ESA ignite talk 2014. Data Citations. Here is the dataset I collected for slide 19 of the talk to examine the relative efficacy of data citations to capture usage of dateasets my colleagues and I have published. Outcome: Do not capture usage. Abstract: For better or worse, citations are here stay. Citations have the capacity to serve as a proxy estimate of uptake or use by the community of ones products. Fortunately, the range of acceptable scientific products is rapidly expanding, datasets in many forms continue to serve as pivotal resources, and big data syntheses are reshaping the standards for acceptable derived evidence. Data citations are defined, general rules provided, and the unique elements of datasets described such as versioning and persistent identifiers. The cultural and scientific discovery implications of data citations are also described focusing on emerging linked-data futures.

Data for Figure 3.5 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.5 shows the standard deviation of annually averaged zonal-mean near-surface air temperature.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

List of data provided

• Simulated (CMIP6) standard deviation of near-surface air temperature
• Observed standard deviation of near-surface air temperature

CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.

Data provided in relation to figure

Datafile: fig_3_5.nc, black lines: - HadCRUT5: model = 62 - BerkleyEarth: model = 61 - NOAAGlobalTemp-Interim: model = 60 - Kadow: model = 59 - colored lines: model = 0, 1, ..., 58

Where HadCRUT5, BerkleyEarth, NOAAGlobalTemp-Interim, and Kadow are gridded datasets of global historical surface temperature.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

Sentences and citation contexts identified from the PubMed Central open access articles ---------------------------------------------------------------------- The dataset is delivered as 24 tab-delimited text files. The files contain 720,649,608 sentences, 75,848,689 of which are citation contexts. The dataset is based on a snapshot of articles in the XML version of the PubMed Central open access subset (i.e., the PMCOA subset). The PMCOA subset was collected in May 2019. The dataset is created as described in: Hsiao TK., & Torvik V. I. (manuscript) OpCitance: Citation contexts identified from the PubMed Central open access articles. Files: • A_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with A. • B_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with B. • C_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with C. • D_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with D. • E_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with E. • F_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with F. • G_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with G. • H_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with H. • I_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with I. • J_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with J. • K_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with K. • L_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with L. • M_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with M. • N_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with N. • O_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with O. • P_p1_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 1). • P_p2_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 2). • Q_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with Q. • R_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with R. • S_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with S. • T_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with T. • UV_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with U or V. • W_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with W. • XYZ_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with X, Y or Z. Each row in the file is a sentence/citation context and contains the following columns: • pmcid: PMCID of the article • pmid: PMID of the article. If an article does not have a PMID, the value is NONE. • location: The article component (abstract, main text, table, figure, etc.) to which the citation context/sentence belongs. • IMRaD: The type of IMRaD section associated with the citation context/sentence. I, M, R, and D represent introduction/background, method, results, and conclusion/discussion, respectively; NoIMRaD indicates that the section type is not identifiable. • sentence_id: The ID of the citation context/sentence in the article component • total_sentences: The number of sentences in the article component. • intxt_id: The ID of the citation. • intxt_pmid: PMID of the citation (as tagged in the XML file). If a citation does not have a PMID tagged in the XML file, the value is "-". • intxt_pmid_source: The sources where the intxt_pmid can be identified. Xml represents that the PMID is only identified from the XML file; xml,pmc represents that the PMID is not only from the XML file, but also in the citation data collected from the NCBI Entrez Programming Utilities. If a citation does not have an intxt_pmid, the value is "-". • intxt_mark: The citation marker associated with the inline citation. • best_id: The best source link ID (e.g., PMID) of the citation. • best_source: The sources that confirm the best ID. • best_id_diff: The comparison result between the best_id column and the intxt_pmid column. • citation: A citation context. If no citation is found in a sentence, the value is the sentence. • progression: Text progression of the citation context/sentence. Supplementary Files • PMC-OA-patci.tsv.gz – This file contains the best source link IDs for the references (e.g., PMID). Patci [1] was used to identify the best source link IDs. The best source link IDs are mapped to the citation contexts and displayed in the *_journal IntxtCit.tsv files as the best_id column. Each row in the PMC-OA-patci.tsv.gz file is a citation (i.e., a reference extracted from the XML file) and contains the following columns: • pmcid: PMCID of the citing article. • pos: The citation's position in the reference list. • fromPMID: PMID of the citing article. • toPMID: Source link ID (e.g., PMID) of the citation. This ID is identified by Patci. • SRC: The sources that confirm the toPMID. • MatchDB: The origin bibliographic database of the toPMID. • Probability: The match probability of the toPMID. • toPMID2: PMID of the citation (as tagged in the XML file). • SRC2: The sources that confirm the toPMID2. • intxt_id: The ID of the citation. • journal: The first letter of the journal title. This maps to the *_journal_IntxtCit.tsv files. • same_ref_string: Whether the citation string appears in the reference list more than once. • DIFF: The comparison result between the toPMID column and the toPMID2 column. • bestID: The best source link ID (e.g., PMID) of the citation. • bestSRC: The sources that confirm the best ID. • Match: Matching result produced by Patci. [1] Agarwal, S., Lincoln, M., Cai, H., & Torvik, V. (2014). Patci – a tool for identifying scientific articles cited by patents. GSLIS Research Showcase 2014. http://hdl.handle.net/2142/54885 • Supplementary_File_1.zip – This file contains the code for generating the dataset.

