The Dark Matter Data Center (integrated into ODSL)

Open Data, Open Science!

Open science has become a pillar in the research world and it's fuelling exchange of knowledge, data and ideas. The extraordinary impact of open science accelerates scientific research and the creation of new knowledge. We believe that open data is deeply rooted in the scope and spirit of fundamental research and we support this culture, offering a place where data from experiments and phenomenology can meet.

Dark Matter

Dark matter searches are an extraordinary endeavor of the human kind to shed light on one of the biggest mysteries of the cosmos and the physics that governs it. The understanding of the composition of our Universe expands through a variety of experimental approaches and a rich zoo of models and ideas. The discovery of dark matter and the investigation of its nature must follow complementary paths, for no single evidence would uniquely identify the nature of dark matter making up our Universe.

Bringing Experiments and Theories Together

With the ORIGINS Dark Matter Data Center we want to fully leverage the potential of open science to bring together observations from different experiments, the implications of different models and all the associated software. At the DMDC we aim at increasing accessibility to scientific process and knowledge, open data and open source software: key ingredients for the nourishing of open science (From "Open Data to Open Science" Earth and Space Science doi:10.1029/2020EA001562), by offering a repository for experimental data, models and code. The Dark Matter Data Center supports data comparison, combination and interpretation using clear and reproducible methodologies, easing the usability of this data, enabling one to make the most out of it. Our sights are set on sharing knowledge in all its relevant forms: data, methodologies and software with the ultimate goal of offering a consistent and unified view of the field in all its facets.

Dark matter experiments are based on widely varying technologies and the data from these experiments are also very different in the way they need to be understood and analysed. Here you can,

• Look at the data from a variety of experiments in their available formats and download them directly along with their citeable resources. Go to the Collaboration pages to look at the data from all their experiments, you will also find a detailed description of the experiments there.
• Understand how the data needs to be interpreted and analysed.
• Know how the data has been obtained and how the Collaborations get from their observations to their results.
• Visualise the data in an integrated plotting platform. Here you can plot different datasets and compare any set of observables you wish. Edit the plots or do basic analysis directly on our website! You can download your plots for later use as well! No one else can see what you are looking at and the figures you generate are lost the moment you close the window.

Publish Dark Matter data directly on the Dark Matter Data Centre!

• The data will be published for public use under the Ceative Commons Attribution License (CC-BY) with an assigned DOI.
• Link it with the associated publication. We will pass these DOIs along with that of your data as citeable resources.
• Not a few specific regions of interest (ROI's), but as much of the data as possible -- this is what we would prefer.
• We need all the information that goes into processing your data to obtain the exclusions you publish.
• All the information required to simulate an expected signal for your experiment, given a specific model of physics. 

Have you just published a model of physics concerning the presence of Dark Matter? Publish the software implementation of your model!

Have you designed a new simulator for DM-nucleus/electron events? Publish your code here!

• Your software will be published as an open source code under the Creative Commons Attribution License (CC-BY) with an assigned DOI.
• Covenient storage for long-term utility and reproducibility.
• It will help those coming after you to learn and build upon your work.
• It will add to the repository of hypotheses that can be analysed in tandem and compared for a unified view of the work that has already been done.
• We will include verified model and simulator softwares as options in our upcoming online Simulator tool.

• You can simulate DM events for specific experiments using data from the DMDC website on our integrated binder for Python and Julia.
• Use your model implementation to simulate events on our integrated simulator for any of the experiments listed on DMDC.

  Coming Soon!

• You can evaluate posteriors for your model parameters using data from any subset of the experiments listed at the DMDC. Inputs required: Model implementation, likelihood function and priors. You can choose to use either PyHF or BAT for the analysis.

  Coming Soon!