cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.16313760
title: DOSE - Global dataset of reported subnational economic output

description: DOSE V2.11 is an update of DOSE V2. We made a few corrections and additions to the data where various users had identified gaps or inaccuracies. Please see 'DOSEV2.11_changes.pdf' for details. 

DOSE – the MCC-PIK Database Of Sub-national Economic Output. DOSE v2 contains harmonised data on reported economic output for:



1,661 sub-national regions

across 83 countries

from 1953 to 2020

with sectoral detail for the agricultural, manufacturing and services sectors.


To avoid interpolation, values were assembled from numerous statistical agencies, yearbooks and the literature and harmonised for both aggregate and sectoral output. In addition to regional economic output in local currency units (LCU) at current market prices as collected from the original data sources, DOSE contains per capita estimates in LCU and US dollars at both, current and 2015 market prices to enable comparison across time and space. Population data, market exchanges rates and deflator data used to generate them are included as well. Moreover, we provide temporally and spatially consistent data for regional boundaries, enabling matching with geo-spatial data such as climate observations. Annual temperature and precipitation data for each region are already included. Overall, DOSE provides the opportunity for detailed analyses of economic development at the subnational level, consistent with reported values.

A peer-reviewed data descriptor with detailed documentation of the different data assembling, processing and validation steps as well as illustrative plots of the data set's coverage and examples for its application can be found here: 

L. Wenz, R.D. Carr, N. Koegel, M. Kotz, M. Kalkuhl. DOSE – Global data set of reported sub-national economic output. Nature Scientific Data. 2023. https://doi.org/10.1038/s41597-023-02323-8
DOI: 10.5281/zenodo.4681305
title: DOSE - Global dataset of reported subnational economic output

description: DOSE V2.11 is an update of DOSE V2. We made a few corrections and additions to the data where various users had identified gaps or inaccuracies. Please see 'DOSEV2.11_changes.pdf' for details. 

DOSE – the MCC-PIK Database Of Sub-national Economic Output. DOSE v2 contains harmonised data on reported economic output for:



1,661 sub-national regions

across 83 countries

from 1953 to 2020

with sectoral detail for the agricultural, manufacturing and services sectors.


To avoid interpolation, values were assembled from numerous statistical agencies, yearbooks and the literature and harmonised for both aggregate and sectoral output. In addition to regional economic output in local currency units (LCU) at current market prices as collected from the original data sources, DOSE contains per capita estimates in LCU and US dollars at both, current and 2015 market prices to enable comparison across time and space. Population data, market exchanges rates and deflator data used to generate them are included as well. Moreover, we provide temporally and spatially consistent data for regional boundaries, enabling matching with geo-spatial data such as climate observations. Annual temperature and precipitation data for each region are already included. Overall, DOSE provides the opportunity for detailed analyses of economic development at the subnational level, consistent with reported values.

A peer-reviewed data descriptor with detailed documentation of the different data assembling, processing and validation steps as well as illustrative plots of the data set's coverage and examples for its application can be found here: 

L. Wenz, R.D. Carr, N. Koegel, M. Kotz, M. Kalkuhl. DOSE – Global data set of reported sub-national economic output. Nature Scientific Data. 2023. https://doi.org/10.1038/s41597-023-02323-8
DOI: 10.15468/dl.akh8uc
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020529-250802193616735/datasets/export for details.
DOI: 10.17632/7k9bcy5byw
title: Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part I)

description: Cell-free layer measurement in a low shear rate microfluidic device (Part I). The data contained here is a series of images of horse blood resuspended in dextran 500 to induce rouleaux formation. Each folder contains the images for a given pressure drop. The pressure is controlled by hydrostatic head, so the folder labels are given as the volume removed from the outlet water column. The volume converted to pressure drop for each measurement is:

0 uL = 0 Pa
300 uL = 6.77 Pa
400 uL = 9.02 Pa
500 uL = 11.3 Pa
1500 uL = 33.8 Pa

This dataset contains only part I of the images, which are 0, 300, and 400 uL. The link to Part II is:

Farrington, Sean (2025), “Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part II)”, Mendeley Data, V1, doi: 10.17632/c9m6sznxhc.1
DOI: 10.17632/7k9bcy5byw.1
title: Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part I)

description: Cell-free layer measurement in a low shear rate microfluidic device (Part I). The data contained here is a series of images of horse blood resuspended in dextran 500 to induce rouleaux formation. Each folder contains the images for a given pressure drop. The pressure is controlled by hydrostatic head, so the folder labels are given as the volume removed from the outlet water column. The volume converted to pressure drop for each measurement is:

0 uL = 0 Pa
300 uL = 6.77 Pa
400 uL = 9.02 Pa
500 uL = 11.3 Pa
1500 uL = 33.8 Pa

This dataset contains only part I of the images, which are 0, 300, and 400 uL. The link to Part II is:

Farrington, Sean (2025), “Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part II)”, Mendeley Data, V1, doi: 10.17632/c9m6sznxhc.1
DOI: 10.15468/dl.r7cf6y
title: Occurrence Download

description: A dataset containing 413 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phyllastrephus poensis)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 413 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020531-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mtvc65
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Triplophysa turpanensis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020533-250802193616735/datasets/export for details.
DOI: 10.15468/dl.3vnt64
title: Occurrence Download

description: A dataset containing 219 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phyllastrephus poliocephalus)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 219 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020534-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ma8uz
title: Occurrence Download

description: A dataset containing 21154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Living Specimen)",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-97.50652 32.76084,-97.39356 32.76084,-97.39356 32.84452,-97.50652 32.84452,-97.50652 32.76084))",
 "OccurrenceStatus is Present",
 "Year 2020-2025"
 ]
}

 The dataset includes 21154 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020530-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/27xk9v8z73
title: Dataset and codes for the paper "Leveraging Synthetic Degradation for Effective Training of Super-Resolution Models in Dermatological Images"

description: Dataset and codes for the work: "Leveraging Synthetic Degradation for Effective Training of Super-Resolution Models in Dermatological Images", Francesco Branciforti, Kristen M. Meiburger, and Massimo Salvi

Abstract: 
Background: Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image en-hancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. Methods: We developed a customized image degradation pipeline simulating common artifacts in dermatological images, in-cluding blur, noise, downsampling, and compression. This synthetic degradation ap-proach enabled effective training of DermaSR-GAN, a super-resolution Generative Adversarial Network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH2) using structural similarity and no-reference quality metrics. Results: DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Conclusions: Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for en-hancing teledermatology applications and computer-aided diagnosis systems.
DOI: 10.17632/27xk9v8z73.1
title: Dataset and codes for the paper "Leveraging Synthetic Degradation for Effective Training of Super-Resolution Models in Dermatological Images"

description: Dataset and codes for the work: "Leveraging Synthetic Degradation for Effective Training of Super-Resolution Models in Dermatological Images", Francesco Branciforti, Kristen M. Meiburger, and Massimo Salvi

Abstract: 
Background: Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image en-hancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. Methods: We developed a customized image degradation pipeline simulating common artifacts in dermatological images, in-cluding blur, noise, downsampling, and compression. This synthetic degradation ap-proach enabled effective training of DermaSR-GAN, a super-resolution Generative Adversarial Network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH2) using structural similarity and no-reference quality metrics. Results: DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Conclusions: Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for en-hancing teledermatology applications and computer-aided diagnosis systems.
DOI: 10.17632/c9m6sznxhc
title: Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part II)

description: Cell-free layer measurement in a low shear rate microfluidic device (Part II). The data contained here is a series of images of horse blood resuspended in dextran 500 to induce rouleaux formation. Each folder contains the images for a given pressure drop. The pressure is controlled by hydrostatic head, so the folder labels are given as the volume removed from the outlet water column. The volume converted to pressure drop for each measurement is:

0 uL = 0 Pa
300 uL = 6.77 Pa
400 uL = 9.02 Pa
500 uL = 11.3 Pa
1500 uL = 33.8 Pa

This dataset contains only part II of the images, which are 500 and 1500 uL. The link to Part I is:

Farrington, Sean (2025), “Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part I)”, Mendeley Data, V1, doi: 10.17632/7k9bcy5byw.1
DOI: 10.17632/c9m6sznxhc.1
title: Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part II)

description: Cell-free layer measurement in a low shear rate microfluidic device (Part II). The data contained here is a series of images of horse blood resuspended in dextran 500 to induce rouleaux formation. Each folder contains the images for a given pressure drop. The pressure is controlled by hydrostatic head, so the folder labels are given as the volume removed from the outlet water column. The volume converted to pressure drop for each measurement is:

0 uL = 0 Pa
300 uL = 6.77 Pa
400 uL = 9.02 Pa
500 uL = 11.3 Pa
1500 uL = 33.8 Pa

This dataset contains only part II of the images, which are 500 and 1500 uL. The link to Part I is:

Farrington, Sean (2025), “Cell-Free Layer Measurement for Dextran Red Blood Cell Suspensions (Part I)”, Mendeley Data, V1, doi: 10.17632/7k9bcy5byw.1
DOI: 10.15468/dl.9c2sqr
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thunbergia alata",
 "Country is Panama",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020535-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3htr438mcw
title: Camera-traps and the city: spatiotemporal adaptations of wildlife to urban environments

description: The dataset is a camera trap record on animal and human presence across urban sites in Florence over time. It consists of nearly 7879 individual entries, each corresponding to a discrete detection event captured by camera traps. Every entry includes a timestamp and contextual information such as the phase of the moon, sky coverage, and the specific time of day. These temporal and environmental variables suggest that the dataset is designed to explore how conditions like lighting, weather, and lunar cycles influence the visibility or activity patterns of species within urban environments.
Each observation is geolocated through the 'Site' variable. Observations span across multiple years, months, and seasons, providing a rich temporal spread for ecological analysis. Additionally, the dataset distinguishes between day and night events, allowing researchers to assess diurnal and nocturnal activity patterns. The inclusion of lunar data, such as moon phase and epact, suggests an interest in understanding behavioral shifts tied to lunar illumination, which can significantly affect the activity of nocturnal species.
Biologically, the dataset captures species identity through the 'Sps' column and counts the number of individuals observed. The dataset reflects not just presence-absence data but also relative abundance, as indicated by the number of individuals.
DOI: 10.17632/3htr438mcw.1
title: Camera-traps and the city: spatiotemporal adaptations of wildlife to urban environments

description: The dataset is a camera trap record on animal and human presence across urban sites in Florence over time. It consists of nearly 7879 individual entries, each corresponding to a discrete detection event captured by camera traps. Every entry includes a timestamp and contextual information such as the phase of the moon, sky coverage, and the specific time of day. These temporal and environmental variables suggest that the dataset is designed to explore how conditions like lighting, weather, and lunar cycles influence the visibility or activity patterns of species within urban environments.
Each observation is geolocated through the 'Site' variable. Observations span across multiple years, months, and seasons, providing a rich temporal spread for ecological analysis. Additionally, the dataset distinguishes between day and night events, allowing researchers to assess diurnal and nocturnal activity patterns. The inclusion of lunar data, such as moon phase and epact, suggests an interest in understanding behavioral shifts tied to lunar illumination, which can significantly affect the activity of nocturnal species.
Biologically, the dataset captures species identity through the 'Sps' column and counts the number of individuals observed. The dataset reflects not just presence-absence data but also relative abundance, as indicated by the number of individuals.
DOI: 10.17632/xjnsg4yy33
title: NuMA mechanically reinforces the spindle independently of its partner dynein

description: Summary of work: During cell division, both motor and non-motor proteins organize microtubules to build the spindle and maintain it against opposing forces. NuMA, a long microtubule-binding protein, is essential to spindle structure and function. NuMA recruits the motor dynein to actively cluster spindle microtubule minus-ends, but whether NuMA performs other spindle roles remains unknown. Here, we show that NuMA acts independently of dynein to passively reinforce the mammalian spindle. NuMA that cannot bind dynein is sufficient to protect spindle poles against fracture under external force. In contrast, NuMA with a shorter coiled-coil or disrupted self-interactions cannot protect spindle poles, and NuMA turnover differences cannot explain mechanical differences. In vitro, NuMA’s C-terminus self-interacts and bundles microtubules without dynein, dependent on residues essential to pole protection in vivo. Together, this suggests that NuMA reinforces spindle poles by crosslinking microtubules, using its long coiled-coiled and self-interactions to reach multiple, far-reaching pole microtubules. We propose that NuMA acts as a mechanical “multitasker” targeting contractile motor activity and separately crosslinking microtubules, both functions synergizing to drive spindle mechanical robustness.

