Sentence examples for a lack of databases from inspiring English sources

However, if there is insufficient information regarding such probabilities (such as a lack of databases or experimental studies), the probabilities are represented as a variable which can be studied across a range of possible values.

Also, there is a lack of databases that assemble oil crop species together with annotations based on comparative genomics.

Historically, the lack of studies based on georeferenced data of marine biodiversity was due to a lack of macroscale databases compiling this kind of information.

At present, there is a lack of comprehensive databases that provide a resource for experimentally verified data on miRNA-sponges and ceRNA interactions in different species.

Regions that work well for these systems suffer from a lack of curated databases and the persistence of indels, length variation, etc., making the analysis more complex.

Despite the rapid accumulation of information regarding the telomeric interaction network, there remains a lack of a database that enables rapid access and integrated analysis of the interaction data.

While there are a couple of well curated databases for M. tuberculosis data through TBDB [ 14, 15] and TubercuList [ 16] and a database devoted to M. abscessus in MabsBASE [ 17], there remains a lack of well-curated databases for mycobacterium genomes as a whole.

A lack of information in databases for contamination risk assessment about the transport behaviour of the trivalent f-orbital elements in groundwater systems where calcite is at equilibrium motivated this study of Eu3+ uptake.

Industry executives and observers recalled the early 2000s, when many banks pulled out of the business on record defaults that stemmed from lax lending practices and a lack of national credit databases.

It said efforts to develop the sophisticated software needed to sniff out sensitive content in Chinese were being hampered by a lack of proper vocabulary databases in Tibetan and other ethnic-minority languages.

Why? First, whenever we spoke with machine learning experts (data scientists focused on training and testing predictive models) about the most difficult part of their job, they said again and again, "the data is a mess". Initially, taking that statement literally, we imagined it referred to well-known issues with data — missing values or a lack of coherence across databases.