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+## Bechhofer et al. (2010)
+
+Research Objects: Towards Exchange and Reuse of Digital Knowledge
+
+### Objectives
+
+Research Object = rich aggregations of resources that provide the “units of knowledge"
+= data + methods + people
+
+- “borrow strength" from other research
+- framework that facilitates the reuse and exchange of digital knowledge
+
+digital exchange of data → straightforward
+digital exchange of knowledge → non-trivial
+
+goal = create a class of artefacts that can encapsulate our digital knowledge and provide
+a mechanism for sharing and discovering assets of reuseable research and scientific knowledge.
+
+Reusable 
+Repurposeable = reuse constituent parts (for another purpose)
+Repeatable = other can repeat the same study (data + services)
+Reproducible = use same material and methods and see if the same results can be obtained
+Replayable = metadata recording the provenance of data and results
+Traceability = 
+
+## Wilkinson et al. (2016)
+
+### Objective
+
+reuse of data alone or with newly acquired data
+> "Collect once, use many times"
+
+machines to automatically find and use the data
+
+Not only data but also other research objects
+- algorithms
+- tools, and 
+- analytical workflows 
+that led to that data.
+
+ALL research objects!
+
+both for human and machine
+
+### Origin
+
+"Jointly Designing a Data Fairport" workshop in 2014
+
+
+### Examples
+
+* Dataverse (https://dataverse.org): Open source research data repository software; unbreakable link between articles in your journal and associated data
+* FigShare (http://figshare.com): stores any type of file and visualizes hundreds of file formats; allow for sharing and publish research objects
+* Dryad (https://datadryad.or): data in any file format from any field of research to satisfy publisher and funder requirements for preservation and availability
+* Mendeley Data (https://data.mendeley.com): free and secure cloud-based communal repository where you can store your data
+* Zenodo (http://zenodo.org): house closed and restricted content, so that artefacts can be captured and stored safely 
+* DataHub (http://datahub.io): easily publish and share datasets from GitHub
+* DANS (http://www.dans.knaw.nl): Data Archiving and Network Service; not widely used in oceanography
+* EUDAT (https://www.eudat.eu): address the full lifecycle of research data
+* FAIRDOM (https://fair-dom.org):  data, documents operating procedures and models
+
+__Issue__ few restrictions on the dataset → less integration
+
+
+
+## DOI
+
+DOI = 
+- most widely known and used PIDs. 
+- designed for research objects
+
+Structure: resolver service / assigning body / resource
+
+Managed by the Registration Agencies
+
+Alternative metrics
+
+## White et al. (2013)
+
+Nine simple ways to make it easier to (re)use your data
+
+### Objectives
+
+making your data
+- understandable
+- easy to work with
+- available to the wider community of scientists. 
+
+1. Share → required by funding agencies, journals and by law
+2. Provide metadata → standards
+3. Provide raw data → more flexibility
+4. Use standard data formats → files, tables, cells...
+5. Use good null values
+6. Make it easy to combine your data with other datasets
+7. Perform basic quality control
+8. Use an established repository → http://databib.org and http://re3data.org
+9. Use an established and open license
+
+## Sandve (2013)
+
+Ten Simple Rules for Reproducible Computational Research
+
+reproducible research 
+replication: clear protocol
+culture of reproducibility
+
+good habits of reproducibility may actually turn out to be a time-saver in the longer run.
+
+exponential number of possible combinations of 
+- software versions, 
+- parameter values, 
+- pre-processing steps
+
+__Benefits:__ increase trust and citation
+
+### Rules
+
+1. For Every Result, Keep Track of How It Was Produced → workflow, versionning
+2. Avoid Manual Data Manipulation Steps → copy/paste etc
+3. Archive the Exact Versions of All External Programs Used → code, packages, dependencies, ... virtual machine
+4. Version Control All Custom Scripts
+5. Record All Intermediate Results, When Possible in Standardized Formats → re-running code, finding errors
+6. For Analyses That Include Randomness, Note Underlying Random Seeds → 
+7. Always Store Raw Data behind Plots → store also code to produce the code
+8. Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected 
+9. Connect Textual Statements to Underlying Results
+10. Provide Public Access to Scripts, Runs, and Results
+
+> As a minimum, you should submit the main data and source code as supplementary material, and be prepared to respond to any requests for further data or methodology details by peers.
+
+## FAIR assessment tools
+
+* Devaraju, A., & Huber, R. (2023). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400
+
+* Vesa Akerman, Linas Cepinskas, Maaike Verburg, & Mokrane Mustapha. (2021). FAIR-Aware: Assess Your Knowledge of FAIR (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.5084861 
+
+### FAIR Assessment Tools: Towards an “Apples to Apples” Comparisons
+
+Same DO assessment by different tools often exhibits widely different results
+
+FAIR assessment tool creators had no guidance on how to apply metrics to a digital object. 
+
+## References (to add to BibTeX)
+
+[x] Crosas, M. "The Dataverse Network®: An Open-Source Application for Sharing, Discovering and Preserving Data". D-Lib Mag 17 (1), p2 (2011).
+
+[x] White, H. C., Carrier, S., Thompson, A., Greenberg, J. & Scherle, R. The Dryad data repository: A Singapore framework metadata architecture in a DSpace environment. Univ. Göttingen, p157 (2008).
+
+[x] Lecarpentier, D. et al. EUDAT: A New Cross-Disciplinary Data Infrastructure for Science. Int. J. Digit. Curation 8, 279–287 (2013).
+
+[x] White, E. et al. Nine simple ways to make it easier to (re)use your data. Ideas Ecol. Evol. 6 (2013).
+
+-- Sandve, G. K., Nekrutenko, A., Taylor, J. & Hovig, E. Ten Simple Rules for Reproducible Computational Research. PLoS Comput. Biol. 9, e1003285 (2013).
+
+
+## Application
+
+EMODnet Chemistry Eutrophication - Mediterranean Sea
+
+https://emodnet.ec.europa.eu/en/eutrophication
+
+Testing with F-UJI:
+
+1) With the DOI: 10.6092/ep6n-tp63 → 58%
+2) https://sextant.ifremer.fr/documentation/emodnet_chemistry/api/catalogue.html#/metadata/a6d89ed2-17d0-4a8a-97fe-7e99d8e6520d → 4% ???
+3) https://emodnet.ec.europa.eu/geonetwork/emodnet/eng/catalog.search#/metadata/74158cb0-a21f-42ea-8a29-72a96b2a0da2 → 8%
+4)