📘 Description

This dataset has been created to support research in knowledge management, information retrieval, and user-centered services in academic libraries. It contains search queries in English and Chinese, along with metadata about user preferences, retrieved resources, and relevance feedback.

The dataset reflects realistic library search interactions, where students, researchers, and faculty members look for academic resources and provide feedback on the relevance of search results. It is designed to help evaluate and improve information retrieval efficiency, personalized recommendations, and user satisfaction in modern academic libraries.

📊 Dataset Structure

Query_ID – Unique identifier for each query

Language – English or Chinese

User_Query – Original search query from the user

Preprocessed_Query – Cleaned & normalized query

Named_Entities – Extracted keywords and entities from the query

Sense_Disambiguation – Disambiguated meanings for ambiguous terms

Retrieved_Docs – List of system-retrieved document IDs

Relevant_Docs – Ground-truth relevant documents

User_Preference – User’s domain preference (e.g., AI, Medicine, Education)

User_Feedback – Relevance feedback (Relevant / Irrelevant / Partially Relevant)

Precision, Recall, F1_Score – Retrieval performance metrics

🌍 Potential Applications

Digital library research

Bilingual information retrieval (English & Chinese)

Academic search system evaluation

Personalized resource recommendation

User-centered knowledge management studies

Bibliographic databases are essential research tools. In medicine, key databases are MEDLINE/PubMed, Embase, and Cochrane Central (MEC). In education, the Education Resource Information Center (ERIC) is a major database. Medical education, situated between medicine and education, has no dedicated database of its own. Many medical education researchers use MEC, some use ERIC and some do not. We performed a descriptive analysis using search strategies to retrieve medical education references from MEC and ERIC. ERIC references which were duplicates with MEC references were removed. Unique ERIC references were tallied. Between 1977 and 2022, MEC has 359,354 unique references relevant to medical education. ERIC provided 3925 unique references for the same period, all of which would be missed by searching only MEC. The mean unique ERIC medical education references per year for all 46 years is 85 (SD = ±29), or 119 (SD = ±15) for the last 10 years from 2013 to 2022. ERIC consistently offered a small yet significant number of unique references relevant to medical education for decades. We recommend the use of ERIC for medical education research when comprehensive literature searches are required, such as in systematic reviews, scoping reviews, evidence synthesis, or guideline development.

Abstract: Introduction: The growing importance given to research training in medical education globally is notorious and reflects the growing scientific production on this topic. In this scenario, the state of the question (EQ) emerges as a bibliographic production tool that promotes an overview of the re-search produced on a given topic, being able to provide a closer approach to the research object and to estimate the possible advances that research in that field can reach. Objective: This study aimed to identify what has been studied about research training in medical education and classify them considering the location, type, and intentions of the research. Method: This is a EQ whose object of study is research training in medical education. Descriptors related to research training and medical education in Portuguese, Spanish, and English were used. Two databases for national dissertations and theses (Biblioteca Digital de Teses e Dissertações and Catálogos de Teses e Dissertações da Capes) and two databases for international scientific journals (Portal de Periódicos da Capes and PubMed) were searched. Result: We identified 33 scientific articles from 18 countries on all continents. Four articles (12%) were presented as opinion articles where the authors theorized about the importance of inserting research elements at the curricular and disciplinary level. Seven articles (21%) portrayed, through case reports, the structural changes and the paths traced in their institutions, as well as the implications of these actions in medical education and the educational process. The remaining 22 articles (67%) documented empirical research with different intentions, with the most recurrent theme being the identification of factors that influence the student body’s search for participation in research. Conclusion: Research training in medical education has a body of qualified researchers and spe-cialized periodicals for its dissemination on a global level. We emphasize that no studies were found in the context of theses and dissertations, making this an open field for researchers to deepen and accumulate knowledge.

