aThe percentages for each city were computed from [58] using the countr y's percentage of children under 20 years old. Taiwan's percentage was obtained from [59].

This file contains data associated with "Building Word-Problem Solving and Working Memory Capacity: A Randomized Controlled Trial Comparing Three Intervention Approaches", a publication in the Journal of Education Psychology (2022), 2022, Vol. 114, No. 7, 1633–1653. Specifically this includes data on: nesting variables, general participant information, pre, post, delayed post on working memory (WM), word-problem solving (WPS), and arithmetic.

The purpose of this study was to contrast alternative approaches to structure word-problem solving (WPS) intervention, including previously validated structured WPS intervention, the same WPS intervention with embedded working memory (WM) training, and general WM training with contiguous math practice. Second-grade students with WPS difficulty were randomly assigned to four conditions: validated structured WPS intervention (Pirate Math), Pirate Math with embedded WM training, general WM training with contiguous math practice, and a business-as-usual control group. Each of the three active intervention conditions involved 45 30-min sessions conducted one-to-one for 15 weeks. WM, WPS, and arithmetic were assessed before and 1-3 weeks after intervention. WPS and arithmetic were assessed again 4-6 weeks later. Multilevel models testing main effects and testing WM as a mediator of significant main effects were conducted. Baseline WM was assessed in a secondary set of moderation analyses.

This dataset contains a set of typically developing students (i.e., pretest WPS greater than or equal to the 30th percentile standard scores below 80 on both subtests of the Wechsler Abbreviated Intelligence Scale; and WM > 60th percentile). Data for this project was collected in person at schools and via email mail, with each student containing two time points worth of data in the current sample. Data were collected at: Time point 1-September-October; Time point 2-April, Time point 3- May in academic years 2016-2017, 2017-2018, and 2018-2019.
This dataset contains information related to: Cognitive Processes, Executive Function, Math, Number Problems, Shema, Word Problems, Working Memory Arithmetic, Calculation; containing data from the following measures: Story Problems (Jordan & Hanich, 2000), WASI (Wechsler, 2011), Working Memory Test Battery for Children (WMTB-C; Pickering & Gathercole, 2001)–Listening Recall and Counting Recall, Automated Working Memory Assessment (AWMA) Odd-One Out, Arithmetic (Addition 0-12, Addition 5-18, Subtraction 0-12, Subtraction 5-18), Second-Grade Word Problems.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.

Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)

Method - demographic fractions

Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.

To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:

\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)

Where:

- \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset.

- \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year.

- \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.

The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.

For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as:

\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)

Where:

- \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c.

- \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c.

The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.

Method - demographic totals

Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.

The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.

Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

TEDS-M examined how different countries prepare their teachers to teach mathematics in primary and lower-secondary schools. The study gathered information on various characteristics of teacher education institutions, programs, and curricula. It also collected information on the opportunities to learn within these contexts, and on future teachers’ knowledge and beliefs about mathematics and learning mathematics. TEDS-M Educational measurements and tests Target population: Teachers of Mathematics TEDS-M surveyed teacher education institutions, educators of future teachers, and future teachers (primary and secondary levels). STRATIFIED TWO-STAGE CLUSTER SAMPLE DESIGN

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Note that the pre-registration can be found here: https://osf.io/xs5wf

The error reported is the standard error of calculating for three separate chains of each dataset.Denotes ().

The colours for inferred populations correspond to those seen in Figure 2. The error reported for the continents is the standard error of calculated for three separate chains of each dataset. No such error is reported for inferred populations because the designations for inferred populations differ between chains.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data. Single cell RNA-seq of MDA-MB-231 cell line with chemotherapy treatment

This is a hybrid gridded dataset of demographic data for China from 1979 to 2100, given as 21 five-year age groups of population divided by gender every year at a 0.5-degree grid resolution.

The historical period (1979-2020) part of this dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4, UN WPP-Adjusted Population Count) with gridded population from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, Histsoc gridded population data).

The projection (2010-2100) part of this dataset is resampled directly from Chen et al.’s data published in Scientific Data.

This dataset includes 31 provincial administrative districts of China, including 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government (Taiwan, Hong Kong, and Macao were excluded due to missing data).

Method - demographic fractions by age and gender in 1979-2020

Age- and gender-specific demographic data by grid cell for each province in China are derived by combining historical demographic data in 1979-2020 with the national population census data provided by the National Statistics Bureau of China.

