cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18798379
title: Dataset related to sub-study one of the PainSMART-project (RCT-PROMs primary and secondary outcomes).

description: Data supporting the results of this study comprise patient reported outcome measures (PROMs) and baseline information used as covariates in adjusted models. 

Data were collected for the two groups (PainSMART-group and Control Group) via online questionnaires at four time points: Baseline, follow-up 1 (24-72 hours pre-initial physiotherapy consultation), follow-up 2 (24 hours post-initial physiotherapy consultation) and follow-up 3 (three months post-baseline).

The following PROMs were collected:

Primary outcomes

-    Average pain intensity in the last 24 hours (NRS-average pain, 0-10).

-    Pain self-efficacy (Pain Self Efficacy Questionnaire, PSEQ-10, 0-60).

Secondary outcomes

-    Best pain intensity in the last 24 hours (NRS-best pain, 0-10).

-    Worst pain intensity in the last 24 hours (NRS-worst pain, 0-10).

-    MSKP-related illness perceptions (Brief Illness Perception Questionnaire, BIPQ, 0-80).

-    MSKP-management strategies (Brief Pain Coping Inventory-2:SWE Traditional Pain Management Strategies subscale, BPCI-2:SWE TPMS, 0-56).

-    Psychological flexibility (BPCI-2:SWE Psychological Flexibility, PF-subscale, 0-77).

-    Reassurance of the benign nature of MSKP (NRS-reassurance, 0-10).

-    Self-reported physical activity levels in the preceding week (FASTA-score, 3-19).

- Self-reported sitting time (hours per day, 0-16hrs).

- Global rating of change scale (GRoC, -5 to +5).

The following baseline covariates were used to adjust the models.

Age, biological sex, body-mass-index, highest educational level, comorbidities (yes/no), work status (working/not working), sickness absence (absent/not absent), pain duration (acute/chronic with 3-month cut-off), pain localisation (regional pain/widespread pain), time from baseline to consultation (days), healthcare consultations for presenting MSKP in preceding 3-months (yes/no), Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) risk category (high >50 points or low ≤50), analgesic use (yes/no).
description: LiU secure repository is a data repository that hosts research data which cannot be openly available for ethical or legal reasons. LiU secure repository is managed by Linköping University Library as a service to researchers at Linköping University (Sweden). For requests of access or further information, please contact datamanagement@liu.se
DOI: 10.5281/zenodo.18798378
title: Dataset related to sub-study one of the PainSMART-project (RCT-PROMs primary and secondary outcomes).

description: Data supporting the results of this study comprise patient reported outcome measures (PROMs) and baseline information used as covariates in adjusted models. 

Data were collected for the two groups (PainSMART-group and Control Group) via online questionnaires at four time points: Baseline, follow-up 1 (24-72 hours pre-initial physiotherapy consultation), follow-up 2 (24 hours post-initial physiotherapy consultation) and follow-up 3 (three months post-baseline).

The following PROMs were collected:

Primary outcomes

-    Average pain intensity in the last 24 hours (NRS-average pain, 0-10).

-    Pain self-efficacy (Pain Self Efficacy Questionnaire, PSEQ-10, 0-60).

Secondary outcomes

-    Best pain intensity in the last 24 hours (NRS-best pain, 0-10).

-    Worst pain intensity in the last 24 hours (NRS-worst pain, 0-10).

-    MSKP-related illness perceptions (Brief Illness Perception Questionnaire, BIPQ, 0-80).

-    MSKP-management strategies (Brief Pain Coping Inventory-2:SWE Traditional Pain Management Strategies subscale, BPCI-2:SWE TPMS, 0-56).

-    Psychological flexibility (BPCI-2:SWE Psychological Flexibility, PF-subscale, 0-77).

-    Reassurance of the benign nature of MSKP (NRS-reassurance, 0-10).

-    Self-reported physical activity levels in the preceding week (FASTA-score, 3-19).

- Self-reported sitting time (hours per day, 0-16hrs).

- Global rating of change scale (GRoC, -5 to +5).

The following baseline covariates were used to adjust the models.