This dataset documents an experiment in which one research question — how AI-based systems are used in scholarly literature search, and with what implications for research, higher education and university libraries — was searched in two ways: with a Boolean strategy across seven bibliographic databases (Scopus, Web of Science, LISTA, LISA, ERIC, ACM Digital Library, Academic Search Complete), and with four AI search tools that retrieve before answering (Claude Pro, Microsoft Copilot, Keenious, Scopus AI) under a three-tier prompt protocol. Two further conditions used the Consensus index (through an MCP connector and through its own interface). A control ran the same prompts on OpenAI GPT-5.1 without retrieval access.

All 1,584 unique records are included, each checked for whether the cited work exists and (for those that did) graded 0–5 for relevance by a single grader. The package contains the master record table, three readable lists (CSV, RIS, Excel), the identifier and existence checks, the raw exports of the AI conditions (bibliographic fields only), verbatim transcripts and prompts, a description of the models and tools used, the review protocol, and scripts that regenerate every figure in the accompanying master data document. The file README.md provides a fuller introduction to the materials, so please start there.

Please note, that a large part of the work of building and checking this package was done with generative AI (Anthropic's Claude): the scripts, deduplication and matching, most identifier and existence checks, and the documentation. All relevance grades and all decisions are the author's own. README.md, section "How this package was made", states what was done by AI, what by hand, and what that means for any error a user may find.

The experiment is part of the scoping review (to be published separately later) "Searching with AI: Practices and Implications for Scholarly Discovery and University Libraries" (protocol registration:
https://doi.org/10.17605/OSF.IO/AKZ7R). The Boolean database searches were run by the information specialists of the scoping review team: Minna Suikka (Scopus), Anne Ojanen (Web of Science), Sanna Raninen (LISA), Päivi Helminen (ERIC), Katri Larmo (ACM Digital Library) and Rosa Venäläinen (Academic Search Complete). Warm thanks to all of them.

IFLA stands for The International Federation of Library Associations and Institutions. The IFLA World Library and Information Congress 2016 and 2nd IFLA General Conference and Assembly, ‘Connections. Collaboration. Community’ took place 13–19 August 2016 at the Greater Columbus Convention Center (GCCC) in Columbus, Ohio, United States. The official hashtag of the conference was #WLIC2016.This spreadsheet contains the results of a text analysis of 22327 Tweets publicly labeled with #WLIC2016 between Sunday 14 and Thursday 18 August 2015. The collection of the source dataset was made with a Twitter Archiving Google Spreadsheet and the automated text analysis was done with the Terms tool from Voyant Tools. The spreadsheet contains:A sheet containing a table summarising the source archive A sheet containing a table detailing tweet counts per day. Sheets containing the 'raw' (no stop words, no manual refining) tables of top 300 most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).Sheets containing the 'edited' (edited English stop word filter applied, manually refined) tables of top 50 Most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).A sheet containing a comparison table of the top 50 per day.Other ConsiderationsOnly Tweets published by accounts with at least one follower were included in the source archive.Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (González-Bailon, Sandra, et al, 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that each and every Tweet tagged with #WLIC2016 during the indicated period was analysed. The dataset was shared for archival, comparative and indicative educational research purposes only.Only content from public accounts, obtained from the Twitter Search API, was analysed. The source data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.This file contains the results of analyses of Tweets that were published openly on the Web with the queried hashtag; the source Tweets are not included. The content of the source Tweets is responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. This work is shared to archive, document and encourage open educational research into scholarly activity on Twitter. The resulting dataset does not contain complete Tweets nor Twitter metadata. No private personal information was shared. The collection, analysis and sharing of the data has been enabled and allowed by Twitter's Privacy Policy. The sharing of the results complies with Twitter's Developer Rules of the Road. A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). Tweets published publicly by scholars or other professionals during academic conferences are often publicly tagged (labeled) with a hashtag dedicated to the conference in question. This practice used to be the confined to a few 'niche' fields; it is increasingly becoming the norm rather than the exception. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association and the American Pyschological Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual Tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. Though this work has limitations and might not be thoroughly systematic, it is hoped it can contribute to developing new insights into a discipline's public concerns as expressed on Twitter over time.As it is increasingly recommended for data sharing, the CC-0 license has been applied to the resulting output in the repository. It is important however to bear in mind that some terms appearing in the dataset might be licensed individually differently; copyright of the source Tweets -and sometimes of individual terms- belongs to their authors. Authorial/curatorial/collection work has been performed on the shared file as a curated dataset resulting from analysis, in order to make it available as part of the scholarly record. If this dataset is consulted attribution is always welcome.Ideally for proper reproducibility and to encourage other studies the whole archive dataset should be available. Those wishing to obtain the whole Tweets should still be able to get them themselves via text and data mining methods.