To combine the national population census data with the historical demographics, we constructed the provincial fractions of demographic in each age groups and each gender according to the fourth, fifth and sixth national population census, which cover the year of 1979-1990, 1991-2000 and 2001-2020, respectively. The provincial fractions can be computed as:

\(\begin{align*} \begin{split} f_{year,province,age,gender}= \left \{ \begin{array}{lr} POP_{1990,province,age,gender}^{4^{th}census}/POP_{1990,province}^{4^{th}census} & 1979\le\mathrm{year}\le1990\\ POP_{2000,province,age,gender}^{5^{th}census}/POP_{2000,province}^{5^{th}census} & 1991\le\mathrm{year}\le2000\\ POP_{2010,province,age,gender}^{6^{th}census}/POP_{2010,province}^{6^{th}census}, & 2001\le\mathrm{year}\le2020 \end{array} \right. \end{split} \end{align*}\)

Where:

- \( f_{\mathrm{year,province,age,gender}}\)is the fraction of population for a given age, a given gender in each province from the national census from 1979-2020.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province,age,gender}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for a given age, a given gender in each province from the X^th national census.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for all ages and both genders in each province from the X^th national census.

Method - demographic totals by age and gender in 1979-2020

The yearly grid population for 1979-1999 are from ISIMIP Histsoc gridded population data, and for 2000-2020 are from the GPWv4 demographic data adjusted by the UN WPP (UN WPP-Adjusted Population Count, v4.11, https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11), which combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP to improve accuracy. These two gridded time series are simply joined at the cut-over date to give a single dataset - historical demographic data covering 1979-2020.

Next, historical demographic data are mapped onto the grid scale to obtain provincial data by using gridded provincial code lookup data and name lookup table. The age- and gender-specific fraction were multiplied by the historical demographic data at the provincial level to obtain the total population by age and gender for per grid cell for china in 1979-2020.

Method - demographic totals and fractions by age and gender in 2010-2100

The grid population count data in 2010-2100 under different shared socioeconomic pathway (SSP) scenarios are drawn from Chen et al. published in Scientific Data with a resolution of 1km (~ 0.008333 degree). We resampled the data to 0.5 degree by aggregating the population count together to obtain the future population data per cell.

This previously published dataset also provided age- and gender-specific population of each provinces, so we calculated the fraction of each age and gender group at provincial level. Then, we multiply the fractions with grid population count to get the total population per age group per cell for each gender.

Note that the projected population data from Chen’s dataset covers 2010-2020, while the historical population in our dataset also covers 2010-2020. The two datasets of that same period may vary because the original population data come from different sources and are calculated based on different methods.

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. Spatial or temporal consistency across dataset boundaries cannot be guaranteed.

The Third International Mathematics and Science Study, known as TIMSS 1995, was the largest and most ambitious international study of student achievement conducted up to that time. In 1994 - 1995, it was conducted at five grade levels in more than 40 countries (the third, fourth, seventh, and eighth grades, and the final year of secondary school).

Students were tested in mathematics and science and extensive information about the teaching and learning of mathematics and science was collected from students, teachers, and school principals. Altogether, TIMSS tested and gathered contextual data for more than half a million students and administered questionnaires to thousands of teachers and school principals.

Also, TIMSS investigated the mathematics and science curricula of the participating countries through an analysis of curriculum guides, textbooks, and other curricular materials. The TIMSS results were released in 1996 and 1997 in a series of reports, providing valuable information to policy makers and practitioners in the participating countries about mathematics and science instruction and the achievement of their students. Technical reports and the complete international database also have been published.

The TIMSS international database contains a myriad of educational variables collected in more than 40 countries, including achievement results in mathematics and science for third-, fourth-grade students (Population 1), seventh-, and eighth-grade students (Population 2), the final year of secondary school students (Population 3), their teachers, and their school principals.

Participating countries include: Argentina, Australia, Austria, Belgium (Flemish), Belgium (French), Bulgaria, Canada, Colombia, Cyprus, Czech Republic, Denmark, England, France, Germany, Greece, Hong Kong, Hungary, Iceland, Indonesia, Iran, Ireland, Israel, Italy, Japan, Korea, Kuwait, Latvia, Lithuania, Mexico, Netherlands, New Zealand, Norway, Philippines, Portugal, Romania, Russian Federation, Scotland, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, United States.