Data associated with the paper: "NuMA mechanically reinforces the spindle independently of its partner dynein", Cho et al. Current Biology (DOI: https://doi.org/10.1016/j.cub.2025.07.028)

Here, we include data used to generate the figures in this paper. Files are created and can be opened with GraphPad Prism.
DOI: 10.17632/xjnsg4yy33.2
title: NuMA mechanically reinforces the spindle independently of its partner dynein

description: Summary of work: During cell division, both motor and non-motor proteins organize microtubules to build the spindle and maintain it against opposing forces. NuMA, a long microtubule-binding protein, is essential to spindle structure and function. NuMA recruits the motor dynein to actively cluster spindle microtubule minus-ends, but whether NuMA performs other spindle roles remains unknown. Here, we show that NuMA acts independently of dynein to passively reinforce the mammalian spindle. NuMA that cannot bind dynein is sufficient to protect spindle poles against fracture under external force. In contrast, NuMA with a shorter coiled-coil or disrupted self-interactions cannot protect spindle poles, and NuMA turnover differences cannot explain mechanical differences. In vitro, NuMA’s C-terminus self-interacts and bundles microtubules without dynein, dependent on residues essential to pole protection in vivo. Together, this suggests that NuMA reinforces spindle poles by crosslinking microtubules, using its long coiled-coiled and self-interactions to reach multiple, far-reaching pole microtubules. We propose that NuMA acts as a mechanical “multitasker” targeting contractile motor activity and separately crosslinking microtubules, both functions synergizing to drive spindle mechanical robustness.

Data associated with the paper: "NuMA mechanically reinforces the spindle independently of its partner dynein", Cho et al. Current Biology (DOI: https://doi.org/10.1016/j.cub.2025.07.028)

Here, we include data used to generate the figures in this paper. Files are created and can be opened with GraphPad Prism.
DOI: 10.17632/2rn7f8rsd9
title: The non-pathogenic strain Fusarium oxysporum FO12 mitigates simultaneous Verticillium dahliae and Fe deficiency stresses in Arabidopsis thaliana

description: qRT-PCR data files from Arabidopsis thaliana in calcareous soil inoculatedwith the nos pathogenic Fusarium oxysporum strain FO12 and subjected to Verticicilium dahliae V180
DOI: 10.17632/2rn7f8rsd9.2
title: The non-pathogenic strain Fusarium oxysporum FO12 mitigates simultaneous Verticillium dahliae and Fe deficiency stresses in Arabidopsis thaliana

description: qRT-PCR data files from Arabidopsis thaliana in calcareous soil inoculatedwith the nos pathogenic Fusarium oxysporum strain FO12 and subjected to Verticicilium dahliae V180
DOI: 10.15468/dl.w6zjxy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thunbergia alata",
 "Country is Philippines",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020536-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kw49s7w48j
title: Health-Promoting Workplace through Companion Animals for People with a Disability in Mauritius

description: Health-Promoting Workplace through Companion Animals for People with a Disability in Mauritius
DOI: 10.17632/kw49s7w48j.1
title: Health-Promoting Workplace through Companion Animals for People with a Disability in Mauritius

description: Health-Promoting Workplace through Companion Animals for People with a Disability in Mauritius
DOI: 10.5517/ccdc.csd.cc12vjjz
title: CCDC 1008601: Experimental Crystal Structure Determination

description: Related Article: Sujan Biswas, Puspendu Roy, Deblina Sarkar, Tapan Kumar Mondal
 

|2016|Indian J.Chem.,Sect.A:Inorg.,Bio-inorg.,Phys.,Theor.Anal.Chem.|55A|929|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.