cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bv6c3x22yy
title: Long-term Effectiveness of Cadmium-Stabilizing Materials: Machine Learning and Meta-Analysis Confirm the Sustained Efficacy of Biochar in Soil-Rice Systems

description: Based on the results of long‑term experiments with existing immobilization materials, this study identifies the material type that exhibits the most durable and stable performance and explores the key soil physicochemical properties and material characteristics that govern its long‑term immobilization effectiveness. By systematically collecting published data from long‑term studies on immobilization materials and conducting subgroup analyses and calculating the effect size lnRR in Meta‑Win, the results indicate that biochar is the most effective long‑term immobilization material. The study found that the optimal range of soil physicochemical properties for biochar gradually narrows from the long term to the medium term, suggesting that soil organic matter (SOM), clay content, and cation exchange capacity (CEC) play important regulatory roles in stabilizing biochar’s long‑term performance. Further analysis of biochar’s intrinsic properties revealed that its immobilization effectiveness primarily depends on pyrolysis temperature, inherent pH, and application rate.
DOI: 10.17632/bv6c3x22yy.1
title: Long-term Effectiveness of Cadmium-Stabilizing Materials: Machine Learning and Meta-Analysis Confirm the Sustained Efficacy of Biochar in Soil-Rice Systems

description: Based on the results of long‑term experiments with existing immobilization materials, this study identifies the material type that exhibits the most durable and stable performance and explores the key soil physicochemical properties and material characteristics that govern its long‑term immobilization effectiveness. By systematically collecting published data from long‑term studies on immobilization materials and conducting subgroup analyses and calculating the effect size lnRR in Meta‑Win, the results indicate that biochar is the most effective long‑term immobilization material. The study found that the optimal range of soil physicochemical properties for biochar gradually narrows from the long term to the medium term, suggesting that soil organic matter (SOM), clay content, and cation exchange capacity (CEC) play important regulatory roles in stabilizing biochar’s long‑term performance. Further analysis of biochar’s intrinsic properties revealed that its immobilization effectiveness primarily depends on pyrolysis temperature, inherent pH, and application rate.
DOI: 10.17632/wp4rk3nd2s
title: Ultrafast Quantum Light–Matter Dynamics in a Single Ni₂ Optical Resonator

description: Femtosecond transient absorption spectra
DOI: 10.17632/wp4rk3nd2s.1
title: Ultrafast Quantum Light–Matter Dynamics in a Single Ni₂ Optical Resonator

description: Femtosecond transient absorption spectra
DOI: 10.17632/z5jx9wwygr
title: Dataset for fertilizer-derived nitrogen partitioning in rice–soil systems under water-saving and flooded irrigation using a ¹⁵N tracer

description: This repository provides the processed, figure-ready dataset used to generate the tables and figures in the associated manuscript on artificial humic substances (AHS) and irrigation effects on fertilizer nitrogen (N) fate in a paddy rice system. The underlying research hypothesis is that AHS can reallocate fertilizer-derived N toward crop recovery and away from the apparent loss fraction, and that the magnitude of this effect is modulated by irrigation regime (water-saving irrigation vs flooded irrigation). Data were generated from a replicated field experiment using a field-embedded ¹⁵N tracer microplot approach. Treatments combined two irrigation regimes with graded AHS application levels. Plant and soil samples were collected at maturity, and total N and ¹⁵N enrichment were used to quantify fertilizer-derived N recovered in plant pools, retained in soil, and assigned to an apparent loss pool by isotope-based mass balance. The Excel file contains the curated values used for plotting, including (where applicable) treatment identifiers, replication/grouping variables, means and/or replicate-level values, and derived indices such as fertilizer-derived N in plant/soil pools, partitioning proportions, and apparent nitrogen recovery efficiency (NRE). Variable names and units are provided in the spreadsheet to support interpretation. Notable patterns reflected in these plotting data include increased fertilizer-derived N recovery by rice with AHS across irrigation regimes, a shift in fertilizer-N partitioning toward plant (grain/straw) pools, and a reduced relative contribution of fertilizer-N to the apparent loss fraction. The dataset enables readers to reproduce the visualizations and summary comparisons reported in the manuscript and can be reused for cross-study comparisons, meta-analyses, or model evaluation where processed ^15N-based fertilizer-N partitioning metrics are required. This upload contains processed data used for figure construction rather than raw instrument outputs. Users should interpret values in the context of the experimental design (AHS level × irrigation regime) and consult the spreadsheet headers/notes for units, abbreviations, and calculation conventions.
DOI: 10.17632/z5jx9wwygr.1
title: Dataset for fertilizer-derived nitrogen partitioning in rice–soil systems under water-saving and flooded irrigation using a ¹⁵N tracer

