[{"id":239,"title":"European Citizen Science via the web: potentials, expectations and way ahead","sha":"542c815ec7b61db0426c906c6f0559c3","user_id":null,"state":"published","body":"We envision a web-enabled European Citizen Science Platform that opens research supported by the European Union for collaborations with the public. Our initial investigations concentrate on environmental sciences and sustainability research in order to support the living within the limits of our planet.\r\n\r\nThis activity shall advance science by (i) providing easy access to data, services, models, publications, etc.; (ii) listening to emerging debates and areas of interest; and (iii) enabling deep engagement in scientific processes for anybody interested. Web technologies will be a key element in order to reach a wide audience, enable re-use of available resources in innovative applications, and foster collaboration beyond the physical space.\r\n\r\nWe hope to address questions such as:\r\n-\tWhich web-tools would allow you to get as involved in science as desired?\r\n-\tWhat should be done within Europe in order to support European and non-European citizen?\r\n-\tWhat would you expect as the European Commission’s contribution to a Citizen Science Platform?\r\n\r\nThe Open Research Accelerator should help to identify and shape expectations, collect potential components and promising approaches, and avoid developments into undesired directions. This first activity of awareness raising and co-design should initiate a collaborative process beyond the event itself.","created_at":"2015-09-28T09:48:52.458Z","updated_at":"2020-12-07T22:16:18.660Z","zenodo_id":62719,"doi":"http://dx.doi.org/10.5281/zenodo.31512","subject":null,"tags":["open research accelerator","citizen sicience","open knowledge","open data","civic engagement"],"vote_count":9,"view_count":1,"score":9.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":285,"title":"Create standalone simulation tools to facilitate collaboration between HEP and machine learning community","sha":"ff0489d51bdb17359cef823c1d6b7029","user_id":null,"state":"published","body":"Discussions at recent workshops have made it clear that one of the key barriers to collaboration between high energy physics and the machine learning community is access to training data. Recent successes in data sharing through the [HiggsML](http://doi.org/10.7483/OPENDATA.ATLAS.ZBP2.M5T8) and [Flavours of Physics](https://www.kaggle.com/c/flavours-of-physics/data) Kaggle challenges have borne much fruit, but required significant effort to coordinate.\r\n\r\nWhile static simulated datasets are useful for challenges, in the course of investigating new machine learning techniques it is advantageous to be able to generate training data on demand (e.g. Refs. [1](https://archive.ics.uci.edu/ml/datasets/HEPMASS), [2](https://archive.ics.uci.edu/ml/datasets/SUSY), [3](https://archive.ics.uci.edu/ml/datasets/HIGGS) ). \r\nTherefore we recommend efforts be made to produce the ingredients required to facilitate such collaboration:\r\n   * Specific challenges for HEP experiments should be fully specified such that minimal domain-specific knowledge is required to attack them.\r\n   * Stand-alone simulators should be made open source. They should be developed to be easy to use without domain-specific expertise, while still being representative of real experimental challenges. Such a simulation will permit non-HEP researchers to generate realistic HEP datasets for training and testing. These simulators could range from truth-level simulation of a hard scattering to fast simulation like [Delphes](http://dx.doi.org/10.1007/JHEP02(2014)057), to full [GEANT4](http://dx.doi.org/10.1016/S0168-9002(03)01368-8) simulation of sensor arrays.\r\n   * Performance metrics (objective functions) and operational constraints should be defined to evaluate proposed solutions.\r\n","created_at":"2016-02-26T21:34:14.721Z","updated_at":"2016-04-03T17:04:01.541Z","zenodo_id":99761,"doi":"http://dx.doi.org/10.5281/zenodo.46864","subject":null,"tags":["dslhc","machinelearning","datascience","open data","simulation"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]