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| Whether or not you fully buy into the ability of cloud computing to help you laboratory depends on a number of factors, including industry served, current data output, anticipated future data output, the regulations affecting your lab, your organization's budget, and your organization's willingness to adopt and enforce sound risk management policies and controls. A tiny material testing laboratory with relatively simple workflows and little in the way of anticipated growth in the short term may be content with using their in-house systems. A biological research group with several laboratories geographically spread across the continent creating and managing large data sets for developing new medical innovations, all while operating in a competitive environment, may see the cloud as an opportunity to grow the organization and perform more efficient work.
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| In fact, the biomedical sciences in general have been a good fit for cloud computing. "Omics" laboratories in particular have shown promise for cloud-based data management, with many researchers over the years demonstrating various methods of managing the "big data" networking and sharing of omics data in the cloud.<ref name="OnsongoImplem14">{{cite journal |title=Implementation of Cloud based Next Generation Sequencing data analysis in a clinical laboratory |journal=BMC Research Notes |author=Onsong, G.; Erdmann, J.; Spears, M.D. et al. |volume=7 |at=314 |year=2014 |doi=10.1186/1756-0500-7-314 |pmid=24885806 |pmc=PMC4036707}}</ref><ref name="AfganGenomics15">{{cite journal |title=Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud |journal=PLoS One |author=Afgan, E.; Sloggett, C.; Goonasekera, N. et al. |volume=10 |issue=10 |at=e0140829 |year=2015 |doi=10.1371/journal.pone.0140829 |pmid=26501966 |pmc=PMC4621043}}</ref><ref name="NavaleCloud18">{{cite journal |title=Cloud computing applications for biomedical science: A perspective |journal=PLoS Computational Biology |author=Navale, V.; Bourne, P.E. |volume=14 |issue=6 |at=e1006144 |year=2018 |doi=10.1371/journal.pcbi.1006144 |pmid=29902176 |pmc=PMC6002019}}</ref><ref name="OgleNamed21">{{cite journal |title=Named data networking for genomics data management and integrated workflows |journal=Frontiers in Big Data |author=Ogle, C.; Reddick, D.; McKnight, C.; Biggs, T.; Pauly, R.; Ficklin, S.P.; Feltus, F.A.; Shannigrahi, S. |volume=4 |at=582468 |year=2021 |doi=10.3389/fdata.2021.582468 |pmid=33748749 |pmc=PMC7968724}}</ref> These efforts have focused on managing large data sets more efficiently while being able to securely share those data sets with researchers around the world. This data sharing—particularly while considering FAIR data principles that aim to make data findable, accessible, interoperable, and reusable<ref name="WilkinsonTheFAIR16">{{cite journal |title=The FAIR Guiding Principles for scientific data management and stewardship |journal=Scientific Data |author=Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J. et al. |volume=3 |pages=160018 |year=2016 |doi=10.1038/sdata.2016.18 |pmid=26978244 |pmc=PMC4792175}}</ref>—is of significant benefit to research laboratories around the world; controlled access to that data via standards-based cloud computing methods certainly lends to those FAIR principles.<ref name="MonsCloudy17">{{cite journal |title=Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud |journal=Information Services & Use |author=Mons, B.; Neylon, C.; Velterop, J. et al. |volume=37 |issue=1 |pages=49–56 |year=2017 |doi=10.3233/ISU-170824}}</ref>
| | ==''Introduction to Quality and Quality Management Systems''== |
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| | | text = This book should not be considered complete until this message box has been removed. This is a work in progress. |
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| | The goal of this short volume is to act as an introduction to the quality management system. It collects several articles related to quality, quality management, and associated systems. |
