cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/nbjt6d7tp4
title: Impact of Oil Price Shocks on Japanese Industries

description: This upload provides the replication code and the processed dataset used in the empirical analysis of the paper. Code.ipynb is the Python notebook that reproduces the main analysis (data loading, transformations, estimation, and creation of figures/tables). 分析用データ.csv contains processed monthly series for Japan’s Index of Industrial Production (IIP; 2015=100, seasonally adjusted) and Corporate Goods Price Index (CGPI; 2015=100, seasonally adjusted), where IIP is obtained from the Ministry of Economy, Trade and Industry (METI) and CGPI from the Bank of Japan (BOJ). OECD_plus6_industrial_production.csv provides the monthly World Industrial Production (WIP) series (proxy for global real economic activity), retrieved from James D. Hamilton’s website. WTISPLC1.csv provides the monthly average spot crude oil price WTI (West Texas Intermediate), retrieved from FRED. All series are monthly. The included data are processed/compiled for replication purposes; users should cite the original data providers listed above when using the underlying sources.
DOI: 10.17632/nbjt6d7tp4.1
title: Impact of Oil Price Shocks on Japanese Industries

description: This upload provides the replication code and the processed dataset used in the empirical analysis of the paper. Code.ipynb is the Python notebook that reproduces the main analysis (data loading, transformations, estimation, and creation of figures/tables). 分析用データ.csv contains processed monthly series for Japan’s Index of Industrial Production (IIP; 2015=100, seasonally adjusted) and Corporate Goods Price Index (CGPI; 2015=100, seasonally adjusted), where IIP is obtained from the Ministry of Economy, Trade and Industry (METI) and CGPI from the Bank of Japan (BOJ). OECD_plus6_industrial_production.csv provides the monthly World Industrial Production (WIP) series (proxy for global real economic activity), retrieved from James D. Hamilton’s website. WTISPLC1.csv provides the monthly average spot crude oil price WTI (West Texas Intermediate), retrieved from FRED. All series are monthly. The included data are processed/compiled for replication purposes; users should cite the original data providers listed above when using the underlying sources.
DOI: 10.34740/kaggle/dsv/14793583
title: THROMBUS VS NON-THROMBUS Dataset Images
DOI: 10.15468/dl.fkax6m
title: Occurrence Download

description: A dataset containing 150 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pachira retusa",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 150 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005071-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48544/5605d6d4-78c6-4ebb-934b-a3b78f0c1563
title: Arbeidsoverproductiviteit : voorbij de menselijke maat
DOI: 10.5281/zenodo.18590680
title: LINF_320040500

description: DnaJ domain-containing protein | JDP45 | J45; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18590679
title: LINF_320040500

description: DnaJ domain-containing protein | JDP45 | J45; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.35cf7p
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyphessobrycon pyrrhonotus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005074-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dx2shm
title: Occurrence Download

description: A dataset containing 7613 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bidens subalternans",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 7613 records from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005073-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cyzj2bd9n2
title: ALIW-farm

description: This dataset comprises multi-sensor data collected from a custom unmanned ground vehicle (UGV) platform navigating through diverse unstructured agricultural environments, including open farmlands, papaya groves, and GPS-denied betel nut forests.
Sensor Configuration:

 LiDAR: Livox Mid-360 with integrated IMU (Inertial Measurement Unit)
 Wheel Odometry: High-precision wheel encoders for linear and angular velocity measurements
 Extrinsic Calibration: Pre-calibrated spatial relationships between LiDAR-IMU and wheel odometry frames

Environmental Coverage:
The dataset captures challenging scenarios typical of agricultural robotics applications:

 Open farmland: Feature-sparse arable land with sparse geometric constraints
 Orchard environments: Papaya groves with dense canopy structures
 GPS-denied areas: Betel nut forests with severe signal occlusion

Data Characteristics:

 High-frequency IMU measurements for state propagation
 Wheel odometry data providing velocity and scale constraints
 3D LiDAR point clouds exhibiting non-uniform spatial distribution
 Severe motion disturbances and terrain vibrations representative of real-world farm operations

Intended Use:
This dataset is specifically designed for evaluating tightly-coupled multi-sensor SLAM algorithms (LiDAR-IMU-Wheel fusion) in geometrically degraded agricultural environments.It serves as a complementary benchmark to existing public datasets (e.g., CitrusFarm) by providing:

 Diverse environmental diversity across different crop types
 Explicit LiDAR-IMU extrinsic parameters
 Real-world validation scenarios for robustness analysis

File Format:
ROS bag format with synchronized sensor topics including IMU raw data, wheel encoder readings, and LiDAR point cloud streams.
DOI: 10.17632/cyzj2bd9n2.1
title: ALIW-farm

description: This dataset comprises multi-sensor data collected from a custom unmanned ground vehicle (UGV) platform navigating through diverse unstructured agricultural environments, including open farmlands, papaya groves, and GPS-denied betel nut forests.
Sensor Configuration:

