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FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows [article]

Sonia Natalie Mitchell, Andrew Lahiff, Nathan Cummings, Jonathan Hollocombe, Bram Boskamp, Ryan Field, Dennis Reddyhoff, Kristian Zarebski, Antony Wilson, Bruno Viola, Martin Burke, Blair Archibald (+26 others)
2022 arXiv   pre-print
while tracing the provenance of scientific outputs back through the analytical source code to data sources.  ...  Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses,  ...  the volunteers in the Scottish COVID-19 Response Consortium formed as part of that initiative -https://www.gla.ac.uk/scrc -who contributed to this project, and in particular to the team at Man Group for  ... 
arXiv:2110.07117v2 fatcat:xoxyycp5uvhp7lwqb7uozxg6o4

Ocean FAIR Data Services

Toste Tanhua, Sylvie Pouliquen, Jessica Hausman, Kevin O'Brien, Pip Bricher, Taco de Bruin, Justin J. H. Buck, Eugene F. Burger, Thierry Carval, Kenneth S. Casey, Steve Diggs, Alessandra Giorgetti (+22 others)
2019 Frontiers in Marine Science  
Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and  ...  To achieve this, data should be findable, accessible, interoperable, and reusable (FAIR). Here, we outline how these principles apply to ocean data and illustrate them with a few examples.  ...  Significant time is invested in these activities before the actual research or data utilization can begin, while provenance and traceability are required for the sake of reproducibility.  ... 
doi:10.3389/fmars.2019.00440 fatcat:akify7xyxrc6tcglxf4kwfotla

FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober
2019 Data Intelligence  
This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.  ...  data provenance.  ...  Acknowledgements (TBC) KP is funded by the German Network for Bioinformatics Infrastructure (de.NBI) and acknowledges BMBF funding under grant number 031L0107.  ... 
doi:10.1162/dint_a_00033 fatcat:44wqwahponccfe3dbmx77bslda

Turning FAIR data into reality: interim report from the European Commission Expert Group on FAIR data

Simon Hodson, Sarah Jones, Sandra Collins, Françoise Genova, Natalie Harrower, Leif Laaksonen, Daniel Mietchen, Rūta Petrauskaité, Peter Wittenburg
2018 Zenodo  
The FAIR Data Expert Group was also asked to contribute to the evaluation of the Horizon 2020 Data Management Plan template and future revisions in light of harmonisation with funders across the EU, including  ...  A FAIR Data Action Plan has also been proposed.  ...  This includes the algorithms, tools, workflows, and analytical pipelines that lead to creation of the data and give it meaning.  ... 
doi:10.5281/zenodo.1285272 fatcat:cayevf4vkfeuhbfcfc3srfhqpi

Draft recommendations for FAIR Photon and Neutron Data Management

Daniel Salvat, Alejandra Gonzalez-Beltran, Heike Görzig, Brian Matthews, Abigail McBirnie, Majid Ounsy, Sylvie Da Graca Ramos, Andrei Vukolov
2020 Zenodo  
data for National RIs.  ...  These recommendations give a framework for setting standards for common metadata formats and provide a basis for assessing the FAIRness of data generated by facilities.  ...  In such a context, data provenance and workflow transparency are very important to good research data management.  ... 
doi:10.5281/zenodo.4312825 fatcat:ose4lsdlgbawlfgwe652gh7xl4

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Koenraad De Smedt, Dimitris Koureas, Peter Wittenburg
2020 Publications  
In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable  ...  tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability  ...  The ability of a scientist to verify the relevance, provenance, completeness and fitness of data is an essential process in the scientific pipeline.  ... 
doi:10.3390/publications8020021 fatcat:hedoebfl25h6vducuyml64ggca

Final recommendations for FAIR Photon and Neutron Data Management

Nicolas Soler, Abigail McBirnie, Alejandra Gonzalez-Beltran, Andrey Vukolov, Carlo Minotti, Heike Goerzig, Krisztian Pozsa, Brian Matthews
2022 Zenodo  
The present deliverable pursues the work initiated in ExPaNDS deliverable 2.2, which established a common metadata framework for FAIR data generated in Photon and Neutron (PaN) facilities.  ...  It also provides the reader with guidelines on current tools and schemata available to record provenance and digital preservation information.  ...  The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 3 Matthews, B. et al. (2012).  ... 
doi:10.5281/zenodo.6799105 fatcat:3tdacgkvgjacrb74y2inv53fta

