Publication speed, impact, uncited share, self-citation, retractions and scale for Writing Systems Research and Perspectives on Language Learning and Education, each metric with its unit and which direction is better.

Publication speed, impact, uncited share, self-citation, retractions and scale for Reading and Writing and Journal of Research on Educational Effectiveness, each metric with its unit and which direction is better.

Purpose:This project focuses on optimizing the window size parameter for semantic classification models to identify important in-text citations within research papers. The goal is to enhance the accuracy and efficiency of citation importance detection, which aids in understanding the contextual and intent-based significance of citations in academic writing. Datasets Used: SciCite: A benchmark dataset for citation intent classification. ACL-ARC: A dataset commonly used for citation context analysis. Methodology: Multiple machine learning and deep learning models were implemented, including: Machine Learning Models: SVM, Naive Bayes, Decision Tree. Deep Learning Models: CNN, LSTM, GRU. The models were evaluated at different window sizes to determine the one that yields optimal performance. Metrics such as accuracy, precision, and recall were used to assess the models. Results: The experiments revealed that a window size of 10 produced the most accurate and reliable results across various models. This finding highlights the importance of parameter tuning in citation classification tasks. Conclusion:This project demonstrates the critical role of selecting an optimal window size in improving the performance of semantic classification models. The results provide valuable insights for future research in citation analysis and related fields. Significance:Accurately classifying important citations can: Help in evaluating the influence of research work. Improve tools for literature reviews and scientific discovery. Aid automated systems in summarizing or prioritizing references in academic writing. Repository Structure:The project repository contains the following: data/: Preprocessed datasets from SciCite and ACL-ARC. models/: Implemented machine learning and deep learning models. results/: Performance metrics and visualizations for different window sizes. scripts/: Code for training and evaluating the models

Publication speed, impact, uncited share, self-citation, retractions and scale for College Of Basic Education Research Journal and Writing Chinese, each metric with its unit and which direction is better.

Publication speed, impact, uncited share, self-citation, retractions and scale for Reading and Writing Quarterly and Journal of Research on Educational Effectiveness, each metric with its unit and which direction is better.

Writer Identification Dataset

High-quality Bangladeshi english handwriting collected from Varendra University, Rajshahi, Bangladesh for writer identification research

Short summary

This dataset contains 424 standardized RGB JPG handwriting images collected from 91 Bangladeshi university students. Images were cleaned and normalized for reproducible writer identification and document-image experiments. Some writers have fewer than five usable samples; unreadable or corrupted files were omitted during processing.

Dataset overview

• Writers: 91
• Total images: 424
• Average images per writer: ~4.66
• Image format: JPG (RGB)
• Max dimension: 1024 × 1024 (aspect-ratio preserved)
• JPEG quality: 95
• License: CC BY-NC-SA 4.0

Important note on image count

The raw collection originally targeted five samples per writer (91 × 5 = 455). During cleaning and standardization some source files were missing or unreadable; the released dataset therefore contains 424 verified, usable images. This dataset represents the cleaned, usable data rather than a forced completion of missing samples.

Contents and file layout

standardized_dataset/
├── images/        # standardized JPG images
├── metadata.csv     # per-image metadata (image_path, writer_id, writer_number, ...)
├── README.md       # dataset overview and usage notes
├── DESCRIPTION.md    # (this file) full Kaggle-ready description
├── dataset_info.json   # processing summary
└── LICENSE        # CC BY-NC-SA 4.0

Files of interest: - images/: standardized handwriting images named {writer_id}_{number:03d}.jpg. - metadata.csv: contains image_path, writer_id, writer_number, original_filename, width, height, file_size_kb.

Preprocessing and standardization

• Converted all images to RGB and saved as JPEG.
• Aspect ratio preserved; images resized using thumbnailing so neither side exceeds 1024 pixels.
• Transparency flattened onto a white background where present.
• Saved with quality=95 to balance fidelity and file size.
• Corrupted or unreadable files were skipped and logged in standardization.log.

Collection, privacy and ethics

Samples were collected from Varendra university students in Bangladesh and anonymized by numeric writer IDs. No personal names or identifiable metadata are included. Users must respect the dataset license (CC BY-NC-SA 4.0) and ensure ethical, non-commercial use. If you plan to republish derivative datasets or use the data for commercial purposes, obtain explicit permission from the dataset owner.

Suggested uses

• Writer identification (multi-class classification)
• Writer verification (pairwise or siamese networks)
• Transfer learning and fine-tuning experiments
• Feature extraction and classical machine-learning baselines
• Digital image processing coursework and lab exercises

Recommended practices for experiments

• Use writer_number from metadata.csv as the modeling label.
• For reproducible evaluations, generate or publish explicit stratified train/validation/test splits by writer (not by image) to avoid sample leakage.
• Report per-writer metrics (accuracy, precision, recall) as well as macro-averaged scores to account for imbalance.

Citation

If you use this dataset, please cite:

Handwritten Writer Identification Dataset, standardized from raw writer folders into RGB JPG format. Version 1.0, 91 writers, 424 images.