description: This repository provides the processed, figure-ready dataset used to generate the tables and figures in the associated manuscript on artificial humic substances (AHS) and irrigation effects on fertilizer nitrogen (N) fate in a paddy rice system. The underlying research hypothesis is that AHS can reallocate fertilizer-derived N toward crop recovery and away from the apparent loss fraction, and that the magnitude of this effect is modulated by irrigation regime (water-saving irrigation vs flooded irrigation). Data were generated from a replicated field experiment using a field-embedded ¹⁵N tracer microplot approach. Treatments combined two irrigation regimes with graded AHS application levels. Plant and soil samples were collected at maturity, and total N and ¹⁵N enrichment were used to quantify fertilizer-derived N recovered in plant pools, retained in soil, and assigned to an apparent loss pool by isotope-based mass balance. The Excel file contains the curated values used for plotting, including (where applicable) treatment identifiers, replication/grouping variables, means and/or replicate-level values, and derived indices such as fertilizer-derived N in plant/soil pools, partitioning proportions, and apparent nitrogen recovery efficiency (NRE). Variable names and units are provided in the spreadsheet to support interpretation. Notable patterns reflected in these plotting data include increased fertilizer-derived N recovery by rice with AHS across irrigation regimes, a shift in fertilizer-N partitioning toward plant (grain/straw) pools, and a reduced relative contribution of fertilizer-N to the apparent loss fraction. The dataset enables readers to reproduce the visualizations and summary comparisons reported in the manuscript and can be reused for cross-study comparisons, meta-analyses, or model evaluation where processed ^15N-based fertilizer-N partitioning metrics are required. This upload contains processed data used for figure construction rather than raw instrument outputs. Users should interpret values in the context of the experimental design (AHS level × irrigation regime) and consult the spreadsheet headers/notes for units, abbreviations, and calculation conventions.
DOI: 10.17632/j9vmh6sfj5
title: A First Systemised Collection of Unattested Kula (Tanglapui) Toponyms from Alor Island (Southeastern Indonesia)

description: This dataset collects a first systemised list of so far undocumented place names from the Kula (Tanglapui) language and territory in Alor Island (Alor-Pantar Archipelago, Southeast Indonesia, Timor Area).
It contains thirty-one entries of place names, documenting the toponymic landscape of the Kula-speaking region in East Alor. Each record in the dataset comprises the following core fields (when available): 
1. Place Name
2. Name variant(s)
3. Gloss / Folk Etymology
4. Natural Source(s)
5. Story/History
6. Settlement Type
7. Location
8. Habitation Status (inhabited/abandoned)
9. Tribal Links (within Kula)
10. Tribal Links (outside Kula)

The collection was supported by the research grant “Place Names and Cultural Identity: Toponyms and Their Diachronic Evolution among the Kula People from Alor Island”—Research Development Fund (RDF) Grant, Grant Number: RDF-23-01-014 (Principal Investigator: Dr Francesco Perono Cacciafoco), funded by the School of Humanities and Social Sciences (HSS), Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou (Jiangsu), China, 2024–2025.
DOI: 10.17632/j9vmh6sfj5.1
title: A First Systemised Collection of Unattested Kula (Tanglapui) Toponyms from Alor Island (Southeastern Indonesia)

description: This dataset collects a first systemised list of so far undocumented place names from the Kula (Tanglapui) language and territory in Alor Island (Alor-Pantar Archipelago, Southeast Indonesia, Timor Area).
It contains thirty-one entries of place names, documenting the toponymic landscape of the Kula-speaking region in East Alor. Each record in the dataset comprises the following core fields (when available): 
1. Place Name
2. Name variant(s)
3. Gloss / Folk Etymology
4. Natural Source(s)
5. Story/History
6. Settlement Type
7. Location
8. Habitation Status (inhabited/abandoned)
9. Tribal Links (within Kula)
10. Tribal Links (outside Kula)

