---
esco_uri: http://data.europa.eu/esco/skill/8f6ed69b-29d0-4c01-81ba-f05920a185f3
esco_label: "digital curation"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# digital curation — Data Scientist

For a Data Scientist, 'digital curation' isn’t about archiving old websites; it's about ensuring the *reliability and reproducibility* of your entire analytical pipeline. Daily tasks involve meticulously documenting data provenance – where did the data come from? What transformations were applied (cleaning, feature engineering)? Which versions of libraries/code were used? Think beyond just storing raw files; you’re building a traceable history. This means using version control (Git is essential), employing metadata standards to describe datasets and models, and potentially leveraging data catalogs like Amundsen or Marquez to make this information searchable for yourself *and* collaborators.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
