cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/x9nynx5dt8.3
title: Data and code supporting "Market Professional Performance under Insider Information Leakage"

description: This dataset contains all the necessary files to replicate the findings, tables, and figures presented in the manuscript “Market Professional Performance under Insider Information Leakage.”
1. Data File (dataset.dta): The final, cleaned panel dataset used for all analyses in the study.
Source: The raw data were originally obtained from the CSMAR (China Stock Market & Accounting Research Database) and RESSET databases. This file contains the processed and merged variables as described in the manuscript.

2. Analysis Script (empirical_analysis.do): A Stata *.do file that executes the main econometric analyses and generates the results reported in the manuscript (e.g., regression tables).

3. Visualization Scripts (generate_Figure_1.py): Python scripts used to generate all figures presented in the manuscript.
DOI: 10.34808/e8gs-sm18
title: Raw files from digital image correlation analysis of total shrinkage of 3D-printed concrete mixture with magnetite aggregate. Part 3/4

description: Raw image files recorded by the ARAMIS digital image correlation system for 3D-printed concrete mixture with megnetite aggregate (M20M). The files were used for displacement and strain analysis. Sample designation in the files is consistent with the sample designation used in the manuscript associated with the dataset. Part 3/4
M0 - reference sampleM20B - 20% of aggregate replaced with brick aggregateM20M - 20% of aggregate replaced with magnetite aggregate
DOI: 10.5281/zenodo.14181443
title: Probiotic sequences dataset based on Machine Learning

description: ProbioSML is a database comprising 1,071 genes associated with microbial genera that have been demonstrated to possess probiotic properties.
DOI: 10.5281/zenodo.14181444
title: Probiotic sequences dataset based on Machine Learning

description: ProbioSML is a database comprising 1,071 genes associated with microbial genera that have been demonstrated to possess probiotic properties.
DOI: 10.17632/sf6fd7y4fs
title: India-wide Threshold Atlas for Extreme Rainfall Analysis under Extreme Value Theory assumptions.

description: This dataset contains the research results associated with the manuscript: "EVT-AutoThresh: Automated Right-Tail Threshold Selection for Peaks-Over-Threshold Extreme Value Analysis."

Contents:

India POT Threshold Dataset: A national-scale atlas of extreme rainfall thresholds and corresponding percentiles for 4,612 grid cells (0.25° resolution) across India under Extreme Value Theory assumptions.

Data Source: Derived from the India Meteorological Department (IMD) daily gridded rainfall dataset (1901-2024).

Formats: Provided as geospatial shapefiles and tabular CSV for statistical analysis.

Software Link: The source code for the EVT-AutoThresh tool used to generate this data is available at: https://github.com/hysunburg2407/EVT-AutoThresh
DOI: 10.17632/sf6fd7y4fs.1
title: India-wide Threshold Atlas for Extreme Rainfall Analysis under Extreme Value Theory assumptions.

description: This dataset contains the research results associated with the manuscript: "EVT-AutoThresh: Automated Right-Tail Threshold Selection for Peaks-Over-Threshold Extreme Value Analysis."

Contents:

India POT Threshold Dataset: A national-scale atlas of extreme rainfall thresholds and corresponding percentiles for 4,612 grid cells (0.25° resolution) across India under Extreme Value Theory assumptions.

Data Source: Derived from the India Meteorological Department (IMD) daily gridded rainfall dataset (1901-2024).

Formats: Provided as geospatial shapefiles and tabular CSV for statistical analysis.

Software Link: The source code for the EVT-AutoThresh tool used to generate this data is available at: https://github.com/hysunburg2407/EVT-AutoThresh
DOI: 10.17632/tnbty8kjjh
title: Dry whole corn optimizes rumen fermentation and gastrointestinal nutrient digestion in sheep by regulating cazymes-microbial synergy in the rumen