Contents

The four CSV files are the data used for the evaluation in:

Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.

DOI: 10.1007/978-3-030-45439-5_15

Code: github.com/IllDepence/ecir2020

The evaluation was conducted in a citation re-prediction setting.

CSV Format

7 columns divided by \u241E

cited document ID

for *_nomarker.csv: citation marker position ambiguous

for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context

adjacent cited document IDs

only given in citrec_unarxive_*.csv

divided by \u241F

order matches 'CIT' markers in citation context

citing document ID

citation context

MAG field of study IDs

divided by \u241F

predicate:argument tuples generated based on PredPatt

JSON

noun phrases

for *_nomarker.csv: divided by \u241F

for *_withmarker.csv:

divided by \u241D into

noun phrases

noun phrase directly preceding citation marker

Data Sources

citrec_unarxive_cs_withmarker.csv

data set

unarXive

Paper DOI: 10.1007/s11192-020-03382-z

Data DOI: 10.5281/zenodo.2553522

filter

citing doc from computer science

cited doc is cited at least 5 times

citrec_mag_cs_en.csv

data set

Microsoft Academic Graph (MAG)

Paper DOI: 10.1145/2740908.2742839

filter

citing doc from computer science and in English

citing doc abstract in MAG given

cited doc is cited at least 50 times

citrec_refseer.csv

data set

RefSeer

Paper URL: ojs.aaai.org/index.php/AAAI/article/view/9528

Data URL: psu.app.box.com/v/refseer

filter

for citing and cited docs title, venue, venuetype, abstract, and year not NULL

citrec_acl-arc_withmarker.csv

data set

ACL ARC

Paper URL: aclanthology.org/L08-1005

Data URL: acl-arc.comp.nus.edu.sg/

filter

cited doc has a DBLP ID

Paper Citation

@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, }

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

The Shannon–Wiener index is a popular nonparametric metric widely used in ecological research as a measure of species diversity. We used the Web of Science database to examine cases where papers published from 1990 to 2015 mislabelled this index. We provide detailed insights into causes potentially affecting use of the wrong name ‘Weaver’ instead of the correct ‘Wiener’. Basic science serves as a fundamental information source for applied research, so we emphasize the effect of the type of research (applied or basic) on the incidence of the error. Biological research, especially applied studies, increasingly uses indices, even though some researchers have strongly criticized their use. Applied research papers had a higher frequency of the wrong index name than did basic research papers. The mislabeling frequency decreased in both categories over the 25-year period, although the decrease lagged in applied research. Moreover, the index use and mistake proportion differed by region and aut...

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).

