Salah Eddine El Herrag - Artificial intelligence for data curation and

Conference

Salah Eddine El Herrag, Noria Harir, Soraya Moulessehoul
1st National Scientific Day, 2025th ed., Djillali Liabès University of Sidi Bel Abbes, 2025 Nov 22

DOI: 10.17605/OSF.IO/F3SZU

Abstract

Cite

APA Click to copy
Herrag, S. E. E., Harir, N., & Moulessehoul, S. (2025). Artificial intelligence for data curation and sustainable research in the biological sciences current trends and challenges (2025th ed.). Djillali Liabès University of Sidi Bel Abbes. https://doi.org/10.17605/OSF.IO/F3SZU

Chicago/Turabian Click to copy
Herrag, Salah Eddine El, Noria Harir, and Soraya Moulessehoul. “Artificial Intelligence for Data Curation and Sustainable Research in the Biological Sciences Current Trends and Challenges.” 2025th ed. 1st National Scientific Day. Djillali Liabès University of Sidi Bel Abbes, 2025.

MLA Click to copy
Herrag, Salah Eddine El, et al. Artificial Intelligence for Data Curation and Sustainable Research in the Biological Sciences Current Trends and Challenges. 2025th ed., Djillali Liabès University of Sidi Bel Abbes, 2025, doi:10.17605/OSF.IO/F3SZU.

BibTeX Click to copy

@conference{salah2025a,
  title = {Artificial intelligence for data curation and sustainable research in the biological sciences current trends and challenges},
  year = {2025},
  month = nov,
  day = {22},
  edition = {2025},
  organization = {Djillali Liabès University of Sidi Bel Abbes},
  series = {1st National Scientific Day},
  doi = {10.17605/OSF.IO/F3SZU},
  author = {Herrag, Salah Eddine El and Harir, Noria and Moulessehoul, Soraya},
  month_numeric = {11}
}

Abstract

Introduction: Artificial Intelligence (AI) is reshaping the biological sciences by facilitating the integration, analysis, and interpretation of increasingly large and complex datasets. Its applications now extend across genomics, proteomics, biotechnology, agriculture, and environmental science. Despite its transformative potential, the widespread adoption of AI introduces new challenges in data quality, reproducibility, privacy, and sustainability.

Methods: A critical review of recent literature was conducted to evaluate the implementation of AI-driven tools in biological data curation and sustainable research. The analysis focused on five key domains: data integration, automated annotation, environmental sustainability, data governance, and interdisciplinary collaboration.

Results: AI technologies enable large-scale data integration and predictive modeling, improving hypothesis generation and discovery across subdisciplines. Deep learning and natural language processing algorithms enhance data annotation while reducing human error, though they also risk propagating misinformation through synthetic data. AI contributes to sustainable research by optimizing agricultural practices, bioremediation, and resource management. However, persistent concerns regarding data provenance, reproducibility, and privacy highlight the need for transparent, ethical, and energy-efficient AI systems. The Open and Sustainable AI (OSAI) framework emerges as a promising model to promote trust and accountability.

Conclusions: AI represents a paradigm shift in biological research, offering unprecedented capabilities for data curation and sustainable innovation. Yet, realizing its full benefits requires addressing systemic challenges in data management, ethical governance, and cross-disciplinary collaboration to ensure that AI integration remains both scientifically rigorous and environmentally responsible.

Keywords: Artificial intelligence, data curation, sustainability, biological sciences.

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Poster: Artificial intelligence for data curation and sustainable research in the biological sciences current trends and challenges