User:Shawndouglas/sandbox/sublevel12 - Revision history

Shawndouglas at 13:29, 13 May 2024

2024-05-13T13:29:21Z

← Older revision		Revision as of 13:29, 13 May 2024
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	==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		This brief topical article will examine

Shawndouglas at 22:10, 9 May 2024

2024-05-09T22:10:15Z

← Older revision		Revision as of 22:10, 9 May 2024
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	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>		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 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>		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.		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.

Shawndouglas at 21:43, 9 May 2024

2024-05-09T21:43:40Z

← Older revision		Revision as of 21:43, 9 May 2024
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	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.		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.		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>		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>

Shawndouglas at 21:30, 9 May 2024

2024-05-09T21:30:08Z

← Older revision		Revision as of 21:30, 9 May 2024
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	===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===		===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 ~~databases~~.		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.		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.

Shawndouglas: /* Implications of the FAIR concept to laboratory informatics software */

2024-05-08T00:43:54Z

Implications of the FAIR concept to laboratory informatics software

← Older revision		Revision as of 00:43, 8 May 2024
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	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 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. 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 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===		===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===
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	<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>		<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>		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==

Shawndouglas: /* The "FAIR-ification" of research objects and software */

2024-05-08T00:09:34Z

The "FAIR-ification" of research objects and software

← Older revision		Revision as of 00:09, 8 May 2024
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	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" />		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" />

	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:		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" />:

	<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>		<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>

Shawndouglas: /* 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 */

2024-05-07T23:21:28Z

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

← Older revision		Revision as of 23:21, 7 May 2024
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	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>		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 ~~journal~~ \|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 ~~- FAIR and Knowledge graphs~~ \|url=https://zenodo.org/record/6783564 \|~~journal~~=~~Zenodo~~ \|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 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>		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 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.		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.

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	==The "FAIR-ification" of research objects and software==		==The "FAIR-ification" of research objects and software==
	First discussed during a 2014 FORCE-11 workshop dedicated to "overcoming data discovery and reuse obstacles," the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship\|FAIR ~~Guiding 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" />		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" />

	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.		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.

Shawndouglas at 19:22, 7 May 2024

2024-05-07T19:22:44Z

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	<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>		<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]."~~<nowiki>~~<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.~~</nowiki>~~<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>		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==

Shawndouglas at 23:39, 3 May 2024

2024-05-03T23:39:41Z

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	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>		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:		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 findability of FAIR research objects for further AI-driven scientific discovery;

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	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>		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 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>		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.		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.

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	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>		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 ~~journal~~ \|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 ~~- FAIR and Knowledge graphs~~ \|url=https://zenodo.org/record/6783564 \|~~journal~~=~~Zenodo~~ \|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 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>		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 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.		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.