Making data sticky
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This document discusses the importance of making data "sticky" by using shared identifiers that create links between data sources. It notes that identifiers should be globally unique, resolvable by both humans and machines, and widely used. Examples of shared identifiers that enable discovery and metrics include DOIs, specimen identifiers from databases like GBIF, and citations. The value, it argues, comes from the links between nodes, not just the nodes themselves.
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https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
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D paul ecn2013
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SLiDInG6 talk on biodiversity knowledge graph
This document discusses building knowledge graphs by connecting various data sources using identifiers and vocabularies. It identifies technical and social obstacles to doing so, including the need for globally unique identifiers, agreed-upon vocabularies, and standards for transmitting the graph. It notes that some of these obstacles are being overcome with solutions like DOIs, JSON-LD, and Wikidata, but that measuring progress on connectivity is more difficult than linear growth. It concludes that knowledge graphs already exist but are not evenly distributed, and that any combined graph should be free, open, and used for good.
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Open data is freely available with attribution as currency, while funding work and demonstrating value can be difficult. Closed data requires monetary access through subscriptions, where development is funded and value is obvious financially. The document proposes using micropayments of bitcoins for accessing raw or cleaned biodiversity data, with the community mining bitcoins to fund processing raw data into cleaned datasets.
Towards a biodiversity knowledge graph
The document discusses obstacles to and progress in building knowledge graphs. It outlines technical obstacles like needing globally unique identifiers and agreed-upon vocabularies. Social obstacles include economic issues. Identifiers are key to connecting knowledge graphs. Metrics are needed to measure graph connectivity rather than just growth. Network effects are important to make graphs truly useful. Wikidata and Google's knowledge graph are examples of existing large knowledge graphs.
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This document summarizes the capabilities and potential of the Biodiversity Heritage Library (BHL) and BioStor resources. It discusses how BHL provides open access to digitized biodiversity literature but does not easily surface the journal articles. However, the data is downloadable and has APIs, allowing the articles within to be programmatically extracted. It proposes linking specimens to articles for improved findability and creating a "PubMed Central for biodiversity" to better organize and surface taxonomic facts and documents.
Cisco Digital Catapult
Digitisation involves making physical specimens, literature, and metadata available in digital form, including DNA barcodes. This allows joining up different types of biological data that were previously separate, such as specimens, published literature, and genetic sequences. Digitising collections allows broader access and use of biodiversity data through databases like GBIF, which has over 500 million occurrence records available online. However, different intellectual property policies can apply to literature, specimens, and genetic data. Efforts are underway to better link related biological records and publications through identifiers like ORCID for people, DOIs for publications, and LSIDs for plant names. The goal is to build a comprehensive knowledge graph that integrates all these different types of digital biological data.
Built in the 19th century, rebuilt for the 21st
The document discusses several topics related to digitization of biological data collections:
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2) Debates around creating GenBank in 1982 illuminated different moral economies regarding collecting/sharing data and attributing credit.
3) Both experimentalism and natural history traditions have shaped new ways of producing knowledge in life sciences through articulating these approaches in databases.
Two graphs, three responses
This document discusses two graphs related to taxonomy and barcoding. It then presents three potential responses: 1) Modernize taxonomy by digitizing specimens, literature, and linking data sources. 2) Imagine a world where only barcodes exist and how we would visualize biodiversity. 3) Integrate taxonomy and barcodes by linking names to barcodes using specimens as the connection point, though challenges remain around building complete trees and visualizing communities. Integrating data from sources like BOLD, GBIF, and literature would help but requires resolving issues of duplicate records and name interpretations.
GrBio Workshop talk
This document discusses three problems related to linking specimens to evidence in biological databases: 1) linking specimens to DNA barcodes, sequences, and literature citations in databases like GBIF, 2) the relationship between physical biological collections and institutions and their digital representations in databases like GBIF, and 3) making Wikipedia pages on species more structured and machine-readable similar to Bouchout declarations. Suggestions are made to map specimen identifiers to other databases, provide live updates of digitization efforts, and allow for user-editable structured data.
