Daily situation for Tennessee counties as reported by the Tennessee Department of Health. The data are posted on the department's coronavirus disease web page: https://www.tn.gov/health/cedep/ncov.html. Date on testing results and deaths was posted beginning March 31, 2020.CountyNS (County GNIS code)NAMELSAD (Legal/statistical area) -County of residence of COVID-19 casesCounty identifier (GEOID) - County FIPS codeCombined statistical area code (CBSAFP) - Metropolitan/Micropolitan Area codeCore-based area name (CBSA_TITLE) - Metropolitan/Micropolitan Area nameCore-based statistical area type (MSA_TYPE) - Core-based statistical area typeCore-based area county type (MSA_COUNTY_TYPE) - Type of county in core-based statistical areasHealth Department Region (HEALTH_DEPT_REG)Health Department Type (HEALTH_DEPT_TYPE)TN ECD Urban Rural Classification (ECD_URBAN_RURAL_CLASS)Positive Tests (TEST_POS) - Total number of people ever to test positive for COVID-19Negative Tests (TEST_NEG) - Total number of people with a negative COVID-19 test resultTotal Tests (TEST_TOT) - Total number of COVID-19 tests with reported resultNew Tests (TEST_NEW) - Number of new tests results posted since the previous dayTotal Cases (CASES_TOT) - Total number of people ever to have a confirmed or probably case of COVID-19 by countyNew Cases (CASES_NEW) - The number of new cases reported to have a confirmed case of COVID-19 since the report on the previous dayTotal Hospitalizations (HOSPITALIZED_TOT) - Number of patients that were ever hospitalized during their illness, it does not indicate the number of patients currently hospitalizeNew Hospitalizations (HOSPITALIZED_NEW) - Number of patients that were ever hospitalized in the previous 24-hour period. Does not indicate the number of patients currently hospitalizedTotal Recovered (RECOV_TOT) - Total Number of inactive/recovered COVID cases. Includes people 14 days beyond illness onset date, specimen collection date, investigation report date, or investigation start date.New Recovered (RECOV_NEW) - Change in the number of new inactive/recovered cases since the previous day.Total Deaths (DEATHS_TOT) - Number of COVID-19 related deaths that were ever reported by countyNew Deaths (DEATHS_NEW) - Number of COVID-19 related deaths that were reported since the previous dayActive Cases (ACTIVE_TOT) - Calculated as the total number of confirmed COVID-19 cases, less the number of recovered and deaths reportedNew Active Cases (ACTIVE_NEW) - Change in the number of active COVID-19 cases since the previous dayPopulation Estimate 2019 (POPESTIMATE2019) - 2019 vintage estimated population for counties by the U.S. Census BureauNOWcast Current (NOWCast_CURRENT) - UTK COVID-19 NOWCast estimate of the number of new daily casesEffective Rate Transmission (EffectiveR) - Effective reproduction or R is an estimate of the average number of new infections caused by a single infected individualEffect Rate Transmission Label (EffectiveR_LABEL)

For English, see below The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is passed on with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. For the calculation of the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the GGD. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl) . Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the GGD notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - From September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - From March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registration. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGD. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the GGD test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the GGD. Date: Date for which the reproduction number was estimated Rt_low: Lower bound 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks. -------------------------------------------------- --------------------------------------------- Covid-19 reproduction number The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is reported with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. To calculate the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the PHS. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl). Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the PHS notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic till October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - As of March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registry. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the PHS. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the PHS test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the PHS. Date: Date for which the reproduction number was estimated Rt_low: Lower limit 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

The WHO have identified 20 neglected tropical diseases (NTDs), both communicable and non-communicable, that prevail in tropical and subtropical conditions in 149 countries. \r \r The NTD portfolio currently includes:\r •\tBuruli ulcer\r •\tChagas disease\r •\tDengue and Chikungunya\r •\tDracunculiasis (guinea-worm disease)\r •\tEchinococcosis\r •\tFoodborne trematodiases\r •\tHuman African trypanosomiasis (sleeping sickness)\r •\tLeishmaniasis\r •\tLeprosy (Hansen's disease)\r •\tLymphatic filariasis\r •\tMycetoma, chromoblastomycosis and other deep mycoses\r •\tOnchocerciasis (river blindness)\r •\tRabies\r •\tScabies and other ectoparasites\r •\tSchistosomiasis\r •\tSoil-transmitted helminthiases\r •\tSnakebite envenoming\r •\tTaeniasis/Cysticercosis\r •\tTrachoma\r •\tYaws (Endemic treponematoses)\r \r Of the currently noted NTDs, only chikungunya, dengue, leprosy (Hansen’s disease) and rabies are nationally notifiable in Australia.\r \r Chikungunya\r \r Chikungunya is not currently endemic in Australia. There have been no reported cases of locally-acquired chikungunya in Australia, though mosquitoes capable of spreading the virus are present in some areas of Queensland. From 2015 to 2020, the number of notified chikungunya cases in Australia has ranged between 32 and 114 annually, with a mean of 81 cases (Table 1).\r \r Between 2015 and 2020, notified Chikungunya infections in Australia were most frequently acquired in areas of South and South East Asia, particularly India and Indonesia, and the Pacific Islands. Trends in overseas acquisition are influenced by the volume and frequency of travel to source countries and their local chikungunya epidemiology.\r \r \r Dengue\r \r Dengue is not currently endemic in Australia, but outbreaks associated with locally acquired cases do occur in coastal areas of mainland North Queensland, where the Aedes aegypti mosquito is present in suitable environments near susceptible populations. The number of notified dengue cases in Australia from 2015 to 2020 have ranged between 222 and 2,238 annually, with a mean of 1,284 cases (Table 1).\r \r In Australia, overseas-acquired dengue infections are most frequently acquired in South East Asia, particularly Indonesia. Trends in overseas acquisition are influenced by the volume and frequency of travel to source countries and their local dengue epidemiology. On average, over 90% of dengue cases reported annually in Australia are overseas acquired. \r \r \r Leprosy\r \r Leprosy is an uncommon disease in Australia with the majority of cases being diagnosed in migrants from leprosy endemic countries and occasionally in local Aboriginal and Torres Strait Islander populations. \r \r In 2020, a total of 6 cases of leprosy were notified (Table 1), representing a rate of less than 0.1 case per 100,000 population. Between 2015 and 2020, annual notifications of leprosy in Australia have ranged from 6 to 21 cases per year (Table 1).\r \r \r Rabies\r \r Australia is considered to be free of rabies with the last overseas acquired case being reported in 1987.\r \r *The data provided were extracted from the National Notifiable Disease Surveillance System (NNDSS) on 23 February 2021. Due to the dynamic nature of the NNDSS, data in this extract are subject to retrospective revision and may vary from data reported in published NNDSS reports and reports of notification data by states and territories.\r \r Trachoma\r \r Australia is a signatory to the World Health Organization (WHO) Alliance for the Global Elimination of Trachoma by 2020. Elimination of trachoma as a public health problem is defined by the WHO as ‘community prevalence of trachoma in children aged 1-9 years of less than 5%’.\r \r As part of its WHO obligation to eliminate trachoma, Australia is required to regularly collect data on trachoma prevalence. The National Trachoma Surveillance and Reporting Unit, managed by the Kirby Institute, University of NSW, provides surveillance and annual reporting of trachoma prevalence, using State and Territory Governments’ data.\r \r Trachoma program activities, data collection and analysis are guided by the National Guidelines for the Public Health Management of Trachoma in Australia (revised in 2013 and published in 2014 – see link). The below information should be read in conjunction with the Guidelines.\r \r In 2019, 115 communities were identified as being ‘at-risk’ of trachoma. A total of 4419 people received antibiotic (azithromycin) treatment for trachoma (including people diagnosed with trachoma, their household contacts and community members as required by the Guidelines). This is fewer doses of azithromycin delivered in 2019 as compared to 2018 (4419 compared to 6576). \r \r Strong progress has been made in reducing the overall prevalence of active trachoma rate from 14% in 2009 to 4.5% in 2019. \r \r Further information can be found at:\r http://www.health.gov.au/internet/main/publishing.nsf/Content/health-oatsih-pubs-trachreport \r ; and\r http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-cdna-pubs-trachoma.htm \r

Current occupancy data P+R in the VVO

We introduce FUNSD-r and CORD-r in Token Path Prediction, the revised VrD-NER datasets to reflect the real-world scenarios of NER on scanned VrDs.

In FUNSD and CORD, segment layout annotations are aligned with labeled entities, which makes them not reflect the reading order issue of NER on scanned VrDs, and thus are unsuitable for evaluating current methods. In FUNSD-r and CORD-r, we automatically reannotate the layouts using PP-OCRv3 OCR system, and manually reannotate the named entities as word sequences based on the new layout annotations. Their segment layout annotations are aligned with real-world situations and entity mentions are labeled on words.

The proposed FUNSD-r consists of 199 document samples including the image, layout annotation of segments and words, and labeled entities of 3 categories. For the detailed summary statistics, please refer to the original paper.

BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies.

METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider.

RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001.

CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Annual and monthly program statistics about the Australian Government Hearing Services Voucher Program, including claiming information, hearing devices and vouchers issued, number of practitioners and number of calls processed by the Client Contact Line.\r \r The monthly statistics provides current information on hearing services vouchers issued and vouchers serviced which is updated on a monthly basis.\r \r The Annual statistics provides current information across a broad range of areas of the Voucher Program including number of clients, devices, business sites, hearing service providers and business sites. This information is updated on a six monthly basis.\r \r The Previous statistics section provides statistical information for previous reporting periods.

