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We continue to look at 20 broad industry categories and the laboratories associated with them. For each you'll find a brief description with common services and how the lab type affects the average person. As discussed previously, using our client type + function model we dig into examples found in the private, government, and academic sectors and then outline function through activities, sciences, test types, equipment, and unique attributes.
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| text      = This is sublevel12 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p>
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<div align="center">-----Return to [[LII:The Laboratories of Our Lives: Labs, Labs Everywhere!|the beginning]] of this guide-----</div>
==Sandbox begins below==
__TOC__
<div class="nonumtoc">__TOC__</div>
[[File:FAIRResourcesGraphic AustralianResearchDataCommons 2018.png|right|520px]]
'''Title''': ''What are the potential implications of the FAIR data principles to laboratory informatics applications?''


==5. Labs by industry: Part 3==
'''Author for citation''': Shawn E. Douglas
===Geology and mining===
[[File:Mining near the city of Tomsk in Russia.jpg|left|400px]]
{{clear}}
Geology and mining [[Laboratory|laboratories]] are responsible for analyzing rocks, minerals, and metals; monitoring and reporting on the status of mining operation effects on the environment; and teaching and promoting research of geological and mining science and engineering concepts. These labs are involved at most stages of geological and mining operations, from exploration and production to remediation. These labs are found in the private, government, and academic sectors and provide many different services, including (but not limited to)<ref name="RajasthanTesting">{{cite web |url=http://www.dmg-raj.org/docs/Vol%2024(4).doc |title=Testing Facilities Available at Mines & Geology Department Laboratory |work=Department of Mines & Geology |publisher=Government of Rajasthan |accessdate=03 June 2017}}{{Dead link}}</ref><ref name="AGATMetals">{{cite web |url=https://agatlabs.com/minerals/ |title=Minerals and Metals - Overview |publisher=AGAT Laboratories Ltd |accessdate=29 June 2022}}</ref><ref name="MichTechGeo">{{cite web |url=https://www.mtu.edu/geo/labs/equipment/ |title=Labs and Equipment |publisher=Michigan Tech |accessdate=29 June 2022}}</ref>:


* chemical analysis
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
* physical testing
* earth magnetism measurement
* petrological imaging
* soil suitability and fertility
* environmental analysis and remediation
* drill core analysis
* purity testing
 
''How do geology and mining laboratories intersect the average person's life on a daily basis?''
 
Tracking down how these labs intersect our lives is, comparatively, a bit more difficult than the industries we've looked at previously. From an environmental standpoint, when regulated, contamination testing is important to the ecosystems in and around a mining site. The oxidation of sulfide minerals and the corresponding acidification of the environment is well known in the mining community, requiring tested and standardized methods to limit the effects.<ref name="AGICanWe">{{cite web |url=https://www.americangeosciences.org/critical-issues/faq/can-we-mitigate-environmental-impacts-mining |title=Can we mitigate environmental impacts from mining? |publisher=American Geosciences Institute |accessdate=29 June 2022}}</ref> Secondarily, research coming out of geology and mining labs is helping to make current and future mining activities safer for humans and guiding the implementation of early-warning systems for earthquakes.<ref name="USGSEarthquake">{{cite web |url=https://www.usgs.gov/programs/earthquake-hazards/science/early-warning |title=Early Warning |work=Earthquake Hazards Program |publisher=U.S. Geological Survey |accessdate=29 June 2022}}</ref> Without these laboratories in place, there's a higher likelihood humans and animals alike would face a higher risk of poisoning or death.
 
====Client types====
 
'''Private''' - These labs focus on providing third-party analysis and consultation services to industry and government, including explorations services, environmental chemistry, and purity testing.
 
Examples include:
 
* [https://agatlabs.com/minerals/ AGAT Laboratories]
* [https://www.huffmanlabs.com/?page_id=154 Huffman Hazen Laboratories]
* [https://www.intertek.com/minerals/mine-site-laboratories/ Intertek Group PLC]
 
'''Government''' - Many governments around the world have geology and mining departments, divisions, etc. responsible for contamination testing, water quality monitoring, and applied research. They also occasionally offer their services to outside parties and agencies.
 
Examples include:
 
* [https://dmg.kerala.gov.in/?option=com_content&view=article&id=87&Itemid=72 Government of Kerala, Department of Mining and Geology Chemical Laboratory]
* [https://www.ggmc.gov.gy/services/all/chemical-laboratory Guyana Geology and Mines Chemical Laboratory]
* [https://jis.gov.jm/government/agencies/mines-and-geology-division/ Jamaica's Mines and Geology Division]
 
'''Academic''' - Like other industries, academic labs in geology and mining programs contribute diverse research programs to society while teaching the next generation of geologists, engineers, and miners.
 
Examples include:
 
* [https://geology.mines.edu/Laboratories/ Colorado School of Mines' Geology and Geological Engineering Laboratories]
* [https://www.mtu.edu/geo/labs/equipment/ Michigan Tech's Geological Engineering Laboratories]
* [https://engineering.und.edu/academics/geology-and-geological/ University of North Dakota's Harold Hamm School of Geology and Geological Engineering]
 
====Functions====
 
''What are the most common functions?''  analytical, QA/QC, research/design, and teaching
 
''What materials, technologies, and/or aspects are being analyzed, researched, and quality controlled?'' alloys, base and minor metals, minerals, precious metals, sediment, soil, water
 
''What sciences are being applied in these labs?'' chemistry, environmental science, geology, geotechnical engineering, metallurgy, mineralogy, mining engineering, petrology, seismology
 
''What are some examples of test types and equipment?''
 
'''Common test types include''':
 
Absorption, Age determination, Angle of repose, Atterberg limits, Bioaccumulation, Carbon-hydrogen ratio, Characterization, Compression, Compaction, Consolidation, Density, Durability, Geochemistry, Geophysics, Grain and particle size, Grindability, Hydraulic conductivity, Identification, Inclusion, Isotope analysis, Macroetch, Metallurgical analysis, Mobility, Moisture, Nuclear density, Organic carbon, Oxidation reduction potential, Passivation, Permeability, pH, Proficiency, Radioactivity, Radiochemical, Refractive index, Seismic, Shear, Stability, Stress corrosion cracking, Ultraviolet
 
'''Industry-related lab equipment may include''':
 
autoclave, balance, calorimeter, chromatographic, compressive strength tester, furnace, jaw crusher, magnetic separator, microscope, mill (various), pH meter, photoelectric flame photometer, reflectance/gloss meter, roll crusher, sieve shaker, spectrophotometer, titrator, thermogravimetric analyzer, viscometer
 
''What else, if anything, is unique about the labs in the clinical research industry?''
 
While many geology laboratories are indoors, outdoor labs—i.e., field studies—are an important part of the industry. Those that are indoors tend to stand out: take for instance the approximately 20 luminescence geological dating laboratories in the U.S., responsible for dating geological substances.<ref name="USGSOtherUSLabs">{{cite web |url=https://gec.cr.usgs.gov/projects/lumlab/other_labs.shtml |archiveurl=https://web.archive.org/web/20170123152325/https://gec.cr.usgs.gov/projects/lumlab/other_labs.shtml |title=For Prospective Users: Other U.S. Laboratories for Luminescence Dating |work=Geosciences and Environmental Change Science Center |publisher=U.S. Geological Survey |date=26 March 2015 |archivedate=23 January 2017 |accessdate=29 June 2022}}</ref> Also note there is often industry crossover with the petrochemical industry, which depends on sound geological science for much of its operations.
 
====Informatics in the geology and mining industry====
The most obvious place where informatics intersects geology and mining operations can be seen in the geographic information system (GIS), a data management tool for capturing, storing, analyzing, and visualizing spatial or geographic data. While used in other industries such as power and utility, agriculture, and logistics, the GIS serves as a valuable tool for mineral exploration, production scheduling, and mine remediation. Informatics methods are being applied to mines in other ways as well, including using remote-operated drone data to map and characterize voids in underground mines.<ref name="OVE011Mine16">{{cite web |url=https://research.csiro.au/robotics/mine-informatics/ |title=Mine Informatics |author=OVE011 |work=Robotics and Autonomous Systems Group at CSIRO |publisher=Commonwealth Scientific and Industrial Research Organisation |date=12 July 2016 |accessdate=29 June 2022}}</ref> Companies like Thermo Fisher offer [[laboratory information management system]]s (LIMS) to the industry, allowing their associated laboratories to more efficiently analyze mineral, water, and other samples in an automated or on-demand fashion.<ref name="ThermoLIMSMining">{{cite web |url=https://www.thermofisher.com/us/en/home/digital-solutions/lab-informatics/lims-metal-mining-industry.html |title=Mining and Metals LIMS |publisher=Thermo Fisher Scientific |accessdate=29 June 2022}}</ref> And international conferences such as the International Multidisciplinary Scientific GeoConference SGEM bring together researchers and practitioners to discuss many aspects of the industry, including applications of informatics such as data modeling, remote sensing, database development, and geo-visualization of temporal data.<ref name="SGEMInformatics17">{{cite web |url=https://www.sgem.org/index.php/conference-topics/informatics-geoinformatics-and-remote-sensing |title=Informatics, Geoinformatics and Remote Sensing |work=22nd International Multidisciplinary Scientific GeoConference SGEM 2022 |publisher=SGEM World Science |accessdate=29 June 2022}}</ref>
 
====LIMSwiki resources====
 
* [[Geoinformatics]]


====Further reading====
'''Publication date''': May 2024


* {{cite book |url=https://books.google.com/books?id=n7i2ywEACAAJ&printsec=frontcover |title=Laboratory Manual in Physical Geology |editor=Cronin, V.S.;  |publisher=Pearson Education, Inc |year=2020 |pages=480 |isbn=9780135836972}}
==Introduction==
https://www.limswiki.org/index.php/Journal:Infrastructure_tools_to_support_an_effective_radiation_oncology_learning_health_system


