cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6974286
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-08-03)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: The previous version of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The previous version of this data set (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-07-27.
DOI: 10.5281/zenodo.7055449
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-09-06)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The version of this data set associated with the publication (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-08-31.
DOI: 10.5281/zenodo.7143035
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-10-04)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The version of this data set associated with the publication (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-09-28.
DOI: 10.5281/zenodo.7307316
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-11-09)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The version of this data set associated with the publication (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-11-02.
DOI: 10.5281/zenodo.5745338
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The version of this data set associated with the publication (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-11-29.
DOI: 10.5281/zenodo.7426515
title: Data for SARS­-CoV-­2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)

description: This version contains a single file, with time series data for the most recent monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data. Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. DOI: 0.1126/science.abn4947 For code and more details see: https://github.com/jrcpulliam/reinfections/releases/tag/v3.0 or 10.5281/zenodo.6108448 The version of this data set associated with the publication (available via the links above) included the following files: <code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>) <code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or &gt;0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>) <code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) <code>sim_90_null.RDS</code> - simulation results (as used in the manuscript) <code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)
description: The data provided here cover the period from 2021-03-04 to 2022-11-29.
DOI: 10.5281/zenodo.7379487
title: das data on lake

description: The data will be uploaded here.
DOI: 10.25381/cput.21673370
title: UV Data, ABTS & FRAP antioxidant activities and Total Phenolics of Helichrysum Pandurifolium Schrank.xlsx

description: RAW data and calculations for UV Bio-assays performed on extracts of Helichrysum Pandurifolium Schrank and phytochemical isolates.
DOI: 10.25381/cput.21673370.v1
title: UV Data, ABTS & FRAP antioxidant activities and Total Phenolics of Helichrysum Pandurifolium Schrank.xlsx

description: RAW data and calculations for UV Bio-assays performed on extracts of Helichrysum Pandurifolium Schrank and phytochemical isolates.
DOI: 10.25381/cput.21640619
title: DESIGN AND IMPLEMENTATION OF AN INTELLIGENT REQUIREMENTS ENGINEERING TOOL FOR INTERNET OF THINGS APPLICATIONS IN AN AGILE ENVIRONMENT

description: Ref:2020FEREC-STD-019 <br> The dataset comprised of epic agile requirements which needs to be decomposed.Subsequent to their decomposition, the system initiate task assignment algorithm.The algorithm accepts square matrix as an input. The model (Hungerian algorithm output proposed task assigments. Each developer is assigned tasks in the form sprint planning <br>
DOI: 10.25381/cput.21640619.v1
title: DESIGN AND IMPLEMENTATION OF AN INTELLIGENT REQUIREMENTS ENGINEERING TOOL FOR INTERNET OF THINGS APPLICATIONS IN AN AGILE ENVIRONMENT

description: Ref:2020FEREC-STD-019 <br> The dataset comprised of epic agile requirements which needs to be decomposed.Subsequent to their decomposition, the system initiate task assignment algorithm.The algorithm accepts square matrix as an input. The model (Hungerian algorithm output proposed task assigments. Each developer is assigned tasks in the form sprint planning <br>
DOI: 10.25381/cput.21581418
title: The Comparative analysis of adsorbents suitable for thermal desalination system.

description: The datat published is based on finding the isosteric enthalpy.
DOI: 10.25381/cput.21581418.v1
title: The Comparative analysis of adsorbents suitable for thermal desalination system.

description: The datat published is based on finding the isosteric enthalpy.
DOI: 10.5281/zenodo.5841139
title: Longitudinal corpus of privacy policies

description: This is a corpus of 56,416 unique privacy policy texts spanning the years 1996-2021. policy-texts.zip contains a directory of text files with the policy texts. File names are the hashes of the policy text. policy-metadata.zip contains two CSV files (can be imported into a pandas dataframe) with policy metadata including readability measures for each policy text. labeled-policies.zip contains CSV files with content labels for each policy. Labeling was done using a BERT classifier. corpus_readability_metrics.csv contains readability measures for a sample of 1,500 policy texts computed by six different readability libraries. Details on the methodology can be found in the accompanying paper.
DOI: 10.25381/cput.21602817
title: STAKEHOLDERS' INVOLVEMENT IN THE DEVELOPMENT AND IMPLEMENTATION OF TOURISM-RELATED POLICIES IN A SELECTED DISTRICT MUNICIPALITY IN THE EASTERN CAPE

description: <strong>Ethics Clearance: 2019FOBREC738</strong> Semi-structured interviews were performed online using tools like Microsoft Teams and Zoom. These platforms were only employed because of the COVID-19 pandemic to follow health and safety procedures. The researcher documented every interview with a participant and made sure that every response was accurately recorded.
DOI: 10.25381/cput.21602817.v1
title: STAKEHOLDERS' INVOLVEMENT IN THE DEVELOPMENT AND IMPLEMENTATION OF TOURISM-RELATED POLICIES IN A SELECTED DISTRICT MUNICIPALITY IN THE EASTERN CAPE

