cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25452/figshare.plus.29603915.v1
title: Drone Mapping Dataset – Summer 2023 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina during <b>Summer 2023</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.
DOI: 10.13140/rg.2.2.11315.23848
title: anysz_foremny_SKB.docx
DOI: 10.13140/rg.2.2.27908.95367
title: Estadística de la lectura de artículos para la implementación de referencias he información en mi investigación
DOI: 10.25452/figshare.plus.29440793
title: Drone Mapping Dataset – Fall 2023 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Fall 2023</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.<br>
DOI: 10.25452/figshare.plus.29440793.v1
title: Drone Mapping Dataset – Fall 2023 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Fall 2023</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.<br>
DOI: 10.15468/uafufg
title: Données naturalistes opportunistes issues de la plateforme INaturalist-Données naturalistes opportunistes "juliefernandes_c1"

description: Données naturalistes opportunistes saisies sur iNaturalist à partir du compte "juliefernandes_c1"
DOI: 10.5281/zenodo.17382222
title: Vogtareuth Rehabilitation Pose Dataset

description: Vogtareuth Rehablitation (VtR) is a large-scale human posture dataset specific to physical rehablitation. It contains depth images and 2D and 3D pose annotations. The dataset has varied samples of children and a few adults, different genders, a variety of poses such as standing, sitting, bending, etc. and also instances of limited range of motion for some joints.

12 children (6 males and 6 females) were recorded using an Azure Kinect sensor while they played games designed and developed by reFit for rehabilitation. The actions involved in the games are sideways trunk movement in the frontal plane, i.e., torso abduction and adduction, arm abduction and adduction, arm flexion and extension, torso rotation, knee raise, bending, and some free movements.

The pose annotations were extracted using the Azure Kinect Body Tracking SDK . The 3D poses available in this dataset are in the camera coordinate space and the 2D poses are in the depth image space. Altogether 25 joints were recorded.

Skeleton Structure of VtR Dataset




SPINE_BASE
1


SPINE_MID
2


NECK
3


HEAD
4


SHOULDER_LEFT
5


ELBOW_LEFT
6


WRIST_LEFT
7


NOSE
8


SHOULDER_LEFT
9


ELBOW_RIGHT
10


WRIST_RIGHT
11


HAND_RIGHT
12


HIP_LEFT
13


KNEE_LEFT
14


ANKLE_LEFT
15


FOOT_LEFT
16


HIP_RIGHT
17


KNEE_RIGHT
18


ANKLE_RIGHT
19


FOOT_RIGHT
20


SPINE_SHOULDER
21


HANDTIP_LEFT
22


THUMB_LEFT
23


HANDTIP_RIGHT
24


THUMB_RIGHT
25




30 recordings are provided in this dataset. Every recording has three associated files with the following naming convention.



Record_<Recording_Date>_<UserId>_<Recording_Number>_depth.zip: contains the anonymized depth images.

Record_<Recording_Date>_<UserId>_<Recording_Number>_export_2D_depth.csv: contains the 2D posture in the depth image space.

Record_<Recording_Date>_<UserId>_<Recording_Number>_export_3D.csv: contains the 3D posture in camera coordinate space.
DOI: 10.25452/figshare.plus.29881397
title: Drone Mapping Dataset – Winter 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina during <b>Winter 2024 </b>as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.
DOI: 10.5281/zenodo.17382221
title: Vogtareuth Rehabilitation Pose Dataset

description: Vogtareuth Rehablitation (VtR) is a large-scale human posture dataset specific to physical rehablitation. It contains depth images and 2D and 3D pose annotations. The dataset has varied samples of children and a few adults, different genders, a variety of poses such as standing, sitting, bending, etc. and also instances of limited range of motion for some joints.

12 children (6 males and 6 females) were recorded using an Azure Kinect sensor while they played games designed and developed by reFit for rehabilitation. The actions involved in the games are sideways trunk movement in the frontal plane, i.e., torso abduction and adduction, arm abduction and adduction, arm flexion and extension, torso rotation, knee raise, bending, and some free movements.

