Difference between revisions of "User:Shawndouglas/sandbox/sublevel12"

From LIMSWiki
Jump to navigationJump to search
 
(153 intermediate revisions by the same user not shown)
Line 8: Line 8:
==Sandbox begins below==
==Sandbox begins below==
<div class="nonumtoc">__TOC__</div>
<div class="nonumtoc">__TOC__</div>
[[File:|right|350px]]
[[File:FAIRResourcesGraphic AustralianResearchDataCommons 2018.png|right|520px]]
'''Title''': ''Is there a benefit to utilizing both a LIMS and an ELN in the lab?''
'''Title''': ''What are the potential implications of the FAIR data principles to laboratory informatics applications?''


'''Author for citation''': Shawn E. Douglas
'''Author for citation''': Shawn E. Douglas
Line 15: Line 15:
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]


'''Publication date''': March 2023
'''Publication date''': May 2024


==Introduction==
==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 ELN and what it does outside the scope of a LIMS==
==The "FAIR-ification" of research objects and software==
To answer the question, knowledge of what an ELN does and what it addresses outside of a LIMS is important. An ELN is a modern electronic equivalent of the traditional paper-based [[laboratory notebook]], which historically has served as a collection of scribblings—often with individual, regional, or temporal idiosyncratic styles of "subjectivity, unruliness, and privacy"<ref name="HolmesArchRework03">{{cite book |title=Reworking the Bench - Research Notebooks in the History of Science |chapter=Introduction |series=Archimedes - New Studies in the History and Philosophy of Science and Technology |editor=Holmes, F.L.; Renn, J.; Rheinberger, H.-J. |publisher=Kluwer Academic Publishers |volume=7 |pages=vii–xv |year=2003 |isbn=9780306481529 |doi=10.1007/0-306-48152-9}}</ref>—concerning the notes and protocols of one or more particular scientific research endeavors.<ref name="HolmesArchRework03" /><ref name="NussbeckTheLab14">{{cite journal |last=Nussbeck |first=Sara Y |last2=Weil |first2=Philipp |last3=Menzel |first3=Julia |last4=Marzec |first4=Bartlomiej |last5=Lorberg |first5=Kai |last6=Schwappach |first6=Blanche |year=2014 |title=The laboratory notebook in the 21 st century: The electronic laboratory notebook would enhance good scientific practice and increase research productivity |url=https://www.embopress.org/doi/10.15252/embr.201338358 |journal=EMBO reports |language=en |volume=15 |issue=6 |pages=631–634 |doi=10.15252/embr.201338358 |issn=1469-221X |pmc=PMC4197872 |pmid=24833749}}</ref> In recent times, these scribblings have become more recognizably organized and thorough as a necessary part of presenting all the details of experiments, observations, and analyses such that the results can be reproduced and verified by peers in the scientific community (often referred to as part of a broader "reproducibility crisis").<ref name="NussbeckTheLab14" /><ref name="DirnaglAPocket16">{{cite journal |last=Dirnagl |first=Ulrich |last2=Przesdzing |first2=Ingo |date=2016-01-04 |title=A pocket guide to electronic laboratory notebooks in the academic life sciences |url=https://f1000research.com/articles/5-2/v1 |journal=F1000Research |language=en |volume=5 |pages=2 |doi=10.12688/f1000research.7628.1 |issn=2046-1402 |pmc=PMC4722687 |pmid=26835004}}</ref><ref>{{Cite journal |last=Hunter |first=Philip |date=2017-09 |title=The reproducibility “crisis”: Reaction to replication crisis should not stifle innovation |url=https://www.embopress.org/doi/10.15252/embr.201744876 |journal=EMBO reports |language=en |volume=18 |issue=9 |pages=1493–1496 |doi=10.15252/embr.201744876 |issn=1469-221X |pmc=PMC5579390 |pmid=28794201}}</ref> As laboratory research has increasingly incorporated more digital sources of data and information from instruments and other sources, labs conducting laboratory notebook-assisted research today—in both academic and industrial environments—have had to necessarily look at old paper notebook formats as antiquated and incompatible with modern research methods and increasingly digitalized workflows.<ref name="NussbeckTheLab14" /><ref name="DirnaglAPocket16" />
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" />


