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==Sandbox begins below==
==Sandbox begins below==
<div class="nonumtoc">__TOC__</div>
<div class="nonumtoc">__TOC__</div>
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'''Title''': ''Why are the FAIR data principles increasingly important to research laboratories and their software?''
'''Title''': ''What are the key elements of a LIMS for animal feed testing?''


'''Author for citation''': Shawn E. Douglas
'''Author for citation''': Shawn E. Douglas
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==Introduction==
==Introduction==


==The growing importance of the FAIR principles to research laboratories==
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" /> Since being published, other researchers have taken the somewhat broad set of principles and refined them to their own scientific disciplines, as well as to other types of research objects, including the research software being used by those researchers to generate research objects.<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><ref name="HasselbringFromFAIR20">{{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 name=":0">{{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 name=":1">{{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 name=":2">{{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>


But why are research laboratories increasingly pushing for more findable, accessible, interoperable, and reusable research objects and software? The short answer, as evidenced by the Wilkinson ''et al.'' quote above, is that greater innovation can be gained through improved knowledge discovery. The discovery process necessary for that greater innovation—whether through traditional research methods or [[artificial intelligence]] (AI)-driven methods—is enhanced when research objects and software are compatible with the core ideas of FAIR.<ref name="WilkinsonTheFAIR16" /><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><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>
This brief topical article will examine ...


A slightly longer answer, suitable for a Q&A topic, requires looking at a few more details of the FAIR principles as applied to both research objects and research software. Research laboratories exist to innovate. That innovation can come in the form of discovering new materials, developing a pharmaceutical to improve patient outcomes for a particular disease, or modifying an existing food or beverage recipe, among others. In academic research labs, this usually looks like advancement of theoretical knowledge and the publishing of research results, whereas in industry research labs, this typically looks like more practical applications of research concepts to new or existing products or services. In both cases, research software was likely involved at some point, whether it be something like a researcher-developed [[bioinformatics]] application or a commercial vendor-developed [[electronic laboratory notebook]] (ELN).  
'''Note''': Any citation leading to a software vendor's site is not to be considered a recommendation for that vendor. The citation should however still stand as a representational example of what vendors are implementing in their systems.


===FAIR research objects===
==Feed testing laboratory workflow, workload, and information management==
Regarding research objects themselves, the FAIR principles essentially say "vast amounts of data and information in largely heterogeneous formats spread across disparate sources both electronic and paper make modern research workflows difficult, tedious, and at times impossible. Further, repeatability, reproducibility, and replicability of openly published or secure internal research results is at risk, giving less confidence to academic peers in the published research or to critical stakeholders in the viability of a researched prototype." As such, research objects (which include not only their inherent data and information but also any [[metadata]] that describe features of that data and information) need to be<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=Introducing the FAIR Principles |doi=10.5281/ZENODO.6783564}}</ref>:
A feed testing lab can operate within a number of different production, research and development (R&D&#59; academic and industry), and public health contexts. They can<ref name="WardObtain24">{{cite web |url=https://animal.ifas.ufl.edu/media/animalifasufledu/dairy-website/ruminant-nutrition-symposium/archives/12.-WardRNS2024.pdf |format=PDF |author=Ward, R. |title=Obtaining value from a feed/forage lab engagement |work=Florida Ruminant Nutrition Symposium |date=27 February 2024 |accessdate=22 May 2024}}</ref>:


*''findable'', with globally unique and persistent identifiers, rich metadata that link to the identifier of the data described, and an ability to be indexed as an effectively searchable resource;
*act as a third-party consultant, interpreting analytical data;
*''accessible'', being able to be retrieved (including metadata of data that is no longer available) by identifiers using secure standardized communication protocols that are open, free, and universally implementable with authentication and authorization mechanisms;
*provide research and development support for new and revised formulations;
*''interoperable'', represented using formal, accessible, shared, and relevant language models and vocabularies that abide by FAIR principles, as well as with qualified linkage to other metadata; and
*provide analytical support for nutrition and contaminant determinations;
*''reusable'', being richly described by accurate and relevant metadata, released with a clear and accessible data usage license, associated with sufficiently detailed provenance information, and compliant with discipline-specific community standards.
*provide development support for analytical methods;
*ensure quality to specifications, accreditor standards, and regulations;
*develop informative databases and data libraries for researchers;
*manage in-house and remote sample collection, labeling, and registration, including on farms; and
*report accurate and timely results to stakeholders, including those responsible for monitoring public health.


