The phrase "and thorough data on" is correct and usable in written English.
You can use it when discussing the need for comprehensive information or analysis regarding a specific topic or subject matter.
Example: "The report should include a detailed analysis and thorough data on market trends to support our strategy."
Alternatives: "comprehensive information about" or "detailed statistics regarding".
Doing such an inventory has little short-term payoff, and is, as Amsterdam CTO Baron puts it, "a boring, boring job". But successful analytics projects depend on solid infrastructures and thorough data, and on building a process to keep up with the (relentlessly) growing data supply.
Stringent quality control and thorough data normalization are crucial when analysing this type of data.
Given both past and present-day conditions, the lack of thorough data on atrazine concentrations in lentic systems, and the common use of agricultural ponds, ditches, and wetlands by amphibians and fish, we suggest that concentrations near or below historical EECs (≤ 500 μg/L) are ecologically relevant when considering the findings of this meta-analysis.
As with all types of network data, thorough data on sibship sets are difficult to collect because the process is time-consuming and tedious both for respondents and for fieldworkers.
This study uses new, and very thorough, data obtained from CARES on current reinforcement properties to re-examine the requirements for a suitable partial safety factor for current reinforcement production.
Regardless of which camp you decide to join, it does seem evident that until more extensive research is done, and until more thorough data can be compiled on the adverse effects of the herb, a physician should be consulted for long-term use to ensure that the regimen is appropriate.
Cummings argued that move was necessary and based on thorough data analysis, but declined to issue a full report justifying the decision until after it took effect.
But such methodologies would generally involve a thorough data collection and analysis on a large sample of sites, which seems hard to implement by practitioners in the routine situations we considered in this paper.
By focusing attention on thorough data analysis, design issues can be understood and their impact or bias can be estimated, on average, and then ideally accounted for in the interpretation of data.
Our analysis is based on a thorough data set of more than 5000 interspecific phylogenies and a sample of 67 intraspecific phylogenies (see Text S1), thereby testing the universality of the results derived across scales.
