PFAIR23: Practically FAIR 2023 Coimbra, Portugal, April 15-16, 2023 |
Conference website | https://sites.google.com/view/pfair23/home |
Submission link | https://easychair.org/conferences/?conf=pfair23 |
Submission deadline | January 27, 2023 |
This workshops seeks new contributions working to answer questions about FAIR Data Principles as well as offering practical coding/software engineering aspects of adopting FAIR within an application or system to help others see potential hurdles and a way it was crossed successfully. We are looking for community discussion and participation on the above topics plus the following. First, provenance collection, metadata across domains, automatic metadata collection, metadata formats, domain specific metadata content, and system support for these and other relevant activities. Second, techniques to ensure trustworthiness and security of FAIR Data, enabling tracing data across Big Data and AI workflows. Third, portability and reproducibility of tools and performance considerations of FAIR Data Principles across different platforms. Finally, issues with FAIR Data Principles when managing large datasets, formata across domains, derived data space considerations.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
Full papers describing the emerging techniques for adapting FAIR Data Principles into applications and platforms that cover from traditional HPC, to AI+ML, and Cloud environments. Understanding the challenges and the tradeoffs in terms of performance and scalability when including FAIR Data Principles into the scientific discovery, as well as the benefits into obtaining findable, accesible, interoperable, and reusable digital assets and results for accelerating science.
Delinating the metadata that needs to be collected and interpretable across domains and to all users along the data evolution. Formalizing the format, content, storage approach, and the standards of data and metadata that fit into the FAIR Data Principles. Getting all the different types of data across domains to be FAIR is difficult but a necessary task in this data driven world, so understanding and defining the challenges and incompatbilities that limit the adoption of FAIR Data Principles allow new developers to tackle them and provide future solutions.
Defining protocols along the adaptation of FAIR Data Principles for authentication and authorisation of accessing and reusing data to ensure trustworthiness and security of the data, which is critical to trust scientific discovery and more for fields where privacy is a key aspect (e.g., health data).
PFAIR aims to dive into the practical aspects of adapting FAIR Data Principles, sharing experiences and exploring the various technological infrastructure needs to support effective, convenient FAIR Data across a broad spectrum of HPC environments from clusters to supercomputers to cloud systems and across applications (AI+ML, simulations).
Paper submission guidelines:
Papers should be formatted in ACM format following ICPE formatting rules and can be 5 pages not including references. The format of the paper should be of double column text. Templates are available from this link.
List of Topics
- Position and Experience papers related to scientific applications and platforms on related topics (particularly the topics listed below)
- Big Data or AI workflow systems like Spark, Hadoop, and Tensorflow in conjunction with data management and reproducibility efforts and techniques
- Domain specific metadata hierarchy development efforts
- Data formatting and chunking issues and related support libraries
- Approaches for testing FAIR compliance automaticallyApproaches for dealing with the derived data space for FAIR
- Data authentication approaches with low barriers to support while still offering strong guarantees
- Programming framework support to better address FAIR principles
- Testing of claimed FAIR data compliance to validate what the compliance level actually is for a third party investigator
- Cross-language and cross-platform data portability issues that would affect FAIR compliance
- Provenance tracking for FAIR marked artifacts within a workflow
- Working with non-data FAIR digital assets using any technique or addressing any problem like those described above or domain specific examples
- And any related topics.
Committees
Program Committee
- Dmitry Duplyakin (University of Utah)
- Rosa Filgueira (University of St. Andrews)
- Balzs Gerofi (Intel)
- Bingsheng He (University of Singapore)
- Shadi Ibrahim (INRIA)
- Biran Kocoloski (ISI)
- Jakob Luettgau (UTK)
- Tom Peterka (ANL)
- Reed Milewicz (SNL)
- Karly Harrod (ORNL)
- Hariharan Devarajan (LLNL)
- Matthre Wolf (ORNL)
Organizing committee
- Jay Lofstead (Sandia)
- Paula Olaya (UTK)
Contact
All questions about submissions should be emailed to gflofst@sandia.gov