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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 BezuidenhoutDataSciJo2017 16.png|240px]]</div>
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'''"[[Journal:Technology transfer and true transformation: Implications for open data|Technology transfer and true transformation: Implications for open data]]"'''
'''"[[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 />


When considering the “openness” of data, it is unsurprising that most conversations focus on the online environment—how data is collated, moved, and recombined for multiple purposes. Nonetheless, it is important to recognize that the movements online are only part of the data lifecycle. Indeed, considering where and how data are created—namely, the research setting—are of key importance to open data initiatives. In particular, such insights offer key understandings of how and why scientists engage with in practices of openness, and how data transitions from personal control to public ownership. This paper examines research settings in low/middle-income countries (LMIC) to better understand how resource limitations influence open data buy-in. ('''[[Journal:Technology transfer and true transformation: Implications for open data|Full article...]]''')<br />
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Latest 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: