University of South-Eastern Norway

URGE – Universal Readme Generator.

Philosophical Investigations from the ResponsAbility Podcast

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181 to 190 of 2,264 Results

Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies Feb 26, 2026 Barahmand, Zahir, 2026, "Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies", https://doi.org/10.18710/WEZMJE, DataverseNO, V1 This repository provides a large-scale, reproducibly labeled dataset of feedstock terms reported in the biomass and waste conversion literature. The dataset contains 133,000+ labeled feedstock items extracted from 121,000+ peer-reviewed studies. It is derived from upstream, review-based technology corpora hosted in DataverseNO, where feedstock desc...
00_ReadMe.txt Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies Plain Text - 68.2 KB - MD5: 3da021a86b9625ab4eb183d051e16459
FinalDataset_FeedstockLabels_GlobalDictionary.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 3.2 MB - MD5: b0042f40e1544fd7aa882930ba3f1db7
L1_labeling_MaterialStatus_Renewability.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 58.3 MB - MD5: 8daaf02bd6a1ad7f580d1e4366eff4d2
L2_labeling_Hazardness_LoW.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 31.0 MB - MD5: 1e5f5675fe991662dd347f5c48c82d70
L3_labeling_primary_biomass_taxonomy.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 14.9 MB - MD5: 07c32432a1cf064f5c8feb140213e7a5
L4_final_audit_cross_technology_dictionary.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 41.9 MB - MD5: 1671e52b586ffed27fcafabee49aecb4
References.zip Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies ZIP Archive - 135.8 MB - MD5: 73fdd2a1c34ffe059be03c81389d15e6
Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production Feb 24, 2026 Neville Aloysius D’Souza; Pfeiffer, Carlos; Mirlekar, Gaurav, 2026, "Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production", https://doi.org/10.18710/KIJEWJ, DataverseNO, V1 The data set consists of simulated time‑series measurements from two gas‑lifted subsea oil wells, used to develop and evaluate data‑driven virtual flow metering (VFM) models for oil and gas flow rate prediction. Purpose: To assess a range of machine learning algorithms (10 methods, including LSTM, MLP, XGBoost, SVR, tree‑based and linear methods) f...
00_ReadMe.txt Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production Plain Text - 4.1 KB - MD5: d876283a934a820f7703dd26e950e133

Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

Feb 26, 2026

Barahmand, Zahir, 2026, "Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies", https://doi.org/10.18710/WEZMJE, DataverseNO, V1

This repository provides a large-scale, reproducibly labeled dataset of feedstock terms reported in the biomass and waste conversion literature. The dataset contains 133,000+ labeled feedstock items extracted from 121,000+ peer-reviewed studies. It is derived from upstream, review-based technology corpora hosted in DataverseNO, where feedstock desc...

00_ReadMe.txt

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

Plain Text - 68.2 KB -

FinalDataset_FeedstockLabels_GlobalDictionary.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 3.2 MB -

L1_labeling_MaterialStatus_Renewability.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 58.3 MB -

L2_labeling_Hazardness_LoW.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 31.0 MB -

L3_labeling_primary_biomass_taxonomy.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 14.9 MB -

L4_final_audit_cross_technology_dictionary.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 41.9 MB -

References.zip

Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies

ZIP Archive - 135.8 MB -

Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production

Feb 24, 2026

Neville Aloysius D’Souza; Pfeiffer, Carlos; Mirlekar, Gaurav, 2026, "Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production", https://doi.org/10.18710/KIJEWJ, DataverseNO, V1

The data set consists of simulated time‑series measurements from two gas‑lifted subsea oil wells, used to develop and evaluate data‑driven virtual flow metering (VFM) models for oil and gas flow rate prediction. Purpose: To assess a range of machine learning algorithms (10 methods, including LSTM, MLP, XGBoost, SVR, tree‑based and linear methods) f...

00_ReadMe.txt

Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production

Plain Text - 4.1 KB -

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