Age, biological sex, body-mass-index, highest educational level, comorbidities (yes/no), work status (working/not working), sickness absence (absent/not absent), pain duration (acute/chronic with 3-month cut-off), pain localisation (regional pain/widespread pain), time from baseline to consultation (days), healthcare consultations for presenting MSKP in preceding 3-months (yes/no), Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) risk category (high >50 points or low ≤50), analgesic use (yes/no).
description: LiU secure repository is a data repository that hosts research data which cannot be openly available for ethical or legal reasons. LiU secure repository is managed by Linköping University Library as a service to researchers at Linköping University (Sweden). For requests of access or further information, please contact datamanagement@liu.se
DOI: 10.15468/dl.pzu76f
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Asplenium komarovii"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001584-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17591258
title: I- BioLex dataset on organisations

description: This dataset was compiled as part of the I-Biolex research project funded by the French National Agency for research (ANR-20-CE26-0007-01 ; PI: A. Mahalatchimy). More information on the project can be found at: https://elsibi.hypotheses.org/i-biolex

The dataset presented (organisations) was compiled to identify the organisations participating in consultations on guidelines and regulations in the field of biological medicinal products since the 1990s. It is partly based on the I-BioLex dataset on guidance (available here: https://doi.org/10.5281/zenodo.17591115). But it has been extended with the integration of selected European Union legislations. Our main research objective was to identify organisations involved in the field, and to understand the extent to which different organizations have taken part in these consultations, depending on the subject and scope of the documents.   

Our main contribution has been to provide a clear and concise picture of the organisations involved in the field of biological medicinal products through answers to public consultations in the European Union. Several open access papers have or will be published on the basis of this initial dataset in the context of I-BioLex.

This dataset can be used for research purposes beyond I-BioLex, upon request and clear reference to the source, as provided here.
DOI: 10.5281/zenodo.17591257
title: I- BioLex dataset on organisations

description: This dataset was compiled as part of the I-Biolex research project funded by the French National Agency for research (ANR-20-CE26-0007-01 ; PI: A. Mahalatchimy). More information on the project can be found at: https://elsibi.hypotheses.org/i-biolex

The dataset presented (organisations) was compiled to identify the organisations participating in consultations on guidelines and regulations in the field of biological medicinal products since the 1990s. It is partly based on the I-BioLex dataset on guidance (available here: https://doi.org/10.5281/zenodo.17591115). But it has been extended with the integration of selected European Union legislations. Our main research objective was to identify organisations involved in the field, and to understand the extent to which different organizations have taken part in these consultations, depending on the subject and scope of the documents.   

Our main contribution has been to provide a clear and concise picture of the organisations involved in the field of biological medicinal products through answers to public consultations in the European Union. Several open access papers have or will be published on the basis of this initial dataset in the context of I-BioLex.

This dataset can be used for research purposes beyond I-BioLex, upon request and clear reference to the source, as provided here.
DOI: 10.15468/dl.d8an69
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Polysteganus coeruleopunctatus"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001585-260226173443078/datasets/export for details.
DOI: 10.13140/rg.2.2.21476.85120
title: Formula of iodine dioxide (IO2).
DOI: 10.5281/zenodo.18695332
title: VisionFault-350K: A Large-Scale Fault Injection Dataset for Robotic Vision Systems

description: Dataset Description: 

A large-scale fault-augmented dataset contains 350,751 images derived from real robotic camera recordings. To simulate real-world edge cases, original frames were transformed via Stable Diffusion (img2img) across a wide range of fault scenarios.

Technical Specifications

• Total Images: 350751

• Primary Tasks: Lane Following and Obstacle Detection.

• LLM Engine: Fault scenarios designed using GPT-OSS 120B.

• Synthesis Engine: Perturbations synthesized using Stable Diffusion 2.1 Base.

•Categories: Features diverse fault types, such as Camera Failures, Motion Blur, Extreme Weather (Ice, Rain, Fog), Low Light (Tunnel, Night, Backlight), Lens Distortions, etc.

