The Place of Schema.org in Linked Ocean Data
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This document discusses using Schema.org to describe marine data and link ocean data on the web. It provides background on linked data and Schema.org. It describes work done by various organizations to apply Schema.org to describe datasets, organizations, projects, and other marine data. This includes developing schemas and cataloging various types of marine data. Future work is discussed, such as supporting tabular data and linking to other vocabularies for different data types.
The Place of Schema.org in Linked Ocean Data
- 1. The place of Schema.org in Linked Ocean Data Adam Leadbetter1 Rob Thomas1 Adam Shepherd2 Doug Fils3 Kevin O’Brien4 1. Marine Institute, Ireland 2. Woods Hole Oceanographic Institution, USA 3. Consortium for Ocean Leadership, USA 4. National Oceanic and Atmospheric Administration, USA
- 2. Acknowledgments Bob Simons (NOAA) Rob Fuller, Andrew Conway, Nigel Burke (Marine Institute, Ireland) Natasha Noy, Chris Sater (Google) 2 of 73
- 3. Why? 3 of 73
- 6. Why? 6 of 73
- 10. Why? 10 of 73
- 11. Why? 11 of 73
- 13. Background “The Semantic Web isn't just about putting data on the web. It is about making links, so that a person or machine can explore the web of data. With Linked Data, when you have some of it, you can find other, related, data.” Sir Tim Berners-Lee, 2006 13 of 73
- 14. Background 1. Use web addresses to name things 2. Allow those addresses to be looked up 3. Use web standards when the addresses are looked up 4. Include links to other web resources 14 of 73
- 15. Background 1. Use web addresses to name things 2. Allow those addresses to be looked up 3. Use web standards when the addresses are looked up 4. Include links to other web resources 15 of 73
- 19. Background 1. Use web addresses to name things 2. Allow those addresses to be looked up 3. Use web standards when the addresses are looked up 4. Include links to other web resources 19 of 73
- 21. Background 1. Use web addresses to name things 2. Allow those addresses to be looked up 3. Use web standards when the addresses are looked up 4. Include links to other web resources 21 of 73
- 23. Background 1. Use web addresses to name things 2. Allow those addresses to be looked up 3. Use web standards when the addresses are looked up 4. Include links to other web resources 23 of 73
- 29. What about marine data? 29 of 73
- 30. What about marine data? https://schema.org/Dataset 30 of 73
- 31. What about marine data? https://schema.org/Dataset 31 of 73
- 32. What have we done so far? 32 of 73
- 33. What have we done so far? http://erddap.marine.ie 33 of 73
- 34. What have we done so far? http://erddap.marine.ie 34 of 73
- 35. What have we done so far? 35 of 73
- 36. What have we done so far? 36 of 73
- 37. What have we done so far? 37 of 73
- 38. What have we done so far? 38 of 73
- 39. What have we done so far? https://toolbox.google.com/datasetsearch/search?query=site%3Amarine.ie&docid=ku6SAZZKhVNKgC0sAAAAAA%3D%3D 39 of 73
- 40. What have we done so far? 40 of 73
- 41. What have we done so far? http://data.marine.ie 41 of 73
- 42. What have we done so far? 42 of 73
- 43. What have we done so far? 43 of 73
- 44. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Datasets (EDMED) Projects (EDMERP) Common Data Inventory Cruise Summary Reports Observing Systems (EDIOS) 44 of 73
- 45. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Projects (EDMERP) Common Data Inventory Cruise Summary Reports Observing Systems (EDIOS) 45 of 73
- 46. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Datasets Projects (EDMERP) Common Data Inventory Cruise Summary Reports Observing Systems (EDIOS) 46 of 73
- 47. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Dataset Projects (EDMERP) Project (pending Schema) Common Data Inventory Cruise Summary Reports Observing Systems (EDIOS) 47 of 73
- 48. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Dataset Projects (EDMERP) Project Common Data Inventory Dataset Cruise Summary Reports Observing Systems (EDIOS) 48 of 73
- 49. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Dataset Projects (EDMERP) Project Common Data Inventory Dataset Cruise Summary Reports Event Observing Systems (EDIOS) 49 of 73
- 50. What have we done so far? Catalogue Schema.org schema Organisations (EDMO) Organization Datasets (EDMED) Dataset Projects (EDMERP) Project Common Data Inventory Dataset Cruise Summary Reports Event Observing Systems (EDIOS) Thing 50 of 73
- 51. What have we done so far? 51 of 73
- 52. What have we done so far? 52 of 73 Worked with NSF data facilities to leverage schema.org for dataset description, indexing and discovery
