1. Reference Year: 2023/2024 2. The Department of Education’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

Land cover and land use are highly visible indicators of climate change and human disruption to natural processes. While land cover is frequently monitored over a large area using satellite data, ground-based reference data is valuable as a comparison point. The NASA-funded GLOBE Observer (GO) program provides volunteer-collected land cover photos tagged with location, date and time, and, in some cases, land cover type. When making a full land cover observation, volunteers take six photos of the site, one facing north, south, east, and west (N-S-E-W), respectively, one pointing straight up to capture canopy and sky, and one pointing down to document ground cover. Together, the photos document a 100-meter square of land. Volunteers may then optionally tag each N-S-E-W photo with the land cover types present. Volunteers collect the data through a smartphone app, also called GLOBE Observer, resulting in consistent data. While land cover data collected through GLOBE Observer is ongoing, this paper presents the results of a data challenge held between June 1 and October 15, 2019. Called “GO on a Trail,” the challenge resulted in more than 3,300 land cover data points from around the world with concentrated data collection in the United States and Australia. GLOBE Observer collections can serve as reference data, complementing satellite imagery for the improvement and verification of broad land cover maps. Continued collection using this protocol will build a database documenting climate-related land cover and land use change into the future.

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.

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?

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

Black scientists are major contributors to the advancement of Science, Technology, Engineering, and Mathematics (STEM). Yet, most of us know very little about these accomplishments. Here, we provide the first volume of the Atlas of Black Scholarship (A.B.S.) for inclusion to help science educators in the Life Sciences and Chemistry integrate the work of Black scientists into their curricula.

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.

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

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.

Polymer engineering and science CiteScore 2025-2026 - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

Fostering equity in undergraduate science, technology, engineering, and mathematics (STEM) programs can be accomplished by incorporating learner-centered pedagogies, resulting in the closing of opportunity gaps (defined in this research as the difference in grades earned by minoritized and non-minoritized students). We assessed STEM courses that exhibit small and large opportunity gaps at a minority-serving, research-intensive university, and evaluated the degree to which their syllabi are learner-centered, according to a previously validated rubric. We specifically chose syllabi as they are often the first interaction a student has with a course and can serve to establish expectations for course policies and practices. We found that STEM courses with more learner-centered syllabi had smaller opportunity gaps. The syllabus rubric factor that most correlated with smaller opportunity gaps was Power and Control, which reflects the Student's Role, Outside Resources, and Syllabus Focus. This..., This dataset is composed of rubric scores for 50 course syllabi of STEM classes in a research-intensive university with a large population of minoritized students as well as some institutional data (here defined as African-American, Latinx, Pacific Islander, and American Indian). We wanted to examine the relationship between racial grade gaps (here labeled as opportunity gaps) and the degree of learner-centeredness of the syllabi since course syllabi are good representations of classroom pedagogy according to the previous literature. The 50 syllabi were evaluated with a previously validated and peer-reviewed rubric designed by Cullen and Harris in 2009 and published in Assessment and Evaluation in Higher Education journal. The rubric measures the degree of learner-centeredness of syllabi. It has 13 items categorized under 3 factors plus the number of pages of the syllabi. We have modified the rubric to be on a 5-point scale (0-4). Zero represents the lowest degree of learner-centerednes..., , # How syllabi relate to outcomes in higher education: An evaluation of syllabi learner-centeredness and grade inequities in STEM

https://doi.org/10.7280/D1NH6N

DATA-SPECIFIC INFORMATION

Eslami_2022_How_Syllabi_Relate_to_Outcomes_in_Higher_Education.csv

Number of variables: 26

Number of cases/rows: 50

Variable names with descriptions and/or their values in parenthesis:

small_opportunity_gap; delta_GP (average grade point difference between minoritized and non-minoritized students in a STEM course); additional_item_Length_of_Syllabus (number of pages for each syllabus);

For the definition of rubric items, refer to the rubric designed by Cullen & Harris in 2009 published in Assessment and Evaluation in Higher Education journal; Rubric items are scored on a 5-point scale (0, 1, 2, 3, and 4): rubric_item_Accessibility_of_Teacher; rubric_item_Learning_Rationale; rubric_item_Collaboration; rubric_item_Teachers_Role; rubric_item_Stud...

✅ Polymer engineering and science ISSN - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

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.

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.

The dataset is freely available via Google Cloud Storage buckets (more info here). Stay tuned for weekly updates to the dataset!

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

The release of this dataset was featured further in a Kaggle blog post here.

https://storage.googleapis.com/kaggle-public-downloads/arXiv.JPG" alt="">

See here for more information.