Biodiversity Knowledge Graphs
This document discusses building knowledge graphs to connect biodiversity data from various sources like publications, specimens, sequences, and locations. It notes some challenges like an increasing amount of sequencing data not connected to formal taxonomic names, disconnects between taxonomy and genomics, and much of the historical taxonomic literature being difficult to access. It also discusses imbalances in available data across regions, taxa, and between well-studied and under-studied species. Integrating these diverse data sources into knowledge graphs could help address these issues but also comes with its own challenges.
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Biodiversity informatics: digitising the living world
The document discusses biodiversity informatics and efforts to digitize information about the living world. It notes that GBIF has over 500 million specimen records but that none of the Kenya data actually comes from Kenya. It also discusses challenges with the Biodiversity Heritage Library in emphasizing older publications over recent scientific articles and representing information by scanned book pages rather than articles. The document raises issues about ensuring all scientific names are published and unique, and about making publications containing names accessible online.
Ebbe Nielsen Challenge GBIF #gb21
The document discusses the Ebbe Nielsen GBIF Challenge, which is a competition that offers cash prizes. The challenge is open to individuals, teams, and companies and asks participants to submit innovative uses of data mobilized by the Global Biodiversity Information Facility (GBIF). Winning submissions could include new visualizations of biodiversity data or utilizing new types of data. Prizes will be awarded to semifinalists and top finishers, with the top prize being €20,000.
GBIF Science Committee Report GB21, Delhi, India
The GBIF Science Committee met in Glasgow where they awarded prizes and developed the Ebbe Nielsen Challenge. CSIRO's Tony Rees was named the 2014 Ebbe Nielsen Prize winner. Young Researcher Awards were given to Vijay Barve and Caoimhe Marshall. The GBIF Ebbe Nielsen Challenge will foster innovation using GBIF data and have cash prizes. The committee discussed challenges around data types from sources like DNA sequences and literature, as well as data quality and coverage issues.
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Making data sticky
- 2. Data
- 3. More data is good…
- 4. …but this data is not sticky
- 5. Location
- 8. Tags name name
- 9. name Namen
- 10. Identifiers
- 12. Shared identifiers are sticky
- 13. Identifiers • Globally unique • Resolvable (human and machine) • Used
- 14. Identifiers • http://dx.doi.org/10.1186/1471-2105-12-187 Extracting scientific articles from a large digital archive: BioStor and the Biodiversity Heritage Library • http://dx.doi.org/10.5061/dryad.3pc64 Nexus file with the characters-taxa matrix used for the phylogenetic analysis • http://dx.doi.org/10.1601/nm.5037 Halobacillus
- 15. Links
- 16. PMID:948206
- 20. BHL and GBIF as biomedical databases http://iphylo.blogspot.co.uk/2012/03/bhl-and-gbif-as-biomedical-databases.html
- 21. Metrics
- 22. In an attempt to live up to that increasing demand for documentation, the leadership of the Natural History Museum of Denmark has issued an order to its curatorial staff - The staff members are requested to document which publications from 2011, written entirely by external scientists, that in one way or another are based on material in the collections of the Museum. @TAXACOM http://markmail.org/message/opv2we7fkmro2nen
- 24. https://twitter.com/edwbaker/status /205595933159858176 https://twitter.com/edwbaker/status/205595933159858176
- 26. Cited, linkable specimens NMNH Vertebrate Zoology 11194 Herpetology Collections Herpetology Collection (University of Kansas Biodiversity Research 9619 Center) CAS Herpetology Collection Catalog 6720 MCZ Herpetology Collection 5818 http://iphylo.blogspot.co.uk/2012/02/gbif-specimens-in-biostor-who-are-top.html
- 27. The value is in the links, not the nodes
- 28. Summary • Make your data sticky… • …by using other people’s identifiers • Shared identifiers create links… • …which enable discovery (and metrics) • Value is in the links (think Google, Facebook)
Editor's Notes
- http://markmail.org/message/opv2we7fkmro2nen