The collection was supported by the research grant “Place Names and Cultural Identity: Toponyms and Their Diachronic Evolution among the Kula People from Alor Island”—Research Development Fund (RDF) Grant, Grant Number: RDF-23-01-014 (Principal Investigator: Dr Francesco Perono Cacciafoco), funded by the School of Humanities and Social Sciences (HSS), Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou (Jiangsu), China, 2024–2025.
DOI: 10.5281/zenodo.18320785
title: RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package

description: Title: RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package

Authors: Stephen John Warnett; Uwe Zdun

About: This is the replication package artefact for the paper entitled "RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0".

Paper Abstract: Machine learning operations (MLOps) automate common tasks throughout the entire life cycle of a machine learning model. In Industry 4.0 environments, cyber-physical production systems increasingly utilise reinforcement learning (RL) models to optimise and automate production processes, giving rise to reinforcement learning operations (RLOps). However, manually configuring RLOps pipelines requires specialised development operations (DevOps) knowledge, and this can limit the potential for automation. We propose a template-based approach for rapidly creating and deploying RLOps pipelines in Industry 4.0 environments that minimises the required programming effort and expertise. Based on the Pipes and Filters pattern, our modular solution leverages large language models (LLMs) for automated pipeline creation. It enables fully automated execution, including model training, testing and deployment, with built-in quality control to ensure correct configurations. We validate our approach through evaluation with multiple LLMs in an Industry 4.0 context. Our results demonstrate that our solution, used with a suitable LLM, can reliably generate and execute RLOps pipelines with low error rates, thereby reducing development time and the need for specialised DevOps knowledge.
DOI: 10.5281/zenodo.18320786
title: RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package

description: Title: RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package

Authors: Stephen John Warnett; Uwe Zdun

About: This is the replication package artefact for the paper entitled "RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0".

Paper Abstract: Machine learning operations (MLOps) automate common tasks throughout the entire life cycle of a machine learning model. In Industry 4.0 environments, cyber-physical production systems increasingly utilise reinforcement learning (RL) models to optimise and automate production processes, giving rise to reinforcement learning operations (RLOps). However, manually configuring RLOps pipelines requires specialised development operations (DevOps) knowledge, and this can limit the potential for automation. We propose a template-based approach for rapidly creating and deploying RLOps pipelines in Industry 4.0 environments that minimises the required programming effort and expertise. Based on the Pipes and Filters pattern, our modular solution leverages large language models (LLMs) for automated pipeline creation. It enables fully automated execution, including model training, testing and deployment, with built-in quality control to ensure correct configurations. We validate our approach through evaluation with multiple LLMs in an Industry 4.0 context. Our results demonstrate that our solution, used with a suitable LLM, can reliably generate and execute RLOps pipelines with low error rates, thereby reducing development time and the need for specialised DevOps knowledge.
DOI: 10.17632/bxkyc7bdww
title: A Multicenter Randomized Controlled Trial: Optimizing Concentrated Growth Factor Therapy: Sodium Citrate Minimizes Pain and Maximizes Efficacy in Hair Loss

description: This document contains supplemental tables, figures and video providing detailed data from a comparative study on the use of different blood collection tube additives (No anticoagulant, Sodium Citrate, and Heparin Sodium) and their effects on procedural outcomes, safety, and quality of life.
DOI: 10.17632/bxkyc7bdww.1
title: A Multicenter Randomized Controlled Trial: Optimizing Concentrated Growth Factor Therapy: Sodium Citrate Minimizes Pain and Maximizes Efficacy in Hair Loss

