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==Sandbox begins below==
==Sandbox begins below==
<div class="nonumtoc">__TOC__</div>
<div class="nonumtoc">__TOC__</div>
[[File:Team Tomato (6904) (8754464273).jpg|right|250px]]
[[File:FAIRResourcesGraphic AustralianResearchDataCommons 2018.png|right|520px]]
'''Title''': ''How does a LIMS benefit microbiological sampling and testing in the food and beverage industry?''
'''Title''': ''What are the potential implications of the FAIR data principles to laboratory informatics applications?''


'''Author for citation''': Shawn E. Douglas
'''Author for citation''': Shawn E. Douglas
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'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]


'''Publication date''': January 2024
'''Publication date''': May 2024


==Introduction==
==Introduction==
https://www.limswiki.org/index.php/Journal:Infrastructure_tools_to_support_an_effective_radiation_oncology_learning_health_system


==LIMS and microbiological sampling and analysis of foods and beverages==
This brief topical article will examine
In the world of microbiological testing of foods, beverages, and their processing environment, the importance of proper sampling practices—including having a sound sampling plan—rarely gets understated.<ref name="JacksonManage23">{{cite book |last=Jackson, T. |year=2023 |editor-last=Andersen, V.; Lelieveld, H.; Motarjemi, Y. |title=Food Safety Management: A Practical Guide for the Food Industry |url=https://books.google.com/books?id=3TpwEAAAQBAJ&printsec=frontcover |chapter=Management of Microbiological Hazards: Role of Testing as Verification |edition=2nd |publisher=Elsevier, Inc |pages=851–72 |isbn=9780128200131}}</ref><ref name="ErkmanSamp22">{{cite book |last=Erkmen |first=Osman |date=2022 |title=Microbiological analysis of foods and food processing environments |chapter=Practice 1: Sampling and sample preparation techniques |url=https://books.google.com/books?id=6kU6EAAAQBAJ&printsec=frontcover |publisher=Academic Press, an imprint of Elsevier |place=London San Diego, CA Cambridge, MA Kidlington, Oxford |pages=3–12 |isbn=978-0-323-91651-6}}</ref><ref name="FockerDesign23">{{cite journal |last=Focker |first=M. |last2=van Asselt |first2=E.D. |last3=van der Fels-Klerx |first3=H.J. |year=2023 |title=Designing a risk-based monitoring plan for pathogens in food: A review |url=https://linkinghub.elsevier.com/retrieve/pii/S0956713522005126 |journal=Food Control |language=en |volume=143 |pages=109319 |doi=10.1016/j.foodcont.2022.109319}}</ref><ref name="DeLoyHemdrikxSampling18">{{cite book |last=De Loy-Hendrickx, A.; Vermeulen, A.; Jacxxens, L. et al. |year=2018 |editor-last=Mieke, U. |title=Microbiological Guidelines: Support for Interpretation of Microbiological Test Results of Foods |url=https://books.google.com/books?id=pG1UDwAAQBAJ&pg=PT108 |chapter=Part II: Sampling |publisher=Die Keure |pages=95–136 |isbn=9782874035036}}</ref><ref name="ICMSFMicro18">{{cite book |url=http://link.springer.com/10.1007/978-3-319-68460-4 |title=Microorganisms in Foods 7: Microbiological Testing in Food Safety Management |author=International Commission on Microbiological Specifications for Foods |publisher=Springer International Publishing  |year=2018 |edition=2nd |doi=10.1007/978-3-319-68460-4 |isbn=978-3-319-68458-1}}</ref>


Sampling plans essentially take identified sampling points within a controlled environment and define the frequency at which those sampling points are monitored and drawn from, while linking those collected samples to one or more specific standardized test methods and analyses. The frequency may be daily, monthly, quarterly, annual, or custom-defined (or even randomized<ref name="AIAutoInt20">{{cite web |url=https://www.autoscribeinformatics.com/resources/blog/autoscribe-integrates-field-sample-planning-and-scheduling-management |title=Autoscribe Integrates Field Sample Planning and Scheduling Management |publisher=Autoscribe Informatics, Inc |work=Autoscribe Blog |date=28 April 2020 |accessdate=09 February 2024}}</ref>), with each sampling point having it's own requirements, often different from sample point to sample point.
==The "FAIR-ification" of research objects and software==
First discussed during a 2014 FORCE-11 workshop dedicated to "overcoming data discovery and reuse obstacles," the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR data principles]] were published by Wilkinson ''et al.'' in 2016 as a stakeholder collaboration driven to see research "objects" (i.e., research data and [[information]] of all shapes and formats) become more universally findable, accessible, interoperable, and reusable (FAIR) by both machines and people.<ref name="WilkinsonTheFAIR16">{{Cite journal |last=Wilkinson |first=Mark D. |last2=Dumontier |first2=Michel |last3=Aalbersberg |first3=IJsbrand Jan |last4=Appleton |first4=Gabrielle |last5=Axton |first5=Myles |last6=Baak |first6=Arie |last7=Blomberg |first7=Niklas |last8=Boiten |first8=Jan-Willem |last9=da Silva Santos |first9=Luiz Bonino |last10=Bourne |first10=Philip E. |last11=Bouwman |first11=Jildau |date=2016-03-15 |title=The FAIR Guiding Principles for scientific data management and stewardship |url=https://www.nature.com/articles/sdata201618 |journal=Scientific Data |language=en |volume=3 |issue=1 |pages=160018 |doi=10.1038/sdata.2016.18 |issn=2052-4463 |pmc=PMC4792175 |pmid=26978244}}</ref> The authors released the FAIR principles while recognizing that "one of the grand challenges of data-intensive science ... is to improve knowledge discovery through assisting both humans and their computational agents in the discovery of, access to, and integration and analysis of task-appropriate scientific data and other scholarly digital objects."<ref name="WilkinsonTheFAIR16" />


Despite the wide variability that can be found in a sample plan, they tend to fall into two categories: attributes plans and variables plans. Broadly speaking, attributes plans are useful when little or no information is known about a food processing method or the past performance of a food or beverage producer but presence/absence testing is required; this is common for regulated testing at points of entry, as found with adulteration testing. Variables sampling plans, on the other hand, are useful where the frequency distribution of microorganisms within a given food or beverage lot is known or can easily be assumed. This type of sample plan is more applicable to food and beverage manufacturers conducting verification and end-point testing in their production processes.<ref name="ICMSFMicro18-C7">{{cite book |url=http://link.springer.com/10.1007/978-3-319-68460-4 |title=Microorganisms in Foods 7: Microbiological Testing in Food Safety Management |chapter=Chapter 7: Sampling Plans |author=International Commission on Microbiological Specifications for Foods |publisher=Springer International Publishing  |year=2018 |edition=2nd |pages=145–64 |doi=10.1007/978-3-319-68460-4 |isbn=978-3-319-68458-1}}</ref>
Since 2016, other research stakeholders have taken to publishing their thoughts about how the FAIR principles apply to their fields of study and practice<ref name="NIHPubMedSearch">{{cite web |url=https://pubmed.ncbi.nlm.nih.gov/?term=fair+data+principles |title=fair data principles |work=PubMed Search |publisher=National Institutes of Health, National Library of Medicine |accessdate=30 April 2024}}</ref>, including in ways beyond what perhaps was originally imagined by Wilkinson ''et al.''. For example, multiple authors have examined whether or not the software used in scientific endeavors itself can be considered a research object worth being developed and managed in tandem with the FAIR data principles.<ref>{{Cite journal |last=Hasselbring |first=Wilhelm |last2=Carr |first2=Leslie |last3=Hettrick |first3=Simon |last4=Packer |first4=Heather |last5=Tiropanis |first5=Thanassis |date=2020-02-25 |title=From FAIR research data toward FAIR and open research software |url=https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html |journal=it - Information Technology |language=en |volume=62 |issue=1 |pages=39–47 |doi=10.1515/itit-2019-0040 |issn=2196-7032}}</ref><ref name="GruenpeterFAIRPlus20">{{Cite web |last=Gruenpeter, M. |date=23 November 2020 |title=FAIR + Software: Decoding the principles |url=https://www.fairsfair.eu/sites/default/files/FAIR%20%2B%20software.pdf |format=PDF |publisher=FAIRsFAIR “Fostering FAIR Data Practices In Europe” |accessdate=30 April 2024}}</ref><ref>{{Cite journal |last=Barker |first=Michelle |last2=Chue Hong |first2=Neil P. |last3=Katz |first3=Daniel S. |last4=Lamprecht |first4=Anna-Lena |last5=Martinez-Ortiz |first5=Carlos |last6=Psomopoulos |first6=Fotis |last7=Harrow |first7=Jennifer |last8=Castro |first8=Leyla Jael |last9=Gruenpeter |first9=Morane |last10=Martinez |first10=Paula Andrea |last11=Honeyman |first11=Tom |date=2022-10-14 |title=Introducing the FAIR Principles for research software |url=https://www.nature.com/articles/s41597-022-01710-x |journal=Scientific Data |language=en |volume=9 |issue=1 |pages=622 |doi=10.1038/s41597-022-01710-x |issn=2052-4463 |pmc=PMC9562067 |pmid=36241754}}</ref><ref>{{Cite journal |last=Patel |first=Bhavesh |last2=Soundarajan |first2=Sanjay |last3=Ménager |first3=Hervé |last4=Hu |first4=Zicheng |date=2023-08-23 |title=Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool |url=https://www.nature.com/articles/s41597-023-02463-x |journal=Scientific Data |language=en |volume=10 |issue=1 |pages=557 |doi=10.1038/s41597-023-02463-x |issn=2052-4463 |pmc=PMC10447492 |pmid=37612312}}</ref><ref>{{Cite journal |last=Du |first=Xinsong |last2=Dastmalchi |first2=Farhad |last3=Ye |first3=Hao |last4=Garrett |first4=Timothy J. |last5=Diller |first5=Matthew A. |last6=Liu |first6=Mei |last7=Hogan |first7=William R. |last8=Brochhausen |first8=Mathias |last9=Lemas |first9=Dominick J. |date=2023-02-06 |title=Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software |url=https://link.springer.com/10.1007/s11306-023-01974-3 |journal=Metabolomics |language=en |volume=19 |issue=2 |pages=11 |doi=10.1007/s11306-023-01974-3 |issn=1573-3890}}</ref> Researchers quickly recognized that any planning around updating processes and systems to make research objects more FAIR would have to be tailored to specific research contexts, recognize that digital research objects go beyond data and information, and recognize "the specific nature of software" and not consider it "just data."<ref name="GruenpeterFAIRPlus20" /> The end result has been applying the core concepts of FAIR but differently from data, with the added context of research software being more than just data, requiring more nuance and a different type of planning from applying FAIR to digital data and information.


