Figure 1
Schema of the three-phase workflow implementation. Overview of the three phases and the involved tools of the implementation of a FAIR data workflow for ion-exchange chromatography.
Table 1
Ontology development phases of the Methontology method (Fernández-López, Gomez-Perez and Juristo, 1997).
| PHASE | DESCRIPTION |
|---|---|
| Specification | Purpose, level of formality and scope of the ontology |
| Knowledge acquisition | Collection of information from experts, books, handbooks, etc. according to the prior specified purpose and scope |
| Conceptualization | Structured model of the collected domain knowledge that describes the purpose of the ontology |
| Integration | Reuse of already defined ontology terms to lessen the number of new definitions |
| Implementation | Technical realization of the ontology based on the concept model |
| Evaluation | Technical validation of the ontology (e.g., consistency checks) |
| Documentation | Formal description of the ontology in natural language (e.g., logic definitions or journal publications) additionally to the ontology code |
Table 2
Ontology-metadata schema mapping excerpt based on important metadata from the aquaculture example data set (full mapping in supplementary file number 1).
| METADATA SCHEMA FIELD | ONTOLOGY IRI | CLASS LABEL | EXAMPLE VALUE |
|---|---|---|---|
| Sequence name | http://purl.obolibrary.org/obo/MS_4000089 | Injection sequence label | 2022-02-22_Wasser_Born |
| Date of measurement begin | http://purl.obolibrary.org/obo/AGRO_00010151 | Experiment date | 23.02.2022 |
| Runtime [min] | https://plasma-mds.org/ontology/IC_Ontology/IC_C0077 | Runtime | 40 |
| Injection volume [µL] | http://purl.allotrope.org/ontologies/result#AFR_0001267 | Autosampler Injection Volume Setting | 5 |
| Pump mode | https://plasma-mds.org/ontology/IC_Ontology/IC_C0009 | Pump Mode | Isocratic |
| Eluent mode | https://plasma-mds.org/ontology/IC_Ontology/IC_C0006 | Eluent Mode | Isocratic |
| Hydroxide suppression [mM] | https://plasma-mds.org/ontology/IC_Ontology/IC_C0061 | Hydroxide Suppression Current | 23 |
Figure 2
Graph example for representation in ArangoDB. Graph representation of the sample dataset and suitable properties within ArangoDB using the ontologies Plasma-O and IC-O.
Table 3
The FAIR guiding principles according Wilkinson et al. (Wilkinson et al., 2016).
| ID | PRINCIPLE |
|---|---|
| F | To be Findable: |
| F1 | (meta)data are assigned a globally unique and persistent identifier |
| F2 | data are described with rich metadata (defined by R1 below) |
| F3 | metadata clearly and explicitly include the identifier of the data it describes |
| F4 | (meta)data are registered or indexed in a searchable resource |
| A | To be Accessible: |
| A1 | (meta)data are retrievable by their identifier using a standardized communications protocol |
| A1.1 | the protocol is open, free, and universally implementable |
| A1.2 | the protocol allows for an authentication and authorization procedure, where necessary |
| A2 | metadata are accessible, even when the data are no longer available |
| I | To be Interoperable: |
| I1 | (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation |
| I2 | (meta)data use vocabularies that follow FAIR principle |
| I3 | (meta)data include qualified references to other (meta)data |
| R | To be Reusable: |
| R1 | meta(data) are richly described with a plurality of accurate and relevant attributes |
| R1.1 | (meta)data are released with a clear and accessible data usage license |
| R1.2 | (meta)data are associated with detailed provenance |
| R1.3 | (meta)data meet domain-relevant community standards |
Table 4
Overview of the FAIR compliance for the current FAIR data workflow for IC.
| ID | COMPLIANCE | |
|---|---|---|
| F | Satisfied | Not (fully) satisfied |
| F1 | X | |
| F2 | X | |
| F3 | X | |
| F4 | X | |
| A | ||
| A1 | X | |
| A1.1 | X | |
| A1.2 | X | |
| A2 | X | |
| I | ||
| I1 | X | |
| I2 | X | |
| I3 | X | |
| R | ||
| R1 | X | |
| R1.1 | X | |
| R1.2 | X | |
| R1.3 | X | |
Figure 3
Comparison of the results of the ARDC FAIR self-assessment. Results of the ARDC FAIR self-assessment for the current workflow implementation (A) and the scores of the full implementation (B) in comparison.
Figure 4
Degree of FAIR implementation according to the ARDC assessment. Radar graph displaying the degree of FAIR implementation in the designed workflow. The blue line displays the maximum achievable score in the ARDC FAIR self-assessment tool for the future full implementation, With the green area showing the current achievements according to the ARDC assessment. The table summarizes the normalized achievement for each FAIR section, whereas 100% define the max. score from the full workflow implementation.
Figure 5
Result of DSW assessment for current workflow implementation. Summary report of the Data Stewardship Wizard software using the Common DSW Knowledge Model. The report displays the FAIR compliance score of the implemented IC workflow at the current state (A) and the contribution of the selected areas of the questionnaire to the individual scores (B).
Figure 6
Result of DSW assessment for planned full workflow implementation. Summary report of the Data Stewardship Wizard software using the Common DSW Knowledge Model. The report displays the FAIR compliance score of the implemented IC workflow (A) and the contribution of the selected areas of the questionnaire to the individual scores (B) in case that the workflow is fully implemented.
Figure 7
Degree of FAIR implementation according to the DSW assessment. Radar graph displaying the degree of FAIR implementation in the designed workflow. The blue line represents the maximum achievable score in the DSW Knowledge model for the planned full implementation. The green area indicates the scores currently achieved in this assessment. The table summarizes the normalized achievement for each FAIR section, whereas the 100% define the max. score from the full workflow implementation.