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| Another area where laboratory-driven cloud computing makes sense is that of the [[internet of things]] (IoT) and networked sensors that collect data. From wearable sensors, monitors, and point-of-care diagnostic systems to wireless sensor networks, Bluetooth-enabled mobile devices, and even IoT-enabled devices and equipment directly in the laboratory, connecting those devices to cloud services and storage provides near-seamless integration of data producing instruments with laboratory workflows.<ref name="MayerAMega19">{{cite journal |title=A Megatrend Challenging Analytical Chemistry: Biosensor and Chemosensor Concepts Ready for the Internet of Things |journal=Chemical Reviews |author=Mayer, M.; Baeumner, A.J. |volume=119 |issue=13 |pages=7996–8027 |year=2019 |doi=10.1021/acs.chemrev.8b00719 |pmid=31070892}}</ref><ref name="BorfitzIoT20">{{cite web |url=https://www.bio-itworld.com/news/2020/04/17/iot-in-the-lab-includes-digital-cages-and-instrument-sensors |title=IoT In The Lab Includes Digital Cages And Instrument Sensors |author=Borfitz, D. |work=BioIT World |date=17 April 2020 |accessdate=21 August 2021}}</ref> Monitoring temperature, humidity, and other ambient temperatures of freezers, fridges, and incubators while maintaining calibration and maintenance data becomes more automated.<ref name="BorfitzIoT20" /> Usage data on high-use instruments can be uploaded to the cloud and analyzed to enable predictive maintenance down the road.<ref name="BorfitzIoT20" /> And outdoor pollution monitoring systems that use low-cost, networked sensors can, upon taking a reading (a trigger event), send the result to a cloud service (at times with the help of edge computing), where a bit of uploaded code processes the data upload on-demand.<ref name="IdreesEdge18">{{cite journal |title=Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development |journal=Sensors |author=Idrees, Z.; Zou, Z.; Zheng, L. |volume=18 |issue=9 |at=3021 |year=2018 |doi=10.3390/s18093021 |pmid=30201864 |pmc=PMC6163730}}</ref> In all these cases, the automated collection and analysis of data using cloud components—which in turn makes that data accessible from anywhere in the world with internet access—allows laboratorians to rapidly gain advantages in how they work.
| | ;1. What is quality? |
| | :''Key terms'' |
| | :[[Quality (business)|Quality]] |
| | :[[Quality assurance]] |
| | :[[Quality control]] |
| | :''The rest'' |
| | :[[Data quality]] |
| | :[[Information quality]] |
| | :[[Nonconformity (quality)|Nonconformity]] |
| | :[[Service quality]] |
| | ;2. Processes and improvement |
| | :[[Business process]] |
| | :[[Process capability]] |
| | :[[Risk management]] |
| | :[[Workflow]] |
| | ;3. Mechanisms for quality |
| | :[[Acceptance testing]] |
| | :[[Conformance testing]] |
| | :[[Clinical quality management system]] |
| | :[[Continual improvement process]] |
| | :[[Corrective and preventive action]] |
| | :[[Good manufacturing practice]] |
| | :[[Malcolm Baldrige National Quality Improvement Act of 1987]] |
| | :[[Quality management]] |
| | :[[Quality management system]] |
| | :[[Total quality management]] |
| | ;4. Quality standards |
| | :[[ISO 9000]] |
| | :[[ISO 13485]] |
| | :[[ISO 14000|ISO 14001]] |
| | :[[ISO 15189]] |
| | :[[ISO/IEC 17025]] |
| | :[[ISO/TS 16949]] |
| | ;5. Quality in software |
| | :[[Software quality]] |
| | :[[Software quality assurance]] |
| | :[[Software quality management]] |
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| Panning outward, we see other benefits of cloud to a broad set of laboratories. Agilent Technologies, an analytical instrument developer and manufacturer, argues that the cloud can transform siloed, disparate data and information in a non-cloud, on-premises [[Informatics (academic field)|informatics]] solution into more actionable knowledge and wisdom, which by extension adds value to the laboratory. They also argue overall value to the lab is increased by<ref name="AgilentCloud19">{{cite web |url=https://www.agilent.com/cs/library/whitepaper/public/whitepaper-cloud-adoption-openlab-5994-0718en-us-agilent.pdf |format=PDF |title=Cloud Adoption for Lab Informatics: Trends, Opportunities, Considerations, Next Steps |author=Agilent Technologies |publisher=Agilent Technologies |date=21 February 2019 |accessdate=21 August 2021}}</ref>:
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| * "providing a higher level of connectivity and consistency for lab informatics systems and processes";
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| * enabling "lab managers to integrate functionality and bring context to every phase of the continuum of value, without increasing cost or risk";
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| * allowing IT personnel "to do more with less";
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| * enabling "faster, easier, more mobile access to data and tools" for lab technicians (globally);
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| * allowing lab leaders to reduce costs (including capital expenditure costs) and "increase team morale by enabling streamlined, self-service access to resources" ; and
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| * enabling labs to expand their "digital transformation initiatives," with cloud as the catalyst.