A LIMS can make enacting a lab's sampling plan easier in a number of ways. First, resource planning and quality control (QC) within the lab are important to accurate high-volume sampling and analyses, which better ensures resources such as instruments, equipment, and personnel are more efficiently scheduled, maintained, and put to positive use, and that sampling and analytical procedures are true to standardized methods and standard operating procedures.<ref name="LWHighVol21">{{cite web |url=https://www.labware.com/blog/meet-challenges-high-sample-volumes-environmental-monitoring |title=High Volume Environmental Monitoring: LabWare LIMS Solutions |publisher=LabWare, Inc |work=LabWare blog |date=06 April 2021 |accessdate=09 February 2024}}</ref> Given the variable frequency, and the differences of sampling and analytical methods at differing sampling points, a LIMS' ability to manage schedules, enforce standardized methods and workflows, and send alerts further helps the lab enact its sampling plan. In some cases, microbiological sampling and analyses may be required outside the manufacturing plant, for example at a point of distribution or at an ingredient or raw food supplier's facility. In this case, a well-developed LIMS that has strong mobile support, as well as scheduling of samplers and analysts to remote locations, can better allow for field sampling and the management of on-site microbiological test results, in turn supporting any field-based sampling plans.<ref name="AIAutoInt20" /> In summation, a LIMS' ability to handle numerous aspects of sample and resource management, from scheduling to final analysis and reporting, streamlines sampling workflows and enhances the accuracy of results, bolstering any microbiology lab's sampling plan.
A 2019 survey by Europe's FAIRsFAIR found that researchers seeking and re-using relevant research software on the internet faced multiple challenges, including understanding and/or maintaining the necessary software environment and its dependencies, finding sufficient documentation, struggling with accessibility and licensing issues, having the time and skills to install and/or use the software, finding quality control of the source code lacking, and having an insufficient (or non-existent) software sustainability and management plan.<ref name="GruenpeterFAIRPlus20" /> These challenges highlight the importance of software to researchers and other stakeholders, and the roll FAIR has in better ensuring such software is findable, interoperable, and reusable, which in turn better ensures researchers' software-driven research is repeatable (by the same research team, with the same experimental setup), reproducible (by a different research team, with the same experimental setup), and replicable (by a different research team, with a different experimental setup).<ref name="GruenpeterFAIRPlus20" />


Second, a sampling plan is further enhanced by a LIMS' ability to improve the scientifically and legally defensible nature of sampling and analytical data. For example, a LIMS that supports barcoded and RFID sample management will minimize hand-entered sample IDs (and in turn reduce errors in sample receipt, scheduling, and tracking) and better ensure proper metadata (e.g., time, date, sampler) are associated with each sample. When properly entered into the system as such, the LIMS can then quickly and accurately move it to the next step of workflow, or it can identify an abnormality or missing metadata and flag or alert it for immediate action so that the timely nature of sampling and analysis and can be maintained. Of course, the audit trail of the LIMS itself also aids in the defensibility of sampling data, as when implemented properly, it will document every step of the process, including any modifications, overrides, or approvals of results. The LIMS can also enforce hold times and requirements for blanks, and some even allow for environmental monitoring of sample preservation locations (e.g., tracking of ambient and storage temperature) to ensure regulatory requirements are being met.<ref name="TyerHowToEnsure">{{cite web |url=https://www.hargis.com/blog/how-to-ensure-environmental-sampling-data-are-scientifically-sound-and-legally-defensible/ |title=How to Ensure Environmental Sampling Data Are Scientifically Sound and Legally Defensible |author=Tyer, M. |work=Hargis & Associates Blog |publisher=Hargis & Associates, Inc |accessdate=09 February 2024}}</ref><ref name=PFPLTGFoodFeed13">{{cite web |url=https://www.fda.gov/media/88973/download |format=PDF |title=Food/Feed Testing Laboratories Best Practices Manual (Draft) |author=Partnership for Food Protection Laboratory Task Group |date=01 November 2013}}</ref><ref name="PFPLSWHumanAnim18">{{cite web |url=https://www.aphl.org/programs/food_safety/APHL%20Documents/LBPM_Dec2018.pdf |format=PDF |title=Human and Animal Food Testing Laboratories Best Practices Manual |author=Partnership for Food Protection Laboratory Science Workgroup |date=December 2018}}</ref>
At this point, the topic of what "research software" represents must be addressed further, and, unsurprisingly, it's not straightforward. Ask 20 researchers what "research software" is, and you may get 20 different opinions. Some definitions can be more objectively viewed as too narrow, while others may be viewed as too broad, with some level of controversy inherent in any mutual discussion.<ref name="GruenpeterDefining21">{{Cite journal |last=Gruenpeter, Morane |last2=Katz, Daniel S. |last3=Lamprecht, Anna-Lena |last4=Honeyman, Tom |last5=Garijo, Daniel |last6=Struck, Alexander |last7=Niehues, Anna |last8=Martinez, Paula Andrea |last9=Castro, Leyla Jael |last10=Rabemanantsoa, Tovo |last11=Chue Hong, Neil P. |date=2021-09-13 |title=Defining Research Software: a controversial discussion |url=https://zenodo.org/record/5504016 |journal=Zenodo |doi=10.5281/zenodo.5504016}}</ref><ref name="JulichWhatIsRes24">{{cite web |url=https://www.fz-juelich.de/en/rse/about-rse/what-is-research-software |title=What is Research Software? |work=JuRSE, the Community of Practice for Research Software Engineering |publisher=Forschungszentrum Jülich |date=13 February 2024 |accessdate=30 April 2024}}</ref><ref name="vanNieuwpoortDefining24">{{Cite journal |last=van Nieuwpoort |first=Rob |last2=Katz |first2=Daniel S. |date=2023-03-14 |title=Defining the roles of research software |url=https://upstream.force11.org/defining-the-roles-of-research-software |language=en |doi=10.54900/9akm9y5-5ject5y}}</ref> In 2021, as part of the FAIRsFAIR initiative, Gruenpeter ''et al.'' made a good-faith effort to define "research software" with the feedback of multiple stakeholders. Their efforts resulted in this definition<ref name="GruenpeterDefining21" />:


'''Pg 865''': "Although statistically limited, finished product and raw material testing may be conducted where there is limited information available about the hygienic status of a product lot (for example, a regulator’s analysis of imported product or a food producer’s analysis of raw materials). Testing may also be used for the evaluation of the suitability of finished products or raw materials where there is information from other verification activities that indicates an increased risk of contamination.
<blockquote>Research software includes source code files, algorithms, scripts, computational workflows, and executables that were created during the research process, or for a research purpose. Software components (e.g., operating systems, libraries, dependencies, packages, scripts, etc.) that are used for research but were not created during, or with a clear research intent, should be considered "software [used] in research" and not research software. This differentiation may vary between disciplines. The minimal requirement for achieving computational reproducibility is that all the computational components (i.e., research software, software used in research, documentation, and hardware) used during the research are identified, described, and made accessible to the extent that is possible.</blockquote>


