BackgroundGlobally, with a neonatal mortality rate of 27/1000 live births, Sub-Saharan Africa has the highest rate in the world and is responsible for 43% of all infant fatalities. In the first week of life, almost three-fourths of neonatal deaths occur and about one million babies died on their first day of life. Previous studies lack conclusive evidence regarding the overall estimate of early neonatal mortality in Sub-Saharan Africa. Therefore, this review aimed to pool findings reported in the literature on magnitude of early neonatal mortality in Sub-Saharan Africa.MethodsThis review’s output is the aggregate of magnitude of early neonatal mortality in sub-Saharan Africa. Up until June 8, 2023, we performed a comprehensive search of the databases PubMed/Medline, PubMed Central, Hinary, Google, Cochrane Library, African Journals Online, Web of Science, and Google Scholar. The studies were evaluated using the JBI appraisal check list. STATA 17 was employed for the analysis. Measures of study heterogeneity and publication bias were conducted using the I2 test and the Eggers and Beggs tests, respectively. The Der Simonian and Laird random-effect model was used to calculate the combined magnitude of early neonatal mortality. Besides, subgroup analysis, sensitivity analysis, and meta regression were carried out to identify the source of heterogeneity.ResultsFourteen studies were included from a total of 311 articles identified by the search with a total of 278,173 participants. The pooled magnitude of early neonatal mortality in sub-Saharan Africa was 80.3 (95% CI 66 to 94.6) per 1000 livebirths. Ethiopia had the highest pooled estimate of early neonatal mortality rate, at 20.1%, and Cameroon had the lowest rate, at 0.5%. Among the included studies, both the Cochrane Q test statistic (χ2 = 6432.46, P

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

Collective data of Japan's birth-related statistics from 1899 to 2022. Some data are missing between the years 1944 and 1946 due to records lost during World War II.

For use case and analysis reference, please take a look at this notebook Japan Birth Demographics Analysis

Feature Descriptions

• year: The year.
• birth_total: The total number of births.
• birth_male: The total number of male births.
• birth_female: The total number of female births.
• birth_rate: The birth rate. Equation is birth_total / population_total * 1,000
• birth_gender_ratio: The birth gender ratio. Equation is birth_male / birth_female * 1,000
• total_fertility_rate: The average number of children that are born to a woman over her lifetime.
• population_total: The total population.
• population_male: The total male population.
• population_female: The total female population.
• infant_death_total: The total infant deaths.
• infant_death_male: The total male infant deaths.
• infant_death_female: The total female infant deaths.
• infant_death_unknown_gender: The total unknown gender infant deaths.
• infant_death_rate: The infant death rate. Equation is infant_death_total / birth_total * 1,000
• infant_death_gender_ratio: The infant death gender ratio. Equation is infant_death_male / infant_death_female * 1,000
• infant_deaths_in_total_deaths: The infant death ratio among other deaths.
• stillbirth_total: The total number of stillbirths (dead born).
• stillbirth_male: The total number of male stillbirths.
• stillbirth_female: The total number of female stillbirths.
• stillbirth_unknown_gender: The total number of unknown gender stillbirths.
• stillbirth_rate: The stillbirth rate. Equation is stillbirth_total / (birth_total + stillbirth_total) * 1,000
• stillbirth_gender_ratio: The stillbirth gender ratio. Equation is stillbirth_male / stillbirth_female * 1,000
• firstborn: The number of firstborns.
• secondborn: The number of secondborns.
• thirdborn: The number of thirdborns.
• forthborn: The number of forthborns.
• fifthborn_and_above: The number of fifthborns and above.
• weeks_under_28: The number of births occurred under week 28. Early terms.
• weeks_28-31: The number of births occurred between weeks 28 and 31. Early terms.
• weeks_32-36: The number of births occurred between weeks 32 and 36. Early terms.
• weeks_37-41: The number of births occurred between weeks 37 and 41. Full terms.
• weeks_over_42: The number of births occurred over week 42. Late terms.
• mother_age_avg: The mother's average age.
• mother_age_firstborn: The mother's average age of the firstborn.
• mother_age_secondborn: The mother's average age of the secondborn.
• mother_age_thirdborn: The mother's average age of the thirdborn.
• mother_age_under_19: The number of births by mothers under age 19.
• mother_age_20-24: The number of births by mothers between age 20 and 24.
• mother_age_25-29: The number of births by mothers between age 25 and 29.
• mother_age_30-34: The number of births by mothers between age 30 and 34.
• mother_age_35-39: The number of births by mothers between age 35 and 39.
• mother_age_40-44: The number of births by mothers between age 40 and 44.
• mother_age_over_45: The number of births by mothers over 45.
• father_age_avg: The father's average age.
• father_age_firstborn: The father's average age of the firstborn.
• father_age_secondborn: The father's average age of the secondborn.
• father_age_thirdborn: The father's average age of the thirdborn.
• legitimate_child: The Number of births under married parents.
• illegitimate_child: The number of births under non-married parents.