As a modern substitute for the paper-based laboratory notebook, the ELN at its core intends to similarly provide a means to document experiments, observations, and analyses but in a more organized, consistent, readable, searchable, and shareable way. Because it is software, additional thought has gone into the development of an ELN to allow users to do their research more effectively while integrating with other digital instruments and software solutions to capture and manage data and information closer to real-time. As a result, today's ELNs take many shapes and forms, many of them being developed to address the needs of specific research activities, such as biology and DNA [[sequencing]]<ref name="BarillariOpenBIS16">{{Cite journal |last=Barillari |first=Caterina |last2=Ottoz |first2=Diana S. M. |last3=Fuentes-Serna |first3=Juan Mariano |last4=Ramakrishnan |first4=Chandrasekhar |last5=Rinn |first5=Bernd |last6=Rudolf |first6=Fabian |date=2016-02-15 |title=openBIS ELN-LIMS: an open-source database for academic laboratories |url=https://academic.oup.com/bioinformatics/article/32/4/638/1743839 |journal=Bioinformatics |language=en |volume=32 |issue=4 |pages=638–640 |doi=10.1093/bioinformatics/btv606 |issn=1367-4811 |pmc=PMC4743625 |pmid=26508761}}</ref><ref name=":3">{{Cite journal |last=Plass |first=Fabian |last2=Englisch |first2=Silvan |last3=Apeleo Zubiri |first3=Benjamin |last4=Pflug |first4=Lukas |last5=Spiecker |first5=Erdmann |last6=Stingl |first6=Michael |date=2023-11-22 |title=Using OpenBIS&nbsp;as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the CRC 1411 Design of Particulate Products |url=https://account.datascience.codata.org/index.php/up-j-dsj/article/view/1500 |journal=Data Science Journal |volume=22 |pages=44 |doi=10.5334/dsj-2023-044 |issn=1683-1470}}</ref> or chemical analysis.<ref name=":3" /><ref>{{Cite journal |last=Tremouilhac |first=Pierre |last2=Nguyen |first2=An |last3=Huang |first3=Yu-Chieh |last4=Kotov |first4=Serhii |last5=Lütjohann |first5=Dominic Sebastian |last6=Hübsch |first6=Florian |last7=Jung |first7=Nicole |last8=Bräse |first8=Stefan |date=2017-12 |title=Chemotion ELN: an Open Source electronic lab notebook for chemists in academia |url=https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0240-0 |journal=Journal of Cheminformatics |language=en |volume=9 |issue=1 |pages=54 |doi=10.1186/s13321-017-0240-0 |issn=1758-2946 |pmc=PMC5612905 |pmid=29086216}}</ref> However, the sheer number of research use cases and workflows across numerous disciplines and experiment types, as well as varying vendor approaches to ELN development, can lead to difficulty in finding the right ELN for a given lab<ref name=":0">{{Cite journal |last=Higgins |first=Stuart G. |last2=Nogiwa-Valdez |first2=Akemi A. |last3=Stevens |first3=Molly M. |date=2022-02 |title=Considerations for implementing electronic laboratory notebooks in an academic research environment |url=https://www.nature.com/articles/s41596-021-00645-8 |journal=Nature Protocols |language=en |volume=17 |issue=2 |pages=179–189 |doi=10.1038/s41596-021-00645-8 |issn=1754-2189}}</ref><ref name=":1">{{Cite web |last=Loveluck, J. |date=08 October 2020 |title=Finding the Right Electronic Lab Notebook with the Corey Lab |work=Harvard Research Data Management |url=https://datamanagement.hms.harvard.edu/news/finding-right-electronic-lab-notebook-corey-lab |publisher=Harvard Medical School |accessdate=05 March 2024}}</ref>, even leading some to develop their own custom ELNs<ref name="LiscouskiTheApp21">{{cite web |url=https://www.limswiki.org/index.php/LII:The_Application_of_Informatics_to_Scientific_Work:_Laboratory_Informatics_for_Newbies#Expanding_the_research_team |title=LII:The Application of Informatics to Scientific Work: Laboratory Informatics for Newbies |author=Liscouski, J. |editor=Douglas, S.E. |work=LIMSwiki |date=April 2021 |accessdate=05 March 2024}}</ref>, often as an extension of some other piece of software like a knowledge management system<ref>{{Cite journal |last=Khan |first=Arshad M. |last2=Hahn |first2=Joel D. |last3=Cheng |first3=Wei-Cheng |last4=Watts |first4=Alan G. |last5=Burns |first5=Gully A. P. C. |date=2006 |title=NeuroScholar\'s Electronic Laboratory Notebook and Its Application to Neuroendocrinology |url=http://link.springer.com/10.1385/NI:4:2:139 |journal=Neuroinformatics |language=en |volume=4 |issue=2 |pages=139–162 |doi=10.1385/NI:4:2:139 |issn=1539-2791 |pmc=PMC4476904 |pmid=16845166}}</ref>, course management system<ref>{{Cite journal |last=Cardenas, M. |year=2014 |title=An Implementation of Electronic Laboratory Notebooks (ELN) Using a Learning Management System Platform in an Undergraduate Experimental Engineering Course |url=https://peer.asee.org/an-implementation-of-electronic-laboratory-notebooks-eln-using-a-learning-management-system-platform-in-an-undergraduate-experimental-engineering-course.pdf |format=PDF |journal=Proceedings of the 121st ASEE Annual Conference & Exposition |pages=24.164.1–16 |at=9040}}</ref>, or note-taking application.<ref>{{Cite journal |last=Guerrero |first=Santiago |last2=López-Cortés |first2=Andrés |last3=García-Cárdenas |first3=Jennyfer M. |last4=Saa |first4=Pablo |last5=Indacochea |first5=Alberto |last6=Armendáriz-Castillo |first6=Isaac |last7=Zambrano |first7=Ana Karina |last8=Yumiceba |first8=Verónica |last9=Pérez-Villa |first9=Andy |last10=Guevara-Ramírez |first10=Patricia |last11=Moscoso-Zea |first11=Oswaldo |date=2019-05-09 |editor-last=Ouellette |editor-first=Francis |title=A quick guide for using Microsoft OneNote as an electronic laboratory notebook |url=https://dx.plos.org/10.1371/journal.pcbi.1006918 |journal=PLOS Computational Biology |language=en |volume=15 |issue=5 |pages=e1006918 |doi=10.1371/journal.pcbi.1006918 |issn=1553-7358 |pmc=PMC6508581 |pmid=31071077}}</ref>
Since 2016, other research stakeholders have taken to publishing their thoughts about how the FAIR principles apply to their fields of study and practice<ref name="NIHPubMedSearch">{{cite web |url=https://pubmed.ncbi.nlm.nih.gov/?term=fair+data+principles |title=fair data principles |work=PubMed Search |publisher=National Institutes of Health, National Library of Medicine |accessdate=30 April 2024}}</ref>, including in ways beyond what perhaps was originally imagined by Wilkinson ''et al.''. For example, multiple authors have examined whether or not the software used in scientific endeavors itself can be considered a research object worth being developed and managed in tandem with the FAIR data principles.<ref>{{Cite journal |last=Hasselbring |first=Wilhelm |last2=Carr |first2=Leslie |last3=Hettrick |first3=Simon |last4=Packer |first4=Heather |last5=Tiropanis |first5=Thanassis |date=2020-02-25 |title=From FAIR research data toward FAIR and open research software |url=https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html |journal=it - Information Technology |language=en |volume=62 |issue=1 |pages=39–47 |doi=10.1515/itit-2019-0040 |issn=2196-7032}}</ref><ref name="GruenpeterFAIRPlus20">{{Cite web |last=Gruenpeter, M. |date=23 November 2020 |title=FAIR + Software: Decoding the principles |url=https://www.fairsfair.eu/sites/default/files/FAIR%20%2B%20software.pdf |format=PDF |publisher=FAIRsFAIR “Fostering FAIR Data Practices In Europe” |accessdate=30 April 2024}}</ref><ref>{{Cite journal |last=Barker |first=Michelle |last2=Chue Hong |first2=Neil P. |last3=Katz |first3=Daniel S. |last4=Lamprecht |first4=Anna-Lena |last5=Martinez-Ortiz |first5=Carlos |last6=Psomopoulos |first6=Fotis |last7=Harrow |first7=Jennifer |last8=Castro |first8=Leyla Jael |last9=Gruenpeter |first9=Morane |last10=Martinez |first10=Paula Andrea |last11=Honeyman |first11=Tom |date=2022-10-14 |title=Introducing the FAIR Principles for research software |url=https://www.nature.com/articles/s41597-022-01710-x |journal=Scientific Data |language=en |volume=9 |issue=1 |pages=622 |doi=10.1038/s41597-022-01710-x |issn=2052-4463 |pmc=PMC9562067 |pmid=36241754}}</ref><ref>{{Cite journal |last=Patel |first=Bhavesh |last2=Soundarajan |first2=Sanjay |last3=Ménager |first3=Hervé |last4=Hu |first4=Zicheng |date=2023-08-23 |title=Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool |url=https://www.nature.com/articles/s41597-023-02463-x |journal=Scientific Data |language=en |volume=10 |issue=1 |pages=557 |doi=10.1038/s41597-023-02463-x |issn=2052-4463 |pmc=PMC10447492 |pmid=37612312}}</ref><ref>{{Cite journal |last=Du |first=Xinsong |last2=Dastmalchi |first2=Farhad |last3=Ye |first3=Hao |last4=Garrett |first4=Timothy J. |last5=Diller |first5=Matthew A. |last6=Liu |first6=Mei |last7=Hogan |first7=William R. |last8=Brochhausen |first8=Mathias |last9=Lemas |first9=Dominick J. |date=2023-02-06 |title=Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software |url=https://link.springer.com/10.1007/s11306-023-01974-3 |journal=Metabolomics |language=en |volume=19 |issue=2 |pages=11 |doi=10.1007/s11306-023-01974-3 |issn=1573-3890}}</ref> Researchers quickly recognized that any planning around updating processes and systems to make research objects more FAIR would have to be tailored to specific research contexts, recognize that digital research objects go beyond data and information, and recognize "the specific nature of software" and not consider it "just data."<ref name="GruenpeterFAIRPlus20" /> The end result has been applying the core concepts of FAIR but differently from data, with the added context of research software being more than just data, requiring more nuance and a different type of planning from applying FAIR to digital data and information.