description: <strong>Ethics Clearance: 2019FOBREC738</strong> Semi-structured interviews were performed online using tools like Microsoft Teams and Zoom. These platforms were only employed because of the COVID-19 pandemic to follow health and safety procedures. The researcher documented every interview with a participant and made sure that every response was accurately recorded.
DOI: 10.6084/m9.figshare.20290587.v2
title: Increased nighttime oxidants over China despite widespread decrease across the globe

description: Datasets that support the findings of the study "Global Production and Trends in Nighttime Oxidants – Increases in China, Decreases Elsewhere"
DOI: 10.25381/cput.21534564
title: Topside Facilities’ Ability to Handle a New Blend of Crude Oil

description: 2021FEBEREC-STD-047 <br> Aspen Hysys process simulation input and output results
DOI: 10.25381/cput.21534564.v1
title: Topside Facilities’ Ability to Handle a New Blend of Crude Oil

description: 2021FEBEREC-STD-047 <br> Aspen Hysys process simulation input and output results
DOI: 10.17632/b3d66r56k8.1
title: Spatial variability of heavy metal concentration in urban pavement joints – A case study

description: Dataset: heavy_metal_pollution_indice_data.xslx Dataset description: Concertation of trace metals (Al, Fe) and heavy metals (V, Cr, Co, Ni, Cu, Sn, As, Cd, Pb) given in mg/kg soil dry weight and calculated pollution indices (Igeo: Geoaccumulation Index; PLI: Pollution load index; RI: Potential ecological risk index). For methods of analyses and index calculation see method chapter in above mentioned publication Dataset: pavement_joint_features_Marburg_city.xlsx Dataset description: Features of pavement joints in the inner-city of Marburg (Hesse, Germany) recorded during field work. Distance_street: Distance (m) of each sampling point to next street. Height: Heigth (m a.s.l.) taken from DEM. Av_joint_size: Average joint size (cm) from 1 square meter pavement measured during field work. Plant_coverage: Plant coverage classified from 0 to 5. For further description of classes see above mentioned publication. Runoff:_accumulation: Runoff accumulation classified in three classes: 0 – 2.5 - 5. For further description of classes see above mentioned publication. pH: pH (KCl) values of pavement joint soil material. OM: Organic matter content (mass-%) of pavement joint soil material.
DOI: 10.5683/sp2/xlim93
title: Data and R script for "Consequences of nest site selection vary along a tidal gradient"

description: These data were used to investigate variation in the costs and benefits of nesting at various positions along an intertidal gradient. These data were collected from one laboratory and two field experiments carried out in 2018 and 2019. The focal species, the plainfin midshipman Porichthys notatus, is a marine toadfish that nests in the intertidal and shallow subtidal zones of beaches along the Pacific Coast of North America.
DOI: 10.6084/m9.figshare.13317404.v1
title: Measurements of fecal cortisol metabolites (fGMC) and activity budget of male spotted hyenas in the Ngorongoro Crater (Tanzania)

description: This excel table contains 3 sheets:<br> (1) fGMC, with the fecal cortisol metabolite concentrations and associated activity of male spotted hyenas,<br> (2) Activity Budget, with the proportion of sightings where males engaged in each of 4 categories of activities<br> (3) Quality of courted females, with the mean social rank of the females that were courted by males.<br> <br> These data were used for the original publication: "The interplay between social rank, physiological constraints and investment in courtship in male spotted hyenas" Davidian et al. (2020) Functional Ecology. https://doi.org/10.1111/1365-2435.13733 <br>
DOI: 10.6084/m9.figshare.9798491
title: Data for Social information use and collective foraging in a pursuit diving seabird

description: Data for Social information use and collective foraging in a pursuit diving seabird<br>
DOI: 10.7295/w9t72fmz
title: Cervical (C5), unilateral spinal cord injury with diverse injury modalities, multiple behavioral outcomes, and histopathology. Open Data Commons for Spinal Cord Injury

description: STUDY PURPOSE: This project was a multivariate validation study of unilateral cervical contusion (hemi-contusion) model-development efforts. DATA COLLECTED: The dataset includes N=159 rats with hemisections (n=9), NYU MASCIS weight drop contusions: sham (n=10), 6.25 mm (n=10), 12.5 mm (n=32); Infinite Horizon Impactor: sham (n=6), 75 kdyn (n=58), 100 kdyn (n=34). Behavioral recovery was monitored over 6 weeks using mutiple assements taken from the same subjects: Grooming Score, Paw Placement in a Cylinder, BBB locomotor subscore, Forelimb Open Field Score, and numerous Catwalk footprint analysis metrics. Histopathology metrics included lesion epicenter gray matter sparing, white matter sparing, total tissue sparing, and motorneuron numbers. PRIMARY CONCLUSION: Principal component analysis (PCA)-based multidimensional pattern detectors can resolve the ‘syndrome space’ across multiple models of SCI, allowing direct comparison of subjects with diverse injury types, outcome sets and therapeutics.