cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6b6kvv
title: Occurrence Download

description: A dataset containing 4469 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Boerhavia coccinea",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 4469 records from 259 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011178-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4jqr8q
title: Occurrence Download

description: A dataset listing the 2827 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Reef Life Survey: Global reef fish dataset",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 2827 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011165-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4536sd
title: Occurrence Download

description: A dataset listing the 35 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.43825 39.80122,-2.29951 39.80122,-2.29951 39.85677,-2.43825 39.85677,-2.43825 39.80122))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 1991-2026"
 ]
}

 The dataset's 35 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011151-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18478427
title: Data associated with community-scale metabolic models for vaginal microbiome
DOI: 10.5281/zenodo.18478426
title: Data associated with community-scale metabolic models for vaginal microbiome
DOI: 10.15468/dl.rhqqxm
title: Occurrence Download

description: A dataset containing 1099108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((5.82949 57.65926,5.68886 54.82494,7.53354 55.18127,8.3972 55.11965,8.58383 54.93728,8.63557 54.91168,8.66018 54.90846,8.66792 54.91117,8.67945 54.91068,8.69101 54.90712,8.70991 54.89875,8.72783 54.89245,8.73809 54.89272,8.74754 54.89497,8.75388 54.89447,8.83786 54.89673,8.9623 54.89782,9.0493 54.87073,9.06244 54.87072,9.07945 54.87115,9.12972 54.87299,9.1425 54.8731,9.16987 54.86672,9.24004 54.84984,9.24424 54.84385,9.2358 54.8359,9.23531 54.83223,9.24106 54.82403,9.25024 54.80911,9.27067 54.81229,9.28401 54.80947,9.28645 54.80434,9.29318 54.80238,9.29306 54.80629,9.29404 54.80837,9.32829 54.80483,9.34285 54.80079,9.34322 54.80275,9.34285 54.80593,9.34616 54.80642,9.35325 54.81302,9.35925 54.81535,9.36769 54.81828,9.37393 54.82036,9.37552 54.82501,9.37686 54.8288,9.37576 54.83003,9.37576 54.83309,9.37858 54.83358,9.38115 54.83578,9.38078 54.83786,9.38286 54.83884,9.38873 54.83761,9.40194 54.84055,9.40329 54.83884,9.40733 54.84214,9.41271 54.84067,9.41687 54.83712,9.41577 54.83553,9.41968 54.83125,9.43254 54.82582,9.45458 54.82611,9.5013 54.84257,9.54273 54.86137,9.57417 54.87342,9.58592 54.88576,9.60297 54.88694,9.62383 54.88312,9.62618 54.87371,9.6159 54.86372,9.6159 54.8505,9.6588 54.83728,9.72139 54.8217,9.77176 54.81793,9.85081 54.78559,9.90112 54.82692,9.97119 54.79637,10.29998 54.60592,10.75455 54.49632,11.02585 54.65443,11.32589 54.48914,11.62774 54.33462,11.93857 54.33103,12.21166 54.47656,12.53327 54.70294,13.06509 54.82332,13.40467 54.82153,13.97422 54.61491,14.66954 54.42805,15.26783 54.3993,15.58225 54.51609,15.8805 54.67599,15.8841 54.92214,15.77809 55.21859,15.29119 55.46833,14.70367 55.53661,14.22036 55.14493,13.33639 55.08744,12.66982 55.22039,12.66982 55.3264,12.84589 55.50427,12.86027 55.60848,12.76781 55.90275,12.66578 56.02925,12.59607 56.09528,12.45599 56.25116,12.31577 56.52421,11.36747 57.75294,10.46921 58.39211,8.12433 57.65098,5.82949 57.65926))",
 "OccurrenceStatus is Present",
 "Year 1950-2026"
 ]
}

 The dataset includes 1099108 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011143-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rgpcba
title: Occurrence Download

description: A dataset containing 640 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ericaria crinita",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 640 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011180-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18374095
title: Synthetic flood debris dataset (Flood-SHAB)

description: This repository contains a synthetic image dataset of a floating-debris for bridge and near-pier water-surface scenes. The dataset is designed for object-detection training (debris vs. no-debris) and for validating robustness under diverse bridge geometries, camera viewpoints, and water-surface conditions.

All images are synthetic (AI-generated) and provided as JPG files. Object-detection annotations are provided in YOLO format. The dataset is synthetic; domain shift is expected.

Dataset size and splitTotal images: 1,400- Images with debris (positive): 700- Images without debris (negative): 700

Splits (balanced 50/50):- train: 1,120 images (560 positive, 560 negative)- valid:   280 images (140 positive, 140 negative)

Directory structureThe dataset follows a YOLO-style split:

dataset.zip/    train/    images/      *.jpg    labels/      *.txt  valid/    images/      *.jpg    labels/      *.txt

Notes:- Each image have a corresponding label file in YOLO TXT format with the same filename stem.- Images without debris have empty label file.- Each line: <class_id> <x_center> <y_center> <width> <height>- All coordinates are normalised to [0, 1] relative to image width/height.

