_2022">here</a>.<br><br>The manuscript and supplementary information are available at the QJE, <a href="https://doi.org/10.1093/qje/qjac020">here</a>.</p>
WiDS mortality dataset - APACHE diagnoses enriched
<p>The WiDS mortality dataset was modified, adding the APACHE diagnoses using the original column "apache_3j_diagnosis_code".</p> <p>This dataset is a merge from:</p> <ol> <li><strong>Mortality data</strong>: https://www.kaggle.com/competitions/widsdatathon2020/data</li> <li><strong>APACHE</strong>: https://www.kaggle.com/datasets/danofer/apache-iiij-icu-diagnosis-codes?select=icu-apache-Subdiagnosis-codes-ANZICS.csv</li> </ol>
A ten-year (2009–2018) database of cancer mortality rates in Italy
<p>AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies.</p>
Datasets used on the analysis of Mediterranean Mass mortality events during the 2015-2019 period
<p>This upload contains three datasets in CSV files and a PDF file with the specific description of the CSV files. These data was used for the analysis of the mass mortality events reported during the period 2015-2019 across the Mediterranean.</p> <p>The datasets are 1) a CSV file with the data used for the description of the spatial-temporal, depth and biological patterns of mortality observed in the Mediterranean Sea in the 2015-2019 period; 2) a CSV file with the data used to conduct the analyses on the relationship between marine heatwaves (MHW) days found on the surface (averaged per monitored area and year) and the corresponding mass mortality incidence of benthic organisms; 3) a CSV file with the data used to conduct the analyses on the relationship between in-situ MHW days (averaged per monitored area, depth and year) and the corresponding mass mortality incidence. </p> <p>Data were obtained through benthic community field surveys conducted by 33 research teams from 11 Mediterranean countries. Surveys covered thousands of kms of coastline, spanning 13º of latitude (32 °S to 45 °N) and 40º of longitude (-5°W to 35°E) in the Mediterranean Sea. The dataset provides the most updated inventory of mass mortality events records for benthic species between 2015-2019 in the region. The surveys were conducted in 142 monitoring areas. Monitoring areas were considered as geographic areas (10-25 km coastline, e.g., a marine protected area and the nearby coast) sharing common environmental features. </p> <p>In situ temperature conditions datasets base consists of high frequency (hourly) time series obtained using HOBO data loggers (accuracy ± 0.21°C) set-up at standard depths along rocky walls by divers, generally every 5 m from the surface to 40 m depth.This dataset as in the case of the mortality was assembled under the T-MEDNet initiative (<a href="http://www.t-mednet.org/">www.t-mednet.org</a>).</p> <p>Satellite derived sea surface temperature (SST) across the Mediterranean Sea was obtained from CMEMS (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=SST_MED_SST_L4_REP_OBSERVATIONS_010_021). The data consists of daily (night-time), gap free, optimally interpolated foundation SST at ~4 km resolution from AVHRR with improved accuracy and stability over the 1982-2019 period</p>
COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries
<p>"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"</p> <p>Dataset for preprint titled <br> "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe"<br> https://doi.org/10.1101/2021.01.27.21250658 </p> <p>by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA</p> <p>Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.</p> <p> </p> <p>===========<br> Dataset file: <br> 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv</p> <p><br> Dataset graphical preview: <br> 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png</p> <p>DATASET:<br> 444 rows (records), with the following fields:</p> <p>"Country" :<br> Country name; 37 European countries included.</p> <p>"Date": <br> Date stamp at the collection time.<br> Data collection was performed in the last day of every month. <br> Date format: YYYY-MM-DD</p> <p>"Month_Key" : <br> Date stamp at the collection time, formatted for easier monthly time series analysis.<br> Date format: YYYY-MM</p> <p>"Month_Fct2020"<br> Date stamp at the collection time,formatted for easier graphing, as a string with names of the months<br> (in English). </p> <p>"Deaths_per_1Mpop" :<br> Monthly mortality from COVID-19 raported in the country, <br> reported as number of COVID-19 deaths per 1 million population of the country, <br> in that particular month / country. <br> NB: it is reported as million population, not patients. </p> <p>"LogDeaths_per_1Mpop" :<br> Log10 transformation of "Deaths_per_1Mpop"</p> <p>"Insolation_Average" :<br> Insolation average (solar irradiance at ground level),<br> in that particular month / country. <br> It is expressed in Watt / square meter of the ground surface. <br> Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, <br> CERES / FLASHFlux team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )</p> <p>"Cloud_Fraction" :<br> Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),<br> as decimal fraction of the sky obscured by clouds, <br> in that particular month / country. <br> Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,<br> MODIS Atmosphere Science Team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )</p> <p>"CENTR_latitude" and<br> "CENTR_longitude" :<br> Latitude