Project Details
Description
Situated at the interface between the philosophy of science computer science, the development of innovative algorithmic solutions will be informed by the
combination of three research perspectives. First, we will examine the use of research data in publications, in different disciplines, and for different research tasks, so as to understand to which extent scientific discoveries are based on data availability and how they are affected by data sharing cultures. Second, we will contribute new semantic technologies to support dataset search, to match research data with different user groups and scientific communities, and to generate research dataset search engine result pages. Third, using key insights into research dataset use and semantics, we will develop information retrieval algorithms for unsupervised dataset search and for predicting user interactions with dataset search engine results. These two will be combined into a self-learning method for retrieving and recommending research datasets.
combination of three research perspectives. First, we will examine the use of research data in publications, in different disciplines, and for different research tasks, so as to understand to which extent scientific discoveries are based on data availability and how they are affected by data sharing cultures. Second, we will contribute new semantic technologies to support dataset search, to match research data with different user groups and scientific communities, and to generate research dataset search engine result pages. Third, using key insights into research dataset use and semantics, we will develop information retrieval algorithms for unsupervised dataset search and for predicting user interactions with dataset search engine results. These two will be combined into a self-learning method for retrieving and recommending research datasets.
| Status | Finished |
|---|---|
| Effective start/end date | 12/1/16 → 12/1/20 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Findable and reusable? Data discovery practices in research
Gregory, K., 2021Research output: Other contribution › peer-review
Open Access -
Characterizing and predicting downloads in academic search
Li, X. & de Rijke, M., May 2019, In: Information Processing and Management. 56, 3, p. 394-407 14 p.Research output: Contribution to journal › Article › peer-review
18 Link opens in a new tab Scopus citations -
Data discovery paradigms: User requirements and recommendations for data repositories
Wu, M., Psomopoulos, F., de Waard, A. & Khalsa, S. J., Jan 1 2019, In: Data Science Journal. 18, 1, 3.Research output: Contribution to journal › Article › peer-review
Open Access26 Link opens in a new tab Scopus citations
Press/Media
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300 miljoen extra voor Amsterdams AI-onderzoek
Hoekstra, R. & Tsatsaronis, G.
12/12/19
1 Media contribution
Press/Media
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The new faces of data science
Augenstein, I., Hobby, M., Gao, Y., Gregory, K., Siebert, M., Haak, W., Groth, P., Hoekstra, R. & Tsatsaronis, G.
10/1/17
1 Media contribution
Press/Media