By Fritz Venter LEFT and Andrew Stein The human brain simultaneously processes millions of images, movement, sound and other esoteric information from multiple sources. The brain is exceptionally efficient and effective in its capacity to prescribe and direct a course of action and eclipses any computing power available today. Smartphones now record and share images, audios and videos at an incredibly increasing rate, forcing our brains to process more. Technology is catching up to the brain.
And this is particularly true for scientific data. This happens also for business data, but here they had more time to learn.
They implemented data architectures, created data warehouse and used data mining to extract information from their data. So why don't study and implement something similar for scientific data? The solution can be to setup a Scientific Data Management architecture. Scientists normally limit the meaning of Data Management to the mere physical data storage and access layer.
But the scope of Scientific Data Management is much boarder: Below I listed common problem and opportunities in scientific data access. Then I collected what are considered the parts of a Data Management solution.
A list of references and examples of data access and scientific data collections follow. The paper ends with more implementation oriented issues: Most of this paper notes and information have been collected and studied for one specific project.
But really the ideas collected are generally applicable to the kind of scientific projects that uses the CSCS computational and visualization services.
I try my best to fix them, but not always succeed. Problems and opportunities Problems that can be found in current scientific projects are for example: Limited file and directory naming schemes.
Some project data repositories are simply big flat directories. Scientists retrieve entire files to ascertain relevance. No access to important metadata in scientists' notebooks and heads.
Un-owned data with dubious content after the end of project or PhD thesis. But the increasing of scientific data collections size brings not only problems, but also a lot of opportunities.
One of the biggest opportunities is the possibility of reuse existing data for new studies.
The idea is summarized below: Another virtuous effect can be called "discovery by browsing". If the data is well described and the data access method is quite flexible, the user can establish unexpected correlations between data items thus facilitating serendipitous discoveries.
Last, but not least, remember that the data is composed not only by bytes, but also by workflow definitions, computation parameters, environment setup and so on.
They also have huge data sets to be managed, but they must comply also with industry regulations and rigidly enforce intellectual property protection. The second point is important for each science field, but not as vital as in industry.
In this paper we don't touch those specific problems. Here I collected a quick list of the most important ones: Creation of logical collections The primary goal of a Data Management system is to abstract the physical data into logical collections.
The resulting view of the data is a uniform homogeneous library collection. Physical data handling This layer maps between the physical to the logical data views. Here you find items like data replication, backup, caching, etc. Interoperability support Normally the data does not reside in the same place, or various data collection like star catalogues should be put together in the same logical collection.
Security support Data access authorization and change verification. This is the basis of trusting your data.Homeschooling, also known as home education is the education of children at home or a variety of other places. Home education is usually conducted by a parent or tutor or online teacher.
Many families use less formal ways of educating. " Homeschooling" is the term commonly used in North America, whereas "home education" is commonly used in the United Kingdom, Europe, and in many Commonwealth.
In unstructured observation, the researcher enters the field with some general ideas of what might be salient, but not of what specifically will be observed. Structured or unstructured In structured observation, the researcher specifies in detail what is to be observed and how the measurements are to be recorded.
It is appropriate when the problem is clearly defined and the information needed is specified. About the Authors. Bonnie Piller is an Assistant Professor of Language Literacy and Culture at California State University San Bernardino.
Her scholarly interest in teaching English as a Second Language began when she taught in East Africa. She is continuing this international comparative education focus with research in Belize and Thailand.
structured observation method, social scientists are able to look selectively at the social phenomena they are attempting to study. For this reason, structured observation is a popular method of conducting an experiment or observing a phenomenon for the explicit purpose of testing a specific hypothesis.
Controlled observations (usually a structured observation) are likely to be carried out in a psychology laboratory. The researcher decides where the observation will take place, at what time, with which participants, in what circumstances and uses a standardised initiativeblog.com: Saul Mcleod.