All that talk of unique persistent identifiers, communication protocols, authentication mechanisms, language models (e.g., [[Ontology (information science)|ontology]] languages), standardized vocabularies, provenance information, and more could make one's head spin. And, to be fair, it has been challenging for research groups to adopt FAIR, with few international efforts to translate the FAIR principles to broad research. The ''FAIR Cookbook'' represents one example of such an international collaborative effort, providing "a combination of guidance, technical, hands-on, background and review types to cover the operation steps of FAIR data management."<ref name="Rocca-SerraFAIRCook22-1">{{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=Introduction |doi=10.5281/ZENODO.6783564}}</ref> In fact, the Cookbook is illustrative of the challenges of implementing FAIR in research laboratories, particularly given the diverse array of vocabularies used across the wealth of scientific disciplines, such as [[biobanking]], [[biomedical engineering]], [[botany]], [[food science]], and [[materials science]]. The way a botanical research organization makes its research objects FAIR is going to require a set of different vocabularies and frameworks than the materials science research organization. But all of them will turn to [[Informatics (academic field)|informatics]] software, data management plans, database tools, and more to not only transform existing non-FAIR research objects to be FAIR but also better ensure newly created research objects are FAIR.
This wide variety of roles further highlights the already obvious cross-disciplinary nature of analyzing animal feed ingredients and products, and interpreting the resulting data. The human [[Biology|biological]] sciences, [[Veterinary medicine|veterinary sciences]], [[environmental science]]s, [[chemistry]], [[microbiology]], [[radiochemistry]], [[botany]], [[epidemiology]], and more may be involved within a given animal feed analysis laboratory.<ref>{{Cite journal |last=Schnepf |first=Anne |last2=Hille |first2=Katja |last3=van Mark |first3=Gesine |last4=Winkelmann |first4=Tristan |last5=Remm |first5=Karen |last6=Kunze |first6=Katrin |last7=Velleuer |first7=Reinhard |last8=Kreienbrock |first8=Lothar |date=2024-02-06 |title=Basis for a One Health Approach—Inventory of Routine Data Collections on Zoonotic Diseases in Lower Saxony, Germany |url=https://www.mdpi.com/2813-0227/4/1/7 |journal=Zoonotic Diseases |language=en |volume=4 |issue=1 |pages=57–73 |doi=10.3390/zoonoticdis4010007 |issn=2813-0227}}</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 |accessdate=22 May 2024}}</ref><ref name=":0">{{Cite journal |last=Wood |first=Hannah |last2=O'Connor |first2=Annette |last3=Sargeant |first3=Jan |last4=Glanville |first4=Julie |date=2018-12 |title=Information retrieval for systematic reviews in food and feed topics: A narrative review |url=https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1289 |journal=Research Synthesis Methods |language=en |volume=9 |issue=4 |pages=527–539 |doi=10.1002/jrsm.1289 |issn=1759-2879}}</ref> Given this significant cross-disciplinarity, it's arguably more challenging for software developers creating [[laboratory informatics]] solutions like a [[laboratory information management system]] (LIMS) that has the breadth to cover the production, R&D, and public health contexts of animal feed testing. In fact, an industry lab performing [[quality control]] (QC) work for a company will likely have zero interest in public health reporting functionality, and a LIMS that focuses on QC workflows may be more highly desirable.  