This brief topical article will examine


<div align="center"><hr width="50%"></div>
==The "FAIR-ification" of research objects and software==
First discussed during a 2014 FORCE-11 workshop dedicated to "overcoming data discovery and reuse obstacles," the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR data principles]] were published by Wilkinson ''et al.'' in 2016 as a stakeholder collaboration driven to see research "objects" (i.e., research data and [[information]] of all shapes and formats) become more universally findable, accessible, interoperable, and reusable (FAIR) by both machines and people.<ref name="WilkinsonTheFAIR16">{{Cite journal |last=Wilkinson |first=Mark D. |last2=Dumontier |first2=Michel |last3=Aalbersberg |first3=IJsbrand Jan |last4=Appleton |first4=Gabrielle |last5=Axton |first5=Myles |last6=Baak |first6=Arie |last7=Blomberg |first7=Niklas |last8=Boiten |first8=Jan-Willem |last9=da Silva Santos |first9=Luiz Bonino |last10=Bourne |first10=Philip E. |last11=Bouwman |first11=Jildau |date=2016-03-15 |title=The FAIR Guiding Principles for scientific data management and stewardship |url=https://www.nature.com/articles/sdata201618 |journal=Scientific Data |language=en |volume=3 |issue=1 |pages=160018 |doi=10.1038/sdata.2016.18 |issn=2052-4463 |pmc=PMC4792175 |pmid=26978244}}</ref> The authors released the FAIR principles while recognizing that "one of the grand challenges of data-intensive science ... is to improve knowledge discovery through assisting both humans and their computational agents in the discovery of, access to, and integration and analysis of task-appropriate scientific data and other scholarly digital objects."<ref name="WilkinsonTheFAIR16" />


===Law enforcement and forensics===
Since 2016, other research stakeholders have taken to publishing their thoughts about how the FAIR principles apply to their fields of study and practice<ref name="NIHPubMedSearch">{{cite web |url=https://pubmed.ncbi.nlm.nih.gov/?term=fair+data+principles |title=fair data principles |work=PubMed Search |publisher=National Institutes of Health, National Library of Medicine |accessdate=30 April 2024}}</ref>, including in ways beyond what perhaps was originally imagined by Wilkinson ''et al.''. For example, multiple authors have examined whether or not the software used in scientific endeavors itself can be considered a research object worth being developed and managed in tandem with the FAIR data principles.<ref>{{Cite journal |last=Hasselbring |first=Wilhelm |last2=Carr |first2=Leslie |last3=Hettrick |first3=Simon |last4=Packer |first4=Heather |last5=Tiropanis |first5=Thanassis |date=2020-02-25 |title=From FAIR research data toward FAIR and open research software |url=https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html |journal=it - Information Technology |language=en |volume=62 |issue=1 |pages=39–47 |doi=10.1515/itit-2019-0040 |issn=2196-7032}}</ref><ref name="GruenpeterFAIRPlus20">{{Cite web |last=Gruenpeter, M. |date=23 November 2020 |title=FAIR + Software: Decoding the principles |url=https://www.fairsfair.eu/sites/default/files/FAIR%20%2B%20software.pdf |format=PDF |publisher=FAIRsFAIR “Fostering FAIR Data Practices In Europe” |accessdate=30 April 2024}}</ref><ref>{{Cite journal |last=Barker |first=Michelle |last2=Chue Hong |first2=Neil P. |last3=Katz |first3=Daniel S. |last4=Lamprecht |first4=Anna-Lena |last5=Martinez-Ortiz |first5=Carlos |last6=Psomopoulos |first6=Fotis |last7=Harrow |first7=Jennifer |last8=Castro |first8=Leyla Jael |last9=Gruenpeter |first9=Morane |last10=Martinez |first10=Paula Andrea |last11=Honeyman |first11=Tom |date=2022-10-14 |title=Introducing the FAIR Principles for research software |url=https://www.nature.com/articles/s41597-022-01710-x |journal=Scientific Data |language=en |volume=9 |issue=1 |pages=622 |doi=10.1038/s41597-022-01710-x |issn=2052-4463 |pmc=PMC9562067 |pmid=36241754}}</ref><ref>{{Cite journal |last=Patel |first=Bhavesh |last2=Soundarajan |first2=Sanjay |last3=Ménager |first3=Hervé |last4=Hu |first4=Zicheng |date=2023-08-23 |title=Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool |url=https://www.nature.com/articles/s41597-023-02463-x |journal=Scientific Data |language=en |volume=10 |issue=1 |pages=557 |doi=10.1038/s41597-023-02463-x |issn=2052-4463 |pmc=PMC10447492 |pmid=37612312}}</ref><ref>{{Cite journal |last=Du |first=Xinsong |last2=Dastmalchi |first2=Farhad |last3=Ye |first3=Hao |last4=Garrett |first4=Timothy J. |last5=Diller |first5=Matthew A. |last6=Liu |first6=Mei |last7=Hogan |first7=William R. |last8=Brochhausen |first8=Mathias |last9=Lemas |first9=Dominick J. |date=2023-02-06 |title=Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software |url=https://link.springer.com/10.1007/s11306-023-01974-3 |journal=Metabolomics |language=en |volume=19 |issue=2 |pages=11 |doi=10.1007/s11306-023-01974-3 |issn=1573-3890}}</ref> Researchers quickly recognized that any planning around updating processes and systems to make research objects more FAIR would have to be tailored to specific research contexts, recognize that digital research objects go beyond data and information, and recognize "the specific nature of software" and not consider it "just data."<ref name="GruenpeterFAIRPlus20" /> The end result has been applying the core concepts of FAIR but differently from data, with the added context of research software being more than just data, requiring more nuance and a different type of planning from applying FAIR to digital data and information.
[[File:Day 253 - West Midlands Police - Forensic Science Lab (7969822920).jpg|left|400px]]
{{clear}}
The [[Forensic science|forensic]] laboratory is responsible for aiding in crime investigations, helping investigators identify remains, place an alleged killer at a particular crime scene, or identify and characterize crime scene evidence. They also serve as training grounds for future forensic scientists. Less occasionally, forensic laboratories operate as private, third-party contract labs that work with government investigators or private industry to analyze DNA, fire debris, paint, etc. These labs provide many different services, including (but not limited to)<ref name="FBILabServ">{{cite web |url=https://www.fbi.gov/services/laboratory |title=Laboratory Services |publisher=Federal Bureau of Investigation |accessdate=29 June 2022}}</ref><ref name="ArmstrongServ">{{cite web |url=https://www.aflab.com/services/ |title=Forensic Services |publisher=Armstrong Forensic Laboratory, Inc |accessdate=29 June 2022}}</ref><ref name="LSUFACESServ">{{cite web |url=https://lsu.edu/faceslab/ |title=LSU Faces Laboratory |publisher=Louisiana State University |accessdate=29 June 2022}}</ref>:


* DNA analysis
A 2019 survey by Europe's FAIRsFAIR found that researchers seeking and re-using relevant research software on the internet faced multiple challenges, including understanding and/or maintaining the necessary software environment and its dependencies, finding sufficient documentation, struggling with accessibility and licensing issues, having the time and skills to install and/or use the software, finding quality control of the source code lacking, and having an insufficient (or non-existent) software sustainability and management plan.<ref name="GruenpeterFAIRPlus20" /> These challenges highlight the importance of software to researchers and other stakeholders, and the roll FAIR has in better ensuring such software is findable, interoperable, and reusable, which in turn better ensures researchers' software-driven research is repeatable (by the same research team, with the same experimental setup), reproducible (by a different research team, with the same experimental setup), and replicable (by a different research team, with a different experimental setup).<ref name="GruenpeterFAIRPlus20" />
* fire debris analysis
* metallurgical analysis
* firearms and ballistics analysis
* vehicle fluid analysis
* trauma analysis
* skeletal identification
* body fluid identification
* evidence screening
* facial reconstruction
* audio/image enhancement
* carbon dating of remains


''How do law enforcement and forensic laboratories intersect the average person's life on a daily basis?''  
At this point, the topic of what "research software" represents must be addressed further, and, unsurprisingly, it's not straightforward. Ask 20 researchers what "research software" is, and you may get 20 different opinions. Some definitions can be more objectively viewed as too narrow, while others may be viewed as too broad, with some level of controversy inherent in any mutual discussion.<ref name="GruenpeterDefining21">{{Cite journal |last=Gruenpeter, Morane |last2=Katz, Daniel S. |last3=Lamprecht, Anna-Lena |last4=Honeyman, Tom |last5=Garijo, Daniel |last6=Struck, Alexander |last7=Niehues, Anna |last8=Martinez, Paula Andrea |last9=Castro, Leyla Jael |last10=Rabemanantsoa, Tovo |last11=Chue Hong, Neil P. |date=2021-09-13 |title=Defining Research Software: a controversial discussion |url=https://zenodo.org/record/5504016 |journal=Zenodo |doi=10.5281/zenodo.5504016}}</ref><ref name="JulichWhatIsRes24">{{cite web |url=https://www.fz-juelich.de/en/rse/about-rse/what-is-research-software |title=What is Research Software? |work=JuRSE, the Community of Practice for Research Software Engineering |publisher=Forschungszentrum Jülich |date=13 February 2024 |accessdate=30 April 2024}}</ref><ref name="vanNieuwpoortDefining24">{{Cite journal |last=van Nieuwpoort |first=Rob |last2=Katz |first2=Daniel S. |date=2023-03-14 |title=Defining the roles of research software |url=https://upstream.force11.org/defining-the-roles-of-research-software |language=en |doi=10.54900/9akm9y5-5ject5y}}</ref> In 2021, as part of the FAIRsFAIR initiative, Gruenpeter ''et al.'' made a good-faith effort to define "research software" with the feedback of multiple stakeholders. Their efforts resulted in this definition<ref name="GruenpeterDefining21" />:


Your average person won't feel much impact from a forensic lab, at least in a direct sense. Indirectly, forensic labs help capture criminals, which in theory reduces the chances of a criminal running free to cross paths with you. Should you find yourself in the unfortunate situation of requiring the services of a forensic laboratory (whether to help solve a crime that has impacted you or help clear you of wrongdoing), you'll feel the impact more succinctly; this lab depends on tried and true techniques employed by knowledgeable laboratorians to solve crimes and give some measure of peace to those negatively affected by them. Without these labs, we'd arguably have more criminals get away with their crimes, leaving more cases unsolved.
<blockquote>Research software includes source code files, algorithms, scripts, computational workflows, and executables that were created during the research process, or for a research purpose. Software components (e.g., operating systems, libraries, dependencies, packages, scripts, etc.) that are used for research but were not created during, or with a clear research intent, should be considered "software [used] in research" and not research software. This differentiation may vary between disciplines. The minimal requirement for achieving computational reproducibility is that all the computational components (i.e., research software, software used in research, documentation, and hardware) used during the research are identified, described, and made accessible to the extent that is possible.</blockquote>
====Client types====


'''Private''' - These labs are less common than government and academic labs, but where they do exist, they tend to take on contract analysis and consultation work for a variety of clients.
Note that while the definition primarily recognizes software created during the research process, software created (whether by the research group, other open-source software developers outside the organization, or even commercial software developers) "for a research purpose" outside the actual research process is also recognized as research software. This notably can lead to disagreement about whether a proprietary, commercial spreadsheet or [[laboratory information management system]] (LIMS) offering that conducts analyses and visualizations of research data can genuinely be called research software, or simply classified as software used in research. van Nieuwpoort and Katz further elaborated on this concept, at least indirectly, by formally defining the roles of research software in 2023. Their definition of the various roles of research software—without using terms such as "open-source," "commercial," or "proprietary"—essentially further defined what research software is<ref name="vanNieuwpoortDefining24" />:


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


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


'''Government''' - Government forensic labs make up a significant chunk of the bunch, whether at the federal, state, or local level.
Ultimately, assuming that some laboratory informatics software can be considered research software and not just "software used in research," it's tough not to arrive at some deeper implications of research organizations' increasing need for FAIR data objects and software, particularly for laboratory informatics software and the developers of it.


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


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


'''Academic''' - Academic forensic labs may be used by undergraduate students, but they are largely reserved for graduate level training of students. Some university forensic labs may also provide their facilities and services to government agencies and coroner's offices.
This brings us to our first point: the application of small-scale, FAIR-driven academic research software engineering practices and elements to the larger development of more commercial laboratory informatics software, and vice versa with the application of commercial-scale development practices to small FAIR-focused academic and institutional research software engineering efforts, has the potential to help better support all research laboratories using both independently-developed and commercial research software.  


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


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


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


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


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


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


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


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


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


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


balance, binocular microscope, blood analyzer, burette, centrifuge, chemical storage cabinet, chromatographic, compound microscope, confocal microscope, cryostat, elecrophoresis equipment, evaporator, evidence drying cabinet, extractor, fingerprint development chamber, fluorescent plate reader, freezers and refrigerators, FTIR microscope, fume hood, fuming chamber, graphite furnace, hyperspectral imaging system, microplate handler, microscope, microtome, PCR system, refractometer, spectrometer, spectrophotometer, stereo microscope, viscometer
*greater findability of FAIR research objects for further AI-driven scientific discovery;
*greater reproducibility of FAIR research objects and any AI models published with them;
*improved generalization of AI-driven medical research models when exposed to diverse and FAIR research objects;
*improved reporting of AI-driven research results using FAIRified research objects, lending further credibility to those results;
*more uniform comparison of AI models using well-defined hyperstructure and information training conditions from FAIRified research objects;
*more developed and interoperable "data e-infrastructure," which can further drive a more effective "AI services layer";
*reduced bias in AI-driven processes through the use of FAIR research objects and AI models; and
*improved surety of scientific correctness where reproducibility in AI-driven research can't be guaranteed.


''What else, if anything, is unique about the labs in the law enforcement and forensics industry?''
In the end, developers of research software (whether discipline-specific research software or broader laboratory informatics solutions) would be advised to keep in mind the growing trends of FAIR research, FAIR software, and ML- and AI-driven research, especially in the [[life sciences]], but also a variety of other fields.<ref name="HuertaFAIRForAI23" />


Forensic science is significantly cross-discipline in nature, with anthropology, biology, chemistry, cryptography, entomology, medical science, toxicology, and a host of other disciplines getting involved with the analysis and characterization of a wide variety of evidence types. As such, gaps may exist in knowledge and know-how in some areas of analysis, requiring the recruitment of outside help for more esoteric analyses.<ref name="NRCStrength09">{{cite book |title=Strengthening Forensic Science in the United States: A Path Forward |author=National Research Council |publisher=National Academies Press |year=2009 |pages=348 |doi=10.17226/12589 |url=https://nap.nationalacademies.org/catalog/12589/strengthening-forensic-science-in-the-united-states-a-path-forward}}</ref>
===Restricted clinical data and its FAIRification for greater research innovation===
Broader discussion in the research community continues to occur in regards to how best to ethically make restricted or privacy-protected clinical data and information FAIR for greater innovation and, by extension, improved patient outcomes, particularly in the wake of the [[COVID-19]] [[pandemic]].<ref name="MaxwellFAIREthic23">{{Cite journal |last=Maxwell |first=Lauren |last2=Shreedhar |first2=Priya |last3=Dauga |first3=Delphine |last4=McQuilton |first4=Peter |last5=Terry |first5=Robert F |last6=Denisiuk |first6=Alisa |last7=Molnar-Gabor |first7=Fruzsina |last8=Saxena |first8=Abha |last9=Sansone |first9=Susanna-Assunta |date=2023-10 |title=FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries |url=https://linkinghub.elsevier.com/retrieve/pii/S2589750023001292 |journal=The Lancet Digital Health |language=en |volume=5 |issue=10 |pages=e712–e736 |doi=10.1016/S2589-7500(23)00129-2 |pmc=PMC10552001 |pmid=37775189}}</ref><ref name="Queralt-RosinachApplying22">{{Cite journal |last=Queralt-Rosinach |first=Núria |last2=Kaliyaperumal |first2=Rajaram |last3=Bernabé |first3=César H. |last4=Long |first4=Qinqin |last5=Joosten |first5=Simone A. |last6=van der Wijk |first6=Henk Jan |last7=Flikkenschild |first7=Erik L.A. |last8=Burger |first8=Kees |last9=Jacobsen |first9=Annika |last10=Mons |first10=Barend |last11=Roos |first11=Marco |date=2022-12 |title=Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic |url=https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-022-00263-7 |journal=Journal of Biomedical Semantics |language=en |volume=13 |issue=1 |pages=12 |doi=10.1186/s13326-022-00263-7 |issn=2041-1480 |pmc=PMC9036506 |pmid=35468846}}</ref><ref>{{Cite journal |last=Martínez-García |first=Alicia |last2=Alvarez-Romero |first2=Celia |last3=Román-Villarán |first3=Esther |last4=Bernabeu-Wittel |first4=Máximo |last5=Luis Parra-Calderón |first5=Carlos |date=2023-05 |title=FAIR principles to improve the impact on health research management outcomes |url=https://linkinghub.elsevier.com/retrieve/pii/S2405844023029407 |journal=Heliyon |language=en |volume=9 |issue=5 |pages=e15733 |doi=10.1016/j.heliyon.2023.e15733 |pmc=PMC10189186 |pmid=37205991}}</ref> (Note that while there are other types of restricted and privacy-protected data, this section will focus largely on clinical data and research objects as the most obvious type.)


====Informatics in the law enforcement and forensics industry====
These efforts have usually revolved around pulling reusable clinical patient or research data from [[hospital information system]]s (HIS), [[electronic medical record]]s (EMRs), [[clinical trial management system]]s (CTMSs), and research databases (often relational in nature) that either contain de-identified data or can de-identify aspects of data and information before access and extraction. Sometimes that clinical data or research object may have already in part been FAIRified, but often it may not be. In all cases, the concepts of privacy, security, and anonymization come up as part of any desire to gain access to that clinical material. However, any FAIRified clinical data isn't necessarily readily open for access. As Snoeijer ''et al.'' note: "The authors of the FAIR principles, however, clearly indicate that 'accessible' does not mean open. It means that clarity and transparency is required around the conditions governing access and reuse."<ref name="SnoeijerProcess19">{{cite book |url=https://phuse.s3.eu-central-1.amazonaws.com/Archive/2019/Connect/EU/Amsterdam/PAP_SA04.pdf |format=PDF |chapter=Paper SA04 - Processing big data from multiple sources |title=Proceedings of PHUSE Connect EU 2019 |author=Snoeijer, B.; Pasapula, V.; Covucci, A. et al. |publisher=PHUSE Limited |year=2019 |accessdate=03 May 2024}}</ref>
A 2014 paper in the ''Australian Journal of Forensic Sciences'' highlighted "both the organizational challenges and the information system architecture" of forensic informatics software implemented in Queensland, "which established workflows tailored to the timely production of forensic intelligence to reduce, disrupt and prevent crime."<ref name="O'MalleyForensic14">{{cite journal |title=Forensic informatics enabling forensic intelligence |journal=Australian Journal of Forensic Sciences |author=O'Malley, T. |volume=47 |issue=1 |pages=27–35 |year=2014 |doi=10.1080/00450618.2014.922618}}</ref> Indeed, that goal is similar to forensic laboratories around the world: how can data management systems and other informatics technology improve forensic intelligence? Informatics can support forensic pathology and death investigations, which often involve a significant amount of textual and image data associated with both autopsy and scene of death.<ref name="LevyTheNeed15">{{cite journal |title=The need for informatics to support forensic pathology and death investigation |journal=Journal of Pathology Informatics |author=Levy, B. |volume=6 |pages=32 |year=2015 |doi=10.4103/2153-3539.158907}}</ref> Additionally, informatics can better guide investigations into computer and network forensics, including data recovery, intrusion detection and analysis, and computer fraud.<ref name="USC_CNF15">{{cite web |url=https://datascience.usc.edu/wp-content/uploads/2017/05/inf528.pdf |format=PDF |title=Computer and Network Forensics - INF 528 (3 Units) |publisher=USC Viterbi School of Engineering |date=2015 |accessdate=29 June 2022}}</ref>


Offshoots of informatics application to forensic science also occur, as can be seen in the IEEE Intelligence and Security Informatics conference, which discusses the intersections of informatics, IT, medical and bioinformatics, forensic science, and many other fields with the goal of governments' "anticipation, prevention, preparedness and response to security events, in physical, cyber, enterprise, and societal spaces."<ref name="IEEEISI17">{{cite web |url=http://www.isi-conf.org/ |title=IEEE ISI 2017 |accessdate=29 June 2022}}</ref>
This is being mentioned in the context of laboratory informatics applications for a couple of reasons. First, a well-designed commercial LIMS that supports clinical research laboratory workflows is already going to address privacy and security aspects, as part of the developer recognizing the need for those labs to adhere to regulations such as the [[Health Insurance Portability and Accountability Act]] (HIPAA) and comply with standards such as [[ISO 15189]]. However, such a system may not have been developed with FAIR data principles in mind, and any built-in metadata and ontology schemes may be insufficient for full FAIRification of laboratory-based clinical trial research objects. As Queralt-Rosinach ''et al.'' note, however, "interestingly, ontologies may also be used to describe data access restrictions to complement FAIR metadata with information that supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" /> Essentially, the authors are suggesting that while a HIS or LIS may have built-in access management tools, setting up ontologies and metadata mechanisms that link privacy aspects of a research object (e.g., "has consent form for," "is de-identified," etc.) to the object's metadata allows for even more flexible, FAIR-driven approaches to privacy and security. Research software developers creating such information management tools for the regulated clinical research space may want to apply FAIR concepts such as this to how access control and privacy restrictions are managed. This will inevitably mean any research objects exported with machine-readable privacy-concerning metadata will be more reusable in a way that still "supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" />


