The phrase "adaptable data" is correct and usable in written English.
It can be used when referring to data that can be modified or adjusted to fit different contexts or requirements.
Example: "In our project, we need to ensure that the adaptable data can be easily integrated into various systems without losing its integrity."
Alternatives: "flexible data" or "modifiable data".
Farmeron provides an adaptable data management tool for farmers with a delightful, fun interface a world away form dull, agro-industry rivals - of which there are only two.
ADLAM [33] proposes an adaptable data migration model based on the heat of data to determine the next hot data.
Therefore, it is important to create flexible systems with adaptable data bases and analytical tools to integrate knowledge of future climate change in agronomy for impact studies and adaptation planning.
As such, a new generation of adaptive algorithms are needed, ones that learn in real-time, capture the multivariate nonlinear spatio-temporal dependencies and are easily adaptable to new data (e.g. weather or crowdsourced data) and changes in network structure, without having to retrain and/or redeploy the entire system.
This highlights the need for an adaptable approach to data collection, inclusive of both paper-based and electronic means of collecting and sharing data.
In this scenario, we implement a typical method for digital phase locking and make it adaptable to different data rates.
The CASCADE detector uses an ASIC electronic front-end paired with an adaptable integrated FPGA data processing unit to provide high rate capacity and realtime event reconstruction.
Additionally, the use of size-dependent scatter matrices to compute the activation of the neurons makes the algorithm more adaptable to different data distributions.
This still is a nightmare due to the lack of domain-specific adaptable tool and data integration solutions which support maintenance of traceability links, semi-automatic consistency checking as well as update propagation.
In addition, since we focus on changes in signal in different portions of the transcript, the analytic approaches presented here should be adaptable to new data types, e.g., mRNA-seq [8], [37].
In contrast, nonparametric methods are much more adaptable to variable data in general.
