This dataset contains a wide range of signed networks from different disciplines. For more information about the data, one may refer to the paper below:Aref, S., and Wilson, M. C. Balance and frustration in signed networks. Journal of Complex Networks (2019). doi: 10.1093/comnet/cny015.

This dataset supports the paper:

Singh CK, Barme E, Ward R, Tupikina L, Santolini M (2022)
Quantifying the rise and fall of scientific fields.
PLOS ONE 17(6): e0270131. https://doi.org/10.1371/journal.pone.0270131

It provides the processed metadata and relational mappings derived from the arXiv preprint repository used to quantify the temporal dynamics of 175 scientific fields across Physics, Mathematics, Computer Science, Quantitative Biology, and Quantitative Finance.

Files Information

arXiv_data_with_Rescaled_times.csv

CSV file containing article-level features used for analyzing field evolution, including rescaled time variables derived from Gumbel distribution fits.

Each row represents an article and includes the following columns:

• id: arXiv ID

• categories: arXiv field tags (e.g., 'hep-th')

• doi: DOI if the article has been published

• created: Date of first submission to arXiv

• authors: List of last names of authors

• authors_orcid: ORCID ID of authors wherever possible

• NumCitationsArxiv: Number of arXiv articles citing this article

• NumReferencesArxiv: Number of arXiv articles this article cites

• year: Year of submission

• Rescaled Times: Rescaled time values based on Gumbel parameters per field

• Min RT: Minimum rescaled time across the article’s fields

article_metadata.tsv

Tab-separated file with supplementary metadata parsed from arXiv and journal records:

• id: arXiv ID (e.g., "0704.0001")

• journal.ref: Journal reference string (if published)

• doi: DOI of published version (if available)

• num.versions: Number of arXiv versions submitted

• num.pages: Estimated number of pages (from PDF parsing, may be NA)

• num.figures: Estimated number of figures (from PDF parsing, may be NA)

orcid_ids_to_articles.json

JSON list of triples associating author ORCID IDs to arXiv article IDs, allowing disambiguation of authors.

Each entry links a disambiguated author (via ORCID) to an arXiv preprint, in the form:

Used for author trajectory reconstruction across fields.

internal-citations.json

JSON dictionary representing the arXiv internal citation network.

• Keys: arXiv IDs of citing articles

• Values: Lists of arXiv IDs cited by the article

Example:

This file enables construction of the citation network used to compute the Disruptive Index and other bibliometric indicators.

Citation

Please cite the original article if using this dataset:

Singh CK, Barme E, Ward R, Tupikina L, Santolini M (2022)
Quantifying the rise and fall of scientific fields.
PLOS ONE 17(6): e0270131. https://doi.org/10.1371/journal.pone.0270131

Also cite the dataset itself:

Chakresh Kumar Singh, Emma Barme, Robert Ward, Liubov Tupikina, & Marc Santolini. (2022).
Dataset for: Quantifying the rise and fall of scientific fields (Version v2) [Data set].
Zenodo. https://doi.org/10.5281/zenodo.16738242

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to use, share, and adapt the materials with proper attribution.

A factsheet and call for papers created for the launch of REPO4EU's Diamond Open Access, peer-reviewed Network Medicine Journal, focusing on interdisciplinary approaches to exploiting the power of big data by applying network science and systems thinking to medicine.

This is a dataset of 637 journal papers applying neural networks for various tasks in seismology spanning January 1988 to January 2022. The dataset mainly includes peer reviewed papers and does not contain duplicated works. It follows a hierarchical classification of papers based on seismological tasks (i.e. category, sub_category_I, sub_category_II, task, and sub_task). For each paper following information are provided: 1) first author's last name, 2) publication year, 3) paper's title, 4) journal 's name, 5) machine learning method used, 6) the type of used neural network, 7) the name of neural network architecture, 8) the number of neurons/kernels in each hidden layer, 9) type of training process, i.e. supervised, semi-supervised, etc, 10) input data into the network, 11) output data, 12) data domain, i.e. time, frequency, feature, etc, 13) the type of data used for training, e.g. synthetic or real data, 14) the size of training set, 15) the metrics used to measure the performance, 16) performance scores, 17) the baseline method used for evaluation, and 18) a short note summarizing the paper's objective, its approach, and its significance.

An updating version of the dataset can be find from here: https://smousavi05.github.io/dl_seismology/ and here:https://github.com/smousavi05/dl_seismology/tree/main/docs.

An updating glossary of seismological tasks and relevant machine learning techniques and papers are provided here: https://smousavi05.gitbook.io/mlseismology/

VOSviewer was used in the present study because it provides for both the visualization and clustering of subnetworks and thus meets the purpose of the present study. In addition, the subgroup structures of the scholarly network were performed by the VOS clustering algorithm. The obtained clustering results yielded a comparative understanding of the main foci of GN and GS researchers in the investigated field. Please using the attached two files with VOSviewer to construct the Full network diagram of information science scholarly network on AI research 1. Map.txt 2.Network.txt

Portal for Science Dynamics projects with open source framework to provide way to query datasets. Code framework, including tutorials for project, titled Over Optimization of Academic Publishing Metrics Observing Goodhart Law in Action, to interactively explore and understand how various properties of journals have changed over time. Datasets, software implementations, code tutorials and interactive web interface for investigating studied networks.