Usage



 If "Download All" is not working, please download the files one by one. To merge the parts after downloading:



For Linux/Mac:



cat part* > dataset.zip 



For Windows (PowerShell - Recommended):



Get-Content part* -ReadCount 0 -Encoding Byte | Set-Content dataset.zip -Encoding Byte



For Windows (CMD): 



copy /b part* dataset.zip

if not working : cmd /c copy /b part* dataset.zip






 

License & Disclaimer

• License: Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer:



Dataset Integrity: This dataset is generated via LLM+LDM and provided "as-is" for robotic vision system testing and research.

Version 2.0 Enhancement: Version 2 features a refined image corpus with optimized visual quality.
DOI: 10.5281/zenodo.18323988
title: VisionFault-350K: A Large-Scale Fault Injection Dataset for Robotic Vision Systems

description: Dataset Description: 

A large-scale fault-augmented dataset contains 350,751 images derived from real robotic camera recordings. To simulate real-world edge cases, original frames were transformed via Stable Diffusion (img2img) across a wide range of fault scenarios.

Technical Specifications

• Total Images: 350751

• Primary Tasks: Lane Following and Obstacle Detection.

• LLM Engine: Fault scenarios designed using GPT-OSS 120B.

• Synthesis Engine: Perturbations synthesized using Stable Diffusion 2.1 Base.

•Categories: Features diverse fault types, such as Camera Failures, Motion Blur, Extreme Weather (Ice, Rain, Fog), Low Light (Tunnel, Night, Backlight), Lens Distortions, etc.

Usage



 If "Download All" is not working, please download the files one by one. To merge the parts after downloading:



For Linux/Mac:



cat part* > dataset.zip 



For Windows (PowerShell - Recommended):



Get-Content part* -ReadCount 0 -Encoding Byte | Set-Content dataset.zip -Encoding Byte



For Windows (CMD): 



copy /b part* dataset.zip

if not working : cmd /c copy /b part* dataset.zip






 

License & Disclaimer

• License: Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer:



Dataset Integrity: This dataset is generated via LLM+LDM and provided "as-is" for robotic vision system testing and research.

Version 2.0 Enhancement: Version 2 features a refined image corpus with optimized visual quality.
DOI: 10.15468/dl.b66fes
title: Occurrence Download

description: A dataset containing 33874 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is India",
 "Geometry POLYGON((80 13.3,80.3 13.3,80.3 13.7,80 13.7,80 13.3))",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2015-2025"
 ]
}

 The dataset includes 33874 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001586-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.13088.24325
title: Formula of bromine dioxide (BrO2).
DOI: 10.15468/dl.2tx5ue
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Rhabdosargus thorpei"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001587-260226173443078/datasets/export for details.
DOI: 10.5281/zenodo.11501835
title: Blaze Fire Classification – Segmentation Dataset

description: The dataset will be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’. If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following paper:



M. Siavrakas, C. Papaioannidis and I.Pitas, “BLAZE: A dataset for wildfire and burnt area UAV image classification and segmentation”, IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA, 13-17 September, 2025.


 

Dataset Structure

CSV files are provided containing the frames taken from every video, the class that has been assigned to them, the path to the respective segmentation mask along with the mask for the segmentation subset and the related links to the public videos and the 2 public datasets.More details on the dataset are available in the following papers:



de Venâncio, P.V.A.B., Lisboa, A.C. & Barbosa, A.V. An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. Neural Comput & Applic 34, 15349–15368 (2022). DOI

Tiago F.R. Ribeiro, Fernando Silva, José Moreira, Rogério Luís de C. Costa,Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks,ISPRS Journal of Photogrammetry and Remote Sensing,Volume 202,2023,Pages 565-580,ISSN 0924-2716. DOI


 

Details on acquiring the dataset can be found here.
DOI: 10.5281/zenodo.11501836
title: Blaze Fire Classification – Segmentation Dataset

description: The dataset will be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’. If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following paper:



M. Siavrakas, C. Papaioannidis and I.Pitas, “BLAZE: A dataset for wildfire and burnt area UAV image classification and segmentation”, IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA, 13-17 September, 2025.


