Key measures of English and maths progress over the last three years.

New York State Math Test Results

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1937611%2Fd08067a1c078259d0026dde197817660%2F_7ceb67e5-e417-4a42-8b01-9b7cb8d78871.jpeg?generation=1715699004202376&alt=media" alt="">

This dataset presents detailed information on math examination results administered in New York State from 2006 to 2012. It includes the following categories:

• Report Category: The high-level grouping for each report.
• Geographic Subdivision: The individual schools or geographic subregion for each of the main categories.
• Grade: The school grade for which the test was administered.
• Year: The year the test was administered.
• Student Category: Reflects the set of students who were tested.
• Number Tested: Total number of students tested.
• Mean Scale Score: Average score of total students tested.
• Num Level 1: Number of students who scored in Level 1 range.
• Pct Level 1: Percentage of students who scored in Level 1 range.
• Num Level 2: Number of students who scored in Level 2 range.
• Pct Level 2: Percentage of students who scored in Level 2 range.
• Num Level 3: Number of students who scored in Level 3 range.
• Pct Level 3: Percentage of students who scored in Level 3 range.
• Num Level 4: Number of students who scored in Level 4 range.
• Pct Level 4: Percentage of students who scored in Level 4 range.
• Num Level 3 and 4: Number of students who scored in Level 3 and 4 range combined.
• Pct Level 3 and 4: Percentage of students who scored in Level 3 and 4 range.

ObjectiveTo provide a practical guidance for the analysis of N-of-1 trials by comparing four commonly used models.MethodsThe four models, paired t-test, mixed effects model of difference, mixed effects model and meta-analysis of summary data were compared using a simulation study. The assumed 3-cycles and 4-cycles N-of-1 trials were set with sample sizes of 1, 3, 5, 10, 20 and 30 respectively under normally distributed assumption. The data were generated based on variance-covariance matrix under the assumption of (i) compound symmetry structure or first-order autoregressive structure, and (ii) no carryover effect or 20% carryover effect. Type I error, power, bias (mean error), and mean square error (MSE) of effect differences between two groups were used to evaluate the performance of the four models.ResultsThe results from the 3-cycles and 4-cycles N-of-1 trials were comparable with respect to type I error, power, bias and MSE. Paired t-test yielded type I error near to the nominal level, higher power, comparable bias and small MSE, whether there was carryover effect or not. Compared with paired t-test, mixed effects model produced similar size of type I error, smaller bias, but lower power and bigger MSE. Mixed effects model of difference and meta-analysis of summary data yielded type I error far from the nominal level, low power, and large bias and MSE irrespective of the presence or absence of carryover effect.ConclusionWe recommended paired t-test to be used for normally distributed data of N-of-1 trials because of its optimal statistical performance. In the presence of carryover effects, mixed effects model could be used as an alternative.

The MathE Mathematics Learning and Assessment Dataset is a comprehensive collection of student responses to mathematical questions from higher education institutions. The dataset includes 9,546 records, with each record representing a student's answer to a specific question. The dataset is designed to support various machine learning tasks, including classification, regression, and clustering. It provides detailed information on student demographics, question difficulty, and mathematical topics, making it a valuable resource for educational data analysis and predictive modeling.

Key Features

• Student Information: Includes student ID and country of origin.
• Question Details: Question ID, level (basic or advanced), topic, subtopic, and keywords.
• Answer Type: Binary indicator of whether the answer was correct (1) or incorrect (0).

The case-cohort study design combines the advantages of a cohort study with the efficiency of a nested case-control study. However, unlike more standard observational study designs, there are currently no guidelines for reporting results from case-cohort studies. Our aim was to review recent practice in reporting these studies, and develop recommendations for the future. By searching papers published in 24 major medical and epidemiological journals between January 2010 and March 2013 using PubMed, Scopus and Web of Knowledge, we identified 32 papers reporting case-cohort studies. The median subcohort sampling fraction was 4.1% (interquartile range 3.7% to 9.1%). The papers varied in their approaches to describing the numbers of individuals in the original cohort and the subcohort, presenting descriptive data, and in the level of detail provided about the statistical methods used, so it was not always possible to be sure that appropriate analyses had been conducted. Based on the findings of our review, we make recommendations about reporting of the study design, subcohort definition, numbers of participants, descriptive information and statistical methods, which could be used alongside existing STROBE guidelines for reporting observational studies.

