Engineering Journal Impact Factor 2024-2025 - ResearchHelpDesk - Engineering Journal (Eng J) is a open-access, peer-reviewed, and bi-monthly online-published international journal for the complete coverage of all topics in engineering related areas. EJ consists of two major sections in the engineering field. Environment, Energy and Natural Resources (EJEEN) - A rapidly growing sector in engineering research including virtually all aspects of the environment, energy and natural resources fields: from agricultural systems and engineering, aquaculture and aquatic resource management, food engineering and bioprocess technology, pulp and paper technology, regional and rural development planning and urban environmental management, renewable energy such as solar power, to oil exploration technologies, superconductivity, and nuclear generation. Modern Engineering Technology (EJMET) - This section contains topics in the combined domain of engineering, technology and applied science, and focuses on solving technical problems. This section disseminates results from the applications of engineering and modern technology such as information technology, biotechnology, nanotechnology and several technologies fueling the imaginations and research budgets of scientists and engineers. Great research emphasis is placed on chemicals, material, agriculture, healthcare, disaster mitigation, transportation, telecommunications, survey, space, chips, computer hardware, computer software, entertainment and telephony. We accept original, unpublished research papers and review articles which are not being considered elsewhere. Provided that the submitted manuscript meets all our minimum requirements, the turnaround time for the first round of double-blind peer review is approximately 2 - 3 months. EJ ranks in the 2nd Quartile (Cr. Scopus) in the General Engineering subject category, and is currently indexed in: Emerging Sources Citation Index (ESCI) - (ISI) Web of Science Scopus IET Inspec Chemical Abstracts Service (CAS) Asean Citation Index (ACI) Thai-Journal Citation Index (TCI) Directory of Open Access Journals (DOAJ)

High Quality Publications (Q1): the number of publications that an institution publishes in the most influential scholarly journals of the world. These are those ranked in the first quartile (25%) in their categories as ordered by SCImago Journal Rank (SJRII) indicator (Miguel, Chinchilla-Rodríguez and Moya-Anegón, 2011; Chinchilla-Rodríguez, Miguel, and Moya-Anegón, 2015). Size-dependent indicator.

PurposeThere is a general inquisition regarding the monetary value of a research output, as a substantial amount of funding in modern academia is essentially awarded to good research presented in the form of journal articles, conferences papers, performances, compositions, exhibitions, books and book chapters etc., which, eventually leads to another question if the value varies across different disciplines. Answers to these questions will not only assist academics and researchers, but will also help higher education institutions (HEIs) make informed decisions in their administrative and research policies.Design and methodologyTo examine both the questions, we applied the United Kingdom’s recently concluded national research assessment exercise known as the Research Excellence Framework (REF) 2014 as a case study. All the data for this study is sourced from the openly available publications which arose from the digital repositories of REF’s results and HEFCE’s funding allocations.FindingsA world leading output earns between £7504 and £14,639 per year within the REF cycle, whereas an internationally excellent output earns between £1876 and £3659, varying according to their area of research. Secondly, an investigation into the impact rating of 25315 journal articles submitted in five areas of research by UK HEIs and their awarded funding revealed a linear relationship between the percentage of quartile-one journal publications and percentage of 4* outputs in Clinical Medicine, Physics and Psychology/Psychiatry/Neuroscience UoAs, and no relationship was found in the Classics and Anthropology/Development Studies UoAs, due to the fact that most publications in the latter two disciplines are not journal articles.Practical implicationsThe findings provide an indication of the monetary value of a research output, from the perspectives of government funding for research, and also what makes a good output, i.e. whether a relationship exists between good quality output and the source of its publication. The findings may also influence future REF submission strategies in HEIs and ascertain that the impact rating of the journals is not necessarily a reflection of the quality of research in every discipline, and this may have a significant influence on the future of scholarly communications in general.OriginalityAccording to the author’s knowledge, this is the first time an investigation has estimated the monetary value of a good research output.

Systematic literature review data. The review was limited to scientific journals specialized in educational technology, rated Q1 in the Journal Citations Reports (JCR) in the analyzed period (2009–2018) with a first-quartile presence percentage of 80% or higher and within the category of Education & Educational Research: Computers & Education (100%), British Journal of Educational Technology (100%) and Internet and Higher Education (80%). The sequence of filters used in SCOPUS was the following: ISSN (...) AND KEY (e-learning)) AND DOCTYPE (ar) AND PUBYEAR > 2008 AND PUBYEAR < 2019.

NeuroScape: A Curated Dataset of Neuroscientific Articles from 1999 to 2023

Description

This dataset comprises a collection of neuroscientific articles published between January 1, 1999, and December 31, 2023. The compilation includes information on articles and research domain clusters in multiple formats, including CSV, GraphML, and HDF5.

Scope and Selection Criteria

• Source Journals: The articles in this dataset were selectively retrieved from journals ranked in the first and second quartile (Q1 and Q2) in the field of neuroscience according to the SCImago Journal Rank. Additionally, articles from Q1 multidisciplinary journals such as Nature, Science, and PLOS One were included.
• Search Methodology: PubMed searches were conducted for each year using the journal name and publication year as query terms. All articles returned from these searches were initially included.
• Discipline Classification: A neural network classifier was employed to filter articles specifically related to neuroscience. Articles that did not meet the classifier's threshold were excluded.
• Non-Exhaustiveness: This dataset does not encompass all neuroscientific articles published in the given period. Articles without abstracts or key metadata were omitted, and classification errors may have led to the exclusion of some relevant publications.

Directory Structure

.
├── Code
│  ├── notebooks
│  │  ├── keyword_search.ipynb
│  │  ├── exploring_clusters.ipynb
│  │  ├── loading_article_shards.ipynb
│  │  ├── traversing_article_graph.ipynb
│  │  ├── discipline_classification.ipynb
│  │  └── from_generic_to_domain_embedding.ipynb
│  ├── requirements.txt
│  └── src
│    ├── data_types.py
│    └── utils.py
└── Data
  ├── CSV
  │  ├── neuroscience_articles_1999-2023.csv
  │  ├── neuroscience_clusters_1999-2023.csv
  │  └── neuroscience_dimensions_1999-2023.csv
  ├── Graphs
  │  ├── cluster_citation_density.graphml
  │  ├── article_similarity.graphml
  ├── HDF5
  │  ├── DomainEmbeddings
  │  │  └── 2037 shard_#SHARD_ID.h5 files containing 200 articles
  │  └── VoyageAIEmbeddings
  │    ├── Large_02_Instruct
  │    │  └── 2037 shard_#SHARD_ID.h5 files containing 200 articles
  │    └── Lite_02_Instruct
  │      └── 2037 shard_#SHARD_ID.h5 files containing 200 articles
  └── Models
    ├── discipline_classification_model.pth
    └── domain_embedding_model.pth

Code

The Code folder contains minimal example code to help users get started with the dataset. It includes:

• Jupyter Notebooks demonstrating how to work with thet data with minimal usage examples.
• Python Scripts with basic utilities for handling the dataset.

These examples provide a simple foundation for working with the dataset. More advanced analysis and demonstrations are covered in the accompanying publication.

CSV Files

Neuroscience Articles (neuroscience_articles_1999-2023.csv)

This file contains metadata on neuroscientific articles from 1999 to 2023.

Variables:

• Pmid: PubMed ID (unique identifier).
• Doi: Digital Object Identifier.
• Type: Article type (Review or Research).
• Title: Article title.
• Year: Year of publication.
• Month: Month of publication.
• Age: Age of the article as of January 3, 2025.
• Citations: Total number of citations.
• Citation Rate: Citations divided by article age.
• Cluster ID: The research cluster the article belongs to (neuroscience_clusters_1999-2023.csv).
• Journal: The journal where the article was published.
• Disciplines: Disciplines published by the journal as classified by SCImago.The article does NOT necessarily qualify for all listed disciplines.
• Abstract: The abstract of the article.

Neuroscience Clusters (neuroscience_clusters_1999-2023.csv)

Clusters of related articles based on research themes.

Variables:

• Cluster ID: Unique identifier for the cluster.
• Title: Title of the research cluster.
• Size: Number of articles in the cluster.
• Year First Article: Year of the earliest article in the cluster.
• MCR Research: Median citation rate for research articles.
• MCR Review: Median citation rate for review articles.
• Reference Krackhardt: Measure of internal vs. external references.
• Citation Krackhardt: Measure of internal vs. external citations.
• Most Cited Cluster: Cluster most frequently cited by articles in this cluster.
• Most Citing Cluster: Cluster that cites this cluster the most.
• Keywords: Keywords describing the cluster.
• Description: A summary of the research in the cluster.
• Focus: Whether the cluster is focused on content or methodology.
• Most Similar Cluster: Cluster most semantically similar to this one.
• Similarity: Cosine similarity score with the most similar cluster.
• Distinguishing Features: Key features distinguishing the cluster from its similar cluster.
• Open Questions: Outstanding research questions within the cluster.
• Dimensions: Evaluation of dimensions including appliedness, modality, spatiotemporal scale, cognitive complexity, species focus, theoretical engagement, theorey scope, methodological approach, and interdisciplinarity.
• Trends: Emerging or declining trends between Jan 2021 and December 2023.