The development and application of acceptance criteria for finished products and raw materials is discussed extensively by the ICMSF, 2002. Lot acceptance criteria are expressed in sampling plans outlining the pathogen or indicator organism(s) of concern, the number of samples to be taken from a lot (n), the limits of acceptance (c, m and M) and the methodology to be used in verifying conformance. Sampling plans in specifications are most often defined as two-class attributes plans (acceptable and unacceptable) and three-class attributes plans (acceptable, marginally acceptable and unacceptable). Two-class attributes plans are defined by m, the level separating acceptable from unacceptable and c, the maximum allowable number of sample units yielding a result greater than m. For pathogens m is often set at 0, indicating an absence of the organism in the analytical unit tested. Three-class attributes plans are defined by m, the level separating acceptable from marginally acceptable, M, the level separating marginally acceptable from unacceptable, and c, the maximum allowable number of sample units yielding a result greater than m and less than M. If any sample is above M in a three-class plan the lot is rejected. Three-class plans are most often applied in criteria for quantitative hygienic indicator organisms as they account for variability in levels and allow identification and correction of trends before levels exceed criteria that would result in lot rejection."
Note that while the definition primarily recognizes software created during the research process, software created (whether by the research group, other open-source software developers outside the organization, or even commercial software developers) "for a research purpose" outside the actual research process is also recognized as research software. This notably can lead to disagreement about whether a proprietary, commercial spreadsheet or [[laboratory information management system]] (LIMS) offering that conducts analyses and visualizations of research data can genuinely be called research software, or simply classified as software used in research. van Nieuwpoort and Katz further elaborated on this concept, at least indirectly, by formally defining the roles of research software in 2023. Their definition of the various roles of research software—without using terms such as "open-source," "commercial," or "proprietary"—essentially further defined what research software is<ref name="vanNieuwpoortDefining24" />:


'''Pg 869''': "Sampling for field testing may incorporate the sampling plans and operating characteristic curves used for finished product or ingredient testing, with the testing “lot” as the sampled area of the field or production block. Samples may be taken using a variety of approaches to obtain a samples representative of the field (UF, 2010; Western Growers, 2021). Xu and Buchanan (2019) compared the performance of three field collection methods, including random sampling, stratified random sampling, and sampling using a Z collection pattern ...
*Research software is a component of our instruments.
*Research software is the instrument.
*Research software analyzes research data.
*Research software presents research results.
*Research software assembles or integrates existing components into a working whole.
*Research software is infrastructure or an underlying tool.
*Research software facilitates distinctively research-oriented collaboration.


Development of Microbiological Specifications for Finished Products
When considering these definitions<ref name="GruenpeterDefining21" /><ref name="vanNieuwpoortDefining24" /> of research software and their adoption by other entities<ref name="F1000Open24">{{cite web |url=https://www.f1000.com/resources-for-researchers/open-research/open-source-software-code/ |title=Open source software and code |publisher=F1000 Research Ltd |date=2024 |accessdate=30 April 2024}}</ref>, it would appear that at least in part some [[laboratory informatics]] software—whether open-source or commercially proprietary—fills these roles in academic, military, and industry research laboratories of many types. In particular, [[electronic laboratory notebook]]s (ELNs) like open-source [[Jupyter Notebook]] or proprietary ELNs from commercial software developers fill the role of analyzing and visualizing research data, including developing molecular models for new promising research routes.<ref name="vanNieuwpoortDefining24" /> Even more advanced LIMS solutions that go beyond simply collating, auditing, securing, and reporting analytical results could conceivably fall under the umbrella of research software, particularly if many of the analytical, integration, and collaboration tools required in modern research facilities are included in the LIMS.


Finished product specifications take into account relevant regulatory or customer requirements, the hazards that may be present in raw materials and the environment, the nature of the product and process, and intended use of the material as determined in the HACCP study. Specifications include pathogens of concern as well as relevant indicator organisms, defined sampling plans and methodology. Sampling plans included in specifications should follow ICMSF format, with stringency based upon the severity of the pathogen of concern, the use of the product and the sensitivity of the consumer. Stringency may also be increased for new products or production lines, or where prior history of the product or process indicates a heightened concern. Sampling plan limits for m and M should be based upon an understanding of the raw materials and processes and ideally the results of testing of products manufactured under good conditions on a variety of production days."
Ultimately, assuming that some laboratory informatics software can be considered research software and not just "software used in research," it's tough not to arrive at some deeper implications of research organizations' increasing need for FAIR data objects and software, particularly for laboratory informatics software and the developers of it.
<ref name="JacksonManage23" />


'''Pg 3''': Sampling plan<ref name="ErkmanSamp22" />
==Implications of the FAIR concept to laboratory informatics software==
===The global FAIR initiative affects, and even benefits, commercial laboratory informatics research software developers as much as it does academic and institutional ones===
To be clear, there is undoubtedly a difference in the software development approach of "homegrown" research software by academics and institutions, and the more streamlined and experienced approach of commercial software development houses as applied to research software. Moynihan of Invenia Technical Computing described the difference in software development approaches thusly in 2020, while discussing the concept of "research software engineering"<ref name="MoynihanTheHitch20">{{cite web |url=https://invenia.github.io/blog/2020/07/07/software-engineering/ |title=The Hitchhiker’s Guide to Research Software Engineering: From PhD to RSE |author=Moynihan, G. |work=Invenia Blog |publisher=Invenia Technical Computing Corporation |date=07 July 2020}}</ref>:


<blockquote>Since the environment and incentives around building academic research software are very different to those of industry, the workflows around the former are, in general, not guided by the same engineering practices that are valued in the latter. That is to say: there is a difference between what is important in writing software for research, and for a user-focused software product. Academic research software prioritizes scientific correctness and flexibility to experiment above all else in pursuit of the researchers’ end product: published papers. Industry software, on the other hand, prioritizes maintainability, robustness, and testing, as the software (generally speaking) is the product. However, the two tracks share many common goals as well, such as catering to “users” [and] emphasizing performance and reproducibility, but most importantly both ventures are collaborative. Arguably then, both sets of principles are needed to write and maintain high-quality research software.</blockquote>


This brings us to our first point: the application of small-scale, FAIR-driven academic research software engineering practices and elements to the larger development of more commercial laboratory informatics software, and vice versa with the application of commercial-scale development practices to small FAIR-focused academic and institutional research software engineering efforts, has the potential to help better support all research laboratories using both independently-developed and commercial research software.


The concept of the research software engineer (RSE) began to take full form in 2012, and since then universities and institutions of many types have formally developed their own RSE groups and academic programs.<ref name="WoolstonWhySci22">{{Cite journal |last=Woolston |first=Chris |date=2022-05-31 |title=Why science needs more research software engineers |url=https://www.nature.com/articles/d41586-022-01516-2 |journal=Nature |language=en |pages=d41586–022–01516-2 |doi=10.1038/d41586-022-01516-2 |issn=0028-0836}}</ref><ref name="KITRSE@KIT24">{{cite web |url=https://www.rse-community.kit.edu/index.php |title=RSE@KIT |publisher=Karlsruhe Institute of Technology |date=20 February 2024 |accessdate=01 May 2024}}</ref><ref name="PUPurdueCenter">{{cite web |url=https://www.rcac.purdue.edu/rse |title=Purdue Center for Research Software Engineering |publisher=Purdue University |date=2024 |accessdate=01 May 2024}}</ref> RSEs range from pure software developers with little knowledge of a given research discipline, to scientific researchers just beginning to learn how to develop software for their research project(s). While in the past, broadly speaking, researchers often cobbled together research software with less a focus on quality and reproducibility and more on getting their research published, today's push for FAIR data and software by academic journals, institutions, and other researchers seeking to collaborate has placed a much greater focus on the concept of "better software, better research."<ref name="WoolstonWhySci22" /><ref name="CohenTheFour21">{{Cite journal |last=Cohen |first=Jeremy |last2=Katz |first2=Daniel S. |last3=Barker |first3=Michelle |last4=Chue Hong |first4=Neil |last5=Haines |first5=Robert |last6=Jay |first6=Caroline |date=2021-01 |title=The Four Pillars of Research Software Engineering |url=https://ieeexplore.ieee.org/document/8994167/ |journal=IEEE Software |volume=38 |issue=1 |pages=97–105 |doi=10.1109/MS.2020.2973362 |issn=0740-7459}}</ref> Elaborating on that concept, Cohen ''et al.'' add that "ultimately, good research software can make the difference between valid, sustainable, reproducible research outputs and short-lived, potentially unreliable or erroneous outputs."<ref name="CohenTheFour21" />


The concept of [[software quality management]] (SQM) has traditionally not been lost on professional, commercial software development businesses. Good SQM practices have been less prevalent in homegrown research software development; however, the expanded adoption of FAIR data and FAIR software approaches has shifted the focus on to the repeatability, reproducibility, and interoperability of research results and data produced by a more sustainable research software. The adoption of FAIR by academic and institutional research labs not only brings commercial SQM and other software development approaches into their workflow, but also gives commercial laboratory informatics software developers an opportunity to embrace many aspects of the FAIR approach to laboratory research practices, including lessons learned and development practices from the growing number of RSEs. This doesn't mean commercial developers are going to suddenly take an open-source approach to their code, and it doesn't mean academic and institutional research labs are going to give up the benefits of the open-source paradigm as applied to research software.<ref>{{Cite journal |last=Hasselbring |first=Wilhelm |last2=Carr |first2=Leslie |last3=Hettrick |first3=Simon |last4=Packer |first4=Heather |last5=Tiropanis |first5=Thanassis |date=2020-02-25 |title=From FAIR research data toward FAIR and open research software |url=https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html |journal=it - Information Technology |language=en |volume=62 |issue=1 |pages=39–47 |doi=10.1515/itit-2019-0040 |issn=2196-7032}}</ref> However, as Moynihan noted, both research software development paradigms stand to gain from the shift to more FAIR data and software.<ref name="MoynihanTheHitch20" /> Additionally, if commercial laboratory informatics vendors want to continue to competitively market relevant and sustainable research software to research labs, they frankly have little choice but to commit extra resources to learning about the application of FAIR principles to their offerings tailored to those labs.