The pose annotations were extracted using the Azure Kinect Body Tracking SDK . The 3D poses available in this dataset are in the camera coordinate space and the 2D poses are in the depth image space. Altogether 25 joints were recorded.

Skeleton Structure of VtR Dataset




SPINE_BASE
1


SPINE_MID
2


NECK
3


HEAD
4


SHOULDER_LEFT
5


ELBOW_LEFT
6


WRIST_LEFT
7


NOSE
8


SHOULDER_LEFT
9


ELBOW_RIGHT
10


WRIST_RIGHT
11


HAND_RIGHT
12


HIP_LEFT
13


KNEE_LEFT
14


ANKLE_LEFT
15


FOOT_LEFT
16


HIP_RIGHT
17


KNEE_RIGHT
18


ANKLE_RIGHT
19


FOOT_RIGHT
20


SPINE_SHOULDER
21


HANDTIP_LEFT
22


THUMB_LEFT
23


HANDTIP_RIGHT
24


THUMB_RIGHT
25




30 recordings are provided in this dataset. Every recording has three associated files with the following naming convention.



Record_<Recording_Date>_<UserId>_<Recording_Number>_depth.zip: contains the anonymized depth images.

Record_<Recording_Date>_<UserId>_<Recording_Number>_export_2D_depth.csv: contains the 2D posture in the depth image space.

Record_<Recording_Date>_<UserId>_<Recording_Number>_export_3D.csv: contains the 3D posture in camera coordinate space.
DOI: 10.25452/figshare.plus.29881397.v1
title: Drone Mapping Dataset – Winter 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina during <b>Winter 2024 </b>as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.
DOI: 10.13140/rg.2.2.22364.60805
title: 2.Inflammatory bowel disease
DOI: 10.13140/rg.2.2.19657.97129
title: IMG 9600 358 socioeducational landascape image
DOI: 10.13140/rg.2.2.22174.55361
title: IMG 9600 350 socioeducational landascape image
DOI: 10.13140/rg.2.2.25929.76646
title: ADRENAL GLAND disease
DOI: 10.13140/rg.2.2.28885.44007
title: IMG 9600 349 socioeducational landascape image
DOI: 10.13140/rg.2.2.32240.88324
title: IMG 9600 347 socioeducational landascape image
DOI: 10.13140/rg.2.2.11374.22088
title: IMG 9628 366 socioeducational landascape image
DOI: 10.13140/rg.2.2.17036.53128
title: IMG 9628 382 socioeducational landascape image
DOI: 10.13140/rg.2.2.24219.27685
title: mc cu v0.001 1.7.2024 ddb
DOI: 10.13140/rg.2.2.28177.65127
title: wasp-gall location
DOI: 10.15468/dl.pgz5v6
title: Occurrence Download

description: A dataset containing 2580 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Assiminea)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2580 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048335-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25452/figshare.plus.29924753
title: Drone Mapping Dataset - Spring 2024 - Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Spring 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and
DOI: 10.25452/figshare.plus.29924753.v1
title: Drone Mapping Dataset - Spring 2024 - Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Spring 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and
DOI: 10.13140/rg.2.2.34560.85762
title: Lady's Bedstraw
DOI: 10.15468/gww9td
title: Données naturalistes opportunistes issues de la plateforme INaturalist-Données naturalistes opportunistes "faunetastique"

