CITATION.cff

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cff-version: 1.2.0
title: MIE Conference
message: >-
  If you use this dataset, please cite it using the metadata
  from this file.
type: dataset
authors:
  - given-names: Ehsan
    family-names: Bitaraf
    affiliation: >-
      Rajaei Cardiovascular Reserach Institute Iran
      University of Medical Sciences Tehran, Iran 
    orcid: 'https://orcid.org/0000-0002-6588-7349'
  - given-names: Maryam
    family-names: Jafarpour
    affiliation: >-
      Center for Medical Data Science Medical University of
      Vienna Vienna, Austria
    orcid: 'https://orcid.org/0000-0001-7266-5018'
identifiers:
  - type: doi
    value: 10.48550/arXiv.2410.04602
    description: arXiv Paper
  - type: doi
    value: 10.6084/m9.figshare.27174759
    description: Dataset
repository-code: 'https://github.com/EhsanBitaraf/dataset-mie-literature'
repository-artifact: 'https://doi.org/10.6084/m9.figshare.27174759'
abstract: >-
  The rapid expansion of medical informatics literature
  presents significant challenges in synthesizing and
  analyzing research trends. This study introduces a novel
  dataset derived from the Medical Informatics Europe (MIE)
  Conference proceedings, addressing the need for
  sophisticated analytical tools in the field. Utilizing the
  Triple-A software, we extracted and processed metadata and
  abstract from 4,606 articles published in the "Studies in
  Health Technology and Informatics" journal series,
  focusing on MIE conferences from 1996 onwards. Our
  methodology incorporated advanced techniques such as
  affiliation parsing using the TextRank algorithm. The
  resulting dataset, available in JSON format, offers a
  comprehensive view of bibliometric details, extracted
  topics, and standardized affiliation information. Analysis
  of this data revealed interesting patterns in Digital
  Object Identifier usage, citation trends, and authorship
  attribution across the years. Notably, we observed
  inconsistencies in author data and a brief period of
  linguistic diversity in publications. This dataset
  represents a significant contribution to the medical
  informatics community, enabling longitudinal studies of
  research trends, collaboration network analyses, and
  in-depth bibliometric investigations. By providing this
  enriched, structured resource spanning nearly three
  decades of conference proceedings, we aim to facilitate
  novel insights and advancements in the rapidly evolving
  field of medical informatics. 
keywords:
  - Medical informatics
  - dataset
  - Topic Extraction
  - Affiliation Parsing
  - Bibliometric Analysis
  - MIE Conference
license: CC-BY-4.0