1. Reference Year: 2024/2025 2. The Department of Education & Youth’s P-POD system is the source of this data with it capturing school returns for pupil enrolments by grade, programme and subject 3. Strong differences can be seen in the percentages of girls and boys when it comes to STEM (Science, Technology, Engineering and Mathematics), particularly when biology is excluded 4. Data is reported on an academic year basis which starts in September of a year and finishes in June of the following year (First week of June for post-primary schools and last week of June for primary schools), other than attainment data, which is just for the end of the academic year, i.e., June 2023 5. STEM subjects at Leaving Certificate: agricultural science, mathematics, applied mathematics, biology, physics, chemistry, physics and chemistry, engineering, construction studies, design and communication graphics and technology 6. STEM subjects at Junior Certificate: wood technology, graphics, engineering, applied technology, mathematics and science 7. Schools offering all three science subjects (physics, chemistry, biology) is based on pupils taking these subjects on P-POD, i.e., a school may offer physics but have no pupils taking the subject 8. All %'s are a ratio between 0 – 1

STEM Dataset

📃 [Paper] • 💻 [Github] • 🤗 [Dataset] • 🏆 [Leaderboard] • 📽 [Slides] • 📋 [Poster]

This dataset is proposed in the ICLR 2024 paper: Measuring Vision-Language STEM Skills of Neural Models. We introduce a new challenge to test the STEM skills of neural models. The problems in the real world often require solutions, combining knowledge from STEM (science, technology, engineering, and math). Unlike existing datasets, our dataset requires the understanding of… See the full description on the dataset page: https://huggingface.co/datasets/stemdataset/STEM.

We created a STEM (Science, Technology, Engineering and Mathematics) corpus by filtering wikipedia articles based on their category metadata. During extraction of wiki page contents, we mitigated the frequent rendering issues (number, equations & symbols) prevalent in existing wiki datasets.

For filtering, we first defined a set of seed wikipedia categories related to STEM topics such as Category:Concepts in physics, Category:Physical quantities, etc. For each category, recursively collect the member pages and subcategories up to a certain depth. We next extracted the page contents of the collected wiki URLs using Wikipedia-API (400k+ pages).

Chunking: We first split the full text from each article based on different sections. The longer sections were further broken down into smaller chunks containing approximately 300 tokens (deberta-v3 tokenizer).

This dataset can be embedded and used for RAG over STEM wiki.

References: - Wiki STEM url collection: https://www.kaggle.com/code/conjuring92/d01-wiki-urls/notebook - Extraction of page content: https://www.kaggle.com/code/conjuring92/s04-stem-wiki-fetch - Chunking: https://www.kaggle.com/code/conjuring92/d504-chunking/notebook

Description

This dataset contains 1.5 million English science and engineering test questions, including mathematics, physics, chemistry, biology, and other STEM subjects at the university level. Each questions contain title, answer, parse, type, subject, grade. The dataset can be used for large model subject knowledge enhancement tasks. For more details, please refer to the link: https://www.nexdata.ai/datasets/llm/1881?source=Huggingface

  Content

Science subjects… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-AI/1.5-Million-English-STEM-Test-Questions-Data-Sample.

A Large-scale Dataset of STEM Science as PROCESS, METHOD, MATERIAL, and DATA Named Entities

This repository hosts data as a follow-up study to the following publications

D'Souza, J., Hoppe, A., Brack, A., Jaradeh, M., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 2192–2203). European Language Resources Association.

Brack, A., D’Souza, J., Hoppe, A., Auer, S., Ewerth, R. (2020). Domain-Independent Extraction of Scientific Concepts from Research Articles. In: , et al. Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. Springer, Cham. https://doi.org/10.1007/978-3-030-45439-5_17

Supporting dataset link https://data.uni-hannover.de/dataset/stem-ecr-v1-0

Description

Roughly 60,000 titles and abstracts of scholarly articles with the CC-BY redistributable license were downloaded from Elsevier. The articles spanned 10 STEM domains which were the most prolific on Elsevier viz., Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, and Mathematics. The STEM NER system reported in the publication above was applied on these articles. An automatically extracted dataset of 4 typed entities, viz., Process, Method, Material, and Data was created.

What this repository contains?

Aggregated lists of Process, Method, Material, and Data entities with respective occurrence counts extracted from 59,984 scholarly publications organized per the 10 STEM domains considered.

Additionally, the list of Elsevier CC-BY articles used in this study are provided in the raw-data directory of the repository.