===FAIR research software===
That said, this Q&A article will examine LIMS functionality that addresses the needs of all three contexts for animal feed analyses. Understand that the LIMS solution your feed lab may be looking for doesn't require some of the functionality addressed here, particularly in the specialty LIMS requirements section. But also understand the broader context of feed testing and how it highlights some of the challenges of finding a feed testing LIMS that is just right for your lab.
Discussion on research software and its FAIRness is more complicated. It is beyond the scope of this article to go into great detail about the concepts surrounding FAIR research software, but a brief overview will be attempted. When the FAIR principles were first published, the framework was largely being applied to research objects. However, 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. This led to recognizing that digital research objects go beyond data and information, and that there is a "specific nature of software" used in research; that research software should not be considered "just data."<ref name="GruenpeterFAIRPlus20" /> The end result has seen researchers begin to apply the core concepts of FAIR to research software, but slightly differently from research objects.<ref name="NIHPubMedSearch" /><ref name="HasselbringFromFAIR20" /><ref name="GruenpeterFAIRPlus20" /><ref name=":0" /><ref name=":1" /><ref name=":2" />


Unsurprisingly, what researchers consider to be "research software" for purposes of FAIR has historically been interpreted numerous ways. Does the commercial spreadsheet software used to make calculations to research data deserve to be called research software in parallel with the lab-developed bioinformatics application used to generate that data? Given the difficulties of gaining a consensus definition of the term, a 2021 international initiative called FAIRsFAIR made a good-faith effort to define "research software" with the feedback of multiple stakeholders. The short version of their resulting definition is that, "[r]esearch software includes source code files, algorithms, scripts, computational workflows, and executables that were created during the research process, or for a research purpose."<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> Of note is the last part, acknowledging that software clearly designed for research can be developed in the lab during the research process or developed beforehand by, for example, a commercial software developer. As such, Microsoft Excel may not be looked upon as research software, but an ELN or [[laboratory information management system]] (LIMS) thoughtfully developed with research activities in mind could be considered research software.  
==Base LIMS requirements for animal feed testing==
Given the above ...


More often than not, research software is going to be developed in-house. A growing push for the FAIRification of that software, as well as commercial research solutions, has seen the emergence of "research software engineering" as a domain of practice.<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><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> While in the past 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>, with research software engineering efforts focusing on that concept being vital to future research outcomes. 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" />
What follows is a list of system functionality important to most any feed testing laboratory, with a majority of that functionality found in many vendor software solutions.<ref name="WardObtain24" /><ref name="PFPLSWHumanAnim18" />


Hasselbring ''et al.'' state that "it is essential [for academic research groups] to publish research software in addition to research data," to increase trust in the peer review system, build new research on top of existing research, and ensure greater reproducibility of any published results.<ref name="HasselbringFromFAIR20" /> As such, they extend FAIR data principles to FAIR research software, noting that<ref name="HasselbringFromFAIR20" />:
'''Test, sample and result management'''


*''findable'' software acknowledges that "the first step in (re)using ... software is to find it";
*Sample log-in and management, with support for unique IDs
*''accessible'' software acknowledges that once found, the researcher needs to know how to best access the software, recognizing authentication or authentication mechanisms may need to be in place;
*Sample batching
*''interoperable'' software acknowledges that the software will need to eventually integrate with other research objects and software, demanding FAIR-driven methods and tools in the software's development; and
*[[Barcode]] and RFID support
*''reusable'' software acknowledges that the software will need to not only produce research objects that can be reused, combined, and extended, but that the software itself needs to have metadata that helps make it retrievable and reusable.
*End-to-end sample and inventory tracking, through to reporting and disposition
*Pre-defined and configurable industry-specific test and method management, including for bacteria (i.e., microbiology), heavy metals (i.e., chemistry), radionuclides (i.e., radiochemistry), and other substances
*Pre-defined and configurable industry-specific workflows, including for production, R&D, and public health contexts
*Configurable screens and data fields
*Specification management
*Test, sampling, instrument, etc. scheduling and assignment
*Test requesting
*Data import and export
*Raw data management
*Robust query tools
*Analytical tools, including [[data visualization]], statistical analysis, and [[data mining]] tools
*Document and image management
*Version control
*Project and experiment management
*Method and protocol management
*Investigation management
*Facility and sampling site management
*Storage management and monitoring