The citation checking software market is experiencing robust growth, driven by the increasing demand for accuracy and efficiency in legal research and academic writing. The market's expansion is fueled by several key factors. Firstly, the rising volume of legal documents and scholarly publications necessitates reliable tools for ensuring accurate citations. Secondly, the growing adoption of digital platforms and cloud-based solutions within legal and academic institutions provides a fertile ground for the market's expansion. Furthermore, increasing legal complexities and the need to avoid plagiarism are contributing to the market’s growth trajectory. Competition is fierce, with established players like Bloomberg Law and vLex vying for market share alongside innovative startups such as LegalEase Citations and Casetext. The market is segmented by software type (standalone vs. integrated), user type (legal professionals, academics, students), and deployment model (cloud-based vs. on-premise). While precise market sizing data is unavailable, based on the readily available information about similar software markets and the significant number of players in this sector, we can estimate that the global market size in 2025 was approximately $300 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033), the market is projected to reach a substantial size by 2033. This estimate takes into account potential market saturation, competitive pressures, and economic factors. The market’s future trajectory will be shaped by several trends. The integration of artificial intelligence (AI) and machine learning (ML) technologies into citation checking software will improve accuracy and efficiency, leading to enhanced user experience. This will drive adoption among a broader user base. Furthermore, the rising emphasis on data security and privacy will influence the development of robust security features in these software solutions. The increasing use of mobile devices and tablets will also influence the market, leading to the development of mobile-compatible software. However, challenges remain. The high cost of sophisticated citation checking software can limit its accessibility, particularly for smaller legal firms and individual researchers. The need for continuous software updates to stay compliant with evolving citation styles also poses a challenge for both providers and users.

A modelled dataset derived from a range of national datasets, describing the distribution of woody linear feature boundaries in Great Britain. The dataset presents linear features which have a high likelihood of being a woody linear feature. The dataset was created by a predictive model developed at the Centre for Ecology & Hydrology, Lancaster in 2016.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Early Islamic Mosques Database" data publication.

Automated monthly plot of citation ratios using OpenAlex data. This includes a PDF visualization and supporting CSV generated from OpenAlex (CC0) data.

The reference management software market is experiencing robust growth, projected to reach $319.9 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.5% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume of research publications and the consequent need for efficient organization and citation management are primary factors. Furthermore, the growing adoption of cloud-based solutions offers enhanced collaboration and accessibility, boosting market penetration. Academic institutions and researchers are increasingly recognizing the productivity gains offered by these tools, leading to higher adoption rates. The market is also witnessing a trend towards more sophisticated features, including AI-powered functionalities for automated citation generation and literature discovery. While data privacy concerns and the need for user-friendly interfaces present some challenges, the overall market trajectory is strongly positive, driven by the fundamental needs of researchers and professionals across various disciplines. The competitive landscape is dynamic, with established players like Mendeley, EndNote, and RefWorks alongside newer entrants like Paperpile and Zotero vying for market share. This competition fosters innovation and drives the development of user-friendly interfaces and advanced features. The market’s segmentation is likely driven by pricing models (freemium vs. subscription), software features (basic citation management vs. advanced research tools), and user types (students, academics, professionals). Geographic expansion, particularly in emerging economies with growing research activities, presents a significant opportunity for growth. The forecast period of 2025-2033 suggests continued market expansion, with a potential market size exceeding $500 million by 2033, driven by factors mentioned above, assuming a consistent adoption rate and continued technological advancements.

Dataset includes the following information:

Metadata (columns J-U) of 994 articles published in Nature Index journals and identified in the Web of Science, with the phrase “Retracted Article:” in their titles.

Citation counts (columns V-BP) for these 994 articles, also obtained from Web of Science.

Retraction reasons and other metadata (such as Countries, Paywalled?, Notes, etc.) extracted from the Retraction Watch Database (columns C-I).

Pre-retraction and post-retraction citation ratios columns BR-BS).

Date: March, 2025

This study will examine the impact of loss-gain frames used in civic education campaigns in a multi-country study. The content of the videos focuses on the “rights” dimension of democracy. We frame the information in terms of “gains” or “losses” based on prospect theory. In total, participants will be randomly allocated to one of three videos: 1) loss-framed rights video, 2) gain-framed rights video and 3) placebo. This study builds on Finkel-Neundorf-Rascon (AJPS, 2023) and Ozturk-Finkel-Neundorf-Rascon (working paper, 2023) where they find that loss-gain frames can be effective at increasing democratic values. Based on theoretical work on prospect theory by Kahneman and Tversky (1979), our study will extend this research by evaluating to what extent treatment effects of loss-gain civic education (CE) campaigns depend on reference points.