'''Sampling plan and LIMS for food''': https://www.csolsinc.com/blog/use-samplemanager-lims-stop-food-borne-pathogens/
===The focus on data types and metadata within the scope of FAIR is shifting how laboratory informatics software developers and RSEs make their research software and choose their database approaches===
Close to the core of any deep discussion of the FAIR data principles are the concepts of data models, data types, [[metadata]], and persistent unique identifiers (PIDs). Making research objects more findable, accessible, interoperable, and reusable is no easy task when data types and approaches to metadata assignment (if there even is such an approach) are widely differing and inconsistent. Metadata is a means for better storing and characterizing research objects for the purposes of ensuring provenance and reproducibility of those research objects.<ref name="GhiringhelliShared23">{{Cite journal |last=Ghiringhelli |first=Luca M. |last2=Baldauf |first2=Carsten |last3=Bereau |first3=Tristan |last4=Brockhauser |first4=Sandor |last5=Carbogno |first5=Christian |last6=Chamanara |first6=Javad |last7=Cozzini |first7=Stefano |last8=Curtarolo |first8=Stefano |last9=Draxl |first9=Claudia |last10=Dwaraknath |first10=Shyam |last11=Fekete |first11=Ádám |date=2023-09-14 |title=Shared metadata for data-centric materials science |url=https://www.nature.com/articles/s41597-023-02501-8 |journal=Scientific Data |language=en |volume=10 |issue=1 |pages=626 |doi=10.1038/s41597-023-02501-8 |issn=2052-4463 |pmc=PMC10502089 |pmid=37709811}}</ref><ref name="FirschenAgile22">{{Cite journal |last=Fitschen |first=Timm |last2=tom Wörden |first2=Henrik |last3=Schlemmer |first3=Alexander |last4=Spreckelsen |first4=Florian |last5=Hornung |first5=Daniel |date=2022-10-12 |title=Agile Research Data Management with FDOs using LinkAhead |url=https://riojournal.com/article/96075/ |journal=Research Ideas and Outcomes |volume=8 |pages=e96075 |doi=10.3897/rio.8.e96075 |issn=2367-7163}}</ref> This means as early as possible implementing a software-based approach that is FAIR-driven, capturing FAIR metadata using flexible domain-driven [[Ontology (information science)|ontologies]] (i.e., controlled vocabularies) at the source and cleaning up old research objects that aren't FAIR-ready while also limiting hindrances to research processes as much as possible.<ref name="FirschenAgile22" /> And that approach must value the importance of metadata and PIDs. As Weigel ''et al.'' note in a discussion on making laboratory data and workflows more machine-findable: "Metadata capture must be highly automated and reliable, both in terms of technical reliability and ensured metadata quality. This requires an approach that may be very different from established procedures."<ref>{{Cite journal |last=Weigel |first=Tobias |last2=Schwardmann |first2=Ulrich |last3=Klump |first3=Jens |last4=Bendoukha |first4=Sofiane |last5=Quick |first5=Robert |date=2020-01 |title=Making Data and Workflows Findable for Machines |url=https://direct.mit.edu/dint/article/2/1-2/40-46/9994 |journal=Data Intelligence |language=en |volume=2 |issue=1-2 |pages=40–46 |doi=10.1162/dint_a_00026 |issn=2641-435X}}</ref> Enter non-relational RDF [[knowledge graph]] [[database]]s.


'''Sampling plan and LIMS for food''': https://foodsafetytech.com/tag/laboratory-information-management-system/
This brings us to our second point: given the importance of metadata and PIDs to FAIRifying research objects (and even research software), established, more traditional research software development methods using common relational databases may not be enough, even for commercial laboratory informatics software developers. Non-relational [[Resource Description Framework]] (RDF) knowledge graph databases used in FAIR-driven, well-designed laboratory informatics software help make research objects more FAIR for all research labs.


'''Sampling plan and LIMS''': https://eudl.eu/pdf/10.4108/eai.24-2-2023.2330696
Research objects can take many forms (i.e., data types), making the storage and management of those objects challenging, particularly in research settings with great diversity of data, as with materials research. Some have approached this challenge by combining different database and systems technologies that are best suited for each data type.<ref name="AggourSemantics24">{{Cite journal |last=Aggour |first=Kareem S. |last2=Kumar |first2=Vijay S. |last3=Gupta |first3=Vipul K. |last4=Gabaldon |first4=Alfredo |last5=Cuddihy |first5=Paul |last6=Mulwad |first6=Varish |date=2024-04-09 |title=Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data |url=https://link.springer.com/10.1007/s40192-024-00348-4 |journal=Integrating Materials and Manufacturing Innovation |language=en |doi=10.1007/s40192-024-00348-4 |issn=2193-9764}}</ref> However, while query performance and storage footprint improves with this approach, data across the different storage mechanisms typically remains unlinked and non-compliant with FAIR principles. Here, either a full RDF knowledge graph database or similar integration layer is required to better make the research objects more interoperable and reusable, whether it's materials records or specimen data.<ref name="AggourSemantics24" /><ref name="GrobeFromData19">{{Cite journal |last=Grobe |first=Peter |last2=Baum |first2=Roman |last3=Bhatty |first3=Philipp |last4=Köhler |first4=Christian |last5=Meid |first5=Sandra |last6=Quast |first6=Björn |last7=Vogt |first7=Lars |date=2019-06-26 |title=From Data to Knowledge: A semantic knowledge graph application for curating specimen data |url=https://biss.pensoft.net/article/37412/ |journal=Biodiversity Information Science and Standards |language=en |volume=3 |pages=e37412 |doi=10.3897/biss.3.37412 |issn=2535-0897}}</ref>


'''Sampling plans and an optimal risk-based monitoring plan for microbiological hazards'''<ref name="FockerDesign23" />
It is beyond the scope of this Q&A article to discuss RDF knowledge graph databases at length. (For a deeper dive on this topic, see Rocca-Serra ''et al.'' and the FAIR Cookbook.<ref name="Rocca-SerraFAIRCook22">{{Cite book |last=Rocca-Serra, Philippe |last2=Sansone, Susanna-Assunta |last3=Gu, Wei |last4=Welter, Danielle |last5=Abbassi Daloii, Tooba |last6=Portell-Silva, Laura |date=2022-06-30 |title=D2.1 FAIR Cookbook |url=https://zenodo.org/record/6783564 |chapter=FAIR and Knowledge graphs |doi=10.5281/ZENODO.6783564}}</ref>) However, know that the primary strength of these databases to FAIRification of research objects is their ability to provide [[Semantics|semantic]] transparency (i.e., provide a framework for better understanding and reusing the greater research object through basic examination of the relationships of its associated metadata and their constituents), making these objects more easily accessible, interoperable, and machine-readable.<ref name="AggourSemantics24" /> The resulting knowledge graphs, with their "subject-property-object" syntax and PIDs or uniform resource identifiers (URIs) helping to link data, metadata, ontology classes, and more, can be interpreted, searched, and linked by machines, and made human-readable, resulting in better research through derivation of new knowledge from the existing research objects. The end result is a representation of heterogeneous data and metadata that complies with the FAIR guiding principles.<ref name="AggourSemantics24" /><ref name="GrobeFromData19" /><ref name="Rocca-SerraFAIRCook22" /><ref name="TomlinsonRDF23">{{cite web |url=https://21624527.fs1.hubspotusercontent-na1.net/hubfs/21624527/Resources/RDF%20Knowledge%20Graph%20Databases%20White%20Paper.pdf |format=PDF |title=RDF Knowledge Graph Databases: A Better Choice for Life Science Lab Software |author=Tomlinson, E. |publisher=Semaphore Solutions, Inc |date=28 July 2023 |accessdate=01 May 2024}}</ref><ref name="DeagenFAIRAnd22">{{Cite journal |last=Deagen |first=Michael E. |last2=McCusker |first2=Jamie P. |last3=Fateye |first3=Tolulomo |last4=Stouffer |first4=Samuel |last5=Brinson |first5=L. Cate |last6=McGuinness |first6=Deborah L. |last7=Schadler |first7=Linda S. |date=2022-05-27 |title=FAIR and Interactive Data Graphics from a Scientific Knowledge Graph |url=https://www.nature.com/articles/s41597-022-01352-z |journal=Scientific Data |language=en |volume=9 |issue=1 |pages=239 |doi=10.1038/s41597-022-01352-z |issn=2052-4463 |pmc=PMC9142568 |pmid=35624233}}</ref><ref>{{Cite journal |last=Brandizi |first=Marco |last2=Singh |first2=Ajit |last3=Rawlings |first3=Christopher |last4=Hassani-Pak |first4=Keywan |date=2018-09-25 |title=Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach |url=https://www.degruyter.com/document/doi/10.1515/jib-2018-0023/html |journal=Journal of Integrative Bioinformatics |language=en |volume=15 |issue=3 |pages=20180023 |doi=10.1515/jib-2018-0023 |issn=1613-4516 |pmc=PMC6340125 |pmid=30085931}}</ref> This concept can even be extended to ''post factum'' visualizations of the knowledge graph data<ref name="DeagenFAIRAnd22" />, as well as the FAIR management of computational laboratory [[workflow]]s.<ref>{{Cite journal |last=de Visser |first=Casper |last2=Johansson |first2=Lennart F. |last3=Kulkarni |first3=Purva |last4=Mei |first4=Hailiang |last5=Neerincx |first5=Pieter |last6=Joeri van der Velde |first6=K. |last7=Horvatovich |first7=Péter |last8=van Gool |first8=Alain J. |last9=Swertz |first9=Morris A. |last10=Hoen |first10=Peter A. C. ‘t |last11=Niehues |first11=Anna |date=2023-09-28 |editor-last=Palagi |editor-first=Patricia M. |title=Ten quick tips for building FAIR workflows |url=https://dx.plos.org/10.1371/journal.pcbi.1011369 |journal=PLOS Computational Biology |language=en |volume=19 |issue=9 |pages=e1011369 |doi=10.1371/journal.pcbi.1011369 |issn=1553-7358 |pmc=PMC10538699 |pmid=37768885}}</ref>