description: This document contains supplemental tables, figures and video providing detailed data from a comparative study on the use of different blood collection tube additives (No anticoagulant, Sodium Citrate, and Heparin Sodium) and their effects on procedural outcomes, safety, and quality of life.
DOI: 10.17632/v6tt2kmdvx
title: Does the Enforcement of a Stricter Seat Belt Law Reduce Motor Vehicle Crash Fatalities
DOI: 10.17632/v6tt2kmdvx.1
title: Does the Enforcement of a Stricter Seat Belt Law Reduce Motor Vehicle Crash Fatalities
DOI: 10.17632/7whtznkdrc.1
title: CNFDI_JIE2026_replication_package

description: The replication package is organized to facilitate transparent reproduction of all tables and figures in the paper, "Firm and Labor Adjustments to FDI Liberalization." It contains a `code/` folder with all Stata and R scripts (coordinated by a master script), a `data/` folder with cleaned and intermediate datasets, and `output/table/` and `output/graph/` folders with the LaTeX tables and PDF figures reported in the paper. Due to confidentiality restrictions imposed by the Ministry of Finance of Taiwan, raw worker-level administrative data are not included; scripts that must be run inside the MOF Financial Information Agency secure data center are clearly marked. All file paths are relative, and once the root directory is set, the replication can be executed without further modification.
DOI: 10.17632/hvk9f2by6k
title: Data for: Ensemble Learning: Predicting Human Pathogenicity of Hematophagous Arthropod Vector-Borne Viruses

description: Overview: This dataset supports the study on predicting the human pathogenicity of viruses carried by blood-feeding arthropods (mosquitoes, ticks, etc.) using ensemble learning. It integrates large-scale epidemiological data with genomic functional annotations to assess zoonotic spillover risks.   
Dataset Components:
Epidemiological Characteristics Dataset: Covers 294 viruses and 37 distinct features categorized into:
Virus Properties: Baltimore classification and taxonomy.   
Vector Host Features: Family and genus of vectors (e.g., Culicidae, Ixodidae).   
Non-vector Host Diversity: Distribution across 15 groups, emphasizing the impact of Perissodactyla and Carnivora orders on pathogenicity.   
Viral Sequence Pathogenic Function Dataset: Includes functional annotations for 71,623 viral sequences. Using SeqScreen, 10 key Functional Signatures of Concern (FunSoCs) were identified, such as:   
Viral Adhesion: Found in 62% of sequences, crucial for host cell entry.   
Host Xenophagy & Viral Counter Signaling: Key features for immune evasion.   
Viral Invasion: Associated with non-pathogenic traits in this specific context.   
Technical Application: The data were utilized to develop and validate XGBoost-based models:
Regression Model: Achieved an R² of 90.6%, correlating host diversity with pathogenicity.   
Classification Model: Achieved an F1 score of 96.79% for identifying pathogenic potential at the sequence level.   
External Validation: Includes predictions for 228 sequences, highlighting potential risks from Palma and Zaliv Terpeniya viruses.   
Research Value: This resource allows for the strain-level prediction of pathogenicity within metagenomic data, providing a robust framework for early warning systems of emerging zoonotic threats.
DOI: 10.7910/dvn/wpdsek
title: Replication Data for: Helical electric dipole moments induced by orbit-orbit coupling in chiral chains

description: Dataset for helical electric dipole moments induced by orbit-orbit coupling in chiral chains
DOI: 10.17632/hvk9f2by6k.1
title: Data for: Ensemble Learning: Predicting Human Pathogenicity of Hematophagous Arthropod Vector-Borne Viruses

