Health Data Re-Identification: Assessing Adversaries and Potential Harms.

Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions.

Data Provenance in Biomedical Research: Scoping Review.

Background: Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research.

Open tools for quantitative anonymization of tabular phenotype data: literature review.

Precision medicine relies on molecular and systems biology methods as well as bidirectional association studies of phenotypes and (high-throughput) genomic data. However, the integrated use of such data often faces obstacles, especially in regards to data protection. An important prerequisite for research data processing is usually informed consent.

Composite modeling of leaf shape along shoots discriminates species better than individual leaves.

Premise: Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines ( spp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves.