In the modern age of artificial intelligence (AI), scientific research has entered an era of unprecedented opportunities. From predicting disease outbreaks to advancing quantum physics, AI is transforming the way research is conducted. However, as promising as AI is, it relies heavily on one critical factor: data readiness.
What is Data Readiness for AI and Why It Matters
Data readiness for AI “refers to the process of preparing and ensuring the quality, accessibility, and suitability of datasets before using them for AI applications” . This involves making sure that data is properly collected, clean, structured, and “appropriately annotated, with sufficient metadata to support reliable, appropriate post-model explainability analysis” .
For example, in scientific research, AI-ready data means that the data is organized in a way that algorithms can process efficiently and interpret meaningfully. This is not an easy task. In fact, many scientists spend significant time (often up to 80%) preparing their datasets for AI use .
But why should we worry about data readiness? Because:
- Data needs to be well-structured, cleaned, and pre-processed before it can be used for training.
- Investing time and resources upfront to ensure data is ready for AI can save significant costs in the long run.
- Properly prepared datasets reduce the risks of data bias in model outcomes.
- Well-annotated data improves transparency and reproducibility.
What Role Do the FAIR Principles Play in AI Data Readiness?
Incorporating the FAIR principles—Findability, Accessibility, Interoperability, and Reusability— into data management practices provides a solid foundation for ensuring the correct training of AI models and significantly improving their efficiency and effectiveness. This can be done thanks to platforms like the EOSC EU Node, which primarily supports multi-disciplinary and multi-national research, promoting the use of FAIR data and supplementary services across Europe and beyond. Within this environment, researchers can find easy-to-use tools and the necessary support to plan, execute, disseminate, and assess their research workflows and outcomes across the EOSC ecosystem .
From FAIR to FAIR-R
However, for AI models to be highly effective, data must meet additional criteria for machine readability and quality. To address the specific needs of AI, the FAIR-R conceptual framework extends the original FAIR principles by incorporating AI-readiness. It emphasizes that datasets should not only be findable, accessible, interoperable, and reusable, but also structured to meet the quality standards required for AI applications. For example, it ensures that data is appropriately labeled for supervised learning tasks or provides comprehensive and representative coverage for unsupervised learning .
References
5942012
{16784933:Y9JRDMG9},{16784933:6W36BK7U},{16784933:XN7GIGVH},{16784933:5WG43CHH},{16784933:9P2643VY}
1
apa
50
creator
desc
2604
https://opensciencelab.uc3m.es/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22XN7GIGVH%22%2C%22library%22%3A%7B%22id%22%3A16784933%7D%2C%22meta%22%3A%7B%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3B%26lt%3Bi%26gt%3BEOSC%20EU%20Node%20%7C%20European%20Open%20Science%20Cloud%20-%20EU%20Node%26lt%3B%5C%2Fi%26gt%3B.%20%28n.d.%29.%20Retrieved%20September%2022%2C%202025%2C%20from%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fopen-science-cloud.ec.europa.eu%5C%2Fabout%5C%2Feosc-eu-node%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fopen-science-cloud.ec.europa.eu%5C%2Fabout%5C%2Feosc-eu-node%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22webpage%22%2C%22title%22%3A%22EOSC%20EU%20Node%20%7C%20European%20Open%20Science%20Cloud%20-%20EU%20Node%22%2C%22creators%22%3A%5B%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fopen-science-cloud.ec.europa.eu%5C%2Fabout%5C%2Feosc-eu-node%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%225QC7GHMJ%22%5D%2C%22dateModified%22%3A%222025-09-22T08%3A51%3A12Z%22%7D%7D%2C%7B%22key%22%3A%22Y9JRDMG9%22%2C%22library%22%3A%7B%22id%22%3A16784933%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wilkinson%20et%20al.%22%2C%22parsedDate%22%3A%222016-03-15%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BWilkinson%2C%20M.%20D.%2C%20Dumontier%2C%20M.%2C%20Aalbersberg%2C%20Ij.%20J.%2C%20Appleton%2C%20G.%2C%20Axton%2C%20M.%2C%20Baak%2C%20A.%2C%20Blomberg%2C%20N.%2C%20Boiten%2C%20J.-W.%2C%20da%20Silva%20Santos%2C%20L.%20B.%2C%20Bourne%2C%20P.%20E.%2C%20Bouwman%2C%20J.%2C%20Brookes%2C%20A.%20J.%2C%20Clark%2C%20T.%2C%20Crosas%2C%20M.%2C%20Dillo%2C%20I.%2C%20Dumon%2C%20O.%2C%20Edmunds%2C%20S.%2C%20Evelo%2C%20C.%20T.%2C%20Finkers%2C%20R.%2C%20%26%23x2026%3B%20Mons%2C%20B.%20%282016%29.%20The%20FAIR%20Guiding%20Principles%20for%20scientific%20data%20management%20and%20stewardship.%20%26lt%3Bi%26gt%3BScientific%20Data%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B3%26lt%3B%5C%2Fi%26gt%3B%281%29%2C%20160018.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1038%5C%2Fsdata.2016.18%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1038%5C%2Fsdata.2016.18%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22The%20FAIR%20Guiding%20Principles%20for%20scientific%20data%20management%20and%20stewardship%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mark%20D.%22%2C%22lastName%22%3A%22Wilkinson%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michel%22%2C%22lastName%22%3A%22Dumontier%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22IJsbrand%20Jan%22%2C%22lastName%22%3A%22Aalbersberg%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gabrielle%22%2C%22lastName%22%3A%22Appleton%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Myles%22%2C%22lastName%22%3A%22Axton%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Arie%22%2C%22lastName%22%3A%22Baak%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Niklas%22%2C%22lastName%22%3A%22Blomberg%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jan-Willem%22%2C%22lastName%22%3A%22Boiten%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Luiz%20Bonino%22%2C%22lastName%22%3A%22da%20Silva%20Santos%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Philip%20E.%22%2C%22lastName%22%3A%22Bourne%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jildau%22%2C%22lastName%22%3A%22Bouwman%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anthony%20J.%22%2C%22lastName%22%3A%22Brookes%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tim%22%2C%22lastName%22%3A%22Clark%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Merc%5Cu00e8%22%2C%22lastName%22%3A%22Crosas%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ingrid%22%2C%22lastName%22%3A%22Dillo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Olivier%22%2C%22lastName%22%3A%22Dumon%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Scott%22%2C%22lastName%22%3A%22Edmunds%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Chris%20T.%22%2C%22lastName%22%3A%22Evelo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Richard%22%2C%22lastName%22%3A%22Finkers%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Alejandra%22%2C%22lastName%22%3A%22Gonzalez-Beltran%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Alasdair%20J.%20G.%22%2C%22lastName%22%3A%22Gray%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Paul%22%2C%22lastName%22%3A%22Groth%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Carole%22%2C%22lastName%22%3A%22Goble%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jeffrey%20S.%22%2C%22lastName%22%3A%22Grethe%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jaap%22%2C%22lastName%22%3A%22Heringa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Peter%20A.%20C.%22%2C%22lastName%22%3A%22%5Cu2019t%20Hoen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rob%22%2C%22lastName%22%3A%22Hooft%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tobias%22%2C%22lastName%22%3A%22Kuhn%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ruben%22%2C%22lastName%22%3A%22Kok%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Joost%22%2C%22lastName%22%3A%22Kok%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Scott%20J.