Find research datasets worth reusing

opencc-by-4.0May 2024View details →

E4warning_2020_Population_Age_Sex

Worldpop Human 2020 population by Age and Gender.  Abstract: Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males and all females as well as total populations. Filenames are are follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = minimum age; OO= Maximum age

WDXRF analysis of Iberian unfired potsherds from the Late Iron Age

This data set contains 15 chemical analyses of unfired potsherds from the Iberian workshop of the Mas de Moreno (Teruel, Spain). The chemical composition of the samples was obtained by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF). Method The outer surfaces of all the samples were mechanically removed prior to analysis. The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. 0.8 g of powder was mixed with 3.2 g of Spectroflux 110 flux (Johnson Matthey; 33.5% metaborate and 66.5% lithium tetraborate) and melted in gold-platinum crucibles with an autofluxer melting device (Breitländer). The data were collected with a SRS 3400 (Bruker) spectrometer of 3 kW power (working at 60 kV, 100 mA max.). The spectrometer was equipped with a rhodium tube window, four analyser crystals (OVO-55, LiF200, LiF220, PET), two collimators (0.46° and 0.15°) and two sensors (a proportional Ar/CH4 gas-flow counter and a scintillation counter). The calibration of the instrument was carried out on 40 international certified reference materials. Data description <ul> <li>sample: sample reference.</li> <li>date: date of the analysis.</li> <li>laboratory: analysis laboratory.</li> <li>stratigraphy: stratigraphic unit (all dated to the first half of the 1st century BC).</li> <li>artefact: typology.</li> <li>part: analysed part of the artefact.</li> <li>LOI: loss on ignition (percent).</li> <li>CaO, Fe2O3, TiO2 , K2O, SiO2 , Al2O3 , MgO, MnO, Na2O, P2O5: oxide mass percents.</li> <li>Zr, Sr, Rb, Zn, Cr, Ni, La, Ba, V, Ce, Y, Th, Pb, Cu: ppm.</li> </ul> Data below the following limits should be considered unreliable: <ul> <li>Na2O: 0.5 %</li> <li>La: 24 ppm</li> <li>Y: 15 ppm</li> <li>Th: 15 ppm</li> <li>Pb: 20 ppm</li> <li>Cu: 10 ppm</li> </ul>

WDXRF analysis of Iberian potsherds from the Late Iron Age

This data set contains 99 chemical analyses of ceramic potsherds from the Iberian workshop of the Mas de Moreno (Teruel, Spain). The chemical composition of the samples was obtained by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF). Method The outer surfaces of all the samples were mechanically removed prior to analysis. The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. 0.8 g of powder was mixed with 3.2 g of Spectroflux 110 flux (Johnson Matthey; 33.5% metaborate and 66.5% lithium tetraborate) and melted in gold-platinum crucibles with an autofluxer melting device (Breitländer). The data were collected with a SRS 3400 (Bruker) spectrometer of 3 kW power (working at 60 kV, 100 mA max.). The spectrometer was equipped with a rhodium tube window, four analyser crystals (OVO-55, LiF200, LiF220, PET), two collimators (0.46° and 0.15°) and two sensors (a proportional Ar/CH4 gas-flow counter and a scintillation counter). The calibration of the instrument was carried out on 40 international certified reference materials. Data description <ul> <li>sample: sample reference.</li> <li>date: date of the analysis.</li> <li>laboratory: analysis laboratory.</li> <li>stratigraphy: stratigraphic unit.</li> <li>artefact: typology.</li> <li>part: analysed part of the artefact.</li> <li>decoration: did the sampled artefact carry a painted decoration?</li> <li>LOI: loss on ignition (percent).</li> <li>CaO, Fe2O3, TiO2 , K2O, SiO2 , Al2O3 , MgO, MnO, Na2O, P2O5: oxide mass percents.</li> <li>Zr, Sr, Rb, Zn, Cr, Ni, La, Ba, V, Ce, Y, Th, Pb, Cu: ppm.</li> </ul> Data below the following limits should be considered unreliable: <ul> <li>Na2O: 0.5 %</li> <li>La: 24 ppm</li> <li>Y: 15 ppm</li> <li>Th: 15 ppm</li> <li>Pb: 20 ppm</li> <li>Cu: 10 ppm</li> </ul>

