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1,423 results for “Death”
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Global Violent Deaths (GVD) database 2004-2021, 2023 update, version 1.0
<p>The <a href="https://www.smallarmssurvey.org/database/global-violent-deaths-gvd">Global Violent Deaths (GVD) database</a> integrates indicators on the major causes of lethal interpersonal and communal violence—intentional and unintentional homicides, killings in legal interventions, and direct conflict deaths—and combines them in a single violent deaths indicator. These indicators are also reported in a disaggregated format by the sex of the victim and perpetration mechanism, namely firearm killings. The GVD database tracks this information across 222 countries and territories worldwide yearly from 2004 and reports both crude counts and rates per 100,000 population. The input data is retrieved from reliable sources, such as governments, national and international organizations, trusted non-governmental organizations, and verified media outlets. Missing data points are estimated using the methods described in this document.</p> <p>The GVD database is updated annually by the <a href="https://www.smallarmssurvey.org/">Small Arms Survey</a>, an associated programme of the Geneva Graduate Institute, which strengthens the capacity of governments and practitioners to reduce illicit arms flows and armed violence. This is done through three mutually reinforcing activities: the generation of policy-relevant knowledge, the development of authoritative resources and tools, and the provision of training and other services. The GVD database benefits from financial support from governments and organizations, and notably its core donors, who are publicly disclosed <a href="https://www.smallarmssurvey.org/who-we-are/funding-and-finance">online</a>. The Small Arms Survey follows rigorous procedures to ensure that the input data, the applied methods, and the results are of reasonable quality. If the user encounters apparent errors, they should contact us via email at <a href="mailto:media@smallarmssurvey.org">media@smallarmssurvey.org</a>.</p> <p>Regions, sub-regions, countries, and territories are defined based on the classification system used by the UN Statistical Division (2013 revision), except for Kosovo, England and Wales, Northern Ireland, and Scotland. The names and designations reported in the database do not imply any sort of endorsement by the Small Arms Survey.</p>
COVID-19 case fatality derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a><br> doi:<a href="https://doi.org/10.5281/zenodo.3787930">10.5281/zenodo.3787930</a></p> <p>This is the plot of the COVID-19 risk of death adjusted by age based on the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library published on April 3, 2020, (release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 case-fatality-derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3787931"><em>https://zenodo.org/record/3787931</em></a><br> doi:10.5281/zenodo.3787931</p> <p>This is the plot of the COVID-19 risk of death adjusted by age derived from the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library initially published on April 3, 2020, (with the release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 age-adjusted risk of death derived from case fatality
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><em>https://zenodo.org/record/3787931</em><br> doi:10.5281/zenodo.3787931</p> <p>This is a plot of the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.0 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
COVID-19 risk of death by age
<p><strong>This is a version of the plot without split into gender-adjusted risks. For the newer version with gender-adjusted curves, please visit </strong><em>https://zenodo.org/record/3787931</em><br> doi:10.5281/zenodo.3787931</p> <p>This figure plots the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.1 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
COVID-19 case fatality-derived risk of death adjusted by age and gender
<p>This figure plots the risk of death in COVID-19 adjusted by the age and gender, which was modeled with the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project <em>Data Science for COVID-19 in South Korea (DS4C)</em>, which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was built with the release 2.2 of the open-sourced Python & C library for macOS and Linux initially published under <strong>GPLv3</strong> license on April 3, 2020, on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit <a href="https://github.com/yuryatin/covid19_age_adjusted_mortality"><strong>https://github.com/yuryatin/covid19_age_adjusted_mortality</strong></a> for more detailed description of the model and for the source code.</p>
Social networks predict the life and death of honey bees - Data
<p><strong>Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a></p> <p>See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code.</p> <p><strong>The following files are included:</strong></p> <p><strong>interaction_networks_20160729to20160827.h5</strong></p> <p>The social interaction networks as a dense tensor and metadata.</p> <p>Keys:</p> <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> <p><strong>alive_bees_bayesian.csv </strong></p> <p>This file contains the results of the bayesian lifetime model with one row for each bee.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> <p><strong>bee_daily_data.csv</strong></p> <p>This file contains one row per bee per day that she was alive for the focal period.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> <p><strong>location_data.csv</strong></p> <p>This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used.</p> <p>Columns:</p> <p>In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:</p> <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul> <p> </p>
