White Paper: Data In Science Technologies
Leveraging the DataLogger for Metadata Cataloging establishes a singular view of the meaningful attributes for your data and identifies access rights to this data.
Data in Science Technologies is proposing the concept of a central Data Catalog called DataLogger, analyzes identified data sets and extracts the metadata into a searchable catalog.
Read this informative whitepaper to learn more about how Metadata cataloging helps management make informed compliance decisions around Metadata and data created.
What are the features provided by DataLogger when it augments with HPC and analytics systems?
How does DataLogger help in solving the Data Management issues?
How does Data Logging work? What are the research facilities provided by the DataLogger?
How research data can be systematically identified with a Data Catalog System?
Implementation of Data Catalog System
Metadata Management Strategy around Research
DataLogger Security and Features
Taking full control of your Data Management using DataLogger
Identifying what data exists in the environment
By: EClinical Solutions, LLC
What is the need of having a Clinical Data Repository and Analytics solution? Well, implementing a Clinical Data Repository (CDR) within a meaningful timeframe and a reasonable budget does not have to be a major IT initiative. With the right technology partner, a CDR can be implemented in 90 days. The capabilities are growing quickly and robust CDRs are available that allow companies to reap considerable value from clinical trial data. This informative whitepaper talks about the desired functionality of the platform, and demonstrates the best practices for implementing a CDR while heeding to queries like: How to maximize and utilize all clinical and operational data for real-time healthcare analytics? What are the critical components in successfully implementing the CDR platform? What are the technology benefits of implementing a next generation CDR?
By: Data In Science Technologies
The crux of disaster recovery planning is a detailed recovery plan based on a disaster recovery strategy tailored to the HPC environment. When things go awry, it's important to have a robust, targeted, and well-tested Disaster Recovery Plan. This whitepaper discusses the development, maintenance and testing of the strategy for a Disaster Recovery Plan in a HPC environment, as well as addressing the following questions: What are the steps taken by the larger strategic Disaster Recovery Plan which can be invoked to provide a limited set of benefits in a disaster situation? What are the common challenges faced by HPC Environment for the Disaster Recovery? What is the main purpose of a Business Continuity and Disaster Recovery Plan? Download this white paper which examines how Data in Science Technologies solves the problem of Disaster Recovery for a midsize HPC environment running an isolated system for research scientist and learn about: Top critical factors for the success of an IT Disaster Recovery Planning Process Requirements analysis in order to define the strategy for a Disaster Recovery Plan Strategic and tactical steps to provide a Disaster Recovery Solution (Disaster Recovery Strategy Examples) for the Bayesian Information Criterion (BIC) Cluster
By: Avere Systems
Employing hybrid cloud architecture can help IT departments more easily address both technical and business challenges. The availability of so much functionality and economy in High-Performance computing begs the...
What is Technology ?
Technology is the use of scientific knowledge for creating tools, processing actions and extracting of materials whether in industry or in our everyday lives. We apply technology in nearly all things that we do in our lives, we use technology at work, in communication, transportation, making food, extracting and securing information, running an organization and many more tasks, pretty much everywhere. Types of technology include information technology, banking technology, medical technology,
What is Data Management ?
Data management is the development and execution of policies and procedures in order to manage the information lifecycle needs of an enterprise ensuring the accessibility, reliability, and timeliness of the data for its users. Data Management enables organizations and enterprises to use data in: Organizing the enterprise data, Storing and preserving data for future re-use, Making data ready to use anytime, Share data with colleagues
What is Analytics ?
Analytics is the process of obtaining an optimal and realistic decision based on examining existing data, typically large sets of business data, with the aid of mathematics, statistics, specialized systems and software. In the last few years use of analytical methods to extract useful insights from data have gained immediate importance and has helped several companies improve their business performances.