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

You can access each paper directly on ArXiv using these links: - https://arxiv.org/abs/{id}: Page for this paper including its abstract and further links - https://arxiv.org/pdf/{id}: Direct link to download the PDF

Bulk access

The full set of PDFs is available for free in the GCS bucket gs://arxiv-dataset or through Google API (json documentation and xml documentation).

You can use for example gsutil to download the data to your local machine. ```

List files:

gsutil cp gs://arxiv-dataset/arxiv/

Download pdfs from March 2020:

gsutil cp gs://arxiv-dataset/arxiv/arxiv/pdf/2003/ ./a_local_directory/

Download all the source files

gsutil cp -r gs://arxiv-dataset/arxiv/ ./a_local_directory/ ```

Update Frequency

We're automatically updating the metadata as well as the GCS bucket on a weekly basis.

License

Creative Commons CC0 1.0 Universal Public Domain Dedication applies to the metadata in this dataset. See https://arxiv.org/help/license for further details and licensing on individual papers.

Acknowledgements

The original data is maintained by ArXiv, huge thanks to the team for building and maintaining this dataset.

We're using https://github.com/mattbierbaum/arxiv-public-datasets to pull the original data, thanks to Matt Bierbaum for providing this tool.

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.

Polymer engineering and science Abbreviation ISO4 - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

Abstract:This dataset presents survey responses from first-year engineering students on their use of ChatGPT and other AI tools in a project-based learning environment. Collected as part of a study on AI’s role in engineering education, the data captures key insights into how students utilize ChatGPT for coding assistance, conceptual understanding, and collaborative work. The dataset includes responses on frequency of AI usage, perceived benefits and challenges, ethical concerns, and the impact of AI on learning outcomes and problem-solving skills.With AI increasingly integrated into education, this dataset provides valuable empirical evidence for researchers, educators, and policymakers interested in AI-assisted learning, STEM education, and academic integrity. It enables further analysis of student perceptions, responsible AI use, and the evolving role of generative AI in higher education.By making this dataset publicly available, we aim to support future research on AI literacy, pedagogy, and best practices for integrating AI into engineering and science curricula..................................................................................................................................................................Related PublicationThis dataset supports the findings presented in the following peer-reviewed article:ChatGPT in Engineering Education: A Breakthrough or a Challenge?Davood KhodadadPublished: 7 May 2025 | Physics Education, Volume 60, Number 4© 2025 The Author(s). Published by IOP Publishing LtdCitation: Davood Khodadad 2025 Phys. Educ. 60 045006DOI: 10.1088/1361-6552/add073If you use or reference this dataset, please consider citing the above publication......................................................................................................................................................................Description of the data and file structureTitle: ChatGPT in Engineering Education: Survey Data on AI Usage, Learning Impact, and CollaborationDescription of Data Collection:This dataset was collected through a survey distributed via the Canvas learning platform following the completion of group projects in an introductory engineering course. The survey aimed to investigate how students engaged with ChatGPT and other AI tools in a project-based learning environment, particularly in relation to coding, report writing, idea generation, and collaboration.The survey consisted of 15 questions:12 multiple-choice questions to capture quantitative insights on AI usage patterns, frequency, and perceived benefits.3 open-ended questions to collect qualitative perspectives on challenges, ethical concerns, and students' reflections on AI-assisted learning.Key areas assessed in the survey include:Students’ prior familiarity with AI tools before the course.Frequency and purpose of ChatGPT usage (e.g., coding assistance, conceptual learning, collaboration).Perceived benefits and limitations of using AI tools in an engineering learning environment.Ethical considerations, including concerns about over-reliance and academic integrity.The dataset provides valuable empirical insights into the evolving role of AI in STEM education and can support further research on AI-assisted learning, responsible AI usage, and best practices for integrating AI tools in engineering education.

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.

The High School Longitudinal Study of 2009 (HSLS:09) is a nationally representative, longitudinal study of more than 21,000 9th graders in 944 schools who will be followed through their secondary and postsecondary years. The study focuses on understanding students' trajectories from the beginning of high school into postsecondary education, the workforce, and beyond. What students decide to pursue when, why, and how are crucial questions for HSLS:09, especially, but not solely, in regards to science, technology, engineering, and math (STEM) courses, majors, and careers.High School Longitudinal Study of 2009 (HSLS:09) Features Nationally representative, longitudinal study of 23,000+ 9th graders from 944 schools in 2009, with a first follow-up in 2012 and a second follow-up in 2016Students followed throughout secondary and postsecondary yearsSurveys of students, their parents, math and science teachers, school administrators, and school counselorsA new student assessment in algebraic skills, reasoning, and problem solving for 9th and 11th grades10 state representative datasets HSLS:09 Data Collection Waves Base Year (2009)First Follow-up (2012)2013 Update (2013)High School Transcripts (2013-2014)Second Follow-up (2016)Postsecondary Transcripts (2017-18)