cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17900481
title: Training data and simulations; Modeling uncertainty with engression: a deep generative time-series approach

description: This collection contains training data and simulations for the paper:

Basil Kraft, Steven Stalder, William Hugo Aeberhard, Nicolás Herrington Ruiz, Nicolai Meinshausen, Xinwei Shen, and Lukas Gudmundsson: Modeling uncertainty with engression: a deep generative time-series approach. Authorea. May 27, 2025.



data.zip (harmonized_basins.zarr): training data

engression_runs.zip: engression runs

quantiles_runs.zip: quantile regression runs


We recommend using this training data only to reproduce our work. In this case, please cite all original datasets (see below). Otherwise, it is highly recommended to use the CAMELS-CH dataset directly.

The training data contains data from the following sources, aggregated to catchment scale:



CAMELS-CH data  



https://zenodo.org/records/15025258 



Static catchment properties:



The New Swiss Glacier Inventory SGI2016: From a Topographical to a Glaciological Dataset, Linsbauer et al (2021): 10.3389/feart.2021.704189

Hochauflösende Bodenkarten für den Schweizer Wald, Baltensweiler et al. (2022): 10.3188/szf.2022.0288

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product: Price et al. (2023), 10.3390/rs15030643

swissALTI3D, swisstopo (2018): https://www.swisstopo.admin.ch/en/height-model-swissalti3d#Additional-information, last access: 1 April 2024

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, Poggio et al. (2021): 10.5194/soil-7-217-2021



Meteorological features were retrieved from MeteoSwiss.



https://www.meteoschweiz.admin.ch/dam/jcr:818a4d17-cb0c-4e8b-92c6-1a1bdf5348b7/ProdDoc_TabsD.pdf, last access: 1 April 2024

https://www.meteoschweiz.admin.ch/dam/jcr:4f51f0f1-0fe3-48b5-9de0-15666327e63c/ProdDoc_RhiresD.pdf, last access: 1 April 2024
DOI: 10.5281/zenodo.17830658
title: ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

description: Code and datasets for the 'ProteoBench: the community-curated platform for comparing proteomics data analysis workflows' manuscript.
DOI: 10.5281/zenodo.17830657
title: ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

description: Code and datasets for the 'ProteoBench: the community-curated platform for comparing proteomics data analysis workflows' manuscript.
DOI: 10.15468/dl.kegu9u
title: Occurrence Download

description: A dataset containing 17205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Asystasia gangetica"
 ]
}

 The dataset includes 17205 records from 382 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053593-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dk8xmc
title: Occurrence Download

description: A dataset listing the 238 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Patrones biogeográficos de vegas de la Puna Argentina"
 ]
}

 The dataset's 238 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053589-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9dz5wk
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ligularia dolichobotrys"
 ]
}

 The dataset includes 68 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053594-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17939855
title: Metrics katalia
DOI: 10.5281/zenodo.17939856
title: Metrics katalia
DOI: 10.57702/ni28izvr
title: Wind Turbine SCADA Data For Early Fault Detection

description: This dataset is published together with the paper "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. 
The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines
from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. 
The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.
The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), 
and consists of 5 wind turbines of an onshore wind farm in Portugal. 
It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. 
From this data 22 datasets were selected to be included in this data collection. 
The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.
Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files
More detailed information can be found in the included README-file.
Notes
In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.
DOI: 10.6084/m9.figshare.30884855
title: UntitleTo see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditionsd Item

description: To see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditions
DOI: 10.6084/m9.figshare.30884855.v1
title: UntitleTo see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditionsd Item

description: To see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditions
DOI: 10.6084/m9.figshare.30884915
title: Modelling smoke dynamics and hazards of smouldering fire in complex large space building

description: Smouldering fires produce significant quantities of toxic smoke and gases that are responsible for severe casualties while rarely considered in building fire safety design. This work simulates smouldering smoke transport using a surrogate model with prescribed mass-loss rate, surface temperature, and CO/CO<sub>2</sub> yields. It quantifies the hazards of low-buoyancy, CO-rich smoke from indoor smouldering fires by tracking the carbon monoxide concentration and smoke flow patterns. As the smouldering burning temperature increases, the smoke pattern changes from (1) the stagnation flow on the ground to (2) the boundary wall flow and finally to (3) two-zone structure, because a low temperature smouldering fuel induces a much weaker smoke buoyancy than a flame. Smoke stratification under a hot ceiling becomes easy to occur for a smouldering fire, preventing smoke flowing towards ceiling fire sensors and delaying the fire detection. The available safe egress time (ASET) of smouldering fire can be shorter than flaming fire under the same fuel-burning rate, showing a greater fire hazard. Building design features like roof shape, slab extension, and smoke extraction affect the smouldering smoke flow, where a sawtooth roof reduces ASET by 18% compared to a flat roof atrium. When a smouldering fire source is located under the slab extension, ASET may be reduced to less than a minute due to rapid smoke spread at floor level, while a mechanical extraction system can effectively remove low buoyancy smouldering fire smoke. This work improves our understanding of smouldering fire hazards in complex buildings and provides scientific guidelines for a more comprehensive design evaluation of building fire safety.
DOI: 10.6084/m9.figshare.30884915.v1
title: Modelling smoke dynamics and hazards of smouldering fire in complex large space building

description: Smouldering fires produce significant quantities of toxic smoke and gases that are responsible for severe casualties while rarely considered in building fire safety design. This work simulates smouldering smoke transport using a surrogate model with prescribed mass-loss rate, surface temperature, and CO/CO<sub>2</sub> yields. It quantifies the hazards of low-buoyancy, CO-rich smoke from indoor smouldering fires by tracking the carbon monoxide concentration and smoke flow patterns. As the smouldering burning temperature increases, the smoke pattern changes from (1) the stagnation flow on the ground to (2) the boundary wall flow and finally to (3) two-zone structure, because a low temperature smouldering fuel induces a much weaker smoke buoyancy than a flame. Smoke stratification under a hot ceiling becomes easy to occur for a smouldering fire, preventing smoke flowing towards ceiling fire sensors and delaying the fire detection. The available safe egress time (ASET) of smouldering fire can be shorter than flaming fire under the same fuel-burning rate, showing a greater fire hazard. Building design features like roof shape, slab extension, and smoke extraction affect the smouldering smoke flow, where a sawtooth roof reduces ASET by 18% compared to a flat roof atrium. When a smouldering fire source is located under the slab extension, ASET may be reduced to less than a minute due to rapid smoke spread at floor level, while a mechanical extraction system can effectively remove low buoyancy smouldering fire smoke. This work improves our understanding of smouldering fire hazards in complex buildings and provides scientific guidelines for a more comprehensive design evaluation of building fire safety.
DOI: 10.6084/m9.figshare.30295497
title: What does Dr Google tell us about tendinopathies? An Italian analysis of online content

description: This study aimed to identify the most frequently searched keywords and questions related to the Achilles, knee, shoulder, and elbow tendons in Italy. It further aimed to evaluate the credibility, readability, and content of the most visited web pages. Semrush Inc. (2008) machine learning models were used for data mining in December 2024. Credibility and readability of the most visited web pages were assessed through the QUality Evaluation Scoring Tool (QUEST) and Gulpease index, respectively. A content analysis of web pages was used to determine alignment with evidence-based literature. The most searched question was “How to treat foot tendonitis?” (2,750 searches). Only two web pages (2.2%) were rated as providing unbiased information using the QUEST, with credibility values ranging from 4.0 (±1.6) to 11.4 (±4.0) across all searches. Gulpease indices ranged from 34.0 (±2.1) to 42.8 (±2.9) across all web pages. Notably, content analysis revealed only a small percentage of web pages that aligned to best available evidence. Credibility, readability, and overall quality of online content on tendons were poor. Healthcare professionals may play a role in promoting accurate terminology and supporting the production of high-quality, evidence-based web page content to improve public health literacy. ”Dr. Google,” is used by individuals to seek health-related information for common issues like tendon pain. However, it is unknown if the information found online is trustworthy and easy to understand. This study investigated what people in Italy search for online about the most common tendinopathies: Achilles, knee, shoulder, and elbow. The most popular web pages were examined for credibility (trustworthiness), readability (easy to understand), and how web page content aligns with best available evidence. Patients with tendon pain were consulted in this research project. Our findings demonstrated that online information is often poor quality. Most web pages used outdated terms like “tendinitis” which may give end-users incorrect information about their condition. It was found that very few web pages were trustworthy and unbiased. The most visited web pages were found to have poor readability, even for people with a high level of education. Furthermore, advice for tendinopathy conditions viewed online rarely aligned with expert recommendations. Poor-quality online information can affect the end-users understanding of their health and the subsequent choices they make. There is a need for healthcare professionals to help create clear and accurate online resources for tendinopathy, using terminology that facilitates appropriate assessment and management of the condition.
DOI: 10.6084/m9.figshare.30295497.v1
title: What does Dr Google tell us about tendinopathies? An Italian analysis of online content

