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

2. Quality:

  • 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?

3. Impact:

  • 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

7. Reproducibility:

  • 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