The future of agricultural data-sharing policy in Europe: stakeholder insights on the EU Code of Conduct
Bibliographic record
Abstract
In 2018, the EU Code of Conduct of Agricultural Data Sharing by Contractual Agreement (EUCC) was published. This voluntary initiative is considered a basis for rights and responsibilities for data sharing in the agri-food sector, with a specific farmer orientation. While the involved industry associations agreed on its content, there are limited insights into how and to what extent the EUCC has been received and implemented within the sector. In 2024, the Data Act was introduced, a horizontal legal framework that aims to enforce specific legal requirements for data sharing across sectors. Yet, it remains to be seen if it will be the ultimate solution for the agricultural sector, as some significant agricultural data access issues remain. It is thus essential to determine if the EUCC may still play a significant role to address sector-specific issues in line with the horizontal rules of the Data Act. During six workshops across Europe with 89 stakeholders, we identified how the EUCC has been (1) received by stakeholders, (2) implemented, and (3) its future use (particularly in response to the Data Act). Based on the workshop results and continued engagements with researchers and stakeholders, we conclude that the EUCC is still an important document for the agricultural sector but should be updated in response to the content of the Data Act. Hence we propose the following improvements to the EUCC: 1. Provide clear, practical examples for applying the EUCC combined with the Data Act; 2. Generate model contractual terms based on the EUCC provisions; 3. Clarify GDPR-centric concepts like anonymisation and pseudonymisation in the agricultural data-sharing setting; 4. Develop a functional enforcement and implementation framework; and 5. Play a role in increasing interoperability and trust among stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.147 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".