'''Sampling for food safety''': https://www.campdenbri.co.uk/blogs/sampling-for-safety.php
While rare, some commercial laboratory informatics vendors like Semaphore Solutions have already recognized the potential of RDF knowledge graph databases to FAIR-driven laboratory research, having implemented such structures into their offerings.<ref name="TomlinsonRDF23" /> (The use of knowledge graphs has already been demonstrated in academic research software, such as with the ELN tools developed by RSEs at the University of Rostock and University of Amsterdam.<ref>{{Cite journal |last=Schröder |first=Max |last2=Staehlke |first2=Susanne |last3=Groth |first3=Paul |last4=Nebe |first4=J. Barbara |last5=Spors |first5=Sascha |last6=Krüger |first6=Frank |date=2022-12 |title=Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation |url=https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-021-00257-x |journal=Journal of Biomedical Semantics |language=en |volume=13 |issue=1 |pages=4 |doi=10.1186/s13326-021-00257-x |issn=2041-1480 |pmc=PMC8802522 |pmid=35101121}}</ref>) As noted in the prior point, it is potentially advantageous to not only laboratory informatics vendors to provide but also research labs to use relevant and sustainable research software that has the FAIR principles embedded in the software's design. Turning to knowledge graph databases is another example of keeping such software relevant and FAIR to research labs.


'''Effective sampling plans during pandemic''': https://www.dksh.com/global-en/lab-solutions/insights/how-effective-sampling-plans-can-improve-quality-assurance-and-food-safety-during-a-pandemic#accordion-1578360548811-0-collapse
===Applying FAIR-driven metadata schemes to laboratory informatics software development gives data a FAIRer chance at being ready for machine learning and artificial intelligence applications===
The third and final point for this Q&A article highlights another positive consequence of engineering laboratory informatics software with FAIR in mind: FAIRified research objects are much closer to being usable for the trending inclusion of [[machine learning]] (ML) and [[artificial intelligence]] (AI) tools in laboratory informatics platforms and other companion research software. By developing laboratory informatics software with a focus on FAIR-driven metadata and database schemes, not only are research objects more FAIR but also "cleaner" and more machine-ready for advanced analytical uses as with ML and AI.


'''Implementation of Food Safety Management Systems along with Other Management Tools''': https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468768/
To be sure, the FAIRness of any structured dataset alone is not enough to make it ready for ML and AI applications. Factors such as classification, completeness, context, correctness, duplicity, integrity, mislabeling, outliers, relevancy, sample size, and timeliness of the research object and its contents are also important to consider.<ref name="HinidumaDataRead24">{{Cite journal |last=Hiniduma |first=Kaveen |last2=Byna |first2=Suren |last3=Bez |first3=Jean Luca |date=2024 |title=Data Readiness for AI: A 360-Degree Survey |url=https://arxiv.org/abs/2404.05779 |journal=arXiv |doi=10.48550/ARXIV.2404.05779}}</ref><ref name="FletcherFAIRRe24">{{Cite journal |last=Fletcher |first=Lydia |date=2024-04-16 |others=The University Of Texas At Austin, The University Of Texas At Austin |title=FAIR Re-use: Implications for AI-Readiness |url=https://repositories.lib.utexas.edu/handle/2152/124873 |doi=10.26153/TSW/51475}}</ref> When those factors aren't appropriately addressed as part of a FAIRification effort towards AI readiness (as well as part of the development of research software of all types), research data and metadata have a higher likelihood of revealing themselves to be inconsistent. As such, searches and analytics using that data and metadata become muddled, and the ultimate ML or AI output will also be muddled (i.e., "garbage in, garbage out"). Whether retroactively updating existing research objects to a more FAIRified state or ensuring research objects (e.g., those originating in an ELN or LIMS) are more FAIR and AI-ready from the start, research software updating or generating those research objects has to address ontologies, data models, data types, identifiers, and more in a thorough yet flexible way.<ref name="OlsenEmbracing23">{{cite web |url=https://www.pharmasalmanac.com/articles/embracing-fair-data-on-the-path-to-ai-readiness |title=Embracing FAIR Data on the Path to AI-Readiness |author=Olsen, C. |work=Pharma's Almanac |date=01 September 2023 |accessdate=03 May 2024}}</ref>


'''Sampling and testing: What are the objectives'''<ref name="DeLoyHemdrikxSampling18" />
Noting that Wilkinson ''et al.'' originally highlighted the importance of machine-readability of FAIR data, Huerta ''et al.'' add that that core principle of FAIRness "is synergistic with the rapid adoption and increased use of AI in research."<ref name="HuertaFAIRForAI23">{{Cite journal |last=Huerta |first=E. A. |last2=Blaiszik |first2=Ben |last3=Brinson |first3=L. Catherine |last4=Bouchard |first4=Kristofer E. |last5=Diaz |first5=Daniel |last6=Doglioni |first6=Caterina |last7=Duarte |first7=Javier M. |last8=Emani |first8=Murali |last9=Foster |first9=Ian |last10=Fox |first10=Geoffrey |last11=Harris |first11=Philip |date=2023-07-26 |title=FAIR for AI: An interdisciplinary and international community building perspective |url=https://www.nature.com/articles/s41597-023-02298-6 |journal=Scientific Data |language=en |volume=10 |issue=1 |pages=487 |doi=10.1038/s41597-023-02298-6 |issn=2052-4463 |pmc=PMC10372139 |pmid=37495591}}</ref> They go on to discuss the positive interactions of FAIR research objects with FAIR-driven, AI-based research. Among the benefits include<ref name="HuertaFAIRForAI23" />:


'''LIMS in a Shop Floor IT Landscape + sample plans''': https://www.frontwell-solutions.com/blog/lims-3/lims-in-a-shop-floor-it-landscape-21
*greater findability of FAIR research objects for further AI-driven scientific discovery;
*greater reproducibility of FAIR research objects and any AI models published with them;
*improved generalization of AI-driven medical research models when exposed to diverse and FAIR research objects;
*improved reporting of AI-driven research results using FAIRified research objects, lending further credibility to those results;
*more uniform comparison of AI models using well-defined hyperstructure and information training conditions from FAIRified research objects;
*more developed and interoperable "data e-infrastructure," which can further drive a more effective "AI services layer";
*reduced bias in AI-driven processes through the use of FAIR research objects and AI models; and
*improved surety of scientific correctness where reproducibility in AI-driven research can't be guaranteed.