opencc-by-4.0May 2024View details →

Animal bones from Iron Age settlements in Scania, Southern Sweden

This data is a compilation of the zooarchaeological record from Iron Age settlements in Scania, southern Sweden. It consists of data from various technical reports produced between 1961 to 2019, by different analysts. Published reports and unpublished but archived communications are included. This data may be of interest to anyone interested in archaeological themes involving animals in any kind, such as economy, animal husbandry, animal production, hunting, fishing, and so on. It may also be of paleozoological interest, as it contains valuable fauna historical information such as presence of wild species of different kinds.   The database is the basis for the published catalogue included in the book "Animal husbandry in Iron Age Scania, with a catalogue" published 2022. The book is open acess and you can download it via this link: https://www.ht.lu.se/en/series/9128370/ The data can bee accessed through a one .csv-file, which is an export of the data set which was originally recorded in a MS Access-database. Both files are published in this version. The dataset consists of data on 130 animal bone assemblages from 101 Scanian settlement sites. The original Access-database, with two levels, one (Site) with descriptive information on the archaeological site (totally 12 variables), and one (zooarch-overview) with quantitative data on number of specimens, in general and per recorded taxa (totally 35 variables). Presence of bird, fish, amphibian and wild mammalian taxa is also included.  Included is a READ ME (.csv) describing the data set in more detail. ERRATA (READ ME-file): No of observations is 130, not 131.

opencc-by-4.0Sep 2021View details →

Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))

Data supporting the figures and findings presented in the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), Atmos. Chem. Phys..

opencc-by-4.0Sep 2021View details →

Data in: Aging power spectrum of membrane protein transport and other subordinated random walks

Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are: Numerical simulations  RWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, H=0.3 and \(\alpha\)=0.4. RWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, H=0.7 and \(\alpha\)=0.4. RWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, H=0.75 and \(\alpha\)=0.8. RWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), \(\alpha\)=0.7. Spectra of simulations PSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, H=0.3 and \(\alpha\)=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16. PSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, H=0.7 and \(\alpha\)=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16. PSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, H=0.75 and \(\alpha\)=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18. PSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), \(\alpha\)=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16. Experimental data of Nav1.6 channels in the soma of hippocampal neurons NavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s). NavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.

opencc-by-4.0Oct 2022View details →

ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration

VaLRun: Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration" (Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file) The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.

opencc-by-4.0Nov 2022View details →

Weekly CoMix contact matrices for UKHSA COVID-19 dashboard and ONS COVID-19 infection survey age-groups

Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard  (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey (2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+). The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey.   Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795). 1. Munday, J.D., Jarvis, C.I., Gimma, A. et al. Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. BMC Med 19, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0

XRD analysis of Iberian unfired potsherds from the Late Iron Age

This dataset contains 11 mineralogical analyses of ceramic potsherds by powder X-ray diffraction (XRD). The samples come from the Iberian workshop of the Mas de Moreno (Teruel, Spain). Method The outer surfaces of all the samples were mechanically removed prior to analysis. All samples were manualy powdered in an agate mortar. The data were collected with a D8 Advance (Bruker) diffractometer in Bragg-Brentano configuration working at 1.6 kW (40 kV, 40 mA) and equipped with a copper anode source (kα1 = 1.5406 ; the kβ ray being removed by a Ni-filter in the diffracted beam). An 8 mm anti-scattering slit was mounted in front of the LynxEye© CCD detector. The explored area covered the 3-70° (2θ) range, with an angle step of 0.02° and a time step of 2 seconds. The stability of the instrument was checked between the different series of measurements by analyzing a standard (corundum crystal, NIST 1976). Data description File format: Bruker raw. The "XRD_clay_raw.zip" archive contains the raw diffractograms. All file names start with the sample code (a code starting with "BDX" followed by a 5-digit number), followed by a capital "P" (for powder diffraction). The "XRD_clay_raw.csv" file contains all results expressed in counts, one sample per row. The first column gives the angular position (2 thêta).

