This dataset contains all the citation data (in CSV format) included in POCI, released on 27 December 2022. In particular, each line of the CSV file defines a citation, and includes the following information:

[field "oci"] the Open Citation Identifier (OCI) for the citation; [field "citing"] the PMID of the citing entity; [field "cited"] the PMID of the cited entity; [field "creation"] the creation date of the citation (i.e. the publication date of the citing entity); [field "timespan"] the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity); [field "journal_sc"] it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal); [field "author_sc"] it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).

This version of the dataset contains:

717,654,703 citations; 26,024,862 bibliographic resources.

The size of the zipped archive is 9.6 GB, while the size of the unzipped CSV file is 50 GB. Additional information about POCI at official webpage.

Self-citation analysis data based on PubMed Central subset (2002-2005) ---------------------------------------------------------------------- Created by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik on April 5th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Fegley BD, Diesner J, Torvik VI (2018) Self-Citation is the Hallmark of Productive Authors, of Any Gender. PLOS ONE. It contains files for running the self citation analysis on articles published in PubMed Central between 2002 and 2005, collected in 2015. The dataset is distributed in the form of the following tab separated text files: * Training_data_2002_2005_pmc_pair_First.txt (1.2G) - Data for first authors * Training_data_2002_2005_pmc_pair_Last.txt (1.2G) - Data for last authors * Training_data_2002_2005_pmc_pair_Middle_2nd.txt (964M) - Data for middle 2nd authors * Training_data_2002_2005_pmc_pair_txt.header.txt - Header for the data * COLUMNS_DESC.txt file - Descriptions of all columns * model_text_files.tar.gz - Text files containing model coefficients and scores for model selection. * results_all_model.tar.gz - Model coefficient and result files in numpy format used for plotting purposes. v4.reviewer contains models for analysis done after reviewer comments. * README.txt file ## Dataset creation Our experiments relied on data from multiple sources including properitery data from Thompson Rueter's (now Clarivate Analytics) Web of Science collection of MEDLINE citations. Author's interested in reproducing our experiments should personally request from Clarivate Analytics for this data. However, we do make a similar but open dataset based on citations from PubMed Central which can be utilized to get similar results to those reported in our analysis. Furthermore, we have also freely shared our datasets which can be used along with the citation datasets from Clarivate Analytics, to re-create the datased used in our experiments. These datasets are listed below. If you wish to use any of those datasets please make sure you cite both the dataset as well as the paper introducing the dataset. * MEDLINE 2015 baseline: https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html * Citation data from PubMed Central (original paper includes additional citations from Web of Science) * Author-ity 2009 dataset: - Dataset citation: Torvik, Vetle I.; Smalheiser, Neil R. (2018): Author-ity 2009 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4222651_V1 - Paper citation: Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3), 1–29. https://doi.org/10.1145/1552303.1552304 - Paper citation: Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2004). A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140–158. https://doi.org/10.1002/asi.20105 * Genni 2.0 + Ethnea for identifying author gender and ethnicity: - Dataset citation: Torvik, Vetle (2018): Genni + Ethnea for the Author-ity 2009 dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9087546_V1 - Paper citation: Smith, B. N., Singh, M., & Torvik, V. I. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL ’13. ACM Press. https://doi.org/10.1145/2467696.2467720 - Paper citation: Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington DC, USA. http://hdl.handle.net/2142/88927 * MapAffil for identifying article country of affiliation: - Dataset citation: Torvik, Vetle I. (2018): MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4354331_V1 - Paper citation: Torvik VI. MapAffil: A Bibliographic Tool for Mapping Author Affiliation Strings to Cities and Their Geocodes Worldwide. D-Lib magazine : the magazine of the Digital Library Forum. 2015;21(11-12):10.1045/november2015-torvik * IMPLICIT journal similarity: - Dataset citation: Torvik, Vetle (2018): Author-implicit journal, MeSH, title-word, and affiliation-word pairs based on Author-ity 2009. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4742014_V1 * Novelty dataset for identify article level novelty: - Dataset citation: Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1 - Paper citation: Mishra S, Torvik VI. Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib magazine : The Magazine of the Digital Library Forum. 2016;22(9-10):10.1045/september2016-mishra - Code: https://github.com/napsternxg/Novelty * Expertise dataset for identifying author expertise on articles: * Source code provided at: https://github.com/napsternxg/PubMed_SelfCitationAnalysis Note: The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check here for information to get PubMed/MEDLINE, and NLMs data Terms and Conditions Additional data related updates can be found at Torvik Research Group ## Acknowledgments This work was made possible in part with funding to VIT from NIH grant P01AG039347 and NSF grant 1348742. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## License Self-citation analysis data based on PubMed Central subset (2002-2005) by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik is licensed under a Creative Commons Attribution 4.0 International License. Permissions beyond the scope of this license may be available at https://github.com/napsternxg/PubMed_SelfCitationAnalysis.