The applicability of these principles is clear to academic research software developed in-house, with the concept of open science driving FAIR development and release of that software, including on platforms like GitHub.<ref name="HasselbringFromFAIR20" /> It's less clear for commercial developers making research software. The growing prevalence of FAIR data and software practices in research laboratories 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 name="HasselbringFromFAIR20" /> However, both research software development paradigms stand to gain from the shift to more FAIR data and software.<ref name="MoynihanTheHitch20" /> Additionally, if commercial vendors of research software 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 FAIR-abiding research labs.
'''Quality, security, and compliance'''


===FAIRer research objects + better software = the potential for greater innovation===
*[[Quality assurance]] / [[quality control]] mechanisms
Greater research innovation can be gained through improved knowledge discovery, which is enabled by FAIR research objects and software. The FAIR principles say that when research objects and software are created, managed, updated, and developed such that they are more findable, accessible, interoperable, and reusable, then researchers and other stakeholders benefit. Published research results are more reputable, reproducible, and reusable, benefiting the overall research community. However, this extends beyond academic research. The provenance of industry research—e.g., as with the pharmaceutical industry—performed with the help of and documented within ELNs and other research management software, is better maintained using FAIR principles. As a result, clinical and preclinical studies are more reproducible, ensuring proper funneling of research funding, limiting resource waste, and limiting potential suffering of research participants.<ref>{{Cite journal |last=Sahoo |first=Satya S. |last2=Valdez |first2=Joshua |last3=Kim |first3=Matthew |last4=Rueschman |first4=Michael |last5=Redline |first5=Susan |date=2019-01 |title=ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata |url=https://linkinghub.elsevier.com/retrieve/pii/S1386505618302697 |journal=International Journal of Medical Informatics |language=en |volume=121 |pages=10–18 |doi=10.1016/j.ijmedinf.2018.10.009 |pmc=PMC6343667 |pmid=30545485}}</ref> Finally, patients suffering from rare diseases may benefit from FAIRer data practices that help prevent the data silos of testing, medical device use, patient outcome, treatment history, and clinical trial data. If these types of data were made more FAIR, "new diagnostics, treatments, and health care policies to benefit patients" could be developed.<ref>{{Cite journal |last=van Lin |first=Nawel |last2=Paliouras |first2=Georgios |last3=Vroom |first3=Elizabeth |last4=’t Hoen |first4=Peter A.C. |last5=Roos |first5=Marco |date=2021-11-02 |title=How Patient Organizations Can Drive FAIR Data Efforts to Facilitate Research and Health Care: A Report of the Virtual Second International Meeting on Duchenne Data Sharing, March 3, 2021 |url=https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JND-210721 |journal=Journal of Neuromuscular Diseases |volume=8 |issue=6 |pages=1097–1108 |doi=10.3233/JND-210721 |pmc=PMC8673524 |pmid=34334415}}</ref> However, in all these cases, laboratories are involved, and their software's ability to effectively ensure FAIR research objects are created is vital. As such, the implications of FAIR research objects and software on modern research laboratories' operations are undoubtable. Greater innovation and improved patient outcomes are only a few of the many benefits of the FAIR principles to society.
*Mechanisms for compliance with ISO 17025 and HACCP, including support for critical control point (CCP) specifications and limits
*Result, method, protocol, batch, and material validation, review, and release
*Data validation
*Trend and control charting for statistical analysis and measurement of uncertainty
*User qualification, performance, and training management
*[[Audit trail]]s and [[chain of custody]] support
*Configurable and granular role-based security
*Configurable system access and use (i.e., authentication requirements, account usage rules, account locking, etc.)
*[[Electronic signature]] support
*Data [[encryption]] and secure communication protocols
*Archiving and [[Data retention|retention]] of data and information
*Configurable data [[backup]]s
*Status updates and alerts
*Environmental monitoring support
*Incident and non-conformance notification, tracking, and management
 
'''Operations management and reporting'''
 
