experimental

Entry: 4. Experimental

URI: http://reference.metoffice.gov.uk/DataCategories/data-readiness/Experimental    

Experimental data is created in the process of conducting domain specific research and developing new techniques and approaches. It is used to explore new and niche ideas. It is often highly customised and specific. It does not have to be generally useful, nor well described or discoverable outside of the small group of specialists who are collaborating around it. It is often in the process of being validated and verified and not in a state to be shared with people who are not experts in the area of interest. It is only appropriate to use within the scope of the research activities. Experimental data consumers are often both the producers and consumers of the data – so change is driven by extremely short and rapid feedback loops. The inherent risks in use of this data are that they are experimental, change rapidly, maybe misleading, and may not be valid in the context of use. Mitigation is that it is narrowly shared with experts. Experimental is the default data readiness category. In the absence of definitive labelling, the Experimental category may be assumed.

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is a Concept | data readiness
submitted bymo-marqh
accepted on 14 Jun 2022 08:02:47.835

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date accepted 14 Jun 2022 08:02:47.835
date submitted 1 Jun 2022 08:23:07.015
definition
entity 4. Experimental
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description Experimental data is created in the process of conducting domain specific research and developing new techniques and approaches. It is used to explore new and niche ideas. It is often highly customised and specific. It does not have to be generally useful, nor well described or discoverable outside of the small group of specialists who are collaborating around it. It is often in the process of being validated and verified and not in a state to be shared with people who are not experts in the area of interest. It is only appropriate to use within the scope of the research activities. Experimental data consumers are often both the producers and consumers of the data – so change is driven by extremely short and rapid feedback loops. The inherent risks in use of this data are that they are experimental, change rapidly, maybe misleading, and may not be valid in the context of use. Mitigation is that it is narrowly shared with experts. Experimental is the default data readiness category. ...
item class Concept | data readiness
label 4. Experimental
notation Experimental
register data readiness
status status experimental
submitter
account name https://api.github.com/users/mo-marqh
name mo-marqh

type register item
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Definition

description Experimental data is created in the process of conducting domain specific research and developing new techniques and approaches. It is used to explore new and niche ideas. It is often highly customised and specific. It does not have to be generally useful, nor well described or discoverable outside of the small group of specialists who are collaborating around it. It is often in the process of being validated and verified and not in a state to be shared with people who are not experts in the area of interest. It is only appropriate to use within the scope of the research activities. Experimental data consumers are often both the producers and consumers of the data – so change is driven by extremely short and rapid feedback loops. The inherent risks in use of this data are that they are experimental, change rapidly, maybe misleading, and may not be valid in the context of use. Mitigation is that it is narrowly shared with experts. Experimental is the default data readiness category. ...
label 4. Experimental
notation Experimental
type Concept | Data Readiness