====LIMSwiki resources====
Second, a well-designed research software solution working with clinical data will provide not only support for open, community-supported data models and vocabularies for clinical data, but also standardized community-driven ontologies that are specifically developed for access control and privacy. Queralt-Rosinach ''et al.'' continue<ref name="Queralt-RosinachApplying22" />:


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


====Further reading====
Also of note here is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its OHDSI standardized vocabularies. In all these cases, a developer-driven approach to research software that incorporates community-driven standards that support FAIR principles is welcome. However, as Maxwell ''et al.'' noted in their ''Lancet'' review article in late 2023, "few platforms or registries applied community-developed standards for participant-level data, further restricting the interoperability of ... data-sharing initiatives [like FAIR]."<ref name="MaxwellFAIREthic23" /> As the FAIR principles continue to gain ground in clinical research and diagnostics settings, software developers will need to be more attuned to translating old ways of development to ones that incorporate FAIR data and software principles. Demand for FAIR data will only continue to grow, and any efforts to improve interoperability and reusability while honoring (and enhancing) privacy and security aspects of restricted data will be appreciated by clinical researchers. However, just as FAIR is not an overall goal for researchers, software built with FAIR principles in mind is not the end point of research organizations managing restricted and privacy-protected research objects. Ultimately, those organizations will have make other considerations about restricted data in the scope of FAIR, including addressing data management plans, data use agreements, disclosure review practices, and training as it applies to their research software and generated research objects.<ref>{{Cite journal |last=Jang |first=Joy Bohyun |last2=Pienta |first2=Amy |last3=Levenstein |first3=Margaret |last4=Saul |first4=Joe |date=2023-12-06 |title=Restricted data management: the current practice and the future |url=https://journalprivacyconfidentiality.org/index.php/jpc/article/view/844 |journal=Journal of Privacy and Confidentiality |volume=13 |issue=2 |doi=10.29012/jpc.844 |issn=2575-8527 |pmc=PMC10956935 |pmid=38515607}}</ref>


* {{cite book |url=https://books.google.com/books?id=jlAXBAAAQBAJ&printsec=frontcover |title=Forensic Science and the Administration of Justice: Critical Issues and Directions |author=Strom, K.J.; Hickman, M.J. |publisher=SAGE Publications |year=2014 |pages=312 |isbn=9781483324401 |doi=10.4135/9781483368740}}
==Conclusion==
 
Laboratory informatics developers will also need to remember that FAIRification of research in itself is not a goal for research laboratories; it is a continual process that recognizes improved scientific research and greater innovation as a more likely outcome.<ref name="WilkinsonTheFAIR16" /><ref name="OlsenEmbracing23" /><ref name="HuertaFAIRForAI23" />
 
<div align="center"><hr width="50%"></div>
 
===Life sciences and biotechnology===
[[File:PAPRs in use 01.jpg|left|400px]]
{{clear}}
[[Life sciences industry|Life sciences]] is a broad category of scientific disciplines associated with the study of living organisms. Studies at the molecular level, as well as the use of living systems and organisms to make products for human purposes (biotechnology), have expanded the concept of life sciences even further. Biological and health sciences are at the heart of life science and biotechnology laboratories, with a broad array of branches/disciplines falling under the umbrella. From the plant experiments and analyses at Space Florida's Space Life Sciences Lab<ref name="SFSLSL">{{cite web |url=https://www.spaceflorida.gov/facilities/ |title=Space Life Sciences Lab |publisher=Space Florida |accessdate=29 June 2022}}</ref> to the neurological and brain studies at the Neuroinformatics and Brain Connectivity Lab at Florida International University<ref name="FIUNBCL">{{cite web |urlhttps://nbclab.github.io/ |title=Neuroinformatics and Brain Connectivity Lab |author=Florida International University |publisher=GitHub |accessdate=29 June 2022}}</ref>, just about anything to do with living organisms and their components is being analyzed, researched, and synthesized in a life science and biotechnology lab. These laboratories are often research-focused, intent on making discoveries to improve plant, animal, and human life. These labs are found in the private, government, and academic sectors and provide many different services, including (but not limited to):
 
* researching neuropsychiatric disorders<ref name="SFSLSL" />
* researching plant stress tolerances<ref name="FIUNBCL" />
* molecular imaging<ref name="WillardGenomic08">{{cite book |url=https://books.google.com/books?id=5RBXqL7x-bcC&printsec=frontcover |title=Genomic and Personalized Medicine |editor=Willard, H.F.; Ginsburg, G.S. |publisher=Academic Press |year=2008 |pages=1558 |isbn=9780080919034}}</ref>
* gene targeting<ref name="WillardGenomic08" />
* gene base sequence analysis<ref name="WillardGenomic08" />
* antibody analysis<ref name="WillardGenomic08" />
* protein and peptide analysis<ref name="WillardGenomic08" />
* DNA sequencing and fragment analysis<ref name="WillardGenomic08" />
* biomarker discovery and validation<ref name="WillardGenomic08" />
 
''How do life sciences and biotechnology laboratories intersect the average person's life on a daily basis?''
 
Have you received treatment for cancer? A life science lab was behind the development and/or improvement of that therapy. Have you ever eaten a soybean? The plant that grew it was likely improved in some way by the research at life science lab. From the new medicine you take for your medical condition to the new advances in genetics that allow you to detect disease earlier, don't forget your life has most likely been touched by a life science and biotechnology lab in some way.
 
====Client types====
 
'''Private''' - Some private labs in the life sciences are foundations or institutes, others are companies.
 
Examples include:
 
* [https://macrogen.com/en/rnd/institute/bioinformatics Macrogen Bioinformatics Institute]
* [https://neogenomics.com/ NeoGenomics Laboratories]
* [https://www.jax.org/about-us The Jackson Laboratory]
 
'''Government''' - Government-based life science labs are often part of a branch, agency, etc. and have focused goals either as part of the branch/agency or as mandated research from higher up in the government.
 
Examples include:
 
* [https://www.ils.res.in/ India Institute of Life Sciences]
* [https://www.spaceflorida.gov/facilities/ Space Florida, Space Life Sciences Lab]
* [https://www.fda.gov/about-fda/buildings-and-facilities/white-oak-campus-information U.S. Food and Drug Administration, White Oak Campus's life science laboratories]
 
'''Academic''' - These labs are typically graduate-level and act as hotbeds for researchers of all types.
 
Examples include:
 
* [https://nbclab.github.io/ Florida International University's Neuroinformatics and Brain Connectivity Laboratory]
* [https://cafnrfaculty.missouri.edu/nguyenlab/ University of Missouri's Molecular Genetics and Soybean Genomics Laboratory]
* [https://crl.berkeley.edu/ University of California - Berkeley's Cancer Research Laboratory]
 
====Functions====
 
''What are the most common functions?'' analytical, QA/QC, research/design, and teaching
 
''What materials, technologies, and/or aspects are being analyzed, researched, and quality controlled?'' biological specimens, cancers, DNA, genes, organs and systems, plant materials, proteins
 
''What sciences are being applied in these labs?'' anatomy, bioinformatics, biology, botany, cardiology, genetics, genomics, hematology, kinesiology, medical imaging, microbiology, molecular biology, nephrology, neurology, oncology, pathology, physiology, proteomics, pulmonology, toxicology, and many more
 
''What are some examples of test types and equipment?''
 
'''Common test types include''':
 
Absorption, Adhesion, Age determination, Aging, Amino acid analysis, Antimicrobial, Antigen, Biomolecular, C- and N-terminal, Carcinogenicity, Circular dichroism, Colorimetric, Compression, Cytology, De novo protein, Degradation, Detection, Developmental and reproductive toxicology, Dietary exposure, Disulfide bridge, DNA hybridization, Electrophoresis, Genotype, Identification, Isotope analysis, Macro- and microstructure, Microfluidics, Minimum bactericidal concentration, Minimum inhibitory concentration, Molecular weight, Pathogenicity, Peptide mapping, Post-translational modification, Proficiency, Protein analysis, Protein characterization, Terrestrial toxicology
 
'''Industry-related lab equipment may include''':
 
balance, bioreactor, biosafety cabinet, cell counter, centrifuge, DNA sequencer, dry bath, electrophoresis equipment, Erlenmeyer flask, flow cytometer, freezer, fume hood, gel documentation system, immunoassay system, incubator, laminar flow cabinet, microplate equipment, mixer/shaker, molecular imager, osmometer, PCR workstation, pipettor, protein sequencer, reagents, spectrometer, spectrophotometer, thermal cycler
 
''What else, if anything, is unique about the labs in the life sciences and biotechnology industry?''
 