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| Other benefits provided to labs by cloud computing include:
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| * providing "the ability to easily increase or decrease their use [of infrastructure and services] as business objectives change and keep the organization nimble and competitive" (i.e., added scalability while operating more research tasks with massive data sets)<ref name="WardCloud19">{{cite web |url=https://www.labmanager.com/business-management/cloud-computing-for-the-laboratory-736 |title=Cloud Computing for the Laboratory: Using data in the cloud - What it means for data security |author=Ward, S. |work=Lab Manager |date=09 October 2019 |accessdate=21 August 2021}}</ref>;
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| * limiting responsibility of physical (in-person) access and protection of stored data to the CSP (though this comes with its own caveats concerning backing up with another provider)<ref name="APHLBreaking17">{{cite web |url=https://www.aphl.org/aboutAPHL/publications/Documents/INFO-2017Jun-Cloud-Computing.pdf |format=PDF |title=Breaking Through the Cloud: A Laboratory Guide to Cloud Computing |author=Association of Public Health Laboratories |publisher=Association of Public Health Laboratories |date=2017 |accessdate=21 August 2021}}</ref>;
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| * limiting responsibility for technical hardware and other assets to the CSP as the organization grows and changes<ref name="APHLBreaking17" />;
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| * inheriting the existing security protocols and compliance procedures of the provider (though again with caveats concerning vetting the CSP's security, and the inability to do so at times)<ref name="APHLBreaking17" />; and
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| * ensuring "that different teams are not simultaneously replicating workloads – creating greater efficiencies throughout organizations" (under the scope of the real-time update capacity of cloud globally).<ref name="IFAhelp20">{{cite web |url=https://www.mynewlab.com/blog/a-helpful-guide-to-cloud-computing-in-a-laboratory/ |title=A Helpful Guide to Cloud Computing in a Laboratory |work=InterFocus Blog |publisher=InterFocus Ltd |date=05 October 2020 |accessdate=21 August 2021}}</ref>
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| To sum this all up, let's look at what cloud computing is, care of Chapter 1:
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| <blockquote>an internet-based computing paradigm in which standardized and [[Virtualization|virtualized]] resources are used to rapidly, elastically, and cost-effectively provide a variety of globally available, "always-on" computing services to users on a continuous or as-needed basis</blockquote>
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| First, cloud technology is standardized, just like many of the techniques used in laboratories. A standardized approach to cloud computing assists with cloud services remaining compliant, which laboratories also must do. Yes, there's plenty of responsibility in ensuring all data management and use in the cloud is done so in a compliant fashion, but standardized approaches based on sound security principles help limit a laboratory's extended risk. Second, cloud technology is virtualized, meaning compute services and resources are more readily able to be recovered should system failure or disaster strike. Compare this to traditional infrastructures that inherently end up with longer periods of downtime, something which most laboratories cannot afford to have. Third, cloud services are built to be provisioned rapidly, elastically, and cost-effectively. In the case of laboratories and their workflows—especially in high-throughput labs—having scalable compute services that can be ramped up rapidly on a pay-what-you-use basis is certainly appealing to the overall business model. Finally, having those services available from anywhere with internet service, at any time, greatly expands numerous aspects of laboratory operations. Laboratorians can access data from anywhere at any time, facilitating research and discovery. Additionally, this enables remote and wireless data collection and upload from all but the most remote of locations, making environmental or even public health laboratory efforts more flexible and nimble.
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| ==References==
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| {{Reflist|colwidth=30em}}
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