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.

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.

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.

Nature Communications Acceptance Rate - ResearchHelpDesk - Nature Communications is an open-access journal that publishes high-quality research from all areas of the natural sciences. Papers published by the journal represent important advances of significance to specialists within each field. Nature Communications is open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, and Earth sciences. Papers published by the journal aim to represent important advances of significance to specialists within each field. We are committed to providing an efficient service for both authors and readers. Our team of independent editors makes rapid and fair publication decisions. Prompt dissemination of accepted papers to a wide readership and beyond is achieved through a program of continuous online publication. Article and journal metrics Article metrics such as number of downloads, citations and online attention are available from each article page and provide an overview of the attention received by a paper. The 2018 journal metrics for Nature Communications are as follows: 2-year Impact Factor: 11.878 5-year Impact Factor: 13.811 Immediacy Index: 2.107 Eigenfactor® score: 1.10329 Article Influence Score: 5.402 2-year Median: 8 Nature Communications Abstract & Indexing DOAJ, MEDLINE, Web of Science, Scopus and Google Scholar. Nature Communications started in the year 2010 and has been growing ever since. Nature Communications doesn’t have a fixed publishing frequency. Their publishing frequency is continuous and upon acceptance. They have a very strict acceptance rate of 7.7%. They get over 50,000+ submissions every year. Nature Communications Article-processing charges Nature Communications is an open-access journal. To publish in Nature Communications, authors are required to pay an article-processing charge (APC). The APC for all published papers is as follows, plus VAT or local taxes where applicable: £3,790 (UK) $5,380 (The Americas, China, and Japan) €4,380 (Europe and rest of world) Nature communications ranking Title Type SJR H index Total Docs. (2018) Total Docs. (3years) Total Refs. (2018) Total Cites (3years) Citable Docs. (3years) Cites / Doc. (2years) Ref. / Doc. (2018) Nature Communications journal 5.992 Q1 248 5664 11692 273530 141425 10983 11.80 48.29 Nature communications details Country: United Kingdom H Index: 248 Subject Area and Category: Biochemistry, Genetics, and Molecular Biology, Biochemistry, Genetics and Molecular Biology (miscellaneous), Chemistry, Chemistry (miscellaneous), Physics and Astronomy, Physics and Astronomy (miscellaneous) Publisher: Nature Publishing Group Publication Type: Journals ISSN: 20411723 Coverage: 2010-ongoing

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.

Changelog

Version 1.0.1 (Latest)

• Fixed incorrect cluster citation graph: The previous version had an incorrect cluster_citation_density.graphml file. This has now been corrected.

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

This dataset was used as part of results of an scientific article whom abstract is:

The relationship between international collaboration and scientific impact is studied in the context of South American universities. This study aims to comprehensively analyze the strength of this relationship using nonparametric statistical methods. The records are the 244300 papers published in journals indexed in Scopus (2011-2020) by researchers affiliated to 10 South American public universities and extracted with Scival support. There is a marked trend of collaborative work, since 93% of publications were collaborative at institutional, national or international level, with a higher percentage of international collaboration. A refined analysis of the geographic collaboration of publications in Q1 journals further evidences the frequency of international collaboration. In the top 4 collaborating partner institutions for each university, the presence of the Centre National de la Recherche Scientifique of France
(CNRS) is observed, followed by the National Council for Scientific and Technical Research of Argentina (Conicet). It is proven that there is a statistically significant relationship (p < .01) in each of the 10 universities between collaboration (number of
countries) and normalized impact (FWCI). The results confirmed the hypothesis of this study and the authors provide practical recommendations for science policy makers and researchers, including the promotion of strategic collaboration between different
institutional sectors of society to increase the impact of publications.

