Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

Excel file showing the table and the graph for figure 1 in the manuscript: "Knowledge and attitudes among life scientists towards reproducibility within journal articles: a research survey."

Demographic table.

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

The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

The Demographic Profile Summary File contains 23 tables derived from the responses to the 2010 Census questionnaire, including tabulations of the population by age and sex, race, and Hispanic or Latino origin; household population by household relationship; group quarters population by sex and group quarters type; households by type; and housing units by occupancy and tenure. The Census Bureau computed the tables for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan and micropolitan statistical areas, counties, county subdivisions, places, congressional districts, state legislative districts, school districts, American Indian Areas, Alaska Native Areas, Hawaiian Home Lands, ZIP code tabulation areas, and census tracts. Additionally, for some summary areas the tables are iterated for "geographic components," e.g., the urban and rural portions of metropolitan statistical areas and the portions of states inside and outside metropolitan statistical areas. With one variable per table cell and additional variables with geographic information, the Demographic Profile Summary File comprises 106 data files, two per state, the District of Columbia, Puerto Rico, and the National File. ICPSR supplies this data collection in 54 ZIP archives. There is a separate archive for each state, the District of Columbia, Puerto Rico and the National File, and an archive with a Microsoft Access database shell and instructions on how to use the shell.

Objectives: Inborn error of immunity (IEI) comprises a broad group of inherited immunological disorders that usually display an overlap in many clinical manifestations challenging their diagnosis. The identification of disease-causing variants comprises the gold-standard approach to ascertain IEI diagnosis. The efforts to increase the availability of clinically relevant genomic data for these disorders constitute an important improvement in the study of rare genetic disorders. This work aims to make available whole-exome sequencing (WES) data of Brazilian patients' suspicion of IEI without a genetic diagnosis. We foresee a broad use of this dataset by the scientific community in order to provide a more accurate diagnosis of IEI disorders. Data description: Twenty singleton unrelated patients treated at four different hospitals in the state of Rio de Janeiro, Brazil were enrolled in our study. Half of the patients were male with mean ages of 9±3, while females were 12±10  years old. The WES was performed in the Illumina NextSeq platform with at least 90% of sequenced bases with a minimum of 30 reads depth. Each sample had an average of 20,274 variants, comprising 116 classified as rare pathogenic or likely pathogenic according to ACMG guidelines. The genotype-phenotype association was impaired by the lack of detailed clinical and laboratory information, besides the unavailability of molecular and functional studies which, comprise the limitations of this study. Overall, the access to clinical exome sequencing data is limited, challenging exploratory analyses and the understanding of genetic mechanisms underlying disorders. Therefore, by making these data available, we aim to increase the number of WES data from Brazilian samples despite contributing to the study of monogenic IEI-disorders.

Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species’ environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on..., We conducted a keyword-based search in the Web of Science to quantify how often temporal components related to phenology and demographic stages are explicitly considered in plant SDMs. A full list of keywords is provided in the Supporting Information Table S1. We used a nested set of keywords to identify all studies that mentioned SDMs (or common synonyms), were focused on plants, and were listing relevant keywords related to phenology or to demographic stages, respectively. The search was carried out on 5-Oct-2023 and was restricted to English-language journal articles in the period 1945-2022 (no studies using SDMs were published before that start year). Overall, we found more than 40,000 articles mentioning SDM and over 10,000 articles in our refined search for plant SDMs, with a strong increase in the number of articles over time. Among these, phenology (or related search terms) was mentioned in 970 articles and demographic stages (or related terms) in 1188 articles, each averaging c..., , # The niche through time: considering phenology and demographic stages in plant distribution models

https://doi.org/10.5061/dryad.sn02v6xct

Description of the data and file structure

Columns from WoS (Web of Science) search – these are identical in both excel sheets

These columns are the standard columns provided as WoS search output. If the entries contain "n/a", then no information was provided by WoS because those items are not applicable. For example, a journal article does not have any entries for book authors.