Despite the diverse needs of a wide variety of academic and industrial research labs in regards to ELNs, there are some functional aspects that stand out as being somewhat unique to the software, even when compared to common [[laboratory informatics]] solutions like a LIMS or [[laboratory information system]] (LIS). This includes<ref name="NussbeckTheLab14" /><ref name="DirnaglAPocket16" /><ref name=":3" /><ref name=":0" /><ref name=":1" /><ref name="KanzaElect17">{{Cite journal |last=Kanza |first=Samantha |last2=Willoughby |first2=Cerys |last3=Gibbins |first3=Nicholas |last4=Whitby |first4=Richard |last5=Frey |first5=Jeremy Graham |last6=Erjavec |first6=Jana |last7=Zupančič |first7=Klemen |last8=Hren |first8=Matjaž |last9=Kovač |first9=Katarina |date=2017-12 |title=Electronic lab notebooks: can they replace paper? |url=https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0221-3 |journal=Journal of Cheminformatics |language=en |volume=9 |issue=1 |pages=31 |doi=10.1186/s13321-017-0221-3 |issn=1758-2946 |pmc=PMC5443717 |pmid=29086051}}</ref><ref name="KnippenbergBest18">{{Cite web |last=Knippenberg, R. |title=Best Practices for Electronic Laboratory Notebook Implementation in R&D Labs |work=Astrix Insights |url=https://astrixinc.com/best-practices-for-electronic-laboratory-notebook-implementation-in-rd-labs/ |publisher=Astrix, Inc |archiveurl=https://web.archive.org/web/20231208184311/https://astrixinc.com/best-practices-for-electronic-laboratory-notebook-implementation-in-rd-labs/ |archivedate=08 December 2023 |date=30 June 2018 |accessdate=05 March 2024}}</ref><ref name="CoveyElectronic19">{{cite web |url=https://www.rockefeller.edu/markus-library/uploads/www.rockefeller.edu/sites/207/2019/05/Electronic-Notebooks-CCTS.pdf |format=PDF |title=Electronic Lab Notebooks: From paper to screen, keeping track of your research |author=Covey, M.; Goto, R.; Ceglia, I. |publisher=Rita and Frits Markus Library, Rockefeller University |date=May 2019 |accessdate=05 March 2024}}</ref><ref name=":2">{{Cite journal |last=Argento |first=Nicolas |date=2020-03-04 |title=Institutional ELN/LIMS deployment: Highly customizable ELN/LIMS platform as a cornerstone of digital transformation for life sciences research institutes |url=https://www.embopress.org/doi/10.15252/embr.201949862 |journal=EMBO reports |language=en |volume=21 |issue=3 |pages=e49862 |doi=10.15252/embr.201949862 |issn=1469-221X |pmc=PMC7054672 |pmid=32129000}}</ref>:
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" />