 LiDAR: Livox Mid-360 with integrated IMU (Inertial Measurement Unit)
 Wheel Odometry: High-precision wheel encoders for linear and angular velocity measurements
 Extrinsic Calibration: Pre-calibrated spatial relationships between LiDAR-IMU and wheel odometry frames

Environmental Coverage:
The dataset captures challenging scenarios typical of agricultural robotics applications:

 Open farmland: Feature-sparse arable land with sparse geometric constraints
 Orchard environments: Papaya groves with dense canopy structures
 GPS-denied areas: Betel nut forests with severe signal occlusion

Data Characteristics:

 High-frequency IMU measurements for state propagation
 Wheel odometry data providing velocity and scale constraints
 3D LiDAR point clouds exhibiting non-uniform spatial distribution
 Severe motion disturbances and terrain vibrations representative of real-world farm operations

Intended Use:
This dataset is specifically designed for evaluating tightly-coupled multi-sensor SLAM algorithms (LiDAR-IMU-Wheel fusion) in geometrically degraded agricultural environments.It serves as a complementary benchmark to existing public datasets (e.g., CitrusFarm) by providing:

 Diverse environmental diversity across different crop types
 Explicit LiDAR-IMU extrinsic parameters
 Real-world validation scenarios for robustness analysis

File Format:
ROS bag format with synchronized sensor topics including IMU raw data, wheel encoder readings, and LiDAR point cloud streams.
DOI: 10.17632/hg9ymt3px9
title: Genome-wide mapping of the Galleria mellonella transcription start sites during fungal infection and treatment

description: The catalog contains the data files for the article
DOI: 10.17632/hg9ymt3px9.3
title: Genome-wide mapping of the Galleria mellonella transcription start sites during fungal infection and treatment

description: The catalog contains the data files for the article
DOI: 10.17632/95htt984ws
title: Aluminum-Aqueous Ammonia Reactions

description: This raw data is part of a study investigating the reactivity of activated aluminum in 30 wt% aqueous ammonia. Unlike pure water, ammonium solution plays a dual role in aluminum reactions as both solvent and gas promoter, enabling the generation of more gas (potential energy) per unit volume of solvent, a crucial feature for compact, energy-dense-seeking applications, such as underwater vehicles.
DOI: 10.17632/95htt984ws.1
title: Aluminum-Aqueous Ammonia Reactions

description: This raw data is part of a study investigating the reactivity of activated aluminum in 30 wt% aqueous ammonia. Unlike pure water, ammonium solution plays a dual role in aluminum reactions as both solvent and gas promoter, enabling the generation of more gas (potential energy) per unit volume of solvent, a crucial feature for compact, energy-dense-seeking applications, such as underwater vehicles.
DOI: 10.15468/dl.776e3w
title: Occurrence Download

description: A dataset containing 785 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geoffroea spinosa",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 785 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005075-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hngfc9dhmm
title: Pan-cancer analysis of ASF1B

description: This research investigated the role of ASF1B in pan-cancer and examined its potential mechanisms. The study employed public databases to explore the relationships of ASF1B expression with prognosis, genomic instability, DNA repair, cancer stemness, and immune infiltration in pan-cancer. Additionally, we used a single-cell dataset and the SpatialDB database to examine the association between ASF1B expression and macrophage infiltration. Furthermore, we applied cMap to screen compounds targeting ASF1B and used the Protein Data Bank alongside AutoDock Vina to determine the three-dimensional model of ASF1B and perform molecular docking.
DOI: 10.17632/hngfc9dhmm.1
title: Pan-cancer analysis of ASF1B

description: This research investigated the role of ASF1B in pan-cancer and examined its potential mechanisms. The study employed public databases to explore the relationships of ASF1B expression with prognosis, genomic instability, DNA repair, cancer stemness, and immune infiltration in pan-cancer. Additionally, we used a single-cell dataset and the SpatialDB database to examine the association between ASF1B expression and macrophage infiltration. Furthermore, we applied cMap to screen compounds targeting ASF1B and used the Protein Data Bank alongside AutoDock Vina to determine the three-dimensional model of ASF1B and perform molecular docking.
DOI: 10.17632/yt42s8zv7x
title: The range dataset of statistical characteristics of soil salinizationin and nutrients in the saline-alkali profile soil of the Qaidam Basin

description: This data includes the content data of total salt, salt-separated ions and nutrients in the saline-alkali soil of the Qaidam Basin in China.
DOI: 10.17632/yt42s8zv7x.1
title: The range dataset of statistical characteristics of soil salinizationin and nutrients in the saline-alkali profile soil of the Qaidam Basin