Neuroscience Dimensions (neuroscience_dimensions_1999-2023.csv)

Provides various research dimensions assessed for each cluster. Each dimension comes with specific binarized categories.

Key Variables:

• Appliedness: Fundamental, translational, or clinical focus.
• Modality: Auditory, visual, olfactory, gustatory, somatosensory.
• Spatiotemporal Scale: Focus on molecular, cellular, system-level neuroscience.
• Cognitive Complexity: Simple vs. complex cognitive processes.
• Species: Human, non-human primate, rodent, etc.
• Theory Engagement: Data-driven vs hypothesis-driven research.
• Theory Scope: Scope of theoretical frameworks utilized by the cluster.
• Methodological Approach: Experimental, observational, computational, meta-analytic.
• Interdisciplinarity: Low to very high.

HDF5 Files

The HDF5 directory contains two sets of embeddings for the abstracts of articles. All folders contain 2037 HDF5 shard files, each holding about 200 articles (using a custom defined article filetype).

Article Datatypes:

• pmid, doi, title, type, journal, year, age, citationcount, citationrate, abstract: Corresponds directly with the CSV data.
• embedding: Text embedding of the article's abstract. There are two versions.
• out_links: List of PubMed IDs for articles in the dataset that are cited by this article (references).
• in_links: List of PubMed IDs for articles in the dataset that cite this article (citations).

Please note that abstracts of articles in the subfolders of HDF5/VoyageAIEmbeddings have been embedded using Voyage AI's voyage-lite-02-instruct and voyage-large-02-instruct models, respectively. Those in the folder HDF5/DomainEmbeddings are voyage-large-02-instructembeddings that have subsequently been further transformed into a domain-specific lower dimensional embedding using a custom neural network (domain_embedding_model.pth).

Graph-Based Data

Article Similarity Graph (article_similarity.graphml)

A graph representation of article similarity based on cosine similarity between abstract embeddings (using domain-specific embedding reuslting from domain_embedding_model.pth).

• Vertices: Each article is a node with pmid (PubMed ID) as an attribute.
• Edges: The top 50 nearest neighbor articles (by cosine similarity) form edges.
• Edge Weight: The cosine similarity score between the two articles.

Citation Density Graph (cluster_citation_density.graphml)

Represents citation relationships between research

BackgroundThe US Food and Drug Administration Amendments Act requires results from clinical trials of Food and Drug Administration–approved drugs to be posted at ClinicalTrials.gov within 1 y after trial completion. We compared the timing and completeness of results of drug trials posted at ClinicalTrials.gov and published in journals.Methods and FindingsWe searched ClinicalTrials.gov on March 27, 2012, for randomized controlled trials of drugs with posted results. For a random sample of these trials, we searched PubMed for corresponding publications. Data were extracted independently from ClinicalTrials.gov and from the published articles for trials with results both posted and published. We assessed the time to first public posting or publishing of results and compared the completeness of results posted at ClinicalTrials.gov versus published in journal articles. Completeness was defined as the reporting of all key elements, according to three experts, for the flow of participants, efficacy results, adverse events, and serious adverse events (e.g., for adverse events, reporting of the number of adverse events per arm, without restriction to statistically significant differences between arms for all randomized patients or for those who received at least one treatment dose).From the 600 trials with results posted at ClinicalTrials.gov, we randomly sampled 50% (n = 297) had no corresponding published article. For trials with both posted and published results (n = 202), the median time between primary completion date and first results publicly posted was 19 mo (first quartile = 14, third quartile = 30 mo), and the median time between primary completion date and journal publication was 21 mo (first quartile = 14, third quartile = 28 mo). Reporting was significantly more complete at ClinicalTrials.gov than in the published article for the flow of participants (64% versus 48% of trials, p