XRD analysis of Iberian potsherds from the Late Iron Age

This data set contains 76 mineralogical analyses of ceramic potsherds by powder X-ray diffraction (XRD). The samples come from the Iberian workshop of the Mas de Moreno (Teruel, Spain) and the settlements of Torre Cremada (Valdeltormo, Teruel) and El Palao (Alcañiz, Teruel). Method The outer surfaces of all the samples were mechanically removed prior to analysis. All samples were manualy powdered in an agate mortar. The data were collected with a D8 Advance (Bruker) diffractometer in Bragg-Brentano configuration working at 1.6 kW (40 kV, 40 mA) and equipped with a copper anode source (kα1 = 1.5406 ; the kβ ray being removed by a Ni-filter in the diffracted beam). An 8 mm anti-scattering slit was mounted in front of the LynxEye© CCD detector. The explored area covered the 3-70° (2θ) range, with an angle step of 0.02° and a time step of 2 seconds. The stability of the instrument was checked between the different series of measurements by analyzing a standard (corundum crystal, NIST 1976). Data description File format: Bruker raw. The "XRD_ceramic_raw.zip" archive contains the raw diffractograms. All file names start with the sample code (a code starting with "BDX" followed by a 5-digit number), followed by a capital "P" (for powder diffraction). The "XRD_ceramic_raw.csv" file contains all results expressed in counts, one sample per column. The first column gives the angular position (2 thêta).

opencc-by-4.0Jul 2023View details →

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Abstract Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks. Data Description This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.  As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those. Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets. Paper & Code The original paper for this article can be accessed here: <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> To access the codes relevant for this project, please access the project GitHub Repos: <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> If using this work, please cite it based on the above paper, or using the following BibTex: <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre>   Data Access The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi Funding ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

opencc-by-4.0Mar 2023View details →

Phylogenetic and epidemiologic data relating to age-specific HIV incidence and transmission in Rakai, Uganda, 2003-2018.

This repository contains the data for the analyses presented in the paper Growing gender inequity in HIV infection in Africa: sources and policy implications by M. Monod, A. Brizzi, R. Galiwango, R. Ssekubugu, Y. Chen, X. Xi et al. available in the pre-print <a href="https://doi.org/10.1101/2023.03.16.23287351">https://doi.org/10.1101/2023.03.16.23287351</a>  We thank all contributors, program staff and participants to the Rakai Community Cohort Study; all members of the PANGEA-HIV consortium, the <a href="https://www.rhsp.org/index.php">Rakai Health Sciences Program</a>, and CDC Uganda for comments on an earlier version of the manuscript. We also extend our gratitude to the <a href="https://doi.org/10.14469/hpc/2232">Imperial College Research Computing Service</a> and the <a href="https://www.bdi.ox.ac.uk/about/biomedical-research-computing">Biomedical Research Computing Cluster</a> at the University of Oxford for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the <a href="https://www.niaid.nih.gov/">National Institute for Allergy and Infectious Diseases</a> for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app.  All analysis code is available from <a href="https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender">https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender</a>.