Abstract We present results of a study which objective was the open access documental production characterization of the Spanish-speaking community in Mathematics Education for middle school. From a taxonomy of key terms specific to Mathematics Education, we made a semantic approach to the documentation content, which was produced by this community of teachers and researchers between 1986 and 2017. We defined the following study variables: educational level, document type, school mathematics, and curriculum. We crossed the educational level variable with the other variables and, with a normalization process, we identified the values of these variables in which the middle school is different from the other educational levels, since it has the highest or lowest percentage of documents published. In comparison with other educational levels, middle school is distinguished by having the highest or one of the highest production levels in activities. It stands out for addressing issues of probability, calculation, measurement and algebra, and classroom and learning.

Korean Test Questions Structured Analysis Processing Data, around 2.4 million questions, contains question types, questions, answers, explanations, etc..For subjects, include [Primary School] Korean, Mathematics, English, Social Studies, Science; [Middle School] Korean, English, Mathematics, Science, Social Studies; [High School] Korean, English, Mathematics, Physics, Chemistry, Biology, History, Geography; question Types indlude single-choice question, fill-in question, true or false question, short answer question, etc. This dataset can be used for large-scale subject knowledge enhancement tasks.

Data content Korean K12, university test question

Amount around 2.4 million questions

Data fields Contains question types, questions, answers, explanations, etc.

Subject and Grade Level K12, university；contains math,physics,chemistry,biology

Question Types single-choice question, fill-in question, true or false question, short answer question, etc.

Format Jsonl

Language Korean

This dataset was collected through a survey conducted in 2021 and includes responses from 349 students (boys and girls) attending lower secondary schools in Norway. The primary objective of the data collection was to investigate how task difficulty labels influence students’ self-efficacy and performance in mathematics, with particular attention to gender differences. Variables included in the dataset: (1) Gender, (2) Self-efficacy related to three mathematics tasks, measured both before and after the tasks were presented with difficulty labels, (3) Performance on the same three mathematics tasks, (4) Task difficulty labels assigned to each task (experimental variable: easy, medium, or difficult). This dataset enables analysis of how labelling mathematics tasks as “easy”, “medium”, or “difficult” affects students’ self-efficacy and performance, and how these effects may differ across genders.

About the Math-Students Dataset

This dataset, originally sourced from the UCI Machine Learning Repository, offers a rich collection of data on student performance in a math program. It provides detailed insights into both the academic achievements and the socio-demographic backgrounds of the students, making it an excellent resource for educational data mining and predictive analytics.

Key Features & Attributes

• Demographics & Background:

  • School: Identifies the student's school (e.g., Gabriel Pereira or Mousinho da Silveira).
  • Sex & Age: Basic demographic information to help explore performance trends among different groups.
  • Address & Family Size: Details about the student’s home environment, including whether they live in an urban or rural area and their family size.

• Parental & Household Information:

  • Parental Cohabitation & Education: Data on whether parents live together and their education levels, which can correlate with student support and academic outcomes.
  • Parental Occupation: Information on the mother’s and father’s jobs, providing further context on socioeconomic factors.

• Educational & Behavioral Variables:

  • Study Time & Failures: Weekly study time and history of past class failures help gauge academic dedication and potential challenges.
  • Support & Extracurricular Activities: Records on whether the student has received extra educational support or participates in extracurricular activities, which can influence overall performance.
  • School-Related Factors: Travel time to school, attendance (absences), and participation in additional paid classes contribute to a holistic view of the educational environment.

• Lifestyle & Social Factors:

  • Internet Access, Free Time & Socializing: Variables like internet availability, free time, and how often students go out with friends help capture lifestyle and behavioral patterns.
  • Health & Well-being: Self-reported health status and alcohol consumption patterns during weekdays and weekends offer insights into personal well-being, which may impact academic performance.

• Academic Performance:

  • Grades: The dataset includes three key assessments—G1 (first period grade), G2 (second period grade), and G3 (final grade). G3, the final grade, serves as the primary target variable for predictive models.