Useful Links

• https://github.com/elsevierlabs/OA-STM-Corpus/
• https://orkg.org/orkg

A Large-scale Dataset of STEM Science as PROCESS, METHOD, MATERIAL, and DATA Named Entities This repository hosts data as a follow-up study to the following publications D'Souza, J., Hoppe, A., Brack, A., Jaradeh, M., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 2192–2203). European Language Resources Association. Brack, A., D’Souza, J., Hoppe, A., Auer, S., Ewerth, R. (2020). Domain-Independent Extraction of Scientific Concepts from Research Articles. In: , et al. Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. Springer, Cham. https://doi.org/10.1007/978-3-030-45439-5_17 Supporting dataset link https://data.uni-hannover.de/dataset/stem-ecr-v1-0 Description Roughly 60,000 titles and abstracts of scholarly articles with the CC-BY redistributable license were downloaded from Elsevier. The articles spanned 10 STEM domains which were the most prolific on Elsevier viz., Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, and Mathematics. The STEM NER system reported in the publication above was applied on these articles. An automatically extracted dataset of 4 typed entities, viz., Process, Method, Material, and Data was created. What this repository contains?

Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources

The STEM ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises annotations for scientific entities in scientific Abstracts drawn from 10 disciplines in Science, Technology, Engineering, and Medicine. The annotated entities are further grounded to Wikipedia and Wiktionary, respectively.

What this repository contains?

The dataset is organized in the following folders:

• Scientific Entity Annotations: Contains annotations for Process, Material, Method, and Data scientific entities in the STEM dataset.
• Scientific Entity Resolution: Annotations for the STEM dataset scientific entities with Entity Linking (EL) annotations to Wikipedia and Word Sense Disambiguation (WSD) annotations to Wiktionary.

Annotation Guidelines

The annotation guidelines that supported the creation of this corpus can be found here.

Supporting Publication

D'Souza, J., Hoppe, A., Brack, A., Jaradeh, M., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 2192–2203). European Language Resources Association.

Useful Links

• https://github.com/elsevierlabs/OA-STM-Corpus/
• https://brat.nlplab.org/
• https://dumps.wikimedia.org/enwiki/20190920/
• https://dumps.wikimedia.org/enwiktionary/20190920/
• https://dkpro.github.io/dkpro-jwpl/
• https://dkpro.github.io/dkpro-jwktl/
• https://orkg.org/orkg

🔬 EAI-Taxonomy STEM w/ DCLM

🏆 Website | 🖥️ Code | 📖 Paper A high-quality STEM dataset curated from web data using taxonomy-based filtering, containing 1742 billion tokens of science, technology, engineering, and mathematics content.

  🎯 Dataset Overview

This dataset is part of the Essential-Web project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional STEM datasets that require… See the full description on the dataset page: https://huggingface.co/datasets/EssentialAI/eai-taxonomy-stem-w-dclm.

The lack of diversity in Science, Technology, Engineering, and Mathematics (STEM) is a significant issue for the sector. Many organisations and educators have identified lack of representation of historically marginalised groups within teaching materials as a potential barrier to students feeling that a Science, Technology, Engineering, and Mathematics (STEM) career is something that they can aspire to. A key barrier to addressing the issue is providing accessible and effective evidence-based approaches for educators to implement. In this study, we explore the potential for adapting presentation slides within lectures to ‘humanise’ the scientists involved, presenting their full names and photographs alongside a Harvard style reference. The intervention stems from an initial assumption that many formal scientific referencing systems are demographic-neutral and exacerbate prevailing perceptions that STEM is not diverse. We adopt a questionnaire based methodology surveying 161 bioscience undergraduates and postgraduates at a UK civic university. We first establish that students project assumptions about the gender, location, and ethnicity of the author of a hypothetical reference, with over 50% of students assuming they are male and Western. We then explore what students think of the humanised slide design, concluding that many students see it as good pedagogical practice with some students positively changing their perceptions about diversity in science. We were unable to compare responses by participant ethnic group, but find preliminary evidence that female and non-binary students are more likely to see this as good pedagogical practice, perhaps reflecting white male fragility in being exposed to initiatives designed to highlight diversity. We conclude that humanised powerpoint slides are a potentially effective tool to highlight diversity of scientists within existing research-led teaching, but highlight that this is only a small intervention that needs to sit alongside more substantive work to address the lack of diversity in STEM.

