This is the large data set as featured in the OCR H240 exam series.

Questions about this dataset will be featured in the statistics paper

The LDS is a .xlsx file containing 5 tables, four data, one information. The data is drawn from the UK censuses from the years 2001 and 2011. It is designed for you to make comparisons and analyses of the changes in demographic and behavioural features of the populace. There is the age structure of each local authority and the method of travel within each local authority.

Analysis of ‘aqalds’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tombutton/aqalds on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Data for 3827 cars including make, engine size, mass, year registered and emissions. The data features five different makes of car from two years (2002 and 2016).

This is the large data set for the AQA A level Mathematics specification: https://www.aqa.org.uk/subjects/mathematics/as-and-a-level/mathematics-7357/assessment-resources?f.Resource+type%7C6=Assessment+materials

Guidance on current European emissions standards applicable to vehicles in the UK can be found at the link below: "">http://www.dft.gov.uk/vca/fcb/exhaust-emissions-testing.asp

Acknowledgements

An extract from the UK Department for Transport Stock Vehicle Database, 2017. Contains public sector information licensed under the Open Government Licence v3.0

--- Original source retains full ownership of the source dataset ---

Dataset Card for Big-Math-RL-Verified-Processed

This is a processed version of SynthLabsAI/Big-Math-RL-Verified where we have applied the following filters:

Removed samples where llama8b_solve_rate is None Removed samples that could not be parsed by math-verify (empty lists)

We have also created 5 additional subsets to indicate difficulty level, similar to the MATH dataset. To do so, we computed quintiles on the llama8b_solve_rate values and then filtered the dataset into the… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/Big-Math-RL-Verified-Processed.

Dataset of complex math problems with questions and answers.

This is originally from huggingface. This data is not mine, I am just uploading it.

https://huggingface.co/datasets/Putnam-AXIOM/putnam-axiom-dataset

@article{fronsdal2024putnamaxiom, title={Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning}, author={Kai Fronsdal and Aryan Gulati and Brando Miranda and Eric Chen and Emily Xia and Bruno de Moraes Dumont and Sanmi Koyejo}, journal={NeurIPS 2024 Workshop on MATH-AI}, year={2024}, month={October}, url={https://openreview.net/pdf?id=YXnwlZe0yf}, note={Published: 09 Oct 2024, Last Modified: 09 Oct 2024}, keywords={Benchmarks, Large Language Models, Mathematical Reasoning, Mathematics, Reasoning, Machine Learning}, abstract={As large language models (LLMs) continue to advance, many existing benchmarks designed to evaluate their reasoning capabilities are becoming less challenging. These benchmarks, though foundational, no longer offer the complexity necessary to evaluate the cutting edge of artificial reasoning. In this paper, we present the Putnam-AXIOM Original benchmark, a dataset of 236 challenging problems from the William Lowell Putnam Mathematical Competition, along with detailed step-by-step solutions. To address the potential data contamination of Putnam problems, we create functional variations for 53 problems in Putnam-AXIOM. We see that most models get a significantly lower accuracy on the variations than the original problems. Even so, our results reveal that Claude-3.5 Sonnet, the best-performing model, achieves 15.96% accuracy on the Putnam-AXIOM original but experiences more than a 50% reduction in accuracy on the variations dataset when compared to its performance on corresponding original problems.}, license={Apache 2.0} }

InCube - Large Math Dataset

  Overview

This dataset contains over 175 million mathematical questions and their answers, designed for training and evaluating machine learning models on mathematical reasoning tasks. It spans 17 different types of mathematical operations with varying levels of complexity.

  Dataset Details





  Size

Total examples: ~175 million File format: JSON Examples per operation: ~10 million (with some variation due to mathematical… See the full description on the dataset page: https://huggingface.co/datasets/evanto/Incube-large-math-dataset.

Mathematics database.

This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli).

Example usage:

train_examples, val_examples = tfds.load(
  'math_dataset/arithmetic_mul',
  split=['train', 'test'],
  as_supervised=True)

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('math_dataset', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Maths-Grade-School I am releasing large Grade School level Mathematics datatset. This extensive dataset, comprising nearly one million instructions in JSON format, encapsulates a diverse array of topics fundamental to building a strong mathematical foundation. This dataset is in instruction format so that model developers, researchers etc. can easily use this dataset. Following Fields & sub Fields are covered: Calculus Probability Algebra Liner Algebra Trigonometry Differential Equations… See the full description on the dataset page: https://huggingface.co/datasets/pt-sk/Maths-Grade-School.