We will identify reference points at the macro and individual level. Macro level reference points will be defined using an objective measure of the current level of democracy of the country, using the V-Dem liberal democracy index. Micro level reference points will be defined using subjective data on how the study’s participant perceives the current level of democracy in the country and what is the expected level they believe the country will experience in 5 years’ time. Both measures of micro (individual) reference points will be collected prior to the exposure to the interventions. In total, we will consider 3 types of reference points: macro (country) level of current situation, micro (individual) assessment of current situation and individual expectations about the future. For all three types of reference points we will distinguish Low, Medium, and High levels.

This is the supplementary data for an article published in Journal of Informetrics. The abstract of article follows. In this study, we investigated the associations of two national culture models with citation impact of nations (measured by the proportion of papers belonging to the 10% and 1% most cited papers in the corresponding fields, PPtop 10% and PPtop 1%). Bivariate statistical analyses showed that of six Hofstede’s national culture dimensions (HNCD), uncertainty avoidance and power distance had a statistically significant negative association, while individualism and indulgence had a statistically significant positive association with both citation impact indicators (PPtop 10% and PPtop 1%). The study also revealed that of two Inglehart-Welzel cultural values (IWCV), the value survival versus self-expression is statistically significantly related to both citation impact indicators (PPtop 10% and PPtop 1%). We additionally calculated multiple regression analyses controlling for the possible effects of confounding factors including national self-citations, international co-authorships, investments in research and development, international migrant stock, number of researchers of each nation, language, and productivity. The results revealed that the statistically significant associations of HNCD with citation impact indicators disappeared. But the statistically significant relationship between survivals versus self-expression values and both citation impact indicators remained stable even after controlling for the confounding variables. Thus, the freedom of expression and trust in society might contribute to better scholarly communication systems, higher level of international collaborations, and further quality research.

Citations are to ‘citable documents’ (as classified by Thomson Reuters), which include standard research articles and reviews; distributions contain citations accumulated in 2015 to citable documents published in 2013 and 2014. Data was extracted using the “Purchased Database Method” detailed in the V. Larivière et. al. BioRxiv article. To facilitate direct comparison, distributions are plotted with the same range of citations (0-100) in each plot; articles with more than 100 citations are shown as a single bar at the right of each plot. Copyright held by Thomson Reuters prohibits publication of the raw data.

Abstract (en): Please cite the corresponding article if this dataset is used to support additional findings:Leon-Moreta, A, and Totaro, V. "Workforce Capacity in Municipal Government." Public Administration Review. (Forthcoming).The data described here were used in the analysis and findings reported in the article reference above. A STATA (V. 13) do-file for calculating variables is also included. Please cite the corresponding article if this dataset is used to support additional findings. Municipalities in the United States

PubMed “related citations” versus ranked noun phrases.

95% CI = 95% confidence interval†n/a = not applicable (variable was not included in the model)*Since a significant interaction term between these two variables was included in the final multivariable model, their effects are dependent on each other and are presented together.

Although sodium channels have been a hot multidisciplinary focus for decades and most of nerve system drugs worked on alerting sodium channel function, the trends and future directions of sodium channel studies have not been comprehensive analyzed bibliometrically. Herein, we collected the scientific publications of sodium channels research and constructed a model to evaluate the current trend systematically. Publications were selected from the Web of Science Core Collection (WoSCC) database from 2013 to 2017. Microsoft Excel 2016, Prism 6, and CiteSpace V software were used to analyze publication outputs, journal sources, countries, territories, institutions, authors, and research areas. A total of 4,275 publications on sodium channel research were identified. PLoS ONE ranked top for publishing 170 papers. The United States of America had the largest number of publications (1,595), citation frequency (19,490), and H-index (53). S. G. Waxman (62 publications) and W. A. Catterall (585 citations) were the most productive authors and had the greatest co-citation counts. This is the first report that shows the trends and future development in sodium channel publications, and our study provides a clear profile for the contribution to this field by countries, authors, keywords, and institutions.