Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reprod... more Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reproducibility, accessibility, and scalability of experimental biology. Funding and training programs will enable academics to overcome barriers to adopting such technology.
Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reprod... more Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reproducibility, accessibility, and scalability of experimental biology. Funding and training programs will enable academics to overcome barriers to adopting such technology. What does the future of experimental biology look like? "Cloud labs" are one ambitious answer to this question. The concept is that one day, rather than working at the bench, scientists will instead program their experiments to be executed at a remote facility using laboratory automation. Cloud labs draw an analogy to cloud computing: Just as cloud computing services give researchers access to open-source libraries of software that can be deployed at scale, on demand, cloud laboratories could enable researchers to execute standardized, robust, scalable, biological protocols in robotically operated laboratories. Perhaps the most surprising feature of the cloud lab concept is that they already exist. Commercial cloud labs like Strateos and Emerald Cloud Lab have existed for nearly a decade and are primarily used by industry. A few intrepid academics have managed to experiment with commercial cloud labs [1,2], but largely this technology has not had an impact on academic science. This is a shame, as cloud science has the potential to improve the reproducibility, accessibility, and scalability of life science research. With cloud lab tools, academic researchers could more easily reproduce scientific experiments [3], build new methods on top of existing protocols, and share their new open-source contributions to continue the scientific cycle. Life science experiments are notoriously tricky to make robust and reliable, often flummoxing scientists and slowing progress [4]. In an era characterized by a "reproducibility crisis," science would benefit from experimenting with new ideas about how to standardize and share methods. Symbolic lab languages [5-7] enable researchers to describe experiments in an intuitive, standardized format that enables biological protocols to be shared in the same manner that open-source software is shared today. Furthermore, cloud labs democratize access to advanced instruments, eliminating the up-front costs associated with purchasing equipment, space, and long-term service contracts. These facilities give academic groups instant access to an extensive variety of specialized instruments that may not be available at their local institution, enabling groups with limited resources to access top-of-the-line equipment. So, if cloud labs exist and can help to alleviate many of the problems facing life science research today, why do scientists not use them? And what can be done to enable academia to adopt and benefit from the cloud science approach?
Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reprod... more Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reproducibility, accessibility, and scalability of experimental biology. Funding and training programs will enable academics to overcome barriers to adopting such technology.
Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reprod... more Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reproducibility, accessibility, and scalability of experimental biology. Funding and training programs will enable academics to overcome barriers to adopting such technology. What does the future of experimental biology look like? "Cloud labs" are one ambitious answer to this question. The concept is that one day, rather than working at the bench, scientists will instead program their experiments to be executed at a remote facility using laboratory automation. Cloud labs draw an analogy to cloud computing: Just as cloud computing services give researchers access to open-source libraries of software that can be deployed at scale, on demand, cloud laboratories could enable researchers to execute standardized, robust, scalable, biological protocols in robotically operated laboratories. Perhaps the most surprising feature of the cloud lab concept is that they already exist. Commercial cloud labs like Strateos and Emerald Cloud Lab have existed for nearly a decade and are primarily used by industry. A few intrepid academics have managed to experiment with commercial cloud labs [1,2], but largely this technology has not had an impact on academic science. This is a shame, as cloud science has the potential to improve the reproducibility, accessibility, and scalability of life science research. With cloud lab tools, academic researchers could more easily reproduce scientific experiments [3], build new methods on top of existing protocols, and share their new open-source contributions to continue the scientific cycle. Life science experiments are notoriously tricky to make robust and reliable, often flummoxing scientists and slowing progress [4]. In an era characterized by a "reproducibility crisis," science would benefit from experimenting with new ideas about how to standardize and share methods. Symbolic lab languages [5-7] enable researchers to describe experiments in an intuitive, standardized format that enables biological protocols to be shared in the same manner that open-source software is shared today. Furthermore, cloud labs democratize access to advanced instruments, eliminating the up-front costs associated with purchasing equipment, space, and long-term service contracts. These facilities give academic groups instant access to an extensive variety of specialized instruments that may not be available at their local institution, enabling groups with limited resources to access top-of-the-line equipment. So, if cloud labs exist and can help to alleviate many of the problems facing life science research today, why do scientists not use them? And what can be done to enable academia to adopt and benefit from the cloud science approach?
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