ArticleHeliyon2025
Agriculture data sharing review.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Lessons in implementing open farm data for diverse stakeholders at Harper Adams Future Farm.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Agriculture data sharing review.Heliyon · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study conducts a bibliometric analysis of 252 scientific publications from 2001 to 2023, exploring the evolution and emerging trends in agricultural data spaces. Analyzing articles from the Web of Science and Scopus databases, we address six research questions: the current and interconnected key topics in agricultural data spaces (RQ1), the evolution of research themes over time (RQ2), emerging trends in the field (RQ3), the identification of leading researchers (RQ4), and the primary funding sources for this research area (RQ5), the relationship among research data and small farmers (RQ6). Our findings reveal a shift from traditional to innovative research themes, such as the increasing focus on the Internet of Things (IoT), Blockchain, and Digital Storage. This indicates a trend toward modernizing agricultural practices through technology. We found that the rise of these topics is not correlated with the results shown by Google Scholar for these same terms but is correlated with the economic impact of such areas. Prominent authors and significant funding sources, including the European Union, the United States Department of Agriculture, and Chinese research programs, have been identified, proving the global interest and investment in the digitalization of agriculture. We continue our analysis by identifying some barriers that prevent small farmers from using or sharing research data, among them cultural contexts, lack of trust in providers, and ignorance of terms and conditions. This study offers valuable insights for researchers, policymakers, and practitioners, focusing on the evolution of the dynamic landscape of agricultural data spaces and potential future innovations in fields like data analytics and Artificial Intelligence, Internet of Things, Blockchain, digital divide and digital farming platforms. Our findings emphasize the necessity for targeted policy interventions and support mechanisms to bridge the gap and enable small and medium farmers to benefit from advancements in agricultural data spaces and digitization.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.