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Academic Visualisation Publications Dataset
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About
This dataset provides detailed information on IEEE Visualization (IEEE VIS) publications from 1990 to 2023, encompassing papers from conferences such as SciVis, InfoVis, VAST, and Vis. It also includes IEEE TVCG and IEEE CG&A articles that were presented at IEEE VIS. The collection offers crucial details such as paper titles, authors, Digital Object Identifiers (DOIs), abstracts, and direct links to the papers. Additionally, it lists citations to other previous VIS papers, making it an invaluable resource for analysing trends, understanding citation networks, and exploring the evolution of visualisation research. The dataset is designed to provide analytical tools for uncovering relevant patterns and to assist with navigating content within extensive text collections.
Columns
- Conference: The short title of the conference in which the paper appeared, such as SciVis, InfoVis, VAST, or Vis.
- Year: The year the paper was presented at the conference. This may differ from the actual publication year.
- Title: The title of the paper.
- DOI: The Digital Object Identifier for the paper. If a valid DOI was not available, a syntactically valid but fictitious DOI, beginning with "10.0000", was entered, serving as a unique identifier for each paper.
- Link: A direct link to the paper within the IEEE digital library, derived from the DOI.
- FirstPage: The first page number of the paper in the printed proceedings. This information has not been thoroughly checked.
- LastPage: The last page number of the paper in the printed proceedings. This information has not been thoroughly checked and may occasionally include a second page number, particularly for older years where separate colour pages for pictures were included.
- PaperType: Indicates the type of paper: 'C' for conference paper, 'J' for journal paper, or 'M' for miscellaneous. To filter for scientific research articles, 'C' and 'J' types are relevant. Papers marked with 'M' are not typically considered archival publications.
- Abstract: A summary of the paper's content.
- AuthorNames-Deduped: A list of co-authors, with names separated by semicolons. Authors are listed in the order they appear on the paper, using de-duped full author names provided by DBLP.
Distribution
The dataset is typically provided in a Google spreadsheet format, which can be downloaded as a CSV file or other formats. It contains information on 3,753 unique DOIs and covers a substantial period from 1990 to 2023. Based on paper type, approximately 50% are Journal (J) papers, 42% are Conference (C) papers, and 8% fall into other categories. Regarding conference representation, Vis papers constitute 48% of the collection, InfoVis papers 24%, and other conferences 28%. The data is organised across multiple tabs: the main tab contains IEEE VIS papers, a second tab details journal papers presented at VIS, and a third tab provides an overview of paper submission numbers and acceptance rates across the years.
Usage
This dataset is ideally suited for various analytical and research purposes, including:
- Analysing trends and patterns within the IEEE Visualization publication landscape.
- Developing and evaluating text mining techniques for academic literature.
- Exploring citation relationships and the impact of research within the visualisation community.
- Filtering for specific types of scientific research articles (conference or journal papers).
- Investigating conference submission volumes and acceptance rates over time.
- Aiding with content navigation and discovery within large collections of academic texts.
Coverage
The dataset spans a significant time range from 1990 to 2023, offering a historical perspective on research in visualisation. While it does not include specific geographic demographics, the scope of IEEE VIS publications is considered global. The dataset specifically covers publications from IEEE Visualization (VIS) conferences, as well as IEEE TVCG and IEEE CG&A articles presented at VIS. It is structured to allow for clear distinction between different paper categories and provides a summary of yearly publication statistics.
License
CC-BY-NC
Who Can Use It
- Academic researchers and scholars focused on bibliometrics, publication trends, and the history of visualisation.
- Data scientists and machine learning practitioners interested in natural language processing (NLP) and text analysis.
- Students undertaking academic projects or literature reviews related to computer graphics and visualisation.
- Conference organisers seeking insights into academic publishing patterns and acceptance statistics.
- Developers creating tools for academic search, data exploration, or content analysis.
Dataset Name Suggestions
- IEEE VIS Publication Data
- Visualisation Research Papers Collection
- Academic Visualisation Publications (1990-2023)
- VIS Conference and Journal Dataset
- IEEE VIS Bibliographic Data
Attributes
Original Data Source: 📜 Publication Dataset
QUALITY