Code and derived outputs for the article "Mapping Five Decades of Life Skills Education Research in Compulsory Schooling: A Dual-Database Bibliometric and Science Mapping Analysis (1977-2026)". This is the open half of a two-record deposit. It holds the complete processing pipeline and every output the article draws on, so that each number, table and figure can be traced to the code that produced it. The raw Web of Science and Scopus exports the pipeline consumes are deposited separately, at https://doi.org/10.5281/zenodo.23439446, under restricted access: the terms of the database subscriptions under which they were retrieved do not permit redistribution, and that record releases them on request. CONTENTS code/ — the processing pipeline, fourteen steps, run end to end by code/run_all.sh derived/ — every intermediate and final output the manuscript draws on, as JSON, CSV and GraphML out/figures/ — the five figures at 400 dpi out/validation_coded.xlsx — the author's coding of the 300-record validation sample, made blind to the machine decision Supplement_S1_turkish_terminology.csv — the Turkish-terminology sensitivity search response_letter.md — the point-by-point response to reviewers README.md — contents, provenance, and the errors found and corrected during preparation LICENSE — MIT, for the code Running the deposited pipeline against the raw exports reproduces every derived output file and all five figures byte for byte. This was verified by unpacking the archive into a clean directory and re-running it. THE STUDY IN NUMBERS Searches run 16 October 2026 on Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, ESCI) and Scopus. 8,051 records carried forward, 2,834 indexed in both databases, 5,200 screened after deduplication. A positive compulsory-schooling criterion retained 2,071 records; the permissive corpus that adds back a withdrawn single-cue rule is 2,737. The screen was validated against the author's own coding of a random sample of 300 records, made blind to the machine decision: kappa .646, 95% CI [.558, .735], agreement .830. The co-word network has 54 nodes, 80 edges and 7 communities at modularity .698; sensitivity to the resolution parameter, the keyword threshold and the edge threshold is reported for every setting. 87,298 cited-reference instances resolve to 70,446 distinct references, 43.5% of them identifier-keyed. REPRODUCIBILITY python3 -m pip install networkx matplotlib openpyxl python-docx bash code/run_all.sh Fourteen steps, about a minute, with the raw exports restored to data/. The script fixes PYTHONHASHSEED as well as the algorithm seed, because Python randomises string hashing per process and Louvain is sensitive to node insertion order. No step contacts a network service. The only values not derived from the exports are the two pre-limit record totals at the identification step, which the exports cannot carry because they are already document-type filtered; they come from the deposited search log and the code says so. Errors found and corrected during preparation are listed in README.md, because a pipeline whose errors are not stated should not be trusted. LICENCE Code: MIT. Derived outputs and figures: CC BY 4.0.