Trends in International Mathematics and Science Study Advanced 2008 TIMSS Advanced 2008 compared and contrasted the achievement of students enrolled in the most advanced secondary-school programs in mathematics and science. The study provided countries with an excellent opportunity to consider the effectiveness of their academic programs for students graduating from their secondary schools. The study also measured changes in students’ achievement between 1995 and 2008 for the countries that participated in both TIMSS Advanced cycles. TIMSS Advanced was administered in 1995 and 2008. For the advanced mathematics assessment, the target population consisted of students in their final year of secondary school who were taking, or had taken, courses in advanced mathematics. For physics, the target population was final-year secondary students who were taking, or had taken, courses in physics. TIMSS Educational measurements and tests Target population (students) All students in their final year of secondary school (often 12th grade) who are engaged in advanced mathematics and physics studies that prepare them to enter STEM programs in higher education

Educational tests and questionaires Target population (students) All students in their final year of secondary school (often 12th grade) who are engaged in advanced mathematics and physics studies that prepare them to enter STEM programs in higher education

Trends in International Mathematics and Science Study Advanced 1995 - TIMSS advanced 1995, the largest and most ambitious international study of student achievement conducted up to that time, was the first cycle of assessments of trends in students’ mathematics and science achievement, now known as the Trends in International Mathematics and Science Study. Questionnaires gathered extensive information about the teaching and learning of mathematics and science from students, as well as from their teachers and school principals. The study also investigated the mathematics and science curricula of the participating countries by conducting an analysis of curriculum guides, textbooks, and other curricular materials. TIMSS 1995 was conducted at five grade levels: students enrolled in the two grades containing the largest proportion of 9-year-old students (third and fourth grade in most countries) students enrolled in the two grades containing the largest proportion of 13-year-old students (seventh and eighth grade in most countries) students in their final year of secondary education Advanced: As an additional option, countries could test two special sub-groups of these students: students taking advanced courses in mathematics and/or students taking advanced courses in physics. For the advanced mathematics assessment, the target population consisted of students in their final year of secondary school who were taking, or had taken, courses in advanced mathematics. For physics, the target population was final-year secondary students who were taking, or had taken, courses in physics. TIMSS Educational measurements and tests

This dataset tracks annual math proficiency from 2012 to 2023 for Norristown Area School District vs. Pennsylvania

This dataset tracks annual math proficiency from 2011 to 2023 for La Canada High School vs. California and La Canada Unified School District

This dataset tracks annual math proficiency from 2012 to 2023 for Sandy Creek High School vs. Georgia and Fayette County School District

This dataset contains the following ".PDF", ".R", and ".RData" files: (1) A PDF file "Description of the SimuBP function.PDF"; (2) R scripts for Algorithm 1 (SimuBP), Algorithm 2, and Algorithm 3; (3) R scripts for Simulations S1a, S1b, S1c, S2a, S2b, S2c, and S3a; (4) An R script "pLD.R" used in Simulation S1c. (5) Results generated in Simulations S1a, S1b, S1c, S2a, S2b, and S3a.

TIMSS 2011 is the fifth in IEA’s series of international assessments of student achievement dedicated to improving teaching and learning in mathematics and science. A globally cooperative enterprise, TIMSS conducts comprehensive state-of the-art assessments of student achievement supported with extensive data about country, school, and classroom learning environments. There is enormous diversity among the TIMSS countries—in terms of economic development, geographical location, and population size. Fundamental to IEA’s vision is the notion that the diversity of educational philosophies, models, and approaches that characterize the world’s education systems constitute a natural laboratory in which each country can learn from the experiences of others. TIMSS Educational measurements and tests Target population (students) All students enrolled in the grades representing 4 years of formal schooling respectively, counting from the first year of ISCED Level 1. STRATIFIED TWO-STAGE CLUSTER SAMPLE DESIGN

This dataset tracks annual math proficiency from 2011 to 2023 for Sherwood High School vs. Maryland and Montgomery County School District

All data refer to pupils in schools with grades according to the target and knowledge-related grading system. Statistics as per cent from 2013. ‘Local municipality’ means pupils in both municipal and independent schools located in the municipality, regardless of where they are registered in the population register. Source: The National Agency for Education (Siris).