cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5880/icgem.2026.001
title: HUST-Grace2026s: unconstrained GRACE and GRACE Follow-On static gravity field solution

description: The new unconstrained static gravity field HUST-Grace2026s is recently developed at Huazhong University of Science and Technology. During retrieving our model, the reprocessed GRACE L1b RL03 data and GRACE-FO RL04 data are used, and the newly de-aliasing product AOD1B RL07 is applied in the time span from 2002-04 to 2025-03. In addition, a hybrid processing chain is applied to improve the quality of final solutions. Further details are presented in Zhou et al. (2024) and Zheng et al. (2026).
DOI: 10.21979/n9/kvo4jm
title: Seismic receiver function data of Aceh, Indonesia

description: This dataset archives P-wave receiver functions derived from seismograms of 155 Z-land short-period nodal stations and 2 broadband stations deployed in Aceh, northern Sumatra.
DOI: 10.34740/kaggle/dsv/14622212
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.5281/zenodo.18243601
title: Pretrain anndata
DOI: 10.5281/zenodo.18295469
title: Pretrain anndata
DOI: 10.5281/zenodo.18161789
title: Development and Validation of the Generative Artificial Intelligence-Assisted Engineering Creativity Scale (GECS) among Chinese Students
DOI: 10.5281/zenodo.18161788
title: Development and Validation of the Generative Artificial Intelligence-Assisted Engineering Creativity Scale (GECS) among Chinese Students
DOI: 10.5281/zenodo.18373261
title: AI-driven data lineage verification using temporal analysis with graph-based anomaly detection: A Comparative approach of supervised and unsupervised learning

description: In data-centric industries, such as finance, healthcare, and cybersecurity, maintaining the integrity and accuracy of data lineage is crucial due to compliance requirements. Current methods for verifying data lineage often struggle with the dynamic and multi-sourced nature of datasets, as well as their scale, resulting in reduced performance in detecting anomalies or validating lineage. In this paper, we introduce and provide an empirical assessment of two artificial intelligence-based frameworks for data lineage verification and anomaly detection, which complement each other. In the first phase, we developed an unsupervised approach using Graph Attention Networks (GATs) for structural representation learning and an Isolation Forest for ‘no-label’ anomaly detection. The model surrogate for validation produced over 99.8% accurate reporting on replicated anomaly patterns in unseen test data for Olist, TPC-H, and MIMIC-III. In the second phase, we developed a supervised, multi-modal framework that integrates GNNs, LSTM-Attention networks, DTW for automatic labeling, and Contrastive Learning. To counter the integrated class imbalance of the anomaly detection class, this framework incorporates the Synthetic Minority Over-Sampling Technique (SMOTE) as a fundamental component of its training. Comparing both models on three datasets, the unsupervised model outperforms the supervised model due to its ability to dynamically adapt to data without requiring labels. The supervised model achieves a maximum AUC of 0.96, and the Unsupervised model achieves an AUC of 1.00, indicating better prediction efficiency. The multi-faceted comparison of performance, feature importance, and operational dashboards provides the user with valuable insights, thereby confirming the effectiveness of the first unsupervised model and the second supervised multi-modal model, while fully retaining the explainability, governance, and scalability of data lineage in a comparative pair.
DOI: 10.5281/zenodo.18373262
title: AI-driven data lineage verification using temporal analysis with graph-based anomaly detection: A Comparative approach of supervised and unsupervised learning

description: In data-centric industries, such as finance, healthcare, and cybersecurity, maintaining the integrity and accuracy of data lineage is crucial due to compliance requirements. Current methods for verifying data lineage often struggle with the dynamic and multi-sourced nature of datasets, as well as their scale, resulting in reduced performance in detecting anomalies or validating lineage. In this paper, we introduce and provide an empirical assessment of two artificial intelligence-based frameworks for data lineage verification and anomaly detection, which complement each other. In the first phase, we developed an unsupervised approach using Graph Attention Networks (GATs) for structural representation learning and an Isolation Forest for ‘no-label’ anomaly detection. The model surrogate for validation produced over 99.8% accurate reporting on replicated anomaly patterns in unseen test data for Olist, TPC-H, and MIMIC-III. In the second phase, we developed a supervised, multi-modal framework that integrates GNNs, LSTM-Attention networks, DTW for automatic labeling, and Contrastive Learning. To counter the integrated class imbalance of the anomaly detection class, this framework incorporates the Synthetic Minority Over-Sampling Technique (SMOTE) as a fundamental component of its training. Comparing both models on three datasets, the unsupervised model outperforms the supervised model due to its ability to dynamically adapt to data without requiring labels. The supervised model achieves a maximum AUC of 0.96, and the Unsupervised model achieves an AUC of 1.00, indicating better prediction efficiency. The multi-faceted comparison of performance, feature importance, and operational dashboards provides the user with valuable insights, thereby confirming the effectiveness of the first unsupervised model and the second supervised multi-modal model, while fully retaining the explainability, governance, and scalability of data lineage in a comparative pair.
DOI: 10.15468/dl.9n45jn
title: Occurrence Download

description: A dataset containing 353 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "TaxonKey is Citrus maxima"
 ]
}

 The dataset includes 353 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011866-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7303/syn72399776.1
title: BSSO NSD
DOI: 10.15468/dl.ukw28q
title: Occurrence Download

description: A dataset containing 213574 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Fusarium"
 ]
}

 The dataset includes 213574 records from 408 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011821-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21979/n9/gvcvo0
title: Replication Data for: Electrically Tunable Topological Notch Filter for THz Integrated Photonics

description: Electromagnetic filtering is essential for emerging integrated photonic technologies and widely adaptable processing of high-bandwidth signals. Conventional electro-optic modulator-based photonic approach for signal filtering at microwave frequencies could not be implemented at terahertz (THz) frequencies due to the unavailability of the THz signal-driven optical modulator. Here, we demonstrate an electrically tunable on-chip THz photonic notch filter based on the topologically protected valley hall waveguide-cavity platform. Our device shows a significantly large notch suppression depth of more than 20 dB with a return loss of 13 dB at in the entire tuning range of notch frequency. The feedback control circuit enabled precise control of the notch frequency shift with a minimum step size of 7 MHz. Our work extends the application of topological photonic crystals in developing THz integrated photonic devices for transformative technologies, including sixth generation (6G) communication and high-resolution spectral sensing.
DOI: 10.5281/zenodo.18372718
title: Data from: High-power test of normal conducting cavities with real-time resonant frequency tracking

description: Normal-conducting RF cavities suffer from thermal detuning during high-power operation, traditionally requiring manual frequency adjustments or mechanical tuning. This work presents a digital low-level RF system that autonomously tracks and compensates for resonant frequency shifts using an embedded phase-locked loop and real-time spectrum analysis. The approach maintains reflected power below 8.5% under detuning exceeding 200 kHz, offering a compact, fully electronic alternative to conventional methods.
DOI: 10.5281/zenodo.18372717
title: Data from: High-power test of normal conducting cavities with real-time resonant frequency tracking

description: Normal-conducting RF cavities suffer from thermal detuning during high-power operation, traditionally requiring manual frequency adjustments or mechanical tuning. This work presents a digital low-level RF system that autonomously tracks and compensates for resonant frequency shifts using an embedded phase-locked loop and real-time spectrum analysis. The approach maintains reflected power below 8.5% under detuning exceeding 200 kHz, offering a compact, fully electronic alternative to conventional methods.
DOI: 10.15468/dl.p649vd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Boloria euphrosyne"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011856-260120142942310/datasets/export for details.
DOI: 10.21979/n9/yl4act
title: Replication Data for: 327 Gbps THz Silicon Photonic Interconnect with Sub-λ Bends

description: Miniaturized photonic devices at the terahertz (THz) band are envisioned to bring significant enhancement to data transfer capacity and integration density for computing and future wireless communications. Broadband silicon waveguiding technology has continuously matured to advance low-loss platforms for integrated solutions. However, challenges are faced in realizing compact waveguiding platforms with different degrees of bends due to bend induced losses and mode distortion. Here, we demonstrate multiple bend incorporated photonic crystal waveguide platform for multi-carrier on-chip transmission. Our silicon interconnect device exhibits optimized bending radius to the free space wavelength ratio of 0.74, without signal distortion and transmission bandwidth of 90 GHz, representing 25.4% fractional bandwidth at 355 GHz. The broadband waveguide interconnect enables an aggregate data transfer rate of 327 Gbps by sending the complex modulated data over multiple carriers. This work augments the development of THz photonic integrated circuit (TPIC) for the future generations of on-chip high data-rate interconnect and wireless communication, ranging from the sixth to X generation (6G to XG).
DOI: 10.15468/dl.8nujzt
title: Occurrence Download

description: A dataset containing 10948 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sageretia"
 ]
}

 The dataset includes 10948 records from 313 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011868-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4syug4
title: Occurrence Download

description: A dataset containing 3775194 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae, Undaria pinnatifida, Lindavia intermedia)",
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 0",
 "DecimalLongitude is greater than or equal to -180",
 "DecimalLatitude is greater than or equal to -90",
 {
 "not" : {
 "or" : [
 "Issue is Zero coordinate",
 "Issue is Coordinate out of range",
 "Issue is Country coordinate mismatch"
 ]
 }
 }
 ]
}

 The dataset includes 3775194 records from 310 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011841-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18153415
title: Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers

description: Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers

The package includes datasets, refactoring scripts, experimental results, and survey materials used in the study.

release/├── dataset/│   ├── apps-5000.csv│   └── mbpp.csv├── prompts/│   ├── baseline-Prompt.md│   └── CDDRefactorER-Prompt.md├── results/│   ├── apps-baseline-gpt-5-nano.csv│   ├── apps-baseline-kimi-k2.csv│   ├── apps-refactor-cdd-gpt-5-nano.csv│   ├── apps-refactor-cdd-kimi-k2.csv│   ├── mbpp-baseline-gpt-5-nano.csv│   ├── mbpp-baseline-kimi-k2.csv│   ├── mbpp-refactor-cdd-gpt-5-nano.csv│   └── mbpp-refactor-cdd-kimi-k2.csv├── scripts/│   ├── baseline_llm.py│   ├── baseline_llm_ollama.py│   ├── cdd_refactor_llm.py│   ├── cdd_refactor_llm_ollama.py│   ├── metric-codebleu.py│   └── metric-complexity.py└── survey/    ├── task_list.csv    ├── pre-test survey questions.md    ├── post-test survey questions.md    └── Code Comprehension Study – Participant Instructions.pdf

 

The project evaluates baseline and refactoring oriented prompting strategies for code generation and refactoring tasks. It uses programming benchmarks and applies automated metrics alongside a human code comprehension study.

Two task settings are covered:





Baseline code generation or refactoring.




CDD based refactoring with an emphasis on readability and maintainability.



Datasets

Located in release/dataset/.





apps-5000.csvA subset of programming problems derived from the APPS benchmark.




mbpp.csvProblems from the MBPP benchmark, commonly used for evaluating code generation and reasoning.



Each dataset is provided in CSV format and is consumed directly by the experiment scripts.

Prompts

Located in release/prompts/.





baseline-Prompt.mdPrompt template for the baseline condition.




CDDRefactorER-Prompt.mdPrompt template for the CDD refactoring condition, focused on improving code clarity and structure.



Scripts

Located in release/scripts/.





baseline_llm.py and baseline_llm_ollama.pyRun baseline experiments using API based or Ollama based model execution.




cdd_refactor_llm.py and cdd_refactor_llm_ollama.pyApply the CDD refactoring prompt to code samples.




metric-codebleu.pyComputes CodeBLEU scores for generated or refactored code.




metric-complexity.pyComputes code complexity related metrics.



Scripts are written in Python and assume datasets and prompts are referenced via relative paths.

Results

Located in release/results/.

Result files are CSVs containing model outputs and evaluation metrics for:





Dataset type: APPS or MBPP




Method: baseline or CDD refactor




Model variant



Each file corresponds to a specific experimental configuration.
DOI: 10.5281/zenodo.17199739
title: Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers

description: Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers

The package includes datasets, refactoring scripts, experimental results, and survey materials used in the study.

release/├── dataset/│   ├── apps-5000.csv│   └── mbpp.csv├── prompts/│   ├── baseline-Prompt.md│   └── CDDRefactorER-Prompt.md├── results/│   ├── apps-baseline-gpt-5-nano.csv│   ├── apps-baseline-kimi-k2.csv│   ├── apps-refactor-cdd-gpt-5-nano.csv│   ├── apps-refactor-cdd-kimi-k2.csv│   ├── mbpp-baseline-gpt-5-nano.csv│   ├── mbpp-baseline-kimi-k2.csv│   ├── mbpp-refactor-cdd-gpt-5-nano.csv│   └── mbpp-refactor-cdd-kimi-k2.csv├── scripts/│   ├── baseline_llm.py│   ├── baseline_llm_ollama.py│   ├── cdd_refactor_llm.py│   ├── cdd_refactor_llm_ollama.py│   ├── metric-codebleu.py│   └── metric-complexity.py└── survey/    ├── task_list.csv    ├── pre-test survey questions.md    ├── post-test survey questions.md    └── Code Comprehension Study – Participant Instructions.pdf

 

The project evaluates baseline and refactoring oriented prompting strategies for code generation and refactoring tasks. It uses programming benchmarks and applies automated metrics alongside a human code comprehension study.

Two task settings are covered:





Baseline code generation or refactoring.




CDD based refactoring with an emphasis on readability and maintainability.



Datasets

Located in release/dataset/.





apps-5000.csvA subset of programming problems derived from the APPS benchmark.




mbpp.csvProblems from the MBPP benchmark, commonly used for evaluating code generation and reasoning.



Each dataset is provided in CSV format and is consumed directly by the experiment scripts.

Prompts

Located in release/prompts/.





baseline-Prompt.mdPrompt template for the baseline condition.




CDDRefactorER-Prompt.mdPrompt template for the CDD refactoring condition, focused on improving code clarity and structure.



Scripts

Located in release/scripts/.





baseline_llm.py and baseline_llm_ollama.pyRun baseline experiments using API based or Ollama based model execution.




cdd_refactor_llm.py and cdd_refactor_llm_ollama.pyApply the CDD refactoring prompt to code samples.




metric-codebleu.pyComputes CodeBLEU scores for generated or refactored code.




metric-complexity.pyComputes code complexity related metrics.



Scripts are written in Python and assume datasets and prompts are referenced via relative paths.

Results

Located in release/results/.

Result files are CSVs containing model outputs and evaluation metrics for:





Dataset type: APPS or MBPP




Method: baseline or CDD refactor




Model variant



Each file corresponds to a specific experimental configuration.
DOI: 10.15468/dl.88xep7
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "TaxonKey is Citrus medica"
 ]
}

 The dataset includes 109 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011867-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34847/nkl.ba661iti
title: 9 Death Records 1862
title: 9 Morts à Paris 1862

description: Ce fichier vise à apparier les renseignements que fournissent les déclarations de décès à l’Etat civil et ceux des sources fiscales pour comparer la situation des parisiens au décès selon qu’ils détiennent ou non de la richesse. Parmi les morts de janvier 1862 mentionnés dans les registres de l’Etat civil, il sélectionne les adultes ayant leur domicile légal à Paris dont le nom commence par les lettres A, B, C, D et T pour les apparier aux indications sur la richesse relevée dans les Tables de successions et absences et les déclarations de mutations par décès. On peut ainsi comparer la situation des riches et des pauvres en fonction d’un même ensemble de critères : l’âge, le sexe, la situation maritale, la profession, l’adresse du domicile ainsi que le lieu de décès (à l’hôpital ou à domicile).
description: This file aims to match the information provided by civil‑registration death records with that found in fiscal sources, in order to compare the situation of Parisians at the time of death depending on whether or not they possessed wealth. Among the individuals who died in January 1862 and are listed in the civil‑registration registers, it selects adults whose legal residence was in Paris and whose surname begins with the letters A, B, C, D, or T, in order to match them with the wealth information recorded in the Tables of Successions and Absences and in the declarations of estate transfers upon death. This makes it possible to compare the situation of the rich and the poor using the same set of criteria: age, sex, marital status, occupation, home address, and place of death (in hospital or at home).
DOI: 10.15151/esrf-es-2297671811
title: Benchmarking Gas Binding to Pd(0) Entatic States via High-Pressure In-Crystallo Synthesis and Crystallography

description: We propose an innovative approach combining in-crystallo synthesis and high-pressure crystallography to study gas binding to Pd(0) complexes, with a particular focus on hydrogen activation. Our study centers on the formation of a highly bent Pd(0) complex, representing the entatic state for oxidative addition in cross-coupling catalysis. This complex will be synthesized within the crystal lattice, allowing for direct structural characterization. Using a diamond anvil cell as a gas dosing system, we will introduce H₂, He, and Ne to probe their binding affinities and structural effects on Pd(0). By systematically benchmarking these interactions, we aim to gain fundamental insights into gas binding and activation mechanisms, which could lead to advances in catalyst design, hydrogen storage, and energy-efficient industrial processes. Our findings will provide a deeper understanding of Pd-mediated transformations, aiding the development of more selective and sustainable catalytic systems.
DOI: 10.5281/zenodo.18053515
title: Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests

description: In recent years, the automatic generation of pull requests (PRs) using AI agents has become increasingly common.Although AI-generated PRs are fast and easy to create, their merge rates tend to be lower than those created by humans.In this study, we conduct a large-scale empirical analysis of 40,214 PRs collected from the AIDev dataset.We extract 64 features spanning six families, and fit statistical regression models to analyze and compare the features associated with PR merge outcomes for human and agentic PRs, as well as across three prominent AI agents.The findings of this study provide insights into improving PR quality through human–AI collaboration.
DOI: 10.5281/zenodo.18373332
title: Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests

description: In recent years, the automatic generation of pull requests (PRs) using AI agents has become increasingly common.Although AI-generated PRs are fast and easy to create, their merge rates tend to be lower than those created by humans.In this study, we conduct a large-scale empirical analysis of 40,214 PRs collected from the AIDev dataset.We extract 64 features spanning six families, and fit statistical regression models to analyze and compare the features associated with PR merge outcomes for human and agentic PRs, as well as across three prominent AI agents.The findings of this study provide insights into improving PR quality through human–AI collaboration.
DOI: 10.15468/dl.zh25ye
title: Occurrence Download

description: A dataset containing 428065 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vicia sepium",
 "Year 1000-2022"
 ]
}

 The dataset includes 428065 records from 1920 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011858-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28861931
title: Learning Diary Template

description: Template of Learning Diaries for Capston Projects