description: Processing corn grain alters its ruminal degradation rate and nutritional value in small ruminants. In this study, feeding DWC to sheep prolonged ruminal retention time compared with DRC or DGC, resulting in slower, more sustained starch release. This led to higher total-tract and foregut starch digestibility, increased ruminal propionate production, enrichment of starch-degrading bacteria (e.g., Bacteroides and Pseudobutyrivibrio spp.) and carbohydrate-active enzymes, and reduced starch flow and fermentation intensity in the hindgut. No differences were observed in blood parameters. Overall, DWC optimized energy utilization and rumen health, supporting its use in high-starch diets for sheep and potentially other small ruminants such as dairy goats.
DOI: 10.17632/tnbty8kjjh.1
title: Dry whole corn optimizes rumen fermentation and gastrointestinal nutrient digestion in sheep by regulating cazymes-microbial synergy in the rumen

description: Processing corn grain alters its ruminal degradation rate and nutritional value in small ruminants. In this study, feeding DWC to sheep prolonged ruminal retention time compared with DRC or DGC, resulting in slower, more sustained starch release. This led to higher total-tract and foregut starch digestibility, increased ruminal propionate production, enrichment of starch-degrading bacteria (e.g., Bacteroides and Pseudobutyrivibrio spp.) and carbohydrate-active enzymes, and reduced starch flow and fermentation intensity in the hindgut. No differences were observed in blood parameters. Overall, DWC optimized energy utilization and rumen health, supporting its use in high-starch diets for sheep and potentially other small ruminants such as dairy goats.
DOI: 10.15468/dl.5c6f9j
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carex baccans",
 "Year 1970-2000"
 ]
}

 The dataset includes 50 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002136-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/489mxgtz5b
title: Effects of Biochar, Vermicompost, and Inorganic N-P Nutrients on Soil Physical Properties, Maize Growth, and Yield in Northwest Ethiopia

description: Description:
This dataset contains the numerical results and validation data from the associated study. It provides a comprehensive record of soil physical properties (before planting and after two years of field experimentation), maize crop growth measurements (leaf area and leaf area index), harvest index, and grain yield. Crop-related data were collected over two growing seasons, while soil data are presented for the initial state and after the two-year experimental period.
Data Processing and Software Compatibility:
The data were checked for normality using the Shapiro–Wilk test, and homogeneity of variance was tested using Levene’s test. Multiple linear regression assumptions were evaluated using the Variance Inflation Factor (VIF). Statistical analyses were performed using SAS 9.4, employing the PROC GLM procedure. Mean comparisons were conducted using the Least Significant Difference (LSD) test at P &lt; 0.05. Lettering for mean separation was computed using factorial analysis in Statistix 8.1. Economic analysis was performed via partial budget analysis, including calculation of the marginal rate of return.
Dataset Includes:
Table 1: Selected soil physical properties determined and analyzed in this study. Table 2: Leaf area (normalized, coded data) for the 2023/24 and 2024/25 farming seasons. Table 3: Leaf area index (normalized, coded data) for the 2023/24 and 2024/25 farming seasons. Table 4: Harvest index (normalized, coded data) for the 2023/24 and 2024/25 seasons. Table 5: Maize grain yields (normalized, coded data) for the 2023/24 and 2024/25 seasons.
Table 6: Correlation analysis between two-year crop mean data and after-harvest soil physical properties.
This dataset provides a comprehensive resource for understanding the effects of integrated nutrient management on soil physical properties and maize performance in Northwest Ethiopia. It can be used for further research, comparative studies, or modeling purposes, and serves as a reference for improving maize productivity through the combined use of biochar, vermicompost, and inorganic N-P fertilizers.
DOI: 10.17632/489mxgtz5b.1
title: Effects of Biochar, Vermicompost, and Inorganic N-P Nutrients on Soil Physical Properties, Maize Growth, and Yield in Northwest Ethiopia

