Výsledky vyhľadávania - Working with challenging data: Complex data, models and formats

Making Environmental Monitoring Data More Open Using a Classification Model Based on the FAIR Principles and the Five Star Open Data Model

Autori: Taberman, Ida Asplund, Mikaela Hallstan, Simon a ďalší

Predmety: Working with challenging data: Data on a large scale: big data or large collections of long-tail data, IDCC25, Working with challenging data: Universal standards and expectations and that of context and discipline specific adaptations, long-tail data, Sustainability and strategy: Environmental sustainability of digital curation, reusability, governmental collaboration, dynamic datasets, Working with challenging data: Complex data, models and formats, Open data act

An Attempt To Make Open Data FAIR. How SLU Made Data Discoverable and Reusable by Creating the Environmental Data Catalogue

Autori: Asplund, Mikaela Hallstan, Simon Taberman, Ida

Predmety: IDCC25, Open data, long-tail data, distributed data spaces, metadata catalogue, Working with challenging data: Complex data, models and formats

Evolution from Data Silos to FAIR Digital Objects - Decade of Data Management at IPK Gatersleben

Autori: Schüler, Danuta orcid:0000-0003-4277- Arend, Daniel a ďalší

Predmety: IDCC25, Papers, Curation infrastructure: Tools, systems and services that are in development, Curation infrastructure: Proposals for new approaches to large-scale service delivery, Education and training: How effective is current training in delivering knowledge and skills?, Sustainability and strategy: Work on curation processes, techniques, costs, and workflows, Working with challenging data: Data on a large scale: big data or large collections of long-tail data, Working with challenging data: Complex data, models and formats, Sustainability, Research Data Management Infrastructure, LIMS, Training concepts and formats

Relation: https://zenodo.org/communities/idcc25/; https://zenodo.org/records/14989455; oai:zenodo.org:14989455; https://doi.org/10.5281/zenodo.14989455

Dostupnosť: https://doi.org/10.5281/zenodo.14989455
https://zenodo.org/records/14989455

'The Data Came in Waves': Fluid Data Sharing and Reuse During Covid-19

Autori: Yakel, Elizabeth orcid:0000-0002-8792- Lee, Jian-Sin a ďalší

Predmety: IDCC25, Papers, Working with challenging data: Complex data, models and formats, Working with challenging data: Universal standards and expectations and that of context and discipline specific adaptations, Sharing data: Demonstrating research integrity, Data sharing, Data reuse, COVID-19, Collaboration, Crisis informatics

Relation: https://zenodo.org/communities/idcc25/; https://zenodo.org/records/15008870; oai:zenodo.org:15008870; https://doi.org/10.5281/zenodo.15008870

Dostupnosť: https://doi.org/10.5281/zenodo.15008870
https://zenodo.org/records/15008870

Curating Archive and Library Special Collections for Large-Scale Environmental Data Collection: A Case Study

Autori: Wood, Nicole Elizabeth

Predmety: Curation infrastructure: Innovative research and exploration into new curation methods, Education and training: What knowledge and skills are needed?, Education and training: What innovations are being explored?, Working with challenging data: Data on a large scale: big data or large collections of long-tail data, Working with challenging data: Complex data, models and formats, Digital curation, Environmental data, Information retrieval, Ethics, Case study, IDCC25, Lightning Talks

Relation: https://zenodo.org/communities/idcc25/; https://zenodo.org/records/14947884; oai:zenodo.org:14947884; https://doi.org/10.5281/zenodo.14947884

Dostupnosť: https://doi.org/10.5281/zenodo.14947884
https://zenodo.org/records/14947884

Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries.

Autori: Nagabhyru, Kushvanth Chowdary A., Jyothi Babu

Zdroj: Metallurgical & Materials Engineering; 2025, Vol. 31 Issue 7, p122-141, 20p

Predmety: GENERATIVE artificial intelligence, DECISION making, DATA integration, COOPERATION, KNOWLEDGE acquisition (Expert systems), TECHNOLOGICAL innovations

Crossing the streams: improving data quality and integration across the One Health genomics continuum with data standards and implementation strategies.