In the end, developers of research software (whether discipline-specific research software or broader laboratory informatics solutions) would be advised to keep in mind the growing trends of FAIR research, FAIR software, and ML- and AI-driven research, especially in the [[life sciences]], but also a variety of other fields.<ref name="HuertaFAIRForAI23" />


'''Microbiological testing: what food businesses need to know''': https://www.safefood.net/professional/food-safety/laboratories/micro-food-testing
===Restricted clinical data and its FAIRification for greater research innovation===
Broader discussion in the research community continues to occur in regards to how best to ethically make restricted or privacy-protected clinical data and information FAIR for greater innovation and, by extension, improved patient outcomes, particularly in the wake of the [[COVID-19]] [[pandemic]].<ref name="MaxwellFAIREthic23">{{Cite journal |last=Maxwell |first=Lauren |last2=Shreedhar |first2=Priya |last3=Dauga |first3=Delphine |last4=McQuilton |first4=Peter |last5=Terry |first5=Robert F |last6=Denisiuk |first6=Alisa |last7=Molnar-Gabor |first7=Fruzsina |last8=Saxena |first8=Abha |last9=Sansone |first9=Susanna-Assunta |date=2023-10 |title=FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries |url=https://linkinghub.elsevier.com/retrieve/pii/S2589750023001292 |journal=The Lancet Digital Health |language=en |volume=5 |issue=10 |pages=e712–e736 |doi=10.1016/S2589-7500(23)00129-2 |pmc=PMC10552001 |pmid=37775189}}</ref><ref name="Queralt-RosinachApplying22">{{Cite journal |last=Queralt-Rosinach |first=Núria |last2=Kaliyaperumal |first2=Rajaram |last3=Bernabé |first3=César H. |last4=Long |first4=Qinqin |last5=Joosten |first5=Simone A. |last6=van der Wijk |first6=Henk Jan |last7=Flikkenschild |first7=Erik L.A. |last8=Burger |first8=Kees |last9=Jacobsen |first9=Annika |last10=Mons |first10=Barend |last11=Roos |first11=Marco |date=2022-12 |title=Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic |url=https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-022-00263-7 |journal=Journal of Biomedical Semantics |language=en |volume=13 |issue=1 |pages=12 |doi=10.1186/s13326-022-00263-7 |issn=2041-1480 |pmc=PMC9036506 |pmid=35468846}}</ref><ref>{{Cite journal |last=Martínez-García |first=Alicia |last2=Alvarez-Romero |first2=Celia |last3=Román-Villarán |first3=Esther |last4=Bernabeu-Wittel |first4=Máximo |last5=Luis Parra-Calderón |first5=Carlos |date=2023-05 |title=FAIR principles to improve the impact on health research management outcomes |url=https://linkinghub.elsevier.com/retrieve/pii/S2405844023029407 |journal=Heliyon |language=en |volume=9 |issue=5 |pages=e15733 |doi=10.1016/j.heliyon.2023.e15733 |pmc=PMC10189186 |pmid=37205991}}</ref> (Note that while there are other types of restricted and privacy-protected data, this section will focus largely on clinical data and research objects as the most obvious type.)


'''Sampling and testing: What are the objectives'''<ref name="DeLoyHemdrikxSampling18" />
These efforts have usually revolved around pulling reusable clinical patient or research data from [[hospital information system]]s (HIS), [[electronic medical record]]s (EMRs), [[clinical trial management system]]s (CTMSs), and research databases (often relational in nature) that either contain de-identified data or can de-identify aspects of data and information before access and extraction. Sometimes that clinical data or research object may have already in part been FAIRified, but often it may not be. In all cases, the concepts of privacy, security, and anonymization come up as part of any desire to gain access to that clinical material. However, any FAIRified clinical data isn't necessarily readily open for access. As Snoeijer ''et al.'' note: "The authors of the FAIR principles, however, clearly indicate that 'accessible' does not mean open. It means that clarity and transparency is required around the conditions governing access and reuse."<ref name="SnoeijerProcess19">{{cite book |url=https://phuse.s3.eu-central-1.amazonaws.com/Archive/2019/Connect/EU/Amsterdam/PAP_SA04.pdf |format=PDF |chapter=Paper SA04 - Processing big data from multiple sources |title=Proceedings of PHUSE Connect EU 2019 |author=Snoeijer, B.; Pasapula, V.; Covucci, A. et al. |publisher=PHUSE Limited |year=2019 |accessdate=03 May 2024}}</ref>


This is being mentioned in the context of laboratory informatics applications for a couple of reasons. First, a well-designed commercial LIMS that supports clinical research laboratory workflows is already going to address privacy and security aspects, as part of the developer recognizing the need for those labs to adhere to regulations such as the [[Health Insurance Portability and Accountability Act]] (HIPAA) and comply with standards such as [[ISO 15189]]. However, such a system may not have been developed with FAIR data principles in mind, and any built-in metadata and ontology schemes may be insufficient for full FAIRification of laboratory-based clinical trial research objects. As Queralt-Rosinach ''et al.'' note, however, "interestingly, ontologies may also be used to describe data access restrictions to complement FAIR metadata with information that supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" /> Essentially, the authors are suggesting that while a HIS or LIS may have built-in access management tools, setting up ontologies and metadata mechanisms that link privacy aspects of a research object (e.g., "has consent form for," "is de-identified," etc.) to the object's metadata allows for even more flexible, FAIR-driven approaches to privacy and security. Research software developers creating such information management tools for the regulated clinical research space may want to apply FAIR concepts such as this to how access control and privacy restrictions are managed. This will inevitably mean any research objects exported with machine-readable privacy-concerning metadata will be more reusable in a way that still "supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" />
Second, a well-designed research software solution working with clinical data will provide not only support for open, community-supported data models and vocabularies for clinical data, but also standardized community-driven ontologies that are specifically developed for access control and privacy. Queralt-Rosinach ''et al.'' continue<ref name="Queralt-RosinachApplying22" />:
<blockquote>Also, very important for accessibility and data privacy is that the digital objects ''per se'' can accommodate the criteria and protocols necessary to comply with regulatory and governance frameworks. Ontologies can aid in opening and protecting patient data by exposing logical definitions of data use conditions. Indeed, there are ontologies to define access and reuse conditions for patient data such as the Informed Consent Ontology (ICO), the Global Alliance for Genomics and Health Data Use Ontology (DUO) standard, and the Open Digital Rights Language (ODRL) vocabulary recommended by W3C.</blockquote>
Also of note here is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its OHDSI standardized vocabularies. In all these cases, a developer-driven approach to research software that incorporates community-driven standards that support FAIR principles is welcome. However, as Maxwell ''et al.'' noted in their ''Lancet'' review article in late 2023, "few platforms or registries applied community-developed standards for participant-level data, further restricting the interoperability of ... data-sharing initiatives [like FAIR]."<ref name="MaxwellFAIREthic23" /> As the FAIR principles continue to gain ground in clinical research and diagnostics settings, software developers will need to be more attuned to translating old ways of development to ones that incorporate FAIR data and software principles. Demand for FAIR data will only continue to grow, and any efforts to improve interoperability and reusability while honoring (and enhancing) privacy and security aspects of restricted data will be appreciated by clinical researchers. However, just as FAIR is not an overall goal for researchers, software built with FAIR principles in mind is not the end point of research organizations managing restricted and privacy-protected research objects. Ultimately, those organizations will have make other considerations about restricted data in the scope of FAIR, including addressing data management plans, data use agreements, disclosure review practices, and training as it applies to their research software and generated research objects.<ref>{{Cite journal |last=Jang |first=Joy Bohyun |last2=Pienta |first2=Amy |last3=Levenstein |first3=Margaret |last4=Saul |first4=Joe |date=2023-12-06 |title=Restricted data management: the current practice and the future |url=https://journalprivacyconfidentiality.org/index.php/jpc/article/view/844 |journal=Journal of Privacy and Confidentiality |volume=13 |issue=2 |doi=10.29012/jpc.844 |issn=2575-8527 |pmc=PMC10956935 |pmid=38515607}}</ref>


==Conclusion==
==Conclusion==
 
Laboratory informatics developers will also need to remember that FAIRification of research in itself is not a goal for research laboratories; it is a continual process that recognizes improved scientific research and greater innovation as a more likely outcome.<ref name="WilkinsonTheFAIR16" /><ref name="OlsenEmbracing23" /><ref name="HuertaFAIRForAI23" />


==References==
==References==
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Latest revision as of 13:29, 13 May 2024

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FAIRResourcesGraphic AustralianResearchDataCommons 2018.png

Title: What are the potential implications of the FAIR data principles to laboratory informatics applications?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: May 2024

Introduction

https://www.limswiki.org/index.php/Journal:Infrastructure_tools_to_support_an_effective_radiation_oncology_learning_health_system

This brief topical article will examine

The "FAIR-ification" of research objects and software

First discussed during a 2014 FORCE-11 workshop dedicated to "overcoming data discovery and reuse obstacles," the FAIR data principles were published by Wilkinson et al. in 2016 as a stakeholder collaboration driven to see research "objects" (i.e., research data and information of all shapes and formats) become more universally findable, accessible, interoperable, and reusable (FAIR) by both machines and people.[1] The authors released the FAIR principles while recognizing that "one of the grand challenges of data-intensive science ... is to improve knowledge discovery through assisting both humans and their computational agents in the discovery of, access to, and integration and analysis of task-appropriate scientific data and other scholarly digital objects."[1]

Since 2016, other research stakeholders have taken to publishing their thoughts about how the FAIR principles apply to their fields of study and practice[2], including in ways beyond what perhaps was originally imagined by Wilkinson et al.. For example, multiple authors have examined whether or not the software used in scientific endeavors itself can be considered a research object worth being developed and managed in tandem with the FAIR data principles.[3][4][5][6][7] Researchers quickly recognized that any planning around updating processes and systems to make research objects more FAIR would have to be tailored to specific research contexts, recognize that digital research objects go beyond data and information, and recognize "the specific nature of software" and not consider it "just data."[4] The end result has been applying the core concepts of FAIR but differently from data, with the added context of research software being more than just data, requiring more nuance and a different type of planning from applying FAIR to digital data and information.