*Configurable dashboards for monitoring, by product, process, facility, etc.
*Customizable rich-text reporting, with multiple supported output formats
*Custom and industry-specific reporting, including certificates of analysis (CoAs)
*Industry-compliant labeling
*Email integration and other communication support for internal and external stakeholders
*Instrument interfacing and data management, particularly for [[near-infrared spectroscopy]] (NIRS) instruments
*Third-party software interfacing (e.g., LES, scientific data management system [SDMS], other databases)
*Data import, export, and archiving
*Instrument and equipment management, including calibration and maintenance tracking
*Inventory and material management
*Supplier/vendor/customer management
*Flexible but secure client portal for pre-registering samples, printing labels, and viewing results
*Integrated (or online) system help
 
==Specialty LIMS requirements==
 
*'''Mechanisms to make data and information more FAIR''': Like many other disciplines, modern academic and industrial research of feed ingredient selection, feed formulation, and feed production is plagued by interdisciplinary research data and information (i.e., objects) "in a broad range of [heterogeneous] information formats [that] involve inconsistent vocabulary and difficult‐to‐define concepts."<ref name=":0" /> This makes increasingly attractive data discovery options<ref name=":0" /> such as text mining, cluster searching, and [[artificial intelligence]] (AI) methods less effective, in turn hampering innovation, discovery, and improved health outcomes. As such, research labs of all sorts are increasingly turning to the FAIR principles, which encourage processes that make research objects more findable, accessible, interoperable, and reusable. A handful of software developers have become more attuned to this demand and have developed or modified their systems to produce research objects that are produced using [[metadata]]- and [[Semantics|semantic-driven]] technologies and frameworks.<ref name="DouglasWhyAre24">{{cite web |url=https://www.limswiki.org/index.php/LIMS_Q%26A:Why_are_the_FAIR_data_principles_increasingly_important_to_research_laboratories_and_their_software%3F |title=LIMS Q&A:Why are the FAIR data principles increasingly important to research laboratories and their software? |author=Douglas, S.E. |work=LIMSwiki |date=May 2024 |accessdate=22 May 2024}}</ref> Producing FAIR data is more important to the academic research and public health contexts of feed testing, but can still be useful to other industrial contexts, as having interoperable and reusable data in industry can lead to greater innovation and process improvement.<ref>{{Cite journal |last=van Vlijmen |first=Herman |last2=Mons |first2=Albert |last3=Waalkens |first3=Arne |last4=Franke |first4=Wouter |last5=Baak |first5=Arie |last6=Ruiter |first6=Gerbrand |last7=Kirkpatrick |first7=Christine |last8=da Silva Santos |first8=Luiz Olavo Bonino |last9=Meerman |first9=Bert |last10=Jellema |first10=Renger |last11=Arts |first11=Derk |date=2020-01 |title=The Need of Industry to Go FAIR |url=https://direct.mit.edu/dint/article/2/1-2/276-284/10011 |journal=Data Intelligence |language=en |volume=2 |issue=1-2 |pages=276–284 |doi=10.1162/dint_a_00050 |issn=2641-435X}}</ref> Of course, all animal feed testing labs can benefit when, for example, FAIR-driven, internationally accepted vocabulary and data descriptors for mycotoxin contamination data are used in research and laboratory software.<ref>{{Cite journal |last=Mesfin |first=Addisalem |last2=Lachat |first2=Carl |last3=Vidal |first3=Arnau |last4=Croubels |first4=Siska |last5=Haesaert |first5=Geert |last6=Ndemera |first6=Melody |last7=Okoth |first7=Sheila |last8=Belachew |first8=Tefera |last9=Boevre |first9=Marthe De |last10=De Saeger |first10=Sarah |last11=Matumba |first11=Limbikani |date=2022-02 |title=Essential descriptors for mycotoxin contamination data in food and feed |url=https://linkinghub.elsevier.com/retrieve/pii/S0963996921007833 |journal=Food Research International |language=en |volume=152 |pages=110883 |doi=10.1016/j.foodres.2021.110883}}</ref> This leads into...
*'''Support for standardized and controlled vocabularies''': By extension, this gets into the matter of improved interoperability of feed testing results from different laboratories, particularly government labs in different jurisdictions responsible for monitoring contaminates in animal feed.<ref name="AAFCOSACStrat22">{{cite web |url=https://www.aafco.org/wp-content/uploads/2023/07/SAC_Strategic_Plan_2023-2025.pdf |format=PDF |title=Strategic Plan 2023-2025 |author=The Association of American Feed Control Officials, Strategic Affairs Committee |publisher=AAFCO |page=14 |date=16 November 2022 |accessdate=22 May 2024}}</ref> The Association of American Feed Control Officials (AAFCO) Strategic Affairs Committee (SAC) highlight this in their Strategic Plan for 2023–2025, stating that in order to "promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems," the different LIMS used across various states demand an integrated IT environment where "comparable results from different labs" can effectively be made.<ref name="AAFCOSACStrat22" />