Acknowledgement

E-Stat Demographic Survey

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents. Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date). COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date. Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are spec

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under constant-rate birth-death (crBD) tend to differ from empirical trees, for example with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between crBD and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which crBD differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used crBD priors with those that used other non-BD Bayesian and non-Bayesian methods, we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using crBD in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under crBD, leading to systematic bias in data sets with limited information content. Methods Empirical trees used in the study are trees from the literature, collected by TimeTree (timetree.org). Run Tree_Selection.R to select the empirical phylogenetic trees to be included from TimeTree. The output file final_timetrees.RData contains the final subset of empirical phylogenetic TimeTree trees used for analysis with anonymized tip labels. 2. Run Simulation_And_Analysis.R to fit birth and death parameters (assuming rho = 1) for each of the 1189 empirical trees, simulate 1000 trees per empirical tree, calculate tree index values for both empirical and simulated trees, and calculate z-scores comparing the simulated and empirical trees. Note that calculating the tree index values for the simulated trees is VERY time-consuming due to the number of trees. Run Supplementary_Fig_S1_Analysis.R to generate data for Supplementary Figure S1. 3. Run Meta_analysis.R to run the linear regression models to investigate the role of the prior/analysis type for the subset (n=300) of the included empirical trees. The metadata for the 300 trees can be found in the supplementary files (Table S3). 4. Run Imbalance_Simulation.R to run the simulations for the imbalanced data subset (100 trees). Simulated sequences for each tree were run through RevBayes and IQ-TREE 2, as mentioned previously. Note: To avoid later confusion, the three various substitution rates used (0.5, 0.05, 0.005) are referred to as Rates 2-4 in the code. There is therefore no Rate 1; apologies in advance for any confusion. The shell scripts to run the inferences in each software are as follows: 4a. RevBayes: fasta_to_revbayes_code_rate2.sh, fasta_to_revbayes_code_rate3.sh, fasta_to_revbayes_code_rate4.sh These shell scripts use the following .Rev files: MCMC_Revbayes_code_rate2.Rev, MCMC_Revbayes_code_rate3.Rev, MCMC_Revbayes_code_rate4.Rev And rely on the following supplementary .Rev files: tree_BD.Rev, sub_JC.Rev, clock_global.Rev 4b. IQ-TREE 2: fasta_to_iqtree_code.sh 5. Run Final_Figures.R to visualize the results.

The results are contained in 5 datasets, each of which is described in further detail below. Each dataset is stored as a tar file, which when extracted will be in Hive/Feather format as: {dataset}/age={age}/genotype={genotype}/data.feather Where {age} is the starting age of the simulated individuals (30, 35, 40 or 45) and {genotype} is the genotype of the simulated individuals (path_MLH1, path_MSH2, path_MSH6, path_PMS2). Each data.feather file covers 500 different parameter sets, and for each of these 4 competing options, and for each of these 1000 simulated individuals, i.e., 2 million simulated individuals per data.feather and 32 million simulated individuals overall. patient-level-outcomes This gives patient-level costs, life years and QALYs for conducting the economic evaluation. Each row/observation corresponds to a single simulated patient. Fields:

params_uuid - uniquely identifies the parameter set (can be used to join with other datasets) individual_uuid - uniquely identifies the individual (can be used to join with other datasets) competing_option - text description for which of the four competing options applied to the individual total_costs - total costs over the lifetime for the individual, discounted at the rate params'analysis.discount_rate.cost' total_life_years - total life years for the individual, discounted at the rate params'analysis.discount_rate.ly' total_qalys - total quality-adjusted life years for the individual, discounted at the rate params'analysis.discount_rate.qaly'

Tip: As each observation corresponds to a single individual it is possible to calculate undiscounted life years lived from total_life_years: drc = log(1 + params['analysis.discount_rate.ly'])total_life_years_undiscounted = - log(1 - drc * total_life_years) / drc params The parameters for the simulations. Each row/observation corresponds to a single parameter set.

