Difference between revisions of "User:Shawndouglas/sandbox/sublevel13"
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Given the above ... | 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.<ref name="WardObtain24" /><ref name="PFPLSWHumanAnim18" /> | 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" /><ref name="FAOTheFeed13">{{cite web |url=https://www.fao.org/4/i3535e/i3535e.pdf |format=PDF |title=The Feed Analysis Laboratory: Establishment and Quality Control |author=deJonge, L.H.; Jackson, F.S.; Makkar, H.P.S. |publisher=Food and Agriculture Organization of the United Nations |date=2013 |accessdate=25 May 2024}}</ref><ref name="OpenCoLIMS">{{cite web |url=https://www.openco.it/en/production-laboratory/ |title=ProLabQ - The LIMS system for your production laboratory |publisher=Open-Co S.r.l |accessdate=25 May 2024}}</ref> | ||
'''Test, sample and result management''' | '''Test, sample and result management''' | ||
Line 66: | Line 66: | ||
*Method and protocol management | *Method and protocol management | ||
*Investigation management | *Investigation management | ||
* | *Multi-facility and -sampling site management | ||
*Storage management and monitoring | *Storage management and monitoring | ||
Line 99: | Line 99: | ||
*Data import, export, and archiving | *Data import, export, and archiving | ||
*Instrument and equipment management, including calibration and maintenance tracking | *Instrument and equipment management, including calibration and maintenance tracking | ||
*Inventory and material management | *Inventory and material management, including raw materials | ||
*Supplier/vendor/customer management | *Supplier/vendor/customer management | ||
*Flexible but secure client portal for pre-registering samples, printing labels, and viewing results | *Flexible but secure client portal for pre-registering samples, printing labels, and viewing results | ||
Line 108: | Line 108: | ||
*'''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 name=":1">{{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... | *'''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 name=":1">{{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 - Objective 3.2 - Promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems |author=The Association of American Feed Control Officials, Strategic Affairs Committee |publisher=AAFCO |pages=10–15 |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" /> As of 2024, a standardized, internationally recognized controlled vocabulary for animal feed isn't fully apparent. Efforts such as FEED<ref>{{Cite journal |last=Wall |first=Christine E. |last2=Vinyard |first2=Christopher J. |last3=Williams |first3=Susan H. |last4=Gapeyev |first4=Vladimir |last5=Liu |first5=Xianhua |last6=Lapp |first6=Hilmar |last7=German |first7=Rebecca Z. |date=2011-08 |title=Overview of FEED, the Feeding Experiments End-user Database |url=https://academic.oup.com/icb/article-lookup/doi/10.1093/icb/icr047 |journal=Integrative and Comparative Biology |language=en |volume=51 |issue=2 |pages=215–223 |doi=10.1093/icb/icr047 |issn=1557-7023 |pmc=PMC3135827 |pmid=21700574}}</ref>, Feedipedia<ref>{{Cite web |last=INRAE CIRAD AFZ and FAO |date=2022 |title=Feedipedia: An on-line encyclopedia of animal feeds |work=Feedipedia - Animal Feed Resources Information System |url=https://www.feedipedia.org/content/about-feedipedia |accessdate=25 May 2024}}</ref>, FoodOn<ref>{{Cite journal |last=Dooley |first=Damion M. |last2=Griffiths |first2=Emma J. |last3=Gosal |first3=Gurinder S. |last4=Buttigieg |first4=Pier L. |last5=Hoehndorf |first5=Robert |last6=Lange |first6=Matthew C. |last7=Schriml |first7=Lynn M. |last8=Brinkman |first8=Fiona S. L. |last9=Hsiao |first9=William W. L. |date=2018-12-18 |title=FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration |url=https://www.nature.com/articles/s41538-018-0032-6 |journal=npj Science of Food |language=en |volume=2 |issue=1 |pages=23 |doi=10.1038/s41538-018-0032-6 |issn=2396-8370 |pmc=PMC6550238 |pmid=31304272}}</ref>, and MYTOX-SOUTH<ref name=":1" /> have made inroads into to the area of developing or extending controlled vocabularies that could apply to feed testing, and a LIMS vendor that taps into one or more these options arguably has a leg up on other such vendors. | *'''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 - Objective 3.2 - Promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems |author=The Association of American Feed Control Officials, Strategic Affairs Committee |publisher=AAFCO |pages=10–15 |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" /> As of 2024, a standardized, internationally recognized controlled vocabulary for animal feed isn't fully apparent. Efforts such as FEED<ref>{{Cite journal |last=Wall |first=Christine E. |last2=Vinyard |first2=Christopher J. |last3=Williams |first3=Susan H. |last4=Gapeyev |first4=Vladimir |last5=Liu |first5=Xianhua |last6=Lapp |first6=Hilmar |last7=German |first7=Rebecca Z. |date=2011-08 |title=Overview of FEED, the Feeding Experiments End-user Database |url=https://academic.oup.com/icb/article-lookup/doi/10.1093/icb/icr047 |journal=Integrative and Comparative Biology |language=en |volume=51 |issue=2 |pages=215–223 |doi=10.1093/icb/icr047 |issn=1557-7023 |pmc=PMC3135827 |pmid=21700574}}</ref>, Feedipedia<ref>{{Cite web |last=INRAE CIRAD AFZ and FAO |date=2022 |title=Feedipedia: An on-line encyclopedia of animal feeds |work=Feedipedia - Animal Feed Resources Information System |url=https://www.feedipedia.org/content/about-feedipedia |accessdate=25 May 2024}}</ref>, FoodOn<ref>{{Cite journal |last=Dooley |first=Damion M. |last2=Griffiths |first2=Emma J. |last3=Gosal |first3=Gurinder S. |last4=Buttigieg |first4=Pier L. |last5=Hoehndorf |first5=Robert |last6=Lange |first6=Matthew C. |last7=Schriml |first7=Lynn M. |last8=Brinkman |first8=Fiona S. L. |last9=Hsiao |first9=William W. L. |date=2018-12-18 |title=FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration |url=https://www.nature.com/articles/s41538-018-0032-6 |journal=npj Science of Food |language=en |volume=2 |issue=1 |pages=23 |doi=10.1038/s41538-018-0032-6 |issn=2396-8370 |pmc=PMC6550238 |pmid=31304272}}</ref>, and MYTOX-SOUTH<ref name=":1" /> have made inroads into to the area of developing or extending controlled vocabularies that could apply to feed testing, and a LIMS vendor that taps into one or more these options arguably has a leg up on other such vendors. | ||
*'''Tools that support [[quality management system]] (QMS) initiatives''': In the same AAFCO-SAC Strategic Plan is the recognition of the importance of a QMS to the feed testing lab, in particular in regards to how it should be integrated with laboratory technology such as LIMS and the workflows the LIMS can improve.<ref name="AAFCOSACStrat22" /> As such, a LIMS developed with feed testing in mind will ideally have a variety of tools and functionality that help the lab better achieve its QMS goals. At the farthest end of the scale could be a feed testing LIMS that essentially provides the functionality of an electronic QMS so as to limit data duplication and extra integration considerations. This includes document management, training management, equipment and maintenance management, workflow and method management, quality control and assessment tools, out-of-specification and incident management, batch management, qualification management, and more. Most of this functionality is listed in the base LIMS requirements above, but the combination of them all better supports the QMS needs of a feed testing lab compared to a system that only provides a few of those functionalities. | *'''Tools that support [[quality management system]] (QMS) initiatives''': In the same AAFCO-SAC Strategic Plan is the recognition of the importance of a QMS to the feed testing lab, in particular in regards to how it should be integrated with laboratory technology such as LIMS and the workflows the LIMS can improve.<ref name="AAFCOSACStrat22" /> As such, a LIMS developed with feed testing in mind will ideally have a variety of tools and functionality that help the lab better achieve its QMS goals. At the farthest end of the scale could be a feed testing LIMS that essentially provides the functionality of an electronic QMS so as to limit data duplication and extra integration considerations. This includes document management, training management, equipment and maintenance management, workflow and method management, quality control and assessment tools, out-of-specification and incident management, batch management, qualification management, and more.<ref name="FAOTheFeed13" /><ref name="AAFCOSACStrat22" /> Most of this functionality is listed in the base LIMS requirements above, but the combination of them all better supports the QMS needs of a feed testing lab compared to a system that only provides a few of those functionalities. | ||
==Conclusion== | ==Conclusion== |
Revision as of 16:25, 24 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][5][6]
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
- Multi-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, including raw materials
- 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.[7] 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.[8] 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.[9] 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.[10] 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.[10] As of 2024, a standardized, internationally recognized controlled vocabulary for animal feed isn't fully apparent. Efforts such as FEED[11], Feedipedia[12], FoodOn[13], and MYTOX-SOUTH[9] have made inroads into to the area of developing or extending controlled vocabularies that could apply to feed testing, and a LIMS vendor that taps into one or more these options arguably has a leg up on other such vendors.