BackgroundThere exists a lack of knowledge regarding the quantity and quality of scientific yield in relation to individual cancer types. We aimed to measure the proportion, quality and relevance of oncology-related articles, and to relate this output to their associated disease burden. By incorporating the impact factor(IF) and Eigenfactor™(EF) into our analysis we also assessed the relationship between these indices and the output under study.MethodsAll publications in 2007 were retrieved for the 26 most common cancers. The top 20 journals ranked by IF and EF in general medicine and oncology, and the presence of each malignancy within these titles was analysed. Journals publishing most prolifically on each cancer were identified and their impact assessed.Principal Findings63260 (PubMed) and 126845 (WoS) entries were generated, respectively. 26 neoplasms accounted for 25% of total output from the top medical publications. 5 cancers dominated the first quartile of output in the top oncology journals; breast, prostate, lung, and intestinal cancer, and leukaemia. Journals associated with these cancers were associated with much higher IFs and EFs than those journals associated with the other cancer types under study, although these measures were not equivalent across all sub-specialties. In addition, yield on each cancer was related to its disease burden as measured by its incidence and prevalence.ConclusionsOncology enjoys disproportionate representation in the more prestigious medical journals. 5 cancers dominate yield, although this attention is justified given their associated disease burden. The commonly used IF and the recently introduced EF do not correlate in the assessment of the preeminent oncology journals, nor at the level of individual malignancies; there is a need to delineate between proxy measures of quality and the relevance of output when assessing its merit. These results raise significant questions regarding the best method of assessment of research and scientific output in the field of oncology.

GQS values represented as median (first quartile-third quartile).

Descriptive data of the videos represented as median (first quartile-third quartile).

Values of videos according to content represented as median (first quartile-third quartile).

Modified DISCERN values represented as median (first quartile-third quartile).

Comparison of assessment scores based on sources represented as median (first quartile-third quartile) values.

Significance between number of students who have skipped school in ESCS first quartile.

The risk of psoriasis in GRS-N quartiles relative to the first quartile.

Generalized linear mixed models (GLMMs) estimate fixed and random effects and are especially useful when the dependent variable is binary, ordinal, count or quantitative but not normally distributed. They are also useful when the dependent variable involves repeated measures, since GLMMs can model autocorrelation. This study aimed to determine how and how often GLMMs are used in psychology and to summarize how the information about them is presented in published articles. Our focus in this respect was mainly on frequentist models. In order to review studies applying GLMMs in psychology we searched the Web of Science for articles published over the period 2014–2018. A total of 316 empirical articles were selected for trend study from 2014 to 2018. We then conducted a systematic review of 118 GLMM analyses from 80 empirical articles indexed in Journal Citation Reports during 2018 in order to evaluate report quality. Results showed that the use of GLMMs increased over time and that 86.4% of articles were published in first- or second-quartile journals. Although GLMMs have, in recent years, been increasingly used in psychology, most of the important information about them was not stated in the majority of articles. Report quality needs to be improved in line with current recommendations for the use of GLMMs.

MRI grading system and median scores obtained (first quartile- third quartile).

Background: The quality of a scientific meeting can be quantified by the rate of full publications arising from the presented abstracts and the impact factor of the journals in which the studies were published. Objectives: The aim of this study was to investigate the publication rates of presentations from the 2013 World Society for Stereotactic and Functional Neurosurgery (WSSFN) quadrennial meeting. Methods: Scopus and PubMed databases were searched for the authors of the presentations to identify full publications arising from the relevant abstracts. Author and content matching were used to match an abstract with a full publication. Mann-Whitney U and Kruskal-Wallis tests were used for statistical analysis. Results: In total, 77% (57/74), 56% (44/79), and 50% (79/157) of the paper, flash, and poster presentations, respectively, have been published, with an overall publication rate of 58% (180/310). Articles received a total of 5,227 citations, with an average of 29 ± 64.1 citations per article. The first authors who published their studies had a significantly higher h-index than those who did not publish (p = 0.003). The most preferred journals for publication were Journal of Neurosurgery, Acta Neurochirurgica, and Stereotactic and Functional Neurosurgery. The majority of the articles (117/180 [65%]) were published in a quartile 1 or 2 journal. The average journal impact factor (JIF) was 4.5 for all presentations, and 7.8 for paper session presentations. Studies presented in paper sessions were published in significantly higher-impact factor journals than those presented in poster sessions (p < 0.001). Conclusions: The WSSFN Congress had a relatively high overall publication rate (58%) compared to both other neurosurgical congresses and congresses in other scientific fields. The average JIF of 7.8 is a reflection of the high quality and high impact of the paper session presentations.

Note: Q1 = first quartile or 25th percentile, Q3 = third quartile or 75th percentile.

*Body mass indexData are presented as mean (95%CI) or median [1st quartile; 3rd quartile]