Catalysis research comes with many roles, routines, and “oh no, where did that file go?” moments. Our NFDI4Cat Research Personas turn this diversity into something practical: a set of relatable profiles that help you quickly see how you work, what you need, and which tools or services will actually make your day easier.
Whether you spend your time at the bench, build workflows and integrations, train models, curate datasets, supervise projects, or translate results into industrial practice – you might recognize yourself in one cat… or several.
That’s the point: use the Personas to find your cat(s), then use that match to navigate toward the right infrastructure, guidance, and services for you.
LabCat
Experimental Scientist · Daily Bench Operations
The laboratory researcher forms the operational backbone of catalytic science. Day after day, this person conducts synthesis runs, reactor experiments, and characterization measurements – generating the primary data upon which all downstream analysis depends. Their working environment is instrument-rich but documentation-poor: analytical devices produce proprietary output files, experimental parameters are recorded in scattered notebooks, and sample provenance is often held in memory rather than in a structured system.
From an infrastructure perspective, this researcher needs a low-friction point of entry into digital data management. Electronic laboratory notebook (ELN) integration that connects directly to instruments – automating the capture of measurement files, timestamps, and device metadata – would eliminate the most time-consuming manual steps. Standardized metadata schemas for catalysis (covering catalyst composition, pretreatment conditions, reaction parameters, and performance metrics) must be pre-populated from device outputs wherever possible, reducing transcription errors. A simple, well-designed interface that requires minimal training ensures adoption even under the time pressure of active experimental campaigns. Reliable local data storage with automatic synchronisation to institutional repositories ensures that no measurement is lost between the bench and long-term preservation.
Workflow Architect · Automation & Tool Integration
The digital research engineer sits at the intersection of domain science and computational tooling. Fluent in scripting languages and data pipeline design, this researcher transforms raw experimental outputs into analysis-ready datasets and orchestrates reproducible processing workflows. Their primary frustration is not with data volume but with data heterogeneity: instrument vendors supply proprietary formats, analysis tools expect incompatible inputs, and the absence of standardised interfaces forces constant ad hoc conversion work.
NFDI4Cat infrastructure must meet this researcher with well-documented, stable APIs that expose repository content and metadata in machine-readable form. Support for community data formats – such as HDF5, JSON-LD, and domain-specific standards emerging from the catalysis community – reduces conversion overhead and enables direct tool integration. Versioned data access, provenance tracking, and workflow execution environments (supporting tools such as Snakemake or Nextflow) are essential for maintaining reproducibility across long-running research programmes. A software registry listing community-developed tools, alongside shared workflow templates, would allow hard-won automation solutions to be reused rather than reinvented.
DevCat
DataCat
Computational Analyst · Machine Learning & Modelling
The data scientist operates primarily on existing datasets, seeking to extract patterns, build predictive models, and contribute to the emerging field of data-driven catalyst design. Their work depends critically on the findability and quality of prior experimental results: a dataset without reliable metadata on catalyst preparation or testing conditions is of limited modelling value, regardless of how many data points it contains.
The infrastructure requirements of this persona centre on discovery and access. A powerful, semantically aware search interface – capable of filtering by catalyst system, reaction type, characterisation method, and performance descriptor – is a prerequisite. Metadata must conform to community-agreed ontologies (such as those developed within NFDI4Cat) to enable meaningful cross-dataset harmonisation. Bulk export capabilities and direct API access allow large aggregated datasets to be retrieved programmatically for integration into machine learning pipelines. Data quality indicators and provenance records help this researcher assess the reliability of a dataset before investing modelling effort. Benchmark datasets published with clear licences accelerate model development and community comparison studies.
Graduate Student · Research Data Management in Training
The early-career researcher – whether a master's student, doctoral candidate, or postdoctoral fellow in the first stages of independent work – is simultaneously learning catalytic science and the practices of research data management. This dual learning burden means that complexity without context is a significant barrier: data schemas that are not explained, repositories that offer no guided entry path, and analysis tools without worked examples all erode confidence and slow the development of good data practice.