- 53. What have we done so far? 53 of 73 Describing Publishing Indexing Serving P418 Vocabulary approaches developed, now working with ESIP on governance and evolution 10 NSF facilities publishing * GOALSTATUS
- 54. What have we done so far? 54 of 73 Describing Publishing Indexing Serving P418 Vocabulary approaches developed, now working with ESIP on governance and evolution Worked with facilities to adapt approach to their existing metadata workflow and software. 10 NSF facilities publishing * GOALSTATUS
- 55. What have we done so far? 55 of 73 Describing Publishing Indexing Serving P418 Vocabulary approaches developed, now working with ESIP on governance and evolution Worked with facilities to adapt approach to their existing metadata workflow and software. 10 NSF facilities publishing * Code developed to collect and index the descriptions. Indexes include: text, spatial and graph. GOALSTATUS
- 56. What have we done so far? 56 of 73 Describing Publishing Indexing Serving P418 Vocabulary approaches developed, now working with ESIP on governance and evolution Worked with facilities to adapt approach to their existing metadata workflow and software. 10 NSF facilities publishing * Code developed to collect and index the descriptions. Indexes include: text, spatial and graph. Geodex.org, example notebooks and APIs. GOALSTATUS
- 57. What have we done so far? 57 of 73
- 58. How to do it? 58 of 73
- 59. How to do it? • Add some JSON to your web pages 59 of 73
- 60. How to do it? • Add some JSON to your web pages 60 of 73
- 61. How to do it? • Add some JSON to your web pages • Make sure the pages are in your sitemap 61 of 73
- 62. How to do it? • Add some JSON to your web pages • Make sure the pages are in your sitemap • Submit your sitemap to Google… 62 of 73
- 63. Thoughts for the future 63 of 73
- 64. Thoughts for the future 64 of 73
- 65. Thoughts for the future 65 of 73
- 66. Thoughts for the future 66 of 73 ‘A tabular dataset is one organized primarily in terms of a grid of rows and columns. For pages that embed tabular datasets, you can also create more explicit markup, building on the basic approach described above. At this time we understand a variation of CSVW (“CSV on the Web”), provided in parallel to user- oriented tabular content on the HTML page.’ https://developers.google.com/search/docs/data-types/dataset
- 67. Thoughts for the future 67 of 73 ‘A tabular dataset is one organized primarily in terms of a grid of rows and columns. For pages that embed tabular datasets, you can also create more explicit markup, building on the basic approach described above. At this time we understand a variation of CSVW (“CSV on the Web”), provided in parallel to user- oriented tabular content on the HTML page.’
- 68. Thoughts for the future 68 of 73 ‘A tabular dataset is one organized primarily in terms of a grid of rows and columns. For pages that embed tabular datasets, you can also create more explicit markup, building on the basic approach described above. At this time we understand a variation of CSVW (“CSV on the Web”), provided in parallel to user- oriented tabular content on the HTML page.’
- 69. Thoughts for the future 69 of 73 https://github.com/opengeospatial/netCDF-Classic-LD
- 70. Thoughts for the future 70 of 73 http://vocab.nerc.ac.uk/collection/P01/current/SIGTPR01/
- 71. Thoughts for the future 71 of 73
- 72. Useful Links 72 of 73 Google Dataset Search – https://toolbox.google.com/datasetsearch Schema.org Datasets – https://schema.org/Dataset – https://developers.google.com/search/docs/data-types/dataset Structured Data Testing Tools – https://search.google.com/structured-data/testing-tool – http://linter.structured-data.org/ Project 418 – Code: https://github.com/earthcubearchitecture-project418 – Implementation: https://geodex.org/
- 73. The place of Schema.org in Linked Ocean Data Adam Leadbetter adam.leadbetter@marine.ie
Editor's Notes
- #58: Publishing: Done in collaboration with the facilities. Focused mostly on schema.org type Dataset with an eye toward future extension work. Vocabulary work now at ESIP Indexing (summoning) Go based code “gleaner (summoner)” built that pulls the JSON-LD based schema.org from resources. Driven by sitemap files. Indexing (milling) Go based code “gleaner (miller)” is a set of patterns for adding different indexing (“milling”) workflows to work on the summoned code. Main ones were spatial, test and graph. Also have SHACL, Tika pipelines and alternative indexing and other miller options in the works. Serving APIs and sample interface at https://geodex.org using indexes from millers.