Example:0 0.28125 0.615625 0.528125 0.4where class_id=0 (debris), x_center=0.28125, y_center=0.615625, width=0.528125, height=0.4.

Definition of “debris” in this dataset:- Visible floating debris on or near the water surface that is operationally relevant to near-pier accumulation monitoring.- Bridge structure, shoreline vegetation, and unrelated background clutter are not annotated as debris.- Very small ambiguous artifacts (noise-like patterns) are generally not annotated.

Ownership and redistribution:- All images are synthetic and were created using AI image generation tools. Based on the service terms reviewed at the time of dataset preparation, the dataset creator retains ownership of the input prompts and generated outputs.

Attribution:This dataset includes images generated using multiple tools (Grok, OpenAI tools (e.g., Sora and ChatGPT image generation), and Canva AI). Grok requests attribution; accordingly, when redistributing or using the dataset publicly, please include the statement “Created by Grok”. This attribution requirement should be interpreted as applying to the subset of images generated with Grok and does not imply that every individual image was generated by Grok, nor does it imply endorsement by any tool provider.

Citation:

If you use this dataset, please cite this dataset DOI. In addition, please cite the associated article once published (bibliographic details will be added to a future version of this record).

FundingThis work has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101154316. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor REA can be held responsible for them.
DOI: 10.15468/dl.2jmy53
title: Occurrence Download

description: A dataset containing 393 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Puya raimondii"
 ]
}

 The dataset includes 393 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011181-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18374094
title: Synthetic flood debris dataset (Flood-SHAB)

description: This repository contains a synthetic image dataset of a floating-debris for bridge and near-pier water-surface scenes. The dataset is designed for object-detection training (debris vs. no-debris) and for validating robustness under diverse bridge geometries, camera viewpoints, and water-surface conditions.

All images are synthetic (AI-generated) and provided as JPG files. Object-detection annotations are provided in YOLO format. The dataset is synthetic; domain shift is expected.

Dataset size and splitTotal images: 1,400- Images with debris (positive): 700- Images without debris (negative): 700

Splits (balanced 50/50):- train: 1,120 images (560 positive, 560 negative)- valid:   280 images (140 positive, 140 negative)

Directory structureThe dataset follows a YOLO-style split:

dataset.zip/    train/    images/      *.jpg    labels/      *.txt  valid/    images/      *.jpg    labels/      *.txt

Notes:- Each image have a corresponding label file in YOLO TXT format with the same filename stem.- Images without debris have empty label file.- Each line: <class_id> <x_center> <y_center> <width> <height>- All coordinates are normalised to [0, 1] relative to image width/height.

Example:0 0.28125 0.615625 0.528125 0.4where class_id=0 (debris), x_center=0.28125, y_center=0.615625, width=0.528125, height=0.4.

Definition of “debris” in this dataset:- Visible floating debris on or near the water surface that is operationally relevant to near-pier accumulation monitoring.- Bridge structure, shoreline vegetation, and unrelated background clutter are not annotated as debris.- Very small ambiguous artifacts (noise-like patterns) are generally not annotated.

Ownership and redistribution:- All images are synthetic and were created using AI image generation tools. Based on the service terms reviewed at the time of dataset preparation, the dataset creator retains ownership of the input prompts and generated outputs.

Attribution:This dataset includes images generated using multiple tools (Grok, OpenAI tools (e.g., Sora and ChatGPT image generation), and Canva AI). Grok requests attribution; accordingly, when redistributing or using the dataset publicly, please include the statement “Created by Grok”. This attribution requirement should be interpreted as applying to the subset of images generated with Grok and does not imply that every individual image was generated by Grok, nor does it imply endorsement by any tool provider.

Citation:

If you use this dataset, please cite this dataset DOI. In addition, please cite the associated article once published (bibliographic details will be added to a future version of this record).

FundingThis work has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101154316. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor REA can be held responsible for them.
DOI: 10.15468/dl.anuxaa
title: Occurrence Download

description: A dataset containing 348268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Reef Life Survey: Global reef fish dataset",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 348268 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011170-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hyf44g
title: Occurrence Download

description: A dataset containing 1800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Agrostis alpina, Agrostis alpina, Agrostis alpina, Agrostis alpina, Agrostis alpina, Agrostis alpina, Agrostis alpina, Agrostis alpina)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 1800 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011182-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18478315
title: SuperDARN data in netCDF format (2012-Mar)

description: 2012-Mar SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6521988
title: SuperDARN data in netCDF format (2012-Mar)

description: 2012-Mar SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.e5nfs7
title: Occurrence Download

description: A dataset containing 124 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((33.07024 -19.50779,33.14915 -19.50839,33.1498 -19.43023,33.14786 -19.40739,33.1451 -19.40547,33.06833 -19.40687,33.07024 -19.50779))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 124 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011183-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yy3jt
title: Occurrence Download

description: A dataset containing 905 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Boerhavia dominii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 905 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011185-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5yrrsb
title: Occurrence Download

description: A dataset containing 1041 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is VectorNet data Luxembourg 2023-2024"
 ]
}

 The dataset includes 1041 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011188-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31249339
title: Supplementary Material for: Digitally Delivered Cognitive Behavioral Therapy (CBT) and Inflammation-Guided Nutrition to Enhance Recovery and Survival After Abdominoperineal Resection (APR): A Prospective Non-Randomized Controlled Cohort Study

description: Background:
Abdominoperineal resection (APR) for low rectal cancer results in permanent stoma, psychosocial distress, and systemic inflammation, impairing quality of life and survival. This study evaluated whether app-based cognitive behavioral therapy (CBT) plus biomarker-guided nutrition could improve recovery and survival.

Methods:
In this prospective, single-center, non-randomized controlled cohort, 186 patients with stage I–III low rectal adenocarcinoma undergoing curative abdominoperineal resection (APR) all received standard Enhanced Recovery After Surgery (ERAS) care. Patients were managed with ERAS alone or ERAS plus an integrative program combining digital cognitive behavioral therapy (CBT) and inflammation-guided precision nutrition, tailored according to prognostic nutritional index (PNI), C-reactive protein (CRP), and interleukin-6 (IL-6). Interventions began two weeks preoperatively and continued for six months. Primary outcomes were longitudinal changes in depression, sleep quality, sexual function, and health-related quality of life. Secondary outcomes included inflammatory markers, skeletal muscle index (SMI), and two-year disease-free and overall survival.

Results:
By month 3, the intervention group showed greater improvements in all primary outcomes, sustained through 24 months (all p &lt; 0.001). CRP and IL-6 were reduced by day 7 (both p &lt; 0.001), with faster recovery and shorter hospitalization. SMI was better preserved at 24 months (p = 0.046). Two-year DFS (hazard ratio [HR] = 0.48, 95% confidence interval [CI] 0.26–0.89, p = 0.021) and OS (HR = 0.44, 95% CI 0.20–0.95, p = 0.036) were significantly improved. Findings remained robust in multivariable and sensitivity analyses.

Conclusions:
Among patients undergoing APR managed within an ERAS pathway, the addition of digital CBT and inflammation-guided nutrition was associated with improved multidimensional recovery and favorable survival signals. Given the non-randomized design, these results should be regarded as hypothesis-generating and support further evaluation of ERAS-based integrative strategies in randomized multicenter trials.
DOI: 10.6084/m9.figshare.31249339.v1
title: Supplementary Material for: Digitally Delivered Cognitive Behavioral Therapy (CBT) and Inflammation-Guided Nutrition to Enhance Recovery and Survival After Abdominoperineal Resection (APR): A Prospective Non-Randomized Controlled Cohort Study

description: Background:
Abdominoperineal resection (APR) for low rectal cancer results in permanent stoma, psychosocial distress, and systemic inflammation, impairing quality of life and survival. This study evaluated whether app-based cognitive behavioral therapy (CBT) plus biomarker-guided nutrition could improve recovery and survival.

Methods:
In this prospective, single-center, non-randomized controlled cohort, 186 patients with stage I–III low rectal adenocarcinoma undergoing curative abdominoperineal resection (APR) all received standard Enhanced Recovery After Surgery (ERAS) care. Patients were managed with ERAS alone or ERAS plus an integrative program combining digital cognitive behavioral therapy (CBT) and inflammation-guided precision nutrition, tailored according to prognostic nutritional index (PNI), C-reactive protein (CRP), and interleukin-6 (IL-6). Interventions began two weeks preoperatively and continued for six months. Primary outcomes were longitudinal changes in depression, sleep quality, sexual function, and health-related quality of life. Secondary outcomes included inflammatory markers, skeletal muscle index (SMI), and two-year disease-free and overall survival.

Results:
By month 3, the intervention group showed greater improvements in all primary outcomes, sustained through 24 months (all p &lt; 0.001). CRP and IL-6 were reduced by day 7 (both p &lt; 0.001), with faster recovery and shorter hospitalization. SMI was better preserved at 24 months (p = 0.046). Two-year DFS (hazard ratio [HR] = 0.48, 95% confidence interval [CI] 0.26–0.89, p = 0.021) and OS (HR = 0.44, 95% CI 0.20–0.95, p = 0.036) were significantly improved. Findings remained robust in multivariable and sensitivity analyses.

Conclusions:
Among patients undergoing APR managed within an ERAS pathway, the addition of digital CBT and inflammation-guided nutrition was associated with improved multidimensional recovery and favorable survival signals. Given the non-randomized design, these results should be regarded as hypothesis-generating and support further evaluation of ERAS-based integrative strategies in randomized multicenter trials.
DOI: 10.15468/np5v2z
title: Collezione entomologica di coleotteri di Guido Grandi | Guido Grandi's entomological collection of Coleoptera

description: A collection of beetle specimens gathered by Guido Grandi.
DOI: 10.15468/dl.bdjcb6
title: Occurrence Download

description: A dataset containing 1887 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Acridotheres tristis"
 ]
}

 The dataset includes 1887 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011190-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ddd7pd
title: Occurrence Download

description: A dataset containing 6285 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Agrostis canina, Agrostis canina)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 6285 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011189-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.