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The general data workflow outlines how biomolecular and archaeological data are managed from their generation through various usage and up to structured archival. Figure 1#below visualises the core progression, showing data infrastructure, in this case based on SharePoint worksheets and ARHUT data management system and emphasising feedback loops in data management:
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Instrument outputs–such as mass-spectrometry (IRMS, GC-MS, LC LC-MS/MS) andsequencing files, ormicroscopyvisuals–are collected in vendor-specificrawformats (e.g., RAW, FASTQ), and preferably stored in instrument-related computer and copied into project (shared) folders, securing the back-up versions of initial measurement files. Thisrawdataisthenreferenced and linked incombined worksheets (e.g. SharePoint or Google Sheets)thatrecordinitialmetadata, samplingcontext, and lab-specificidentifiers. Theseworksheets are usedforearly-stagereview and validation.
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Once structured, datasets can be exported (typically as Comma-Separated Values, CSV files) and processed using computational tools tailored to specific research questions, analyses and data types. This includes data interpretation and evaluation, statistical modelling, pattern recognition, and visualisation. Analyses are typically performed in environments like R, Python, or specialised software, such as OxCal, IsoReader, mMass, or MaxQuant.
Analytical outputs must be reproducible and versioned, with all scripts and parameter settings documented and stored alongside the dataset, either in SharePoint or linked repositories (e.g., GitHub).
2.6 Data Validation and Feedback
Structured datasets are subjected to both planned and unplanned quality checks. Users can verify data completeness, coherence, and consistency with raw entries during a formal review process, but often various problems arenoticed while working with data. In some cases, those require contextual knowledge, and it is thus not possible to catch all of those during any formal review.Feedback is communicated via SharePoint comments or tracked changes or or ARHUT comments / tasks in case the dataset has already been entered to the ARHUT system system. Datasets may cycle through multiple revisions before finalization.This feedback mechanism is essential for maintaining data quality and for correcting inconsistencies before deposition.
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Finalizeddatasets are transferredtothethe ARHUT data platform, wherethey are archivedwith:
- Persistent identifiers. Each entity has its own ARHUT link link, that can be used to reference from publications but is essential in linking datasets. Additionally other identifiers can be added e.g.dataDOI.
- Full metadata including sampling context, lab identifiers, and data structure
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Thesedatasetsbecome part of thelong-term record and are linkedtobothinternalsystems (e.g., SharePoint, Archemy, Department of Archaeology) and externalrepositories (e.g Zenodo, Dryad) and databases (e.gBIAD).
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- SharePoint for live collaboration and version-controlled documentation
- ARHUT for for curated, long-term data with controlled access and open publishing options
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Finalised and curated datasets archived in in ARHUT are made available through their dissemination. ARHUT’s web interface (https://arh.ut.ee/) allows for structured querying and access to project-specific datasets, enriched with contextual metadata and persistent identifiers. PaleoMIX O.A.D. builds on the the ARHUT infrastructure,offering public-facing access to selected datasets from PaleoMIX and related projects. This system enables transparent sharing of research outputs, supports interdisciplinary collaboration, and fosters broader reuse by both academic and public audiences.
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