==Conclusion==
==Conclusion==
Eight years after their publishing, the importance of the FAIR data principles to research groups is greater than ever, and this brief Q&A article sought to explain why. The short answer by Wilkinson ''et al.'' is that making research objects more FAIR means better knowledge discovery, which in turn means greater innovation. This gives research labs around the world incentive to continue making existing and new research objects FAIR, for many stakeholders' benefit. The longer answer looks at the specifics of FAIR and the importance of rich metadata, persistent identifiers, standardized vocabularies, common data models, and other ontology- and semantic-driven technologies and frameworks. By implementing these open, standardized technologies and frameworks in a mindful manner to existing and new research objects, heterogeneous and disparate data pools can be unified to the benefit of research stakeholders, whether they are based in academics or industry. Of course, the FAIR concepts have since extended to the research software used by research labs. This primarily affects the software developed in these labs, particularly as it pertains to academic labs and their published research. However, commercial vendors of software made specifically for research labs have incentives to learn about the FAIR principles and research software engineering practices, to make their solutions more compatible for FAIR-driven research labs. These labs, and society at large, can ultimately benefit from innovation, improved products, and better research and patient outcomes.
 


==References==
==References==
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Latest revision as of 21:45, 22 May 2024

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[[File:|right|400px]] Title: What are the key elements of a LIMS for animal feed testing?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: May 2024

Introduction

This brief topical article will examine ...

Note: Any citation leading to a software vendor's site is not to be considered a recommendation for that vendor. The citation should however still stand as a representational example of what vendors are implementing in their systems.

Feed testing laboratory workflow, workload, and information management

A feed testing lab can operate within a number of different production, research and development (R&D; academic and industry), and public health contexts. They can[1]:

  • act as a third-party consultant, interpreting analytical data;
  • provide research and development support for new and revised formulations;
  • provide analytical support for nutrition and contaminant determinations;
  • provide development support for analytical methods;
  • ensure quality to specifications, accreditor standards, and regulations;
  • develop informative databases and data libraries for researchers;
  • manage in-house and remote sample collection, labeling, and registration, including on farms; and
  • report accurate and timely results to stakeholders, including those responsible for monitoring public health.

This wide variety of roles further highlights the already obvious cross-disciplinary nature of analyzing animal feed ingredients and products, and interpreting the resulting data. The human biological sciences, veterinary sciences, environmental sciences, chemistry, microbiology, radiochemistry, botany, epidemiology, and more may be involved within a given animal feed analysis laboratory.[2][3][4] Given this significant cross-disciplinarity, it's arguably more challenging for software developers creating laboratory informatics solutions like a laboratory information management system (LIMS) that has the breadth to cover the production, R&D, and public health contexts of animal feed testing. In fact, an industry lab performing quality control (QC) work for a company will likely have zero interest in public health reporting functionality, and a LIMS that focuses on QC workflows may be more highly desirable.

