Publication details
On this page you will find information about the planned collaboration paper that is to be published at the end of the mock data challenge. It contains the structure of the paper, as well as a few details of the contents of these sections. It also contains information on what will not be covered by the collaboration paper and might, therefore, be useful to be included in a standalone publication on the individual submissions.
We want to highlight that we encourage any participating group to write a publication on their own contribution that can be referenced in the collaboration paper. We also encourage to make the code for training and testing open source such that it can be referenced in the collaboration paper. However, we do not require a standalone paper or open source code for any submission.
The information below may be updated and/or expanded during the course of the challenge.
Here the different groups will be introduced briefly. Each group will be asked to write up to 3 paragraphs on their algorithm. Any supplemental material (e.g. open source code) and accompanying publications describing the algorithm in detail should be referenced here. Please the deadline for submission of this summary in mind.
Each contribution is tested on all 4 data sets, irrespective of the data set it was optimized for. We will mark entries which were not optimized for a specific data set in each table accordingly.
We provide a table summarizing the results of all contributions on the metrics for each data set. The table will have the format shown below. Furthermore, we will give a brief discussion of the contributions optimized for this data set and their specific advantages.
For each of the data sets we will also provide two figures. Both will show the sensitive distance as a function of the false-alarm rate. The first plot will show all contributions, whereas the second one will only show those algorithms optimized on the data set at question. An example plot is shown below.
