This instructional activity introduces students to the application of statistical tools for analyzing biological data, with a focus on measures of center (mean, median, mode) and measures of spread (range, quartiles, standard deviation). Using real-world biological contexts. students learn how to summarize datasets, identify trends, and evaluate variability. The activity integrates the use of MS Excel and TI-84 Plus graphing calculators to calculate descriptive statistics and interpret results. By engaging with authentic biological data, students develop quantitative reasoning skills that enhance their ability to detect patterns, recognize variability, and draw meaningful conclusions about biological systems

According to a survey performed in the United Kingdom (UK) in March 2020, 24 percent of respondents stated their workplace was offering sanitization products eg. hand sanitizer, wipes to help protect employees against coronavirus (COVID-19), while an additional 19 percent reported receiving regular communication about the virus at their workplace. However, 21 percent of respondents mention that nothing had changed in their workplace policy to manage the spread of coronavirus and business was running as usual. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As with the first three regulations imposed by the National Committee for Special Emergency Situations, the next measures taken by the Romanian authorities against the spread of coronavirus (COVID-19) in Romania benefited from a high percentage of approval from the population and were considered to be qualitative. The only measure that was received with a slight disagreement involved helping the Romanian citizens who returned to Romania from abroad. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

There are many open questions pertaining to the statistical analysis of random objects, which are increasingly encountered. A major challenge is the absence of linear operations in such spaces. A basic statistical task is to quantify statistical dispersion or spread. For two measures of dispersion for data objects in geodesic metric spaces, Fréchet variance and metric variance, we derive a central limit theorem (CLT) for their joint distribution. This analysis reveals that the Alexandrov curvature of the geodesic space determines the relationship between these two dispersion measures. This suggests a novel test for inferring the curvature of a space based on the asymptotic distribution of the dispersion measures. We demonstrate how this test can be employed to detect the intrinsic curvature of an unknown underlying space, which emerges as a joint property of the space and the underlying probability measure that generates the random objects. We investigate the asymptotic properties of the test and its finite-sample behavior for various data types, including distributional data and point cloud data. We illustrate the proposed inference for intrinsic curvature of random objects using gait synchronization data represented as symmetric positive definite matrices and energy compositional data on the sphere.

Abstract

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding.

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine.

The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators.

The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well.

The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables.

MAIN RESULTS

• Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level.

• Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time.

• Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion.

• Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively).

• HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing.

• Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis.

• Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care.

• Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women.

• Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Geographic coverage

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

Analysis unit

• Household
• Women age 15-49
• Men age 15-49

Universe

The population covered by the 2007 UDHS is defined as the universe of all women and men age 15-49 in Ukraine.

Kind of data

Sample survey data

Sampling procedure

The 2007 Ukraine Demographic and Health Survey (UDHS) was the first survey of its kind carried out in Ukraine. The survey was a nationally representative sample survey of 15,000 households, with an expected yield of about 7,900 completed interviews of women age 15-49. It was designed to provide estimates on fertility, infant and child mortality, use of contraception and family planning, knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI), and other family welfare and health indicators. Ukraine is made up of 24 oblasts, the Autonomous Republic of Crimea, and two special cities (Kyiv and Sevastopol), which together make up 27 administrative regions, each subdivided into lower-level administrative units. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

A men's survey was conducted at the same time as the women's survey, in a subsample consisting of one household in every two selected for the female survey. All men age 15-49 living in the selected households were eligible for the men's survey. The survey collected information on men's use of contraception and family planning and their knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI).

SAMPLING FRAME

The sampling frame used for the 2007 UDHS was the Ukraine Population Census conducted in 2001 (SSC, 2003a), provided by the State Statistical Committee (SSC) of Ukraine. The sampling frame consisted of about 38 thousand enumeration areas (EAs) with an average of 400-500 households per EA. Each EA is subdivided into 4-5 enumeration units (EUs) with an average of 100 households per EU. An EA is a city block in urban areas; in rural areas, an EA is either a village or part of a large village, or a group of small villages (possibly plus a part of a large village). An EU is a list of addresses (in a neighborhood) that was used as a convenient counting unit for the census. Both EAs and EUs include information about the location, type of residence, address of each structure in it, and the number of households in each structure.