This data release contains records from research focused on understanding social vulnerability to water insecurity, resiliency demonstrated by institutions, and conflict or crisis around water resource management. This data release focuses on social vulnerability to water insecurity. The data is derived from a meta-analysis of studies in the empirical literature which measured factors of social vulnerability associated with conditions of water insecurity. In the water security context this data and associated study identify the indicators used to measure social vulnerability, the frequency at which indicators are used, and the uncertainty associated with measurements based on those indictors. Assessed studies were published between 2000 and 2022 and covered states of the conterminous U.S. located west of the Mississippi River. This meta-analysis is published as ‘Social vulnerability and water insecurity in the western US: A systematic review of framings, indicators, and uncertainty’. It is part of the Social and Economic Drivers Program’s ‘Measuring Intended and Unintended Effects of Water Management Decisions’ study. The data was gathered to provide baseline metrics supporting the development of a set of indicators describing vulnerability of key water-use sectors (agricultural and municipal) to conditions of water insecurity (including concerns of water quality, quantity, and access to the resource). This includes understanding the inherent vulnerabilities of populations dependent on these water-use sectors as well as those decision-making processes that can exacerbate vulnerabilities. This data may further be used to validate social vulnerability metrics, provide the basis from which sociodemographic data can be integrated into models of water use and demand, and improve models of susceptibility to water-related hazards including drought and floods. The data release contains six (6) related datasets and their associated metadata: Papers: Contains bibliographic data and abstract for each scientific paper included in the meta-analysis. Each entry represents a unique model of social vulnerability to water insecurity. In cases where a scientific paper included multiple models that produced different associations between social vulnerability and water insecurity, the paper is recorded separately for each unique model. Literature Results Summary of Indicators of social vulnerability to water insecurity in the Western US 2000-2022: Contains a high-level overview showing how each paper was classified. The table identifies the water-use sector of focus, thematic issue of water security covered, study location, spatial scale, dimension (thematic category) of social vulnerability covered, the determinants (attributes) of social vulnerability measured, and a count of the number of times each social vulnerability determinant (attribute) was measured. Aggregated indicators of social vulnerability to water insecurity in the Western US 2000-2022: For each model studied this table records: the dimensions (thematic category) of social vulnerability covered, the determinants (attributes) of social vulnerability assessed, aggregated indicators (variables) used to measure individual components of each determinant, and a count of the number of individual variables used to measure each aggregated indicator (e.g., the aggregated indicator ‘Dependents’ may be measured by specific indicators for the population aged below 18 years as well as the population above 65 years). Sector Summary of social vulnerability to water insecurity in the Western US 2000-2022: For each determinant (attribute) of social vulnerability assessed, this table presents a summary of the number of indicators measured and number of papers (studies) including those indicators in both the agricultural and municipal water-use sectors. Uncertainty Summary by Determinant of social vulnerability to water insecurity in the Western US 2000-2022: Provides a high-level summary of the amount of evidence available and agreement in the literature for the direction of influence associated with each determinant of social vulnerability found in the meta-analysis. Uncertainty Summary of social vulnerability to water insecurity in the Western US 2000-2022: For each aggregated indicator assessed, this table provides counts of the number of models in the meta-analysis for which specific relationships (positive, negative, no relationship or for which the directionality could not be determined) to conditions of water insecurity were identified. The strength of these relationships is indicated by a count of the number of models recording them. The table also provides an indication of the levels of evidence and agreement between models.

Demographic Data for Boston’s Neighborhoods, 1950-2019

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This dataset and code allows researchers to replicate the analyses in -- Claassen, C. & McLaren, L. (2021). Does Immigration Produce a Public Backlash or Public Acceptance? Time-Series, Cross-Sectional Evidence from 30 European Democracies. British Journal of Political Science including all tables and figures in the main paper and many of those in the supplementary materials.