These data are networks that have published together or cited each other in four European Political Science journals: British Journal of Political Science (BJPS), Political Studies (PS), Politische Vierteljahresschrift (PVS) and Österreichische Zeitschrift für Politikwissenschaft (ÖZP).

The dataset includes replication materials (dataset files, python code) for the article entitled "Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis" published in The Journal of Mathematical Sociology (https://doi.org/10.1080/0022250X.2021.1956917). One can also open file "Mistakes in the main manuscript.docx" to inspect the list of mistakes found in the main manuscript.

Data for paper "Recommending Scientific Datasets Using Author Networks in Ensemble Methods" which is accepted by Data Science Journal. These data contains 1)MAKG (Microsoft Academic Knowledge Graph) co-author network (HDT/RDF format), 2)MAKG paper/dataset title collection (HDT/RDF format), 3) MAKG paper/dataset abstract collection (HDT/RDF format).

Supplementary Material

Journal of business analytics Abstract & Indexing - ResearchHelpDesk - Business analytics research focuses on developing new insights and a holistic understanding of an organisation’s business environment to help make timely and accurate decisions, and to survive, innovate and grow. Thus, business analytics draws on the full spectrum of descriptive/diagnostic, predictive and prescriptive analytics in order to make better (i.e., data-driven and evidence-based) decisions to create business value in the broadest sense. The mission of the Journal of Business Analytics Journal (JBA) is to serve the emerging and rapidly growing community of business analytics academics and practitioners. We aim to publish articles that use real-world data and cases to tackle problem situations in a creative and innovative manner. We solicit articles that address an interesting research problem, collect and/or repurpose multiple types of data sets, and develop and evaluate analytics methods and methodologies to help organisations apply business analytics in new and novel ways. Reports of research using qualitative or quantitative approaches are welcomed, as are interdisciplinary and mixed methods approaches. Topics may include: Applications of AI and machine learning methods in business analytics Network science and social network applications for business Social media analytics Statistics and econometrics in business analytics Use of novel data science techniques in business analytics Robotics and autonomous vehicles Methods and methodologies for business analytics development and deployment Organisational factors in business analytics Responsible use of business analytics and AI Ethical and social implications of business analytics and AI Bias and explainability in analytics and AI Our editorial philosophy is to publish papers that contribute to theory and practice. Journal of Business Analytics is indexed in: AIS eLibrary Australian Business Deans Council (ABDC) Journal Quality List British Library CLOCKSS Crossref Ei Compendex (Engineering Village) Google Scholar Microsoft Academic Portico SCImago Scopus Ulrich's Periodicals Directory

Network data used for the analysis contained in Baccini A, Barabesi L, Gingras Y, Kalfaoui M (2019) Intellectual and social similarity among scholarly journals. An exploratory comparison of the networks of editors, authors and co-citations.

Data are in .net format for Pajek software

CC indicates co-citation network.

IA indicated Interlocking authorship network.

IE indicates interlocking editorship network.

Stat is for statistics; Econ is for economics; ILS is for information and library science.

These data allow to replicate the results of the manuscript entitled: "Quantifying Women’s Marginalisation in Ibero-American Film Culture During the First Half of the Twentieth Century: A Network-Science Proposal" published in Journal of Cultural Analytics (2024). Check the README file for further details.

Bibliographic database that brings together free, full-text scholarly articles from hundreds of universities and colleges worldwide. Curated by university librarians and their supporting institutions, the Network includes a growing collection of peer-reviewed journal articles, book chapters, dissertations, working papers, conference proceedings, and other original scholarly work. A central discipline wheel features ten color-coded disciplines: law, social and behavioral sciences, arts and humanities, life sciences, physical sciences and mathematics, education, engineering, medicine and health sciences, business, and architecture. The size of each color-coded area reflects the size of each discipline's collection relative to the rest of DCN. Users can click on any segment of any layer of the wheel, with the selected discipline, subdiscipline, or subject navigating users to their chosen commons area where they can then proceed to a list of full-text PDFs. To be clear, typing a couple of keywords into the Search Entire Network box, also located on the homepage, might be a more efficient method than mousing around on this graphical browsing element. If you would like to contribute your institution's research to the Digital Commons Network, Use the form provided, http://network.bepress.com/about/

