This behavior is defined by design and cannot be avoided except when using the MVC wrapper of the Grid. When the Grid is created from an existing table, the Grid provides the following column settings that can be defined through the HTML attributes.
Knowledge and Data Management in GRIDs
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In this article. One of the advantages of DC metadata is that it is difficult to find another format which doesn't intersect with its fields. But it is insufficiently formal and unambiguous for machine understanding. Jeffrey then looked at how the European Union now frames the guidelines under which Grid type project proposals are solicited.
The sixth Framework Programme no longer contains some work areas which have become familiar to those who made proposals under the Fifth Framework Programme. The key phrases in documents which prospective applicants for funding might look out for are: 'Information Landscape' a Lorcan Dempsey coinage of several years ago, roughly coincident with the idea of the 'Information Environment' and the 'Knowledge Society'.
This has some relevance to JISC activities, in that FP6 plans to build on and also build across existing national initiatives. National Partnership for advanced computational infrastructure. Moore's presentation was aimed more squarely at the interests of the computing community.
It was about running applications in a distributed environment and interfaces between systems; about brokerage between networks; essentially about a particular vision of what is technically possible within Grid or Grids architecture - distributed computing across platforms and operating systems, rather than the business of searching for research data held in various formats across domains within a platform-independent environment.
In both metadata needs to be a key feature of the architecture, whether we are talking about finding and running software applications in a distributed computing environment, or the management of textual data. Moore talked about Data Grids: these he defined as possessing collections of material, and providing services. So essentially a Data Grid provides services on data collections.
This is, he said, very close to what is proposed for the architecture of digital libraries. The problem is that service providers are faced with the problem of managing data in a distributed environment i. Data Grids offer a way of working over multiple networks, and are the basis for the distributed management of resources. Digital Entities in Moore's terminology are 'images of reality,' and are combinations of Bitstreams and structural relationships. He made some interesting differentiations between data, knowledge and information. The former he allocated to digital objects and streams of bits.
Knowledge was allocated to the relationship between the attributes of the digital entities, and 'information' is 'any targeted data'. The terminology used by Moore was different from that used by the UK speakers in a number of respects, though he was clearly speaking about very similar concepts his information architecture slides made this clear.
We'd been in the lecture theatre for two hours by the time he began his presentation, and probably it would have been fatal to an understanding of what he meant by 'abstraction' and 'transparency' to have missed the beginning of his talk by answering a call of nature. Ariadne was unlucky in this respect.
If identifiers are given locally or institutionally, then the identifier for two separate instances of a resource within the reach of the Data Grids anywhere around the world might be quite different since the service provider might only have knowledge of the one to which they added a persistent identifier, until a researcher links the second resource with the first. In other words, two instances of a resource perhaps different editions might have the same identifier, or else have totally different identifiers.
Which to some extent would defeat the object of giving resources persistent identifiers. Perhaps society as a whole cares, and those who pay for the services.
A critical survey of data grid Re preview & related info | Mendeley
May issues are management and procedural, as well as technical. Also, do we have the technical solutions for the implementation of policy decisions, and vice versa, and do we have the policy making structures for the implementation of the technically possible? Issues of scale were raised - there comes a time when the scale of the enterprise affects the nature of the solution. Is there a business model? Maybe this question needs to be allocated to a couple of economics Ph.
Ds for a study. On the issue of repurposing, it was pointed out that the community will be collecting data for the Grid without knowing how the data will be repurposed. This means associated information is extremely important. It was suggested that annotations are a driving purpose for an archive. As for life-cycle issues,: it was suggested that the community cannot trust the creators of data resources to make appropriate decisions on preservation. But it was suggested that the self-archiving process might function as an enabler of serendipity, since the automation of the process of 'discovery' might be seen to be squeezing this out.
Process capture for data analysis and new methods of design and exploration, resulting in large quantities of stats. We need tools for provenance and rollback, as well as automation of the discovery process. The example of combinatorial chemistry was used - making haystacks to find needles. The process involves data mining the library of information created by the research. Some info stays in the lab the associated metadata which makes it possible for the experiments to be repeated : this information needs to be preserved, and scientists need to understand the importance of this - younger scientists especially need to learn to record associated metadata while they are working in the lab.
Virtual data - a request for missing data may be met by simulation. That is, the characteristics of a particular molecule might be inferred from its place in an array of known molecules and their properties.
This raises questions of the provenance of data, since the actual properties of the molecule are not actually known, but inferred. There are various kinds of metadata: descriptive, annotative, etc.
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We have to understand what kinds of metadata are required for making the Grid viable. It was mentioned that the persistence of the data might be less important the the persistence of the system used to underpin the Grid hardware, software, etc. Also that we might need to build in a look-ahead time for system design because of the rapid development of the technology.
The question of the propagation of underlying data into derived data products was raised. A piece of derived data which turns out to be based on faulty primary data is naturally also false. If the derived information is arrived at as part of an automated process, then mechanisms for automatic correction of the data and even automatic publication of the new data might be desirable. In other words, changes in primary data need to be reflected upwards again this raises the issue of provenance of data, and also the tracking of changes, or rollback.
The Semantic Grid has as its aim the bridging of the gap between current endeavour and the vision of e-science. Ontologies are required for the automation of Grid processes. The conclusion is that scientific data and the associated information need to be closely defined within the context of the Grid and its processes.
Plus we need better tools for creating metadata. We also need to have good processes for working within collaborative workspaces, and the implementation of clear standards. There was discussion of resource discovery, and what the minimum requirements of a researcher are to make a resource discoverable. They also considered the question: what does the publication of data on the Grid actually mean? Possibly a job for a working party to analyse.