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==Sandbox begins below==
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<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 types of testing occur within an animal feed testing laboratory?''


'''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''': May 2024
'''Publication date''': June 2024


==Introduction==
==Introduction==


==What are the FAIR data principles?==
This brief topical article will ...
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>{{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>


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>
'''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.


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, whether located in an organization or contracted out as third parties, exist to innovate. That innovation can come in the form of discovering new materials that may or may not have a future application, developing a pharmaceutical to improve patient outcomes for a particular disease, or modifying (for some sort of improvement) an existing food or beverage recipe, among others. In academic research labs, this usually looks like knowledge advancement 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).  
==Broad testing within the industry==
A feed testing [[laboratory]] can operate within a number of different research and development (R&D&#59; academic and industry), production, and [[public health]] roles. 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=28 May 2024}}</ref>:


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 published (from academic research organizations) or internal (for industry research organizations) research results is at risk, giving less confidence to academic peers in the published research, or less confidence 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:
*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 (business)|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 activities and workflows within these major roles highlights several aspects of the labs operating in the animal feed sector. First, like the more human-based food and beverage industry, the types of testing will vary based upon the role. From R&D and pre-production optimization and [[quality assurance]] (QA) to production and post-production [[quality control]] (QC) and regulatory safety, analytical workflows can differ, sometimes significantly, in the food and beverage industry.<ref name="DouglasFoodBevTest22">{{cite web |url=https://www.limswiki.org/index.php/LIMS_Q%26A:What_types_of_testing_occur_within_a_food_and_beverage_laboratory%3F |title=LIMS Q&A:What types of testing occur within a food and beverage laboratory? |author=Douglas, S.E. |work=LIMSwiki |date=August 2022 |accessdate=11 June 2024}}</ref> This is similarly true for labs in the animal feed industry. As such—regulations and standards aside—we can draw similar parallels in the test types found in feed analysis labs.
 
Second—and also similar to food and beverage testing<ref name="DouglasFoodBevTest22" />—the activities and workflows listed above also highlight the 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=28 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 can be challenging to characterize the full spectrum of testing found within feed testing labs.
 
For the rest of this article, we'll take a similar approach to the food and industry<ref name="DouglasFoodBevTest22" /> and break down testing by the various roles feed testing labs can fill.
 
==Testing within the primary roles of a feed lab==
The type of testing occurring within a feed lab will vary depending on the role it plays within the larger framework of industry needs. The following subsections examine the three primary roles of these labs and the testing required to meet their goals.
 
===R&D roles===
 
 
===Pre-manufacturing and manufacturing roles===
 
 
===Post-production regulation and safety roles===
 
 
==Conclusion==


* ''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;
* ''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;
* ''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
* ''reusable'',


==References==
==References==
{{Reflist|colwidth=30em}}
{{Reflist|colwidth=30em}}
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Latest revision as of 19:30, 11 June 2024

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[[File:|right|450px]] Title: What types of testing occur within an animal feed testing laboratory?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: June 2024

Introduction

This brief topical article will ...

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.

Broad testing within the industry

A feed testing laboratory can operate within a number of different research and development (R&D; academic and industry), production, and public health roles. 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 activities and workflows within these major roles highlights several aspects of the labs operating in the animal feed sector. First, like the more human-based food and beverage industry, the types of testing will vary based upon the role. From R&D and pre-production optimization and quality assurance (QA) to production and post-production quality control (QC) and regulatory safety, analytical workflows can differ, sometimes significantly, in the food and beverage industry.[2] This is similarly true for labs in the animal feed industry. As such—regulations and standards aside—we can draw similar parallels in the test types found in feed analysis labs.

Second—and also similar to food and beverage testing[2]—the activities and workflows listed above also highlight the 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.[3][4][5] Given this significant cross-disciplinarity, it's can be challenging to characterize the full spectrum of testing found within feed testing labs.

For the rest of this article, we'll take a similar approach to the food and industry[2] and break down testing by the various roles feed testing labs can fill.

Testing within the primary roles of a feed lab

The type of testing occurring within a feed lab will vary depending on the role it plays within the larger framework of industry needs. The following subsections examine the three primary roles of these labs and the testing required to meet their goals.

R&D roles

Pre-manufacturing and manufacturing roles

Post-production regulation and safety roles

Conclusion

References

  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 28 May 2024. 
  2. ↑ 2.0 2.1 2.2 Douglas, S.E. (August 2022). "LIMS Q&A:What types of testing occur within a food and beverage laboratory?". LIMSwiki. https://www.limswiki.org/index.php/LIMS_Q%26A:What_types_of_testing_occur_within_a_food_and_beverage_laboratory%3F. Retrieved 11 June 2024. 
  3. ↑ 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. 
  4. ↑ 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 28 May 2024. 
  5. ↑ 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. 


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