and Longitude of the country centroid, for each country. <br> Data derived from Google LLC, "Dataset publishing language: country centroids",<br> https://developers.google.com/public-data/docs/canonical/countries_csv <br> NOTE: This is identical in every month (obviuously); <br> it is redundantly included for easier monthly sectional analysis of the data. </p> <p>===========</p> <p>Versioning of the dataset: <br> MAJOR: changes yearly; 1 = 2020<br> MINOR: changes if new monthly data is added in that particular year. <br> PATCH: Changes only if errors or minor edits were performed. </p> <p><br> ===========<br> CHANGELOG: </p> <p>Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv<br> - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov<br> - These data were gathered, analyzed and introduced in this dataset (2.0.0). <br> - updated links for CERES/FLASHFLUX and MODIS dataset<br> - added DOI link for preprint<br> - minor edits on text. <br> -Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv</p> <p><br> Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv <br> First version<br> Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-09-22_r10.csv</p>
Recovered frog populations coexist with endemic Batrachochytrium dendrobatidis despite load-dependent mortality
<p>Novel infectious diseases, particularly those caused by fungal pathogens, pose considerable risks to global biodiversity. The amphibian chytrid fungus (<em>Batrachochytrium dendrobatidis</em>, <em>Bd</em>) has demonstrated the scale of the threat, having caused the greatest recorded loss of vertebrate biodiversity attributable to a pathogen. Despite catastrophic declines on several continents, many affected species have experienced population recoveries after epidemics. However, the potential ongoing threat of endemic <em>Bd</em> in these recovered or recovering populations is still poorly understood. We investigated the threat of endemic <em>Bd</em> to frog populations that recovered after initial precipitous declines, focusing on the endangered rainforest frog <em>Mixophyes fleayi</em>. We conducted extensive field surveys over four years at three independent sites in eastern Australia. First, we compared <em>Bd</em> infection prevalence and infection intensities within frog communities to reveal species-specific infection patterns. Then, we analyzed capture-recapture data of <em>M. fleayi</em> to estimate the impact of <em>Bd</em> infection intensity on apparent mortality rates and <em>Bd</em> infection dynamics. We found that <em>M. fleayi</em> had lower infection intensities than sympatric frogs across the three sites, and cleared infections at higher rates than they gained infections throughout the study period. By incorporating time-varying individual infection intensities, we show that healthy <em>M. fleayi</em> populations persist despite increased apparent mortality associated with infrequent high <em>Bd</em> loads. Infection dynamics were influenced by environmental conditions, with <em>Bd</em> prevalence, infection intensity, and rates of gaining infection associated with lower temperatures and increased rainfall. However, mortality remained constant year-round despite these fluctuations in <em>Bd</em> infections, suggesting major mortality events did not occur over the study period. Together, our results demonstrate that while <em>Bd</em> is still a potential threat to recovered populations of <em>M. fleayi</em>, high rates of clearing infections and generally low average infection loads likely minimize mortality caused by <em>Bd</em>. Our results are consistent with pathogen resistance contributing to the coexistence of <em>M. fleayi </em>with endemic <em>Bd</em>. We emphasize the importance of incorporating infection intensity into disease models rather than infection status alone. Similar population and infection dynamics likely exist within other recovered amphibian-<em>Bd</em> systems around the globe, promising longer-term persistence in the face of endemic chytridiomycosis.</p>
Fig. 8 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 8 Transmcsscon electron mccroscops of Mikrocytos donaxi n. sp. cnfectcng Donax trunculus mantle collected cn Audcerne Bas. a Dense form of the parascte wcth a vers granulous cstoplasm and the presence of few large vesccles cnscde (arrow); note the eccentrcc posctcon of the nucleus. b Parascte cn tcght contact wcth two mctochondrca cnduccng a depresscon of the parascte membrane (arrow). c Parascte cn the connectcve tcssue at an endosomal stage, presentcng a well-developed anastomoscng endoplasmcc retcculum near the nucleus. Abbreviations: aER, anastomoscng endoplasmcc retcculum; M, mctochondrca; N, nucleus. Scale-bars: a, c, 1 μm; b, 0.5 μm
Fig. 4 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 4 Hcstologccal sectcon of Donax trunculus mantle showcng hsbrcdczatcon of the Mikrocytos Msp probe wcth mccrocell cells (arrows). Scale-bar: 20 μm
Fig. 3 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 3 Hcstologccal haematoxslcn eoscn tcssue sectcons showcng mantle lescons of Donax trunculus cnfected wcth Mikrocytos donaxi n. sp. parasctes from Audcerne Bas. a Dcffuse necroscs of muscular fcbres (arrows) and connectcve tcssue (arrowheads) of the mantle cnfected wcth mccrocell parasctes b Degenerated haemocstes (arrows) and muscular cell necroscs (arrowhead) assoccated wcth mccrocell parasctes (astercsks). c Mikrocytos donaxi n. sp. cnscde a msocste (arrow). Scale-bars: a, 100 μm; b, c, 20 μm
