How Data Migration Impacts the Scientific Community
Data migration is regularly exploited by the scientific community. Scientific data is produced in large numbers but their exploitation is subject to two conditions: their accessibility on the one hand, and their interpretation on the other hand. It is up to the scientific institutions to facilitate this exploitation.
This term does not apply to: laboratory records, preliminary analyzes and draft scientific papers, peer reviewed papers, future work programs, personal communications with colleagues and material objects. Scientific data, or research data, are factual records, which are used as primary sources for scientific research and are generally recognized by the community as necessary to validate research results. A set of research data is a systematic and partial representation of the subject matter of the research. This article explains what scientific data migration is and how to use it.
What Is Data Migration?
Data migration is an essential step in any hardware, operating system or software changes. Migrating becomes necessary from the moment when organizations or people intervene to change systems. Migrating the data is usually accomplished through programming to achieve automated processing, freeing up human resources.
The purpose of this change is to move a physical storage medium to another that would meet current needs, without however modifying the bit stream holding the information. This makes it possible to solve the problem of the obsolescence of physical supports.
Another form of modification is intended to change the format of the files, by segmenting the data on the one hand and the information on the other. We then proceed to the migration to a new format meeting the standards guaranteeing a better durability. Due to the large number of interactions between different software, the technique can sometimes make information difficult to identify. Also, view this link for more data.
In addition, migration is most often irreversible because of the separation of data from their environment. This migration concept requires tracking changes to software versions. It is indeed essential to not switch to a higher version where compatibility would then be ensured only for a defined number of versions.
The purpose of this mutation is to transform all the data that is managed by a source computer system, hardware or software, in order to use them on a different target system. The incompatibilities between the software versions require that the data be modified to be compatible with the new system. Also, view this link for more data.
This modification of the data requires two prerequisites to be effective. First, you must check the rules that must be validated by the transferred data. This checking must be done for the old rules and for the data prior to the migration. Then, you must identify migrated data that does not fit in the new rules.
The purpose of this transfer is to transform software so that it can run on a different computer. The data will have the same organization but the programs will have to be adapted to be compatible with the mode of operation of the new computer.
A scientific set, data set, is the aggregation of organized data records to form a coherent set. The digital datasets are formatted in such a way that they are communicable, interpretable and suitable for computerized processing. To be used and quoted, a data set must be accompanied by descriptive metadata. Some datasets, annotated, peer-reviewed and made available become reference data, for example genomic data available in a database. A database consists of a set of organized and structured datasets to be accessible and operable by means of a computer program.
Data migration is used in peer-to-peer scientific publications to communicate data that lend themselves to multiple uses. The scientific community can therefore benefit freely from the data communicated to them. Indications that contextualize the data allow for future use and cross-fertilization of data to verify hypotheses and develop scientific conclusions. The accuracy of the collection and the scientific method used ensures the reliability and recognition of the author of the work by the scientific community and allows the sharing of research.
However, the tremendous potential of these data is not without its risks. For the time being, the research community alone remains capable of analyzing, confronting hypotheses and validating conclusions by processing these data.
