image/svg+xml
Data Life Cycle (DLC)
'Data Producer'
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)
• Collect data through observations, interviews, simulation
by algorithms etc.
• Means to collect data comprise different technologies,
instruments, (data) sheets, images.
• Before, during and after collection the 5 W’s (What,
where, when, who, how) should be taken into account.
• Capture and create compatible and structured Metadata.
• Prevent discrepancies before data collection (e.g. through
Data Management Plan, GFBio tools and workbenches
‘Diversity Workbench’ and ‘BExIS 2’).
• Detect errors through statistical/graphical analysis
(GFBio tools soon provided).
• Document data cleaning and data quality by flags,
metadata, coding.
• Version your data sets.
• Generate structured information (metadata) that answer
what data were collected and why, where, when and how.
• Enable visibility and replicability for other users who want
to reuse and cite your data.
so-called metadata, that answer what data were collected
• Create metadata early in the research process.
• Use a common standard recommended by one of the
GFBio data centers and archives (e.g. EML, ABCD).
• Transfer your data from a private to a shared research
domain (GFBio-Portal).
• Prefer long-term storage (like in GFBio) so that your data
are save and available for discovery.
• Follow submission guidelines about metadata standards,
reuse and citation.
• Choose the status of availability of your data: preserved
and curated; available, but with restriction or immediately
available for reuse.
• Preservation is more than a backup.
• Data centers and archives are in charge of data
preservation.
• Data preservation ensures the integrity of data.
• Data should be accessible, authentic and lasting.
• Good data management practices facilitate the data
preservation process.
• 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.