Many laboratories in the life sciences and biotechnology sector are funded by significant external investments, grants, and initial public offerings (IPOs). For example, the National Institutes of Health awarded 7,328 grants worth a total of $3.3 billion to California life science labs in 2014.<ref name="CLSACalif16">{{cite web |url=http://califesciences.org/member-resources/industry-intelligence/2016report/ |archiveurl=https://web.archive.org/web/20170608124846/http://califesciences.org/member-resources/industry-intelligence/2016report/ |title=California Life Sciences Industry 2016 Report |author=California Life Sciences Association |date=2016 |archivedate=08 June 2017 |accessdate=29 June 2022}}</ref> Others turn to private charitable foundations or even biotech and pharmaceutical companies to help fund research efforts.<ref name="GrantFollow15">{{cite web |url=http://www.the-scientist.com/?articles.view/articleNo/42799/title/Follow-the-Funding/ |archiveurl=https://web.archive.org/web/20150522055951/http://www.the-scientist.com/?articles.view/articleNo/42799/title/Follow-the-Funding/ |title=Follow the Funding |author=Grant, B. |work=The Scientist |publisher=LabX Media Group |date=01 May 2015 |archivedate=22 May 2015 |accessdate=29 June 2022}}</ref>
 
====Informatics in the life sciences and biotechnology industry====
Referred to as [[bioinformatics]] or life science informatics, the application of [[information management]] and other software systems to the life sciences has become increasingly necessary as data-intensive automated instruments and methods drive today's lab research and experimentation. Tools that can analyze cells, molecules, and even atoms are helping researchers solve challenges of disease diagnosis and therapy production, giving patients better quality of life as a result. Informatics software helps integrate these various instruments and drive new discoveries through the mining, analysis, visualization, and simulation of the disparate data. Journals such as ''Bioinformatics'', ''Cancer Informatics'', and ''Frontiers in Neuroinformatics'', as well as conferences such as the Rocky Mountain Bioinformatics Conference<ref name="ISCBRocky">{{cite web |url=https://www.iscb.org/rocky2022 |title=Rocky Mountain Bioinformatics Conference 2022 |publisher=International Society for Computational Biology |date=2022 |accessdate=29 June 2022}}</ref> and the IEEE International Conference on Bioinformatics and Biomedicine<ref name="IEEE_BIBM">{{cite web |url=http://ieeebibm.org/ |title=IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |publisher=BIBM Steering Committee |accessdate=29 June 2022}}</ref>, help drive further innovation in how informatics can benefit the life sciences. Examples of life science laboratory advancements include the development of computer algorithms to answer biological questions, the improvement of [[next-generation sequencing]] (NGS) data management, and the development of tools to help us better understand the [[epidemiology]] of complex diseases.<ref name="UofH_LSI">{{cite web |url=https://www.helsinki.fi/en/degree-programmes/life-science-informatics-masters-programme/studying |title=Life Science Informatics - Studying |publisher=University of Helsinki |accessdate=29 June 2022}}</ref>
 
====LIMSwiki resources====
 
'''Life sciences'''
* [[Biodiversity informatics]]
* [[Cancer informatics]]
* [[Genome informatics]]
* [[Genomics]]
* [[Life sciences industry]]
* [[Life sciences life cycle]]
* [[Neuroinformatics]]
 
'''Bioinformatics'''
* [[Bioinformatics]]
* [[Bioimage informatics]]
* [[Biotechnology]]
* [[Molecular informatics]]
 
====Further reading====
 
* {{cite book |url=https://books.google.com/books?id=IqLAAgAAQBAJ&printsec=frontcover |title=Laboratory Protocols in Applied Life Sciences |author=Bisen, P.S. |publisher=CRC Press |year=2014 |pages=1826 |isbn=9781466553149}}
 
* {{cite book |url=https://books.google.com/books?id=fJ17zgEACAAJ |title=Basic Laboratory Methods for Biotechnology: Textbook and Laboratory Reference |author=Seidman, L.A.; Moore, C.J.; Mowery, J. |publisher=CRC Press |year=2022 |pages=1210 |isbn=9780429282799}}
 
 
<div align="center"><hr width="50%"></div>
 
===Logistics===
[[File:GRUBER Logistics Nachläufer.jpg|left|340px]]
{{clear}}
Laboratories related to the logistics industry serve several different functions. Academic research laboratories are key to the analysis and development of transportation systems and safety, traffic models, geographic information systems, freight logistics systems, supply chains, and transit systems. Secondarily, private logistics labs may provide third-party analytical services on cargo to verify authenticity and assist in custody transfers. These labs are found in the private and academic sectors, and occasionally in government, providing many different services, including (but not limited to):
 
* analysis of cargo for custody transfer<ref name="CertispecLab">{{cite web |url=https://certispec.myshopify.com/products/laboratory-services |title=Laboratory Services |publisher=Certispec Services, Inc |accessdate=29 June 2022}}</ref>
* analysis of cargo for dispute resolution<ref name="CertispecLab" />
* detection of radiation<ref name="SavannahAnal">{{cite web |url=https://srnl.doe.gov/facilities/analytical.htm |title=Our Facilities - Analytical Laboratories |work=Savannah River National Laboratory |publisher=SRNS Corporate Communications |accessdate=29 June 2022}}</ref>
* detection of explosives and evaluation of detection tools<ref name="HS-TSL">{{cite web |url=https://www.dhs.gov/science-and-technology/transportation-security-laboratory |title=Transportation Security Laboratory |publisher=U.S. Department of Homeland Security |accessdate=29 June 2022}}</ref>
* development and improvement of material flow management components<ref name="HNULogiLab">{{cite web |url=https://www.hnu.de/en/research/institutes-and-competence-centres/institute-for-logistics-risk-and-resource-management-ilr/logistics-laboratory |title=HNU Logistics Laboratory |publisher=Neu-Ulm University of Applied Sciences |accessdate=29 June 2022}}</ref><ref name="TTLOGLab">{{cite web |url=http://ttlog.civ.uth.gr/the-laboratory/ |title=The Laboratory |publisher=University of Thessaly, Department of Civil Engineering |accessdate=29 June 2022}}</ref>
* development and improvement of transportation and routing policies<ref name="HNULogiLab" /><ref name="TTLOGLab" />
* modeling and analysis of traffic and driving behavior<ref name="HNULogiLab" /><ref name="TTLOGLab" />
* analysis of logistics data<ref name="HNULogiLab" /><ref name="TTLOGLab" />
 
''How do logistics laboratories intersect the average person's life on a daily basis?'' Research-based logistics labs produce laboratorians who, for example, may be knowledgeable in the ways of traffic flow and civil engineering. Those individuals may go on to learn more and provide contributions to the transportation department of a city, state, or even federal entity, finding ways to improve your daily commute to work. Those same laboratorians may also have background and experience with electric and self-driving vehicles, contributing their expertise to the growing infrastructure required to run self-driving vehicles effectively, again improving your commute. Secondarily, logistics laboratories may reduce the changes of dangerous materials such as malicious radioactive materials and explosive devices making their way into the country via port, which is beneficial to dock workers and end users of products.
 
====Client types====
 
'''Private''' - Private logistics labs tend to provide analytical testing services of cargo, facilitating custody transfers and providing expertise in legal disputes.
 
Examples include:
 
* [https://group.bureauveritas.com/markets-services/marine-offshore Bureau Veritas]
* [https://www.camincargo.com/agassi/laboratoryservices.aspx Camin Cargo Control]
* [https://certispec.myshopify.com/products/laboratory-services Cetispec Services]
 
'''Government''' - Governments occasionally engage in research into and investigation of logistics issues of a region or country.
 
Examples include:
 
* [https://srnl.doe.gov/ Savannah River National Laboratory]
* [https://www.dhs.gov/science-and-technology/transportation-security-laboratory U.S. Department of Homeland Security Transportation Security Laboratory]
 
'''Academic''' - Universities provide laboratory resources to undergraduates and graduates keen to learn more about logistics issues and apply research to real-life problems.
 
Examples include:
 
* [https://www.labtrans.ufsc.br/en/ Federal University of Santa Catarina's LabTrans Transportation and Logistics Laboratory]
* [https://www.hnu.de/en/research/institutes-and-competence-centres/institute-for-logistics-risk-and-resource-management-ilr/logistics-laboratory Hochschule Neu-Ulm University of Applied Sciences' Logistics Laboratory]
* [https://ise.utk.edu/lab-center/logistics-transportation-and-supply-chain-engineering-lts-lab/ University of Tennessee - Knoxville's Logistics, Transportation, and Supply Chain Engineering Lab]
 
====Functions====
 
''What are the most common functions?'' analytical, research/design, QA/QC, and teaching
 
''What materials, technologies, and/or aspects are being analyzed, researched, and quality controlled?'' coal and coke, concentrates, fertilizers, food stuffs, land transportation, marine transportation, mass transit systems, petrochemicals, policy and governance, supply chains, traffic, user/driving behavior, vegetable oils, waste water
 
''What sciences are being applied in these labs?'' data science, economics, engineering, logistics, management, mathematics, physics, process optimization, risk management, social science, statistics
 
''What are some examples of test types and equipment?''
 
'''Common test types include''':
 
Absorption, Accelerated stress testing, Cargo inspection and sampling, Climatics, Contamination, Corrosion, Counterfeit detection, Damage tolerance, Dimensional, Drop, Durability, Edge crush, Electromagnetic compatibility, Emissions, Flammability, Flash point, Freight flow, Immersion, Impact, Incline impact, Integrity, Last-mile distribution, Leak, Metallurgical analysis, Permeability, Phytosanitary, Proficiency, Radioactivity, Reliability, Safety, Shear, Shock, Stress corrosion cracking, Tear, Tensile, Thermal, Traffic modeling and analysis, Ultraviolet, Vibration, Weathering
 
'''Industry-related lab equipment may include''':
 
autoclave, balance, biohazard container, biosafety cabinet, centrifuge, chromatographic, colorimeter, computer workstations, desiccator, dry bath, fume hood, [[geographic information system]], homogenizer, hotplate, incubator, magnetic stirrer, microcentrifuge tube, microplate reader, microscope, multi-well plate, orbital shaker, personal protective equipment, pH meter, pipettor, powered air purifying respirators, refractometer, simulation software, spectrophotometer, statistics software, syringes, test tube and rack, thermometer, water bath
 
''What else, if anything, is unique about the labs in the clinical research industry?'' A majority of logistics laboratories are dry labs, meaning they're not analyzing "wet" biological samples, applying reagents, etc. Instead they often heavily rely on software systems to conduct their research and educate new students. However, wet labs do exist in the logistics industry, usually for product and commodity testing of shipments — often petrochemicals — for custody transfer and dispute resolution.<ref name="CertispecLab" />
 
====Informatics in the logistics industry====
Though slightly dated at this point, an excerpt from the foreword to Luo's ''Service Science and Logistics Informatics'' seems relevant here<ref name="LuoService10">{{cite book |url=https://books.google.com/books?id=r2e7NmwoFGoC&lpg=PR1&pg=PR17#v=onepage&q&f=false |title=Service Science and Logistics Informatics: Innovative Perspectives |author=Luo, Z. |publisher=IGI Global |year=2010 |page=xvii}}</ref>:
 