*direct real-time recording of data and information in various (standard) formats like text, images, tables, chromatograms, and raw data files;
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" />:
*robust support for tagging, searching, and reusing data, information, files, etc.;
*support for standard vocabularies and [[metadata]] schemes, including semantic enrichment schemes<ref name="PASemantic24">{{cite web |url=https://www.pistoiaalliance.org/projects/current-projects/semantic-enrichment-of-eln-data/ |title=Semantic Enrichment of Electronic Lab Notebook Data |author=Prior, D. |publisher=Pistoia Alliance |date=21 January 2024 |accessdate=06 March 2024}}</ref>;
*lending of structure to data and information through the use of preformatted or customizable templates with drag-and-drop support;
*flexible creation of links between records, including reference managers and other notebooks;
*group, project, and experiment management;
*import and export functionality, particularly in standard, portable file formats;
*storage of fully searchable records in a secure database format, with automatic backup;
*inclusion of messaging and commenting functionality for better collaboration;
*inclusion of safety data, including flags for dangerous chemicals;
*data integrity and security tools like electronic signatures, time-stamped audit logs, controlled access levels, version control, automated import of instrument data, and archiving capabilities;
*generation of secure forms that accept laboratory data input real-time via a computing device and/or laboratory equipment (i.e., integration);
*accommodation of a scheduling option for routine procedures such as equipment qualification and study-related timelines;
*support for standard chemical, genetic, and other molecular libraries, visualization, and nomenclature formats, e.g., LaTeX; and
*inventory management of instruments, reagents, samples, etc. (usually associated with a LIMS or separate system, but useful as an integrated option within an ELN<ref name="DirnaglAPocket16" /><ref name=":3" /><ref name=":1" />).


