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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Sheeba Samuel, Frank Löffler, Birgitta König‐Ries
Lecture notes in computer science (2021), pp. 226-230
Closed Access | Times Cited: 23

Showing 23 citing articles:

Traceability for Trustworthy AI: A Review of Models and Tools
Marçal Mora‐Cantallops, Salvador Sánchez‐Alonso, Elena García‐Barriocanal, et al.
Big Data and Cognitive Computing (2021) Vol. 5, Iss. 2, pp. 20-20
Open Access | Times Cited: 58
Provenance documentation to enable explainable and trustworthy AI: A literature review
Amruta Kale, Tin Nguyen, J. S. Harris, et al.
Data Intelligence (2022) Vol. 5, Iss. 1, pp. 139-162
Open Access | Times Cited: 40
FAIR for AI: An interdisciplinary and international community building perspective
E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 40
Unleashing the potential of AI for pathology: challenges and recommendations
Amina Asif, Kashif Rajpoot, Simon Graham, et al.
The Journal of Pathology (2023) Vol. 260, Iss. 5, pp. 564-577
Open Access | Times Cited: 18
A review of machine learning in scanpath analysis for passive gaze-based interaction
Abdulrahman Mohamed Selim, Michael Barz, Omair Shahzad Bhatti, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 4
Reproducible research policies and software/data management in scientific computing journals: a survey, discussion, and perspectives
José Manuel Hernández Mogollón, Miguel Colom
Frontiers in Computer Science (2025) Vol. 6
Open Access
Lightweight Distributed Provenance Model for Complex Real–world Environments
Rudolf Wittner, Cecilia Mascia, Matej Gallo, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 15
Data journeys: Explaining AI workflows through abstraction
Enrico Daga, Paul Groth
Semantic Web (2023) Vol. 15, Iss. 4, pp. 1057-1083
Open Access | Times Cited: 5
EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments
Samuel O. Idowu, Daniel Strüber, Thorsten Berger
(2022)
Open Access | Times Cited: 9
Enriching the FIDEO ontology with food-drug interactions from online knowledge sources
Rabia Azzi, Georgeta Bordea, Romain Griffier, et al.
Journal of Biomedical Semantics (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology
Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis, et al.
GigaScience (2024) Vol. 13
Open Access | Times Cited: 1
Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance
Jack W. Stokes, P. England, Kevin Kane
MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) (2021), pp. 181-188
Open Access | Times Cited: 9
Workflow provenance in the lifecycle of scientific machine learning
Renan Souza, Leonardo Guerreiro Azevedo, Vítor Lourenço, et al.
Concurrency and Computation Practice and Experience (2021) Vol. 34, Iss. 14
Open Access | Times Cited: 8
Towards a FAIR Dataset for non-functional requirements
María-Isabel Limaylla-Lunarejo, Nelly Condori-Fernández, Miguel R. Luaces
Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (2023), pp. 1414-1421
Open Access | Times Cited: 1
LIFEDATA - A Framework for Traceable Active Learning Projects
Fabian Stieler, Miriam Elia, Benjamin Weigell, et al.
(2023), pp. 465-474
Open Access | Times Cited: 1
An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services
Zerui Wang, Yan Liu, Jun Huang
IEEE Transactions on Cloud Computing (2024) Vol. 12, Iss. 2, pp. 762-776
Open Access
Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines
Adhithya Bhaskar, Victoria Stodden
(2024), pp. 101-109
Closed Access
CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine
Kirubel Biruk Shiferaw, Irina Balaur, Danielle Welter, et al.
(2024)
Open Access
Workflow Provenance in the Computing Continuum for Responsible, Trustworthy, and Energy-Efficient AI
Renan P. Souza, Silvina Caíno‐Lores, Mark Coletti, et al.
(2024), pp. 1-7
Closed Access
CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine
Kirubel Biruk Shiferaw, Irina Balaur, Danielle Welter, et al.
JAMIA Open (2024) Vol. 7, Iss. 4
Open Access
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Chen, J. Duarte, et al.
(2022)
Open Access | Times Cited: 2
An OGC TrainingDML-AI approach for making EO training datasets ready in deep learning frameworks
Shuaiqi Liu, Peng Yue, Hanwen Xu, et al.
(2023), pp. 1-6
Closed Access
Reproducibility in Activity Recognition Based on Wearable Devices: a Focus on Used Datasets
Andrea Fasciglione, Maurizio Leotta, Alessandro Verri
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2022), pp. 3178-3185
Closed Access
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