BibTeX (placeholder):

@dataset{handwriting_writer_identification_dataset,
 title    = {Handwritten Writer Identification Dataset},
 year     = {2026},
 note     = {91 writers, 424 standardized handwriting images},
 howpublished = {Kaggle dataset}
}

Contact and issues

For questions or to report issues, contact the dataset maintainer or open an issue on the dataset page. Include reproducible steps and any relevant original_filename values from metadata.csv when reporting missing or corrupted samples.

This file complements README.md and metadata.csv.

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.

Publication speed, impact, uncited share, self-citation, retractions and scale for Journal of Educational Research and Reading and Writing Quarterly, each metric with its unit and which direction is better.

This dataset includes data files and supplementary files related to the review study "The concept of competence in educational research: a knowledge map of main research traditions and topics". It consists of the following documents. Supplementary file 1: Search process. This text document outlines a) our general search strategy, presented in table format, b) the search query we used to retrieve 62 systematic competence reviews from Scopus, c) the search terms employed by those systematic reviews, presented in table format, and d) the search terms we included in our own query, presented in table format. Supplementary files 2a-aj: Retrieved documents. These data files list the 135,298 documents retrieved through the search, along with their corresponding bibliometric information. Supplementary Files 3a-b: VOSviewer map and network files (csv), generated using VOSviewer software, contain results from the direct citation network analysis. Using these files with VOSviewer, our direct citation network graph can be reconstructed in an interactive form. Supplementary File 3c: DCNA cleaned map file (xlsx) provides a list of documents by cluster (research tradition), along with their bibliometric details and related statistics calculated by VOSviewer. With this file, the data can be easily read, filtered and sorted. When we refer to the extensive library of further reading in the article, we are referring to this file. Supplementary file 4: Yearly number of documents in the main traditions of competence research (jpg/png). This supplementary figure illustrates the historical development in the number of documents within each research tradition. It was used to support the writing of the Results section. Supplementary file 5: Data preprocessing algorithm (term co-occurrence analysis). Conducting the term co-occurrence analysis separately for each research tradition presented a key challenge: VOSviewer only accepts files that strictly follow the format of standard database export files. Therefore, we needed to create a separate file for each research tradition, containing all documents assigned to that tradition and formatted to match the structure of a Scopus export file. We addressed this by developing a custom Python code that first extracted cluster assignments from the direct citation network analysis (Supplementary Files 3a-c). It then processed the full dataset (Supplementary File 2), assigning each row to cluster 1, 2, 3, 4, or none, based on matches in DOI, author name, and document title. Supplementary files 6a-l: VOSviewer results files (term co-occurrence analysis). These files are similar to Supplementary Files 3a–c, but pertain to the term co-occurrence analysis instead. For each research tradition, there are three corresponding files: map files, network files, and cleaned map files. The map files (csv) and network files (csv), generated using VOSviewer software, contain results from the term co-occurrence analysis. Using these files with VOSviewer, our term co-occurrence graphs can be recreated in an interactive form. The cleaned map files (xlsx) contain the 500 most frequently occurring terms for each research tradition, along with related statistics calculated by VOSviewer. With these files, the data can be easily read, filtered and sorted.

Writing App Market size was valued at USD 1.63 Billion in 2025 and is projected to reach USD 3.86 Billion by 2033, growing at a CAGR of 9.20% from 2027 to 2033.The growth of the Writing App Market is driven by rising demand for digital content creation across education, media, marketing, and professional communication. Increasing adoption of remote work and online learning platforms, growing use of mobile and cloud-based writing tools, and ongoing improvements in AI-assisted writing, collaboration features, and grammar support are further supporting market growth.

The development of academic writing competence in English among undergraduate philology students requires more than the acquisition of grammatical accuracy, academic vocabulary, and conventional essay organization. In university contexts, students are increasingly expected to read several academic sources, identify relevant information, evaluate and compare competing perspectives, transform source material through paraphrasing and summarization, establish intertextual relationships, and construct an independent written argument. These requirements make integrated synthesis writing a particularly important component of academic literacy. The present article develops a literature-informed methodology for improving integrated synthesis academic writing skills among philology undergraduate students through systematic work with multi-source texts. The study adopts a qualitative analytical methodology based on established research into integrated writing, source-based writing, reading-to-write processes, discourse synthesis, source use, and academic argumentation. The analysis indicates that synthesis writing should be understood as a recursive reading-writing activity rather than a linear process in which reading simply precedes writing. On this basis, the article proposes a five-stage instructional methodology comprising source awareness, source mapping and comparison, controlled synthesis, guided academic synthesis, and independent research-based writing. The methodology incorporates source matrices, comparative reading, synthesis maps, paraphrasing and transformation tasks, citation and attribution practice, argument construction, peer review, and recursive revision. A multidimensional assessment framework is also proposed, covering source comprehension, source integration, synthesis and argumentation, organization and coherence, and academic language and citation. The proposed framework is particularly relevant to philology education because students routinely engage with interconnected linguistic, literary, theoretical, and critical texts. The article provides a theoretically grounded methodological model that can subsequently be subjected to empirical validation in an undergraduate EFL context.

Publication speed, impact, uncited share, self-citation, retractions and scale for Reading and Writing Quarterly and Learning Disabilities Research and Practice, each metric with its unit and which direction is better.