CGIAR genebank viability data reveal inconsistencies in seed collection management

Management of Inconsistencies in Data Integration

Data integration aims at providing a unified view over data coming from various sources. One of the most challenging tasks for data integration is handling the inconsistencies that appear in the integrated data in an efficient and effective manner. In this chapter, we provide a survey on techniques introduced for handling inconsistencies in data integration, focusing on two groups. The first gr...

Handling Inconsistencies in Data Warehouses

Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding w...

Handling Inconsistencies in Data Warehouses

Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding w...

Rethinking the approach to viability monitoring in seed genebanks

Seed viability monitoring, usually through a germination test, is a key aspect of genebank management; a low viability result triggers the regeneration of an accession in order to ensure that the genetic diversity of the accession is conserved and available for distribution. However, regular viability monitoring of large collections is costly in terms of seeds, labour and other resources. Geneb...

Qualitative Research: Data Collection, Analysis, and Management.