cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/9byn7g2jhg
title: HPRT-YOLO dataset Part3-val

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 3, which contains the val set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.17632/9byn7g2jhg.1
title: HPRT-YOLO dataset Part3-val

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 3, which contains the val set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.17632/c946dnfxjw
title: HPRT-YOLO dataset Part2-train

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 2, which contains the train set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.17632/c946dnfxjw.1
title: HPRT-YOLO dataset Part2-train

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 2, which contains the train set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.15468/dl.ad8smp
title: Occurrence Download

description: A dataset containing 30235 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is New Zealand Garden Bird Survey 2019"
 ]
}

 The dataset includes 30235 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020677-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bc3mwxgzpm
title: HPRT-YOLO dataset Part1-test

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 1, which contains the test set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.17632/bc3mwxgzpm.1
title: HPRT-YOLO dataset Part1-test

description: HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. 
• Source Dataset 1: SeaDronesSee
It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750. The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. 
• Source Dataset 2: Drone-Based Water Rescue and Surveillance
It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. 
To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak. 

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference. 

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 1, which contains the test set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.
DOI: 10.15468/dl.nxgmyv
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Staphylea cochinchinensis",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 27 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020678-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hm9jxhbsm8.2
title: Supplementary material for "Therapeutic Data on 131I-Labeled Glypican-3 Antibody in Hepatocellular Carcinoma"

description: Data for “Therapeutic Data on 131I-Labeled Glypican-3 Antibody in Hepatocellular Carcinoma”
DOI: 10.17632/6fx2zdpfrv
title: Data asset disclosure and cost of debt (data and code)

description: Empirical data and codes for the study "Are more disclosures always better? Data asset information disclosure and cost of debt in China" in Journal "Emerging Markets Review"
DOI: 10.17632/6fx2zdpfrv.1
title: Data asset disclosure and cost of debt (data and code)

description: Empirical data and codes for the study "Are more disclosures always better? Data asset information disclosure and cost of debt in China" in Journal "Emerging Markets Review"
DOI: 10.17632/w36rk7vh93
title: Power Quality Issues Dataset Using Short Time Fourier Transform (STFT)

description: Power Quality (PQ) has become one of the most crucial issues in contemporary power systems because of the large-scale growth of renewable energy sources, the existence of complex industrial loads, and sophisticated electrical and electronic apparatus. A properly designed dataset is necessary to detect and categorize various PQ disturbances with the help of artificial intelligence (AI) techniques. In this work, the PQ dataset is created through a method of Short-Time Fourier Transform (STFT). There are five key disturbances of PQ that are considered, which are voltage sag, swell, harmonics, interruption and transients. To obtain the original dataset, 100 signal samples are produced for each disturbance, and 2091 features are extracted using STFT.
To reduce the burden of computation, the standard deviation method is conducted, which leads to a reduced dataset with 794 features per sample. A total of 500 samples and 794 features are obtained, and all PQ disturbances are numbered as follows: 0 Sag, 1 Swell, 2 Harmonics, 3 Interruption, and 4 Transients.
DOI: 10.17632/w36rk7vh93.1
title: Power Quality Issues Dataset Using Short Time Fourier Transform (STFT)

description: Power Quality (PQ) has become one of the most crucial issues in contemporary power systems because of the large-scale growth of renewable energy sources, the existence of complex industrial loads, and sophisticated electrical and electronic apparatus. A properly designed dataset is necessary to detect and categorize various PQ disturbances with the help of artificial intelligence (AI) techniques. In this work, the PQ dataset is created through a method of Short-Time Fourier Transform (STFT). There are five key disturbances of PQ that are considered, which are voltage sag, swell, harmonics, interruption and transients. To obtain the original dataset, 100 signal samples are produced for each disturbance, and 2091 features are extracted using STFT.
To reduce the burden of computation, the standard deviation method is conducted, which leads to a reduced dataset with 794 features per sample. A total of 500 samples and 794 features are obtained, and all PQ disturbances are numbered as follows: 0 Sag, 1 Swell, 2 Harmonics, 3 Interruption, and 4 Transients.
DOI: 10.17632/rvhh2rdt7t
title: Learning Culture That Delivers Sustainable Innovation Performance: The Hidden Pathway of Sharing and Absorption

description: This repository contains the empirical materials supporting a study that examines how Organisational Learning Culture (OLC) influences Sustainable Innovation Performance (SIP) through two knowledge-based capabilities, Knowledge Sharing Capability (KSC) and Absorptive Capacity (AC). The dataset was collected via a cross-sectional, self-administered online survey using purposive sampling, with respondents required to have at least one year of work experience and familiarity with organisational learning and knowledge practices; a total of 240 valid responses were retained in line with Hair’s rule-of-thumb (10 respondents per indicator across 24 indicators). The hypotheses tested are that OLC positively affects KSC, AC, and SIP, that both KSC and AC positively affect SIP, and that KSC and AC mediate the OLC–SIP relationship. The data show strong measurement quality (high standardised loadings, high reliability and AVE, and satisfactory discriminant validity) and significant structural relationships, including significant indirect effects through both KSC and AC, indicating partial mediation whereby learning culture enhances sustainable innovation by strengthening internal knowledge exchange and external knowledge utilisation capabilities. The repository includes item-level tabulation (DATA TABULATION.xlsx), respondent demographics (RESPONDENT DEMOGRAPHIC.xlsx), measurement outputs (MEASUREMENT RESULT.xlsx and MEASUREMENT MODEL.png), structural outputs including model fit, R², direct paths and specific indirect effects (STRUCTURAL RESULT.xlsx and STRUCTURAL MODEL.png), algorithm outputs (ALGORITHM RESULT.xlsx), and the conceptual framework (CONCEPTUAL FRAMEWORK.png), enabling users to understand what the data represents, how it was gathered, and how to replicate or reuse the analyses for related knowledge management and sustainability-oriented innovation research.
DOI: 10.17632/rvhh2rdt7t.1
title: Learning Culture That Delivers Sustainable Innovation Performance: The Hidden Pathway of Sharing and Absorption

description: This repository contains the empirical materials supporting a study that examines how Organisational Learning Culture (OLC) influences Sustainable Innovation Performance (SIP) through two knowledge-based capabilities, Knowledge Sharing Capability (KSC) and Absorptive Capacity (AC). The dataset was collected via a cross-sectional, self-administered online survey using purposive sampling, with respondents required to have at least one year of work experience and familiarity with organisational learning and knowledge practices; a total of 240 valid responses were retained in line with Hair’s rule-of-thumb (10 respondents per indicator across 24 indicators). The hypotheses tested are that OLC positively affects KSC, AC, and SIP, that both KSC and AC positively affect SIP, and that KSC and AC mediate the OLC–SIP relationship. The data show strong measurement quality (high standardised loadings, high reliability and AVE, and satisfactory discriminant validity) and significant structural relationships, including significant indirect effects through both KSC and AC, indicating partial mediation whereby learning culture enhances sustainable innovation by strengthening internal knowledge exchange and external knowledge utilisation capabilities. The repository includes item-level tabulation (DATA TABULATION.xlsx), respondent demographics (RESPONDENT DEMOGRAPHIC.xlsx), measurement outputs (MEASUREMENT RESULT.xlsx and MEASUREMENT MODEL.png), structural outputs including model fit, R², direct paths and specific indirect effects (STRUCTURAL RESULT.xlsx and STRUCTURAL MODEL.png), algorithm outputs (ALGORITHM RESULT.xlsx), and the conceptual framework (CONCEPTUAL FRAMEWORK.png), enabling users to understand what the data represents, how it was gathered, and how to replicate or reuse the analyses for related knowledge management and sustainability-oriented innovation research.
DOI: 10.5281/zenodo.18650272
title: citharinus_macrolepis_spades_01.fasta.3.fasta
DOI: 10.5281/zenodo.18650273
title: citharinus_macrolepis_spades_01.fasta.3.fasta
DOI: 10.15468/dl.9bpsqp
title: Occurrence Download

