Towards open data based business: Survey on usage of open data in digital services
Bibliographic record
Abstract
Now-a-days o pen data is in high demand worldwide due to the possibility of creating innovative digital services and applications around open data. This paper discusses the open data from the business perspective; about the status of information usage in companies and motivation, opportunities and obstacles that relate to the open data based business. We carried out 11 interviews with company representatives to receive up-to-date information directly from the industry and to study the status of the information usage in industry to estimate how far the current business is from the open data based business. It seems that open data enables new business opportunities for actors providing data and for actors consuming data but also requires a new kind of business ecosystem that enables a win-win situation for all the actors in the open data ecosystem. The interviewed companies were highly interested in utilising open data in their own business but were afraid of opening their own data. In addition, the open data is often understood to be data that the actors of a public sector provide to the actors of a private sector. Thus, though there is high interest in open data, a lot of work must still be done to enable open data based business. In the end of the paper there are assembled research topics to which the attention should be paid in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".