Fig. 2 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 2 Hcstologccal haematoxslcn eoscn tcssue sectcons showcng Mikrocytos veneroïdes n. sp. parasctes cn dcfferent tcssues of Donax trunculus from Oléron Island. a Parasctes cn the adductor muscle. Note the extracellular (arrows) or cntracellular (arrowhead) posctcon of mccrocell parasctes. b Parascte cell (arrows) cn the neuronal ganglcon. c Two parasctes (arrow) cnscde the cstoplasm of a haemocste. Scale-bars: 20 μm
Fig. 1 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 1 Samplcng sctes of Donax trunculus, mortalcts rate estcmatcon and collected cndcvcduals along the Atlantcc coast of France. Abbreviations: AB, Audcerne Bas; DB, Douarnenez Bas; QB, Qucberon Bas; OI, Oléron Island
Fig. 5 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 5 Phslogenetcc tree (50% majorcts-rule consensus) uscng Basescan Inference (MrBases 3.1.2) based on the small subunct rcbosomal gene of Mikrocytos. Numbers at the nodes are Basescan postercor probabclctces. Paramikrocytos canceri was used as the outgroup for Mikrocytos spp. based on the results of Hartckacnen et al. [5]. Astercsks cndccate sequences obtacned cn thcs studs
Fig. 6 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 6 Phslogenetcc tree (50% majorcts-rule consensus) uscng Basescan Inference (MrBases 3.1.2) based on the ITS1-5.8S-ITS2 sequence arras of Mikrocytos. Numbers at the nodes are Basescan postercor probabclctces. Astercsks cndccate sequences obtacned cn thcs studs
Fig. 7 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 7 Transmcsscon electron mccroscops of Mikrocytos veneroïdes n. sp. cnfectcng Donax trunculus mantle collected cn Qucberon Bas. a Parascte cnscde a msocste and near the msofcbrcls. Note the presence of mctochondrca near the parascte cells (arrow). b Parascte cn the cstoplasm of a haemocste; note the presence of parasctophorous vacuole around the parascte (arrows). c Parascte near the msofcbrcls. Mctochondrca tcght agacnst the surface of the parascte or cnscde the parascte cstoplasm; note the depresscon of parascte surface at the pocnt of contact wcth mctochondrca. d Bcnucleate stage of the parascte cn an extracellular posctcon; note the presence of the two nuclec. Abbreviations: M, mctochondrca; MF, msofcbrcls; N, nucleus; P, parascte. Scale-bars: a, b, d, 2 μm; c, 1 μm
Data from: Local environment and coral composition affect recovery and determine long-term coral responses to recurrent mass mortalities in the Lakshadweep Archipelago
<p>A quarter century after the first global coral bleaching event in 1998, reports differ on the relative importance of anthropogenic influences, local environment and bleaching recurrence in determining the resilience of coral reefs. While life history traits largely determine how corals respond to temperature anomalies, it is unclear if these traits also determine how corals fare over time. From 1998 to 2022, we tracked compositional changes in reefs across the Lakshadweep Archipelago to explore how global El Niño events, and local environment (wave climate and depth) influenced coral responses to repeated mass bleaching. From the 1998 to the 2016 bleaching event, the magnitude of coral mortality reduced overall, particularly at deeper reefs (shallow: -38% to -3%; deep: -18% to -0.45%). Post-bleaching recovery correlated positively with higher wave exposure, linked to the creation of stable structures for coral settlement and survival. Across bleaching phases, recovery was initially slow (6-7 years post-mortality), but, given time, showed a much steeper increase, led by space-occupying genera like <em>Acropora</em>. However, recurring mass bleaching maintained coral cover low (~15% across all sites). These broad trends mask dynamic compositional patterns. Genera such as <em>Porites</em>, <em>Pocillopora</em>, and <em>Favia</em> declined less through time compared to <em>Acanthastrea</em>, <em>Turbinaria</em>, <em>Psammocora</em> and <em>Plesiastrea </em>among others. We identified six community clusters that describe contrasting long-term responses to local and global factors, mediated by depth and wave exposure. Interestingly, genera with different functional traits cluster together indicating that bleaching susceptibility interacts with depth and exposure, creating a spatial mosaic of coral assemblages. These clusters serve as a predictive, site-specific framework to understand the dynamically shifting but declining assemblage of Lakshadweep reefs. While local management could help maintain this changing composition, urgent global action is needed to secure the long-term ecological integrity of tropical reefs.</p>
Fig. 2 in Patterns of growth and natural mortality in Lysapsus bolivianus (Anura, Hylidae, Pseudae) in an environmental protection area in the estuary of the Amazon River
Fig. 2. Weight-length relationships in adult male and female Lysapsus bolivianus from the Rio Curiaú EPA on the estuary of the Amazon River, in northern Brazil.
Fig. 4 in Patterns of growth and natural mortality in Lysapsus bolivianus (Anura, Hylidae, Pseudae) in an environmental protection area in the estuary of the Amazon River
Fig. 4. Von Bertalanffy's growth curves for (A) male and (B) female Lysapsus bolivianus from the Rio Curiaú EPA in Amapá, Brazil.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.