This data set included population and expenditure breakdowns by LGA,\r demographic statistics, labor statistics and Socio Economis Indexes for Areas\r (SEIFA) LGA score and ranking per LGA.\r \r Detailed descriptions of this data set include: \r \- LGA name \r \- LGA code \r \- Region \r \- Total Net Expenditure \r \- SEIFA DIS RANK State \r \- SEIFA DIS RANK Country \r \- SEIFA DIS RANK Metro \r \- SEIFA ADV DIS Score \r \- SEIFA ADV DIS RANK State \r \- SEIFA ADV DIS RANK Country \r \- SEIFA ADV DIS RANK Metro \r \- Adult population \r \- Adult population per venue \r \- EGM numbers per 1000 adults \r \- Expenditure per adult \r \- Workforce \r \- Unemployment \r \- Unemployment rate\r \r

Tables are published in EPA Victoria's 2016-17 Annual Report. \r \r . Table 7.2: Total pollution reports \r \r . Table 7.3: Total pollution reports by region\r \r . Table 7.1: Brooklyn Industrial Precinct summary statistics\r \r . Table 7.4: EPA’s performance against key performance indicators during 2016–17\r \r . Table 7.5: Compliance, enforcement and assessment activities\r \r . Table 7.6: Budget Paper No. 3 Service Delivery\r \r . Table 9.2: Performance against OHS management measure\r \r . Table 9.3: EPA Premium Performance rate\r \r . Table 10.1: Full-time equivalent (FTE) staffing trends from 2012 to 2017\r \r . Table 10.2: Summary of employment in June of 2016 and 2017\r \r . Table 10.3: Details of employment levels in June of 2016 and 2017\r \r . Table 10.4: Number of EOs classified into 'ongoing' and 'special projects'\r \r . Table 10.5: Breakdown of EOs into gender for 'ongoing' and 'special projects'\r \r . Table 10.6: Reconciliation of executive numbers\r \r . Table 11.5: Energy use\r \r . Table 11.6: Current performance against Sustainability Plan targets for energy use\r \r . Table 11.7: Waste \r \r . Table 11.8: Current performance against Sustainability Plan targets for waste\r \r . Table 11.9: Paper use\r \r . Table 11.10: Current performance against Sustainability Plan targets for waste\r \r . Table 11.11: Water use (office facilities only)\r \r . Table 11.12: Current performance against Sustainability Plan targets for waste\r \r . Table 11.13: Transport\r \r . Table 11.14: Current performance against Sustainability Plan targets for transport\r \r . Table 11.15: Greenhouse gas emissions\r \r . Table 11.16: Current performance against Sustainability Plan targets for greenhouse gas

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

The environmental and social data considered in the best fit model.

Conserving species requires knowledge of demographic rates (survival, recruitment) that govern population dynamics to allow the allocation of limited resources to the most vulnerable stages of target species' life cycles. Additionally, quantifying drivers of demographic change facilitates the enactment of specific remediation strategies. However, knowledge gaps persist in how similar environmental changes lead to contrasting population dynamics through demographic rates. For sympatric hummingbird species, the population of urban-associated partial-migrant Anna's hummigbird (Calypte anna) has increased, yet the populations of Neotropical migrants including rufous, calliope, and black-chinned hummingbirds have decreased. Here, we developed an integrated population model to jointly analyze 25 years of mark-recapture data and population survey data for these four species. We examined the contributions of demographic rates on population growth and evaluated the effects of anthropogenic stres..., This R data file contains a named list for each species in our study. It has been processed to remove covariates and data that are not public domain but are available for download at the links provided (indicated with * in the readme file). Each species list contains mark-recapture records (y), the known-state records (z), number of years spanned by the analysis (n.years), numbers banded individuals (n.ind), banding station membership (sta), number of banding stations (n.sta), year of first encounter for each individual (first), year of last possible encounter of each individual if it were to be alive (last), first and last years of mark recapture data (first_yr / last_yr), sex (1 = male, 2 = female) and age (1 = juvenile, 2 = adult) membership for each individual, the observed residency information for each individual in each year (r), the partially observed residency state information for each individual (u), the standardized human population density and crop data in the 3 kilometers ..., Data can be opened in R and analyzed using Nimble., ## Hummingbird IPM data file

This data file contains a list of lists named for each of the four species in our analysis: Anna's (ANHU; Calypte anna), black-chinned (BCHU; Archilochus alexandri), calliope (CAHU; Selasphorus calliope), and rufous hummingbirds (RUHU; Selasphorus rufus). Each of these lists contains the required mark-recapture inputs for integrated population modelling in R/Nimble. Raw covariates of human population density, land cover classification, as well as Breeding Bird Survey data can be accessed as described under Sharing/Access information. To load the file in R from the current working directory:

load("./IPM.shared.Rdata")

Description of the data and file structure

Within each named list, there are data for mark-recapture records (NA = station not active, 0 = not captured, 1 = captured; y), the known state, either alive (1) or unknown (NA) , of each individual in each year (z), number of years spanned by the analysis (n.years), nu...