<blockquote>As [the] world economy gets increasingly integrated, logistics and supply chain management, through the use of advanced information and service technologies, become critically important. This requirement entails tight alignment of business strategy and judicious use of advanced information technologies. It also necessitates infrastructures for streamlining front-end and back-end management and business processes, and resolution of emerging global integration and interoperability issues.</blockquote>
 
In supply chain management, prompt reaction to short-term, often abrupt changes in supply networks is critical to the quality of service and sustainability of a logistics business. Informatics is being applied in the lab to analyze these changes, integrating information from numerous sources, and provide valuable information towards favorably altering material flow and production. This can be done real-time, or theoretical work can be performed with simulation software.<ref name="RISCLogiInfo">{{cite web |url=https://www.risc-software.at/en/units/logistics-informatics/ |title=Logistics Informatics |publisher=RISC Software GmbH |accessdate=29 June 2022}}</ref> In cargo testing, information management software such as Cargotrader's Cargotester.com<ref name="CargoHome">{{cite web |url=http://www.cargotester.com/about.html |title=About Cargotester.com |publisher=Cargotrader, Inc. |accessdate=29 June 2022}}</ref> improve logistics labs' ability to improve compliance control and the analysis process itself. These improvements and others are further perpetuated by standards groups such as the IEEE SMC Technical Committee on Logistics Informatics and Industrial Security Systems<ref name="IEEESMC">{{cite web |url=https://www.ieeesmc.org/technical-activities/systems-science-and-engineering/logistics-informatics-and-industrial-security-systems |title=Logistics Informatics and Industrial Security Systems |work=IEEE SMC |publisher=IEEE |accessdate=29 June 2022}}</ref> and the tangentially related International Conference on Logistics, Informatics and Service Sciences.<ref name="ICLISS">{{cite web |url=http://icir.bjtu.edu.cn/liss2022/ |title=12th International Conference on Logistics, Informatics and Service Sciences (LISS2022) |publisher=Beijing Jiatong University |accessdate=29 June 2022}}</ref>
 
====LIMSwiki resources====
 
* None
 
====Further reading====
 
* {{cite book |url=https://books.google.com/books?id=e4p4CgAAQBAJ&printsec=frontcover |title=Transportation Engineering: Theory, Practice and Modeling |editor=Teodorovic, D.; Janic, M. |publisher=Butterworth-Heinemann |year=2016 |pages=900 |isbn=9780128038895}}
 
* {{cite book |url=https://books.google.com/books?id=7DyqCAAAQBAJ |title=The Impact of Virtual, Remote and Real Logistics Labs |series=Communications in Computer and Information Science |editor=Uckelmann, D.; Scholz-Reiter, B.; Rügge, I. et al. |publisher=Springer-Verlag Berlin Heidelberg |volume=282 |year=2012 |pages=172 |isbn=9783642288166}}
 
 
<div align="center"><hr width="50%"></div>
 
===Manufacturing and R&D===
[[File:Hyundai car assembly line.jpg|left|400px]]
{{clear}}
A manufacturing and R&D laboratory is what it sounds like: a lab associated with the manufacturing process as well as the research and development (R&D) activities that come before it. These labs can be found as part of the company structure, inside a manufacturing facility or separate from one. They may also appear as independent, third-party businesses that contract their services and expertise out to manufacturers and inventors who don't have their own laboratory resources or design knowledge.
 
It's important to note that when looking at many of the other industry categories in this guide, prior and after, you should notice serious crossover with manufacturing and R&D. In fact, laboratories associated with the automotive, aerospace, and marine; food and beverage; and pharmaceutical industries are almost entirely affiliated with manufacturing and R&D activities. As such, you may notice some redundancy in the test types listed below and those listed in the previously mentioned industry sections. This section is focused on manufacturing and R&D in a more generic, all-encompassing way.
 
These labs are found heavily in the private sector. Some labs may also appear in the government as part of state-funded effort, and others show up in the academic departments of some universities as an extension of their graduate-level research programs. Manufacturing and R&D laboratories provide many different services, including (but not limited to)<ref name="DuesterbergUSMan03">{{cite web |url=https://books.google.com/books?id=KrU4Bu8pw8AC&printsec=frontcover |title=U.S. Manufacturing: The Engine for Growth in a Global Economy |editor=Duesterberg, T.J.; Preeg, E.H. |publisher=Greenwood Publishing Group |year=2003 |pages=249 |isbn=9780275980412}}</ref>:
 
* development of advanced materials for manufacturing
* reverse engineering of products
* miniaturization of products
* analysis of shelf life
* quality control testing
* biocompatibility testing
* comparison testing
* formulation of recipes
* characterization of materials
* review and evaluation of designs
* innovation of manufacturing processes
 
''How do manufacturing and R&D laboratories intersect the average person's life on a daily basis?'' Unless you live a life of simplicity, growing and making your own food and materials, free of the industrial world, you most likely already know your life is touched by manufacturing and R&D labs on a daily basis. The mobile phone you use, the vehicle you drive or ride in, the pre-packaged food you eat, and the pharmaceuticals you take largely exist because scientists in a laboratory — wet or dry — designed, tested, and quality controlled it. Without these labs, the industrial and technological world you know today would fall apart rapidly.
 
====Client types====
 
'''Private''' - Private manufacturing and R&D labs are a staple of the industry, putting people's ideas to work. From a company's internal labs to third-party contract labs, much of the research, development, and quality control activities in manufacturing runs through here.
 
Examples include:
 
* [https://www.covestro.com/en/innovation Covestro]
* [https://www.lddavis.com/lab-capabilities/ L. D. Davis]
* [https://www.solvay.com/en/innovation Solvay]
 
'''Government''' - While not super common, government at times sets up and/or funds laboratories that are dedicated to advancing the field of manufacturing through new and improved fabrication and engineering techniques.
 
Examples include:
 
* [https://www.anff-nsw.org/anff-nswunsw/epitaxial-growth-laboratory/ Australian National Fabrication Facility, NSW Node, Epitaxial Growth Laboratory]
* [https://engineering.llnl.gov/collaboration/advanced-manufacturing-lab Lawrence Livermore National Laboratory, Advanced Manufacturing Division]
* [https://www.ornl.gov/facility/mdf Oak Ridge National Laboratory, Manufacturing Demonstration Facility]
 
'''Academic''' - These laboratories are typically part of a graduate research program, training future engineers and laboratorians while spawning new ideas. As seen with the examples below, the focus on learning and researching manufacturing processes may specialize into specific industries such as food and beverage or nanotechnology.
 
Examples include:
 
* [https://cals.cornell.edu/food-science/outreach-extension/services/food-processing-and-development-laboratory Cornell University's Food Processing and Development Laboratory]
* [https://lmp.mit.edu/ Massachusetts Institute of Technology's Laboratory for Manufacturing and Productivity]
* [https://ceas.uc.edu/research/centers-labs/micro-and-nano-manufacturing-laboratory.html University of Cincinnati's Micro and Nano Manufacturing Laboratory]
 
====Functions====
 
''What are the most common functions?'' analytical, research/design, QA/QC, and teaching
 
''What materials, technologies, and/or aspects are being analyzed, researched, and quality controlled?'' The list is seemingly infinite, but a few examples include adhesives, battery electrolytes, ceramics, fiber composites, food preservatives, lubricants, metals, nutritional supplements, plant extracts, polymers, rocket engines, semiconductors, and valves.
 
''What sciences are being applied in these labs?'' Again, the list is long, and the type of science used will depend on what is being developed. The most obvious scientific disciplines include all types and variations of biology, biomechanics, chemistry, engineering, food science, materials science, mathematics, molecular science, nanoscience, and physics.
 
''What are some examples of test types and equipment?''
 
'''Common test types include''':
 
Absorption, Accelerated stress testing, Accelerated weathering, Acceleration, Acoustical, Acute contact, Acute oral, Acute toxicity, Adhesion, Aging, Alcohol level, Allergy, Altitude, Antimicrobial, Artificial pollution, Ash, Bioavailability, Bioburden, Biocompatibility, Biodegradation, Biomechanical, Biosafety, Boiling - freezing - melting point, Calorimetry, Carcinogenicity, Case depth, Characterization, Chemical and materials compatibility, Chronic toxicity, Cleanliness, Climatics, Combustion, Compaction, Comparative Tracking Index, Comparison, Compliance/Conformance, Composition, Compression, Conductivity, Contact mechanics, Contamination, Corrosion, Cytotoxicity, Damage tolerance, Deformulation, Degradation, Design review and evaluation, Design verification testing, Detection, Dielectric withstand, Dimensional, Discoloration, Disintegration, Dissolution, Dissolved gas, Drop, Dynamics, Edge crush, Efficacy, Efficiency, Electromagnetic compatibility, Electromagnetic interference, Electrostatic discharge, Elongation, Emissions, Endotoxin, Endurance, Environmental fate, Environmental metabolism, Environmental stress-cracking resistance, Ergonomics, Etching, Expiration dating, Extractables and leachables, Failure, Fatigue, Fault simulation, Flammability, Flash point, Flavor, Fluid dynamics, Fluorescence, Formulation, Fragrance, Friction, Functional testing, Genotoxicity, Grain and particle size, Hazard analysis, Heat resistance, Human factors, Hydraulic, Identification, Immersion, Impact, Impurity, Incident analysis, Incline impact, Inclusion, Inflatability, Ingredient, Ingress, Inhalation, Integrity, Irritation, Iterative, Labeling, Leak, Lightning, Load, Lot release, Lubricity, Macroetch, Macro- and microstructure, Mechanical, Mechanical durability, Metallurgical analysis, Microfluidics, Minimum bactericidal concentration, Minimum inhibitory concentration, Mobility, Moisture, Molecular weight, Mutagenicity, Nanoparticulate, Neurotoxicity, Nutritional, Optical, Oxidation reduction potential, Oxidation stability, Passivation, Pathogen, Penetration, Performance, Permeability, pH, Pharmacokinetic, Photometric, Photostability, Phototoxicity, Plant metabolism, Plating and coating evaluations, Polarimetry, Power quality, Preservative challenge, Pressure, Process safety, Proficiency, Purity, Pyrogenicity, Qualification, Quality control, Radioactivity, Radiochemical, Reflectance, Refractive index, Reliability, Resistance - capacitance - inductance, Safety, Sanitation, Saponification value, Seismic, Sensory, Shear, Shelf life, Shock, Smoke point, Solar, Stability, Sterility, Stress corrosion cracking, Subchronic toxicity, Surface topography, Tear, Tensile, Thermal, Torque, Total viable count, Ultraviolet, Usability, Validation, Velocity and flow, Verification, Vibration, Visibility, Viscosity, Voltage, Weathering, Water activity
 
'''Industry-related lab equipment may include''':
 
Like materials tested and sciences applied, the lab equipment of a manufacturing and R&D lab will vary based upon what is being designed, tested, and quality controlled. Food R&D is going to depend on a somewhat different set of laboratory tools than say a lab developing a jet engine, pharmaceutical, or mobile phone.
 
