Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

Version: 5

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2023/09/05

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v5.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v5.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 5th version
- Information updated: number of journals, URL, document types associated to a specific journal.

Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

Conferences are an essential tool for scientific communication. In disciplines such as Computer Science, over 50% of original research results are published in conference proceedings. In this dataset, there is is a list of conference proceedings, categorized Q1 - Q4 by analogy with SJR journal quartiles. We have analyzed the role of conference proceedings in various disciplines and propose an alternative approach to research evaluation based on conference proceedings and Scimago Journal Rank (SJR). Comparison of the resulting list in Computer Science with the CORE ranking showed a 62% match, as well as an average rank correlation of the distribution by category.

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.

Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Emergency Medicine Category.

Conferences are an essential tool for scientific communication. In disciplines such as Computer Science, over 50% of original research results are published in conference proceedings. In this dataset, there is is a list of conference proceedings, categorized Q1 - Q4 by analogy with SJR journal quartiles. We have analyzed the role of conference proceedings in various disciplines and proposed an alternative approach to research evaluation based on conference proceedings and Scimago Journal Rank (SJR). Comparison of the resulting list in Computer Science with the CORE ranking showed a 62% match, as well as an average rank correlation of the distribution by category.

These raw data belong to the study “Gender inequalities on editorial boards of indexed pediatrics journals”.

The presence of women in decision-making positions, such as on editorial committees of biomedical journals, is not the same as that of men. This paper analyzes the gender composition of editorial committees (EBMs) and editor-in-chief (ECs) positions of pediatric journals. The gender of EBMs and ECs of 125 journals classified in the pediatrics area of the Journal Citation Report (JCR) were analyzed. The following indicators were calculated: gender distribution of ECs and EBMs by journal, publisher, subject speciality, country, quartile of the journal in JCR and country of affiliation of the members. The total number of EBMs was 4,242. The distribution by sex of the ECs was 19.44% women and 80.56% men, while the EBMs were 33.05% women and 66.95% men. Twenty journals exhibited a greater representation of women than men, and in four there was parity. Journals with greater participation of women specialized in nursing, physical therapy and were related to nutrition (lactation and breastfeeding). Only one-fifth of ECs and one-third of EBMs are female. Women's participation is higher in journals related to nursing, physical and occupational therapy, and nutrition. The United States has the highest number of EBMs, followed by the European Union.

This file contains the following variables: Rank, Full Journal Title,Editorial Board Members (EBM), First Name, Gender, Country, Country Code, Geographical Area, Editor-in-Chief (EC), Country Editor-in-Chief, Gender Editor-in-Chief, Url Journals.

The SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators. For comparative purposes, the value of the composite indicator has been set on a scale of 0 to 100. However the line graphs and bar graphs always represent ranks (lower is better, so the highest values are the worst).

HEI’s Q1% and 4*% in anthropology & development studies UoA.

Quartile distribution of file formats of the supplementary material.

✅ Engineering Journal ISSN - 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)

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.

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

Food and non-food expenditure share differences for quartile 1.

Editorial policies and proportion of trials of included journals.

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.

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.

The denominator for the per paper statistics is 3, 845 papers. IQR = inter-quartile range.

Note: Households were stratified into income quartiles, with Quartile 1 as the lowest and Quartile 4 as the highest income quartile.

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