 

Dataset Structure

CSV files are provided containing the frames taken from every video, the class that has been assigned to them, the path to the respective segmentation mask along with the mask for the segmentation subset and the related links to the public videos and the 2 public datasets.More details on the dataset are available in the following papers:



de Venâncio, P.V.A.B., Lisboa, A.C. & Barbosa, A.V. An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. Neural Comput & Applic 34, 15349–15368 (2022). DOI

Tiago F.R. Ribeiro, Fernando Silva, José Moreira, Rogério Luís de C. Costa,Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks,ISPRS Journal of Photogrammetry and Remote Sensing,Volume 202,2023,Pages 565-580,ISSN 0924-2716. DOI


 

Details on acquiring the dataset can be found here.
DOI: 10.15470/6nvsus
title: Database of non-native species in the Pyrenees

description: This checklist is the result of the compilation of non-native species reported in the Pyrenees according to the following inclusion criteria:- Status. We included terrestrial and freshwater species considered non-native according to national checklists or regional atlases (FloraPyr, FaunaPyr). Species native to continental France or Spain were excluded to avoid confusion. This may underestimate species native to other parts of the country but introduced into the Pyrenees. However, it ensures the database is homogeneous and relevant for management. One exception was made for fish species native to continental Spain and/or France, but deliberately introduced into high-altitude Pyrenean lakes, where they are non-native and have strong ecological impacts.?- Taxonomic scope. All taxonomic groups were included except pathogens and microorganisms whose native range is often difficult to determine. The taxonomic level of the database is species. Subspecies were included at the species level only when the entire species can be considered non-native.- Spatial information. We only included species with georeferenced records in the Pyrenees. Species without any information of presence were excluded.- Naturalisation in the wild. We included species that are naturalised in the wild, excluding those clearly associated with cultivated fields and indoor environments with no evidence of escape potential (e.g. home pets, greenhouse species).Given the difficulty of discerning established from casual records and their persistence over time, we followed the precautionary approach and kept all NNS meeting our inclusion criteria. This choice may overestimate the number and distribution of NNS but avoids underreporting.
DOI: 10.6084/m9.figshare.29886719
title: Freshwater macroinvertebrates in the Abruzzo, Lazio, and Molise national park (Central Apennines, Italy): family-level dataset from 16 riverine sites under a protection gradient

description: This dataset includes presence-absence data on benthic macroinvertebrate distribution across a central Italian protected area (The Abruzzo, Lazio, and Molise National Park), which is managed through a zoning design that establishes a protection gradient. Specimens were captured with the kick-net sampling technique and identified at the family level based on morphological characters.
DOI: 10.6084/m9.figshare.29886719.v1
title: Freshwater macroinvertebrates in the Abruzzo, Lazio, and Molise national park (Central Apennines, Italy): family-level dataset from 16 riverine sites under a protection gradient

description: This dataset includes presence-absence data on benthic macroinvertebrate distribution across a central Italian protected area (The Abruzzo, Lazio, and Molise National Park), which is managed through a zoning design that establishes a protection gradient. Specimens were captured with the kick-net sampling technique and identified at the family level based on morphological characters.
DOI: 10.15468/dl.kr472m
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Armatocereus laetus"
 ]
}

 The dataset includes 127 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001588-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bc8vqk
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Sphyraena flavicauda"
 ]
}

 The dataset includes 84 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001589-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.29865.45926
title: Formula of chlorine dioxide (ClO2).
DOI: 10.18715/ipgp.2025.mfv7yzws
title: OH Airglow for Ionospheric Seismology Applications from Instrumental Noise Characterization to Rayleigh Waves Detection Threshold

description: This dataset contains OH airglow images acquired above Japan on 25 February 2024, along with modeling results of a seismic-wave signature in the airglow layer (dI/I). The two image time series are stored in an HDF5 file, and an accompanying README provides detailed information about the data structure.
DOI: 10.5281/zenodo.11502116
title: Mastodon Posts Dataset

description: General description of the dataset

The dataset comprises of 766 social media posts in Greek from the platform “Mastodon” spanning the 2023 wildfires in Greece. Each post was annotated internally with Plutchik-8 emotions. To obtain texts use the Mastodon API (https://docs.joinmastodon.org/api/) along with the provided IDs.

Dataset Structure

The dataset comprises a single CSV file containing pairs of text identifiers and their corresponding sentiment labels.

 

Details on acquiring the dataset can be found here.