description: Overview: This dataset supports the study on predicting the human pathogenicity of viruses carried by blood-feeding arthropods (mosquitoes, ticks, etc.) using ensemble learning. It integrates large-scale epidemiological data with genomic functional annotations to assess zoonotic spillover risks.   
Dataset Components:
Epidemiological Characteristics Dataset: Covers 294 viruses and 37 distinct features categorized into:
Virus Properties: Baltimore classification and taxonomy.   
Vector Host Features: Family and genus of vectors (e.g., Culicidae, Ixodidae).   
Non-vector Host Diversity: Distribution across 15 groups, emphasizing the impact of Perissodactyla and Carnivora orders on pathogenicity.   
Viral Sequence Pathogenic Function Dataset: Includes functional annotations for 71,623 viral sequences. Using SeqScreen, 10 key Functional Signatures of Concern (FunSoCs) were identified, such as:   
Viral Adhesion: Found in 62% of sequences, crucial for host cell entry.   
Host Xenophagy & Viral Counter Signaling: Key features for immune evasion.   
Viral Invasion: Associated with non-pathogenic traits in this specific context.   
Technical Application: The data were utilized to develop and validate XGBoost-based models:
Regression Model: Achieved an R² of 90.6%, correlating host diversity with pathogenicity.   
Classification Model: Achieved an F1 score of 96.79% for identifying pathogenic potential at the sequence level.   
External Validation: Includes predictions for 228 sequences, highlighting potential risks from Palma and Zaliv Terpeniya viruses.   
Research Value: This resource allows for the strain-level prediction of pathogenicity within metagenomic data, providing a robust framework for early warning systems of emerging zoonotic threats.
DOI: 10.17632/pd6xzkgncc
title: How Tax Structure Shapes FDI Attractiveness and Economic Growth in Developing Economies?

description: This replication package provides all publicly available code, documentation, and a synthetic dataset designed to reproduce the full computational workflow of the study, including data preparation, variable construction, model estimation procedures, and output generation. The package aims to ensure transparency and enable independent verification of the empirical methodology and analytical procedures.
Due to strict data access and disclosure restrictions imposed by the project funding agency, we provide a synthetic dataset. The synthetic datasets were generated by replacing real values with random values, while being designed to closely match the original dataset in terms of the number of observations, mean, standard deviation, minimum, and maximum values. These synthetic data are intended to illustrate the empirical workflow and code execution, and to allow users to run the master replication script end-to-end with minimal effort. The synthetic dataset is provided solely for methodological demonstration purposes and is not intended to reproduce the numerical results reported in the paper.
DOI: 10.6084/m9.figshare.31144105
title: Metadata record for narrative interview data on war displacement and housing among older Sudanese refugees

description: This record describes narrative interview data collected for a pilot qualitative study examining the impact of war-related displacement on housing conditions and everyday living experiences among older Sudanese people. The study focused on older Sudanese refugees living in Cairo and explored how displacement journeys intersect with housing provision in later life, with particular attention to needs, priorities, and agency in relation to dwelling conditions.The dataset comprises in-depth narrative interviews with 12 participants aged 51–86 years. Interviews elicited life-history and displacement narratives, including accounts of pre-war life in Sudan, experiences of forced migration, and subsequent settlement in urban displacement settings. Participants reflected on changes in housing conditions over time, degrees of control and adaptability within dwellings, and how displacement has reshaped their experiences of ageing and future-oriented living.The interview data contain highly sensitive personal narratives relating to war, displacement, and housing insecurity. In accordance with the terms of informed consent and the ethical approval governing this study, the data cannot be made publicly available. No interview transcripts, audio recordings, or identifying materials are shared through this record. This Figshare item provides a metadata description only.<br>
DOI: 10.6084/m9.figshare.31144105.v1
title: Metadata record for narrative interview data on war displacement and housing among older Sudanese refugees

description: This record describes narrative interview data collected for a pilot qualitative study examining the impact of war-related displacement on housing conditions and everyday living experiences among older Sudanese people. The study focused on older Sudanese refugees living in Cairo and explored how displacement journeys intersect with housing provision in later life, with particular attention to needs, priorities, and agency in relation to dwelling conditions.The dataset comprises in-depth narrative interviews with 12 participants aged 51–86 years. Interviews elicited life-history and displacement narratives, including accounts of pre-war life in Sudan, experiences of forced migration, and subsequent settlement in urban displacement settings. Participants reflected on changes in housing conditions over time, degrees of control and adaptability within dwellings, and how displacement has reshaped their experiences of ageing and future-oriented living.The interview data contain highly sensitive personal narratives relating to war, displacement, and housing insecurity. In accordance with the terms of informed consent and the ethical approval governing this study, the data cannot be made publicly available. No interview transcripts, audio recordings, or identifying materials are shared through this record. This Figshare item provides a metadata description only.<br>