params_uuid - uniquely identifies the parameter set (can be used to join with other datasets) ... - parameter values, which are mostly scalars but some are vectors

cancer-outcomes Counts of various cancer outcomes. Each row gives the count of a particular cancer outcome for a particular combination of parameter set and competing option. CAUTION: Any rows which would have n=0 have been omitted from this dataset.

params_uuid - as above competing_option - as above site - colorectal, ovarian or endometrial outcome - Incidence, Recurrence or Mortality stage - Stage of cancer at time of diagnosis: I, II, III or IV (missing for mortality) route - Route to cancer diagnosis (only available if outcome is 'Incidence'): RouteToDiagnosis.SYMPTOMATIC_PRESENTATION, RouteToDiagnosis.SURVEILLANCE, or RouteToDiagnosis.RISK_REDUCING_SURGERY n - Number of times the corresponding cancer outcome occurred (for a simulated population of 1000 individuals)

cancer-free-survival For each individual, how long did they survive without a cancer diagnosis or becoming censored (principal reason for censoring is death from non-cancer cause)

individual_uuid - see above params_uuid - see above competing_option - see above age_event - age of the individual when the diagnosis event or censoring happened event - 1 if a cancer diagnosis happened or 0 if censoring happened cancer - CancerSite.ENDOMETRIUM, CancerSite.OVARIES or CancerSite.COLORECTUM stage - see cancer-outcomes route - see cancer-outcomes age_enter - age of the individual when entering the model (=age) sex - will be Sex.FEMALE for all simulated individuals

cancer-survival Each row/observation in this dataset corresponds to survival from a diagnosed cancer in a simulated individual. CAUTION: For each cancer there are two rows, because users may wish to calculate cause-specific survival or crude survival. Ensure that you filter out the calculation type you do not wish to include

individual_uuid - see above params_uuid - see above competing_option - see above survival_type - 'cause-specific' or 'all-cause' (crude) age_event - age at which the individual died or was censored age_diagnosis - age at which the individual was diagnosed with this cancer event - 1 if the individual had an eligible death at age_event (determined by survival_type), 0 otherwise (e.g., died from another cause if survival_type is 'cause_specific', censored) site - see above stage - see above route - see above

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

The World Bank is a treasure trove of information. :- https://data.worldbank.org/

Generally the Gross Domestic Product of a country = the total output of the country = measure of development/total affluence of the country is measured by indicators such as household spending, government spending, level of investments etc.

Please see Bank of England explanation of GDP here :- http://edu.bankofengland.co.uk/knowledgebank/what-is-gdp/

I have argued that GDP could instead be measured better by primary indicators that lead to these what I call "secondary indicators".

Primary indicators are such as :- level of education. I hypothesize that a higher level of education leads to higher household income and hence higher household spending. So does knowing education levels of a country allow us to predict the GDP of the country?

I have used the list of primary indicators below to do a regression of the GDP per person :- (1) Women making informed choices regarding healthcare - The null hypotheses (H0)----> is the higher the level of women's education - the higher the level of national education and lesser infant mortality rates(which might be a stretch) and hence higher household income --> higher household spending ---> higher GDP. (2) Rural Population % - The null hypotheses (H0) is -----> higher rural population ----> lower per capita household income----> lower level of household spending----> lower GDP. (3) Ratio of Population having education ----> similar to above. You get the point hopefully by now... if not read a introductory macroeconomics textbook or course like this :- https://www.edx.org/course/introduction-economics-macroeconomics-snux-snu044-088-2x-0 (4) Legal Rights Strength Index-----> This actually comes from Islam. In Islam - the affluence of a country is related to truthfulness, rule of law being abided in the country etc.. For those who can understand Urdu/Hindi - please watch this video :- https://www.youtube.com/watch?v=XLjicUv0KYs (5) Credit to Private Sector -----> easier it is to open a business, work on ideas-----> higher should be the output of the country (6) Births attended by Skilled Staff ------> less infant mortality ----> indicates higher level of education and health care in the country ------> can indicate higher government spending among other factors ------>and should translate to higher level of GDP. (6) ATMMachines Ratio per 1000 people ---------> Higher level -----> shows finance is easily available -----> institutions are developed -----> maybe even indicates better public infrastructure-----> should indicate higher personal and government funding. (7) Agricultural Machines per hectare of land ------> higher automation -----> better access to finance for rural areas ------> should lead to higher GDP. (8) Literacy Rate Adults -----> the higher level of education in adults ----> higher private spending -----> should lead to higher GDP. (9) Accounts Ratio Financial Institutions -----> how many people have bank accounts who are male and over 15 ------> shows level of private spending-----> level of finance and infrastructure and hence government funding maybe -----> higher GDP.