Final recommendations for FAIR Photon and Neutron Data Management

Nicolas Soler, Abigail McBirnie, Alejandra Gonzalez-Beltran, Andrey Vukolov, Carlo Minotti, Heike Goerzig, Krisztian Pozsa, Brian Matthews
2022 Zenodo  
The present deliverable pursues the work initiated in ExPaNDS deliverable 2.2, which established a common metadata framework for FAIR data generated in Photon and Neutron (PaN) facilities.  ...  It also provides the reader with guidelines on current tools and schemata available to record provenance and digital preservation information.  ...  The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 3 Matthews, B. et al. (2012).  ... 
doi:10.5281/zenodo.6799106 fatcat:ydieelkq7vhn3kxkajuokn6mvy

menoci: Lightweight Extensible Web Portal enabling FAIR Data Management for Biomedical Research Projects [article]

Markus Suhr, Christoph Lehmann, Christian Robert Bauer, Theresa Bender, Cornelius Knopp, Luca Freckmann, Björn Öst Hansen, Christian Henke, Georg Aschenbrandt, Lea Kühlborn, Sophia Rheinländer, Linus Weber, Bartlomiej Marzec (+4 others)
2020 arXiv   pre-print
A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability,  ...  We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190.  ...  Background Emerging data-driven research methods and the push for open and reproducible science amplify the need for strategic approaches to the management of research data across disciplines [1] .  ... 
arXiv:2002.06161v1 fatcat:zgvfprct4ngqjoxdfsoegxigta

Big Data in Laboratory Medicine—FAIR Quality for AI?

Tobias Ueli Blatter, Harald Witte, Christos Theodoros Nakas, Alexander Benedikt Leichtle
2022 Diagnostics  
To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable.  ...  Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated  ...  Acknowledgments: The authors would like to thank Aurélie Pahud de Mortanges for reviewing the manuscript and her outstanding support.  ... 
doi:10.3390/diagnostics12081923 pmid:36010273 pmcid:PMC9406962 fatcat:oyuvwb3wxnbmfbryvgugqku3dy

Provenance of astronomical data [article]

Mathieu Servillat
2022 arXiv   pre-print
The IVOA Provenance Data Model, published in 2020, puts in place the foundations for structuring and managing detailed provenance information, from the acquisition of raw data, to the dissemination of  ...  Provenance is explicitly cited in the FAIR principles, that aims to make research data Findable, Accessible, Interoperable and Reusable.  ...  Additional funding was provided by the INSU (Action Spécifique Observatoire Virtuel, ASOV), the Action Fédératrice CTA at the Observatoire de Paris and the Paris Astronomical Data Centre (PADC).  ... 
arXiv:2204.11486v1 fatcat:mmfr22i4ynhrxmw3wckjfzykpa

FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Daniel Schober, Kristian Peters, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe
2019 Zenodo  
Computational workflows are an increasingly important part of the research landscape, and a key tool for: instrumentation data capture, data processing pipelines, data analytics, predictive modelling and  ...  Properly designed workflows contribute to FAIR data principles [FAIR principles explained in this issue], since they provide the metadata and provenance necessary to describe their data products and they  ...  Acknowledgements (TBC) KP is funded by the German Network for Bioinformatics Infrastructure (de.NBI) and acknowledges BMBF funding under grant number 031L0107.  ... 
doi:10.5281/zenodo.2642531 fatcat:n6eidlfujfhaph5cgt5pucnrgu

Realising Data-Centric Scientific Workflows with Provenance-Capturing on Data Lakes

Hendrik Nolte, Philipp Wieder
2022 Data Intelligence  
Since their introduction by James Dixon in 2010, data lakes get more and more attention, driven by the promise of high reusability of the stored data due to the schema-on-read semantics.  ...  This paper discusses how FAIR Digital Objects can be used in a novel approach to organize a data lake based on data types instead of zones, how they can be used to abstract the physical implementation,  ...  data pipelines can be managed from a single system and interface.  ... 
doi:10.1162/dint_a_00141 fatcat:t2w5qaoobfh6rnnofzr65lng4a

Traceability for Trustworthy AI: A Review of Models and Tools

Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal, Miguel-Angel Sicilia
2021 Big Data and Cognitive Computing  
Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved  ...  Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems.  ...  Data Availability Statement: Data available in a publicly accessible repository. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bdcc5020020 fatcat:qnlnfdbxabc7nldvrs3hsowfya

FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober
2019 Zenodo  
This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.  ...  data provenance.  ...  necessary to describe their data products and they describe the involved data in a formalized, completely traceable way. • FAIR criteria for workflows as digital objects : Workflows are research products  ... 
doi:10.5281/zenodo.3268653 fatcat:xpcie77nvbasvikgbsfsv7f4im
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