Infrastructure for this persona must prioritise clarity and scaffolding. Curated example datasets drawn from published catalysis studies, accompanied by structured tutorials that walk through deposition, annotation, and retrieval workflows, provide the concrete starting points that independent self-teaching rarely delivers. Clear documentation written for domain scientists rather than data engineers lowers the entry threshold. Visualisation tools that allow immediate, interpretable engagement with downloaded data – without requiring advanced programming skills – help connect RDM practice to scientific insight. Pathways that grow in complexity as competence develops ensure that the infrastructure remains relevant from first thesis chapter to dissertation defence.
LearnCat
StewardCat
Data Quality Officer · FAIR Compliance & Curation
The research data steward occupies a critical quality-assurance role within the data lifecycle. Responsible for reviewing, curating, and publishing datasets on behalf of research groups or institutional repositories, this professional must work efficiently across large volumes of heterogeneous submissions – identifying incomplete metadata, enforcing community standards, and guiding depositing researchers toward compliant records without becoming a bottleneck.
The infrastructure needs of this persona are oriented toward validation and workflow efficiency. Automated metadata validation against defined schemas and ontology terms – flagging missing mandatory fields, inconsistent units, or out-of-vocabulary terms before manual review – substantially reduces curation effort. Configurable quality checklists aligned to FAIR principles, combined with audit trails recording every curation action, support both quality assurance and accountability. Streamlined DOI minting and data publication workflows, integrated with community repositories and data journals, allow completed datasets to move from curation to public availability without requiring expertise in each downstream platform. Administrative dashboards providing an overview of submission queues, review status, and publication metrics enable this steward to manage workload across multiple concurrent projects.
Research Group Leader · Project Oversight & Compliance
The principal investigator bears overall responsibility for the scientific output, data integrity, and regulatory compliance of one or more research projects. Operating across multiple teams and funding frameworks simultaneously, this researcher rarely interacts with raw data directly but must be assured that the data management practices of their group meet funder requirements, institutional policies, and the expectations of collaborative partners.
Infrastructure support for the principal investigator is primarily organisational and oversight-oriented. Role-based access control – allowing delegation of data management tasks to group members while preserving the PI's supervisory visibility – is a foundational requirement. Dashboards that aggregate data management status across projects (completeness of metadata, publication readiness, compliance with data management plans) provide the structured transparency needed for reporting to funding agencies such as DFG or the European Commission. Standardised reporting templates aligned to common funder requirements reduce administrative burden at project close-out. Notifications alerting the PI to data records approaching retention deadlines or missing mandatory documentation allow proactive rather than reactive compliance management.
LeadCat
ProcessCat
Industry–Academia Interface · Applied Data Integration
The industrial process engineer engages with academic research data from the perspective of technological application. Operating at the boundary between research outcomes and industrial deployment, this professional seeks to identify catalyst systems, process conditions, and performance data that are directly transferable to scale-up and operational contexts. Their primary challenge is not data quantity but data accessibility and interoperability: research datasets are rarely structured for direct ingestion into industrial data management systems, and the conditions under which published performance data were obtained are often insufficiently documented for reliable extrapolation.
NFDI4Cat infrastructure can serve this user through emphasis on standardised, machine-readable data exports and clear documentation of experimental scope and limitations. Interoperability with industrial data standards – including compatibility with formats common in process engineering and manufacturing environments – enables direct data transfer without bespoke conversion efforts. Well-structured metadata describing reactor type, scale, feed composition, and operating window allows this engineer to rapidly assess the relevance and transferability of a published dataset. Controlled-access mechanisms that enable industry partners to contribute proprietary data to shared resources under defined confidentiality agreements – while still benefiting from community metadata standards and search infrastructure – create a pathway for sustainable public–private data exchange.
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