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'''"[[Journal:Towards a risk catalog for data management plans|Towards a risk catalog for data management plans]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


Although [[Information management|data management]] and its careful planning are not new topics, there is little published research on [[Risk management|risk mitigation]] in data management plans (DMPs). We consider it a problem that DMPs do not include a structured approach for the [[Risk assessment|identification]] or mitigation of risks, because it would instill confidence and trust in the data and its stewards, and foster the successful conduction of data-generating projects, which often are funded research projects. In this paper, we present a lightweight approach for identifying general risk in DMPs. We introduce an initial version of a generic risk catalog for funded research and similar projects. By analyzing a selection of 13 DMPs for projects from multiple disciplines published in the ''Research Ideas and Outcomes'' (''RIO'') journal, we demonstrate that our approach is applicable to DMPs and transferable to multiple institutional constellations. As a result, the effort for integrating risk management in data management planning can be reduced. ('''[[Journal:Towards a risk catalog for data management plans|Full article...]]''')<br />
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

Recently featured: