Abstract: A platform and method for enabling collaboration on data analysis of life sciences data across disparate databases are disclosed. The collaboration platform may allow for performing exploratory analysis for drug discovery and development. The collaboration platform may include a search and graph module for generating a user project and determining and displaying one or more matching data assets and one or more potential collaborators; a collaboration module for coordinating a collaboration between the user and one or more selected collaborators; a data management module for receiving a schema for one or more producer projects, receiving data from the one or more selected data assets, and ingesting the received data using common standards and an ontology; and an insight application for generating disease specific inferences relating to a scientific question using the ingested received data, and receiving a feedback from the user and/or the selected collaborators to improve the search and graph module.

Abstract: A platform and method for enabling collaboration on data analysis of life sciences data across disparate databases are disclosed. The collaboration platform may allow for performing exploratory analysis for drug discovery and development. The collaboration platform may include a search and graph module for generating a user project and determining and displaying one or more matching data assets and one or more potential collaborators; a collaboration module for coordinating a collaboration between the user and one or more selected collaborators; a data management module for receiving a schema for one or more producer projects, receiving data from the one or more selected data assets, and ingesting the received data using common standards and an ontology; and an insight application for generating disease specific inferences relating to a scientific question using the ingested received data, and receiving a feedback from the user and/or the selected collaborators to improve the search and graph module.

Abstract: A computing device generates an audit trail for monitoring clinical data. In particular, the computing device receives clinical data comprising patient data and investigator site data. The computing device accepts and rejects respective portions of the clinical data by applying a risk-based monitoring policy comprising one or more policy rules to the clinical data. The computing device generates an audit trail for the clinical data. The audit trail comprises the clinical data and corresponding audit data. The audit data comprises identification of the accepted and rejected portions of the clinical data, identification of one or more of the policy rules of the risk-based monitoring policy used in the accepting and rejecting of the respective portions of the clinical data, and one or more timestamps corresponding to each of the accepting and rejecting of the respective portions.

Abstract: A computing device generates an audit trail for monitoring clinical data. In particular, the computing device receives clinical data comprising patient data and investigator site data. The computing device accepts and rejects respective portions of the clinical data by applying a risk-based monitoring policy comprising one or more policy rules to the clinical data. The computing device generates an audit trail for the clinical data. The audit trail comprises the clinical data and corresponding audit data. The audit data comprises identification of the accepted and rejected portions of the clinical data, identification of one or more of the policy rules of the risk-based monitoring policy used in the accepting and rejecting of the respective portions of the clinical data, and one or more timestamps corresponding to each of the accepting and rejecting of the respective portions.