Demo Data from SIGIR 2026 / reviewers in the wild / expert
Erik Wijnker
dblp:437/0654
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
plant genomics |
1.0 | 1 | 2026 | Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
BrAPI proxy · 1.0API integration · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementationabstractMOTIVATION: The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. RESULTS: We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. AVAILABILITY AND IMPLEMENTATION: The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). SUPPLEMENTARY INFORMATION: A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature. Matthijs Brouwer, Jens Bauernfeind, Gouripriya Davuluri, Jorge García Brizuela, Patrick König, Matthias Lange 0001, Stephan Weise, Erik Wijnker, Cyril Pommier, Joseph Ruff, Paul J. Kersey |
Bioinform. | 9 |