A 2019 survey by Europe's FAIRsFAIR found that researchers seeking and re-using relevant research software on the internet faced multiple challenges, including understanding and/or maintaining the necessary software environment and its dependencies, finding sufficient documentation, struggling with accessibility and licensing issues, having the time and skills to install and/or use the software, finding quality control of the source code lacking, and having an insufficient (or non-existent) software sustainability and management plan.[4] These challenges highlight the importance of software to researchers and other stakeholders, and the roll FAIR has in better ensuring such software is findable, interoperable, and reusable, which in turn better ensures researchers' software-driven research is repeatable (by the same research team, with the same experimental setup), reproducible (by a different research team, with the same experimental setup), and replicable (by a different research team, with a different experimental setup).[4]

At this point, the topic of what "research software" represents must be addressed further, and, unsurprisingly, it's not straightforward. Ask 20 researchers what "research software" is, and you may get 20 different opinions. Some definitions can be more objectively viewed as too narrow, while others may be viewed as too broad, with some level of controversy inherent in any mutual discussion.[8][9][10] In 2021, as part of the FAIRsFAIR initiative, Gruenpeter et al. made a good-faith effort to define "research software" with the feedback of multiple stakeholders. Their efforts resulted in this definition[8]:

Research software includes source code files, algorithms, scripts, computational workflows, and executables that were created during the research process, or for a research purpose. Software components (e.g., operating systems, libraries, dependencies, packages, scripts, etc.) that are used for research but were not created during, or with a clear research intent, should be considered "software [used] in research" and not research software. This differentiation may vary between disciplines. The minimal requirement for achieving computational reproducibility is that all the computational components (i.e., research software, software used in research, documentation, and hardware) used during the research are identified, described, and made accessible to the extent that is possible.

Note that while the definition primarily recognizes software created during the research process, software created (whether by the research group, other open-source software developers outside the organization, or even commercial software developers) "for a research purpose" outside the actual research process is also recognized as research software. This notably can lead to disagreement about whether a proprietary, commercial spreadsheet or laboratory information management system (LIMS) offering that conducts analyses and visualizations of research data can genuinely be called research software, or simply classified as software used in research. van Nieuwpoort and Katz further elaborated on this concept, at least indirectly, by formally defining the roles of research software in 2023. Their definition of the various roles of research software—without using terms such as "open-source," "commercial," or "proprietary"—essentially further defined what research software is[10]:

  • Research software is a component of our instruments.
  • Research software is the instrument.
  • Research software analyzes research data.
  • Research software presents research results.
  • Research software assembles or integrates existing components into a working whole.
  • Research software is infrastructure or an underlying tool.
  • Research software facilitates distinctively research-oriented collaboration.

When considering these definitions[8][10] of research software and their adoption by other entities[11], it would appear that at least in part some laboratory informatics software—whether open-source or commercially proprietary—fills these roles in academic, military, and industry research laboratories of many types. In particular, electronic laboratory notebooks (ELNs) like open-source Jupyter Notebook or proprietary ELNs from commercial software developers fill the role of analyzing and visualizing research data, including developing molecular models for new promising research routes.[10] Even more advanced LIMS solutions that go beyond simply collating, auditing, securing, and reporting analytical results could conceivably fall under the umbrella of research software, particularly if many of the analytical, integration, and collaboration tools required in modern research facilities are included in the LIMS.

Ultimately, assuming that some laboratory informatics software can be considered research software and not just "software used in research," it's tough not to arrive at some deeper implications of research organizations' increasing need for FAIR data objects and software, particularly for laboratory informatics software and the developers of it.

Implications of the FAIR concept to laboratory informatics software

The global FAIR initiative affects, and even benefits, commercial laboratory informatics research software developers as much as it does academic and institutional ones

To be clear, there is undoubtedly a difference in the software development approach of "homegrown" research software by academics and institutions, and the more streamlined and experienced approach of commercial software development houses as applied to research software. Moynihan of Invenia Technical Computing described the difference in software development approaches thusly in 2020, while discussing the concept of "research software engineering"[12]:

Since the environment and incentives around building academic research software are very different to those of industry, the workflows around the former are, in general, not guided by the same engineering practices that are valued in the latter. That is to say: there is a difference between what is important in writing software for research, and for a user-focused software product. Academic research software prioritizes scientific correctness and flexibility to experiment above all else in pursuit of the researchers’ end product: published papers. Industry software, on the other hand, prioritizes maintainability, robustness, and testing, as the software (generally speaking) is the product. However, the two tracks share many common goals as well, such as catering to “users” [and] emphasizing performance and reproducibility, but most importantly both ventures are collaborative. Arguably then, both sets of principles are needed to write and maintain high-quality research software.

This brings us to our first point: the application of small-scale, FAIR-driven academic research software engineering practices and elements to the larger development of more commercial laboratory informatics software, and vice versa with the application of commercial-scale development practices to small FAIR-focused academic and institutional research software engineering efforts, has the potential to help better support all research laboratories using both independently-developed and commercial research software.

The concept of the research software engineer (RSE) began to take full form in 2012, and since then universities and institutions of many types have formally developed their own RSE groups and academic programs.[13][14][15] RSEs range from pure software developers with little knowledge of a given research discipline, to scientific researchers just beginning to learn how to develop software for their research project(s). While in the past, broadly speaking, researchers often cobbled together research software with less a focus on quality and reproducibility and more on getting their research published, today's push for FAIR data and software by academic journals, institutions, and other researchers seeking to collaborate has placed a much greater focus on the concept of "better software, better research."[13][16] Elaborating on that concept, Cohen et al. add that "ultimately, good research software can make the difference between valid, sustainable, reproducible research outputs and short-lived, potentially unreliable or erroneous outputs."[16]

The concept of software quality management (SQM) has traditionally not been lost on professional, commercial software development businesses. Good SQM practices have been less prevalent in homegrown research software development; however, the expanded adoption of FAIR data and FAIR software approaches has shifted the focus on to the repeatability, reproducibility, and interoperability of research results and data produced by a more sustainable research software. The adoption of FAIR by academic and institutional research labs not only brings commercial SQM and other software development approaches into their workflow, but also gives commercial laboratory informatics software developers an opportunity to embrace many aspects of the FAIR approach to laboratory research practices, including lessons learned and development practices from the growing number of RSEs. This doesn't mean commercial developers are going to suddenly take an open-source approach to their code, and it doesn't mean academic and institutional research labs are going to give up the benefits of the open-source paradigm as applied to research software.[17] However, as Moynihan noted, both research software development paradigms stand to gain from the shift to more FAIR data and software.[12] Additionally, if commercial laboratory informatics vendors want to continue to competitively market relevant and sustainable research software to research labs, they frankly have little choice but to commit extra resources to learning about the application of FAIR principles to their offerings tailored to those labs.

The focus on data types and metadata within the scope of FAIR is shifting how laboratory informatics software developers and RSEs make their research software and choose their database approaches

Close to the core of any deep discussion of the FAIR data principles are the concepts of data models, data types, metadata, and persistent unique identifiers (PIDs). Making research objects more findable, accessible, interoperable, and reusable is no easy task when data types and approaches to metadata assignment (if there even is such an approach) are widely differing and inconsistent. Metadata is a means for better storing and characterizing research objects for the purposes of ensuring provenance and reproducibility of those research objects.[18][19] This means as early as possible implementing a software-based approach that is FAIR-driven, capturing FAIR metadata using flexible domain-driven ontologies (i.e., controlled vocabularies) at the source and cleaning up old research objects that aren't FAIR-ready while also limiting hindrances to research processes as much as possible.[19] And that approach must value the importance of metadata and PIDs. As Weigel et al. note in a discussion on making laboratory data and workflows more machine-findable: "Metadata capture must be highly automated and reliable, both in terms of technical reliability and ensured metadata quality. This requires an approach that may be very different from established procedures."[20] Enter non-relational RDF knowledge graph databases.

This brings us to our second point: given the importance of metadata and PIDs to FAIRifying research objects (and even research software), established, more traditional research software development methods using common relational databases may not be enough, even for commercial laboratory informatics software developers. Non-relational Resource Description Framework (RDF) knowledge graph databases used in FAIR-driven, well-designed laboratory informatics software help make research objects more FAIR for all research labs.

