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Difference between backup and archive: A backup creates a secondary copy of primary data intended for recovery of current data and is generally overwritten periodically (weekly, monthly). Data can be restored from a specific date. The archiving process typically moves fixed data out of the active data workflow to secure unchangeable content in long-term preservation. Archived data should also be backed up or stored redundantly.
Source publication
Proteomics methods, especially high-throughput mass spectrometry analysis have been continually developed and improved over the years. The analysis of complex biological samples produces large volumes of raw data. Data storage and recovery management poses substantial challenges to biomedical or proteomic facilities regarding backup and archiving c...
Citations
... Historically, the technical aspects of data management like data modelling, database technology, storage management, data integrity as well as the management of backup/archiving and recovery have been of utmost importance in life science [1]. Data repositories, in which the research datasets are stored at the end of an investigation, ensure the long-term storage and handle these aspects. ...
This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.
... Such a technique redirects the input/output (I/O) operations to a mirror disk as soon as a physical defect is discovered in the primary disk. Even if the primary disk experiences I/O problems, a backup mirror disk provides the required I/O services, ensuring operational continuity [11,12]. ...
Recently, intelligent transport systems have been applied to vehicle cloud environments. Such technology is especially useful for the systematic management of road traffic. Moreover, automobiles are increasingly equipped with a black box for accident prevention and preservation of evidence. Vehicle black boxes have become mandatory because black box images and voice data have served as forensic evidence in courts. However, the data from black boxes can be forged or modified by man-in-the-middle (MITM) attacks and message hijacking. In this paper, we propose a vehicle cloud computing-based black box service model that can provide integrity for black box data through digital signatures in vehicle cloud computing (VCC) environments. Our proposed model protects against MITM attacks and message hijacking using only a hash value and digital signature. Moreover, a mirroring technique (RAID 1) provides backup and recovery to protect the data from a traffic accident.
... These platforms often have limited security management and do not often support metadata management. More comprehensive data-sharing and publication services are available through data-file archives [25,26]. Examples of well-accepted scientific data repositories are Dryad [27] and figshare.com ...
Background
The life-science community faces a major challenge in handling “big data”, highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the “big data life cycle”. The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall.
Results
e!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed “out-of-the-box” as an on-site repository.
Conclusions
e!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK’s role as a data center in the DataCite consortium, e!DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. Documentation and Software are also available at: http://edal.ipk-gatersleben.de.