description: Description:
This dataset contains the numerical results and validation data from the associated study. It provides a comprehensive record of soil physical properties (before planting and after two years of field experimentation), maize crop growth measurements (leaf area and leaf area index), harvest index, and grain yield. Crop-related data were collected over two growing seasons, while soil data are presented for the initial state and after the two-year experimental period.
Data Processing and Software Compatibility:
The data were checked for normality using the Shapiro–Wilk test, and homogeneity of variance was tested using Levene’s test. Multiple linear regression assumptions were evaluated using the Variance Inflation Factor (VIF). Statistical analyses were performed using SAS 9.4, employing the PROC GLM procedure. Mean comparisons were conducted using the Least Significant Difference (LSD) test at P &lt; 0.05. Lettering for mean separation was computed using factorial analysis in Statistix 8.1. Economic analysis was performed via partial budget analysis, including calculation of the marginal rate of return.
Dataset Includes:
Table 1: Selected soil physical properties determined and analyzed in this study. Table 2: Leaf area (normalized, coded data) for the 2023/24 and 2024/25 farming seasons. Table 3: Leaf area index (normalized, coded data) for the 2023/24 and 2024/25 farming seasons. Table 4: Harvest index (normalized, coded data) for the 2023/24 and 2024/25 seasons. Table 5: Maize grain yields (normalized, coded data) for the 2023/24 and 2024/25 seasons.
Table 6: Correlation analysis between two-year crop mean data and after-harvest soil physical properties.
This dataset provides a comprehensive resource for understanding the effects of integrated nutrient management on soil physical properties and maize performance in Northwest Ethiopia. It can be used for further research, comparative studies, or modeling purposes, and serves as a reference for improving maize productivity through the combined use of biochar, vermicompost, and inorganic N-P fertilizers.
DOI: 10.17632/rjygdtmx43
title: Extreme response analysis of hybrid Tension Leg-type offshore wind turbine platform using inverse reliability method

description: the reliability of
hybrid floating wind turbine platforms against extreme loads is established using the inverse
first order reliability method (IFORM) that includes the randomness in the gross wind
environment and the extreme response given wind conditions. The Environmental Contour
(EC) Method provides practical reliability estimation and information on the combination of
environmental variables like wind speed and turbulence, which are critical to specific wind
turbines.
DOI: 10.17632/rjygdtmx43.1
title: Extreme response analysis of hybrid Tension Leg-type offshore wind turbine platform using inverse reliability method

description: the reliability of
hybrid floating wind turbine platforms against extreme loads is established using the inverse
first order reliability method (IFORM) that includes the randomness in the gross wind
environment and the extreme response given wind conditions. The Environmental Contour
(EC) Method provides practical reliability estimation and information on the combination of
environmental variables like wind speed and turbulence, which are critical to specific wind
turbines.
DOI: 10.17632/6snrjwcpkh
title: UAV-OBB: An Aerial Urban Vehicle Dataset with Oriented Bounding Boxes for Remote Sensing Object Detection in Smart Cities

description: UAV-OBB is an aerial urban vehicle dataset designed for rotation-aware object detection and smart-city traffic monitoring using oriented bounding boxes (OBBs). Many UAV/drone datasets annotate vehicles with axis-aligned rectangles that include unnecessary background and do not encode orientation; UAV-OBB provides tight rotated annotations to support accurate localization, orientation estimation, and downstream traffic analysis.

The dataset contains 1,375 RGB images (JPEG) at 1920×1080 resolution captured from predominantly nadir-view UAV imagery over urban roads in Chongqing, and Wuhan. It provides 35,615 oriented vehicle instances across six classes: bike, bus, car, other_vehicle, taxi, and truck. The data are split into 1,158 training images, 207 validation images, and 10 test images. Collection altitude was approximately 75-108 m under diverse real-world conditions, including morning, midday, evening, rain, and mist/light fog. Both wide field-of-view and zoom settings were used to introduce strong scale variation, ranging from small distant vehicles to large close-up buses and trucks.

Annotations are provided in YOLOv8-OBB label format as plain text files (one per image). Each object is encoded by class id and a rotated rectangle represented by four corner vertices (x1,y1…x4,y4) in normalized image coordinates. All instances were manually annotated with rotation-capable tools and double-checked for quality and consistency. Occluded and truncated vehicles were included when the majority of the object was visible, reflecting realistic urban traffic scenes.