Potential Applications

• Predictive Modeling:
  Researchers and data scientists can build regression models to predict final grades (G3) based on the numerous socio-demographic and educational features.
• Exploratory Data Analysis:
  The dataset is ideal for exploring relationships between family background, lifestyle choices, and academic success. For example, one could analyze how study time or parental education levels correlate with performance.
• Educational Interventions:
  By identifying key factors that contribute to academic outcomes, educators and policymakers can develop targeted interventions to support at-risk students.
• Comparative Studies:
  While this dataset focuses on math scores, its structure is similar to the Portuguese language course dataset. This similarity provides opportunities for cross-domain comparisons in educational research.

Additional Insights

• Data Complexity & Quality:
  Despite its moderate size, the dataset is rich in both categorical and numerical variables. This diversity requires careful preprocessing and feature engineering but also offers the chance to uncover complex interactions between various factors.
• Research Impact:
  The dataset has been widely used in the field of educational data mining. Its comprehensive nature has provided a basis for numerous studies examining the interplay between academic performance and a range of external factors.
• Historical Context:
  Originating from a study presented at the 5th FUBUTEC 2008 conference, the dataset has contributed valuable insights into secondary school performance and continues to serve as a benchmark for educational analytics research.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud). 3. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:
Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.
– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References 1. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873 2. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.
http://www.bmj.com/content/348/bmj.g3725/rr/762595
Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies. Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.
http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6).
PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics: - Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia. - Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez) 1. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242 2. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181 3. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151 4. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles 1. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725 2. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22 3. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106 4. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597 5. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655 6. Katz D. A-holistic view of evidence based medicine
http://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

The data include information about 85 rigorous experimental studies that evaluated 64 programs in grades K-5 mathematics. These data were collected by the research team from studies included in a systematic review of programs for elementary mathematics. The data contain study and finding level information to examine what types of programs are most effective.

The global mathematics software market size was valued at USD XXX million in 2025 and is projected to grow from USD XXX million in 2026 to USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The increasing adoption of mathematics software in various industries and the rising demand for advanced data analytics and optimization techniques are the key factors driving the market growth. The market is segmented based on type into free software and commercial software. The commercial software segment is expected to hold a larger market share during the forecast period due to the growing adoption of paid software solutions by businesses and organizations. Based on application, the market is segmented into school, engineering construction, academic and research institutes, and others. The school segment is expected to grow at a significant rate during the forecast period due to the increasing need for interactive and engaging learning tools in educational institutions. Major companies operating in the market include Wolfram Research, The MathWorks, Saltire Software, Maplesoft, PTC, GAMS Development Corporation, Gurobi Optimization, Civilized Software, Signalysis, and others. Concentration Areas: The mathematics software market is concentrated in a few key areas, including:

Academic and research institutions: These institutions use mathematics software for teaching, research, and development. Engineering and construction: Engineers and construction professionals use mathematics software for design, analysis, and simulation. Financial services: Financial professionals use mathematics software for risk management, trading, and portfolio optimization. Manufacturing: Manufacturers use mathematics software for product design, process optimization, and quality control.

Characteristics of Innovation: The mathematics software market is characterized by a high level of innovation. Software developers are constantly releasing new products and features that improve the performance, usability, and functionality of their software. Key characteristics of innovation in mathematics software include:

User-friendliness: Mathematics software is becoming increasingly user-friendly, with intuitive interfaces and easy-to-use features. Increased automation: Mathematics software is automating more and more tasks, freeing up users to focus on more complex problems. Integration with other software: Mathematics software is becoming increasingly integrated with other software, such as CAD/CAM software and data analysis software. Cloud-based deployment: Mathematics software is increasingly being deployed in the cloud, which provides users with access to the software from anywhere, at any time.

Impact of Regulations: The mathematics software market is subject to a number of regulations, including:

Export controls: Some mathematics software products are subject to export controls, which restrict their sale to certain countries. Data protection laws: Mathematics software that collects and processes personal data is subject to data protection laws, such as the General Data Protection Regulation (GDPR).

Product Substitutes: There are a number of substitutes for mathematics software, including:

Spreadsheet software: Spreadsheet software can be used for basic mathematical calculations and data analysis. Programming languages: Programming languages can be used to develop custom mathematical software solutions. Online calculators: Online calculators can be used for simple mathematical calculations. Specialized software: There are a number of specialized software products that are designed for specific mathematical applications, such as CAD/CAM software and data analysis software.