description: This data includes the content data of total salt, salt-separated ions and nutrients in the saline-alkali soil of the Qaidam Basin in China.
DOI: 10.17632/xjs4bfgzj5
title: A new multi-modal dataset for Dental Plaque Diagnosis of Patients With Fixed Labial Orthodontic Appliances (Part 2 of 2)

description: This dataset represents Part 2 of a two-part release of a multi-modal intraoral image dataset for dental plaque diagnosis in patients with fixed labial orthodontic appliances. Part 1 (DOI: 10.17632/g8yhdvgjy2.2) contains the complementary subset of patient data collected using the same imaging protocol, labeling criteria, and validation process. Together, the two parts constitute the complete dataset and are intended to be used in combination for model training and evaluation.

Objective: This dataset is designed to benchmark and improve plaque diagnosis performance in patients wearing labial fixed orthodontic appliances. It addresses the lack of high-quality, large-scale intraoral image datasets collected from orthodontic patients, where brackets and wires increase diagnostic difficulty.

Data content: The dataset includes two data types collected from 148 patients undergoing fixed orthodontic treatment.

Intraoral images:
* More than 10,000 pre-staining intraoral images spanning from the second premolar on one side to the contralateral second premolar (teeth 5–5), in both the maxillary and mandibular arches.
* Images are annotated into four plaque severity levels with labels 0–1, 2, 3, and 4, representing increasing plaque accumulation.
* Post-staining images were used as a reference for accurate labeling but are not included in the released dataset.
Clinical records:
* Each patient is associated with one clinical examination form.
* Records include gingival bleeding assessment and oral hygiene habits, documented at both patient and tooth levels.

Methodology:
- Data collection: Images were captured using a professional dental camera following a standardized clinical protocol. For each patient, nine images were taken from nine predefined angles to comprehensively cover the anterior dentition.
- Data labeling: All pre-staining images were classified into four plaque severity labels (0–1, 2, 3, 4).
- Peer review: All annotations were independently reviewed by two dentists with over five years of clinical experience.
- Noise creation: For each original image, three noise-augmented versions were generated using image shake, brightness increase, and brightness decrease.
- Data augmentation: Six augmentation techniques were applied, resulting in 72 images per patient to enhance data diversity and robustness.

Usage: The dataset is split into training and testing sets and is intended for training, fine-tuning, and evaluating deep learning models for plaque level diagnosis in patients wearing orthodontic braces.
DOI: 10.17632/xjs4bfgzj5.2
title: A new multi-modal dataset for Dental Plaque Diagnosis of Patients With Fixed Labial Orthodontic Appliances (Part 2 of 2)

description: This dataset represents Part 2 of a two-part release of a multi-modal intraoral image dataset for dental plaque diagnosis in patients with fixed labial orthodontic appliances. Part 1 (DOI: 10.17632/g8yhdvgjy2.2) contains the complementary subset of patient data collected using the same imaging protocol, labeling criteria, and validation process. Together, the two parts constitute the complete dataset and are intended to be used in combination for model training and evaluation.

Objective: This dataset is designed to benchmark and improve plaque diagnosis performance in patients wearing labial fixed orthodontic appliances. It addresses the lack of high-quality, large-scale intraoral image datasets collected from orthodontic patients, where brackets and wires increase diagnostic difficulty.

Data content: The dataset includes two data types collected from 148 patients undergoing fixed orthodontic treatment.

Intraoral images:
* More than 10,000 pre-staining intraoral images spanning from the second premolar on one side to the contralateral second premolar (teeth 5–5), in both the maxillary and mandibular arches.
* Images are annotated into four plaque severity levels with labels 0–1, 2, 3, and 4, representing increasing plaque accumulation.
* Post-staining images were used as a reference for accurate labeling but are not included in the released dataset.
Clinical records:
* Each patient is associated with one clinical examination form.
* Records include gingival bleeding assessment and oral hygiene habits, documented at both patient and tooth levels.

Methodology:
- Data collection: Images were captured using a professional dental camera following a standardized clinical protocol. For each patient, nine images were taken from nine predefined angles to comprehensively cover the anterior dentition.
- Data labeling: All pre-staining images were classified into four plaque severity labels (0–1, 2, 3, 4).
- Peer review: All annotations were independently reviewed by two dentists with over five years of clinical experience.
- Noise creation: For each original image, three noise-augmented versions were generated using image shake, brightness increase, and brightness decrease.
- Data augmentation: Six augmentation techniques were applied, resulting in 72 images per patient to enhance data diversity and robustness.