%22%2C%22lastName%22%3A%22Lusher%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Maryann%20E.%22%2C%22lastName%22%3A%22Martone%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Albert%22%2C%22lastName%22%3A%22Mons%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Abel%20L.%22%2C%22lastName%22%3A%22Packer%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Bengt%22%2C%22lastName%22%3A%22Persson%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Philippe%22%2C%22lastName%22%3A%22Rocca-Serra%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Marco%22%2C%22lastName%22%3A%22Roos%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rene%22%2C%22lastName%22%3A%22van%20Schaik%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Susanna-Assunta%22%2C%22lastName%22%3A%22Sansone%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Erik%22%2C%22lastName%22%3A%22Schultes%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thierry%22%2C%22lastName%22%3A%22Sengstag%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ted%22%2C%22lastName%22%3A%22Slater%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22George%22%2C%22lastName%22%3A%22Strawn%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Morris%20A.%22%2C%22lastName%22%3A%22Swertz%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mark%22%2C%22lastName%22%3A%22Thompson%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Johan%22%2C%22lastName%22%3A%22van%20der%20Lei%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Erik%22%2C%22lastName%22%3A%22van%20Mulligen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jan%22%2C%22lastName%22%3A%22Velterop%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andra%22%2C%22lastName%22%3A%22Waagmeester%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Peter%22%2C%22lastName%22%3A%22Wittenburg%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Katherine%22%2C%22lastName%22%3A%22Wolstencroft%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jun%22%2C%22lastName%22%3A%22Zhao%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Barend%22%2C%22lastName%22%3A%22Mons%22%7D%5D%2C%22abstractNote%22%3A%22There%20is%20an%20urgent%20need%20to%20improve%20the%20infrastructure%20supporting%20the%20reuse%20of%20scholarly%20data.%20A%20diverse%20set%20of%20stakeholders%5Cu2014representing%20academia%2C%20industry%2C%20funding%20agencies%2C%20and%20scholarly%20publishers%5Cu2014have%20come%20together%20to%20design%20and%20jointly%20endorse%20a%20concise%20and%20measureable%20set%20of%20principles%20that%20we%20refer%20to%20as%20the%20FAIR%20Data%20Principles.%20The%20intent%20is%20that%20these%20may%20act%20as%20a%20guideline%20for%20those%20wishing%20to%20enhance%20the%20reusability%20of%20their%20data%20holdings.%20Distinct%20from%20peer%20initiatives%20that%20focus%20on%20the%20human%20scholar%2C%20the%20FAIR%20Principles%20put%20specific%20emphasis%20on%20enhancing%20the%20ability%20of%20machines%20to%20automatically%20find%20and%20use%20the%20data%2C%20in%20addition%20to%20supporting%20its%20reuse%20by%20individuals.%20This%20Comment%20is%20the%20first%20formal%20publication%20of%20the%20FAIR%20Principles%2C%20and%20includes%20the%20rationale%20behind%20them%2C%20and%20some%20exemplar%20implementations%20in%20the%20community.%22%2C%22date%22%3A%222016-03-15%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1038%5C%2Fsdata.2016.18%22%2C%22ISSN%22%3A%222052-4463%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.nature.com%5C%2Farticles%5C%2Fsdata201618%22%2C%22collections%22%3A%5B%225QC7GHMJ%22%5D%2C%22dateModified%22%3A%222025-09-22T08%3A49%3A56Z%22%7D%7D%2C%7B%22key%22%3A%226W36BK7U%22%2C%22library%22%3A%7B%22id%22%3A16784933%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Verhulst%20et%20al.%22%2C%22parsedDate%22%3A%222025-03-04%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BVerhulst%2C%20S.