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

Journal of Environmental Chemical Engineering FAQ - ResearchHelpDesk - The Journal of Environmental Chemical Engineering provides a forum for the publication of original research on the development of sustainable technologies focusing on water and wastewater treatment and reuse; pollution prevention; resource recovery of waste; nanomaterials for environmental applications; sustainability and environmental safety; and recent developments on green chemistry. JECE calls for full-length research papers, critical review papers, perspectives and letters to the Editor that cover the following fields: Physico-chemical processes: Adsorption/biosorption, ion exchange, membrane processes, magnetic separation, particle separation, phase separation, multiphase extraction, thermal/evaporative processes Advanced oxidation processes: Heterogeneous catalysis, UV/H2O2, Fenton oxidation, ozonation, sonolysis, plasma processes, electrochemical treatment, wet air oxidation Nanomaterials for environmental and chemical applications: Adsorbents, catalysts, nanocomposites, metal-organic frameworks, nanocarbon materials Biological processes: Anaerobic process, aerobic process, biofilm process, membrane bioreactorSustainable technologies: Water reclamation and reuse, carbon capture, wast-to-energy/materials, resource recovery JECE also covers the following fields: Occurence, fate, transport and detection of micropollutants, nanoparticles and microplastics Antimicrobial resistance Greenhouse gas mitigation technologies Novel disinfection methods Zero or minimal liquid discharge technologies Biofuel production Advanced water analytics Abstracting and Indexing INSPEC Journal Title Abbreviations CHEM ENG J ISSN 1385-8947 h-index 172 CiteScore SJR SNIP CiteScore Rank 8.47 2.066 1.941 Subject field Quartiles Rank Percentile Category: Engineering Subcategory: Industrial and Manufacturing Engineering Q1 5 / 323 98% Category: Environmental Science Subcategory: Environmental Chemistry Q1 5 / 100 95% Category: Chemical Engineering Subcategory: General Chemical Engineering Q1 8 / 272 97% Category: Chemistry Subcategory: General Chemistry Q1 22 / 371 94%

Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.

The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?

We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:

1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.

2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.

3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1). This data was used to produce the Figure 3a at the original publication.

4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.

To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:

1. Planning steps. This data provides the number of times landscape ecological concepts were addressed in each planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the original publication.

2. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning steps described above?

This dataset is part of EC Horizon 2020 project ALLINTERACT Widening and diversifying citizen engagement in science (872396).
It contains the raw data obtained from the fieldwork, which consists of: 1) Literature Review, 2) Social Media Analytics, 3) Focus Groups, 4) Survey and 5) Social Media Communicative Observation.
1) Literature Review
The objective of the literature review was to address the following topics in gender and education: a) How citizens’ benefit from scientific research, b) Citizen awareness of the impact of scientific research, c) Awareness-raising initiatives succeeding at engaging citizens in scientific participation, including the Open Access movement and citizen science initiatives, d) Awareness-raising actions that foster the recruitment of new talent in sciences and e) Policies that promote awareness-raising actions and citizen engagement in science.
In order to do so, the searches were carried out in the top scientific databases, namely Web of Science (mainly in those journals indexed in Journal Citation Reports) and Scopus. The articles were published between 2010-2021 in journals indexed Q1 or Q2 in JCR or in Q1 journals indexed in Scopus. Relevant reports from EU-funded research projects and official EU documents were also included.
We provide one word file with the following information of each topic (a-e) in gender and education.
- Keywords used
- Criteria of selection
- Identified sources
- Outcomes
- Annexes: Grids with the details of the identified socurces

2) Social Media Analytic
It is the raw data obtained from social media interactions (Twitter, Facebook, Instagram and Reddit) among citizens about citizen participation in science and research with social impact related to two Sustainable Development Goals: Quality Education and Gender Equality.
The data collection followed a twofold strategy 1) Top-Down, in which researchers identified and selected relevant Twitter and Instagram hashtags and Facebook and Reddit pages and 2) Bottom-Up, in which Twitter hashtags were selected based on daily Trending Topics.
The data was collected between March 9th and March 16th 2021 and has been obtained, cleaned and anonymized following Allinteract - Social Media Analytics Protocol (Flecha & Pulido, 2021).
We provide five Excel files (one for each social network explored). Each file contains the main information of the extracted messages, however the information extracted in each case is slightly different.
-Twitter: Tweet ID, Time, Tweet Type, Retweeted By, Number of Retweets, Hashtags
-Facebook: Post ID, Video, Type, Likes, Created Time, Updated Time, Comment ID, Comment Likes, Comment Time, Page Likes
-Instagram: Likes, comments, date
-Reddit: Row ID, sub_id, sub_title, sub_score, sub_date, comment_id, comment_score, comment_date

3) Focus Groups
This data file contains the pseudonymized transcription of a total of 6 focus groups in gender and 6 in education, which were conducted between October 2021 and February 2022. These focus groups are the pre-test and therefore, the groups are distributed in control group or experimental group. The participants of the gender focus groups were women (including vulnerable women) from a women’s group, members of an LGBTQI group and women (including young women) from a women’s group. The participants of the education focus groups were parents, teachers and students.
We provide a word file with the literal transcriptions of the focus groups in the language in which the focus groups were conducted (English, Spanish or Portuguese).