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Seismic profiles, diatom and microfossil assemblages, and radiocarbon ages for constructing a post-glacial sea level curve from Fiordland, New Zealand

Two research cruises (12PL027 and 13PL018) were conducted aboard the University of Otago RV Polaris II in 2012 and 2013 to collect marine sediment cores and 2d seismic data as part of a collaborative research effort to study late-Pleistocene and Holocene environmental change in New Zealand. Data includes 4 Boomer seismic profiles, 4 CHIRP seismic profiles, seismic line GPS tracks, diatom assemblages (raw counts and relative abundances) and microfossil assemblages (raw counts and relative abundances), and radiocarbon ages from 4 marine sediment cores. The data used to construct a post-glacial sea level curve for Fiordland, New Zealand are also included, as well as data from published literature plotted for global comparisons. These data accompany the publication: Dlabola. E.K., Wilson, G.S., Gorman, A.R., Riesselman, C.R., and Moy, C.M. 2015. A post-glacial sea-level curve from Fiordland, New Zealand. Global and Planetary Change, 131, 101-114. https://doi.org/10.1016/j.gloplacha.2015.05.010

openCC (other)Jan 2023View details →

edi48/100

Tree regeneration after fire: Yukon Lodgepole Pine Surveys, tree age analysis

This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409. Ring counts from tree core and disk samples. Note ages will underestimate establishment date because samples were taken from ~30 cm above root collar.

openOpenOct 2003View details →

OpenNeuro44/100

Protecting the Aging Brain (PAgB)

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

OpenNeuro44/100

Aging

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

Neolithic/Copper Age radiocarbon dates from West Carpathian basin and East Alps

Radiocarbon dates used in this study were collected from original publications including grey literature.  The resulting database was cross-referenced with available radiocarbon databases to ensure the quality of data.  We used the latest revision (2019) of CalPal database by Bernhardt Weninger (B. Weninger et al. 2019), EuroEvol database (Manning et al.2016), C14.sk database (Barta et al. 2013) and RADON database (Martin Hinz et al. 2012).    Database structure consist of ten columns, from  site name (Site), unique site code (Scode), site longitude and latitude (Lon and Lat) in degrees, country (Country),  laboratory code (Labcode), the conventional radiocarbon date (Date) in years before present, standard deviation (SD), dated material (Mat) and bibliographic reference (Cit).  Where possible, we referenced the radiocarbon database where the date is recorded, otherwise, the publication where the date is reported or referenced is cited.    Many thanks to Bernhardt Weninger for kindly providing the latest version of CalPal database.   References    B. Weninger, Jöris O., Danzeglocke U. 2019. CalPal-2007. Cologne Radiocarbon & Palaeoclimatic Research Package. http:\\www.calpal.de Manning K., Colledge S., Crema E., Shennan S., Timpson A. 2016. The Cultural Evolution of Neolithic Europe. EUROEVOL Dataset 1: Sites, Phases and Radiocarbon Data. Journal of Open Archaeological Data 5:e2. DOI: http://doi.org/10.5334/joad.40 Martin Hinz M.F., Johannes Müller, Dirk Raetzel-Fabian, Rinne C., Sjögren K.-G., Wotzka H.-P. 2012. RADON - Radiocarbon dates online 2012. Central European database of 14C dates for the Neolithic and Early Bronze Age. http:\\www.jungsteinsite.de Barta P., Demján P., Hladíková K., Kmeťová P., Piatničková K. 2013. Database of radiocarbon dates measured on archaeological samples from Slovakia, Czechia, and adjacent regions. http://www.c14.sk

COVID-19 case fatality derived age-adjusted risk of death

This is an old version of the plot. For the newer version, please visit <a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a> doi:<a href="https://doi.org/10.5281/zenodo.3787930">10.5281/zenodo.3787930</a> 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 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

COVID-19 case-fatality-derived age-adjusted risk of death

This is an old version of the plot. For the newer version, please visit <a href="https://zenodo.org/record/3787931">https://zenodo.org/record/3787931</a> doi:10.5281/zenodo.3787931 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 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

COVID-19 age-adjusted risk of death derived from case fatality

This is an old version of the plot. For the newer version, please visit https://zenodo.org/record/3787931 doi:10.5281/zenodo.3787931 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. 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. Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.