Census maps were available for most of the EAs with marked boundaries. In urban areas, the census maps have marked boundaries/locations of the EUs. In rural areas, the EUs are defined by detailed descriptions available at the SSC local office. Therefore, either the EA or the EU could be used as the primary sampling unit (PSU) for the 2007 UDHS. Because the EAs in urban areas are large (an average of 500 households), using

This​ law​ sets​ out​ the health​ and​ administrative​ measures,​ and​ other​ measures​ to​ be​ taken​ to​ combat​ and​ prevent​ the​ spread​ of​ COVID-19​ and​ other​ deadly​ communicable​ diseases​ now​ and​ in​ the​ future​ to​ protect​ the​ people's​ life,​ public​ health​ and​ public​ order,​ as​ well​ as​ to​ minimize​ the​ impact​ of​ the​ disease​ on​ Cambodia's​ social​ and​ economic​ sectors.

BackgroundGiven the health and economic benefits of family planning (FP), Nigeria’s very low demand for FP satisfied by modern methods (mDFPS) of less than 50% is therefore a major public health concern, especially considering the global target aimed at achieving an mDFPS of at least 75% by year 2030 for all countries. In view of this, together with recognising the possible contextual nature of health outcomes, this study aimed to empirically analyse the mDFPS among married or in-union women of reproductive age (WRA) in Nigeria.Materials and methodsA multilevel binomial logistic model with two levels of analysis was used: individual and community levels. Secondary cross-sectional data were obtained from the 2018 Nigeria Demographic and Health Survey, and analyses were performed using Stata 15.0. The analytical sample size was 9,122 WRA nested in a total of 1,072 communities.ResultsThe mDFPS was approximately 31.0%. The median odds ratio (MOR) estimated from the final multilevel model was 2.245, which was greater than the adjusted odds ratio (aOR) for most of the individual-level variables, suggesting that the unexplained/residual between-community variation in terms of the odds of women having their mDFPS was more relevant than the regression effect of most of the individual-level variables. This was with the exception of the regression effects of the following individual-level variables: women’s husbands that had higher education level in comparison to their counterparts who had husbands with no formal education (aOR = 2.539; 95% CI = 1.896 to 3.399; p

Based on the results of a survey, 70 percent of Indian respondents stated that the government should follow up and track the health of all those who arrived in India from China and Singapore in the month preceding the survey to prevent the spread of the novel coronavirus. On the other hand, around two percent stated that they think precautionary measures were not needed as the virus is still a minor risk in India.

The country went into lockdown on March 25, 2020, the largest in the world, restricting 1.3 billion people.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

General Information

This contains the data for the publication:

Tokita CK, Aslett K, Godel WP, Sanderson Z, Tucker JA, Nagler J, Persily N, Bonneau RA. (2024). Measuring receptivity to misinformation at scale on a social media platform. PNAS Nexus.

Please see the above peer-reviewed article that resulted from this data for more details.

Raw and original data are located in the `data/` directory, while data that is generated from intermediate analysis is found in the `data_derived/` directory.

Please see the directory in the Github repository https://github.com/christokita/news-belief-at-scale for the code that analyzes this data and generates derived data. The code expects both the `data/` and `data_derived/` folders to reside within the same directory.

We also included a README with a description of each directory and subdirectory of the data.

Abstract (for main paper)

Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation's initial spread. Our paper provides a more precise estimate of misinformation's impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

Significance Statement (for main paper)

As social media platforms grapple with misinformation, our study offers a new approach to measure its spread and impact. By combining survey data with social media data, we estimate not only the number of users exposed to false (and true) news but also the number of users likely to believe these news stories. We find that the impact of misinformation is not evenly distributed, with ideologically extreme users being more likely to see and believe false content, often encountering it before others. Our simulations suggest that current interventions may have limited effectiveness in reducing the exposure of receptive users. These findings highlight the need to consider individual user receptiveness when measuring misinformation's impact and developing policies to combat its spread.

Statistical measures extracted from the simulation results.

‘SQ’ denotes the squared returns measure of volatility, ‘GK’ denotes the Garman-Klass measure while ‘RS’ denotes the Rogers-Satchell measure.

In March, the coronavirus pandemic led to a sell-off in Treasury markets and a subsequent period of financial stress. I use one measure of Treasury market pressure, the G-spread, to gauge how liquidity in Treasury markets changed in response to the pandemic and the Federal Reserve’s interventions. I find that timely Federal Reserve interventions restored calm to the Treasury market, and that these interventions stand out in speed and scale compared with interventions in the early days of the 2007–08 financial crisis.