In January 2019, the Asclepias Broker harvested citation links to Zenodo objects from three discovery systems: the NASA Astrophysics Datasystem (ADS), Crossref Event Data and Europe PMC. Each row of our dataset represents one unique link between a citing publication and a Zenodo DOI. Both endpoints are described by basic metadata. The second dataset contains usage metrics for every cited Zenodo DOI of our data sample.

The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon. The API allows for daily refresh and retrieval of enrich citation data from Office Actions mailed from October 1, 2017 to 30 days prior to the current date.

Objectives: To analyse the total number of newspaper articles citing the four leading general medical journals and to describe national citation patterns. Design: Quantitative content analysis Setting/sample: Full text of 22 general newspapers in 14 countries over the period 2008-2015, collected from LexisNexis. The 14 countries have been categorized into four regions: US, UK, Western World (EU countries other than UK, and Australia, New Zealand and Canada) and Rest of the World (other countries). Main outcome measure: Press citations of four medical journals (two American: NEJM and JAMA; and two British: The Lancet and The BMJ) in 22 newspapers. Results: British and American newspapers cited some of the four analysed medical journals about three times a week in 2008-2015 (weekly mean 3.2 and 2.7 citations respectively); the newspapers from other Western countries did so about once a week (weekly mean 1.1), and those from the Rest of the World cited them about once a month (monthly mean 1.1). The New York Times cited above all other newspapers (weekly mean 4.7). The analysis showed the existence of three national citation patterns in the daily press: American newspapers cited mostly American journals (70.0% of citations), British newspapers cited mostly British journals (86.5%), and the rest of the analysed press cited more British journals than American ones. The Lancet was the most cited journal in the press of almost all Western countries outside the US and the UK. Multivariate correspondence analysis confirmed the national patterns and showed that over 85% of the citation data variability is retained in just one single new variable: the national dimension. Conclusion: British and American newspapers are the ones that cite the four analysed medical journals more often, showing a domestic preference for their respective national journals; non-British and non-American newspapers show a common international citation pattern.

Citation metrics are widely used and misused. We have created a publicly available database of over 100,000 top-scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator. Separate data are shown for career-long and single year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 sub-fields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least 5 papers. Career-long data are updated to end-of-2020. The selection is based on the top 100,000 by c-score (with and without self-citations) or a percentile rank of 2% or above.

The dataset and code provides an update to previously released version 1 data under https://doi.org/10.17632/btchxktzyw.1; The version 2 dataset is based on the May 06, 2020 snapshot from Scopus and is updated to citation year 2019 available at https://doi.org/10.17632/btchxktzyw.2

This version (3) is based on the Aug 01, 2021 snapshot from Scopus and is updated to citation year 2020.

Data enclosed in a single zipped folder:

A) DASH-V2 : Data files for final published analysis (J. Informetrics, 2019)

File A1: PubData_DOI_141986_Nc_0_2019.dta

File A2: PubData_DOI_141986_Nc_0_2019_DOFILE

B) DASH-V1 : Data files for preprint version (https://ssrn.com/abstract=2901272)

File B1: PubData_Obs_102741_Nc_10_No2015_CitationsAnalysis.dta

File B2: PubData_Obs_128734_Nc_10_AcceptanceTimeAnalysis.dta

File B3: STATA13_DOFILE

C) Data description common to all .dta files, which contain parsed and merged PLOS ONE and Web of Science metadata:

File A3: UC-DASH_DataDescription_Petersen_V2.pdf

File B4: UC-DASH_DataDescription_Petersen_V1.pdf

The ACL-cite dataset was created for the paper: “On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles” published in NAACL 2021. This dataset contains over 2.7 million sentences extracted from scholarly articles (from ACL Anthology [Bird et al.]) and their corresponding citation worthiness labels. The goal of the citation worthiness task is to determine whether a given sentence requires a citation.