description: This study aimed to identify the most frequently searched keywords and questions related to the Achilles, knee, shoulder, and elbow tendons in Italy. It further aimed to evaluate the credibility, readability, and content of the most visited web pages. Semrush Inc. (2008) machine learning models were used for data mining in December 2024. Credibility and readability of the most visited web pages were assessed through the QUality Evaluation Scoring Tool (QUEST) and Gulpease index, respectively. A content analysis of web pages was used to determine alignment with evidence-based literature. The most searched question was “How to treat foot tendonitis?” (2,750 searches). Only two web pages (2.2%) were rated as providing unbiased information using the QUEST, with credibility values ranging from 4.0 (±1.6) to 11.4 (±4.0) across all searches. Gulpease indices ranged from 34.0 (±2.1) to 42.8 (±2.9) across all web pages. Notably, content analysis revealed only a small percentage of web pages that aligned to best available evidence. Credibility, readability, and overall quality of online content on tendons were poor. Healthcare professionals may play a role in promoting accurate terminology and supporting the production of high-quality, evidence-based web page content to improve public health literacy. ”Dr. Google,” is used by individuals to seek health-related information for common issues like tendon pain. However, it is unknown if the information found online is trustworthy and easy to understand. This study investigated what people in Italy search for online about the most common tendinopathies: Achilles, knee, shoulder, and elbow. The most popular web pages were examined for credibility (trustworthiness), readability (easy to understand), and how web page content aligns with best available evidence. Patients with tendon pain were consulted in this research project. Our findings demonstrated that online information is often poor quality. Most web pages used outdated terms like “tendinitis” which may give end-users incorrect information about their condition. It was found that very few web pages were trustworthy and unbiased. The most visited web pages were found to have poor readability, even for people with a high level of education. Furthermore, advice for tendinopathy conditions viewed online rarely aligned with expert recommendations. Poor-quality online information can affect the end-users understanding of their health and the subsequent choices they make. There is a need for healthcare professionals to help create clear and accurate online resources for tendinopathy, using terminology that facilitates appropriate assessment and management of the condition.
DOI: 10.6084/m9.figshare.30438041
title: AI-guided EMS for chronic musculoskeletal pain: subgroup-level outcomes from a digital health platform

description: Chronic musculoskeletal pain is common and lacks effective long-term therapies. Transcutaneous electrical nerve stimulation (TENS) and electrical muscle stimulation (EMS) offer noninvasive alternatives, but conventional devices are limited by static protocols and poor adherence. The NXTSTIM EcoAI platform integrates TENS/EMS with artificial intelligence to deliver personalized, adaptive therapy. This study examined 24-month real-world outcomes of EcoAI, focusing on usage and subgroup-level efficacy. A retrospective analysis was conducted using de-identified data from 2,050 adults using EcoAI at home. The primary endpoint was change in pain intensity (0–10 numeric rating scale). Secondary endpoints included functional status, session engagement, and qualitative pain self-efficacy. Across ~185,000 sessions, users reported significant pain reduction. Mean pain decreased by 2.4 points (<i>p</i> &lt; 0.001), exceeding 30% improvement, with benefits sustained at 12 and 24 months. Functional interference and mood improved significantly (<i>p</i> &lt; 0.01). Older adults (≥60 years) achieved comparable or greater relief despite slightly lower usage. No serious adverse events occurred. Findings aligned with prior EcoAI analyses showing optimal outcomes with 2–4 daily sessions of 20–59 minutes. EcoAI provided clinically meaningful, durable pain relief with improved function and mood, supporting personalized home-based neuromodulation as a safe, effective adjunct for chronic musculoskeletal pain.
DOI: 10.6084/m9.figshare.30438041.v1
title: AI-guided EMS for chronic musculoskeletal pain: subgroup-level outcomes from a digital health platform

description: Chronic musculoskeletal pain is common and lacks effective long-term therapies. Transcutaneous electrical nerve stimulation (TENS) and electrical muscle stimulation (EMS) offer noninvasive alternatives, but conventional devices are limited by static protocols and poor adherence. The NXTSTIM EcoAI platform integrates TENS/EMS with artificial intelligence to deliver personalized, adaptive therapy. This study examined 24-month real-world outcomes of EcoAI, focusing on usage and subgroup-level efficacy. A retrospective analysis was conducted using de-identified data from 2,050 adults using EcoAI at home. The primary endpoint was change in pain intensity (0–10 numeric rating scale). Secondary endpoints included functional status, session engagement, and qualitative pain self-efficacy. Across ~185,000 sessions, users reported significant pain reduction. Mean pain decreased by 2.4 points (<i>p</i> &lt; 0.001), exceeding 30% improvement, with benefits sustained at 12 and 24 months. Functional interference and mood improved significantly (<i>p</i> &lt; 0.01). Older adults (≥60 years) achieved comparable or greater relief despite slightly lower usage. No serious adverse events occurred. Findings aligned with prior EcoAI analyses showing optimal outcomes with 2–4 daily sessions of 20–59 minutes. EcoAI provided clinically meaningful, durable pain relief with improved function and mood, supporting personalized home-based neuromodulation as a safe, effective adjunct for chronic musculoskeletal pain.
DOI: 10.15468/dl.9hmm9d
title: Occurrence Download

description: A dataset containing 21552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Vanessa atalanta",
 "Year 2004-2004"
 ]
}

 The dataset includes 21552 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053595-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11434400
title: Deeplearning-driven diagnosis of humerus fractures
DOI: 10.13145/bacdive123497.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123498.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123499.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123500.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123501.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123502.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123503.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123504.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123505.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123506.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123507.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123508.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123509.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123510.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123511.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123512.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive123513.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.