Identifying the business model dimensions of data sharing: A value‐based approach
Bibliographic record
Abstract
This study aimed to investigate the underlying business model of organizations that have data sharing at the core of their activities. Previous work has stressed that data‐sharing projects need to be sustainable in the long term, and highlighted the need for a deeper understanding of the operation model of existing data‐sharing initiatives. To investigate this important issue, we took a qualitative approach to uncover the dynamics of value creation in data sharing. Using a case study method, we examined two data‐sharing sites across different areas. We conducted semistructured interviews with managers from data centers and other stakeholders, and reviewed documents about the technical and managerial practices to determine the main characteristics of their business models. In addition, we applied the e3‐value modeling methodology to tease out the value flows within each site. Our findings demonstrated the importance of the value network dimension of a business model, as data sharing relies on a set of actors creating and getting value in the process, and the significance of intangible assets. The main contributions of this study include extending current understanding on data‐sharing business models by analyzing key dimensions, and uncovering how value is created and transferred in data sharing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchScholarly communicationOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".