Purpose: To investigate the risk-stratifying utility of tumor size and a threshold for further stratification on cancer-specific mortality of thyroid cancer (TC) patients in stage IVB.Methods: One thousand three hundred and forty-five patients (620 males and 725 females) with initial distant metastasis over 55 years between 2004 and 2016 from Surveillance, Epidemiology, and End Results databases were investigated, with a median follow-up time of 23 months [interquartile range (IQR), 5–56 months] and a median age of 70 years (IQR, 63–77 years). TC-specific mortality rates were calculated under different classifications. Cox regressions were used to calculate hazard ratios (HRs) and Kaplan-Meier Analyses were conducted to investigate TC-specific survivals.Results: In the whole cohort, patients with tumors >4 cm had the highest TC-specific mortality (67.9%, 330/486), followed by tumor size >1 cm but ≤ 4 cm (43.08%, 190/441), and tumor size ≤ 1 cm (32.69%, 34/104). Kaplan-Meier curves showed the increased tumor size was associated with a statistically significant decrease in TC-specific survival (P < 0.001). Papillary thyroid cancer (PTC) patients with tumors >4 cm had significantly higher hazard ratios (HRs) of 2.84 (1.72–4.70) and 3.11 (1.84–5.26) after adjusting age, gender, race, and radiation treatment, compared with patients with tumors ≤ 1 cm (P < 0.001). The TC-specific mortalities and survivals were further investigated among more detailed subgroups divided by different tumor size, and a threshold of 3 cm could be observed (P < 0.005) for risk stratification.Conclusions: Mortality risk increased with tumor size in PTC patients in stage IVB. Our findings demonstrated the possibility of further stratification in IVB stage in current TNM staging system. Patients with tumor size over 3 cm had an excessively high risk of PTC-specific mortality, which may justify the necessity of more aggressive treatment for them.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

Incidence rates of the 5 symptoms according to the individual microfilaremia status and the EW status.

Absence occurrence: results from frailty model for recurrent events.

Original provider: Minerals Management Service

Dataset credits: Minerals Management Service

Abstract: This dataset is from the marine mammal and seabird surveys of the southern California Bight studies: Southern California Bight low aerial [birds] study code: SB, Contract Number: AA550-CT7-36, Principal investigators: K. S. Norris and B. J. Le Boeuf, University of California, Santa Cruz, and G. L. Hunt, University of California, Irvine.

Time period: May 1975 through March 1978

Study area: The Southern California Bight, from Point Conception to the United States-Mexico boundary and offshore to the 2,000 m isobath.

Methodology: Aerial and ship surveys were conducted along pre-established transects designed to systematically sample marine mammal and seabird abundance in all waters of the study area. Aerial surveys were conducted at two altitudes (200 ft and about 750-1,000 ft ASL) alternating at 2-3 week intervals. Seabirds and pinnipeds were recorded only on the low-altitude surveys which predominantly sampled along eight lines of latitude, separated by 25 nm, and connecting lines of longitude. High-altitude surveys sampled cetacean abundance along 15 Loran lines oriented northeast-southwest and separated by 12-15 nm. On low-altitude surveys, seabirds were counted only within a 50 m corridor on the shaded side of the aircraft. Marine mammals were counted in an unbounded corridor on one side of the aircraft on low-altitude surveys and both sides of the aircraft on high-altitude surveys. A clinometer or marks on the wing-strut were used to estimate the declination, and the measurement or estimate later used to calculate probability density functions of frequency with right-angle distance. Ships were used for surveys of inshore waters along standard (i.e., predetermined and replicate) transects and for search, catch, and tagging/tracking of small cetaceans. The standard ship transect cruises sampled abundance of seabirds and marine mammals over banks, basins, and ridges in waters inshore of the Patton Escarpment (the shelf break). Seabirds and marine mammals were counted on both sides of the cruise track and distance estimated or measured with a range-finder. Catch cruises attempted to find and remain with schools of common dolphin and other small cetaceans; therefore, data on sightings from these cruises cannot be used as samples of animal abundance. The Southern California Bight Study also included ground and aerial censuses of pinniped and seabird colonies, and special studies of productivity, mortality rates, and foraging range.