Note that the more functionality bolted on to an ELN, the greater chance of overall cost and complexity of use increasing, in turn negatively impacting overall adoption of the ELN by laboratorians.<ref name="DirnaglAPocket16" /> The ELN also needs to be intuitive, easy-to-learn, and well-documented in order to better ensure full adoption.<ref name=":0" />
<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>


==Pairing a LIMS and ELN together==
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" />:
After browsing some of the ELN functionality listed above, comparisons to some types of LIMS functionality become easier. From instrument integration and data integrity mechanisms to equipment scheduling and inventory management, the LIMS and ELN can most certainly share functionality.<ref name="DirnaglAPocket16" /><ref name=":3" /><ref name=":0" /><ref name=":1" /><ref name=":2" /><ref name="DouglasLIMSpec22">{{cite web |url=https://www.limswiki.org/index.php/LII:LIMSpec_2022_R2 |title=LIMSpec 2022 R2 |author=Douglas, S.E. |work=LIMSwiki |date=December 2022 |accessdate=06 March 2024}}</ref> This can be viewed as highly desirable by some organizations<ref name=":1" />, while others may be frustrated by the introduction of a similar standalone system and an impediment to the original desire for the time-saving benefits of digitizing and integrating workflows and systems with a LIMS.<ref name=":0" /> After all, it's even more work to learn how to effectively use another system like an ELN after spending time and energy on learning to use a LIMS.<ref name="DirnaglAPocket16" /><ref name=":1" />


So why would a lab pair an ELN with a LIMS? Is there a benefit to utilizing both? First, as noted prior, both academic and industrial research laboratories are the typical users of an ELN, with adoption by academic research labs slightly lagging industrial research labs.<ref name="KanzaElect17" /> The ELN is a replacement for the laboratory notebook, typically used in research settings to document research workflows. (While there may be legitimate non-research use cases for an ELN, it is beyond the scope of this article to address them, and thus this article assumes research-based use.) Use cases of an ELN include<ref name="DePalmaAClose12">{{cite web |url=https://www.labmanager.com/a-close-look-at-lims-and-elns-16191 |title=A Close Look at LIMS and ELNs |author=DePalma, A. |work=Lab Manager |date=06 September 2012 |accessdate=06 March 2024}}</ref><ref name="WuBarrier22">{{cite web |url=https://www.tetrascience.com/blog/barrier-busting-bringing-eln-and-lims-scientific-data-together |title=Barrier Busting: Bringing ELN and LIMS Scientific Data Together |author=Wu, Y. |work=TetraScience Blog |date=27 June 2022 |accessdate=06 March 2024}}</ref><ref name="SLThePotent23">{{cite web |url=https://www.starlims.com/resources/the-potent-combination-of-eln-and-lims-the-fast-track-from-ideation-to-commercialization/ |title=The Potent Combination of ELN and LIMS: The Fast-Track from Ideation to Commercialization |publisher=STARLIMS Corporation |date=03 November 2023 |accessdate=06 March 2023}}</ref>:
*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.


#those working strictly in R&D, focusing on innovation and proof-of-concept delivery as easily and efficiently as possible in order to get to market faster;
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.
#those working beyond strict innovation and proof-of-concept, taking those ideas into more demanding production-based experimentation, where processes and procedures must strictly be followed; and
#advanced, multi-department research and production environments where integrated systems management is essential to ensuring a clean continuum of data and information across an entire product lifecycle, as well as more timely and efficient workflows.


In the first case, a LIMS is most likely not necessary, with the ELN providing the most benefit. It's only with perhaps the second case and often the third case that pairing an ELN with a LIMS begins to make some sense, particularly if there's a greater need for sample traceability and management/enforcement of standard operating procedures and/or test methods.<ref name=":2" /> And even then multiple considerations need to be made. Additionally, these considerations may vary based upon the existing technology environment at the lab, e.g., if the lab is moving in full from a completely paper-based workflow to an electronic workflow that intends to incorporate both systems, or if the lab has one system and wants to add the other. Is there a definitive need for both systems? If so, does it make sense to acquire them separately, or are purpose-built solutions that combine LIMS functionality with ELN functionality a viable solution? What are the cost, vendor lock-in, data and information export, maintenance, IT, integration, data cleansing, change management, and workflow considerations to be made? Do proposed solutions manage both structured and unstructured data well? If two separate solutions (rather than a single combined solution) are being considered, will they both not only integrate well with the labs' instruments and software, but also with each other?<ref name=":0" /><ref name="KnippenbergBest18" /><ref name="DePalmaAClose12" /><ref name="WuBarrier22" />
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.


Those considerations made, there indeed can be some benefit to using both a LIMS and an ELN in the lab. Many of these benefits are assumed upon two different programs working together without sharing much of the same functionality; however, a single solution that incorporates both LIMS and ELN functionality<ref name="BarillariOpenBIS16" /><ref name=":3" /> could also provide these benefits but differently. Examples of benefits to using both systems—whether stand-alone or integrated—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>:


*Adding one of the two to the other provides a necessary and informative opportunity to reflect upon, update, and consolidate existing workflows, as well as any new ones to potentially be introduced down the line. Will adding an ELN or LIMS to the total mix duplicate functionality, or will it be complementary? How will workflows change for the better, and what new challenges may emerge as a result? Look to this as an opportunity to ask these and other questions not only as part of the acquisition process but also as part of maintaining and updating any relevant [[quality management system]] (QMS) or [[continual improvement process]]es.<ref name=":0" />
<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>
*When interfaced well, using the same standard vocabularies and metadata schemes, the duo would allow for greater findability, accessibility, interoperability, and reusability (FAIR) of data and information across a research-driven academic or industrial enterprise.<ref name=":3" /><ref name=":2" /> This in turn can lend to more rapid innovation and discovery, as well as time-to-market.<ref name="SLThePotent23" />
 
*When combined into a single system, a LIMS+ELN can reduce software management, IT, and licensing costs; reduce the complexity of instrument and system integration; reduce the overall burden of learning how to effectively use multiple systems; improve overall throughput throughout the research and/or product lifecycle; and maximize regulatory compliance.<ref name="DePalmaAClose12" /><ref name="WuBarrier22" />
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.  
*In settings where sample or specimen tracking ties readily to the research side, as with bioanalytical labs assisting with toxicological and pharmacological research studies, data and information management is much more cohesive and efficient with a LIMS + ELN pairing. This in turn allows for quicker clinical and preclinical reporting, as well as more streamlined and maximized regulatory compliance and reporting. (The level of regulatory compliance needed with these labs can vary with the type of workflow, however.)<ref name="DePalmaAClose12" /><ref>{{Citation |last=Bennett |first=Patrick |last2=LeLacheur |first2=Richard M. |date=2017 |editor-last=Rocci |editor-first=Mario L. |editor2-last=Lowes |editor2-first=Stephen |title=Logistical and Operational Practice in the Regulated Bioanalysis Laboratory |url=http://link.springer.com/10.1007/978-3-319-54802-9_3 |work=Regulated Bioanalysis: Fundamentals and Practice |language=en |publisher=Springer International Publishing |place=Cham |volume=26 |pages=39–62 |doi=10.1007/978-3-319-54802-9_3 |isbn=978-3-319-54800-5 |accessdate=2024-03-06}}</ref>
 
*Of course, the instrument management, inventory control, data analysis, data visualization, and stability management functionality of a LIMS (the "laboratory control" portion of a lab implementing laboratory informatics solutions) can pair nicely with the experimental work and protocols maintained in the ELN (the "experiment and process control" portion). For example, the David Corey Lab of Harvard Medical School identified a strong need for integrated inventory management among it and its companion labs, which wasn't readily offered in many ELNs. The lab noted: "By integrating protocols and experiments with inventories of reagents and samples, researchers could work more efficiently, keeping track of samples, and knowing when to make orders. The inventory system also allows users to keep track of equipment, or even mice!"<ref name=":1" /> While they ultimately found an ELN that had this functionality, one can imagine a scenario where a lab has additional requirement found in a LIMS and lands on a LIMS + ELN combined solution that not only meets their needs but saves them money and training headaches, while improving overall research efficiencies.<ref name=":1" /><ref name="WuBarrier22" /><ref name="SLThePotent23" />
The concept of the research software engineer (RSE) began to take full form in 2012, and since then universities and institutions of many types have formally developed their own RSE groups and academic programs.<ref name="WoolstonWhySci22">{{Cite journal |last=Woolston |first=Chris |date=2022-05-31 |title=Why science needs more research software engineers |url=https://www.nature.com/articles/d41586-022-01516-2 |journal=Nature |language=en |pages=d41586–022–01516-2 |doi=10.1038/d41586-022-01516-2 |issn=0028-0836}}</ref><ref name="KITRSE@KIT24">{{cite web |url=https://www.rse-community.kit.edu/index.php |title=RSE@KIT |publisher=Karlsruhe Institute of Technology |date=20 February 2024 |accessdate=01 May 2024}}</ref><ref name="PUPurdueCenter">{{cite web |url=https://www.rcac.purdue.edu/rse |title=Purdue Center for Research Software Engineering |publisher=Purdue University |date=2024 |accessdate=01 May 2024}}</ref> RSEs range from pure software developers with little knowledge of a given research discipline, to scientific researchers just beginning to learn how to develop software for their research project(s). While in the past, broadly speaking, researchers often cobbled together research software with less a focus on quality and reproducibility and more on getting their research published, today's push for FAIR data and software by academic journals, institutions, and other researchers seeking to collaborate has placed a much greater focus on the concept of "better software, better research."<ref name="WoolstonWhySci22" /><ref name="CohenTheFour21">{{Cite journal |last=Cohen |first=Jeremy |last2=Katz |first2=Daniel S. |last3=Barker |first3=Michelle |last4=Chue Hong |first4=Neil |last5=Haines |first5=Robert |last6=Jay |first6=Caroline |date=2021-01 |title=The Four Pillars of Research Software Engineering |url=https://ieeexplore.ieee.org/document/8994167/ |journal=IEEE Software |volume=38 |issue=1 |pages=97–105 |doi=10.1109/MS.2020.2973362 |issn=0740-7459}}</ref> Elaborating on that concept, Cohen ''et al.'' add that "ultimately, good research software can make the difference between valid, sustainable, reproducible research outputs and short-lived, potentially unreliable or erroneous outputs."<ref name="CohenTheFour21" />
 
The concept of [[software quality management]] (SQM) has traditionally not been lost on professional, commercial software development businesses. Good SQM practices have been less prevalent in homegrown research software development; however, the expanded adoption of FAIR data and FAIR software approaches has shifted the focus on to the repeatability, reproducibility, and interoperability of research results and data produced by a more sustainable research software. The adoption of FAIR by academic and institutional research labs not only brings commercial SQM and other software development approaches into their workflow, but also gives commercial laboratory informatics software developers an opportunity to embrace many aspects of the FAIR approach to laboratory research practices, including lessons learned and development practices from the growing number of RSEs. This doesn't mean commercial developers are going to suddenly take an open-source approach to their code, and it doesn't mean academic and institutional research labs are going to give up the benefits of the open-source paradigm as applied to research software.<ref>{{Cite journal |last=Hasselbring |first=Wilhelm |last2=Carr |first2=Leslie |last3=Hettrick |first3=Simon |last4=Packer |first4=Heather |last5=Tiropanis |first5=Thanassis |date=2020-02-25 |title=From FAIR research data toward FAIR and open research software |url=https://www.degruyter.com/document/doi/10.1515/itit-2019-0040/html |journal=it - Information Technology |language=en |volume=62 |issue=1 |pages=39–47 |doi=10.1515/itit-2019-0040 |issn=2196-7032}}</ref> However, as Moynihan noted, both research software development paradigms stand to gain from the shift to more FAIR data and software.<ref name="MoynihanTheHitch20" /> Additionally, if commercial laboratory informatics vendors want to continue to competitively market relevant and sustainable research software to research labs, they frankly have little choice but to commit extra resources to learning about the application of FAIR principles to their offerings tailored to those labs.
 
===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.
 
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.<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>
 
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>
 
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.
 
===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.<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>
 
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" />:
 
*greater findability of FAIR research objects for further AI-driven scientific discovery;
*greater reproducibility of FAIR research objects and any AI models published with them;
*improved generalization of AI-driven medical research models when exposed to diverse and FAIR research objects;
*improved reporting of AI-driven research results using FAIRified research objects, lending further credibility to those results;
*more uniform comparison of AI models using well-defined hyperstructure and information training conditions from FAIRified research objects;
*more developed and interoperable "data e-infrastructure," which can further drive a more effective "AI services layer";
*reduced bias in AI-driven processes through the use of FAIR research objects and AI models; and
*improved surety of scientific correctness where reproducibility in AI-driven research can't be guaranteed.
 
In the end, developers of research software (whether discipline-specific research software or broader laboratory informatics solutions) would be advised to keep in mind the growing trends of FAIR research, FAIR software, and ML- and AI-driven research, especially in the [[life sciences]], but also a variety of other fields.<ref name="HuertaFAIRForAI23" />
 
===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.)
 
These efforts have usually revolved around pulling reusable clinical patient or research data from [[hospital information system]]s (HIS), [[electronic medical record]]s (EMRs), [[clinical trial management system]]s (CTMSs), and research databases (often relational in nature) that either contain de-identified data or can de-identify aspects of data and information before access and extraction. Sometimes that clinical data or research object may have already in part been FAIRified, but often it may not be. In all cases, the concepts of privacy, security, and anonymization come up as part of any desire to gain access to that clinical material. However, any FAIRified clinical data isn't necessarily readily open for access. As Snoeijer ''et al.'' note: "The authors of the FAIR principles, however, clearly indicate that 'accessible' does not mean open. It means that clarity and transparency is required around the conditions governing access and reuse."<ref name="SnoeijerProcess19">{{cite book |url=https://phuse.s3.eu-central-1.amazonaws.com/Archive/2019/Connect/EU/Amsterdam/PAP_SA04.pdf |format=PDF |chapter=Paper SA04 - Processing big data from multiple sources |title=Proceedings of PHUSE Connect EU 2019 |author=Snoeijer, B.; Pasapula, V.; Covucci, A. et al. |publisher=PHUSE Limited |year=2019 |accessdate=03 May 2024}}</ref>
 
This is being mentioned in the context of laboratory informatics applications for a couple of reasons. First, a well-designed commercial LIMS that supports clinical research laboratory workflows is already going to address privacy and security aspects, as part of the developer recognizing the need for those labs to adhere to regulations such as the [[Health Insurance Portability and Accountability Act]] (HIPAA) and comply with standards such as [[ISO 15189]]. However, such a system may not have been developed with FAIR data principles in mind, and any built-in metadata and ontology schemes may be insufficient for full FAIRification of laboratory-based clinical trial research objects. As Queralt-Rosinach ''et al.'' note, however, "interestingly, ontologies may also be used to describe data access restrictions to complement FAIR metadata with information that supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" /> Essentially, the authors are suggesting that while a HIS or LIS may have built-in access management tools, setting up ontologies and metadata mechanisms that link privacy aspects of a research object (e.g., "has consent form for," "is de-identified," etc.) to the object's metadata allows for even more flexible, FAIR-driven approaches to privacy and security. Research software developers creating such information management tools for the regulated clinical research space may want to apply FAIR concepts such as this to how access control and privacy restrictions are managed. This will inevitably mean any research objects exported with machine-readable privacy-concerning metadata will be more reusable in a way that still "supports data safety and patient privacy."<ref name="Queralt-RosinachApplying22" />
 
Second, a well-designed research software solution working with clinical data will provide not only support for open, community-supported data models and vocabularies for clinical data, but also standardized community-driven ontologies that are specifically developed for access control and privacy. Queralt-Rosinach ''et al.'' continue<ref name="Queralt-RosinachApplying22" />:
 
<blockquote>Also, very important for accessibility and data privacy is that the digital objects ''per se'' can accommodate the criteria and protocols necessary to comply with regulatory and governance frameworks. Ontologies can aid in opening and protecting patient data by exposing logical definitions of data use conditions. Indeed, there are ontologies to define access and reuse conditions for patient data such as the Informed Consent Ontology (ICO), the Global Alliance for Genomics and Health Data Use Ontology (DUO) standard, and the Open Digital Rights Language (ODRL) vocabulary recommended by W3C.</blockquote>
 
Also of note here is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its OHDSI standardized vocabularies. In all these cases, a developer-driven approach to research software that incorporates community-driven standards that support FAIR principles is welcome. However, as Maxwell ''et al.'' noted in their ''Lancet'' review article in late 2023, "few platforms or registries applied community-developed standards for participant-level data, further restricting the interoperability of ... data-sharing initiatives [like FAIR]."<ref name="MaxwellFAIREthic23" /> As the FAIR principles continue to gain ground in clinical research and diagnostics settings, software developers will need to be more attuned to translating old ways of development to ones that incorporate FAIR data and software principles. Demand for FAIR data will only continue to grow, and any efforts to improve interoperability and reusability while honoring (and enhancing) privacy and security aspects of restricted data will be appreciated by clinical researchers. However, just as FAIR is not an overall goal for researchers, software built with FAIR principles in mind is not the end point of research organizations managing restricted and privacy-protected research objects. Ultimately, those organizations will have make other considerations about restricted data in the scope of FAIR, including addressing data management plans, data use agreements, disclosure review practices, and training as it applies to their research software and generated research objects.<ref>{{Cite journal |last=Jang |first=Joy Bohyun |last2=Pienta |first2=Amy |last3=Levenstein |first3=Margaret |last4=Saul |first4=Joe |date=2023-12-06 |title=Restricted data management: the current practice and the future |url=https://journalprivacyconfidentiality.org/index.php/jpc/article/view/844 |journal=Journal of Privacy and Confidentiality |volume=13 |issue=2 |doi=10.29012/jpc.844 |issn=2575-8527 |pmc=PMC10956935 |pmid=38515607}}</ref>


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


==References==
==References==
{{Reflist|colwidth=30em}}
{{Reflist|colwidth=30em}}
<!---Place all category tags here-->
<!---Place all category tags here-->

Latest revision as of 13:29, 13 May 2024

Sandbox begins below

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.