Demo Data from SIGIR 2026 / reviewers in the wild / expert
Dorina Bratfalean
dblp:241/2101
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 |
Medical and health informatics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › clinical informatics
clinical data management |
0.4 | 1 | 2019 | Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART · Bioinform. 2019 |
Medical and health informatics › clinical informatics
clinical data integration |
0.1 | 1 | 2019 | Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
ETL · 0.4CDISC SDTM · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMARTabstractMOTIVATION: Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. RESULTS: We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. AVAILABILITY AND IMPLEMENTATION: The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS-Master Tree branch, where the discussed examples can be visualized. Adriano Barbosa-Silva, Dorina Bratfalean, Venkata P. Satagopam, Paul Houston, Lauren B. Becnel, Serge Eifes, Fabien Richard, Andreas Tielmann, Sascha Herzinger, Kavita Rege, Rudi Balling, Paul Peeters |
Bioinform. | 2 |