description: A dataset containing 316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tabernaemontana pandacaqui",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 316 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020679-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/892xx5fzts
title: IBCM in Quang Nam data

description: The dataset includes data on IBCM surveillance outcomes, access to rabies post-exposure prophylaxis (PEP), and knowledge of rabies diagnosis in animals among health and veterinary personnel.
DOI: 10.17632/892xx5fzts.1
title: IBCM in Quang Nam data

description: The dataset includes data on IBCM surveillance outcomes, access to rabies post-exposure prophylaxis (PEP), and knowledge of rabies diagnosis in animals among health and veterinary personnel.
DOI: 10.15468/dl.nrfpjm
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Corydoras bilineatus"
 ]
}

 The dataset includes 28 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020681-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rcd75n4yfk
title: Secretome datasets: WT MEFs, Wnt2b⁻/⁻ MEFs, Wnt2b⁻/⁻ + WNT2B-FL, and Wnt2b⁻/⁻ + WNT2B-MD.

description: This secretome dataset was generated from mouse embryonic fibroblasts (MEFs). Specifically, MEFs were infected with AAV to knock out endogenous Wnt2b. Subsequently, Wnt2b-knockout MEFs were transfected with plasmids overexpressing full-length WNT2B (WNT2B-FL) or the WNT2B middle domain (WNT2B-MD), while control cells were transfected with an empty vector. At 48 hours post-transfection, the culture medium was replaced with serum-free medium, and cells were cultured for an additional 24 hours. The conditioned media were then collected and subjected to proteomic analysis for secreted proteins.
DOI: 10.15468/dl.59z6z5
title: Occurrence Download

description: A dataset containing 46659 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is New Zealand Garden Bird Survey 2018"
 ]
}

 The dataset includes 46659 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020680-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rcd75n4yfk.1
title: Secretome datasets: WT MEFs, Wnt2b⁻/⁻ MEFs, Wnt2b⁻/⁻ + WNT2B-FL, and Wnt2b⁻/⁻ + WNT2B-MD.

description: This secretome dataset was generated from mouse embryonic fibroblasts (MEFs). Specifically, MEFs were infected with AAV to knock out endogenous Wnt2b. Subsequently, Wnt2b-knockout MEFs were transfected with plasmids overexpressing full-length WNT2B (WNT2B-FL) or the WNT2B middle domain (WNT2B-MD), while control cells were transfected with an empty vector. At 48 hours post-transfection, the culture medium was replaced with serum-free medium, and cells were cultured for an additional 24 hours. The conditioned media were then collected and subjected to proteomic analysis for secreted proteins.
DOI: 10.17632/svwxnc7gzy
title: Clinical Efficacy and Safety Analysis of 1.5% Ruxolitinib Cream in The Treatment of Facial Vitiligo in Adolescents: A Retrospective Study of 74 Cases

description: This retrospective study evaluates the real-world efficacy and safety of 1.5% ruxolitinib cream in 74 Chinese adolescents (ages 12–18) with facial vitiligo, comparing outcomes between non-segmental (NSV) and segmental (SV) subtypes. Participants applied the cream twice daily for 24 weeks, with efficacy assessed using the Facial Vitiligo Area Scoring Index (F-VASI) at weeks 12 and 24. Results showed significant time-dependent improvement, with higher response rates in progressive, early-stage, and late-onset disease. No statistically significant difference in efficacy was observed between NSV and SV subtypes. Adverse events were mild to moderate, primarily local acneiform eruptions, with no serious events or discontinuations. The study supports topical ruxolitinib as an effective and well-tolerated treatment for adolescent facial vitiligo, highlighting the benefit of early intervention, though larger prospective studies are needed to confirm long-term outcomes.