Sudden_Oak_Death_in_Oregon_Forests: Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests
<p>Release of code associated with the submitted manuscript</p> <p><strong>Authors</strong></p> <p>ZN Kamvar, MM Larsen, AM Kanaskie, EM Hansen, and NJ Grünwald.</p> <p><strong>Title</strong></p> <p>Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests.</p>
Tesla Deaths
<p>Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot crashes, that is instances when Autopilot was activated during a Tesla crash that resulted in death.</p> <p>Latest version of dataset at <a href="https://www.tesladeaths.com">https://www.tesladeaths.com</a>.</p>
Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset
<p>This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.</p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>More information</em></p> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.</p>
Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death
<p>This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021.</p> <p>Abstract:</p> <p>DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.</p>
Aggregated number of officially recorded COVID-19 deaths in 2020 in Poland by county (powiat) with indication of sources
<p>This is a complimentary dataset for the article "Deaths during the first year of the COVID‑19 pandemic: insights from regional patterns in Germany and Poland" by Myck, Oczkowska, Garten, Krol, Brandt in BMC Public Health (DOI: 10.1186/s12889-022-14909-9).</p>
BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>More information</em></p> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.</p>
Data and statistical analysis for: Bacterial nanotubes are a manifestation of cell death
<p>Contains all data and code to reproduce the statistical analysis in the Supplementary file 2 for the paper "Bacterial nanotubes are a manifestation of cell death" to be published in Nature Communications.</p> <p><strong>Contents:</strong></p> <ul> <li>statistical_analysis.Rmd is the main document written in R Markdown</li> <li>statistical_analysis.html is a compiled version of statistical_analysis.Rmd showing all the computed results.</li> <li>The Source data.xls file contains raw data used for the analysis - the sheet names indiciate the figure they refer to. See the main file for code that can read the data.</li> </ul> <p>The code can also be accessed at <a href="https://github.com/cas-bioinf/nanotubes-death">https://github.com/cas-bioinf/nanotubes-death</a></p>
Social determinants of Covid-19 infection and death in a rural Indonesia: A rapid healthcare assessment
<p>Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.</p>
Cell growth dataset for "Suppression of bacterial cell death underlies the antagonistic interaction between ciprofloxacin and tetracycline in Escherichia coli"
<div>This dataset contains growth data of <em>E. coli</em> cells measured by optical density at a wavelenght of 600 nm (OD600) to determine the conditions where the combination of ciprofloxacin (CIP) and tetracycline (TET) is antagonistic (or suppressive), using a medium that supports fast, intermediate and slow growth (M9 based medium supplemented with: glucose and amino acid, glucose, and glycerol, respectively).</div> <div>Two types of assays were performed: a checkerboard assay with a 2-dimension gradient of antibiotic concentrations shown in "Fig-S1-Growth_rates-Checkerboard_assay.zip", and bulk growth rates assay shown in "Fig-S2-Bulk_doubling_rates-Bioreactor.zip". </div> <div>These experiments are presented in the supplementary figures of the following manuscript published as preprint in bioRxiv: https://doi.org/10.1101/2024.04.18.590101.</div> <div> </div> <p><strong>Fig-S1-Growth_rates-Checkerboard_assay.zip:</strong></p> <div>- growth-OD600-Blank_correct.xlsx: contains the blank corrected (subtracted by initial OD of sterile medium) OD600 data for each well measured in the checkerboard assay. The negative control is named as "Blank B" in the spreasheet and the antibiotic concentrations for each well is defined in column C (Content).</div> <div>- growth_data.xlsx: contains the processed data from "Growth-OD600-Blank_correct.xlsx":growth curves were smoothed with a 5-window moving median and outliers corrected using the filloutliers function in MATLAB. Outliers that were missed were manually corrected.</div> <div>- time_h.xlsx: contains the time in hours used by the matlab function to build the dose response figure S1B.</div> <div>- analysis_code_checkerboard_assay.m: matlab function used to build the dose response figure S1B.</div> <p> </p> <p><strong>Fig-S2-Bulk_doubling_rates-Bioreactor.zip:</strong></p> <p>- "bulk_doubling rates_gly/glu/gluaa.csv": contains the calculated doubling rates for each of three experimental replicates (rows) and for each antibiotic treatment (columns: Ctrl, CIP, TET, CIP-TET) as shown in Figure S2. These values were used to calculated the Bliss independence (for further details see https://gitlab.com/MEKlab/single-cell-suppression-2024/figure plotting.ipynb<br> - Folders containing OD600 measurements and calculated growth rates for each growth medium, which are further separated into folders for each experimental replicate. Each replicate folder is labelled as the date the experiment was performed (denoted as "*" hereon). Each contains the following: raw optical density data (*.txt files), summary of experiment and results (*.docx file), the function compute_growth_rates.m, and the script Growth_curves_*.m.</p> <div>For more information on methods, strains used and table header descriptions, please refer to the README.txt. </div>
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
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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.
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.