That said, this Q&A article will examine LIMS functionality that addresses the needs of all three contexts for animal feed analyses. Understand that the LIMS solution your feed lab may be looking for doesn't require some of the functionality addressed here, particularly in the specialty LIMS requirements section. But also understand the broader context of feed testing and how it highlights some of the challenges of finding a feed testing LIMS that is just right for your lab.

Base LIMS requirements for animal feed testing

Given the above ...

What follows is a list of system functionality important to most any feed testing laboratory, with a majority of that functionality found in many vendor software solutions.[1][3]

Test, sample and result management

  • Sample log-in and management, with support for unique IDs
  • Sample batching
  • Barcode and RFID support
  • End-to-end sample and inventory tracking, through to reporting and disposition
  • Pre-defined and configurable industry-specific test and method management, including for bacteria (i.e., microbiology), heavy metals (i.e., chemistry), radionuclides (i.e., radiochemistry), and other substances
  • Pre-defined and configurable industry-specific workflows, including for production, R&D, and public health contexts
  • Configurable screens and data fields
  • Specification management
  • Test, sampling, instrument, etc. scheduling and assignment
  • Test requesting
  • Data import and export
  • Raw data management
  • Robust query tools
  • Analytical tools, including data visualization, statistical analysis, and data mining tools
  • Document and image management
  • Version control
  • Project and experiment management
  • Method and protocol management
  • Investigation management
  • Facility and sampling site management
  • Storage management and monitoring

Quality, security, and compliance

  • Quality assurance / quality control mechanisms
  • Mechanisms for compliance with ISO 17025 and HACCP, including support for critical control point (CCP) specifications and limits
  • Result, method, protocol, batch, and material validation, review, and release
  • Data validation
  • Trend and control charting for statistical analysis and measurement of uncertainty
  • User qualification, performance, and training management
  • Audit trails and chain of custody support
  • Configurable and granular role-based security
  • Configurable system access and use (i.e., authentication requirements, account usage rules, account locking, etc.)
  • Electronic signature support
  • Data encryption and secure communication protocols
  • Archiving and retention of data and information
  • Configurable data backups
  • Status updates and alerts
  • Environmental monitoring support
  • Incident and non-conformance notification, tracking, and management

Operations management and reporting

  • Configurable dashboards for monitoring, by product, process, facility, etc.
  • Customizable rich-text reporting, with multiple supported output formats
  • Custom and industry-specific reporting, including certificates of analysis (CoAs)
  • Industry-compliant labeling
  • Email integration and other communication support for internal and external stakeholders
  • Instrument interfacing and data management, particularly for near-infrared spectroscopy (NIRS) instruments
  • Third-party software interfacing (e.g., LES, scientific data management system [SDMS], other databases)
  • Data import, export, and archiving
  • Instrument and equipment management, including calibration and maintenance tracking
  • Inventory and material management
  • Supplier/vendor/customer management
  • Flexible but secure client portal for pre-registering samples, printing labels, and viewing results
  • Integrated (or online) system help

Specialty LIMS requirements

  • Mechanisms to make data and information more FAIR: Like many other disciplines, modern academic and industrial research of feed ingredient selection, feed formulation, and feed production is plagued by interdisciplinary research data and information (i.e., objects) "in a broad range of [heterogeneous] information formats [that] involve inconsistent vocabulary and difficult‐to‐define concepts."[4] This makes increasingly attractive data discovery options[4] such as text mining, cluster searching, and artificial intelligence (AI) methods less effective, in turn hampering innovation, discovery, and improved health outcomes. As such, research labs of all sorts are increasingly turning to the FAIR principles, which encourage processes that make research objects more findable, accessible, interoperable, and reusable. A handful of software developers have become more attuned to this demand and have developed or modified their systems to produce research objects that are produced using metadata- and semantic-driven technologies and frameworks.[5] Producing FAIR data is more important to the academic research and public health contexts of feed testing, but can still be useful to other industrial contexts, as having interoperable and reusable data in industry can lead to greater innovation and process improvement.[6] Of course, all animal feed testing labs can benefit when, for example, FAIR-driven, internationally accepted vocabulary and data descriptors for mycotoxin contamination data are used in research and laboratory software.[7] This leads into...
  • Support for standardized and controlled vocabularies: By extension, this gets into the matter of improved interoperability of feed testing results from different laboratories, particularly government labs in different jurisdictions responsible for monitoring contaminates in animal feed.[8] The Association of American Feed Control Officials (AAFCO) Strategic Affairs Committee (SAC) highlight this in their Strategic Plan for 2023–2025, stating that in order to "promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems," the different LIMS used across various states demand an integrated IT environment where "comparable results from different labs" can effectively be made.[8]