Journal of business analytics Impact Factor 2024-2025 - ResearchHelpDesk - Business analytics research focuses on developing new insights and a holistic understanding of an organisation’s business environment to help make timely and accurate decisions, and to survive, innovate and grow. Thus, business analytics draws on the full spectrum of descriptive/diagnostic, predictive and prescriptive analytics in order to make better (i.e., data-driven and evidence-based) decisions to create business value in the broadest sense. The mission of the Journal of Business Analytics Journal (JBA) is to serve the emerging and rapidly growing community of business analytics academics and practitioners. We aim to publish articles that use real-world data and cases to tackle problem situations in a creative and innovative manner. We solicit articles that address an interesting research problem, collect and/or repurpose multiple types of data sets, and develop and evaluate analytics methods and methodologies to help organisations apply business analytics in new and novel ways. Reports of research using qualitative or quantitative approaches are welcomed, as are interdisciplinary and mixed methods approaches. Topics may include: Applications of AI and machine learning methods in business analytics Network science and social network applications for business Social media analytics Statistics and econometrics in business analytics Use of novel data science techniques in business analytics Robotics and autonomous vehicles Methods and methodologies for business analytics development and deployment Organisational factors in business analytics Responsible use of business analytics and AI Ethical and social implications of business analytics and AI Bias and explainability in analytics and AI Our editorial philosophy is to publish papers that contribute to theory and practice. Journal of Business Analytics is indexed in: AIS eLibrary Australian Business Deans Council (ABDC) Journal Quality List British Library CLOCKSS Crossref Ei Compendex (Engineering Village) Google Scholar Microsoft Academic Portico SCImago Scopus Ulrich's Periodicals Directory

Please navigate to http://dx.doi.org/10.7910/DVN/E46ZNK for the full set of replication files. Those files contain Stata (v11/12) do and dta files necessary to produce the results from the 1992 CNES, 1996 ISL, and 2008-09 ANES presented in the article: Djupe, Paul A., Scott D. McClurg, and Anand E. Sokhey. Forthcoming. “The Political Consequences of Gender in Social Networks.” British Journal of Political Science.

This bibliometric dataset comprises metadata of research articles published by three prominent Digital Humanities journals: Computers and the Humanities (Chum) 1966 to 2004, Digital Scholarship in the Humanities (DSH) 1986 to 2017, and Digital Humanities Quarterly (DHQ) 2007 to 2017. It has been manually compiled with information such as 'article title', 'authors', 'affiliations', 'year', 'volume number', 'issue number', 'abstract', and 'keywords'. Many records within the dataset are not indexed in the Web of Science citation index. An author co-citation network is constructed based on this dataset, and the node and edge information of the network is also included.

An XLSX Excel file which provides data used by Gregorio González-Alcaide & collaborators in the article “Scientometrics analysis of research activity and collaboration patterns in Chagas cardiomyopathy” The worksheet “labels” describes the variables of the bibliographic databases that allow identifying the documents used to carry out the study. The worksheet “documents” present the following data: PMID: PubMed Identifier. TI: Title of journal article. AU: Author(s) of the document. SO: Source/journal title. PY: Publication year. MH: Medical Subject Headings (controlled vocabulary of biomedical terms) that is used to describe the subject of each journal article in MEDLINE. UT-WOS: Article Identifier in Web of Science (only present in articles indexed in Web of Science). TC: Times Cites in Web of Science Core Collection.

Introduction

This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’[1] that is currently under review.

Data Collection

The data sets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription [2]. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query:

TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”)

The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies only – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary.

All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records 3. After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles.

From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool [4]. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a citing paper in the ‘Source’ column and identifies the cited paper in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list.

We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 [5] to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including all of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles.

After merging duplicates, we then restricted the network to include only articles fully retrieved (1,902), and retrained only those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’).

This left us with a network of 1,892 nodes connected together by 26,019 edges. It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords.

Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the node-attribute-list. We further clustered this network via modularity maximisation via the Leiden algorithm [6]. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave Q=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list.

Data description

We include here two datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Below, we detail their contents:

1. ‘OTSOC-node-attribute-list.csv’ is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to:

Id, the unique identifier

Label, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available).

Wos_id, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.

Title, paper title.

Authors, all named authors.

Journal, journal of publication.

Pub_year, year of publication.

Wos_citations, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021

Indegree, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript.

Cluster, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters)

1. ‘OTSOC-edge -list.csv’ is a comma-separate values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to:

Source, the unique identifier of the citing paper.

Target, the unique identifier of the cited paper.

Type, edges are ‘Directed’, and this column tells Gephi to regard all edges as such.

Syr_date, this contains the date of publication of the citing paper.

Tyr_date, this contains the date of publication of the cited paper.

Software recommended for analysis

Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification.

Notes

[1] Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence.

[2] Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present.

[3] For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’

[4] Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: https://sci2.cns.iu.edu

[5] Bastian, M., Heymann, S., & Jacomy, M. (2009).

Research article - Publication scientifique - Hart, John P., Jennifer Birch, Christian Gates St-Pierre, 2023: «Social Network Analysis of Iroquoian Sites in the St. Lawrence River Valley: AD 1400-1600». Journal of Historical Network Research 8(1): 98-144. https://DOI:10.25517/jhnr.v8i1.71