- Tools that support quality management system (QMS) initiatives: In the same AAFCO-SAC Strategic Plan is the recognition of the importance of a QMS to the feed testing lab, in particular in regards to how it should be integrated with laboratory technology such as LIMS and the workflows the LIMS can improve.[10] As such, a LIMS developed with feed testing in mind will ideally have a variety of tools and functionality that help the lab better achieve its QMS goals. At the farthest end of the scale could be a feed testing LIMS that essentially provides the functionality of an electronic QMS so as to limit data duplication and extra integration considerations. This includes document management, training management, equipment and maintenance management, workflow and method management, quality control and assessment tools, out-of-specification and incident management, batch management, qualification management, and more.[5][10] Most of this functionality is listed in the base LIMS requirements above, but the combination of them all better supports the QMS needs of a feed testing lab compared to a system that only provides a few of those functionalities.
Conclusion
References
- ↑ 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.
- ↑ 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.
- ↑ 3.0 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.
- ↑ 4.0 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.
- ↑ 5.0 5.1 deJonge, L.H.; Jackson, F.S.; Makkar, H.P.S. (2013). "The Feed Analysis Laboratory: Establishment and Quality Control" (PDF). Food and Agriculture Organization of the United Nations. https://www.fao.org/4/i3535e/i3535e.pdf. Retrieved 25 May 2024.
- ↑ "ProLabQ - The LIMS system for your production laboratory". Open-Co S.r.l. https://www.openco.it/en/production-laboratory/. Retrieved 25 May 2024.
- ↑ 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.
- ↑ 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.
- ↑ 9.0 9.1 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.
- ↑ 10.0 10.1 10.2 10.3 The Association of American Feed Control Officials, Strategic Affairs Committee (16 November 2022). "Strategic Plan 2023-2025 - Objective 3.2 - Promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems" (PDF). AAFCO. pp. 10–15. https://www.aafco.org/wp-content/uploads/2023/07/SAC_Strategic_Plan_2023-2025.pdf. Retrieved 22 May 2024.
- ↑ Wall, Christine E.; Vinyard, Christopher J.; Williams, Susan H.; Gapeyev, Vladimir; Liu, Xianhua; Lapp, Hilmar; German, Rebecca Z. (1 August 2011). "Overview of FEED, the Feeding Experiments End-user Database" (in en). Integrative and Comparative Biology 51 (2): 215–223. doi:10.1093/icb/icr047. ISSN 1557-7023. PMC PMC3135827. PMID 21700574. https://academic.oup.com/icb/article-lookup/doi/10.1093/icb/icr047.
- ↑ INRAE CIRAD AFZ and FAO (2022). "Feedipedia: An on-line encyclopedia of animal feeds". Feedipedia - Animal Feed Resources Information System. https://www.feedipedia.org/content/about-feedipedia. Retrieved 25 May 2024.
- ↑ Dooley, Damion M.; Griffiths, Emma J.; Gosal, Gurinder S.; Buttigieg, Pier L.; Hoehndorf, Robert; Lange, Matthew C.; Schriml, Lynn M.; Brinkman, Fiona S. L. et al. (18 December 2018). "FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration" (in en). npj Science of Food 2 (1): 23. doi:10.1038/s41538-018-0032-6. ISSN 2396-8370. PMC PMC6550238. PMID 31304272. https://www.nature.com/articles/s41538-018-0032-6.