''What else, if anything, is unique about the labs in the manufacturing and R&D industry?'' When it comes to labs that are prevalent but behind the scenes, manufacturing and R&D laboratories stand out. It's easy to take for granted the products we use in our lives; most of the time they taste good, function as expected, or cause the desired effect. That's not to say that shoddy laboratory processes, equipment, and raw materials don't produce bad tasting, non-functional, poorly advertised products because they do; the lab is only part of the equation. But in our industrialized, technological world, we shouldn't forget just how ubiquitous these sorts of labs are. In U.S. colleges and universities alone, 211.8 million net assignable square feet of research space were set aside in 2013 for laboratories, panels, and test rooms to conduct research in all types of sciences, clinical and R&D.<ref name="NSBScience16">{{cite web |url=https://www.nsf.gov/statistics/2016/nsb20161/#/report |title=Science & Engineering Indicators 2016 |author=National Science Board |publisher=National Science Foundation |volume=NSB-2016-1 |date=2016 |accessdate=29 June 2022}}</ref> Now think about how many private businesses are doing the same thing? Put together, the National Science Board estimated that U.S. R&D activities totaled $456.1 billion in 2013.<ref name="NSBScience16" />
 
====Informatics in the manufacturing and R&D industry====
Energy optimization and modelling, waste optimization, and methodology improvement are all areas that informatics-friendly manufacturing and R&D labs are looking to improve.<ref name="UofGreenMI">{{cite web |url=http://enterprise.gre.ac.uk/gmg/research-capabilities/digital-design-and-manufacturing-informatics |archiveurl=https://web.archive.org/web/20170816183437/http://enterprise.gre.ac.uk/gmg/research-capabilities/digital-design-and-manufacturing-informatics |title=Manufacturing informatics |work=Greenwich Manufacturing Group |publisher=University of Greenwich |date=2013 |archivedate=16 August 2017 |accessdate=29 June 2022}}</ref> Journals such as ''IEEE Transactions on Industrial Informatics''<ref name="IEEETransII">{{cite web |url=https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 |title=IEEE Transactions on Industrial Informatics |publisher=IEEE |accessdate=29 June 2022}}</ref> and conferences such as the International Conference on Industrial Informatics and Computer Systems<ref name="CIICS2017">{{cite web |url=https://waset.org/industrial-informatics-and-computer-systems-conference |title=International Conference on Industrial Informatics and Computer Systems |publisher=World Academy of Science, Engineering and Technology |accessdate=29 June 2022}}</ref> help expand those and other goals for R&D labs.
 
Additional ways informatics is impacting labs and the industry:
 
* Additive manufacturing (AM) — the process of building up a component layer by layer with a powdered base material (which is relatively new itself) — is being improved through the application of informatics technologies towards the research, development, analysis, and improvement of AM components and tooling, driving down costs, improving part quality, and making processes more efficient.<ref name="MiesOverview16">{{cite journal |title=Overview of Additive Manufacturing Informatics: “A Digital Thread” |journal=Integrating Materials and Manufacturing Innovation |author=Mies, D.; Marsden, W.; Warde, S. |volume=5 |pages=6 |year=2016 |doi=10.1186/s40192-016-0050-7}}</ref>
 
* "There is direct need of assessment tools to monitor and estimate environmental impact generated by different types of manufacturing processes," state Zhao ''et al.'' Though still in its infancy, some R&D labs are beginning to create associations between their product designs and how they impact the environment.<ref name="ZhaoAManuf13">{{cite book |chapter=A Manufacturing Informatics Framework for Manufacturing Sustainability Assessment |title=Overview of Additive Manufacturing Informatics: “A Digital Thread” |journal=Re-engineering Manufacturing for Sustainability |author=Zhao, Y.F.; Perry, N.; Andriankaja, H. |editor=Nee, A.; Song, B.; Ong, S.K. |pages=475–80 |year=2013 |doi=10.1007/978-981-4451-48-2_77 |isbn=9789814451482}}</ref>
 
====LIMSwiki resources====
 
* [[Biomedical engineering]]
* [[Clinical engineering]]
* [[Materials informatics]]
 
====Further reading====
 
* {{cite book |url=https://books.google.com/books?id=qIS4AMYTmgUC&printsec=frontcover |title=Good Clinical, Laboratory and Manufacturing Practices: Techniques for the QA Professional |chapter=Part 3: Good Manufacturing Practice |editor=Carson, P.A.; Deng, N.J. |publisher=Royal Society of Chemistry |pages=371–460 |year=2007 |isbn=9780854048342}}
 
 
<div align="center">-----Go to [[LII:The Laboratories of Our Lives: Labs, Labs Everywhere!/Labs by industry: Part 4|the next chapter]] of this guide-----</div>


==References==
==References==
{{Reflist|colwidth=30em}}
{{Reflist|colwidth=30em}}
 
<!---Place all category tags here-->
==Citation information for this chapter==
'''Chapter''': 5. Labs by industry: Part 3
 
'''Title''': ''The Laboratories of Our Lives: Labs, Labs Everywhere!''
 
'''Edition''': Second edition
 
'''Author for citation''': Shawn E. Douglas
 
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
 
'''Publication date''': July 2022
 
<!--Place all category tags here-->

Latest revision as of 13:29, 13 May 2024

This is sublevel12 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see my discussion page instead.

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FAIRResourcesGraphic AustralianResearchDataCommons 2018.png

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

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: May 2024

Introduction

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

This brief topical article will examine

The "FAIR-ification" of research objects and software

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

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

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

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

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

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

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

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

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

Implications of the FAIR concept to laboratory informatics software

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Restricted clinical data and its FAIRification for greater research innovation