Project and Data Collection Overview This is a collection of policy and procedure documents collected from public, land-grant institutions that were part of dissertation research ("Institutional Counter-surveillance using a Critical Disability Studies Lens"). They were found using an online search protocol that focused on Disability and accessibility-related documents, as well as those geared toward general university audiences. These documents are organized by institution and year. Data Overview This data project consists of 29 data files from Auburn University, 47 data files from The Ohio State University (OSU), 30 data files from Virginia Tech (Virginia Polytechnic Institute and State University), and 5 documentation files including the Data Narrative and an administrative README file. Shared Data Organization Given that I did not generate the documents originally created by various authors at Virginia Tech, Auburn, and OSU, I evaluated their inclusion in the QDR project based on Fair Use principles. Each document that was downloaded has its publicly available URL source included as part of the spreadsheet in this project. Factor 1: Purpose and character of use Fair use favors educational, scholarly, research uses; criticism or commentary; and non-profit/personal use. This QDR project is a proof of concept demonstrating recommendations outlined in Chapter 5 of my dissertation. It has emerged as a necessary product of my dissertation research as it provides part of the foundation for my analysis and critique of current archival practices at institutions of higher education. Factor 2: Nature of copyrighted work Fair use favors published and factual sources. The intentionality of my dissertation research focused the document search on publicly available materials generated by public institutions of higher education. While their presentation on the website (e.g., color scheme, website design and layout, use of graphics, etc.) might include creative elements, the documents themselves are, or were, openly available from the institutions in question to provide information. As these documents were from public institutions, copyright for the material does not belong to an individual; additionally, they were created using public funds for public use. Factor 3: Amount and substantiality of the portion used Fair use favors only as much as is absolutely necessary for a suitably favored “purpose” - given that the purpose of my dissertation research was to identify, find, and collect such documents, having the entire document included is necessary for my proof of concept project. Again, I also provide the source URL for each document, so anyone who wants to use this material can also make the choice to collect it for themselves. Factor 4: Effect on the potential market value for or value of the work Fair use favors work that does not impact the market for the original product. These documents have no resale value, and are in fact never sold - they are created using public funds and are intended for public use and consumption. This use will have no effect on the non-existent market value of these documents.

As the field of computing education grows and matures, bringing together computing education and higher education research becomes essential. Educational research has highlighted that how students study is crucial to their learning progress, and study behaviors have been found to play an important role in students' academic success. This data summarizes the results of a systematic literature review intended to find out what we know about the study behaviors of computing students and the role of educational design in shaping them. A taxonomy of study behaviors was developed and used to clarify and classify the definitions of study behavior, process, strategies, habits, and tactics, as well as identifying their relations to the educational context. The search resulted in 107 included papers, which were analyzed according to defined criteria and variables. Results revealed a fragmented field of research with ambiguous terminology and a tendency to focus on very specific educational contexts. Although computing education as a field is well equipped to expand the knowledge about both study behaviors and the connection to the educational context, the lack of common terminology and theories limits the impact. Finally, this review stresses that future research and practice should consider adopting a common framework to define and systematize study behavior data and contextualize the research in such a way that researchers and educators across institutional borders can compare and utilize results. The main contribution of this work is to provide a comprehensive synthesis of study behaviors in computing education, the paper also discusses the theory behind these definitions and how the field can develop in the future.

This dataset accompanies a systematic review of science field trip research. It contains the records used during the study selection process, including BibTeX exports from Rayyan after full-text screening, together with the PRISMA 2020 flow diagram. The records were identified through systematic searches in the Web of Science and Scopus databases, screened according to predefined eligibility criteria, and managed using Rayyan. The dataset is provided to support transparency, reproducibility, and reuse of the review process.

This dataset contains the bibliographic records used for the bibliometric mapping review of research on Computer Network Education covering the period 1972–2026. Records were retrieved from the Scopus and Web of Science databases using a predefined title-based search strategy. The dataset includes bibliographic information such as article titles, authors, affiliations, publication years, sources, keywords, citations, and other metadata used for bibliometric analysis. Duplicate records were identified and removed prior to analysis. The final screened dataset was used to examine publication trends, citation patterns, leading contributors, thematic structures, and collaboration patterns in Computer Network Education research. The dataset supports the reproducibility and transparency of the bibliometric analyses reported in the associated research article.

