While the procedure for a design effort is standardized — define the requirements, design and develop, and implement — the specific requirements and subsequent designs will vary depending on the needs of the user(s). Metadata requirements are no exception, as different communities have different requirements. Thus historically, metadata content has been approached in a variety of ways depending on the needs of specific user communities; and resulted in the development of multiple, diverse metadata “dialects.” Consequently, sharing data outside specific user communities is hindered by interoperability challenges.
In order to facilitate discoverability/accessibility, usability, understandability, and interoperability of data across disciplines with differing requirements, the gap between the needs/requirements of the provider community and the needs/requirements of other communities must be bridged.
Recall that recommendations are the metadata concepts (elements) that are required, recommended, or suggested for a particular documentation need. This section provides documentation recommendations for a variety of metadata purposes. Included in each documentation recommendation on the following pages is: 1) Recommendation concepts with a brief description thereof, and, 2) a list of XPaths for each concept that best bridges the gap to other dialects. Thus, the recommendations specify both the “what” (concepts) as well as the “how” (XPaths)– which enables the user to get from the needs of a provider community (dialect X) to the needs of other communities (dialect W, Y, Z, etc.). Therefore, through use of these recommendations, data can become available to a significantly larger pool of users.
The recommendations are subdivided into four categories: Discovery, Accessibility/Usability, Understanding.
Discovery[edit | edit source]
Discoverability is the ability of information to be found. Discoverability of data is particularly important in the sciences, as the benefits of scientific investigation can’t be used if people can’t find the data. The discoverability of a dataset depends on the completeness and compatibility of the metadata. The more complete and compatible the metadata, the more likely it is that a user will be able to discover the data they are seeking.
This sub-section aims to clearly depict discoverability documentation needs and concepts and map to them in the different dialects — thereby increasing compatibility and by default, discoverability.
- Data Discovery (ACDD)
- Data Discovery (CSW)
- Data Discovery (DataCite)
- Data Discovery (DCAT)
- Data Discovery (DIF)
- Data Discovery (ECHO)
- Data Discovery (ECS)
- Data Discovery (FGDC)
- Data Discovery (ISO-19115-1)
- Dataset Descriptions (W3C)
Data Accessibility/Usability[edit | edit source]
Once a dataset is discovered, it is equally important that there be a way to access it. Accessibility is the ability to retrieve data. The accessibility of a dataset depends primarily on the discoverability - which in turn is a function of the completeness and compatibility of the metadata. Therefore, the more complete and compatible the metadata, the more accessible the data, and the more the data can be effectively shared.
Usability is the extent to which the data can be applied to achieve the user’s goals. The usability of a dataset depends on the completeness and compatibility of the metadata. Thus, the more detailed the metadata, the more the data can be effectively utilized.
This sub-section aims to clearly depict accessibility and usability documentation needs and concepts and map to them in the different dialects — thereby increasing completeness and compatibility and by default, the usability of the data.
- ACDD Variable Attributes Recommendations
- Service Discovery (ISO 19115-1)
- Service Discovery (SERF)
- Service Description (WSDL)
Data Understanding[edit | edit source]
Understandability is the ability to understand the meaning or construe the significance of data by analyzing contextual information. The who, what, when, where, and under what conditions the data was collected are just a sampling of the contextual information that must be addressed in the metadata in order for data to be interpreted correctly. Thus, understandability of a dataset depends on the completeness of the metadata. This sub-section aims to clearly depict understandability documentation needs and concepts and map to them in the different dialects — thereby facilitating completeness and increasing the understandability of the data.
- Data Understanding - Quality (ISO-19157)
- Data Understanding - External References (ISO-19115-1)
- Data Understanding - Mandatory if Applicable (FGDC)
- Data Understanding - Provenance (ISO-19115-1)
- Data Understanding - User Feedback (ISO-19115-1)
Documentation Recommendation Comparison Tool[edit | edit source]
An initial implementation of a tool for comparing metadata recommendations is available. Select the recommendations you are interested in to generate a comparison of the concepts across the recommendations.