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The IMEx consortium is an international collaboration between a group of major public interaction data providers who have agreed to share curation effort and develop and work to a single set of curation rules when capturing data from both directly deposited interaction data or from publications in peer-reviewed journals, capture full details of an interaction in a “deep” curation model, perform a complete curation of all protein-protein interactions experimentally demonstrated within a publication, make these interaction available in a single search interface on a common website, provide the data in standards compliant download formats, make all IMEx records freely accessible under the Creative Commons Attribution License
The Database of Protein Disorder (DisProt) is a curated database that provides information about proteins that lack fixed 3D structure in their putatively native states, either in their entirety or in part. DisProt is a community resource annotating protein sequences for intrinsically disorder regions from the literature. It classifies intrinsic disorder based on experimental methods and three ontologies for molecular function, transition and binding partner.
ARES (Aerosol Remote Sensing) is the ACTRIS (Aerosol, Cloud and Trace Gases Research Infrastructure) Data Centre unit for aerosol remote sensing profiling. ACTRIS-ARES host and manage the ACTRIS-EARLINET database, that represents a comprehensive, quantitative, and statistically significant collection of data for the aerosol distribution on European scale. ARES provides data curation, data processing and data access services for ACTRIS aerosol remote sensing data coming from lidar and photometer observations. Additionally, ARES hosts and manage the EARLINET web site and offers data services and digital tools to EARLINET and beyond. Major objectives also include the development and validation of new synergetic remote sensing algorithms, and the continuous evaluation of the representation of atmospheric aerosol parameters in climate and weather forecast models.