Directory structure (YOLO-style)
The dataset is released in a YOLO-style directory layout. The root folder UAV-OBB/ contains the train/validation/test splits, the dataset configuration file (data.yaml), and supplementary MP4 evaluation videos, organized as follows:

UAV-OBB/
├─ train/
│ ├─ images/
│ └─ labels/
├─ valid/
│ ├─ images/
│ └─ labels/
├─ test/
│ ├─ images/
│ └─ labels/
├─ data.yaml
└─ test_videos_mp4/

All images are 1920 × 1080 pixels (8-bit RGB) in resolution and in JPEG format, captured by UAV cameras in two Chinese cities. To support practical evaluation beyond static images, UAV-OBB also includes supplementary MP4 video sequences: a short clip with sparsely annotated reference frames for human-verifiable temporal assessment, and two longer unannotated sequences for qualitative evaluation of stability and deployment behavior. UAV-OBB can be used for oriented detection, tracking, counting, density estimation, and traffic-flow analysis in urban UAV surveillance scenarios.

If you use this dataset in your publication or thesis, please cite:

Ahmad, Israr; Fengjun, Shang; Bibi, Kiran; Slaman Pathan, Muhammad (2026), “UAV-OBB: An Aerial Urban Vehicle Dataset with Oriented Bounding Boxes for Remote Sensing Object Detection in Smart Cities”, Mendeley Data, V3, doi: 10.17632/6snrjwcpkh.3
DOI: 10.17632/6snrjwcpkh.3
title: UAV-OBB: An Aerial Urban Vehicle Dataset with Oriented Bounding Boxes for Remote Sensing Object Detection in Smart Cities

description: UAV-OBB is an aerial urban vehicle dataset designed for rotation-aware object detection and smart-city traffic monitoring using oriented bounding boxes (OBBs). Many UAV/drone datasets annotate vehicles with axis-aligned rectangles that include unnecessary background and do not encode orientation; UAV-OBB provides tight rotated annotations to support accurate localization, orientation estimation, and downstream traffic analysis.

The dataset contains 1,375 RGB images (JPEG) at 1920×1080 resolution captured from predominantly nadir-view UAV imagery over urban roads in Chongqing, and Wuhan. It provides 35,615 oriented vehicle instances across six classes: bike, bus, car, other_vehicle, taxi, and truck. The data are split into 1,158 training images, 207 validation images, and 10 test images. Collection altitude was approximately 75-108 m under diverse real-world conditions, including morning, midday, evening, rain, and mist/light fog. Both wide field-of-view and zoom settings were used to introduce strong scale variation, ranging from small distant vehicles to large close-up buses and trucks.

Annotations are provided in YOLOv8-OBB label format as plain text files (one per image). Each object is encoded by class id and a rotated rectangle represented by four corner vertices (x1,y1…x4,y4) in normalized image coordinates. All instances were manually annotated with rotation-capable tools and double-checked for quality and consistency. Occluded and truncated vehicles were included when the majority of the object was visible, reflecting realistic urban traffic scenes.

Directory structure (YOLO-style)
The dataset is released in a YOLO-style directory layout. The root folder UAV-OBB/ contains the train/validation/test splits, the dataset configuration file (data.yaml), and supplementary MP4 evaluation videos, organized as follows:

UAV-OBB/
├─ train/
│ ├─ images/
│ └─ labels/
├─ valid/
│ ├─ images/
│ └─ labels/
├─ test/
│ ├─ images/
│ └─ labels/
├─ data.yaml
└─ test_videos_mp4/

All images are 1920 × 1080 pixels (8-bit RGB) in resolution and in JPEG format, captured by UAV cameras in two Chinese cities. To support practical evaluation beyond static images, UAV-OBB also includes supplementary MP4 video sequences: a short clip with sparsely annotated reference frames for human-verifiable temporal assessment, and two longer unannotated sequences for qualitative evaluation of stability and deployment behavior. UAV-OBB can be used for oriented detection, tracking, counting, density estimation, and traffic-flow analysis in urban UAV surveillance scenarios.

