Status: Recognised & Endorsed
The Metadata IG will concern itself with all aspects of metadata for research data. In particular it will attempt to coordinate the efforts of the WGs concerned with metadata to produce a coherent approach to metadata covering metadata modalities of description, restriction, navigation, provenance, preservation and the use of metadata for the purposes discovery, contextualisation, validation, analytical processing, simulation, visualisation and interoperation. It will also liaise with the other WGs especially Data Foundation and Terminology, PIDs, Standardisation of data categories and codes and Data Citation. This IG activity relates to data management policies and plans of research organisations and researchers, and to policies and standards of research funders and of research communities which may or may not be official standards.
The metadata IG will organise itself through online meetings and face-to-face meetings of members of the IG present at RDA Plenary events. It is proposed that – while membership is open to any RDA registered member – key members will be the leaders of the WGs concerned with metadata. In order to get the renovated IG working I volunteer to initiate this activity but would expect elections and handover to someone else after an initial period.
Metadata Principles - Created and endorsed by all the RDA metadata groups
- The only difference between metadata and data is mode of use
- Metadata is not just for data, it is also for users, software services, computing resources
- Metadata is not just for description and discovery; it is also for contextualisation (relevance, quality, restrictions (rights, costs)) and for coupling users, software and computing resources to data (to provide a VRE)
- Metadata must be machine-understandable as well as human understandable for autonomicity (formalism)
- Management (meta)data is also relevant (research proposal, funding, project information, research outputs, outcomes, impact…)
The RDA Metadata Standards Directory Working Group supports a collaborative, open directory of metadata standards applicable to scientific data. Additions or updates to the directory can be made here.
The metadata groups intend to recommend the following metadata element set. Please note that each element needs 'unpacking' to get to something recognizable and actionable by a computer.
- Unique Identifier (for later use including citation)
- Location (URL)
- Keywords (terms)
- Temporal coordinates
- Spatial coordinates
- Originator (organisation(s) / person(s))
- Facility / equipment
- Availability (licence, persistence)
- Related publications (white or grey)
- Related software
- Medium / format
As noted above, these are elements, not single-valued attributes. Most will have internal syntax (structure) and use of terms that require declared semantics. Also it is not exhaustive; it is expected that particular subject domains will have much greater lists of elements. This list is intended to be the recommend list of elements that should be provided by all within RDA to
- permit discovery,
- support contextualisation (assessment of relevance and value) and
- facilitate action (interoperation including query and integration).
Use Case Analysis
The initial use case Analysis was presented in Session 9 joint meeting of all the metadata groups at Plenary 6 in Paris. Below are some revised slides based on the feedback from that meeting and the master use case spreadsheet showing the process.
The FAIR Guiding Principles for scientific data management and stewardship have been published
Press release: FAIR guiding principles published in Nature journal
Today, March 15 2016, the FAIR Guiding Principles for scientific data management and stewardship were formally published in the Nature Publishing Group journal Scientific Data. The problem the FAIR Principles address is the lack of widely shared, clearly articulated, and broadly applicable best practices around the publication of scientific data. While the history of scholarly publication in journals is long and well established, the same cannot be said of formal data publication. Yet, data could be considered the primary output of scientific research, and its publication and reuse is necessary to ensure validity, reproducibility, and to drive further discoveries. The FAIR Principles address these needs by providing a precise and measurable set of qualities a good data publication should exhibit - qualities that ensure that the data is Findable, Accessible, Interoperable, and Reusable (FAIR).
- 2016-09-15 - 2016-09-17 RDA Plenary 8 Denver
- 2016-03--01 - 2016-03-03 RDA Plenary 7 Tokyo
- 2015-09-23 - 2015-09-25 RDA Plenary 6 Paris
- 2015-03-08 - 2015-03-11 RDA Plenary 5 San Diego
- 2014-09-22 - 2014-09-24 RDA Plenary 4 Amsterdam
- 2014-03-26 - 2014-03-28 RDA Plenary 3 Dublin
- 2014-02-24 - 2014-02-25 RDA Europe Munich Meeting