Databases produced: 1) sightings of seabirds and marine mammals on 24 low-altitude aerial surveys, 2) sightings of cetaceans on 35 high-altitude aerial surveys, 3) sightings of seabirds and marine mammals on 29 ship transect surveys, and 4) sightings of cetaceans on 34 catch cruises.

Included in this database are the following: High Altitude Mammal Observations: 75,489 km of effort, 695 sightings of 68,557 individual animals Low Altitude Mammal Observations: 37,843 km of effort, 1,320 sightings of 15,070 individual animals Low Altitude Bird Observations: 35,445 km of effort, 7,950 sightings of 63,359 individual animals Ship Observations: 17,903 km of effort, 23,519 sightings of 181,287 individual animals

Eyeworm occurrence: results from frailty model for recurrent events.

Original provider: Minerals Management Service

Dataset credits: Minerals Management Service

Abstract: This dataset is from the marine mammal and seabird surveys of the southern California Bight studies: Southern California Bight high aerial [cetaceans] study code: SH, Contract number: AA550-CT7-36, Principal investigators: K. S. Norris and B. J. Le Boeuf, University of California, Santa Cruz, and G. L. Hunt, University of California, Irvine.

Time period: May 1975 through March 1978 Study area: The Southern California Bight, from Point Conception to the U.S.-Mexico boundary and offshore to the 2,000 m isobath. Methodology: Aerial and ship surveys were conducted along pre-established transects designed to systematically sample marine mammal and seabird abundance in all waters of the study area. Aerial surveys were conducted at two altitudes (200 ft and about 750-1,000 ft ASL) alternating at 2-3 week intervals. Seabirds and pinnipeds were recorded only on the low-altitude surveys which predominantly sampled along eight lines of latitude, separated by 25 nm, and connecting lines of longitude. High-altitude surveys sampled cetacean abundance along 15 Loran lines oriented northeast-southwest and separated by 12-15 nm. On low-altitude surveys, seabirds were counted only within a 50 m corridor on the shaded side of the aircraft. Marine mammals were counted in an unbounded corridor on one side of the aircraft on low-altitude surveys and both sides of the aircraft on high-altitude surveys. A clinometer or marks on the wing-strut were used to estimate the declination, and the measurement or estimate later used to calculate probability density functions of frequency with right-angle distance. Ships were used for surveys of inshore waters along standard (i.e., predetermined and replicate) transects and for search, catch, and tagging/tracking of small cetaceans. The standard ship transect cruises sampled abundance of seabirds and marine mammals over banks, basins, and ridges in waters inshore of the Patton Escarpment (the shelf break). Seabirds and marine mammals were counted on both sides of the cruise track and distance estimated or measured with a range-finder. Catch cruises attempted to find and remain with schools of common dolphin and other small cetaceans; therefore, data on sightings from these cruises cannot be used as samples of animal abundance. The Southern California Bight Study also included ground and aerial censuses of pinniped and seabird colonies, and special studies of productivity, mortality rates, and foraging range.

Databases produced: 1) sightings of seabirds and marine mammals on 24 low-altitude aerial surveys, 2) sightings of cetaceans on 35 high-altitude aerial surveys, 3) sightings of seabirds and marine mammals on 29 ship transect surveys, and 4) sightings of cetaceans on 34 catch cruises.

Included in this database are the following: High Altitude Mammal Observations: 75,489 km of effort, 695 sightings of 68,557 individual animals Low Altitude Mammal Observations: 37,843 km of effort, 1,320 sightings of 15,070 individual animals Low Altitude Bird Observations: 35,445 km of effort, 7,950 sightings of 63,359 individual animals Ship Observations: 17,903 km of effort, 23,519 sightings of 181,287 individual animals

Purpose: Objectives of the study were to characterize the marine mammal and seabird fauna of the Southern California Bight, estimate abundance of species, describe the distribution, determine the timing and routes of migrations and movements, and document seasonal changes in numbers or patterns of habitat-use. In addition, studies were conducted to determine the size and status of breeding colonies of seabirds and pinnipeds, their productivity, and trends in growth.

This dataset provides the results of U-Th dating of coral samples obtained from reef matrix percussion cores and death assemblages from Mazie Bay, North Keppel Island, Southern Great Barrier Reef. Data is presented for 117 coral samples ranging in age from 6900 years before present (yr.BP) to modern.