This proposal adds original approaches to the currently scarce body of practical evidence on the application of STEM innovations in the curriculum. A teaching-learning program was designed in a real-world context such as the game of soccer with a STEM (Science, Technology, Engineering and Mathematics) approach through a cooperative problem-solving methodology. The objectives of the research focus on analyzing the effect of the use of this STEM unit on the academic performance of students, taking into account the gender variable; and their appreciation of the activities and methodology used, as well as the challenges encountered and their solutions. The intervention was implemented in the 4th year of Compulsory Secondary Education in a school in Spain with 36 students (24 girls and 12 boys). Academic performance was analyzed taking into account the gender variable, for which a quasi-experimental design was applied before and after with a control group. The appreciation and interest of the experimental group regarding the methodology used as well as the difficulties that arose were studied. As a result, there is an improvement in the academic performance, which is more evident in girls. The methodology has been valued positively and the greatest difficulties refer to the distribution of roles and understanding and carrying out the activities, however, these difficulties were resolved with the help of classmates and the teacher.

Output, citations, h-index, reach, spread and citation timing for Institute of Tissue Engineering and Stem Cells, Capital Medical University and Animal Science, University of Ilorin, each measure with its unit.

About ArXiv For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth.

In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, we present a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more.

Our hope is to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.

ArXiv is a collaboratively funded, community-supported resource founded by **Paul Ginsparg **in 1991 and maintained and operated by Cornell University.

ArXiv On Kaggle Metadata This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. This file contains an entry for each paper, containing:

id: ArXiv ID (can be used to access the paper, see below) submitter: Who submitted the paper authors: Authors of the paper title: Title of the paper comments: Additional info, such as number of pages and figures journal-ref: Information about the journal the paper was published in doi: https://www.doi.org abstract: The abstract of the paper categories: Categories / tags in the ArXiv system versions: A version history

SummarySESTAT is the Scientists and Engineers Statistical Data System. SESTAT was established in 1993 and comprised of three workforce surveys from the National Center for Science and Engineering Statistics within the U.S. National Science Foundation. This integrated data system is a unique source of longitudinal information on the education and employment of the college-educated U.S. science, technology, engineering, and mathematics (STEM) workforce. It was integrated through 2013.The Scientists and Engineers Statistical Data System (SESTAT) comprises three demographic surveys of scientists and engineers sponsored by sponsored by the National Center for Science and Engineering Statistics (NCSES) within the U.S. National Science Foundation (NSF): the National Survey of College Graduates (NSCG), the National Survey of Recent College Graduates (NSRCG), and the Survey of Doctorate Recipients (SDR). The three component surveys used similar questionnaires, survey reference dates, data collection periods, and data-processing procedures to facilitate integration for SESTAT. The three surveys were designed to provide maximum coverage of the target population—namely, scientists and engineers—with special emphasis given to relatively rare populations (e.g., doctorate recipients, recent graduates, and minorities). Overall, SESTAT provides a comprehensive picture of the number and characteristics of individuals in the United States with a bachelor's or higher-level degree and their employment, with a focus on those having science and engineering (S&E) degrees or working in S&E occupations. In the 2000s, this definition was expanded to include S&E-related degrees and occupations.Background. Since 1993, SESTAT has provided a unique source of information on the education and employment of the college-educated U.S. S&E workforce by integrating three surveys: NSCG, NSRCG, and SDR. The establishment of SESTAT design was based on recommendations from a 1989 CNSTAT panel study report, Surveying the Nation's Scientists and Engineers—A Data System for the 1990s. This CNSTAT recommendation encouraged NSF to target the population of college graduates trained in S&E fields and those with employment in S&E occupations, to conduct the postcensal survey of college graduates, to conduct a survey for new graduating bachelor's or master's degree recipients, and to continue to support the ongoing SDR. The 2010 survey cycle introduced the first redesign of SESTAT during its nearly 20 years of existence.

Publication speed, impact, uncited share, self-citation, retractions and scale for STEM Fellowship Journal and Ho Chi Minh City Open University Journal of Science - Engineering and Technology, each metric with its unit and which direction is better.