End User Concentration: The end user market for mathematics software is concentrated in a few key industries, including:

Education: Mathematics software is used in schools, colleges, and universities for teaching and research. Engineering: Mathematics software is used in engineering firms for design, analysis, and simulation. Finance: Mathematics software is used in financial institutions for risk management, trading, and portfolio optimization. Manufacturing: Mathematics software is used in manufacturing firms for product design, process optimization, and quality control.

Level of M&A: The level of M&A in the mathematics software market is relatively low. However, there have been a number of notable acquisitions in recent years, including:

The MathWorks acquisition of Simulink: This acquisition strengthened The MathWorks' position in the simulation software market. Maplesoft acquisition of Virtual Laboratories: This acquisition expanded Maplesoft's product portfolio to include virtual reality and augmented reality software. PTC acquisition of Onshape: This acquisition gave PTC a strong presence in the cloud-based CAD software market.

The joint collection of UNESCO-OECD-Eurostat (UOE) data on formal education systems providesOna annual data on the participation and completion of educational programs by students as well as personnel data, cost and type of resources dedicated to education. The reference period for non-monetary education data is the curSo school and for monetary data it is the calendar year. The International Statistics of Education and Training Systems - Questionnaire UNESCO-UIS / OECD / Eurostat (UOE) aims to provide the data required by international organizationsIn addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Nº Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of EducationAnd Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different educational levels withIn order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. Main results Below is a selection of tables with data derived from this statistic, along with a non-Presentation summary: Summary note 2022-2023

Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive, and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta-analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and further (ii) do not make use of all available data. We describe a multinomial meta-analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some pre-specified or Box-Cox transformation of test results in the diseased and disease-free populations has a logistic distribution. The Box-Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between-study heterogeneity in all parameters. We demonstrate the model using two case study meta-analyses, examining the accuracy of tests for acute heart failure and pre-eclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.

This study investigates the effectiveness of Level I, the first seven lessons, of the Hands-On Equations balance model in improving the equation-solving skills of upper elementary and middle school students. Fourth-grade (n = 123) and eighth-grade (n = 105) students from eleven classes in the United States participated in this study. A pretest-to-posttest design was used to evaluate the performance of the students on six algebraic equations, including three equations with the unknown on both sides of the equal sign. The students were provided with a six-question pretest prior to classroom instruction on the system, a posttest following Lesson 6 using the manipulatives, and a posttest following Lesson 7 without the manipulatives. Students were allowed 15 minutes for each of the tests. Each question answered correctly was given 1 point, regardless of the difficulty level of the question. Although the question paper had a field to enter the check for each problem, only the value for x was used to determine if the problem was answered correctly. The study sought to measure the effect of the program on the 4th grade, 8th grade rural, and 8th grade suburban groups. It also sought to compare the 4th grade results to an 8th grade benchmark. The data set includes the testing instrument, the pre- and post-testing data for each class, as well as an item analysis for the three groups. Methods The math coordinator asked for grade four teachers to volunteer for this study and received six volunteers. These teachers attended a one-day onsite training session. Prior to instruction, they provided their students with a pretest. After instruction, they administered the posttests and entered the data on a summary data sheet. Those sheets were turned over to the math coordinator who in turn forwarded to the researcher for analysis. Since the author of the program has a conflict of interest, the statistical analysis was done by an independent party. The five 8th grade teachers participated in a public workshop and followed the same testing procedure. The assembled data was analyzed using SPSS 28.01.

This dataset captures the academic performance of students across three main subjects—Math, Reading, and Writing—along with detailed background information such as gender, race/ethnicity, parental education, lunch type, and test preparation status. It enables the exploration of how social, economic, and educational factors influence student outcomes and provides a strong foundation for building predictive machine-learning models in the education domain.

✅ Column Descriptions

Column Name Description gender Student’s gender. race/ethnicity Demographic group/category of the student. parental level of education Highest education level of the student’s parents. lunch Type of lunch received (standard or free/reduced). test preparation course Indicates whether the student completed a test-prep course. math score Numeric score obtained in the math exam. reading score Numeric score obtained in the reading exam. writing score Numeric score obtained in the writing exam.

✅ Machine Learning Application Ideas (with Algorithms)

1️⃣ Score Prediction (Regression)

Goal: Predict a student’s math, reading, or writing score. Best Algorithms: Linear Regression, Random Forest Regressor, XGBoost Regressor.