Introduction This archive contains all the necessary data and R script to reproduce the results of the manuscript entitled "tree growth and mortality of 42 timber species in Central Africa" submitted to Forest Ecology and Management journal. It includes cleansed data (text files and Rdata files), computed data (estimates of tree growth and mortality rates, Rdata files), R script to reproduce the computation of the estimates as well as the analyses and figures presented in the main paper and an excel files containing all the supplementary material tables of the manuscript. Cleansed data To produce the cleansed data, raw data was collected for each sites. The different datasets were standardized to store all of them in a single database. Next, consecutive diameter measurements were analyzed and some outliers were discarded (see the explanations in the main manuscript). The cleansed data can be loaded using either text delimited csv files or a Rdata file. It contains the five following tables. Table cleansed_data _species‧csv This table contains information about each study species. Each line corresponds to one species. It contains the following columns: code : species identifying name timber_name : species name as used by the ATIBT species_name_sci : current scientific species name (genus + species) species_name : vernacular species name dme : reference value of the minimum cutting diameter as defined by the Cameroonian Government incr : reference value of diameter increment (cm/year) as defined by the Cameroonian Government cjb_id : species id of the CJB database see_name : species CJB id of synonym names species_name_sci_full : full current scientific name (genus + species + authority) Table cleansed_data _observation_codes‧csv This table contains the description of the codes used in the field to note any particularities of the monitored trees. One line correspond to one code. There are three columns: code : observation code label_fr : French explanation of the code (as used in the field) label_en : English translation of the explanation of the code Table cleansed_data _mortality_codes‧csv This table contains the description of the codes used to characterize the likely cause of recorded tree death. There are three columns: code : mortality code label_fr : French explanation of the code (as used in the field) label_en : English translation of the explanation of the code Table cleansed_data _records‧csv This table contains the information collected for each tree. Each line corresponds to one record for one tree. There are several lines per tree as they were measured several times. It contains the following columns: site : site name id_site : site identifying number id_plot : plot identifying number treatment : treatment (control, exploited or mixed). exploitation_year : year of the exploitation (4 digits) species : species vernacular name (corresponding to species_name column of species table) id_tree : tree identifying number number : tree number (the number that was painted on the tree) id : record identifying number date : record date (yyyy-mm-dd) census_year : year of the census diameter : tree diameter measured at hom (cm) diameter450 : tree diameter measured at 450 cm in height (cm) hom : height of measurement of the diameter (cm) code_observation : observation codes. Multiple codes were sometimes used. They are separated by a dash (corresponding to code column of observation_codes table). code_mortality : mortality codes (corresponding to code column of mortality_codes table) comment : any additional comment Table cleansed_data _Increments‧csv id : id of the initial measurement id_tree : tree id number id_plot : plot id number treatment : treatment (control, exploited) species : species vernacular name (corresponding to species_name of species table) number : tree number (the number that was written on the tree) hom : height of measurement (cm) id_hom : id of HOM (sometimes the HOM had to be changed, e‧x. due to buttresses or wounds) initial_date : date of the first census initial_diameter : the diameter measured at the first census (cm) diameter_increment : The annual diameter increment computed between the two considered census (cm/year) increment_period : The number of years separating the two censuses (years) diameter_observation : Observation codes (corresponding to code of table observation_codes) that were noted during the first and second census. The observation of the two censuses are separated by a “/”. diameter_comment : Additional comments written during the two measurements. They are separated by a “/”. Id_species : species identifying number Id_site : site identifying number Site : name of the site Exploitation_year : year of the exploitation (if any) File cleansed_data.Rdata This Rdata file contains the five tables (species, mortality_codes, observation_codes, records and increment) of the cleansed data It can be used to rapidly load in R. Computed data From the cleansed data, we computed - as explained in the main manuscript - tree growth and mortality rates using an R script (3-computation.R). This script produces “computed data”. The computed data contains six tables that are provided with three additional csv files and one RData files. Computed_data_records‧csv This table is the same as record‧csv but with one additional column: exploitation_date : the assumed date of the exploitation if any (yyyy-mm-dd) Table computed_data_growth_rates‧csv This table contains one line per combination of tree and treatment. It contains the estimates of diameter increment computed over all available records. This table contains the following columns: site : site name id_site : site identifying number treatment : treatment (control or exploited) species : species vernacular name id_plot : plot id number id_tree : tree id number initial_diameter : tree diameter at the begining of the census period (cm) increment_period : length of the census period (year) initial_date : date of the first census (yyyy-mm-dd) diameter_observation : observation codes if any diameter_comment : comment if any exploitation_year : year of the exploitation (4 digits) exploitation_date : assumed date of the last exploitation (if treatement = logged or mixed) mid_point : mid-point of the census period (yyyy-mm-dd) years_after_expl : length of time between the exploitation date and the first measurement n_increment : number of consecutive increment n_hom : number of change of hom during the census period diameter_increment : estimate of the diameter increment (cm/year) Table computed_data_mortality_rates‧csv This table contains estimates of mortality rates for each species and site. This table contains the following columns: id_site : site id number treatment : treatment (control or exploited) time_min : minimum of the length of the census periods time_max : maximum of the length of the census periods time_sd : standard deviation of the length of the census periods -- deleted exploitation_year : exploitation year (if treatment = exploited) years_after_expl_mid : number of years between the assumed exploitation and the mid-period census. years_after_expl_start : number of years between the assumed exploitation and the first census. site : site name species : species vernacular name N0 : number of monitored trees N_surviving : number of surviving trees meantime : mean monitoring period length rate : estimates of the mortality rate lowerCI : lower bound of the confidence interval of the mortaltity rate upperCI : lower bound of the confidence interval of the mortaltity rate File computed_data.Rdata This Rdata file contains the six tables (species, records, growth_rates, mortality_rates, mortality_codes, observation_codes) of the computed data. It can be used to load them in R. Analyses The analyses presented in the main manuscript were produced with a Rmd script (4-analyses.Rmd). This script generates an HTML report (4-analyses.html), as well as the figure that are shown in the manuscript and an Excel file with all the supplementary tables (with one sheet per supplementary table).

