New in 2018 we are opening up the process of deciding panel discussion topics to the SWAT4HCLS public. We have already we have listed 13 fun topics from a brainstorming session. We want your input on these topics in the form of likes, and comments*. You can also recommend panelists for the topic. Specifically we want your topic suggestions too, so you can add additional topics for others to like or comment on. The overall goal is to engage you is a dialog and together discuss topics of interest to our community leading up to the event in December.
You can tweet this panels page to your friends. Join the discussion !
*The comments will be moderated for unsavoury language / obvious SPAM by a SWAT4HCLS organizer.
June 8, 2018 at 6:58 am
“Making all Data FAIR will be more challenging than sequencing the Human Genome”
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June 8, 2018 at 7:05 am
very interesting topic. Enough for me to pay a visit.
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June 8, 2018 at 10:29 am
I know a great panelist for this topic
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August 7, 2018 at 12:53 pm
“All data” sounds too much of an hyperbole, but I think this makes the topic even more relatable. A little of bit of number of datasets estimation in some field, as a Fermi problem, could be an interesting starting point for this session.
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June 8, 2018 at 2:31 pm
“Ontologists will never make it out of the Ivory tower”
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June 8, 2018 at 3:24 pm
“Interoperability is a dirty word in Healthcare”
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June 8, 2018 at 3:24 pm
“Deep Learning is a Billion dollar Cul-De-Sac”
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August 7, 2018 at 12:58 pm
The title is provocative, but maybe Deep Learning is throwing first outcomes in a number of fields. What if we make the title more specific to any given domain:
“Deep Learning in Crop Forecasting is a Billion dollar cul-de-sac”
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June 8, 2018 at 3:25 pm
“Today’s data privacy policies have irreparably failed future generations”
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August 7, 2018 at 1:01 pm
This is a very tricky as we may fail future generations both by limiting too much data use because of privacy policies, or on the other hand, not being bold enough in their use missing important discoveries for the coming future.
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June 8, 2018 at 3:25 pm
“The biggest limitation in industry is that senior decision makers have no understanding of new technologies and aren’t prepared to admit it”
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June 8, 2018 at 3:25 pm
“Semantic publishing is not advancing scientific discovery and never will”
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June 18, 2018 at 11:34 am
I think this is an interesting topic. At the same time, I go against the topic statement.
Semantic publishing of data supporting scientific articles and Semantic publishing of experimental data is definitely leading to a more transparent and reproducible science. Data is the only proof to all the claims in a publication. Data in life sciences has evolved from excel sheets to databases and to linked data graphs. Semantic fairness of data is a way forward and gives power to do scientific discovery via machine-readability and usability of ML and deep learning.
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June 8, 2018 at 3:25 pm
“Investors are like Magpies, they only collect shiny objects with no understanding of what they are good for”
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June 8, 2018 at 3:25 pm
“The reviewers of my SWAT4HCLS paper were fools and should be ashamed to come to the conference”
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August 7, 2018 at 1:04 pm
Make this happen and invite a couple of selected refused papers and another pair of reviewers to also review the paper. Let them discuss their different perspectives. Panelists can wear invisibility cloaks and use voice changers to keep reviewer identity private. Alternatively just ask reviewers to accept that their reviews and identities will be made public. Don’t some publishers already do this ?
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June 8, 2018 at 3:26 pm
If FAIR data is so valuable, why don’t we have companies that make data FAIR as a business, and other companies willing to pay for that?
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June 9, 2018 at 1:37 pm
Micelio already does but other companies that pay for it …… ? Can’t see why not.
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June 14, 2018 at 11:16 am
Maybe a corollary discussion would be on business models for cleansing data.
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June 9, 2018 at 8:45 am
“People think RDF is a pain because it is complicated. The truth is even worse. RDF is painfully simplistic, but it allows you to work with real-world data and problems that are horribly complicated.” quote from Dan Brickley
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June 18, 2018 at 11:50 am
Very interesting topic. Building SPARQL queries over multiple data graphs is indeed horribly complicated. The big question here is how can we mine information from a data graph without knowing the nitty gritty details about it.
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June 11, 2018 at 2:09 pm
Ten simple rules to make the best Linked Data that we can!
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June 12, 2018 at 10:55 am
Examples of good linked data resource and how you can be one too.
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August 7, 2018 at 1:05 pm
Add some smart linked data reuse use cases.
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June 14, 2018 at 11:25 am
solr is for search, sparql is for research
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June 20, 2018 at 11:40 am
I am expecting a comeback for formal logic and ontological knowledge bases, I’m personally very wary of NNs.
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June 20, 2018 at 7:23 pm
The role of Semantic Web Technologies for enabling secondary use of EHR data for Clinical Research and Pharmacovigilance — Current Challenges and Future Outcomes
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August 7, 2018 at 1:09 pm
What will we have first? The Semantic web publishing egg or the AI assisted data integration chicken?
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