Self-citation analysis data based on PubMed Central subset (2002-2005)



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:

* 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.
- 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.
- 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.

* 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.
- 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.
- 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.

* 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.
- 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.

* 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.
- 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:

* Expertise dataset for identifying author expertise on articles:

* Source code provided at:

**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
Date made availableApr 23 2018
PublisherUniversity of Illinois at Urbana-Champaign


  • Self citation
  • PubMed Central
  • Data Analysis
  • Citation Data

Cite this

Mishra, S. (Creator), Fegley, B. D. (Creator), Diesner, J. (Creator), Torvik, V. I. (Creator). (Apr 23 2018): Self-citation analysis data based on PubMed Central subset (2002-2005), University of Illinois at Urbana-Champaign. 10.13012/B2IDB-9665377_V1