There are three CSV files in the dataset:

train.csv: 1,625,268 rows

dev.csv: 539,085 rows

test.csv: 542,081 rows

Each CSV file contains the following columns:

document_id: identifier of the paper the sentence was extracted from

section: name of the section the sentence was extracted from, (e.g. Abstract, Introduction, etc.)

section_id: sequential identifier of the section in the paper

paragraph_id: sequential identifier of the paragraph the sentence was extracted from

sentence: the sentence with the citations removed

raw_sentence: the raw sentence including the citations

sentence_id: sequential identifier of the sentence in the paper

label: citation worthiness label

Note: The train/dev/test splits are done at the document_id level.

A list of all uniform citations from the Louisville Metro Police Department, the CSV file is updated daily, including case number, date, location, division, beat, offender demographics, statutes and charges, and UCR codes can be found in this Link.INCIDENT_NUMBER or CASE_NUMBER links these data sets together:Crime DataUniform Citation DataFirearm intakeLMPD hate crimesAssaulted OfficersCITATION_CONTROL_NUMBER links these data sets together:Uniform Citation DataLMPD Stops DataNote: When examining this data, make sure to read the LMPDCrime Data section in our Terms of Use.AGENCY_DESC - the name of the department that issued the citationCASE_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms and can be used to connect the dataset to the following other datasets INCIDENT_NUMBER:1. Crime Data2. Firearms intake3. LMPD hate crimes4. Assaulted OfficersNOTE: CASE_NUMBER is not formatted the same as the INCIDENT_NUMBER in the other datasets. For example: in the Uniform Citation Data you have CASE_NUMBER 8018013155 (no dashes) which matches up with INCIDENT_NUMBER 80-18-013155 in the other 4 datasets.CITATION_YEAR - the year the citation was issuedCITATION_CONTROL_NUMBER - links this LMPD stops dataCITATION_TYPE_DESC - the type of citation issued (citations include: general citations, summons, warrants, arrests, and juvenile)CITATION_DATE - the date the citation was issuedCITATION_LOCATION - the location the citation was issuedDIVISION - the LMPD division in which the citation was issuedBEAT - the LMPD beat in which the citation was issuedPERSONS_SEX - the gender of the person who received the citationPERSONS_RACE - the race of the person who received the citation (W-White, B-Black, H-Hispanic, A-Asian/Pacific Islander, I-American Indian, U-Undeclared, IB-Indian/India/Burmese, M-Middle Eastern Descent, AN-Alaskan Native)PERSONS_ETHNICITY - the ethnicity of the person who received the citation (N-Not Hispanic, H=Hispanic, U=Undeclared)PERSONS_AGE - the age of the person who received the citationPERSONS_HOME_CITY - the city in which the person who received the citation livesPERSONS_HOME_STATE - the state in which the person who received the citation livesPERSONS_HOME_ZIP - the zip code in which the person who received the citation livesVIOLATION_CODE - multiple alpha/numeric code assigned by the Kentucky State Police to link to a Kentucky Revised Statute. For a full list of codes visit: https://kentuckystatepolice.org/crime-traffic-data/ASCF_CODE - the code that follows the guidelines of the American Security Council Foundation. For more details visit https://www.ascfusa.org/STATUTE - multiple alpha/numeric code representing a Kentucky Revised Statute. For a full list of Kentucky Revised Statute information visit: https://apps.legislature.ky.gov/law/statutes/CHARGE_DESC - the description of the type of charge for the citationUCR_CODE - the code that follows the guidelines of the Uniform Crime Report. For more details visit https://ucr.fbi.gov/UCR_DESC - the description of the UCR_CODE. For more details visit https://ucr.fbi.gov/

Citation of reference material is well established for most traditional sources but remains inconsistent in its application for online resources such as web pages, blog posts and materials generated from underlying database queries. We present some tips on how authors can more effectively cite and archive such resources so they are persistent and sustainable.