This proposal adds original approaches to the currently scarce body of practical evidence on the application of STEM innovations in the curriculum. A teaching-learning program was designed in a real-world context such as the game of soccer with a STEM (Science, Technology, Engineering and Mathematics) approach through a cooperative problem-solving methodology. The objectives of the research focus on analyzing the effect of the use of this STEM unit on the academic performance of students, taking into account the gender variable; and their appreciation of the activities and methodology used, as well as the challenges encountered and their solutions. The intervention was implemented in the 4th year of Compulsory Secondary Education in a school in Spain with 36 students (24 girls and 12 boys). Academic performance was analyzed taking into account the gender variable, for which a quasi-experimental design was applied before and after with a control group. The appreciation and interest of the experimental group regarding the methodology used as well as the difficulties that arose were studied. As a result, there is an improvement in the academic performance, which is more evident in girls. The methodology has been valued positively and the greatest difficulties refer to the distribution of roles and understanding and carrying out the activities, however, these difficulties were resolved with the help of classmates and the teacher.

Output, citations, h-index, reach, spread and citation timing for Institute of Environmental Science and Engineering, Nanyang Technological University and Center for Stem Cell Biology and Regenerative Medicine, Thomas Jefferson University, each measure with its unit.

This proposal adds original approaches to the currently scarce body of practical evidence on the application of STEM innovations in the curriculum. A teaching-learning program was designed in a real-world context such as the game of soccer with a STEM (Science, Technology, Engineering and Mathematics) approach through a cooperative problem-solving methodology. The objectives of the research focus on analyzing the effect of the use of this STEM unit on the academic performance of students, taking into account the gender variable; and their appreciation of the activities and methodology used, as well as the challenges encountered and their solutions. The intervention was implemented in the 4th year of Compulsory Secondary Education in a school in Spain with 36 students (24 girls and 12 boys). Academic performance was analyzed taking into account the gender variable, for which a quasi-experimental design was applied before and after with a control group. The appreciation and interest of the experimental group regarding the methodology used as well as the difficulties that arose were studied. As a result, there is an improvement in the academic performance, which is more evident in girls. The methodology has been valued positively and the greatest difficulties refer to the distribution of roles and understanding and carrying out the activities, however, these difficulties were resolved with the help of classmates and the teacher.

The dataset in this study focuses on the burden of mental health illness, specifically anxiety and depression, across 149 countries over a 26-year period. The research combines national-level data on mental health prevalence (NAP) sourced from the World Health Organization (WHO), which tracks global mental illness data. To analyze the potential drivers of mental health outcomes, the study includes independent variables related to knowledge creation and transfer, such as publication data from Clarivate's Journal Citation Report on science, technology, engineering, mathematics, arts, and humanities. Additionally, it incorporates control variables from organizations like SIPRI, the World Bank, and the Heritage Foundation, which provide macro-level data on population size, economic freedom, internet penetration, R&D investment, political corruption, military spending, economic status, and literacy rates. The dataset, which integrates these variables, helps reduce the limitations found in previous studies regarding sample selection and regional focus, offering a comprehensive framework for understanding the complex relationships between mental health and societal factors.

Tertiary graduates in STEM fields per 1,000 residents aged 20-29. Extracted from Eurostat on 2026-09-17 with filters freq=A, unit=P_THAB, isced11=ED5-8, sex=T, M, F, time=2024. Dataset last updated by the source on 2026-07-17.

This paper presents the design and validation process of a set of instruments to evaluate the impact of an informal learning initiative to promote Science, Technology, Engineering, and Mathematics (STEM) vocations in students, their families (parents), and teachers. The proposed set of instruments, beyond assessing the satisfaction of the public involved, allow collecting data to evaluate the impact in terms of changes in the consideration of the role of women in STEM areas and STEM vocations. The procedure followed to develop the set of instruments consisted of two phases. In the first phase, a preliminary version (v1) of the questionnaires was designed based on the objectives of the Girls4STEM initiative, an inclusive project promoting STEM vocations between 6 and 18 years old boys and girls. Five specific questionnaires were designed, one for the families (post activity), two for the students (pre and post activity) and two for the teachers (pre and post avitivity). A refined version (v2) of each questionnaire was obtained with evidence of content validity after undergoing an expert judgment process. The second phase was the refinement of the (v2) instruments, to ascertain the evidence of reliability and validity so that a final version (v3) was derived. In the paper, a high-quality set of good practices focused on promoting diversity and gender equality in the STEM sector are presented from a Higher Education Institution perspective, the University of Valencia. The main contribution of this work is the achievement of a set of instruments, rigorously designed for the evaluation of the implementation and effectiveness of a STEM promoting program, with sufficient validity evidence. Moreover, the proposed instruments can be a reference for the evaluation of other projects aimed at diversifying the STEM sector.