A U-Th dating approach to understanding past coral reef dynamics and geomorphological constraints on future reef growth potential; Mazie Bay, Southern Great Barrier Reef - Reconstructing coral reef histories at multiple temporal scales provides a window of understanding into their response to changing environments. Using high precision Uranium-Thorium dating of corals from various reef zones we have reconstructed a complete growth history of Mazie Bay reef (North Keppel Island) in the Southern Great Barrier Reef. Our results show that Mazie Bay reef has been dominated by fast-growing branching Acropora spp. corals for the past 7000 years, and that during the mid-Holocene coral growth rapidly filled available accommodation space. The modern veneer of living coral is subject to periods of disturbance and recovery driven by various climatic influences including cyclones, floods and bleaching. Although loss of coral at Mazie Bay in the past was followed by relatively rapid recovery (~15 years), continued or chronic decimation of adult Acropora spp. coral populations could be catastrophic for this region due to the lack of connectivity to reefs outside of the Keppel Islands region.

Methods: This tables contain data for U-Th dating of coral samples collected in February 2012 and June 2013 from a fringing reef in Mazie Bay, North Keppel Island, Southern Great Barrier Reef, including reef flat and slope matrix percussion cores and death assemblage (coral rubble at the sea/sediment interface next to the reef slope). For reef matrix percussion cores, ten-centimetre diameter aluminium cores were manually percussed into the reef matrix in November 2012 and November 2013. Four cores (two proximal to the beach and two distal) were taken from the emergent reef flat. Nine reef slope matrix cores from three discrete location across ~400 m of the modern reef slope were manually percussed on SCUBA at a depth of ~3 metres below lowest astronomical tide (mLAT) following methods described by Roff et al. (2015). Coral fragments were selected for U-Th dating from the base of each core where coral material was present, then up core where suitable skeletal material was available (i.e. from corals with enough unaltered aragonite for U-Th dating purposes). As compaction of the reef matrix occurs during percussion coring internal and external measurements of the cores were taken in the field prior to extracting the cores. Compaction of the cores was calculated as; core length = (Total Length of the core (initial) - External) and percentage compaction of the reef material inside the cores [(Internal-External)/((Total Length (initial) -External) – (Internal-External))*100]. Coral depths are based on linear uncompacted core length and reported relative to depth metres lowest astronomical tide (mLAT) based on 2012/2013 tide data from Maritime Safety Queensland for Rosslyn Bay (Station-024011A). Death assemblages were collected at three sites, adjacent to the reef slope cores, along four consecutive 20 m transects running parallel to the reef front at depths of ~- 3 to - 6 mLAT. Coral rubble was excavated from the benthos within 5 metre intervals of each transect and placed into calico bags (40 cm x 20 cm). Samples of death assemblages for U-Th dating were selected randomly from the calico bags, with sub-samples for U-Th dating being taken as close to the top (most recent) growth section of the corals so as to represent the timing of mortality.

U-Th dating All coral samples were prepared and U-Th dated on a Nu Plasma Multi-Collector Inductively Coupled Plasma Mass Spectrometer (MC ICP-MS) at the Radiogenic Isotope Facility, The University of Queensland following methods described in detail in Clark et al. (2014) and Leonard et al. (2016). U-Th data was calculated using Isoplot 3.75 (Ludwig, 2012). Activity ratios were calculated from atomic ratios using decay constants; lambda 238 = 1.55125 × 10-10 yr-1 (Jaffey et al., 1971), lambda 234 = (2.8262 ± 0.0057) × 10-6 yr-1, lambda 230 = (9.158 ± 0.028) × 10-6 yr-1 (Cheng et al., 2000) and corrected using the two component mixing equation of Clark et al. (2014) with a detrital 230Th/232Th value of 0.62 based on Keppel Islands specific isochron data from Leonard et al. (2016). For ease of comparison to previously reported data from reef matrix cores we report all dates prior to 1950 as yr.BP (years before present; where present is 1950), but consider all ages >1950 from slope core tops and death assemblage as “modern” and report as AD.

Format: The dataset consists of three excel spreadsheets; one for samples U-Th dated to older than 1950 AD (calculated as years before present where present is 1950), and the second is calculated as years AD (post 1950 samples). The third spreadsheet provides lat/long of sample sites.