Note: Due to a system migration, this data will cease to update on March 14th, 2023. The current projection is to restart the updates on or around July 17th, 2024.A list of all uniform citations from the Louisville Metro Police Department, the CSV file is updated daily, including case number, date, location, division, beat, offender demographics, statutes and charges, and UCR codes can be found in this Link.INCIDENT_NUMBER or CASE_NUMBER links these data sets together:Crime DataUniform Citation DataFirearm intakeLMPD hate crimesAssaulted OfficersCITATION_CONTROL_NUMBER links these data sets together:Uniform Citation DataLMPD Stops DataNote: When examining this data, make sure to read the LMPDCrime Data section in our Terms of Use.AGENCY_DESC - the name of the department that issued the citationCASE_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms and can be used to connect the dataset to the following other datasets INCIDENT_NUMBER:1. Crime Data2. Firearms intake3. LMPD hate crimes4. Assaulted OfficersNOTE: CASE_NUMBER is not formatted the same as the INCIDENT_NUMBER in the other datasets. For example: in the Uniform Citation Data you have CASE_NUMBER 8018013155 (no dashes) which matches up with INCIDENT_NUMBER 80-18-013155 in the other 4 datasets.CITATION_YEAR - the year the citation was issuedCITATION_CONTROL_NUMBER - links this LMPD stops dataCITATION_TYPE_DESC - the type of citation issued (citations include: general citations, summons, warrants, arrests, and juvenile)CITATION_DATE - the date the citation was issuedCITATION_LOCATION - the location the citation was issuedDIVISION - the LMPD division in which the citation was issuedBEAT - the LMPD beat in which the citation was issuedPERSONS_SEX - the gender of the person who received the citationPERSONS_RACE - the race of the person who received the citation (W-White, B-Black, H-Hispanic, A-Asian/Pacific Islander, I-American Indian, U-Undeclared, IB-Indian/India/Burmese, M-Middle Eastern Descent, AN-Alaskan Native)PERSONS_ETHNICITY - the ethnicity of the person who received the citation (N-Not Hispanic, H=Hispanic, U=Undeclared)PERSONS_AGE - the age of the person who received the citationPERSONS_HOME_CITY - the city in which the person who received the citation livesPERSONS_HOME_STATE - the state in which the person who received the citation livesPERSONS_HOME_ZIP - the zip code in which the person who received the citation livesVIOLATION_CODE - multiple alpha/numeric code assigned by the Kentucky State Police to link to a Kentucky Revised Statute. For a full list of codes visit: https://kentuckystatepolice.org/crime-traffic-data/ASCF_CODE - the code that follows the guidelines of the American Security Council Foundation. For more details visit https://www.ascfusa.org/STATUTE - multiple alpha/numeric code representing a Kentucky Revised Statute. For a full list of Kentucky Revised Statute information visit: https://apps.legislature.ky.gov/law/statutes/CHARGE_DESC - the description of the type of charge for the citationUCR_CODE - the code that follows the guidelines of the Uniform Crime Report. For more details visit https://ucr.fbi.gov/UCR_DESC - the description of the UCR_CODE. For more details visit https://ucr.fbi.gov/

Extracting and parsing reference strings from research articles is a challenging task. State-of-the-art tools like GROBID apply rather simple machine learning models such as conditional random fields (CRF). Recent research has shown a high potential of deep-learning for reference string parsing. The challenge with deep learning is, however, that the training step requires enormous amounts of labeled data – which does not exist for reference string parsing. Creating such a large dataset manually, through human labor, seems hardly feasible. Therefore, we created GIANT. GIANT is a large dataset with 991,411,100 XML labeled reference strings. The strings were automatically created based on 677,000 entries from CrossRef, 1,500 citation styles in the citation-style language, and the citation processor citeproc-js. GIANT can be used to train machine learning models, particularly deep learning models, for citation parsing. While we have not yet tested GIANT for training such models, we hypothesise that the dataset will be able to significantly improve the accuracy of citation parsing. The dataset and code to create it, are freely available at https://github.com/BeelGroup/.

