Review Criteria for DataTech19 proposals:
1. Fit with the themes announced for DataTech19, or other themes considered to be of wide interest to the data science community
Is the submission technically sound?
Are claims well supported by theoretical analysis and/or experimental results? Solid justification of claims is particularly important for any talks discussing data problems without satisfactory solutions present currently.
Are the authors careful and honest about evaluating both the strengths and weaknesses of their work?
Are the results/conclusions important?
Are others (researchers or practitioners) likely to use the ideas or build on them?
Alternatively, does the talk demonstrate negative results/approaches which others should avoid in the future?
Does the submission address a difficult task in a better way than previous work?
4. Sufficient / selective coverage of the topic:
Well curated collection of information, but without swamping audience, nor keeping things too vague to be useful.
The submissions should focus on concepts (and potentially code), but not minute details and maths: submitters should avoid “decorative math” if this does not add significant insight
Given that the audience will be multidisciplinary, strike a good balance between low and high level-information, plus
Provide examples and context to the problem discussed, in order for the audience to relate to the topic more easily
5. Adequate novelty vs. prior work balance:
Novelty will be sought in the in any of the following: methods (or novel combination of methods), context switch / generalisability, and data sources / collection.
Are the tasks or methods discussed new? (e.g., applying something in a different context can also constitute novelty)
Is the work a novel combination of well-known techniques?
Does it provide novel data collection methods, novel conclusions about existing data, or a novel theoretical or experimental approach?
Is it clear how this work differs from previous contributions?
Is related work adequately credited?
Review-type submissions are also possible in case there is clear value in synthesizing the progress seen over time in a field of work.
6. Clarity and precision in:
Definition of objectives,
Emphasising the importance of the problem, plus any theoretical or practical implications
Flow of arguments and claims, likely to translate into the audience following the talk easily
Will attendees leaving from this talk likely be able to replicate elements from the talk, or at least know how to make a start in tackling the same topic?
Authors are strongly encouraged to make their code and data publicly available whenever possible