%2C%20Zahuranec%2C%20A.%20J.%2C%20%26amp%3B%20Chafetz%2C%20H.%20%282025%29.%20%26lt%3Bi%26gt%3BMoving%20Toward%20the%20FAIR-R%20principles%3A%20Advancing%20AI-Ready%20Data%26lt%3B%5C%2Fi%26gt%3B%20%28SSRN%20Scholarly%20Paper%20No.%205164337%29.%20Social%20Science%20Research%20Network.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.2139%5C%2Fssrn.5164337%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.2139%5C%2Fssrn.5164337%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22preprint%22%2C%22title%22%3A%22Moving%20Toward%20the%20FAIR-R%20principles%3A%20Advancing%20AI-Ready%20Data%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Stefaan%22%2C%22lastName%22%3A%22Verhulst%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrew%20J.%22%2C%22lastName%22%3A%22Zahuranec%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Hannah%22%2C%22lastName%22%3A%22Chafetz%22%7D%5D%2C%22abstractNote%22%3A%22In%20today%26%23039%3Bs%20rapidly%20evolving%20AI%20ecosystem%2C%20making%20data%20ready%20for%20AI-optimized%20for%20training%2C%20fine-tuning%2C%20and%20augmentation-is%20more%20critical%20than%20ever.%20While%20the%20FAIR%20principles%20%28Findability%2C%20Accessibility%2C%20Interoperability%2C%20and%20Reusability%29%20have%20guided%20data%20management%20and%20open%20science%2C%20they%20do%20not%20inherently%20address%20AI-specific%20needs.%20Expanding%20FAIR%20to%20FAIR-R%2C%20incorporating%20Readiness%20for%20AI%2C%20could%20accelerate%20the%20responsible%20use%20of%20open%20data%20in%20AI%20applications%20that%20serve%20the%20public%20interest.%20This%20paper%20introduces%20the%20FAIR-R%20framework%20and%20identifies%20current%20efforts%20for%20enhancing%20AI-ready%20data%20through%20improved%20data%20labeling%2C%20provenance%20tracking%2C%20and%20new%20data%20standards.%20However%2C%20key%20challenges%20remain%3A%20How%20can%20data%20be%20structured%20for%20AI%20without%20compromising%20ethics%3F%20What%20governance%20models%20ensure%20equitable%20access%3F%20How%20can%20AI%20itself%20be%20leveraged%20to%20improve%20data%20quality%3F%20Answering%20these%20questions%20is%20essential%20for%20unlocking%20the%20full%20potential%20of%20AI-driven%20innovation%20while%20ensuring%20responsible%20and%20transparent%20data%20use.%22%2C%22genre%22%3A%22SSRN%20Scholarly%20Paper%22%2C%22repository%22%3A%22Social%20Science%20Research%20Network%22%2C%22archiveID%22%3A%225164337%22%2C%22date%22%3A%222025-03-04%22%2C%22DOI%22%3A%2210.2139%5C%2Fssrn.5164337%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fpapers.ssrn.com%5C%2Fabstract%3D5164337%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%225QC7GHMJ%22%5D%2C%22dateModified%22%3A%222025-09-22T08%3A52%3A18Z%22%7D%7D%2C%7B%22key%22%3A%229P2643VY%22%2C%22library%22%3A%7B%22id%22%3A16784933%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Natalia%20et%20al.%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BNatalia%2C%20N.%2C%20Denisse%2C%20A.%2C%20%26amp%3B%20Vanessa%2C%20M.%20%28n.d.%29.%20Data%20and%20AI%20for%20science%3A%20Key%20considerations.%20%26lt%3Bi%26gt%3BInternational%20Science%20Council%26lt%3B%5C%2Fi%26gt%3B.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.24948%5C%2F2025.11%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.24948%5C%2F2025.11%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Data%20and%20AI%20for%20science%3A%20Key%20considerations%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Norori%22%2C%22lastName%22%3A%22Natalia%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Albornoz%22%2C%22lastName%22%3A%22Denisse%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22McBride%22%2C%22lastName%22%3A%22Vanessa%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20provides%20an%20overview%20of%20the%20technical%2C%20ethical%20and%20environmental%20factors%20to%20consider%20when%20preparing%20scientific%20data%20for%20artificial%20intelligence%20%28AI%29%2C%20and%20how%20these%20factors%20align%20with%20the%20%5Cu2018Open%20Science%5Cu2019%20movement.