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Data Life Cycle (DLC)
'Data Reuser'
Plan
Assure
Collect
Analyze
Describe
Submit
Preserve
Discover
Publish
Integrate
• Think about data management, sharing and
reuse.
• Discover other author’s data within the
GFBio-Portal to validate your research, to
initiate an innovative study or to get new ideas.
• Are data fit for reuse/ suited for your research?
(Data exploration via GFBio-provided tools,
soon available)
• To discover data and metadata, they must be
visible and accessible.
• Discoverable data are published at a data
infrastructure (GFBio-Portal) and assigned
with a persistent identifier (PID).
• Discover available data from other researchers
by using the GFBio-Search-Function and
explore them by using our workbenches and
tools (‘BExIS 2’, ‘Diversity Workbench’ (DWB)).
• Discovery metadata might also help you to
discern the usability of data.
• Integrate data to create a new, larger data set
based on multiple data sets, or to verify your
results, or to start an integrative study, or to
test a new hypothesis for a follow-up study.
• Data should be compatible: common syntax
and terminology right from the start.
• Document the integration process through
a script or workflow (metadata).
• Cite the reused data properly.
• To analyse data, gather from a single or an
integrated data set.
• Document your data analysis through
workflows to ensure reproducibility.
• To explore your data use descriptive statistics,
plots and data mining. (GFBio-Tools can help
with that.)
• Find a model that fits your data the best.
• Use open source software if possible.
• Published data are visible, citable and uniquely
identifiable via a persistent identifier (e.g. DOI).
• Published data increase visibility and paper
citation rates.
• Data publishing is often required by journals
in order to support your findings.
• You can choose an embargo time, in which
only discoverable metadata are available.