Description

unarXive is a scholarly data set containing publications' full-text, annotated in-text citations, and a citation network.

The data is generated from all LaTeX sources on arXiv and therefore of higher quality than data generated from PDF files.

Typical use cases are

• Citation recommendation
• Citation context analysis
• Bibliographic analyses
• Reference string parsing

Note: This Zenodo record is an old version of unarXive. You can find the most recent version at https://zenodo.org/record/7752754 and https://zenodo.org/record/7752615

Access

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┃ D O W N L O A D S A M P L E  ┃
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To download the whole data set send an access request and note the following:

Note: this Zenodo record is a "full" version of unarXive, which was generated from all of arXiv.org including non-permissively licensed papers. Make sure that your use of the data is compliant with the paper's licensing terms.¹

¹ For information on papers' licenses use arXiv's bulk metadata access.

The code used for generating the data set is publicly available.

Usage examples for our data set are provided at here on GitHub.

Citing

This initial version of unarXive is described in the following journal article.

Tarek Saier, Michael Färber: "unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata", Scientometrics, 2020,
[link to an author copy]

The updated version is described in the following conference paper.

Tarek Saier, Michael Färber. "unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network", JCDL 2023.
[link to an author copy]

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 is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:

Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.

Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articles

Open Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation Collection

Definitions for individual data fields:

pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicine

doi: Digital Object Identifier, if available

year: Year the article was published

title: Title of the article

authors: List of author names

journal: Journal name (ISO abbreviation)

is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research article

relative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.

provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.

citation_count: Number of unique articles that have cited this one

citations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.

field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.

expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.

nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.

human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

x_coord: X coordinate of the article on the Triangle of Biomedicine

y_coord: Y Coordinate of the article on the Triangle of Biomedicine

is_clinical: Flag indicating that this paper meets the definition of a clinical article.

cited_by_clin: PMIDs of clinical articles that this article has been cited by.

apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.

cited_by: PMIDs of articles that have cited this one.

references: PMIDs of articles in this article's reference list.

Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.

Comments and questions can be addressed to iCite@mail.nih.gov

Introduction
This document describes the data collection and datasets used in the manuscript "A Matter of Culture? Conceptualising and Investigating ‘Evidence Cultures’ within Research on Evidence-Informed Policymaking" [1].

Data Collection

To construct the citation network analysed in the manuscript, we first designed a series of queries to capture a large sample of literature exploring the relationship between evidence, policy, and culture from various perspectives. Our team of domain experts developed the following queries based on terms common in the literature. These queries search for the terms included in the titles, abstracts, and associated keywords of WoS indexed records (i.e. ‘TS=’). While these are separated below for ease of reading, they combined into a single query via the OR operator in our search. Our search was conducted on the Web of Science’s (WoS) Core Collection through the University of Edinburgh Library subscription on 29/11/2023, returning a total of 2,089 records.

TS = ((“cultures of evidence” OR “culture of evidence” OR “culture of knowledge” OR “cultures of knowledge” OR “research culture” OR “research cultures” OR “culture of research” OR “cultures of research” OR “epistemic culture” OR “epistemic cultures” OR “epistemic community” OR “epistemic communities” OR “epistemic infrastructure” OR “evaluation culture” OR “evaluation cultures” OR “culture of evaluation” OR “cultures of evaluation” OR “thought style” OR “thought styles” OR “thought collective” OR “thought collectives” OR “knowledge regime” OR “knowledge regimes” OR “knowledge system” OR “knowledge systems” OR “civic epistemology” OR “civic epistemologies”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “policy decision” OR “policy decisions” OR “political decision” OR “political decisions” OR “political decision making”))

OR

TS = ((“culture” OR “cultures”) AND ((“evidence-based” OR “evidence-informed” OR “evidence-led” OR “science-based” OR “science-informed” OR “science-led” OR “research-based” OR “research-informed” OR “evidence use” OR “evidence user” OR “evidence utilisation” OR “evidence utilization” OR “research use” OR “researcher user” OR “research utilisation” OR “research utilization” OR “research in” OR “evidence in” OR “science in”) NEAR/1 (“policymaking” OR “policy making” OR “policy maker” OR “policy makers”)))