description: Données naturalistes opportunistes saisies sur iNaturalist à partir du compte "faunetastique"
DOI: 10.13140/rg.2.2.19566.22087
title: Escarabajo Tigre (Coleoptera: Cicindelidae) Asociado a Laderas con Cultivos Hortícolas en Silla Pando, Volcán, Chiriquí, Panamá - Artículo de Divulgación
DOI: 10.25452/figshare.plus.29886167
title: Drone Mapping Dataset – Summer 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina during <b>Summer 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.
DOI: 10.25452/figshare.plus.29886167.v1
title: Drone Mapping Dataset – Summer 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina during <b>Summer 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.
DOI: 10.13140/rg.2.2.28248.10245
title: (29) CEWs Meet All Relevant World Electrical Standards 2023--08-13 001
DOI: 10.13140/rg.2.2.35273.56168
title: Is self-critical rationalism(critical rationalism) more scientific than extreme skepticism (skeptical empiricism)? Specifically how?
DOI: 10.3886/e113599v1-189607
title: Replication data for: The Miracle of Microfinance? Evidence from a Randomized Evaluation

description: This paper reports results from the randomized evaluation of a group-lending microcredit program in Hyderabad, India. A lender worked in 52 randomly selected neighborhoods, leading to an 8.4 percentage point increase in takeup of microcredit. Small business investment and profits of preexisting businesses increased, but consumption did not significantly increase. Durable goods expenditure increased, while "temptation goods" expenditure declined. We found no significant changes in health, education, or women's empowerment. Two years later, after control areas had gained access to microcredit but households in treatment area had borrowed for longer and in larger amounts, very few significant differences persist. (JEL G21, G31, O16, O12, L25, I38)
DOI: 10.25452/figshare.plus.29896316
title: Drone Mapping Dataset – Fall 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Fall 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.<br>
DOI: 10.25452/figshare.plus.29896316.v1
title: Drone Mapping Dataset – Fall 2024 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Fall 2024</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.<br>
DOI: 10.13140/rg.2.2.24342.15681
title: Demostrative model of SCR firing under direct current condictions
DOI: 10.15468/dl.4sfpx4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bambusa balcooa",
 "HasCoordinate is true",
 "Country is Namibia"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048336-251009101135966/datasets/export for details.
DOI: 10.13140/rg.2.2.17146.30400
title: The Last Remedy -18 January 2004
DOI: 10.13140/rg.2.2.19453.17123
title: Correcting Mr. Shahid Javed Burki’s Myopic Views on Punjab’s Role in Pakistan Movement-21 May 2004
DOI: 10.13140/rg.2.2.33713.80489
title: ADRENAL GLAND DISORDERS PROFESSOR/MOHAMMED AHMED BAMASHMOS PROFESSOR OF INTERNAL MEDICINE AND ENDOCRINOLOGY FACULTY OF MEDICINE, SANA'A UNIVERSITY
DOI: 10.15468/69adjx
title: Données naturalistes opportunistes issues de la plateforme INaturalist-Données naturalistes opportunistes "arom24"

description: Données naturalistes opportunistes saisies sur iNaturalist à partir du compte "arom24"
DOI: 10.25452/figshare.plus.29913830
title: Drone Mapping Dataset – Winter 2025 – Sugarloaf Island, NC

description: This dataset includes georeferenced drone-based remote sensing data collected over Sugarloaf Island, North Carolina, during <b>Winter 2025</b> as part of a multi-season shoreline monitoring program (2023–2025). Data were acquired using RTK-GNSS ground control and processed with Structure-from-Motion (SfM) photogrammetry to support coastal mapping, shoreline monitoring, and research on nature-based solutions.<br><br>The dataset contains two independently processed surveys (East and West island segments), each packaged as a zipped folder.<br><br>Each folder contains:<br>- Orthomosaics (0.7 cm resolution)<br>- Digital elevation models (DEMs, non-interpolated, 5 cm resolution)<br>- Dense point clouds and ground point clouds<br>- Drone imagery<br>- RTK-GNSS ground control points and checkpoint data<br>- Metadata and README files describing acquisition parameters, processing workflow, and accuracy metrics<br><br>These data support applications in coastal mapping, shoreline monitoring, nature-based solutions assessment, digital elevation model (DEM) generation, coastal geomorphology, shoreline change analysis, and barrier island restoration monitoring.<br><br>Recommended citation formats and use cases are provided in the included README files.<br>