Autori: Griffiths, Emma J. Jurga, Emil Wajnberg, Gabriel a ďalší

Zdroj: Canadian Journal of Microbiology; 1/20/2025, Vol. 71, p1-14, 14p

Predmety: DATA harmonization, DRUG resistance in microorganisms, INFORMATION resources management, QUALITY control, INFORMATION sharing

Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.

Autori: Kong, Chun Yin Vasquez, Picasso Farhoodimoghadam, Makan a ďalší

Zdroj: BMC Veterinary Research. 10/2/2025, Vol. 21 Issue 1, p1-12. 12p.

Predmet: Machine learning, Electronic health records, Open source software, Patient care, Veterinary medicine, Diagnosis, Classification algorithms, Synchronization

Automated brain extraction for canine magnetic resonance images.

Autori: Lesta, Gloria D. Deserno, Thomas M. Abani, Samira a ďalší

Zdroj: BMC Veterinary Research. 9/16/2025, Vol. 21 Issue 1, p1-13. 13p.

Predmet: Magnetic resonance imaging, Artificial neural networks, Image processing, Computer-assisted image analysis (Medicine), Skull morphology

An overview of the national cattle health- and welfare-related information systems in Estonia, Finland, Sweden, and Germany.

Autori: Kallio, Tuija Timonen, Anri Tamm, Hardi a ďalší

Zdroj: Frontiers in Animal Science. 2025, p1-17. 17p.

Alternative methods for developmental toxicity testing: features, status and future directions.

Autori: Inho Hwang Eui-Bae Jeung

Zdroj: Journal of Veterinary Science. Nov2025, Vol. 26 Issue 6, p1-17. 17p.

Testing and Improving Deep Learning Models for Heterogeneous Domains Using Graph-based Representation

Autori: Nguyen, Thanh Dat

Témy: deep learning, heterogeneous data, property graph, graph neural networks, fault localization, adversarial robustness, backdoor defense, program analysis, visually rich documents, representation learning, PhD thesis

URL: https://hdl.handle.net/11343/356898

2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project.

Autori: Bianco, Rachele Coluccia, Sergio Marinoni, Michela a ďalší

Zdroj: Nutrients. Jul2025, Vol. 18 Issue 13, p2196. 23p.

Animal Anatomical Teaching Models for Enhanced Veterinary Anatomy Education and Learning.

Autori: Choudhary, Om Prakash Sarkar, Rupan

Zdroj: Pakistan Veterinary Journal. 2025, Vol. 45 Issue 3, p961-982. 22p.

Predmet: Veterinary anatomy, Three-dimensional printing, Educational outcomes, Veterinary medicine education, Experiential learning, Clinical competence, Simulation methods & models, Professional ethics

Environmental drivers of rabies in the Volga region of Russia: application of the maxent model.

Autori: Zakharova, Olga I. Korennoy, Fedor I. Liskova, Elena A. a ďalší

Zdroj: Frontiers in Veterinary Science. 2025, p1-13. 13p.

Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis.

Autori: Siddique, Aftab Batchu, Phaneendra Shaik, Arshad a ďalší

Zdroj: Frontiers in Veterinary Science. 2025, p1-11. 11p.

Deciphering Genetic Overlaps: A Comprehensive Study On Viral Host Determination Using Machine Learning And Deep Learning Models.

Autori: Agarwal, Pankaj Yadav, Sapna

Zdroj: Journal of Advanced Zoology. 2024, Vol. 45 Issue 3, p776-788. 13p.

The Vertebrate Breed Ontology: Toward Effective Breed Data Standardization.

Autori: Mullen, Kathleen R. Tammen, Imke Matentzoglu, Nicolas A. a ďalší

Zdroj: Journal of Veterinary Internal Medicine. Jul2025, Vol. 39 Issue 4, p1-13. 13p.

Developing electronic health records as a source of real-world data for veterinary pharmacoepidemiology.

Autori: Davies, Heather Noble, Peter-John Fins, Ivo S. a ďalší

Zdroj: Frontiers in Veterinary Science. 2025, p1-10. 10p.

Veterinarians' Perspectives on the Antimicrobial Resistance (AMR) Dashboard: A Survey of Needs and Preferences to Inform Development.

Autori: Pellissery, Abraham Joseph Denagamage, Thomas Pedersen, Maura a ďalší

Zdroj: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p940. 14p.