Lost In Translation when machines meet STM content… This presentation was designed to be delivered live. To help you understand the content we have added these notes…
Lost In Translation when machines meet STM content
Oct 30, 2014
This slidedeck outlines the Resource Identification Initiative and how partners within the group are working to improve reproducibility in science by making experimental methods more machine readable.
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Lost In Translationwhen machines meet STM content…
This presentation was designed to be delivered live. To help you understand the content we have added these notes…
3Questions…Behind the shared vision held by the partners of the
Resource Identification Initiative lies a number of questions. …
Which one?
REPRODUCIBILITY: In the scientific community it is difficult to find objective qualitative information about research materials. Choosing the wrong products often
means failed experiments…
Where is it?
EFFICIENCY: Poor resource visibility means that labs around the world waste thousands of man hours
duplicating eachothers work. Greater visibility of produced research materials would dramatically improve efficiency
in science and reduce waste. ..
Who has used it?
CONNECTIVITY: By its nature, science is a collaborative endevour. Efficiently identifying knowledge and expertise
when required is key to progressing discovery. ..
The Role of Content.. Research content has evovled over time as a means of communicating conclusions. However, the real untapped
value in content is the information about the journey…
Who has used it?Where is
it?
Which one?
Every article contains valuable information about experimental procedures and materials. When cross
referenced with location, author and time data, powerful new experimental and research insights are revealed…
Challenges.. Todays research articles are designed to be read one at a
time by humans. To cross reference we rely on our notetaking, memory and prior knowledge. Machines have
the potential to dramatically improve the efficiency of how we glean insight from content. But….
3Culture2Ambiguity1XML
1) Every publisher has slightly different XML standards. 2) The vocabularly for describing research entities is
ambiguous.3) There is a poor culture of facilitating data mining and
enforcing best annotation practice in the publishing industry.
XML
The XML produced by different publishers can be significantly different. This makes indexing and analysing content at scale challenging…
Ambiguity
Insufficient annotation and naming in content makes it difficult to disambiguate material entities. Take this glass beads example….
Sigma produces at least 5 variations of glass beads, which version is being referred to in the article?
Culture
VsPublishers have traditionally made money by attracting great content and selling access to as many people as possible . Advances in technology have largely been viewed by publishers as a means to do more of the same at a lower cost. Publishers have been slow to adopt practices that make their content machine accessible…
Who is involved..
The RII is backed a wide group of interests working together to change how experimental resources are
documented in new research content…
The group includes publishers, academic groups, funding agenicies, resource repositories and commercial
companies…
Shared Goals.. The group has a number of shared goals with the aim of
improving the machine accessiblity of STM content in a practical and sustainable way…
1. Unique Identifier
AB_12345781) By agreeing and assigning standard unique identifiers
for all known research materials (commercial and non-commercial)…
2. Editor Awareness
• Drive adoption• Better XML standards• Content machine friendly
2) By working with publishers and other community members to encourage the inclusion of unique indentifiers
at the authoring stage and devising strategies for XML standardisation...
3. Distribution…
3) By developing technology and APIs to diseminate research material information in a standarised form…
4. Annotation
- Pre-publication- Prospective
- Post-Publication- Retrospective
4) While NIF is focussing on research material annotation at the pre-publication stage, scrazzl is working on a
seperate initiative to drive retrospective annotation of published research…
5. Interoperability
5) One of the main aims of the RII is to support the adoption of a standardised public research material
onthology and vocabulary that is interoperable with other exsisting biological onthologies…
So what does success look like?
Our Destination…
Connectivity
Every new article published will contain unique identifiers either in the visible text or in the underlying metadata. This will improve machine
readability and will dramatically improve the semantic connectivity of articles…
Reproducibility
Data driven qualitative metrics of material entities will be available, improving reproducibility and driving efficiency..
Visibility
Improved Geo and time dependent resource availability visualisation will be possible. Finding where resources are and identifying key technical
experts will be more efficient…
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