2️⃣ Student Performance Classification

Goal: Categorize students into Low/Medium/High performance groups. Best Algorithms: Logistic Regression, Random Forest Classifier, SVM (Support Vector Machine).

3️⃣ Factor Importance & Insights

Goal: Identify which features (parent education, lunch, prep course) most affect performance. Best Algorithms: Random Forest Feature Importance, XGBoost Feature Importance, SHAP (model explainability).

4️⃣ Student Grouping (Unsupervised Learning)

Goal: Find natural clusters of students based on their scores and backgrounds. Best Algorithms: K-Means Clustering, Hierarchical Clustering, DBSCAN.

5️⃣ Predict Likelihood of Needing Test Preparation

Goal: Predict which students may benefit from a test-preparation course. Best Algorithms: Decision Tree Classifier, Gradient Boosting Classifier, Naive Bayes.

FSL5.0

Collection description

IMPORTANT:

This is open access data. You must agree to Terms and conditions of using this data before using it, available at:

http://humanconnectome.org/data/data-use-terms/open-access.html

Open Access Data (all imaging data and most of the behavioral data) is available to those who register an account at ConnectomeDB and agree to the Open Access Data Use Terms. This includes agreement to comply with institutional rules and regulations.

This means you may need the approval of your IRB or Ethics Committee to use the data. The released HCP data are not considered de-identified, since certain combinations of HCP Restricted Data (available through a separate process) might allow identification of individuals. Different national, state and local laws may apply and be interpreted differently, so it is important that you consult with your IRB or Ethics Committee before beginning your research. If needed and upon request, the HCP will provide a certificate stating that you have accepted the HCP Open Access Data Use Terms.

Please note that everyone who works with HCP open access data must review and agree to these terms, including those who are accessing shared copies of this data. If you are sharing HCP Open Access data, please advise your co-researchers that they must register with ConnectomeDB and agree to these terms.

Register and sign the Open Access Data Use Terms at ConnectomeDB:

https://db.humanconnectome.org/

Preprocessing Details:
http://www.ncbi.nlm.nih.gov/pubmed/23668970

T-stat maps were generated with FSL's randomise:

randomise -i 4D -o OneSampT -1 -T

and the package TtoZ was used to generate the Z-stat maps:

https://github.com/vsoch/TtoZ

Tasks:
http://humanconnectome.org/documentation/S500/HCP_S500+MEG2_Release_Appendix_VI.pdf

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

language processing fMRI task paradigm

Map type

Z

Description

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

Key Features

The dataset includes the following key features:

1. Countries and Areas: Name of the countries and areas.
2. Latitude: Latitude coordinates of the geographical location.
3. Longitude: Longitude coordinates of the geographical location.
4. OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males.
5. OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females.
6. OOSR_Primary_Age_Male: Out-of-school rate for primary age males.
7. OOSR_Primary_Age_Female: Out-of-school rate for primary age females.
8. OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males.
9. OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females.
10. OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males.
11. OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females.
12. Completion_Rate_Primary_Male: Completion rate for primary education among males.
13. Completion_Rate_Primary_Female: Completion rate for primary education among females.
14. Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males.
15. Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females.
16. Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males.
17. Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females.
18. Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students.
19. Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students.
20. Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education.
21. Primary_End_Proficiency_Math: Proficiency in math at the end of primary education.
22. Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education.
23. Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education.
24. Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24.
25. Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24.
26. Birth_Rate: Birth rate in the respective countries/areas.
27. Gross_Primary_Education_Enrollment: Gross enrollment in primary education.
28. Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education.
29. Unemployment_Rate: Unemployment rate in the respective countries/areas.

Potential Use Cases

• Global Education Analysis: Evaluate the status of education in different countries and regions, identifying disparities and trends.
• Gender Disparities: Analyze gender-based differences in education, including out-of-school rates and literacy.
• Education Policy Evaluation: Use completion rates to assess the effectiveness of education policies.
• Proficiency Analysis: Investigate students' proficiency in reading and math at different education levels.
• Socioeconomic Impact: Study the relationship between education and unemployment rates.
• Geospatial Analysis: Explore geographical patterns of education indicators.

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Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

language processing fMRI task paradigm

Map type

T