Measurement error table: overall and major taxonomic group support for population growth models and overall AICc w and Bayesian information criterion support for non-Allee and Allee models as a percentage of all support.

Note: some of the myopathy Concept ID categories overlapped.

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. A measure of the number of adults diagnosed with any type of cancer in a year who are still alive five years after diagnosis. This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. As of May 2020, please refer to the data tables published by Public Health England (PHE). This publication is released on an annual basis. A link to the PHE publications, within which the data is held, is available via the resource link below. On the publication page select the ‘Data Tables index of cancer survival 20xx to 20xx’. The data for this indicator is available by applying suitable filters to the dataset contained within the 'Data_Complete’ tab. Legacy unique identifier: P01735

Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Nigeria Demographic and Health Survey (NDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

List of tables (supplemental material to JAAD research article "Variation in initial biopsy technique for primary melanoma diagnosis: a population-based cohort study in New South Wales, Australia")

Abstract

This NWO Corona Fast-Track Data study is a two-wave (non-panel) survey of academic researchers working at Dutch research institutions. Through this survey research we aimed to study scientists’ information gathering and spreading behaviour with regard to the latest scientific information on COVID-19. In the midst of the COVID-19 pandemic, the expertise of researchers from medicine and biology, but also social sciences, humanities and economics is urgently needed to counter misinformation. In order to gain timely and otherwise lost insights on how to improve science communication during this crisis, we collected the data in two survey waves. The first wave survey data were collected between 9 June and 31 August; the second between 26 October and 30 November 2020.

Contents:

- Covid-19 Survey Methodology: here you will find a technical report, demographic tables and some general information about the survey project.

- Covid-19 Survey Questionnaire: here you will find the full questionnaire used for the project.

- Covid-19 Survey Codebook: here you will find the variables names and their explanation.

- June5_August31_2020: here you will find the Wave 1 survey data (June-August 2020)

- Oct26_Nov30_2020: here you will find the Wave 2 survey data (October-November 2020)

Funding:

This dataset was funded under the NWO Corona Fast-Track Data scheme. Project id: 440.20.028; Project title: “Collecting systematic survey data on scientists’ information-seeking and information-spreading behaviour in a time of crisis”.

How to cite

Roex, K. & Colavizza, G. (2020). Doing Science in Times of Covid-19: A Survey. [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4313278 .

List of tables and questionnaires (supplemental material to JAAD research article "Variation in initial biopsy technique for primary melanoma diagnosis: a population-based cohort study in New South Wales, Australia")