Conclusion

References

  1. ↑ 1.0 1.1 Ward, R. (27 February 2024). "Obtaining value from a feed/forage lab engagement" (PDF). Florida Ruminant Nutrition Symposium. https://animal.ifas.ufl.edu/media/animalifasufledu/dairy-website/ruminant-nutrition-symposium/archives/12.-WardRNS2024.pdf. Retrieved 22 May 2024. 
  2. ↑ Schnepf, Anne; Hille, Katja; van Mark, Gesine; Winkelmann, Tristan; Remm, Karen; Kunze, Katrin; Velleuer, Reinhard; Kreienbrock, Lothar (6 February 2024). "Basis for a One Health Approach—Inventory of Routine Data Collections on Zoonotic Diseases in Lower Saxony, Germany" (in en). Zoonotic Diseases 4 (1): 57–73. doi:10.3390/zoonoticdis4010007. ISSN 2813-0227. https://www.mdpi.com/2813-0227/4/1/7. 
  3. ↑ 3.0 3.1 Partnership for Food Protection Laboratory Science Workgroup (December 2018). "Human and Animal Food Testing Laboratories Best Practices Manual" (PDF). https://www.aphl.org/programs/food_safety/APHL%20Documents/LBPM_Dec2018.pdf. Retrieved 22 May 2024. 
  4. ↑ 4.0 4.1 4.2 Wood, Hannah; O'Connor, Annette; Sargeant, Jan; Glanville, Julie (1 December 2018). "Information retrieval for systematic reviews in food and feed topics: A narrative review" (in en). Research Synthesis Methods 9 (4): 527–539. doi:10.1002/jrsm.1289. ISSN 1759-2879. https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1289. 
  5. ↑ Douglas, S.E. (May 2024). "LIMS Q&A:Why are the FAIR data principles increasingly important to research laboratories and their software?". LIMSwiki. https://www.limswiki.org/index.php/LIMS_Q%26A:Why_are_the_FAIR_data_principles_increasingly_important_to_research_laboratories_and_their_software%3F. Retrieved 22 May 2024. 
  6. ↑ van Vlijmen, Herman; Mons, Albert; Waalkens, Arne; Franke, Wouter; Baak, Arie; Ruiter, Gerbrand; Kirkpatrick, Christine; da Silva Santos, Luiz Olavo Bonino et al. (1 January 2020). "The Need of Industry to Go FAIR" (in en). Data Intelligence 2 (1-2): 276–284. doi:10.1162/dint_a_00050. ISSN 2641-435X. https://direct.mit.edu/dint/article/2/1-2/276-284/10011. 
  7. ↑ Mesfin, Addisalem; Lachat, Carl; Vidal, Arnau; Croubels, Siska; Haesaert, Geert; Ndemera, Melody; Okoth, Sheila; Belachew, Tefera et al. (1 February 2022). "Essential descriptors for mycotoxin contamination data in food and feed" (in en). Food Research International 152: 110883. doi:10.1016/j.foodres.2021.110883. https://linkinghub.elsevier.com/retrieve/pii/S0963996921007833. 
  8. ↑ 8.0 8.1 The Association of American Feed Control Officials, Strategic Affairs Committee (16 November 2022). "Strategic Plan 2023-2025" (PDF). AAFCO. p. 14. https://www.aafco.org/wp-content/uploads/2023/07/SAC_Strategic_Plan_2023-2025.pdf. Retrieved 22 May 2024. 
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