cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.t35tdc
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lutjanus vivanus (Cuvier, 1828)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 22 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041402-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14524756
title: Topical Application of the HSP90 Inhibitor 17-AAG Reduces Skin Inflammation and Restores Microbial Balance: Implications for Atopic Dermatitis Therapy

description: Title of the publication: Topical Application of the HSP90 Inhibitor 17-AAG Reduces Skin Inflammation and Restores Microbial Balance: Implications for Atopic Dermatitis TherapyAuthors: Krzysztof Sitko, Ewa Piotrowska, Magdalena Podlacha, Michał D. Starke, Magdalena Trzeciak, Stefan Tukaj  

Please carefully read the manuscript first, as it contains the necessary information for understanding this dataset.  For values used to create graphs showing gene expression and sequencing data in this publication please refer to Graph_data.xlsx. The file contains tabs named after the corresponding figures in the manuscript.

For Gut_microbiome and Skin_microbiome folders, a specific sample naming convention was used, as follows:sampleid column of the metadata.csv file contains the subject identifiers:    S1-1 sample material skin-swab S feces F    S1-1 animal identification number ranging 1-5    S1-1 group identification number 1 - Naive, no disease, no treatment (only vehicle) 2 - Vehicle, disease, no treatment (only vehicle) 3 - Treated, disease, treatment (17-AAG 0.5 uM topically)  

For any additional information please kindly contact the corresponding author: stefan.tukaj@ug.edu.pl
DOI: 10.5281/zenodo.14524757
title: Topical Application of the HSP90 Inhibitor 17-AAG Reduces Skin Inflammation and Restores Microbial Balance: Implications for Atopic Dermatitis Therapy

description: Title of the publication: Topical Application of the HSP90 Inhibitor 17-AAG Reduces Skin Inflammation and Restores Microbial Balance: Implications for Atopic Dermatitis TherapyAuthors: Krzysztof Sitko, Ewa Piotrowska, Magdalena Podlacha, Michał D. Starke, Magdalena Trzeciak, Stefan Tukaj  

Please carefully read the manuscript first, as it contains the necessary information for understanding this dataset.  For values used to create graphs showing gene expression and sequencing data in this publication please refer to Graph_data.xlsx. The file contains tabs named after the corresponding figures in the manuscript.

For Gut_microbiome and Skin_microbiome folders, a specific sample naming convention was used, as follows:sampleid column of the metadata.csv file contains the subject identifiers:    S1-1 sample material skin-swab S feces F    S1-1 animal identification number ranging 1-5    S1-1 group identification number 1 - Naive, no disease, no treatment (only vehicle) 2 - Vehicle, disease, no treatment (only vehicle) 3 - Treated, disease, treatment (17-AAG 0.5 uM topically)  

For any additional information please kindly contact the corresponding author: stefan.tukaj@ug.edu.pl
DOI: 10.15468/dl.e8j63n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycenchelys camchatica (Gilbert & Burke, 1912)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041406-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.14384259
title: Accelerometer data of individuals with Huntington's disease and healthy controls from in-clinic assessment

description: Participants with Huntington's Disease (HD) and age-matched healthy controls (HC) were recruited as part of a study designed to evaluate the feasibility, acceptability, and utility of wearable activity monitors in people with early-mid stage HD. Tri-axial accelerometer signals were collected using wrist-worn devices (GENEActiv, Activinsights; 43 × 40 × 13 mm; weight: 16 g; 100 Hz sampling rate) during an in-clinic assessment. Participants completed a set of standardized laboratory-based functional activities, reflecting independent activities of daily living. The in-clinic assessment lasted 60-90 minutes per participant. The in-clinic assessments were video-recorded, and these videos were subsequently used in the labeling procedure of the accelerometer signals. The dataset contains a csv file for each participant with the 3 axis accelerometer recording, as well as labels for walking detection. An anonymized metadata table is also provided with demographic information and clinical scores. Files description:Each csv file is named with a serial number representing a participant. Each file contains 3 axis of accelerometer recordings (i.e., X, Y, Z) that was recorded in a sample rate of 100 Hz.The forth column contains labels for each sample according to the following: 1=walking , 0=non-walking, -1 = cannot be marked (due to the patients being obscured from the camera). Additional metadata file is provided that include the following information regarding each participant in the columns: 



Huntington's Disease patient/healthy control (HD=1, HC=2)

Sex (Male=1, Female=2)

Age

Birthday

Height

Weight

TMS (Total Motor Score)

FA (Functional Assessment)

TFC (Total functional capacity)


Additional relevant information is available in the paper. 

Kindly cite of the following paper when using this data:

D. Schwartz, L. Quinn, N. E. Fritz, L. M. Muratori, J. M. Hausdorff, and R. G. Bachrach, Detecting daily living gait amid huntington’s disease chorea using a foundation deep learning model, 2024. arXiv:2412.11286 [cs.CV]. url: https://arxiv.org/abs/2412.11286.
DOI: 10.5281/zenodo.14384260
title: Accelerometer data of individuals with Huntington's disease and healthy controls from in-clinic assessment

description: Participants with Huntington's Disease (HD) and age-matched healthy controls (HC) were recruited as part of a study designed to evaluate the feasibility, acceptability, and utility of wearable activity monitors in people with early-mid stage HD. Tri-axial accelerometer signals were collected using wrist-worn devices (GENEActiv, Activinsights; 43 × 40 × 13 mm; weight: 16 g; 100 Hz sampling rate) during an in-clinic assessment. Participants completed a set of standardized laboratory-based functional activities, reflecting independent activities of daily living. The in-clinic assessment lasted 60-90 minutes per participant. The in-clinic assessments were video-recorded, and these videos were subsequently used in the labeling procedure of the accelerometer signals. The dataset contains a csv file for each participant with the 3 axis accelerometer recording, as well as labels for walking detection. An anonymized metadata table is also provided with demographic information and clinical scores. Files description:Each csv file is named with a serial number representing a participant. Each file contains 3 axis of accelerometer recordings (i.e., X, Y, Z) that was recorded in a sample rate of 100 Hz.The forth column contains labels for each sample according to the following: 1=walking , 0=non-walking, -1 = cannot be marked (due to the patients being obscured from the camera). Additional metadata file is provided that include the following information regarding each participant in the columns: 



