Learning Goals: • explain importance of data management • identify elements of an organized data sheet • create & manipulate data in a spreadsheet • calculate vital statistics using life tables • collect, manage and analyze data to test hypotheses

Normalized decision matrix.

This dataset is about books. It has 49 rows and is filtered where the book subjects is Management-Mathematical models. It features 9 columns including author, publication date, language, and book publisher.

These are raw data. We selected the actual quality data of the jobsite from May 2017 to January 2018 to prove the actual basis of the study.Line 75 (marked yellow line) is the shield tunneling parameters of ring 343, the ring where the cutter disc accident occurred.This is also the quality data used in this paper. The rest of the index data is obtained from geological investigation report, material records, work records and personnel testing, which are a lot of files and can be upload later if necessary.

Ideal and negative ideal solutions.

This dataset is about books. It has 2 rows and is filtered where the book subjects is Electronic data processing-Distributed processing-Mathematics. It features 9 columns including author, publication date, language, and book publisher.

Biological data were analysed with all 6 methods, the mean period value is reported in the table (standard deviation in brackets). The expected period is 24 h as the clock is entrained by a 24 h light:dark cycle. 1) The data were collected in two different conditions: LD and SD, monitoring 5 output genes in each of them. 2) (All) represents aggregated results from all data sets. 3) NoCAT3 represents aggregated results from all data sets except the CAT3 marker. +) The cases for which mean period is not statistically different from the 24 h are marked with +.

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Science for Action and Development department. It develops as primary mission of producing generic and finalised information, and developing methods, tools and knowhow in its fields of competence which are mathematics and informatics applied to the sectors of food, agriculture and the environment.

This dataset is about books. It has 2 rows and is filtered where the book subjects is Oil wells-Management-Mathematics. It features 9 columns including author, publication date, language, and book publisher.

Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research challenge. In this dissertation, we address key limitations in MWP solving, focusing on the accuracy, generalization ability and efficiency of AI-based mathematical reasoners by applying human-like reasoning methods and principles.

This dissertation introduces several innovative approaches in mathematical reasoning. First, a numeracy-driven framework is proposed to enhance math word problem (MWP) solvers by integrating numerical reasoning into model training, surpassing human-level performance on benchmark datasets. Second, a novel multi-solution framework captures the diversity of valid solutions to math problems, improving the generalization capabilities of AI models. Third, a customized knowledge distillation technique, termed Customized Exercise for Math Learning (CEMAL), is developed to create tailored exercises for smaller models, significantly improving their efficiency and accuracy in solving MWPs. Additionally, a multi-view fine-tuning paradigm (MinT) is introduced to enable smaller models to handle diverse annotation styles from different datasets, improving their adaptability and generalization. To further advance mathematical reasoning, a benchmark, MathChat, is introduced to evaluate large language models (LLMs) in multi-turn reasoning and instruction-following tasks, demonstrating significant performance improvements. Finally, new inference-time verifiers, Math-Rev and Code-Rev, are developed to enhance reasoning verification, combining language-based and code-based solutions for improved accuracy in both math and code reasoning tasks.

In summary, this dissertation provides a comprehensive exploration of these challenges and contributes novel solutions that push the boundaries of AI-driven mathematical reasoning. Potential future research directions are also discussed to further extend the impact of this dissertation.

The euroSAMPL1 challenge was devoted to the prediction of aqueous pKa values of small drug-like molecules provided as SMILES strings and, as a novel challenge component, included a peer evaluation of submissions concerning FAIR criteria as well as reproducibility (FAIR+R).

This data contains X-ray computed tomography (XCT) reconstructed slices of additively manufactured cobalt chrome samples produced with varying laser powder bed fusion (LPBF) processing parameters (scan speed and hatch spacing). A constant laser power of 195 W and a layer thickness of 20 µm were used. Unoptimized processing parameters created defects in these parts. The as-built CoCr disks were 40 mm in diameter and 10 mm in height, with no post-processing step (e.g. heat treatment or hot isostatic pressing) used. Five mm diameter cylinders were cored out of each disk, and regions of interests (ROIs) within the cylinders were measured with XCT. The voxel size is approximately 2.5 µm, and approximately 1000 x 1000 x 1000 voxel three-dimensional images were obtained, for an actual volume of about (pi/4) x (2.5 mm)^3 in case of the approximately 2.5 µm voxel data sets. The data set contains two folders ('raw' and 'segmented') with 5 zipped tiff image folders, one for each sample. The images in the 'raw' folder are the original 16-bit XCT reconstructed images. The images in the 'segmented' folder are the segmented images. 'setn' in the file name represents the sample set and 'samplen' represents the sample number. The final trailing -n represents the number of the image in the stack where higher number is toward the top of the sample.

A numeral classifier is required between a numeral and a noun in Chinese, which comes in two varieties, sortal classifer (C) and measural classifier (M), also known as ‘classifier’ and ‘measure word’, respectively. Cs categorize objects based on semantic attributes and Cs and Ms both denote quantity in terms of mathematical values. The aim of this study was to conduct a psycholinguistic experiment to examine whether participants process C/Ms based on their mathematical values with a semantic distance comparison task, where participants judged which of the two C/M phrases was semantically closer to the target C/M. Results showed that participants performed more accurately and faster for C/Ms with fixed values than the ones with variable values. These results demonstrated that mathematical values do play an important role in the processing of C/Ms. This study may thus shed light on the influence of the linguistic system of C/Ms on magnitude cognition.

In order to become better prepared to support Research Data Management (RDM) practices in sciences and engineering, Queen’s University Library, together with the University Research Services, conducted a research study of all ranks of faculty members, as well as postdoctoral fellows and graduate students at the Faculty of Engineering & Applied Science, Departments of Chemistry, Computer Science, Geological Sciences and Geological Engineering, Mathematics and Statistics, Physics, Engineering Physics & Astronomy, School of Environmental Studies, and Geography & Planning in the Faculty of Arts and Science.

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 dataset was collected to model change point estimation in time-to-event data for a clinical process with dichotomous outcomes, death and survival, where patient mix was present. Modelling was completed using a Bayesian framework. The performance of the Bayesian estimators was investigated through simulation in conjunction with RAST CUSUM control charts for monitoring right censored survival time of patients who underwent cardiac surgery procedures within a follow-up period of 30 days.

The dataset presents the probability of the occurrence of the change point in the last {25, 50, 100, 200, 300, 400, 500} observations prior to signalling for RAST CUSUM (http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0033630.e205&representation=PNG" />) where http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0033630.e206&representation=PNG" /> and http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0033630.e207&representation=PNG" />.

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.1 introduces basic functions from R, as well as from its package tidyverse, for data exploration and management.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for economics and management mathematics in the U.S.

Use of vectors in financial graphs By using mathematical vectors calculations as financial modeling then further into a new form of quantitative analysis instrument for linear financial computation graphs. A new tool in financial data analysis as an indicator.