Observed average proportion and standard deviation of low and high educated in pooled data, definition of scenarios used to estimate educational inequalities mortality in different educational distributions, men and women.

ObjectivesDespite the relationship between physical activity (PA) and learning outcomes, the school system has not been able to support the inclusion of PA throughout the day. A solution to this problem integrates PA into the academic classroom. The objective of this review is to determine the impact of active classrooms compared to traditional sedentary classrooms on educational outcomes of school-aged children.DesignWe searched ERIC, PubMed, PsychINFO, and Web of Science, reference lists of included studies for randomised controlled studies. Independent reviewers screened the texts of potentially eligible studies and assessed the risk of bias. Data were pooled using random-effects models on standardized mean differences.ResultsThis review identified 25 studies examining educational outcomes, including approximately 6,181 students. Risk of bias was assessed as either some or high risk of bias for most of the studies and outcomes. Pooled data from 20 studies and 842 participants measuring academic performance shows a small positive effect of active classrooms compared with traditional, sedentary classrooms (SMD = 0.28, 95% CI: 0.09 to 0.47).ConclusionsPhysically active classrooms may slightly improve academic achievement compared to the traditional sedentary lessons. Future research is needed to ensure that studies are adequately powered, employ appropriate methods of randomization, and measure a wide range of important student outcomes across the full spectrum of the school-age.

Data consists of health and survey data from epidemiological studies at beach sites and water quality measurements. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data can be accessed by request to Tim Wade: wade.tim@epa.gov. Format: Data are stored in comma delimited text files with codebooks in MS Word. This dataset is associated with the following publication: Benjamin-Chung, J., B. Arnold, T. Wade, K. Schiff, J. Griffith, A. Dufour, S. Weisberg, and J. Colford. Coliphages and gastrointestinal illness in recreational waters: pooled analysis of six coastal beach cohorts. EPIDEMIOLOGY. Lippincott Williams & Wilkins, Philadelphia, PA, USA, 28(5): 644-652, (2017).

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

The SORCE Combined XPS, SOLSTICE, and SIM Solar Spectral Irradiance 24-Hour Means product consists of daily averages of the solar spetra from 0.1 to 2412 nm. The SORCE instruments make measurements during each daytime orbit portion, 15 orbits per day. This product combines data from the XPS, SOLSTICE and SIM instruments and merges them into a daily averaged solar spectra. The spectral resolution of SIM varies between 1-34 nm, SOLSTICE is 1 nm, and XPS is 7 nm.The SORCE combined data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), min wavelength, max wavelength, instrument mode, input data version, spectral irradiance, irradiance uncertainty, and a data quality flag. Each row represents a separate day and wavelength.

Values represent mean combined groundwater withdrawals from the surficial and intermediate aquifer systems for the period 1995–2010 in the southeastern United States. Mean withdrawals were computed by multiplying the gross groundwater withdrawals for each county by a coefficient representing the proportion of total groundwater withdrawals coming from each aquifer. Coefficients for each county and water-use category were back-calculated from data published in Marella and Berndt, 2005, Water withdrawals and trends from the Floridan aquifer system in the southeastern United States, 1950–2000: U.S. Geological Survey Circular 1278, 20 p. and also from unpublished data from the U.S. Geological Survey South Atlantic Water Science Center (Georgia District)

The uploaded files contain the indentation measurements in our work. HV means Vickers Hardness and Eint means elastic indentation modulus. T indicates transversal direction and F indicates frontal direction. _pooled means the pooled data from the five parallel slices.

Mean water quality 2010–2011, by marsh type (standard deviations in parentheses; data pooled across regions and seasons).

Supplementary files for article Supplementary information files for Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants.BackgroundComparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents.MethodsFor this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.FindingsWe pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls.InterpretationThe height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks.

Additional file 1. Datasets.

Data and code share.

This record contains data and code (written in MATLAB) to reproduce the results shown in:

Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (in press) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology

Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.

Figure 1

• Fig1C_shapespace.m: draw shape space (as in Figure 1C)
• Fig1EFG_plotpsychometricdata.m: fit psychometric model to pooled data and plot (as in Figure 1EFG)
• getExptShapesHbias.m: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see fitGabPyr2Hbais.m or fitTAEGANfit2Hbais.m)

Figure 2 and 3A

• fig3A_modeval_expt1.m: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).

Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:

Model: GabPyrAE

• note: To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory
• fig2B_GabPyrAEVisFigs.m: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )
• figS2BC_GabPyrAEExp.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.
• getGabPyrAEMod.m: given an adaptor and test image, this function produces the unfit GabPyrAE prediction
• fitGabPyr2Hbias.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from getExptShapesHbias.m. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in fitGabPyrNormMod2Stims.m
• evalGabPyrAE_fitmod_aic.m: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.
• evalGabPyrAE_unfitmod_aic.m: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1

Model: TAE

• fig2CD_TAEmod.m: produce TAE model images for example adaptor and test image (as in Figure 2CD)
• getTAEModonShape.m: given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.
• getTAEGAN_spwt_onShape.m: given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.
• getTAE_spwt_onShape_nn.m: given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.
• fitTAEGAN2Hbias.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from getExptShapesHbias.m. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in fitTAEGANMod2Stims.m
• fitTAENN2Hbias.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from getExptShapesHbias.m. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in fitTAENNMod2Stims.m.
• evalTAEGAN_fitmod_aic.m: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.
• evalTAENN_fitmod_aic.m: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.
• evalTAE_unfitmod_aic.m: evaluate TAE Original model on how well it predicts human biases from experiment 1

Model: PSAE

• fig2EF_PSAEmod.m: produce PSAE model images for example adaptor and test image
• getPos_spwt_ShiftononShape_io.m: given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.
• getPos_spwt_ShiftonShape_io_nn.m: given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.
• fitPSAEGAN2Hbias.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from getExptShapesHbias.m. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in fitPSAEGANMod2Stims.m
• fitPSAENN2Hbias.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from getExptShapesHbias.m. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in fitPSAENNMod2Stims.m.
• evalPSAEGAN_fitmod_aic.m: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.
• evalPSAENN_fitmod_aic.m: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.

Model: ShapeComp and No Adaptation

• eval_ShapeComp _aic.m: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.
• eval_NoAdaptation _aic.m: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.

Figure 3BC

• fig3BC_Experiment2.m: load, analyze, and plot experiment 2 data (as in Figure 3B and C).
• figS4_Expt2_stimuli.m: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)

For non-model organisms, genome-wide information that describes functionally relevant variation may be obtained by RNA-Seq following de novo transcriptome assembly. While sequencing has become relatively inexpensive, the preparation of a large number of sequencing libraries remains prohibitively expensive for population genetic analyses of non-model species. Pooling samples may be then an attractive alternative. To test whether pooled RNA-Seq accurately predicts true allele frequencies, we analyzed the liver transcriptomes of 10 bank voles. Each sample was sequenced both as an individually barcoded library and as a part of a pool. Equal amounts of total RNA from each vole were pooled prior to mRNA selection and library construction. Reads were mapped onto the de novo assembled reference transcriptome. High-quality genotypes for individual voles, determined for 23,682 SNPs, provided information on “true” allele frequencies; allele frequencies estimated from the pool were then compared to these values. “True” frequencies and those estimated from the pool were highly correlated. Mean relative estimation error was 21% and did not depend on expression level. However, we also observed a minor effects of inter-individual variation in gene expression and allele specific gene expression influencing allele frequency estimation accuracy. Moreover we observed strong negative relationship between minor allele frequency and relative estimation error. Our results indicate that pooled RNA-Seq exhibits accuracy comparable to pooled genome resequencing, but variation in expression level between individuals should be assessed and accounted for. This should help in taking account the difference in accuracy between conservatively expressed transcripts and these which are variable in expression level.

This is an integration of 10 independent multi-country, multi-region, multi-cultural social surveys fielded by Gallup International between 2000 and 2013. The integrated data file contains responses from 535,159 adults living in 103 countries. In total, the harmonization project combined 571 social surveys.

These data have value in a number of longitudinal multi-country, multi-regional, and multi-cultural (L3M) research designs. Understood as independent, though non-random, L3M samples containing a number of multiple indicator ASQ (ask same questions) and ADQ (ask different questions) measures of human development, the environment, international relations, gender equality, security, international organizations, and democracy, to name a few [see full list below].

The data can be used for exploratory and descriptive analysis, with greatest utility at low levels of resolution (e.g. nation-states, supranational groupings). Level of resolution in analysis of these data should be sufficiently low to approximate confidence intervals.

These data can be used for teaching 3M methods, including data harmonization in L3M, 3M research design, survey design, 3M measurement invariance, analysis, and visualization, and reporting. Opportunities to teach about para data, meta data, and data management in L3M designs.