If you use this dataset in your publication or thesis, please cite:

Ahmad, Israr; Fengjun, Shang; Bibi, Kiran; Slaman Pathan, Muhammad (2026), “UAV-OBB: An Aerial Urban Vehicle Dataset with Oriented Bounding Boxes for Remote Sensing Object Detection in Smart Cities”, Mendeley Data, V3, doi: 10.17632/6snrjwcpkh.3
DOI: 10.34808/k6td-4z89
title: Raw files from digital image correlation analysis of total shrinkage of 3D-printed concrete mixture with magnetite aggregate. Part 2/4

description: Raw image files recorded by the ARAMIS digital image correlation system for 3D-printed concrete mixture with megnetite aggregate (M20M). The files were used for displacement and strain analysis. Sample designation in the files is consistent with the sample designation used in the manuscript associated with the dataset. Part 2/4
M0 - reference sampleM20B - 20% of aggregate replaced with brick aggregateM20M - 20% of aggregate replaced with magnetite aggregate
DOI: 10.15468/dl.5s4hrn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.767 1.452183, 117.696715 1.446615, 117.628161 1.430048, 117.563028 1.402891, 117.502919 1.365812, 117.449316 1.319724, 117.403537 1.265764, 117.36671 1.205261, 117.339742 1.139703, 117.323295 1.070706, 117.317774 0.999969, 117.323314 0.929233, 117.339778 0.860241, 117.36676 0.79469, 117.403595 0.734195, 117.449377 0.680244, 117.502977 0.634166, 117.563077 0.597096, 117.628197 0.569945, 117.696734 0.553383, 117.767 0.547816, 117.837266 0.553383, 117.905803 0.569945, 117.970923 0.597096, 118.031023 0.634166, 118.084623 0.680244, 118.130405 0.734195, 118.16724 0.79469, 118.194222 0.860241, 118.210686 0.929233, 118.216226 0.999969, 118.210705 1.070706, 118.194258 1.139703, 118.16729 1.205261, 118.130463 1.265764, 118.084684 1.319724, 118.031081 1.365812, 117.970972 1.402891, 117.905839 1.430048, 117.837285 1.446615, 117.767 1.452183))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002140-260120142942310/datasets/export for details.
DOI: 10.6084/m9.figshare.28344203
title: Objective Bayesian trend filtering via adaptive piecewise polynomial regression

description: Several methods have been developed for nonparametric regression problems, including classical approaches such as kernels, local polynomials, smoothing splines, sieves, and wavelets, as well as relatively new methods such as lasso, generalized lasso, and trend filtering. This study proposes an objective Bayesian trend filtering method based on model selection. The procedure followed in this study estimates the functions based on adaptive piecewise polynomial regression models with two components. First, we determine the intervals with varying trends using Bayesian binary segmentation and then evaluate the most reasonable trend via Bayesian model selection at these intervals. This trend filtering procedure follows Bayesian model selection that uses intrinsic priors, which eliminated any subjective input. Additionally, we prove that the proposed method using these intrinsic priors was consistent when applied to large sample sizes. The behavior of the proposed Bayesian trend filtering procedure is compared with the trend filtering using a simulation study and real examples. Finally, we apply the proposed method to detect the variance change points under mean changes, whereas the existing methods yielded inaccurate estimates of the variance change points when the mean varied smoothly, as the sudden-change assumption was violated in such cases.
DOI: 10.6084/m9.figshare.28344203.v1
title: Objective Bayesian trend filtering via adaptive piecewise polynomial regression

description: Several methods have been developed for nonparametric regression problems, including classical approaches such as kernels, local polynomials, smoothing splines, sieves, and wavelets, as well as relatively new methods such as lasso, generalized lasso, and trend filtering. This study proposes an objective Bayesian trend filtering method based on model selection. The procedure followed in this study estimates the functions based on adaptive piecewise polynomial regression models with two components. First, we determine the intervals with varying trends using Bayesian binary segmentation and then evaluate the most reasonable trend via Bayesian model selection at these intervals. This trend filtering procedure follows Bayesian model selection that uses intrinsic priors, which eliminated any subjective input. Additionally, we prove that the proposed method using these intrinsic priors was consistent when applied to large sample sizes. The behavior of the proposed Bayesian trend filtering procedure is compared with the trend filtering using a simulation study and real examples. Finally, we apply the proposed method to detect the variance change points under mean changes, whereas the existing methods yielded inaccurate estimates of the variance change points when the mean varied smoothly, as the sudden-change assumption was violated in such cases.