OR

TS = ((“culture” OR “cultures”) AND (“scientific advice” OR “technical advice” OR “scientific expertise” OR “technical expertise” OR “expert advice”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “political decision” OR “political decisions” OR “political decision making”))

OR

TS = ((“culture” OR “cultures”) AND (“post-normal science” OR “trans-science” OR “transdisciplinary” OR “transdisiplinarity” OR “science-policy interface” OR “policy sciences” OR “sociology of knowledge” OR “sociology of science” OR “knowledge transfer” OR “knowledge translation” OR “knowledge broker” OR “implementation science” OR “risk society”) AND (“policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers”))

Citation Network Construction

All bibliographic metadata on these 2,089 records were downloaded in five batches in plain text and then merged in R. We then parsed these data into network readable files. All unique reference strings are given unique node IDs. A node-attribute-list (‘CE_Node’) links identifying information of each document with its node ID, including authors, title, year of publication, journal WoS ID, and WoS citations. An edge-list (‘CE_Edge’) records all citations from these documents to their bibliographies – with edges going from a citing document to the cited – using the relevant node IDs. These data were then cleaned by (a) matching DOIs for reference strings that differ but point to the same paper, and (b) manual merging of obvious duplicates caused by referencing errors.

Our initial dataset consisted of 2,089 retrieved documents and 123,772 unretrieved cited documents (i.e. documents that were cited within the publications we retrieved but which were not one of these 2,089 documents). These documents were connected by 157,229 citation links, but ~87% of the documents in the network were cited just once. To focus on relevant literature, we filtered the network to include only documents with at least three citation or reference links. We further refined the dataset by focusing on the main connected component, resulting in 6,650 nodes and 29,198 edges. It is this dataset that we publish here, and it is this network that underpins Figure 1, Table 1, and the qualitative examination of documents (see manuscript for further details).

Our final network dataset contains 1,819 of the documents in our original query (~87% of the original retrieved records), and 4,831 documents not retrieved via our Web of Science search but cited by at least three of the retrieved documents. We then clustered this network by modularity maximization via the Leiden algorithm [2], detecting 14 clusters with Q=0.59. Citations to documents within the same cluster constitute ~77% of all citations in the network.

Citation Network Dataset Description

We include two network datasets: (i) ‘CE_Node.csv’ that contains 1,819 retrieved documents, 4,831 unretrieved referenced documents, making for a total of 6,650 documents (nodes); (ii)’CE_Edge.csv’ that records citations (edges) between the documents (nodes), including a total of 29,198 citation links. These files can be used to construct a network with many different tools, but we have formatted these to be used in Gephi 0.10[3].

‘CE_Node.csv’ is a comma-separate values file that contains two types of nodes:

i. Retrieved documents – these are documents captured by our query. These include full bibliographic metadata and reference lists.

ii. Non-retrieved documents – these are documents referenced by our retrieved documents but were not retrieved via our query. These only have data contained within their reference string (i.e. first author, journal or book title, year of publication, and possibly DOI).

The columns in the .csv refer to:

- Id, the node ID

- Label, the reference string of the document

- DOI, the DOI for the document, if available

- WOS_ID, WoS accession number

- Authors, named authors

- Title, title of document

- Document_type, variable indicating whether a document is an article, review, etc.

- Journal_book_title, journal of publication or title of book

- Publication year, year of publication.

- WOS_times_cited, total Core Collection citations as of 29/11/2023

- Indegree, number of within network citations to a given document

- Cluster, provides the cluster membership number as discussed in the manuscript (Figure 1)

‘CE_Edge.csv’ is a comma-separated values file that contains edges (citation links) between nodes (documents) (n=29,198). The columns refer to:

- Source, node ID of the citing document

- Target, node ID of the cited document

Cluster Analysis

We qualitatively analyse a set of publications from seven of the largest clusters in our manuscript.