‘SQ’ denotes the squared returns measure of volatility, ‘GK’ denotes the Garman-Klass measure while ‘RS’ denotes the Rogers-Satchell measure.

In these measurements considered in this dataset, 15 measurement points are deployed in the scenario. They are sorted in two groups. Among line 1, all measurement points are LoS scenarios. Among line 2, measurement points 1, 2, 3, 4, 5,and 7 are LoS scenarios, and measurement points 6, 8, 9 are NLoS scenarios. Due to the limitation of the cable length, measurement data of line 2 locations 1-6 is collected at mmwave band. The heights of the Tx antenna and the Rx antenna are 2.05 m and 1.45 m, respectively.  The bandwidth of the intermediate frequency filter of the adopted VNA is 2 kHz. Both the Tx and the Rx antennas are omnidirectional antennas. 200 snapshots are collect at each Rx location. In terms of the 3-4 GHz  data, the bandwidth is 1 GHz. The number of frequency points swept in each snapshot is 501. In the aspect of 38-39 GHz and 39-40 GHz data, the center frequencies are 38.5 GHz and 39.5 GHz with 1 GHz bandwidth, respectively. The number of frequency points swept in each snapsh...

‘SQ’ denotes the squared returns measure of volatility, ‘GK’ denotes the Garman-Klass measure while ‘RS’ denotes the Rogers-Satchell measure.

In the Hamburg air measurement network (HaLm), air quality measurements for nitrogen dioxide have been carried out in busy road sections at a suction height of 1.5 m for more than 20 years. In order to find out more about the small-scale spread of nitrogen dioxide, measurements were initially taken at two traffic measuring stations at a height of approx. 3.5 m.

We present an up-to-date catalog of intrinsic iron abundance spreads in the 55 Milky Way globular clusters (GCs) for which sufficiently precise spectroscopic measurements are available. Our method combines multiple data sets when possible to improve the statistics, taking into account the fact that different methods and instruments can lead to systematically offset metallicities. Only high spectral resolution (R>14000) studies that measure the equivalent widths of individual iron lines are found to have uncertainties on the metallicities of the individual stars that can be calibrated sufficiently well for the intrinsic dispersion to be separated cleanly from a random measurement error. The median intrinsic iron spread is found to be 0.045dex, which is small but unambiguously measured to be nonzero in most cases. There is large variation between clusters, but more luminous GCs, above 10^5^L_{sun}_, have increasingly large iron spreads on average; no trend between the iron spread and metallicity is found. Cone search capability for table J/ApJS/245/5/table1 (Derived dispersions {sigma}0 and average metallicity [Fe/H] for each cluster)

Negative values mean that spatial aggregation estimates for peak measures were smaller than spatial aggregation differences for onset measures. Bolded values denote mean estimates that we interpret to have statistical significance; that is, the 95% credible intervals did not overlap with zero.

In this paper, we investigate the spread of COVID-19 and the impact of government measures at the early stage of the pandemic (before the introduction of the vaccines) in the Netherlands. We build a multiple linear regression model to predict the effective reproduction rate using key factors and measures and integrate it with a system dynamics model to predict the spread and the impact of measures against COVID-19. Data from February to November 2020 is used to train the model and data until December 2020 is used to validate the model. We use data about the key factors, e.g., disease specific such as basic reproduction rate and incubation period, weather related factors such as temperature, and controllable factors such as testing capacity. We consider particularly the following measures taken by the government: wearing facemasks, event allowance, school closure, catering services closure, and self-quarantine. Studying the strategy of the Dutch government, we control these measures by following four main policies: doing nothing, mitigation, curbing, elimination. We develop a systems dynamic model to simulate the effect of policies. Based on our numerical experiments, we develop the following main insights: It is more effective to implement strict, sharp measures earlier but for a shorter duration than to introduce measures gradually for a longer duration. This way, we can prevent a quick rise in the number of infected cases but also to reduce the number of days under measures. Combining the measures with a high testing capacity and with effective self-quarantine can significantly reduce the spread of COVID-19.

Incremental validity statistics for benign and malevolent schadenfreude, controlling for Krizan and Johar’s scale [11].