Research objects can take many forms (i.e., data types), making the storage and management of those objects challenging, particularly in research settings with great diversity of data, as with materials research. Some have approached this challenge by combining different database and systems technologies that are best suited for each data type.[21] However, while query performance and storage footprint improves with this approach, data across the different storage mechanisms typically remains unlinked and non-compliant with FAIR principles. Here, either a full RDF knowledge graph database or similar integration layer is required to better make the research objects more interoperable and reusable, whether it's materials records or specimen data.[21][22]

It is beyond the scope of this Q&A article to discuss RDF knowledge graph databases at length. (For a deeper dive on this topic, see Rocca-Serra et al. and the FAIR Cookbook.[23]) However, know that the primary strength of these databases to FAIRification of research objects is their ability to provide semantic transparency (i.e., provide a framework for better understanding and reusing the greater research object through basic examination of the relationships of its associated metadata and their constituents), making these objects more easily accessible, interoperable, and machine-readable.[21] The resulting knowledge graphs, with their "subject-property-object" syntax and PIDs or uniform resource identifiers (URIs) helping to link data, metadata, ontology classes, and more, can be interpreted, searched, and linked by machines, and made human-readable, resulting in better research through derivation of new knowledge from the existing research objects. The end result is a representation of heterogeneous data and metadata that complies with the FAIR guiding principles.[21][22][23][24][25][26] This concept can even be extended to post factum visualizations of the knowledge graph data[25], as well as the FAIR management of computational laboratory workflows.[27]

While rare, some commercial laboratory informatics vendors like Semaphore Solutions have already recognized the potential of RDF knowledge graph databases to FAIR-driven laboratory research, having implemented such structures into their offerings.[24] (The use of knowledge graphs has already been demonstrated in academic research software, such as with the ELN tools developed by RSEs at the University of Rostock and University of Amsterdam.[28]) As noted in the prior point, it is potentially advantageous to not only laboratory informatics vendors to provide but also research labs to use relevant and sustainable research software that has the FAIR principles embedded in the software's design. Turning to knowledge graph databases is another example of keeping such software relevant and FAIR to research labs.

Applying FAIR-driven metadata schemes to laboratory informatics software development gives data a FAIRer chance at being ready for machine learning and artificial intelligence applications

The third and final point for this Q&A article highlights another positive consequence of engineering laboratory informatics software with FAIR in mind: FAIRified research objects are much closer to being usable for the trending inclusion of machine learning (ML) and artificial intelligence (AI) tools in laboratory informatics platforms and other companion research software. By developing laboratory informatics software with a focus on FAIR-driven metadata and database schemes, not only are research objects more FAIR but also "cleaner" and more machine-ready for advanced analytical uses as with ML and AI.

To be sure, the FAIRness of any structured dataset alone is not enough to make it ready for ML and AI applications. Factors such as classification, completeness, context, correctness, duplicity, integrity, mislabeling, outliers, relevancy, sample size, and timeliness of the research object and its contents are also important to consider.[29][30] When those factors aren't appropriately addressed as part of a FAIRification effort towards AI readiness (as well as part of the development of research software of all types), research data and metadata have a higher likelihood of revealing themselves to be inconsistent. As such, searches and analytics using that data and metadata become muddled, and the ultimate ML or AI output will also be muddled (i.e., "garbage in, garbage out"). Whether retroactively updating existing research objects to a more FAIRified state or ensuring research objects (e.g., those originating in an ELN or LIMS) are more FAIR and AI-ready from the start, research software updating or generating those research objects has to address ontologies, data models, data types, identifiers, and more in a thorough yet flexible way.[31]

Noting that Wilkinson et al. originally highlighted the importance of machine-readability of FAIR data, Huerta et al. add that that core principle of FAIRness "is synergistic with the rapid adoption and increased use of AI in research."[32] They go on to discuss the positive interactions of FAIR research objects with FAIR-driven, AI-based research. Among the benefits include[32]:

  • greater findability of FAIR research objects for further AI-driven scientific discovery;
  • greater reproducibility of FAIR research objects and any AI models published with them;
  • improved generalization of AI-driven medical research models when exposed to diverse and FAIR research objects;
  • improved reporting of AI-driven research results using FAIRified research objects, lending further credibility to those results;
  • more uniform comparison of AI models using well-defined hyperstructure and information training conditions from FAIRified research objects;
  • more developed and interoperable "data e-infrastructure," which can further drive a more effective "AI services layer";
  • reduced bias in AI-driven processes through the use of FAIR research objects and AI models; and
  • improved surety of scientific correctness where reproducibility in AI-driven research can't be guaranteed.

In the end, developers of research software (whether discipline-specific research software or broader laboratory informatics solutions) would be advised to keep in mind the growing trends of FAIR research, FAIR software, and ML- and AI-driven research, especially in the life sciences, but also a variety of other fields.[32]

Restricted clinical data and its FAIRification for greater research innovation

Broader discussion in the research community continues to occur in regards to how best to ethically make restricted or privacy-protected clinical data and information FAIR for greater innovation and, by extension, improved patient outcomes, particularly in the wake of the COVID-19 pandemic.[33][34][35] (Note that while there are other types of restricted and privacy-protected data, this section will focus largely on clinical data and research objects as the most obvious type.)

These efforts have usually revolved around pulling reusable clinical patient or research data from hospital information systems (HIS), electronic medical records (EMRs), clinical trial management systems (CTMSs), and research databases (often relational in nature) that either contain de-identified data or can de-identify aspects of data and information before access and extraction. Sometimes that clinical data or research object may have already in part been FAIRified, but often it may not be. In all cases, the concepts of privacy, security, and anonymization come up as part of any desire to gain access to that clinical material. However, any FAIRified clinical data isn't necessarily readily open for access. As Snoeijer et al. note: "The authors of the FAIR principles, however, clearly indicate that 'accessible' does not mean open. It means that clarity and transparency is required around the conditions governing access and reuse."[36]

This is being mentioned in the context of laboratory informatics applications for a couple of reasons. First, a well-designed commercial LIMS that supports clinical research laboratory workflows is already going to address privacy and security aspects, as part of the developer recognizing the need for those labs to adhere to regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and comply with standards such as ISO 15189. However, such a system may not have been developed with FAIR data principles in mind, and any built-in metadata and ontology schemes may be insufficient for full FAIRification of laboratory-based clinical trial research objects. As Queralt-Rosinach et al. note, however, "interestingly, ontologies may also be used to describe data access restrictions to complement FAIR metadata with information that supports data safety and patient privacy."[34] Essentially, the authors are suggesting that while a HIS or LIS may have built-in access management tools, setting up ontologies and metadata mechanisms that link privacy aspects of a research object (e.g., "has consent form for," "is de-identified," etc.) to the object's metadata allows for even more flexible, FAIR-driven approaches to privacy and security. Research software developers creating such information management tools for the regulated clinical research space may want to apply FAIR concepts such as this to how access control and privacy restrictions are managed. This will inevitably mean any research objects exported with machine-readable privacy-concerning metadata will be more reusable in a way that still "supports data safety and patient privacy."[34]

Second, a well-designed research software solution working with clinical data will provide not only support for open, community-supported data models and vocabularies for clinical data, but also standardized community-driven ontologies that are specifically developed for access control and privacy. Queralt-Rosinach et al. continue[34]:

Also, very important for accessibility and data privacy is that the digital objects per se can accommodate the criteria and protocols necessary to comply with regulatory and governance frameworks. Ontologies can aid in opening and protecting patient data by exposing logical definitions of data use conditions. Indeed, there are ontologies to define access and reuse conditions for patient data such as the Informed Consent Ontology (ICO), the Global Alliance for Genomics and Health Data Use Ontology (DUO) standard, and the Open Digital Rights Language (ODRL) vocabulary recommended by W3C.

Also of note here is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its OHDSI standardized vocabularies. In all these cases, a developer-driven approach to research software that incorporates community-driven standards that support FAIR principles is welcome. However, as Maxwell et al. noted in their Lancet review article in late 2023, "few platforms or registries applied community-developed standards for participant-level data, further restricting the interoperability of ... data-sharing initiatives [like FAIR]."[33] As the FAIR principles continue to gain ground in clinical research and diagnostics settings, software developers will need to be more attuned to translating old ways of development to ones that incorporate FAIR data and software principles. Demand for FAIR data will only continue to grow, and any efforts to improve interoperability and reusability while honoring (and enhancing) privacy and security aspects of restricted data will be appreciated by clinical researchers. However, just as FAIR is not an overall goal for researchers, software built with FAIR principles in mind is not the end point of research organizations managing restricted and privacy-protected research objects. Ultimately, those organizations will have make other considerations about restricted data in the scope of FAIR, including addressing data management plans, data use agreements, disclosure review practices, and training as it applies to their research software and generated research objects.[37]

Conclusion

Laboratory informatics developers will also need to remember that FAIRification of research in itself is not a goal for research laboratories; it is a continual process that recognizes improved scientific research and greater innovation as a more likely outcome.[1][31][32]

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