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

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

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

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

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

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

Conclusion

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

References

  1. ↑ 1.0 1.1 1.2 Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan-Willem et al. (15 March 2016). "The FAIR Guiding Principles for scientific data management and stewardship" (in en). Scientific Data 3 (1): 160018. doi:10.1038/sdata.2016.18. ISSN 2052-4463. PMC PMC4792175. PMID 26978244. https://www.nature.com/articles/sdata201618. 
  2. ↑ "fair data principles". PubMed Search. National Institutes of Health, National Library of Medicine. https://pubmed.ncbi.nlm.nih.gov/?term=fair+data+principles. Retrieved 30 April 2024. 
  3. ↑ Hasselbring, Wilhelm; Carr, Leslie; Hettrick, Simon; Packer, Heather; Tiropanis, Thanassis (25 February 2020). "From FAIR research data toward FAIR and open research software" (in en). it - Information Technology 62 (1): 39–47. doi:10.1515/itit-2019-0040. ISSN 2196-7032. https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html. 
  4. ↑ 4.0 4.1 4.2 4.3 Gruenpeter, M. (23 November 2020). "FAIR + Software: Decoding the principles" (PDF). FAIRsFAIR “Fostering FAIR Data Practices In Europe”. https://www.fairsfair.eu/sites/default/files/FAIR%20%2B%20software.pdf. Retrieved 30 April 2024. 
  5. ↑ Barker, Michelle; Chue Hong, Neil P.; Katz, Daniel S.; Lamprecht, Anna-Lena; Martinez-Ortiz, Carlos; Psomopoulos, Fotis; Harrow, Jennifer; Castro, Leyla Jael et al. (14 October 2022). "Introducing the FAIR Principles for research software" (in en). Scientific Data 9 (1): 622. doi:10.1038/s41597-022-01710-x. ISSN 2052-4463. PMC PMC9562067. PMID 36241754. https://www.nature.com/articles/s41597-022-01710-x. 
  6. ↑ Patel, Bhavesh; Soundarajan, Sanjay; Ménager, Hervé; Hu, Zicheng (23 August 2023). "Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool" (in en). Scientific Data 10 (1): 557. doi:10.1038/s41597-023-02463-x. ISSN 2052-4463. PMC PMC10447492. PMID 37612312. https://www.nature.com/articles/s41597-023-02463-x. 
  7. ↑ Du, Xinsong; Dastmalchi, Farhad; Ye, Hao; Garrett, Timothy J.; Diller, Matthew A.; Liu, Mei; Hogan, William R.; Brochhausen, Mathias et al. (6 February 2023). "Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software" (in en). Metabolomics 19 (2): 11. doi:10.1007/s11306-023-01974-3. ISSN 1573-3890. https://link.springer.com/10.1007/s11306-023-01974-3. 
  8. ↑ 8.0 8.1 8.2 Gruenpeter, Morane; Katz, Daniel S.; Lamprecht, Anna-Lena; Honeyman, Tom; Garijo, Daniel; Struck, Alexander; Niehues, Anna; Martinez, Paula Andrea et al. (13 September 2021). "Defining Research Software: a controversial discussion". Zenodo. doi:10.5281/zenodo.5504016. https://zenodo.org/record/5504016. 
  9. ↑ "What is Research Software?". JuRSE, the Community of Practice for Research Software Engineering. Forschungszentrum Jülich. 13 February 2024. https://www.fz-juelich.de/en/rse/about-rse/what-is-research-software. Retrieved 30 April 2024. 
  10. ↑ 10.0 10.1 10.2 10.3 van Nieuwpoort, Rob; Katz, Daniel S. (14 March 2023) (in en). Defining the roles of research software. doi:10.54900/9akm9y5-5ject5y. https://upstream.force11.org/defining-the-roles-of-research-software. 
  11. ↑ "Open source software and code". F1000 Research Ltd. 2024. https://www.f1000.com/resources-for-researchers/open-research/open-source-software-code/. Retrieved 30 April 2024. 
  12. ↑ 12.0 12.1 Moynihan, G. (7 July 2020). "The Hitchhiker’s Guide to Research Software Engineering: From PhD to RSE". Invenia Blog. Invenia Technical Computing Corporation. https://invenia.github.io/blog/2020/07/07/software-engineering/. 
  13. ↑ 13.0 13.1 Woolston, Chris (31 May 2022). "Why science needs more research software engineers" (in en). Nature: d41586–022–01516-2. doi:10.1038/d41586-022-01516-2. ISSN 0028-0836. https://www.nature.com/articles/d41586-022-01516-2. 
  14. ↑ "RSE@KIT". Karlsruhe Institute of Technology. 20 February 2024. https://www.rse-community.kit.edu/index.php. Retrieved 01 May 2024. 
  15. ↑ "Purdue Center for Research Software Engineering". Purdue University. 2024. https://www.rcac.purdue.edu/rse. Retrieved 01 May 2024. 
  16. ↑ 16.0 16.1 Cohen, Jeremy; Katz, Daniel S.; Barker, Michelle; Chue Hong, Neil; Haines, Robert; Jay, Caroline (1 January 2021). "The Four Pillars of Research Software Engineering". IEEE Software 38 (1): 97–105. doi:10.1109/MS.2020.2973362. ISSN 0740-7459. https://ieeexplore.ieee.org/document/8994167/. 
  17. ↑ Hasselbring, Wilhelm; Carr, Leslie; Hettrick, Simon; Packer, Heather; Tiropanis, Thanassis (25 February 2020). "From FAIR research data toward FAIR and open research software" (in en). it - Information Technology 62 (1): 39–47. doi:10.1515/itit-2019-0040. ISSN 2196-7032. https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html. 
  18. ↑ Ghiringhelli, Luca M.; Baldauf, Carsten; Bereau, Tristan; Brockhauser, Sandor; Carbogno, Christian; Chamanara, Javad; Cozzini, Stefano; Curtarolo, Stefano et al. (14 September 2023). "Shared metadata for data-centric materials science" (in en). Scientific Data 10 (1): 626. doi:10.1038/s41597-023-02501-8. ISSN 2052-4463. PMC PMC10502089. PMID 37709811. https://www.nature.com/articles/s41597-023-02501-8. 
  19. ↑ 19.0 19.1 Fitschen, Timm; tom Wörden, Henrik; Schlemmer, Alexander; Spreckelsen, Florian; Hornung, Daniel (12 October 2022). "Agile Research Data Management with FDOs using LinkAhead". Research Ideas and Outcomes 8: e96075. doi:10.3897/rio.8.e96075. ISSN 2367-7163. https://riojournal.com/article/96075/. 
  20. ↑ Weigel, Tobias; Schwardmann, Ulrich; Klump, Jens; Bendoukha, Sofiane; Quick, Robert (1 January 2020). "Making Data and Workflows Findable for Machines" (in en). Data Intelligence 2 (1-2): 40–46. doi:10.1162/dint_a_00026. ISSN 2641-435X. https://direct.mit.edu/dint/article/2/1-2/40-46/9994. 
  21. ↑ 21.0 21.1 21.2 21.3 Aggour, Kareem S.; Kumar, Vijay S.; Gupta, Vipul K.; Gabaldon, Alfredo; Cuddihy, Paul; Mulwad, Varish (9 April 2024). "Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data" (in en). Integrating Materials and Manufacturing Innovation. doi:10.1007/s40192-024-00348-4. ISSN 2193-9764. https://link.springer.com/10.1007/s40192-024-00348-4. 
  22. ↑ 22.0 22.1 Grobe, Peter; Baum, Roman; Bhatty, Philipp; Köhler, Christian; Meid, Sandra; Quast, Björn; Vogt, Lars (26 June 2019). "From Data to Knowledge: A semantic knowledge graph application for curating specimen data" (in en). Biodiversity Information Science and Standards 3: e37412. doi:10.3897/biss.3.37412. ISSN 2535-0897. https://biss.pensoft.net/article/37412/. 
  23. ↑ 23.0 23.1 Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Gu, Wei; Welter, Danielle; Abbassi Daloii, Tooba; Portell-Silva, Laura (30 June 2022). "FAIR and Knowledge graphs". D2.1 FAIR Cookbook. doi:10.5281/ZENODO.6783564. https://zenodo.org/record/6783564. 
  24. ↑ 24.0 24.1 Tomlinson, E. (28 July 2023). "RDF Knowledge Graph Databases: A Better Choice for Life Science Lab Software" (PDF). Semaphore Solutions, Inc. https://21624527.fs1.hubspotusercontent-na1.net/hubfs/21624527/Resources/RDF%20Knowledge%20Graph%20Databases%20White%20Paper.pdf. Retrieved 01 May 2024. 
  25. ↑ 25.0 25.1 Deagen, Michael E.; McCusker, Jamie P.; Fateye, Tolulomo; Stouffer, Samuel; Brinson, L. Cate; McGuinness, Deborah L.; Schadler, Linda S. (27 May 2022). "FAIR and Interactive Data Graphics from a Scientific Knowledge Graph" (in en). Scientific Data 9 (1): 239. doi:10.1038/s41597-022-01352-z. ISSN 2052-4463. PMC PMC9142568. PMID 35624233. https://www.nature.com/articles/s41597-022-01352-z. 
  26. ↑ Brandizi, Marco; Singh, Ajit; Rawlings, Christopher; Hassani-Pak, Keywan (25 September 2018). "Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach" (in en). Journal of Integrative Bioinformatics 15 (3): 20180023. doi:10.1515/jib-2018-0023. ISSN 1613-4516. PMC PMC6340125. PMID 30085931. https://www.degruyter.com/document/doi/10.1515/jib-2018-0023/html. 
  27. ↑ de Visser, Casper; Johansson, Lennart F.; Kulkarni, Purva; Mei, Hailiang; Neerincx, Pieter; Joeri van der Velde, K.; Horvatovich, Péter; van Gool, Alain J. et al. (28 September 2023). Palagi, Patricia M.. ed. "Ten quick tips for building FAIR workflows" (in en). PLOS Computational Biology 19 (9): e1011369. doi:10.1371/journal.pcbi.1011369. ISSN 1553-7358. PMC PMC10538699. PMID 37768885. https://dx.plos.org/10.1371/journal.pcbi.1011369. 
  28. ↑ Schröder, Max; Staehlke, Susanne; Groth, Paul; Nebe, J. Barbara; Spors, Sascha; Krüger, Frank (1 December 2022). "Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation" (in en). Journal of Biomedical Semantics 13 (1): 4. doi:10.1186/s13326-021-00257-x. ISSN 2041-1480. PMC PMC8802522. PMID 35101121. https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-021-00257-x. 
  29. ↑ Hiniduma, Kaveen; Byna, Suren; Bez, Jean Luca (2024). "Data Readiness for AI: A 360-Degree Survey". arXiv. doi:10.48550/ARXIV.2404.05779. https://arxiv.org/abs/2404.05779. 
  30. ↑ Fletcher, Lydia (16 April 2024). FAIR Re-use: Implications for AI-Readiness. The University Of Texas At Austin, The University Of Texas At Austin. doi:10.26153/TSW/51475. https://repositories.lib.utexas.edu/handle/2152/124873. 
  31. ↑ 31.0 31.1 Olsen, C. (1 September 2023). "Embracing FAIR Data on the Path to AI-Readiness". Pharma's Almanac. https://www.pharmasalmanac.com/articles/embracing-fair-data-on-the-path-to-ai-readiness. Retrieved 03 May 2024. 
  32. ↑ 32.0 32.1 32.2 32.3 Huerta, E. A.; Blaiszik, Ben; Brinson, L. Catherine; Bouchard, Kristofer E.; Diaz, Daniel; Doglioni, Caterina; Duarte, Javier M.; Emani, Murali et al. (26 July 2023). "FAIR for AI: An interdisciplinary and international community building perspective" (in en). Scientific Data 10 (1): 487. doi:10.1038/s41597-023-02298-6. ISSN 2052-4463. PMC PMC10372139. PMID 37495591. https://www.nature.com/articles/s41597-023-02298-6. 
  33. ↑ 33.0 33.1 Maxwell, Lauren; Shreedhar, Priya; Dauga, Delphine; McQuilton, Peter; Terry, Robert F; Denisiuk, Alisa; Molnar-Gabor, Fruzsina; Saxena, Abha et al. (1 October 2023). "FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries" (in en). The Lancet Digital Health 5 (10): e712–e736. doi:10.1016/S2589-7500(23)00129-2. PMC PMC10552001. PMID 37775189. https://linkinghub.elsevier.com/retrieve/pii/S2589750023001292. 
  34. ↑ 34.0 34.1 34.2 34.3 Queralt-Rosinach, Núria; Kaliyaperumal, Rajaram; Bernabé, César H.; Long, Qinqin; Joosten, Simone A.; van der Wijk, Henk Jan; Flikkenschild, Erik L.A.; Burger, Kees et al. (1 December 2022). "Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic" (in en). Journal of Biomedical Semantics 13 (1): 12. doi:10.1186/s13326-022-00263-7. ISSN 2041-1480. PMC PMC9036506. PMID 35468846. https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-022-00263-7. 
  35. ↑ Martínez-García, Alicia; Alvarez-Romero, Celia; Román-Villarán, Esther; Bernabeu-Wittel, Máximo; Luis Parra-Calderón, Carlos (1 May 2023). "FAIR principles to improve the impact on health research management outcomes" (in en). Heliyon 9 (5): e15733. doi:10.1016/j.heliyon.2023.e15733. PMC PMC10189186. PMID 37205991. https://linkinghub.elsevier.com/retrieve/pii/S2405844023029407. 
  36. ↑ Snoeijer, B.; Pasapula, V.; Covucci, A. et al. (2019). "Paper SA04 - Processing big data from multiple sources" (PDF). Proceedings of PHUSE Connect EU 2019. PHUSE Limited. https://phuse.s3.eu-central-1.amazonaws.com/Archive/2019/Connect/EU/Amsterdam/PAP_SA04.pdf. Retrieved 03 May 2024. 
  37. ↑ Jang, Joy Bohyun; Pienta, Amy; Levenstein, Margaret; Saul, Joe (6 December 2023). "Restricted data management: the current practice and the future". Journal of Privacy and Confidentiality 13 (2). doi:10.29012/jpc.844. ISSN 2575-8527. PMC PMC10956935. PMID 38515607. https://journalprivacyconfidentiality.org/index.php/jpc/article/view/844. 
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