The recent technologies rise today as a tool of significant importance today, especially in the educational context. In this sense, Augmented Reality (AR) is a technology that is achieving a greater presence in educational centers in the last decade. However, Augmented Reality has not been explored in depth at the Secondary Education stage. Due to this, it is essential to analyze and concentrate the scientific research developed around this educational technology at that stage. Therefore, the aim of this research is to describe the influence that Augmented Reality shows on the motivation and academic performance of students in the Secondary Education stage. In relation to the methodology, a systematic review of the literature has been conducted using the Kitchenham protocol, where several factors have been analyzed, such as subjects, activities, and electronic implementation devices, together with the effects on motivation and student's academic performance. The Scopus and Web of Science (WoS) databases have been used to search for scientific papers, with a total of 344 investigations being analyzed between 2012 and 2022. The methodological stages considered were the formulation of research questions, the choice of data sources, search strategies, inclusion and exclusion criteria and quality assessment, and finally, data extraction and synthesis. The results obtained have shown that the use of AR in the classroom provides higher levels of motivation, reflected by factors such as attention, relevance, confidence, and satisfaction, and reflects better results in the tests carried out on the experimental groups compared to the control groups, which means an improvement in the academic performance of students. These results supply a fundamental theoretical basis, where the different teachers should be supported for the incorporation of AR in the classroom, since how this educational technology has been shown offers great opportunities. Likewise, the development of research in areas not so addressed can further clarify the generality of AR based on its influence on learning. In addition, the fields of natural sciences and logical-mathematical have been the most addressed, managing to implement their contents through object modeling. In short, this research highlights the importance of incorporating Augmented Reality into all areas and educational stages, since it is a significant improvement in the teaching and learning process.

No description is available. Visit https://dataone.org/datasets/sha256%3A7e615992408e70e05e4905eec437109e059ab9234bdd6926da9966de60c3a4db for complete metadata about this dataset.

Literature Organizer Market Size and Growth Outlook

Worldwide, the Literature Organizer market size reached USD 2.14 billion in 2024, with a robust year-on-year growth. The market is projected to expand at a CAGR of 10.8% from 2025 to 2033, reaching an estimated USD 5.36 billion by the end of the forecast period. This impressive growth is primarily driven by the increasing digitization of academic and corporate environments, a surge in remote work and learning, and the growing need for efficient knowledge management systems worldwide.

One of the primary growth factors fueling the Literature Organizer market is the rapid adoption of digital tools in academic environments. With the expansion of e-learning platforms, universities and educational institutions are increasingly relying on literature organizer software to streamline research, manage citations, and enhance collaborative learning. The shift to digital libraries and the necessity for seamless integration with other educational technologies are further propelling demand. Additionally, the proliferation of open-access journals and the exponential growth of research publications have made it imperative for students, researchers, and educators to utilize advanced literature management solutions to keep up with the vast amount of information available. This trend is expected to sustain strong market growth throughout the forecast period.

Another significant driver for the Literature Organizer market is the transformation of corporate knowledge management practices. As organizations become more data-driven and collaborative, there is a growing emphasis on centralizing and organizing reference materials, reports, and research documents. Literature organizer software enables professionals to efficiently categorize, retrieve, and share information, fostering innovation and informed decision-making. The integration of artificial intelligence and machine learning features, such as automated tagging, semantic search, and recommendation systems, is making these platforms indispensable for corporate users. Furthermore, the rise of remote and hybrid work models has accelerated the adoption of cloud-based literature organizers, enabling teams to access and collaborate on documents from anywhere in the world.

The increasing awareness of the benefits of literature organization among individuals and institutions is also contributing to market expansion. Personal users, such as students and independent researchers, are leveraging mobile applications and web-based platforms to manage their reading lists, annotate documents, and synchronize references across devices. Libraries and academic institutions are investing in comprehensive literature management solutions to enhance their service offerings and support digital scholarship. These factors, combined with continuous technological advancements and the emergence of user-friendly interfaces, are expected to drive sustained growth in the Literature Organizer market over the next decade.

Regionally, North America leads the Literature Organizer market due to its advanced educational infrastructure, high research output, and rapid adoption of digital solutions in both academic and corporate sectors. Europe follows closely, driven by strong research and development activities and widespread implementation of digital libraries. The Asia Pacific region is experiencing the fastest growth, fueled by increasing investments in education technology, expanding higher education enrollment, and a burgeoning research community. Latin America and the Middle East & Africa are gradually catching up, supported by government initiatives to digitize education and improve knowledge management capabilities. This regional diversification underscores the global relevance and expanding reach of literature organizer solutions.

