Data Quality in Life Cycle Assessment

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Related lectures (32)

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Data Structuring: Intrarecord and Interrecord Techniques

Covers data structuring techniques, error detection, and functional dependencies within records.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Machine Learning in Human Rights: HURIDOCS

Explores machine learning in human rights, focusing on defining goals, handling false positives and negatives, and ensuring transparency and trust.

Data Processing at Massive Scale: Query Optimization Techniques

Discusses query optimization techniques for data processing at massive scale, comparing optimization strategies and sharing opportunities to reduce processing costs.

Distributed Computing: Challenges and Solutions

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Cost Modelling for Sustainable Innovation

Introduces cost modelling as a tool for sustainable innovation and covers topics such as transport CO2 emissions and material properties.

Completeness and Consistency Control

Explores completeness and consistency control in Life Cycle Assessment, emphasizing the importance of data availability and coherence verification.

Quality Management: Control and Tools

Explores quality management, costs, tools, and control processes with a focus on customer expectations and statistical tools.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Introduction to Data: Data Types and Quality

Covers data types, quantity, quality, and representativeness in the world of data.