The country units are an unbalanced panel derived from non-probability samples of countries and respondents> Panels (countries) have left and right censorship and are thusly unbalanced. This design limitation can be overcome to the extent that VOTP panels are harmonized with public measurements from other 3M surveys to establish balance in terms of panels and occasions of measurement. Should L3M harmonization occur, these data can be assigned confidence weights to reflect the amount of error in these surveys.

Pooled public opinion surveys (country means), when combine with higher quality country measurements of the same concepts (ASQ, ADQ), can be leveraged to increase the statistical power of pooled publics opinion research designs (multiple L3M datasets)…that is, in studies of public, rather than personal, beliefs.

The Gallup Voice of the People survey data are based on uncertain sampling methods based on underspecified methods. Country sampling is non-random. The sampling method appears be primarily probability and quota sampling, with occasional oversample of urban populations in difficult to survey populations. The sampling units (countries and individuals) are poorly defined, suggesting these data have more value in research designs calling for independent samples replication and repeated-measures frameworks.

**The Voice of the People Survey Series is WIN/Gallup International Association's End of Year survey and is a global study that collects the public's view on the challenges that the world faces today. Ongoing since 1977, the purpose of WIN/Gallup International's End of Year survey is to provide a platform for respondents to speak out concerning government and corporate policies. The Voice of the People, End of Year Surveys for 2012, fielded June 2012 to February 2013, were conducted in 56 countries to solicit public opinion on social and political issues. Respondents were asked whether their country was governed by the will of the people, as well as their attitudes about their society. Additional questions addressed respondents' living conditions and feelings of safety around their living area, as well as personal happiness. Respondents' opinions were also gathered in relation to business development and their views on the effectiveness of the World Health Organization. Respondents were also surveyed on ownership and use of mobile devices. Demographic information includes sex, age, income, education level, employment status, and type of living area.

Objective

To conduct a systematic review and meta-analysis of studies reporting on risk factors according to the location of the intracerebral hemorrhage.

Methods

We searched PubMed and Embase for cohort and case-control studies reporting on ≥100 patients with spontaneous intracerebral hemorrhage, that specified the location of the hematoma and reported associations with risk factors published until June 27th 2019. Two authors independently extracted data on risk factors. Estimates were pooled with the generic variance-based random effects method.

Results

After screening 10 013 articles, we included 42 studies totaling 26 174 patients with intracerebral hemorrhage (9 141 lobar and 17 033 non-lobar). Risk factors for non-lobar intracerebral hemorrhage were hypertension (risk ratio 4.25, 95% confidence interval 3.05-5.91, I2=92%), diabetes (RR 1.35, 1.11-1.64, I2=37% ), male sex (RR 1.63, 1.25-2.14, I2=61%), alcohol overuse (RR 1.48, 1.21-1.81, I2=19%), underweight (RR 2.12, 1.12-4.01, I2=31%), and being black (RR 2.19, 1.21-3.96, I2=96%) or Hispanic (RR 2.13,0.94-4.81, I2=71%) in comparison with being white. Hypertension, but not any of the other risk factors, was also a risk factor for lobar intracerebral hemorrhage (RR 1.83, 1.39-2.42, I2=76%). Smoking, hypercholesterolemia and obesity were associated with neither non-lobar nor lobar intracerebral hemorrhage.

Conclusions

Hypertension is a risk factor for both non-lobar and lobar intracerebral hemorrhage, although with double the effect for non-lobar intracerebral hemorrhage. Diabetes, male sex, alcohol overuse, underweight, and being black or Hispanic are risk factors for non-lobar intracerebral hemorrhage only. Hence, the term “hypertensive intracerebral hemorrhage” for non-lobar intracerebral hemorrhage is not appropriate.

Methods The uploaded data is supplemental data belonging to the manuscript "Location specific risk factors for intracerebral hemorrhage: Systematic review and meta-analysis"

Methods

Search strategy and selection criteria

We registered our protocol in PROSPERO (CRD42019117543). We searched PubMed and Embase for cohort, case-crossover, and case-control studies on risk factors for ICH published until June 27th 2019 according to the PRISMA statement methodology.18 We used different combinations of the keywords intracerebral hemorrhage and synonyms; cohort, case-control, case-crossover or longitudinal study; and potential risk factors and synonyms (data available from Dryad; see e-1 for detailed search strategy). For this review we did not assess use of (antithrombotic) medication as a risk factor nor did we study genetic risk factors. We used the studies selected in our previous systematic review and meta-analysis for studies published before 2001. We checked reference lists of all included publications and the citation list of our previous systematic review and meta-analysis for additional articles,7 and repeated this until no further studies were found. We applied no language restrictions.