In the realm of academic research, Citation Recommendation Tools have emerged as a pivotal component of literature organizer software. These tools leverage advanced algorithms to suggest relevant citations and references, streamlining the research process for students and scholars. By analyzing the content of a document and comparing it with vast databases of academic publications, citation recommendation tools can identify pertinent sources that a researcher might have overlooked. This capability

In 2025, 33 percent of users with low formal education in Norway responded that they used the internet to search or apply for jobs. Among people with medium formal education, the share amounted to 28 percent. According to the survey, 34 percent of users in Norway with a high degree of formal education searched and applied for jobs online.

Background The geographical distribution of publications as an indicator of the research productivity of individual countries, regions or institutions has become a field of interest. We investigated the geographical distribution of contributions to the two leading journals in the field of medical education, Academic Medicine and Medical Education.

   Methods
   PubMed was used to search Medline. For both journals all journal articles in each year from 1995 to 2000 were included into the study. Then the affiliation was retrieved from the affiliation field of the MEDLINE format. If this was not possible, it was obtained from the paper version of the journal.


   Results
   Academic Medicine published contributions from 25 countries between 1995 and 2000. Authors from 50 countries contributed to Medical Education in the same period of time. Authors from the USA and Canada wrote ca. 95% off all articles in Academic Medicine, whereas authors from the UK, Australia, the USA, Canada and the Netherlands were responsible for ca. 74% of all articles in Medical Education in the investigated period of time.


   Conclusions
   While many countries contributed to both journals, only a few of them were responsible for the majority of all articles.

Diversity enriches the educational experience by improving intellectual engagement, self-motivation, citizenship, cultural engagement, and academic skills like critical thinking, problem-solving, and writing for students of all races. Faculty role models from similar backgrounds are essential for students from traditionally underrepresented groups as it sends a powerful message of support, belonging, and the confidence to pursue higher education. However, in the biomedical sciences, the percentage of historically underrepresented tenure-track faculty is far lower than that of their white colleagues. For this to change, a strong strategic plan and commitment from the university are imperative. This scoping review will assess the size and scope of available peer-reviewed research literature on diversity programs that aim to increase the recruitment and retention of biomedical sciences research faculty and are implemented and evaluated at American Universities. The information provided in this scoping review will help universities identify novel, successful diversity-based approaches for recruiting and retaining biomedical science faculty that might suit their own unique academic and geographic needs and be incorporated into their diversity initiatives and policies. The review follows the Population-Concept-Context methodology for Joanna Briggs Institution Scoping Reviews. Relevant peer-reviewed studies published in English between June 1, 2012, to June 1, 2022, will be identified from the following electronic databases; MEDLINE (PubMed), Scopus (Elsevier), EMBASE (Elsevier), CINAHL (EBSCO), and ERIC (EBSCO). The search strings using the key variables “biomedical research faculty,” “recruitment/retention,” “diversity/ minority/ underrepresented, and “mentoring” will be conducted using Boolean logic. Two independent reviewers will conduct all title and abstract screening, followed by a full article screening and data extraction. Due to the possible heterogeneity of the studies, we hope to use either a narrative analysis and/or descriptive figures/tables to depict the results.

This dataset contains the bibliographic records used for a bibliometric analysis of Technology-Enhanced Hybrid Service-Learning (HSL) in Higher Education. The data were retrieved from the Scopus database and include peer-reviewed journal articles published between 2010 and 2026. The dataset was compiled using a structured search strategy that focused on keywords related to hybrid service-learning, blended service-learning, online service-learning, and e-service-learning within higher education contexts.

The dataset includes essential bibliometric information such as authors, article titles, publication years, source titles, abstracts, author keywords, affiliations, citation counts, and document identifiers. All records were exported directly from Scopus and subsequently cleaned and harmonised to ensure consistency in author names, institutional affiliations, and keyword formatting. Duplicate entries were removed during the data preparation process.