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

AraSTEM is a dataset designed to evaluate the knowledge of large language models (LLMs) in STEM (Science, Technology, Engineering, and Mathematics) subjects in Arabic. It consists of multiple-choice questions covering various topics and difficulty levels, requiring models to demonstrate a deep understanding of scientific Arabic. The dataset includes the question, options, correct answer, subject, level, and a link to the resource.

Supplementary Information files for A gifted SNARC? Directional spatial-numerical associations in gifted children with high-level math skills do not differ from controlsThe SNARC (Spatial-Numerical Association of Response Codes) efect (i.e., a tendency to associate small/large magnitude numbers with the left/right hand side) is prevalent across the whole lifespan. Because the ability to relate numbers to space has been viewed as a cornerstone in the development of mathematical skills, the relationship between the SNARC efect and math skills has been frequently examined. The results remain largely inconsistent. Studies testing groups of people with very low or very high skill levels in math sometimes found relationships between SNARC and math skills. So far, however, studies testing such extreme math skills level groups were mostly investigating the SNARC efect in individuals revealing math difculties. Groups with above average math skills remain understudied, especially in regard to children. Here, we investigate the SNARC efect in gifted children, as compared to normally developing children (overall n=165). Frequentist and Bayesian analysis suggested that the groups did not difer from each other in the SNARC efect. These results are the frst to provide evidence for the SNARC efect in a relatively large sample of gifted (and mathematically highly skilled) children. In sum, our study provides another piece of evidence for no direct link between the SNARC efect and mathematical ability in childhood.

ViP-Bench is a comprehensive benchmark designed to assess the capability of multimodal models in understanding visual prompts across multiple dimensions. It aims to evaluate how well these models interpret various visual prompts, including recognition, OCR, knowledge, math, relationship reasoning, and language generation. ViP-Bench includes a diverse set of 303 images and questions, providing a thorough assessment of visual understanding capabilities at the region level. This benchmark sets a foundation for future research into multimodal models with arbitrary visual prompts.

Math data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains". we use a large-scale dataset of 773k Chinese Question Answering (QA) pairs, collected under authorized licenses from educational websites. This dataset covers three educational levels: elementary, middle, and high school. Unlike well-structured yet small-scale benchmarks such as MATH (Hendrycks et al., 2021b) and GSM8K (Cobbe et al., 2021b), our reference answers are inherently free-form, often interwoven with… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Math-RLVR.

SciBench is a large-scale scientific problem-solving benchmark suite that aims to systematically examine the reasoning capabilities required for complex scientific problem solving. SciBench contains two carefully curated datasets: an open set featuring a range of collegiate-level scientific problems drawn from mathematics, chemistry, and physics textbooks, and a closed set comprising problems from undergraduate-level exams in computer science and mathematics.

Analysis of ‘2008-09 Class Size - School-level Detail’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b5b2fe76-c9ca-4a66-be65-0683a4d68fbb on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This file shows average class sizes and size of smallest and largest class for each school, broken out by grade and program type (General Education, Self-Contained Special Education, Collaborative Team Teaching (CTT)) for grades K-9 (where grade 9 is not reported by subject area), and for grades 5-9 (where available) and 9-12, aggregated by program type (General Education, CTT, and Self-Contained Special Education) and core course (e.g. English 9, Math A, US History, etc.).

Official class size data for grades K-9* is based on October 31, 2008 Audited Registers; Core course class size data for MS CORE and grades 9-12 is based on January 23, 2009 active registers. *Where ninth grade data is not reported by core course - For middle schools using MSPA (ATS) or HSST to program, average class size is reported by core course, as well as by official class.
- For high schools, sections with matching day, period, room and core subject, and combined enrollment less than 34 are assumed to be co-teaching situations. In the report, duplicated sections are subtracted as "MATCHED SECTIONS" and paired sections are added back as "ASSUMED TEAM TEACHING".