Titles and abstracts and subsequently full-text versions were screened independently by two investigators (WMTJ and KW) using the following inclusion criteria: 1) Included patients were 18 years or older; 2) ICH had to be confirmed by CT, MRI, or autopsy in 100% of cases, not only based on International Classification of Diseases (ICD) codes; 3) ICH location had to be specified; 4) A cohort, case-crossover or case-control design; 5) ICH had to be analysed as a separate entity, not in combination with subarachnoid hemorrhage; 6) Reporting on at least 100 patients with ICH. If studies included patients with ICH caused by a vascular malformation, tumour, coagulation disorder (use of antithrombotic medication was allowed), or hemorrhagic transformation of infarction, data extraction needed to allow exclusion of these patients; if not the study was excluded. Conflicts regarding inclusion were resolved by consensus with a third reviewer (CJMK). We used Covidence (www.covidence.org) for standardized screening of articles.

Data extraction

Data were extracted independently by two reviewers (WMTJ and KW) using a pre-specified and piloted extraction form (data available from Dryad; e-2). Discrepancies in extracted data were resolved by discussion, and if necessary a third reviewer (CJMK) was consulted. In case of multiple publications on overlapping cohorts, we included the study that best matched our inclusion criteria and with the largest amount of data relevant to the review. We extracted data on study period, study design, country of study, in- and exclusion criteria, number of cases and controls, mean or median age, proportion of males, and risk factors. Risk factors were assessed according to lobar and non-lobar (deep and infratentorial) ICH location and if possible, for deep (basal ganglia, thalamus and intraventricular) and for infratentorial hemorrhages (brainstem and cerebellum) separately. We assessed methodological quality, including risk of bias, of the included studies according to the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies.19

Statistical analysis

Estimates of cohort and case-control studies were first analysed separately and then combined if 95% confidence intervals (CIs) of the pooled estimates from cohort studies overlapped with those of case-control studies. We also combined maximally adjusted estimates, when available, with unadjusted estimates, if 95% CIs of the pooled unadjusted estimates overlapped with pooled adjusted estimates. If studies used different definitions for risk factors, we standardised risk factors across studies whenever possible or otherwise we accepted the criteria used in the studies. Risk factors reported in at least three studies were combined in meta-analyses; for the different subgroups of a risk factor we accepted two studies for meta-analyses. For the included studies odds ratios (ORs), relative risks (RRs) and hazard ratios (HRs) with corresponding 95% CIs, whichever were available, were obtained for the various risk factors and pooled with the generic variance-based method, weighing individual study results by the inverse of their variance. Heterogeneity was assessed using I2 statistics.20 We used a random-effects model because of the heterogeneous study characteristics. Because ORs accurately estimate RRs when risks of disease are small, we combined ORs with RRs and HRs from the longitudinal studies.21, 22 We performed a sensitivity analysis for studies with a high-quality, defined as studies with >5 points (arbitrarily chosen) on the NOS. Meta-analyses were performed in R (R programming, version 1.1.456), using the meta package (version 4.9-4).23

Data availability

Data used in this study are available to qualified investigators on request to the corresponding and senior authors.

The TRMM combined algorithm (2B31) combines data from the TMI and PR to produce the best rain estimate for TRMM. This combined rainfall product is derived from vertical hydrometeor profiles using data from the PR radar and TMI. It also includes computed correlation-corrected, mass-weighted, mean drop diameter and its standard deviation, and latent heating data. Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km

Data set contains summary data (mean, standard error, sample size) for all measured endpoints from all experiments reported in the published manuscript. This dataset is associated with the following publication: Conley, J., C. Lambright, N. Evans, E. Medlock Kakaley, A. Dixon, D. Jenkins-Hill, J. McCord, M. Strynar, J. Ford, and L. Gray. Cumulative maternal and neonatal effects of combined exposure to a mixture of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) during pregnancy in the Sprague-Dawley rat. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, (107631): 1, (2022).

Mean otolith measurements for fish in study, 2010–2011 (standard deviations in parentheses; data by marsh type are pooled across regions, seasons, and sex; data for region, season, and sex are pooled across marsh types; reference marshes adjacent to the restored and restricted marshes are noted in parentheses).

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data.We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds.We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.