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.

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).

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.

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).

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.

Countries included in the strongest connected component GTNs of the global inbound tourism network*.

A survey on online social network methodologies Online social networks OSNs are an important source of information for scientists in different fields such as computer science sociology economics etc However it is hard to study OSNs as they are very large For these reasons we argue that sampling techniques will be the best technique to study OSNs in the future For this reason here we combine some papers and present some Online social network methodologies that are used for easily find

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/

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

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.

This repository collects the data used in Baccini A, Petrovich E (2021) "Normative versus strategic accounts of acknowledgment data: the case of the top-five journals of economics".

The file "Database.xlsx" contains two tables. The first reports the relevant metadata of the 1218 research articles considered in the study: the Web of Science identification number(ID), the title (TI), the journal (SO),the acknowledgment text (FX), the authors (AF), and the cited references (CR). The second table reports for each article, identified by its WoS ID, the cleaned list of the acknowledgees it mentions.

The file "Acknowledgment_network.net" contains the acknowledgment network in Pajek format. The list of the vertices is first reported, along with their label and coordinates, followed by the list of arcs with their respective weights.

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.

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/

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.

The dataset contains 5 saved predictive models (.h5 files) which were trained using labeled JPL GIMs to predict the number of HDRs in a map and lists of GIM maps used for training and testing.The data can be found in JPL database of Global Ionospheric Maps (GIMs): https://sideshow.jpl.nasa.gov/pub/iono_daily/gim_for_research/jpli/

Journal of business analytics FAQ - 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

International Journal of Computational Intelligence Systems Impact Factor 2024-2025 - ResearchHelpDesk - The International Journal of Computational Intelligence Systems is an international peer reviewed journal and the official publication of the European Society for Fuzzy Logic and Technologies (EUSFLAT). The journal publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. This is an open access journal, i.e. all articles are immediately and permanently free to read, download, copy & distribute. The journal is published under the CC BY-NC 4.0 user license which defines the permitted 3rd-party reuse of its articles. Aims & Scope The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: Autonomous reasoning Bio-informatics Cloud computing Condition monitoring Data science Data mining Data visualization Decision support systems Fault diagnosis Intelligent information retrieval Human-machine interaction and interfaces Image processing Internet and networks Noise analysis Pattern recognition Prediction systems Power (nuclear) safety systems Process and system control Real-time systems Risk analysis and safety-related issues Robotics Signal and image processing IoT and smart environments Systems integration System control System modelling and optimization Telecommunications Time series prediction Warning systems Virtual reality Web intelligence Deep learning

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

Research data to accommodate the article "Overlay journals: a study of the current landscape" (https://doi.org/10.1177/09610006221125208)

Identifying the sample of overlay journals was an explorative process (occurring during April 2021 to February 2022). The sample of investigated overlay journals were identified by using the websites of Episciences.org (2021), Scholastica (2021), Free Journal Network (2021), Open Journals (2021), PubPub (2022), and Wikipedia (2021). In total, this study identified 34 overlay journals. Please see the paper for more details about the excluded journal types.

The journal ISSN numbers, manuscript source repositories, first overlay volumes, article volumes, publication languages, peer-review type, licence for published articles, author costs, publisher types, submission policy, and preprint availability policy were observed by inspecting journal editorial policies and submission guidelines found from journal websites. The overlay journals’ ISSN numbers were identified by examining journal websites and cross-checking this information with the Ulrich’s periodicals database (Ulrichsweb, 2021). Journals that published review reports, either with reviewers’ names or anonymously, were classified as operating with open peer-review. Publisher types defined by Laakso and Björk (2013) were used to categorise the findings concerning the publishers. If the journal website did not include publisher information, the editorial board was interpreted to publish the journal.

The Organisation for Economic Co-operation and Development (OECD) field of science classification was used to categorise the journals into different domains of science. The journals’ primary OECD field of sciences were defined by the authors through examining the journal websites.

Whether the journals were indexed in the Directory of Open Access Journals (DOAJ), Scopus, or Clarivate Analytics’ Web of Science Core collection’s journal master list was examined by searching the services with journal ISSN numbers and journal titles.

The identified overlay journals were examined from the viewpoint of both qualitative and quantitative journal metrics. The qualitative metrics comprised the Nordic expert panel rankings of scientific journals, namely the Finnish Publication Forum, the Danish Bibliometric Research Indicator and the Norwegian Register for Scientific Journals, Series and Publishers. Searches were conducted from the web portals of the above services with both ISSN numbers and journal titles. Clarivate Analytics’ Journal Citation Reports database was searched with the use of both ISSN numbers and journal titles to identify whether the journals had a Journal Citation Indicator (JCI), Two-Year Impact Factor (IF) and an Impact Factor ranking (IF rank). The examined Journal Impact Factors and Impact Factor rankings were for the year 2020 (as released in 2021).

We demonstrate that the disruption index (CD) recently applied to publication and patent citation networks by Park et al. (Nature, 2023) systematically decreases over time due to secular growth in research and patent production, following two distinct mechanisms unrelated to innovation – the first structural and the second behavioral. The structural explanation follows from ‘citation inflation’ (CI) (Petersen et al., Research Policy, 2018), an inextricable feature of real citation networks. One driver of CI is the ever-increasing length of reference lists, which causes the CD index to systematically decrease. The behavioral explanation reflects shifts in scholarly citation practice (e.g. self-citation) that increase the rate of triadic closure in citation networks and confounds efforts to measure disruptive innovation using CD. Combined, these two mechanisms render CD unsuitable for cross-temporal analysis, and call into question the interpretations provided by Park et al., The enclosed supporting data accompanies the following research articles:

Alexander M. Petersen, Felber Arroyave, Fabio Pammolli (2025). The disruption index suffers from citation inflation: re-analysis of temporal CD trend and relationship with team size reveal discrepancies. J. Informetrics 19, 101605 (2025). DOI:10.1016/j.joi.2024.101605

Alexander M. Petersen, Felber Arroyave, Fabio Pammolli (2024). The disruption index is biased by citation inflation. Quantitative Science Studies (2024). DOI:10.1162/qss_a_00333

Enclosed data were generated using a synthetic citation network model developed and reported in: Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. The memory of science: Inflation, myopia, and the knowledge network. Journal of Informetrics 12, 656–678 (2018). To summarize, provided are raw network data produced for 6 citation network scenarios. For each scenario, we include 4 synthetic networks each, for a total of 24 citation networks. Each citation network i..., Enclosed code was developed using Mathematica 13 software, which should be backwards compatible with previous versions since the notebooks do not use any new functionality introduced in v13., # The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice: synthetic citation networks for bibliometric null models

https://doi.org/10.6071/M3G674

Description of the data and file structure

Enclosed data were generated using a synthetic citation network model developed and reported in: Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. The memory of science: Inflation, myopia, and the knowledge network. Journal of Informetrics 12, 656–678 (2018). Data and their description are provided in the enclosed document: ReadMe_DataDescription.pdf. Provided are raw network data produced for 6 citation network scenarios. For each scenario we include 4 synthetic networks each, for a total of 24 citation networks. Each citation network is comprised of 125270 nodes that were systematically added in cohorts, therefore representing null model for evolving citation networks, and thereby useful for ...