[Paper Review] Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers

Kai Li, Jane Greenberg+1 more|arXiv (Cornell University)|Mar 14, 2019

Research Data Management Practices4 citations

TL;DR

This study analyzes data events in 82 biodiversity data papers from GBIF to examine how scientific processes and data transformations are documented. It identifies 17 data event categories, reveals frequent co-occurrence of multiple events in single sentences, and challenges the notion of data papers as a distinct scholarly genre, highlighting the need for improved data publication practices in scientific workflows.

ABSTRACT

The data paper, an emerging scholarly genre, describes research datasets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this paper addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility (GBIF). Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research papers and they describe data-centric research processes in a through way. This paper also discusses how our results could inform a better data publication ecosystem in the future.

Motivation & Objective

• To investigate how data events—such as data collection, transformation, and sharing—are described in data papers.
• To assess the extent to which data papers function as a distinct scholarly genre compared to traditional research articles.
• To understand the representation of scientific processes in data papers, particularly the documentation of data-centric workflows.
• To identify patterns in how data events are inscribed, including frequency and co-occurrence in textual descriptions.
• To inform the development of a more robust and standardized data publication ecosystem in biodiversity research.

Proposed method

• Conducted a content analysis of the full texts of 82 data papers sourced from the Global Biodiversity Information Facility (GBIF).
• Developed a coding framework with 17 distinct categories to classify data events reported in the papers.
• Systematically coded data events across the full text of each paper based on the predefined categories.
• Analyzed co-occurrence patterns of data event categories within individual sentences to assess textual complexity.
• Used qualitative and quantitative analysis to evaluate the structure and representation of data event descriptions.
• Evaluated the implications of findings for data publication standards and scholarly communication in biodiversity science.

Experimental results

Research questions

• RQ1 How are data events such as data collection, processing, and sharing represented in biodiversity data papers?
• RQ2 To what extent do data papers document complex, multi-step data processes in a single sentence?
• RQ3 How do data event descriptions in data papers compare to those in traditional research articles in terms of structure and granularity?
• RQ4 What patterns emerge in the co-occurrence of multiple data event types within individual sentences?
• RQ5 To what degree do data papers function as a distinct scholarly genre from conventional research papers?

Key findings

• A total of 17 distinct data event categories were identified in the analysis of 82 data papers.
• Many data event categories were described together in the same sentence, indicating high textual complexity and potential ambiguity in documentation.
• The co-occurrence of multiple data events in single sentences suggests that data processes in biodiversity research are often described in a dense, unstructured manner.
• The findings challenge the assumption that data papers are a clearly demarcated genre, as their documentation practices resemble those of traditional research articles.
• The study reveals significant variation in how data events are reported, with no standardized approach to documenting data workflows.
• The results suggest that current data paper practices may hinder reproducibility and traceability, necessitating improved documentation standards in data publication.