4) Survey
This data file contains the anonym answers of the survey conducted with participants from 12 countries, through a CATI/CAWI method. The survey was conducted between November 2021 and February 2022 and consists of 59 questions. The exploitation of this data has been carried out with the SPSS software.
We provide an excel file with the 59 questions and the answers of 7507 participants.

5) Social Media Communicative Observation
The Social Media Communicative Observation aims to explore the effects of introducing scientific pieces of evidence in social media interactions as an initiative to increase participation through awareness. In order to do so, scientific evidence on gender and education were introduced in 10 Facebook groups (5 related to gender and 5 to education), 10 Reddit communities (5 related to gender and 5 to education) and 2 Social Impact Platforms (Sappho and Adhyayana).
We provide an excel file with the anonymized interactions among users around the introduced piece of evidence. This Excel file contains the following information: Group of documents, document name, code, start, final, weight, segment, changed by, changed, created, comment, area and percentage (%).

6) Focus Group – Post test

This data file contains the pseudonymized transcription of a total of 6 focus groups post test

Funding: We acknowledge support of this work by the project "ALLINTERACT Widening and diversifying citizen engagement in science” (872396) from the European Commission Horizon 2020 programme.

Contact information
Ramón Flecha (PI): ramon.flecha@ub.edu
Marta Soler Gallart (KMC Coordinator): marta.soler@ub.edu
Pavel Oveiko (Ethics Chair): pavel.ovseiko@rdm.ox.ac.uk
ALLINTERACT Project: allinteract@ub.edu

References
Flecha, R., & Pulido, C. (2021). Allinteract - Social Media Analytics Protocol is licensed under a Creative Commons Attribution - NonCommercial - ShareAlike 4.0 International License is available in https://archive.org/details/@crea_research

How to cite this dataset
Soler-Gallart, M. (2021). D1.1.Allinteract Raw Data is licensed under a Creative Commons Attribution - NonCommercial - ShareAlike 4.0 International License

Correlation between the number of citations in the tenth year after obtaining the PhD.

Correlation between H-index ten years after obtaining the PhD degree.

Nature Communications FAQ - ResearchHelpDesk - Nature Communications is an open-access journal that publishes high-quality research from all areas of the natural sciences. Papers published by the journal represent important advances of significance to specialists within each field. Nature Communications is open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, and Earth sciences. Papers published by the journal aim to represent important advances of significance to specialists within each field. We are committed to providing an efficient service for both authors and readers. Our team of independent editors makes rapid and fair publication decisions. Prompt dissemination of accepted papers to a wide readership and beyond is achieved through a program of continuous online publication. Article and journal metrics Article metrics such as number of downloads, citations and online attention are available from each article page and provide an overview of the attention received by a paper. The 2018 journal metrics for Nature Communications are as follows: 2-year Impact Factor: 11.878 5-year Impact Factor: 13.811 Immediacy Index: 2.107 Eigenfactor® score: 1.10329 Article Influence Score: 5.402 2-year Median: 8 Nature Communications Abstract & Indexing DOAJ, MEDLINE, Web of Science, Scopus and Google Scholar. Nature Communications started in the year 2010 and has been growing ever since. Nature Communications doesn’t have a fixed publishing frequency. Their publishing frequency is continuous and upon acceptance. They have a very strict acceptance rate of 7.7%. They get over 50,000+ submissions every year. Nature Communications Article-processing charges Nature Communications is an open-access journal. To publish in Nature Communications, authors are required to pay an article-processing charge (APC). The APC for all published papers is as follows, plus VAT or local taxes where applicable: £3,790 (UK) $5,380 (The Americas, China, and Japan) €4,380 (Europe and rest of world) Nature communications ranking Title Type SJR H index Total Docs. (2018) Total Docs. (3years) Total Refs. (2018) Total Cites (3years) Citable Docs. (3years) Cites / Doc. (2years) Ref. / Doc. (2018) Nature Communications journal 5.992 Q1 248 5664 11692 273530 141425 10983 11.80 48.29 Nature communications details Country: United Kingdom H Index: 248 Subject Area and Category: Biochemistry, Genetics, and Molecular Biology, Biochemistry, Genetics and Molecular Biology (miscellaneous), Chemistry, Chemistry (miscellaneous), Physics and Astronomy, Physics and Astronomy (miscellaneous) Publisher: Nature Publishing Group Publication Type: Journals ISSN: 20411723 Coverage: 2010-ongoing