Data Dictionary: • Labcode – sample specific labcode in Radiogenic Isotope Facility, The University of Queensland • Sample Name – Sample field name – NK = North Keppel; S = site number; AB/CD = core section followed by uncompacted core depth where samples was taken.
• Genus – Genus of coral • Date of Chemistry – Date column chemistry was completed • U (ppm) – Uranium concentration in parts per million • 232Th (ppb) – Thorium 232 concentration in parts per billion • (230Th/232Th); (230Th/238U); (234U/ 238U) - activity ratios calculated from atomic ratios using decay constants; lambda 238 = 1.55125 × 10-10 yr-1 (Jaffey et al., 1971), lambda 234 = (2.8262 ± 0.0057) × 10-6 yr-1, lambda 230 = (9.158 ± 0.028) × 10-6 yr-1 (Cheng et al., 2000). • uncorr. 230Th Age (ka) - Uncorrected 230Th age was calculated using Isoplot/EX 3.75 program (Ludwig, 2012). All values have been corrected for laboratory procedural blanks and all errors are 2 sigma. • corr. 230Th Age (ka) - 230Th ages were corrected using the two-component correction method of Clark et al. (2014) using 230Th/232Thhyd and 230Th/232Thdet activity ratios of 1.08 ± 0.23 and 0.62 ± 0.14, respectively. • delta 234U - = [(234U/238U) - 1] × 1000 • Depth (mLAT) – Uncompacted core sample depth relative to metres lowest astronomical tide

References: Cheng, H., Edwards, R.L., Hoff, J., Gallup, C.D., Richards, D.A. and Asmerom, Y., 2000. The half-lives of uranium-234 and thorium-230. Chemical Geology, 169(1): 17-33.

Clark, T.R., Roff, G., Zhao, J.-x., Feng, Y.-x., Done, T.J. and Pandolfi, J.M., 2014. Testing the precision and accuracy of the U–Th chronometer for dating coral mortality events in the last 100 years. Quaternary Geochronology, 23(0): 35-45.

Jaffey, A., Flynn, K., Glendenin, L., Bentley, W.t. and Essling, A., 1971. Precision measurement of half-lives and specific activities of U 235 and U 238. Physical review C, 4(5): 1889.

Leonard, N.D., Zhao, J.-x., Welsh, K.J., Feng, Y.-x., Smithers, S.G., Pandolfi, J.M. and Clark, T.R., 2016. Holocene sea level instability in the southern Great Barrier Reef, Australia: high-precision U–Th dating of fossil microatolls. Coral Reefs, 35(2): 625-639.

Ludwig, K., 2012. Isoplot/Ex Version 3.75, a Geochronological Toolkit for Microsoft Excel. Berkeley Geochronology Center. Special Publications. Berkeley Geochronology Center, Berkeley, CA.

Roff, G., Zhao, J.-x. and Pandolfi, J.M., 2015. Rapid accretion of inshore reef slopes from the central Great Barrier Reef during the late Holocene. Geology, 43(4): 343.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\1.3_Coral_Cores

Description

This repository contains the supplementary materials (Supplementary_information.pdf, Supplementary_figures.pdf, Supplementary_tables.pdf) of the manuscript: "Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript.

Datasets

We provide the spatially anonymized R datasets to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:

• Attack_sf.RData: 19,302 analyzed wolf attacks on sheep
  - ID: unique ID of the attack
  - DATE: date of the attack
  - PASTURE: the related pasture ID from "Pasture_sf" where the attack is located
  - STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality
• Pasture_sf.RData: 4987 analyzed pastures grazed by sheep
  - ID: unique ID of the pasture
  - CODE: Official code in the pastoral census
  - FLOCK_SIZE: maximum annual number of sheep grazing in the pasture
  - USED_MONTHS: months for which the pasture is grazed by sheep
• Removal_sf.RData: 232 analyzed single or multiple wolf removals
  - ID: unique ID of the removal
  - OVERLAP: are they single removal ("single" in the manuscript => "NO" here), or not ("multiple" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the exact same operation or "NON-SIMULTANEOUS" if not).
  - DATE_MIN: date of the single removal or date of the first removal of a group
  - DATE_MAX: date of the single removal or date of the last removal of a group
  - CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript
  - SEX: sex of the removed wolves if known
  - AGE: class age of the removed wolves if known
  - BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found.
  - SEASON: season of the removal, as defined in part 2.3.4 of the manuscript
  - MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript
• Mountain_range_buff_sf.RData: one row for each mountain range, corresponding to the buffered mountain ranges where removal control events could be sampled, as defined in part 2.3.3 of the manuscript
• Area_to_exclude_sf.RData: one row for each mountain range, corresponding to the area too close from other mountain ranges, terrestrial and maritime limits, where removal control events could not be sampled, as defined in part 2.3.3 of the manuscript
• Overlapping_removal_sf.RData corresponds to the spatial dataset necessary to run the supplementary figures about overlapping removals (S9)

You can also find the object called Subset_lt.RData, which gives the ID of removals for each dataset (single/multiple removals) or subsets of single removals.