--- Original source retains full ownership of the source dataset ---

Collection description

The individual Brain Charting (IBC) Project is using high resolution fMRI to map 13 subjects that undergo a large number of tasks: the HCP tasks, the so-called ARCHI tasks, a specific language task, video watching, low-level visual stimulation etc. The native resolution of the data is 1.5mm isotropic. Their main value lies in the large number of contrasts probed, the level of detail and the high SNR per subject. This dataset is meant to provide the basis of a functional brain atlas. We upload here smoothed individual SPMs. The uploaded maps comprise session-specific and fixed effects across maps acquired with AP and PA phase encoding directions.

Note that Neurovault collection #4438 is a subset of that one. In the present collections, some details have been fixed, including mroe accurate and unique file naming.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

language processing fMRI task paradigm

Map type

Z

Students’ math motivation can predict engagement, achievement, and career interest in science, technology, engineering, and mathematics (STEM). However, it is not well understood how personality traits and math anxiety may be linked to different types or qualities of math motivation, particularly during high-stress times such as the COVID-19 pandemic. In this study, we examined how fearful or avoidant temperaments contribute to math anxiety and math motivations for college students during the COVID-19 pandemic. Ninety-six undergraduate students from a large public university were assessed on temperamental fear, math anxiety, and math motivation in an online math course. Results showed that higher levels of temperamental fear are directly linked to higher levels of math anxiety. In addition, temperamental fear is indirectly linked to higher levels of autonomous motivation (i.e., intrinsic motivation and identified regulation) and lower levels of controlled motivation (i.e., external regulation) through math anxiety. Results have implications for helping students at high risk for both high math anxiety and for low motivation to engage in math learning.

Analysis of ‘2009 - 2010 Class Size - School-level Detail’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fd7a843a-a7f9-42cc-a36d-b6a2c7ae92a4 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This file shows average class sizes, pupil-teacher ratio, and size of largest and smallest classes for each school, broken out by grade and program type (General Education, Self-Contained Special Education, Collaborative Team Teaching (CTT)) for grades K-9 (where grade 9 is not reported by subject area), and for grades 5-9 (where available) and 9-12, aggregated by program type (General Education, CTT, and Self-Contained Special Education) and core course (e.g. English 9, Math A, US History, etc.)

Based on January 27, 2010 data. * Grade 9 Official Class data is included for 0K-09 schools. Core Course information for these sections is not reported.

--- Original source retains full ownership of the source dataset ---

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

Analysis of ‘2006-07 School Progress Reports - All Schools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c7712a82-1cb7-4120-a9ef-54e0d25e4d3f on 26 January 2022.

--- Dataset description provided by original source is as follows ---

2006/07 Progress Report results for all schools (data as of 1/14/09)

Peer indices are calculated differently depending on School Level. Schools are only compared to other schools in the same School Level (e.g., Elementary, K-8, Middle, High)

1) Elementary & K-8 - peer index is a value from 0-100. We use a composite demographic statistic based on % ELL, % SpEd, % Title I free lunch, and % Black/Hispanic. Higher values indicate student populations with higher need

2) Middle & High - peer index is a value from 1.00-4.50. For middle schools, we use the average 4th grade proficiency ratings in ELA and Math for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores. Lower values indicate student populations with higher need

3) D84 / Charter Schools - the overall score does not include the results of the learning environment survey.

4) Schools for Transfer Students - consists of schools with large populations of high school students transferring from NYC High Schools or from out of state/country. No peer index value is assigned because this set of schools is its own peer group. The reports contain 3 categories with one additional credit section. Unlike the HS Progress Report, the Environment Category is only composed of Survey Results. Performance measures 6-year graduation rate and Progress captures student level improvements in attendance, credit accumulation and Regents passed. The additional credit section rewards schools demonstrating exceptional achievement (11 credits or more earned per year) among overage/under-credit populations.

--- Original source retains full ownership of the source dataset ---

Collection description

The individual Brain Charting (IBC) Project is using high resolution fMRI to map 13 subjects that undergo a large number of tasks: the HCP tasks, the so-called ARCHI tasks, a specific language task, video watching, low-level visual stimulation etc. The native resolution of the data is 1.5mm isotropic. Their main value lies in the large number of contrasts probed, the level of detail and the high SNR per subject. This dataset is meant to provide the basis of a functional brain atlas. We upload here smoothed individual SPMs. The uploaded maps comprise session-specific and fixed effects across maps acquired with AP and PA phase encoding directions.

Note that Neurovault collection #4438 is a subset of that one. In the present collections, some details have been fixed, including mroe accurate and unique file naming.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

language processing fMRI task paradigm

Map type

Z