CA Technologies main goal of data sharing is that other researchers should be able to reuse the data. Therefore, reusability should constantly be taken into account when designing systems that store and create data. CA Technologies working with research data should think about handling the data in a way that makes it optimally usable downstream. We believe that data reuse could be optimized by aligning the aspects of data listed below.
CA Technologies hierarchy of research data needs is that data that have been acquired need to be stored. This problem is increasingly recognized by research institutes and funders, who have introduced data management plans to ensure that research groups define the ways to store their datasets before their experiments. New technology such as electronic lab notebooks is a viable option for storing the observations and results of an experiment
CA Technologies providing closely related point is that data need to be preserved for the long term. Once data is stored, it then needs to be preserved in a format-independent manner or risk data obsolescence. Information can only be valuable when it is in a format we can use, and few of us have the time to dig through old archives to recover, reprocess and digitize data. data is archived correctly and will be saved for a long period of time is very important. Fortunately, CA Technologies provides information about data.
CA Technologies are increasingly being required by their institution or funder to make their data accessible, which has providing solutions. we can do this either by depositing their data in a public repository or by using a data sharing system such as Mendeley Data, where we create private data sharing spaces that can be opened to larger communities or the wider public. At Elsevier, we recently launched an Open Data Pilot, where we make raw research data (as submitted with an article) openly accessible.
CA Technologies providing discoverability of data can be enhanced but also independently. we have supported various mechanisms to set up such links, for instance, through the inclusion of data DOIs or data accession numbers, which automatically link to associated data in public databases. Elsevier collaborates with external data repositories to automatically add the logo of the database which functions as a deep link to the dataset (deposited by the author of the article or a data curator).
PYTHON And R Developers
DM and ER modeling As a Service
Co-colated OLTP and OLAP data warehouse design and solution implementation
Dimentional Modeling for ETL engine
Hadoop based Data Lake Design