The other RData resulted from the analysis. How to read their names:

• First part:
  - Buffer: they link the attacks to removals or of control events.
  - Kernel: they give the results of the kernel density estimation (z), according to the spatial (y) and temporal (x) coordinates, for each dataset.
  - Int: they give, for each distance and time, the total amount of attack intensities before (INT_BEF) and after (INT_AFT) the combined locations and days of removals or control events. 1 unit of distance = 50 meters, 1 unit of time = 1 day.
  - Trend: they give, for each distance and time, the trends in % of the attack intensities when comparing before and after, with their uncertainties and significance. Files with spatshift are designed for figures about spatial shift (unnested scales), contrary to files without spatshift (nested scales).
• Middle parts: (noted with an X after)
  - obs: results from removals
  - jack: results from jackknife samples of the removals
  - ctl: results related to control sets
  - cor: results corrected for livestock presence
  - raw: results uncorrected for livestock presence
  - sim: results for simulated attacks according to livestock presence

Lengths of lists or sublists correspond to:

- 20 elements: the two main datasets (single/multiple) and the 18 subsets of single removals.
- 100 elements: the 100 control sets.
- 1000 elements: the 1000 simulations of attacks (for the livestock presence correction).
- Other lengths and jack within the name: the jackknife samples.

Structure of the repository

Code 1: file Buffer.R

We keep only removals within geographic zones for the analysis (Removal_analyzed_sf), and sample their control events (100 simulations = control sets, Removal_ctl_sf_lt).

We start by delimiting the spatio-temporal buffer for each row of the removal and control datasets.
- We identify the attacks from Attack_sf.RData within each buffer, thanks to the function Buffer_fn, giving the data frames Buffer_X_df (one row per attack)
- We select the pastures from Pasture_sf.RData within each buffer, thanks to the function Buffer_pasture_fn, giving the data frames Buffer_X_sf (one row per removal or control event)

We calculate the spatial correction:
- We spatially slice each buffer into 200 rings with the function Ring_fn, giving the data frame Ring_sf (one row per ring)
- We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT") with the function Spatial_correction_fn, for each attack of each buffer, within Buffer_X_df

We calculate the pastoral correction:
- We create the pastoral matrix for each removal or control event with the function Pastoral_matrix_fn, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, Pastoral_X_mx_lt.RData)
- We simulate, for each removal or control event, the random distribution of the attacks from Buffer_X_df.RData according to Pastoral_X_mx_lt.RData with the function Buffer_sim_fn. The process is done 1000 times (one element per simulation, Buffer_X_sim_lt.RData).

Code 2: file Kernel.R where we estimate the attack intensities

We classified the removals into 2 main datasets and 18 subsets, according to part 2.3.4 of the manuscript (Subset_lt.RData) (one element per set).
We compute the jackknife samples for each dataset or subset (Removal_id_jack_lt).
We perform the kernel estimations with the function Kernel_fn (Kernel_X_lt).
We sum the intensities of attacks before and after the removals or control events, with the function Intensity_fn, giving Int_X_df_lt.RData.

Code 3: file Trend.R where we calculate the trends of attack intensities after removals

We focus on the nested trends first:
- We calculate them (Trend_X_df) with function Trend_fn
- We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (Trend_X_comparison_df)

We focus on the spatial shifts (trends for each specific distance):
- We calculate them (Trend_X_spatshift_df) with function Trend_fn
- We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (Trend_X_comparison_spatshift_df)

- Code 4: file Functions.R where can be found all custom functions called in the other analysis codes

- Code 5: file Figures.R produces part of the figures from the manuscript

Detailed comments are included in each code.

Support

If you have any question or request, do not hesitate to contact us at: oksana.grente@gmail.com

Authors and acknowledgment

Grente Oksana (CEFE, CNRS), Opitz Thomas (INRAE), Duchamp Christophe (OFB), Drouet-Hoguet Nolwenn (OFB), Chamaillé-Jammes Simon (CEFE, CNRS) and Gimenez Olivier (CEFE, CNRS).

License

GNU GENERAL PUBLIC LICENSE 3.0

Arthralgia occurrence: results from frailty model for recurrent events.

This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Mask out water bodies using a satellite-derived water layer • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing