Towards FAIR Data Workflows for Multidisciplinary Science:

Ongoing Endeavors and Future Perspectives in Plasma Technology

Robert Wagner

, Dagmar Waltemath

, Kristina Yordanova

and Markus M. Becker

Leibniz Institute for Plasma Science and Technology (INP), Felix-Hausdorff-Str. 2, Greifswald, Germany

Medical Informatics Laboratory, University Medicine Greifswald, Felix-Hausdorff-Str. 8, Greifswald, Germany

Institute for Data Science, University of Greifswald, Felix-Hausdorff-Str. 18, Greifswald, Germany

Keywords: Data Management, FAIR Data Principles, Graph Database, Plasma Science.

Abstract: This paper focuses on the ongoing process of establishing a FAIR (Findable, Accessible, Interoperable and

Reusable) data workflow for multidisciplinary research and development in applied plasma science. The

presented workflow aims to support researchers in handling their project data while also fulfilling the

requirements of modern digital research data management. The centerpiece of the workflow is a graph

database (utilizing Neo4J) that connects structured data and metadata from multiple sources across the

involved disciplines. The resulting workflow intents to enhance the FAIR compliance of the data, thereby

supporting data integration and automated processing as well as providing new possibilities for user friendly

data exploration and reuse.

1 INTRODUCTION

In times of advancing digitization and global

connectivity, the FAIR (Findable, Accessible,

Interoperable, and Reusable) data principles

(Wilkinson et al., 2016) have become increasingly

crucial for effective data management. Enhancing the

overall FAIR compliance of data can aid in

addressing emerging scientific inquiries. The

growing complexity of these research questions

necessitates a multidisciplinary research approach

(Hadorn et al., 2008). However, multidisciplinary

science poses its own challenges, such as the

variability in data structure, formats, and quality, as

well as the lack of consistent and structured metadata

for data description. One example of these challenges

is the varying structure of generated data and the

frequent lack of measurement-relevant metadata due

to fluctuations of the researchers involved in the

individual projects. The usage of these workflows to

structure the collected metadata and reusing them in

the graph database shall help to present easy to access

https://orcid.org/0000-0002-2762-293X

https://orcid.org/0000-0002-5886-5563

https://orcid.org/0000-0002-6428-1062

https://orcid.org/0000-0001-9324-3236

examples for researchers in the coming projects to

lessen these challenges.

Additionally, there is a need for more consistent

implementation of research data management (RDM)

practices (Birkbeck et al., 2022). Improving the

overall FAIR compliance of data can help mitigate

these challenges and lay the foundation for future data

reuse.

In this work the problem of applying the FAIR

data principles to multidisciplinary laboratory

experiments in applied plasma science and plasma

technology is addressed. The spectrum of disciplines

involved in research and development (R&D) in this

field includes engineering sciences (such as

mechanical and electrical engineering), life sciences

(for example environmental sciences, microbiology

and food sciences), medicine and physics. Engineers

and physicists are needed to design and construct the

plasma sources (Schmidt et al., 2019). On the other

hand, researchers from the life sciences and

biomedical research use the designed plasma sources,

e.g. for decontamination of food (Wagner, Weihe, et

Wagner, R., Waltemath, D., Yordanova, K. and Becker, M.

Towards FAIR Data Workﬂows for Multidisciplinary Science: Ongoing Endeavors and Future Perspectives in Plasma Technology.

DOI: 10.5220/0012808000003756

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pages 471-477

ISBN: 978-989-758-707-8; ISSN: 2184-285X

471

al., 2023) or wound treatment (Emmert et al., 2020).

Their domain knowledge needs to be integrated with

the work of engineers and physicists, forming a

complex scientific network with heterogenous and

domain-specific data sets.

The field-specific knowledge of scientists in the

life sciences includes, for example, the diverse targets

of physical plasma in the field of microbiology and

the resulting physico-chemical reactions. The plasma

source-related knowledge of engineers and the

specialist knowledge of physicists in the field of

plasma physics support the optimal application of the

required plasma. Both sides are necessary to include,

since their domain knowledge are polar opposites that

complement each other.

The most important benefit of the proposed

workflows for all researchers involved is easier

access to the information, which are important to

answer the multidisciplinary scientific questions in

the field of plasma science by utilizing the FAIR

principles. The presented paper reports on recent

endeavors and technical solutions that contribute to

the challenge of handling heterogenous, complex

research data in plasma science. The work contributes

to the implementation of the FAIR data principles in

workflows for R&D, that also take the individual

needs of each discipline involved into account. This

includes a comprehensive strategy for data

management, providing detailed insights into the

potential for interlinking metadata about research

studies. This metadata can encompass information

about the devices used, their properties, samples,

preparation and treatment procedures, and more. The

proposed approach enables the connection between

metadata contained in an electronic lab notebook

(ELN) and the corresponding raw data, typically

stored in a repository, through the utilization of a

graph database such as Neo4J (Webber, 2012).

Furthermore, the context of the interlinked data can

be further enriched by incorporating additional

information and by leveraging the categorization of

entities from the ELN. This integrated approach aims

to enhance the overall management and

interconnectivity of research-related data, facilitating

a more comprehensive and contextual understanding

of the research process and its outcomes.

2 WORKFLOW DESCRIPTION

The developed workflow consists of collecting

metadata and linking it to additional information from

a variety of sources such as local data management

platforms or ELN. Given the varying progress in data

management across different disciplines, the

metadata collection was separated into two distinct

workflows. This division takes the individual levels

of development in structured RDM of each discipline

into account, while simultaneously enhancing the

overall FAIR compliance of the collected metadata.

The metadata linking section of the general workflow

embeds the collected metadata of each discipline into

a graph database.

Nevertheless, the general concept of this work is

no novelty, as other institutes are dealing with similar

issues due to the relevance of the topic. As an

example, (de Oliveira, 2022) and (Crystal-Ornelas,

2022), have already shown similar concepts. In (de

Oliveira, 2022), the metadata collected is also stored

in RDF format and in (Crystal-Ornelas, 2022) the

interaction of data from several disciplines was

addressed.

2.1 Research Without

Discipline-Specific Metadata

Collection Standards

The first step in research without any discipline-

specific metadata collection standards is the

unstructured or generic structured (by generic

metadata schema like DataCite (Group, 2021))

collection of experimental results and experimental

metadata. The consisting elements of collected

experiments are used to design the first draft of a

template, which will be used for the following

collection of similar experiments. For this purpose of

collecting metadata and designing a template the ELN

“eLabFTW” (Carpi et al., 2017) is used. The raw and

processed data from the experiments, that are part of

a publication (or a dataset publication itself) can be

published in the interdisciplinary plasma technology

data platform “INPTDAT” (Becker et al., 2019). The

datasets in INPTDAT or from a location in the central

data storage can be linked to the experiments in the

ELN. The different experiments and used resources

(such as devices and consumables) can be interlinked

and categorized to enable better findability of the

provided information. Furthermore, the laboratory

management can be enhanced by proper organization

of resources in the ELN, including the possibility to

setup the booking of resources in eLabFTW.

The metadata of the collected experiments can be

extracted from the unstructured (before template

design) and (semi-)structured (after usage of

templates) ELN entries. The extracted metadata are

stored as machine-readable JavaScript object notion

(JSON) files. Automatic data processing of the

machine-readable metadata files and the linked raw

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

472

data is part of the workflow (Figure 1). The processed

data in form of tables or graphs can later be linked to

the corresponding ELN entries or the related datasets

published in INPTDAT.

One example of the application of this workflow

in plasma science is the extraction of industrially

relevant ingredients from microalgae (Sommer et al.,

2021). Due to the new developments of systems for

these tasks, the experiments are still subject to

constant changes, so that no schemes have yet been

established. Therefore, this workflow shall assist the

researchers during the metadata collection and shall

also enable the possibility to process and evaluate

their scientific data while utilizing the machine-

readable metadata, which can be extracted from their

experiments. However, it must be noted that the

extracted metadata is strongly influenced by the still

changing structure of the experiments and the lack of

structured metadata schemata and is therefore a

temporary solution.

Figure 1: Workflow schema for R&D without discipline-

specific metadata collection standards.

2.2 Research Using Discipline-Specific

Metadata Collection Standards

The second case (Figure 2) covers research in

disciplines, which can utilize existing discipline-

specific metadata collection standards or RDM

standards. These standards/schemas are used to

structure the collected metadata. The structured

collection of metadata based on metadata schemas is

realized by the RDM tool “Adamant” (Chaerony Siffa

et al., 2022). Adamant can also directly push the

collected metadata into the ELN.

One example for such schemas is based on the

community standard REMBI (Sarkans et al., 2021).

REMBI (REcommended Metadata for Biological

Images) defines the structure for community accepted

descriptions of imaging metadata. The images from

related experiments can be stored along with their

metadata according to REMBI in databases like

“OMERO” (Allan et al., 2012). OMERO is a database

for images that also allows the annotation of images

and datasets with their corresponding metadata via

scripts. The REMBI-structured metadata in OMERO

are further supplemented by the addition of

discipline-specific elements (e.g. via Plasma-MDS

(Franke et al., 2020)) and the OME schema (Goldberg

et al., 2005).

Ontologies can also be integrated into the

workflow to increase the FAIR compliance of the

collected metadata. Semantic annotations contribute

to the machine-readable description of the metadata,

metadata schema and collected data. It is preferable

to reuse existing domain ontologies. However, the

extension or the design of new ontologies is also part

of the later workflow, if no fitting ontology or

ontology terms exist or necessary entities are not well

enough described. The software “Protégé” (Musen &

Protege, 2015) is used to build and maintain the

ontologies in this project. One example of the

application of this workflow in plasma science is the

analysis of plasma-treated liquids in ion

chromatography. Ion chromatography does not yet

have a common metadata schema, but as part of

related research, a metadata schema based on ASTM

1151 ("ASTM E1151:1993 Standard Practice for Ion

Chromatography Terms and Relationships," 1993)

has been designed. This metadata schema has since

been used in this field to collect structured metadata.

Figure 2: Workflow for research, that utilizes discipline-

specific metadata collection standards.

Towards FAIR Data Workﬂows for Multidisciplinary Science: Ongoing Endeavors and Future Perspectives in Plasma Technology

473

2.3 Graph Database for

Multidisciplinary Data

The structured metadata and experimental data

resulting from steps (2.1) or (2.2) need to be

contextualized and interlinked in a flexible and

extensible manner. One approach is the design of an

overarching graph database, see for example (Mazein

et al., 2024). In our design, the entities (so-called

nodes) represent the experiments, researchers,

devices and projects involved in the multidisciplinary

research. The context that describes the relationships

between each entity are represented as directed

arrows, i.e. the edges of the graph. However, a simple

graph consisting of edges and nodes is not sufficient

to cover the complexity of multidisciplinary scientific

questions and to provide a good readability for the

end users. To meet both needs, a property graph (used

in most graph databases) is used. A property graph as

shown in Figure 3 allows to add information to nodes

and edges to describe the context.

Figure 3: Property graph example of the two nodes

“Researcher X” and “Experiment Y” linked by a directed

edge and their assigned properties. Graph A and B show the

difference in labelling an edge considering the origin of the

edge.

In the current approach, these properties are

extracted via python scripts from the structured

metadata provided by (2.1) and (2.2). The edge

properties can include semantic descriptions from

either ontologies or open vocabularies, like

schema.org for persons (Schema.org, 2024). The

properties of the nodes also provide the opportunity

to cater the provenance of the specific nodes. For

example, the raw data location in a research data

repository (e.g. INPTDAT), the specific stack of

images in an image database (e.g. OMERO), the

original experiment in an ELN (e.g. eLabFTW), the

description of the used plasma source from a plasma

device catalogue (e.g. the plasma source catalogue

that is also part of INPTDAT) or the link to the formal

description of the involved laboratory devices in a

device database can be automatically attached to

nodes via scripts.

Figure 4 depicts a small example of a graph in

Neo4J, that contains nodes for experiments from

different researchers using the same device. The

graph also contains a node with the configurations of

the graph and a node with the namespaces used for

the semantic description. Each class of nodes can

easily be distinguished by their color and label. The

properties of each node can be accessed by clicking

on the specific node in Neo4J as shown in Figure 4.

Figure 4: Example of a property graph and the node

property utilization in Neo4J. The red box shows the

incorporation of the ELN into the graph database.

The process of building a graph from these

different sources is automatized by the formulation of

a triple-based Resource Description Framework

(RDF) file, as illustrated by Figure 5.

Figure 5: Generation of the graph based on a RDF file,

containing the extracted information from different sources

for graph design via python scripts.

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Here, the RDF file is translated into the final graph

by a python script in combination with the Neo4J

plugin “neosemantics”, which is also known as

“n10s” (Barrasa & Cowley). The usage of basic

constraints such as node and relationship property

uniqueness shall prevent the accidental duplication of

already ingested data. For example, if two different

experiments done by “Researcher X” are ingested,

only three nodes are created. During the ingestion of

the second experiment, only the experiment node is

created, since the defined node uniqueness prevents

the creation of the already existing “Researcher X”.

The second experiment is then linked to the existing

node of “Researcher X”. Note that the creation of the

RDF as an intermediate step instead of the direct

graph translation of the extracted information via

Cypher (Francis et al., 2018) enables the easy storage

of RDF files, sharing with the community or use by

other graph database management systems (Das et al.,

2020).

3 CONCLUSIONS AND

OUTLOOK

In this paper an approach of applying the FAIR data

principles to a metadata collection and interlinking

workflow for multidisciplinary experiments in

applied plasma science and plasma technology is

described. Two workflows for the collection of

metadata, one for research with discipline-specific

metadata collection standards and another one for

research without such standards, were presented. The

metadata interlinking is achieved by the generation of

a graph database. The foundation for the metadata

collection is set and is also demonstrated in (Wagner,

Chaerony Siffa, et al., 2023) and (Ahmadi et al.,

2023).

The FAIR principles are implemented as follows.

The findability of the relevant metadata is ensured by

the graph database used. The graph database links all

metadata collected by the workflows with the

scientists and devices involved in order to place them

in a scientific context. The clear and user-friendly

structure of a graph also makes it possible to access

the data. The interoperability of the metadata linked

in the graph is achieved by integrating the

information from various relevant media such as the

ELN, INPTDAT and the device database and can be

accessed by the user by clicking on the respective

properties. The connection and clarity achieved in

this way should also facilitate the reuse of the data.

To evaluate the implementation of the FAIR

principles, a structured FAIR assessment for the

proposed workflows is planned and the results of this

assessment will be used to optimize the workflows.

Also, the feedback of all involved researchers has to

be taken into account.

The next steps intend to expand the linking of the

collected metadata. The usage of the generated graph

database by the intended end-users is conceptualized

by the integration of graph exploration and

visualization tools (Jong, 2021). Another aspect that

is planned in the future is the involvement of the end-

users as a fast survey of needed improvements. One

possible approach for the user integration is to enable

the access to the graph database in Adamant via React

hooks as described in (Cowley, 2020). The

combination of both tools is intended to unify the

structured metadata collection and metadata

representation on the one hand and reduce the amount

of software that users need to familiarize themselves

with on the other.

Another but more specific challenge for plasma

science is the absence of a plasma science ontology

and therefore the design and implementation of such

an ontology is crucial to solidify the collected

metadata by the addition of a proper semantic

description. A correct semantic description can avoid

possible misunderstandings between scientists in

different fields of plasma science. An example for this

is the term "matrix", which can describe a carrier

material of analytical samples in the field of

chemistry, the inner fluid of cell organelles in biology

or a mathematical order of numbers. Thus, the usage

of ontologies can explain the context and then clearly

explain the analysis of the matrix for the scientists

involved.

ACKNOWLEDGEMENTS

The work is funded by the Deutsche

Forschungsgemeinschaft (DFG, German Research

Foundation) under the National Research Data

Infrastructure – [NFDI46/1] - 501864659.

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