Huntington's Disease patient/healthy control (HD=1, HC=2)

Sex (Male=1, Female=2)

Age

Birthday

Height

Weight

TMS (Total Motor Score)

FA (Functional Assessment)

TFC (Total functional capacity)


Additional relevant information is available in the paper. 

Kindly cite of the following paper when using this data:

D. Schwartz, L. Quinn, N. E. Fritz, L. M. Muratori, J. M. Hausdorff, and R. G. Bachrach, Detecting daily living gait amid huntington’s disease chorea using a foundation deep learning model, 2024. arXiv:2412.11286 [cs.CV]. url: https://arxiv.org/abs/2412.11286.
DOI: 10.5287/ora-14ndm1opr
title: The Global Multidimensional Poverty Index 2022: harmonised level estimates and their changes over time

description: This Database provides estimates of the global Multidimensional Poverty Index (MPI) an international measure of acute poverty. More specifically, it contains harmonised level estimates and their changes over time of the global MPI and related sub-indices, which have been estimated for the 2022 release. For this Database all deprivation indicators have been harmonised over time.

The Database covers estimates for 84 countries and 810 subnational regions. 35 of the 84 countries have trends for three points in time; while poverty trends in 48 countries are based on two points in time. Gambia is the only country where we have presented results for four points in time. The estimates are based on 205 individual survey datasets, mostly the Demographic Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS). The Database contains one results file that has the level estimates and change estimates. The results file are provided in Stata format (dta). This repository also contains the computer script files to extract and clean the micro data for replication purposes (i.e. the Stata do-files).
DOI: 10.5281/zenodo.14512961
title: CMIP Climate Forcings Dataset: Simple plumes for anthropogenic aerosols

description: The Simple Plumes (SP) parameterization (Stevens et al., 2017) mimics the changes of the global patterns for the optical properties and cloud effects of anthropogenic aerosols. The parameterization was originally developed for use in a set of experiments in the CMIP6-endorsed Radiative Forcing Model Intercomparison Project (RFMIP, Pincus et al., 2016) and helped to explain differences in the effective radiative forcing of anthropogenic aerosols (Fiedler et al., 2023). Code of the SP parameterization is available in the supplementary material of Stevens et al. (2017). Since the release of SP,  models implemented the parameterization also for applications other than RFMIP. Users of the parameterization have requested new input data for SP based on CMIP7 emission data in the framework of the CMIP Climate Forcings Task Team. 

We provide here the SP dataset for the time period 1850 to 2020 inclusive (SPv2), based on the preliminary data set of CEDS for CMIP6plus version CMIP_CEDS-2024-10-21. We use the decadal scaling method for gridded SO2 and NH3 emissions (Fiedler et al., 2019) and transfered it to the historical period to update and extent the earlier time series for 1850 to 2014 (Stevens et al., 2017). The here published SPv2 data are currently tested by early adopters for their applications. Results of the evaluation of SPv2 will be published in a scientific journal and linked once they are available.

The SPv2 data is intended for use in CMIP experiments and other applications. Information about all CMIP forcing datasets are available from input4MIPs.
DOI: 10.15468/dl.zsdj9g
title: Occurrence Download

description: A dataset containing 51418 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Andropogon gayanus Kunth"
 ]
}

 The dataset includes 51418 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041405-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.exs6jb
title: Occurrence Download

description: A dataset containing 607 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycenchelys crotalinus (Gilbert, 1890)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 607 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041408-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2eupc2
title: Occurrence Download

description: A dataset containing 14385 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Platalea minor Temminck & Schlegel, 1849"
 ]
}

 The dataset includes 14385 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041407-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.373yqa
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycenchelys kolthoffi Jensen, 1904",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 58 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041410-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gvr7sw
title: Occurrence Download

description: A dataset containing 51845 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Osmerus mordax (Mitchill, 1814)"
 ]
}

 The dataset includes 51845 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041409-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8f2yua
title: Occurrence Download

description: A dataset containing 668870 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aphididae"
 ]
}

 The dataset includes 668870 records from 630 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041398-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wb8qsy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycenchelys micropora Andriashev, 1955",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041411-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.13955516
title: SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

description: This page only provides the drone-view image dataset. 



For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I).

For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III).


The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values.

All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected.

Specifically, each 7z file includes the following folders:





rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36.




depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.




instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.




obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).




landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters.



⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.
DOI: 10.5281/zenodo.13955515
title: SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

description: This page only provides the drone-view image dataset. 



For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I).

For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III).


The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values.

All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected.

Specifically, each 7z file includes the following folders:





rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36.




depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.




instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.




obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).




landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters.



For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:



{name}_coco.json: This file contains the annotation of each tree in the scene.

{name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.


⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.
DOI: 10.5281/zenodo.14525290
title: SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

description: This page only provides the drone-view image dataset. 



For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I).

For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III).


The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values.

All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected.

Specifically, each 7z file includes the following folders:





rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36.




depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.




instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.




obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).




landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters.



For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:



{name}_coco.json: This file contains the annotation of each tree in the scene.

{name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.


⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.