Usage: The dataset is split into training and testing sets and is intended for training, fine-tuning, and evaluating deep learning models for plaque level diagnosis in patients wearing orthodontic braces.
DOI: 10.17632/g8yhdvgjy2
title: A new multi-modal dataset for Dental Plaque Diagnosis of Patients With Fixed Labial Orthodontic Appliances (Part 1 of 2)

description: This dataset represents Part 1 of a two-part release of a multi-modal intraoral image dataset for dental plaque diagnosis in patients with fixed labial orthodontic appliances. The complete dataset is split into two parts due to repository size constraints. Part 2 (DOI: 10.17632/xjs4bfgzj5.2) contains the remaining patient data collected under the same clinical protocol, annotation scheme, and quality control procedures, and should be used jointly with this dataset for comprehensive benchmarking and analysis.

Objective: This dataset is designed to benchmark and improve plaque diagnosis performance in patients wearing labial fixed orthodontic appliances. It addresses the lack of high-quality, large-scale intraoral image datasets collected from orthodontic patients, where brackets and wires increase diagnostic difficulty.

Data content: The dataset includes two data types collected from 148 patients undergoing fixed orthodontic treatment.

Intraoral images:
* More than 10,000 pre-staining intraoral images spanning from the second premolar on one side to the contralateral second premolar (teeth 5–5), in both the maxillary and mandibular arches.
* Images are annotated into four plaque severity levels with labels 0–1, 2, 3, and 4, representing increasing plaque accumulation.
* Post-staining images were used as a reference for accurate labeling but are not included in the released dataset.
Clinical records:
* Each patient is associated with one clinical examination form.
* Records include gingival bleeding assessment and oral hygiene habits, documented at both patient and tooth levels.

Methodology:
- Data collection: Images were captured using a professional dental camera following a standardized clinical protocol. For each patient, nine images were taken from nine predefined angles to comprehensively cover the anterior dentition.
- Data labeling: All pre-staining images were classified into four plaque severity labels (0–1, 2, 3, 4).
- Peer review: All annotations were independently reviewed by two dentists with over five years of clinical experience.
- Noise creation: For each original image, three noise-augmented versions were generated using image shake, brightness increase, and brightness decrease.
- Data augmentation: Six augmentation techniques were applied, resulting in 72 images per patient to enhance data diversity and robustness.

Usage: The dataset is split into training and testing sets and is intended for training, fine-tuning, and evaluating deep learning models for plaque level diagnosis in patients wearing orthodontic braces.
DOI: 10.15468/dl.azcrct
title: Occurrence Download

description: A dataset containing 9161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Senna spectabilis"
 ]
}

 The dataset includes 9161 records from 413 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005076-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g8yhdvgjy2.2
title: A new multi-modal dataset for Dental Plaque Diagnosis of Patients With Fixed Labial Orthodontic Appliances (Part 1 of 2)

description: This dataset represents Part 1 of a two-part release of a multi-modal intraoral image dataset for dental plaque diagnosis in patients with fixed labial orthodontic appliances. The complete dataset is split into two parts due to repository size constraints. Part 2 (DOI: 10.17632/xjs4bfgzj5.2) contains the remaining patient data collected under the same clinical protocol, annotation scheme, and quality control procedures, and should be used jointly with this dataset for comprehensive benchmarking and analysis.

Objective: This dataset is designed to benchmark and improve plaque diagnosis performance in patients wearing labial fixed orthodontic appliances. It addresses the lack of high-quality, large-scale intraoral image datasets collected from orthodontic patients, where brackets and wires increase diagnostic difficulty.

Data content: The dataset includes two data types collected from 148 patients undergoing fixed orthodontic treatment.

Intraoral images:
* More than 10,000 pre-staining intraoral images spanning from the second premolar on one side to the contralateral second premolar (teeth 5–5), in both the maxillary and mandibular arches.
* Images are annotated into four plaque severity levels with labels 0–1, 2, 3, and 4, representing increasing plaque accumulation.
* Post-staining images were used as a reference for accurate labeling but are not included in the released dataset.
Clinical records:
* Each patient is associated with one clinical examination form.
* Records include gingival bleeding assessment and oral hygiene habits, documented at both patient and tooth levels.

Methodology:
- Data collection: Images were captured using a professional dental camera following a standardized clinical protocol. For each patient, nine images were taken from nine predefined angles to comprehensively cover the anterior dentition.
- Data labeling: All pre-staining images were classified into four plaque severity labels (0–1, 2, 3, 4).
- Peer review: All annotations were independently reviewed by two dentists with over five years of clinical experience.
- Noise creation: For each original image, three noise-augmented versions were generated using image shake, brightness increase, and brightness decrease.
- Data augmentation: Six augmentation techniques were applied, resulting in 72 images per patient to enhance data diversity and robustness.

Usage: The dataset is split into training and testing sets and is intended for training, fine-tuning, and evaluating deep learning models for plaque level diagnosis in patients wearing orthodontic braces.
DOI: 10.15468/dl.y6g2e4
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyphessobrycon robustulus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 8 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005077-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.