This dataset was used to conduct descriptive and network-based bibliometric analyses, including publication trend analysis, citation performance assessment, author and institutional productivity mapping, country contribution analysis, and keyword co-occurrence analysis using bibliometric software tools. The dataset supports transparency and reproducibility of the study findings and may be reused for further research on Hybrid Service-Learning, educational technology integration, and higher education pedagogy.

All data are derived from Scopus-indexed publications and are shared for academic and research purposes only.

This chapter addresses a number of crucial questions for people who wish to learn about giftedness and gifted education. How do you find reliable research on gifted children, and who does it? How can you evaluate the truth-value of claims made about giftedness and gifted education? What type of incentives confront researchers, and how do those affect the research they do? This chapter discusses the state of educational and psychological research (within which giftedness research is embedded) focusing on the human elements that can reduce the reliability and trustworthiness of research results. The causes and consequences of low-quality research are discussed, as well as methods for improving the quality and credibility of research. The chapter closes with advice on how to "read between the lines" to identify weak research findings that are unlikely to hold up.

This replication dataset supports the systematic review entitled “How do digital tools support metacognition in mathematics learning? A systematic review of research on GeoGebra and problem-solving,” published in Educational Research Review, Volume 52 (2026), Article 100827. The dataset documents the systematic review process used to identify, screen, appraise, extract, and synthesise empirical research on metacognition in GeoGebra-mediated mathematics learning. It includes database search strategies and search records from Scopus, Web of Science, ERIC, and Google Scholar; title-and-abstract and full-text screening decisions; backward and forward reference-chaining records; quality-appraisal results; study-characteristics and data-extraction matrices; thematic coding and synthesis materials; and supporting documentation for the 18 studies included in the review. The review examined metacognitive awareness, regulation, evaluation, and tool adaptation or instrumental genesis during mathematics problem-solving. Copyrighted full texts of the reviewed publications are not included in the dataset.

IntroductionIn a global landscape characterized by intense competition and stringent funding criteria, researchers face the dual challenges of limited resources and high demand for innovation—a challenge that Brazil is no exception to. This study aimed to explore the perceptions, barriers, and challenges faced by researchers during the project submission process for approval by funding agencies, with a focus on schools within the Federal Network of Professional, Scientific, and Technological Education Institutions.MethodsA quantitative cross-sectional approach was used to examine the characteristics of researchers at a Brazilian institution in 2023. The sample comprised eighty three researchers who completed an online questionnaire containing eighty three questions on demographic characteristics, factors associated with project submission and approval, and reasons for non-submission or non-approval. The data were analyzed using descriptive statistics, including the Kolmogorov—Smirnov, Pearson's chi-square, and Mann—Whitney U-tests, followed by post hoc analysis and Yates' correction. Logistic regression was applied using the backward elimination method, and significant parameters (p < 0.20) free from multicollinearity were selected.ResultsThis study revealed that most researchers were men (61.4%) with doctoral degrees (91.6%), highlighted the critical role of proposal clarity and relevance in the project evaluation process. Gender (p = 0.011) and academic level (p = 0.025) were significant factors influencing project submission rates, with Brazilian National Council for Scientific and Technological Development (CNPq) fellows and researchers involved in graduate programs submitting more projects. The participants identified “search for funding” and “desire to expand research impact” as their primary motivations while citing “complex funding calls” and “funding limitations” as major barriers. Additionally, age and the number of children were found to affect project approval (p ≤ 0.018), with “proposal clarity” and “researchers' experience” having been critical factors for submission approval (p ≤ 0.03).ConclusionThe study results highlighted a gender disparity, with lower participation among women, and identified key factors influencing project submission, including the search for funding, curriculum development, and structural challenges. Additionally, the findings suggest the adoption of gender-sensitive and early-career grant criteria, targeted support for underrepresented researchers, and flexible mechanisms for those with caregiving responsibilities. These findings underscore the importance of public policies and institutional strategies in promoting equitable and inclusive funding opportunities.