%20The%20information%20presented%20is%20relevant%20to%20researchers%2C%20data%20practitioners%2C%20scientific%20bodies%20and%20policy-makers%20for%20science.%22%2C%22date%22%3A%22%22%2C%22language%22%3A%22en-US%22%2C%22DOI%22%3A%2210.24948%5C%2F2025.11%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.24948%5C%2F2025.11%22%2C%22collections%22%3A%5B%225QC7GHMJ%22%5D%2C%22dateModified%22%3A%222025-09-22T08%3A47%3A13Z%22%7D%7D%2C%7B%22key%22%3A%225WG43CHH%22%2C%22library%22%3A%7B%22id%22%3A16784933%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Hiniduma%20et%20al.%22%2C%22parsedDate%22%3A%222025%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BHiniduma%2C%20K.%2C%20Byna%2C%20S.%2C%20%26amp%3B%20Bez%2C%20J.%20L.%20%282025%29.%20Data%20Readiness%20for%20AI%3A%20A%20360-Degree%20Survey.%20%26lt%3Bi%26gt%3BACM%20Comput.%20Surv.%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B57%26lt%3B%5C%2Fi%26gt%3B%289%29%2C%20219%3A1-219%3A39.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3722214%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3722214%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Data%20Readiness%20for%20AI%3A%20A%20360-Degree%20Survey%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kaveen%22%2C%22lastName%22%3A%22Hiniduma%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Suren%22%2C%22lastName%22%3A%22Byna%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jean%20Luca%22%2C%22lastName%22%3A%22Bez%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20Intelligence%20%28AI%29%20applications%20critically%20depend%20on%20data.%20Poor-quality%20data%20produces%20inaccurate%20and%20ineffective%20AI%20models%20that%20may%20lead%20to%20incorrect%20or%20unsafe%20use.%20Evaluation%20of%20data%20readiness%20is%20a%20crucial%20step%20in%20improving%20the%20quality%20and%20appropriateness%20of%20data%20usage%20for%20AI.%20R%26amp%3BD%20efforts%20have%20been%20spent%20on%20improving%20data%20quality.%20However%2C%20standardized%20metrics%20for%20evaluating%20data%20readiness%20for%20use%20in%20AI%20training%20are%20still%20evolving.%20In%20this%20study%2C%20we%20perform%20a%20comprehensive%20survey%20of%20metrics%20used%20to%20verify%20data%20readiness%20for%20AI%20training.%20This%20survey%20examines%20more%20than%20140%20papers%20published%20by%20ACM%20Digital%20Library%2C%20IEEE%20Xplore%2C%20journals%20such%20as%20Nature%2C%20Springer%2C%20and%20Science%20Direct%2C%20and%20online%20articles%20published%20by%20prominent%20AI%20experts.%20This%20survey%20aims%20to%20propose%20a%20taxonomy%20of%20data%20readiness%20for%20AI%20%28DRAI%29%20metrics%20for%20structured%20and%20unstructured%20datasets.%20We%20anticipate%20that%20this%20taxonomy%20will%20lead%20to%20new%20standards%20for%20DRAI%20metrics%20that%20would%20be%20used%20for%20enhancing%20the%20quality%2C%20accuracy%2C%20and%20fairness%20of%20AI%20training%20and%20inference.%22%2C%22date%22%3A%22Abril%204%2C%202025%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1145%5C%2F3722214%22%2C%22ISSN%22%3A%220360-0300%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdl.acm.org%5C%2Fdoi%5C%2F10.1145%5C%2F3722214%22%2C%22collections%22%3A%5B%225QC7GHMJ%22%5D%2C%22dateModified%22%3A%222025-09-22T08%3A39%3A39Z%22%7D%7D%5D%7D
EOSC EU Node | European Open Science Cloud - EU Node. (n.d.). Retrieved September 22, 2025, from https://open-science-cloud.ec.europa.eu/about/eosc-eu-node
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18
Verhulst, S., Zahuranec, A. J., & Chafetz, H. (2025). Moving Toward the FAIR-R principles: Advancing AI-Ready Data (SSRN Scholarly Paper No. 5164337). Social Science Research Network. https://doi.org/10.2139/ssrn.5164337
Natalia, N., Denisse, A., & Vanessa, M. (n.d.). Data and AI for science: Key considerations. International Science Council. https://doi.org/10.24948/2025.11
Hiniduma, K., Byna, S., & Bez, J. L. (2025). Data Readiness for AI: A 360-Degree Survey. ACM Comput. Surv., 57(9), 219:1-219:39. https://doi.org/10.1145/3722214