Ammar is finishing up his PhD thesis with his research on the use of FAIR towards predictive toxicology. Or, “AI ready”, as the term FAIR is now sometimes explained. Any computational method needs good data, and just FAIR is not enough. It needs to meet community standards, as formalized in R1.3. To me, this includes meeting community standards like minimal reporting standards.
Noting that in the coming week I am not attending the ELIXIR All Hands in Uppsala. Having lived in (and around) Uppsala for more than three years, I am disappointed and with the first stories from colleagues coming in even more. But it has been a way too busy year, I have much to finish up, and I need to take care of myself too. I am not 32 anymore. But in the past two weeks I did attend two workshops.
Europe PMC joins the SoFAIR project Modern science increasingly relies on software, from data collection and analysis to modeling complex systems. As part of our mission, Europe PMC integrates open access literature with research outputs, such as data and software, to support innovation. To further this effort we have joined forces with the SoFAIR project, a collaborative initiative to empower reuse of open research software.
I was about to call this blog post From spreadsheets to RDF , after the post last week. But then I decided to just use the pattern I typically use. Why I wanted to use that shorter term in the first place was that one of the thing I like about the AMBIT software (of OpenTox and eNanoMapper fame) is its RDF support (see doi:10.1186/1756-0500-4-487). But RDF, ontologies, those are hard things.
Making something FAIR is hard, particularly when you do more than making something findable. We’ve seen before that making something usefully findable requires deep indexing, and already that continues to be difficult, because we are not seeing it enough. So, when I thought convert a paper led by Hoet’s lab in Leuven into machine-actionable RDF to make it FAIR, I gravely underestimated the amount of work.
My research is about the interaction of (machine) representation and the impact on the success of data analysis (matchine learning, chemometrics, AI, etc). See the posts about molecular chemometrics. This got me into FAIR: making data interoperable and being able to (really) reuse data is the starting point of doing research.
Kevin Ashley’s (DCC director) closing remark at the IDCC (International Digital Curation Conference) 2024 meeting was “ Reflect, Enjoy, Do something! ”. Hopefully you will enjoy my doing something.
Marking the 10 th anniversary of the formulation of the FAIR principles, we have one of our GigaBlog Q&A’s with Peter-Bram ’t Hoen, Alain van Gool, Anna Niehues and Casper de Visser from the Netherlands X-omics Initiative and Radboud University Medical Center in Nijmegen, authors of a new paper on publishing FAIR workflows.
This paper got published in July already, but I had not had the time yet to blog about this exciting work by Irini Furxhi and Ammar Ammar: A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability (doi:10.1016/j.impact.2023.100475) The study has two sides to it: first, it looks into how far we
It was a year to remember, for more than one reason: 2022 marked the 10th anniversary of GigaScience ’s launch. The journal’s younger sibling GigaByte got an award and continued to innovate with living documents and its first trilingual article. And we published lots of memorable research, featuring, for example, a giant tortoise and 26 deadly snakes.