A list of all uniform citations from the Louisville Metro Police Department, the CSV file is updated daily, including case number, date, location, division, beat, offender demographics, statutes and charges, and UCR codes can be found in this Link.INCIDENT_NUMBER or CASE_NUMBER links these data sets together:Crime DataUniform Citation DataFirearm intakeLMPD hate crimesAssaulted OfficersCITATION_CONTROL_NUMBER links these data sets together:Uniform Citation DataLMPD Stops DataNote: When examining this data, make sure to read the LMPDCrime Data section in our Terms of Use.AGENCY_DESC - the name of the department that issued the citation

CASE_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms and can be used to connect the dataset to the following other datasets INCIDENT_NUMBER: 1. Crime Data 2. Firearms intake 3. LMPD hate crimes 4. Assaulted Officers

NOTE: CASE_NUMBER is not formatted the same as the INCIDENT_NUMBER in the other datasets. For example: in the Uniform Citation Data you have CASE_NUMBER 8018013155 (no dashes) which matches up with INCIDENT_NUMBER 80-18-013155 in the other 4 datasets.

CITATION_YEAR - the year the citation was issued

CITATION_CONTROL_NUMBER - links this LMPD stops data

CITATION_TYPE_DESC - the type of citation issued (citations include: general citations, summons, warrants, arrests, and juvenile)

CITATION_DATE - the date the citation was issued

CITATION_LOCATION - the location the citation was issued

DIVISION - the LMPD division in which the citation was issued

BEAT - the LMPD beat in which the citation was issued

PERSONS_SEX - the gender of the person who received the citation

PERSONS_RACE - the race of the person who received the citation (W-White, B-Black, H-Hispanic, A-Asian/Pacific Islander, I-American Indian, U-Undeclared, IB-Indian/India/Burmese, M-Middle Eastern Descent, AN-Alaskan Native)

PERSONS_ETHNICITY - the ethnicity of the person who received the citation (N-Not Hispanic, H=Hispanic, U=Undeclared)

PERSONS_AGE - the age of the person who received the citation

PERSONS_HOME_CITY - the city in which the person who received the citation lives

PERSONS_HOME_STATE - the state in which the person who received the citation lives

PERSONS_HOME_ZIP - the zip code in which the person who received the citation lives

VIOLATION_CODE - multiple alpha/numeric code assigned by the Kentucky State Police to link to a Kentucky Revised Statute. For a full list of codes visit: https://kentuckystatepolice.org/crime-traffic-data/

ASCF_CODE - the code that follows the guidelines of the American Security Council Foundation. For more details visit https://www.ascfusa.org/

STATUTE - multiple alpha/numeric code representing a Kentucky Revised Statute. For a full list of Kentucky Revised Statute information visit: https://apps.legislature.ky.gov/law/statutes/

CHARGE_DESC - the description of the type of charge for the citation

UCR_CODE - the code that follows the guidelines of the Uniform Crime Report. For more details visit https://ucr.fbi.gov/

UCR_DESC - the description of the UCR_CODE. For more details visit https://ucr.fbi.gov/

This zipped folders contain all the data produced for the research "Uncovering the Citation Landscape: Exploring OpenCitations COCI, OpenCitations Meta, and ERIH-PLUS in Social Sciences and Humanities Journals": the results datasets (dataset_map_disciplines, dataset_no_SSH, dataset_SSH, erih_meta_with_disciplines and erih_meta_without_disciplines).

dataset_map_disciplines.zip contains CSV files with four columns ("id", "citing", "cited", "disciplines") giving information about publications stored in OpenCitations META (version 3 released on February 2023) and part of SSH journals, according to ERIH PLUS (version downloaded on 2023-04-27), specifying the disciplines associated to them and a boolean value stating if they cite or are cited, according to the OpenCitations COCI dataset (version 19 released on January 2023).

dataset_no_SSH.zip and dataset_SSH.zip contain CSV files with the same structure. Each dataset has four columns: "citing", "is_citing_SSH", "cited", and "is_cited_SSH". ”Citing” and “cited” columns are filled with DOIs of publications stored in OpenCitations META that according to OpenCitations COCI are involved in a citation. The "is_citing_SSH" and "is_cited_SSH" columns contain boolean values: "True" if the corresponding publication is associated with a SSH (Social Sciences and Humanities) discipline, according to ERIH PLUS, and "False" otherwise. The two datasets are built starting from the two different subsets obtained as a result of the union between OpenCitations META and ERIH PLUS: dataset_SSH comes from erih_meta_with_disciplines and dataset_no_SSH from erih_meta_without_disciplines. dataset_no_SSH comes from erih_meta_with_disciplines.zip and erih_meta_without_disciplines.zip, as explained before, contain CSV files originating from ERIH PLUS and META. erih_meta_without_disciplines has just one column “id” and contains the DOIs of all the publications in META that do not have any discipline associated, that is, have not been published on a SSH journal, while erih_meta_with_disciplines derives from all the publications in META that have at least one linked discipline and has two columns: “id” and “erih_disciplines”, containing a string with all the disciplines linked to that publication like "History, Interdisciplinary research in the Humanities, Interdisciplinary research in the Social Sciences, Sociology".

Software: https://doi.org/10.5281/zenodo.8326023

Data preprocessed: https://doi.org/10.5281/zenodo.7973159

Article: https://zenodo.org/record/8326044

DMP: https://zenodo.org/record/8324973

Protocol: https://doi.org/10.17504/protocols.io.n92ldpeenl5b/v5

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model. Methods We focus on a subset of the Web of Science (WoS), WoS-Stat, which is a citation network that comprises the citations between papers published in journals whose subject is associated with “Statistics and Probability.” We construct a citation network utilizing a paper identifier (ID), publication year, and reference list (list of paper IDs) for 36 years, from 1981 to 2016. WoS-Stat consists of 179,483 papers and 1,106,622 citations.

Crime DataUniform Citation DataFirearm intakeLMPD hate crimesAssaulted OfficersCITATION_CONTROL_NUMBER links these data sets together:Uniform Citation DataLMPD Stops DataNote: When examining this data, make sure to read the LMPDCrime Data section in our Terms of Use.AGENCY_DESC - the name of the department that issued the citationCASE_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms and can be used to connect the dataset to the following other datasets INCIDENT_NUMBER:1. Crime Data2. Firearms intake3. LMPD hate crimes4. Assaulted OfficersNOTE: CASE_NUMBER is not formatted the same as the INCIDENT_NUMBER in the other datasets. For example: in the Uniform Citation Data you have CASE_NUMBER 8018013155 (no dashes) which matches up with INCIDENT_NUMBER 80-18-013155 in the other 4 datasets.CITATION_YEAR - the year the citation was issuedCITATION_CONTROL_NUMBER - links this LMPD stops dataCITATION_TYPE_DESC - the type of citation issued (citations include: general citations, summons, warrants, arrests, and juvenile)CITATION_DATE - the date the citation was issuedCITATION_LOCATION - the location the citation was issuedDIVISION - the LMPD division in which the citation was issuedBEAT - the LMPD beat in which the citation was issuedPERSONS_SEX - the gender of the person who received the citationPERSONS_RACE - the race of the person who received the citation (W-White, B-Black, H-Hispanic, A-Asian/Pacific Islander, I-American Indian, U-Undeclared, IB-Indian/India/Burmese, M-Middle Eastern Descent, AN-Alaskan Native)PERSONS_ETHNICITY - the ethnicity of the person who received the citation (N-Not Hispanic, H=Hispanic, U=Undeclared)PERSONS_AGE - the age of the person who received the citationPERSONS_HOME_CITY - the city in which the person who received the citation livesPERSONS_HOME_STATE - the state in which the person who received the citation livesPERSONS_HOME_ZIP - the zip code in which the person who received the citation livesVIOLATION_CODE - multiple alpha/numeric code assigned by the Kentucky State Police to link to a Kentucky Revised Statute. For a full list of codes visit: https://kentuckystatepolice.org/crime-traffic-data/ASCF_CODE - the code that follows the guidelines of the American Security Council Foundation. For more details visit https://www.ascfusa.org/STATUTE - multiple alpha/numeric code representing a Kentucky Revised Statute. For a full list of Kentucky Revised Statute information visit: https://apps.legislature.ky.gov/law/statutes/CHARGE_DESC - the description of the type of charge for the citationUCR_CODE - the code that follows the guidelines of the Uniform Crime Report. For more details visit https://ucr.fbi.gov/