cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jfsgrp5kzc.2
title: Presence-Gated VOC Sensing for Urban Search and Rescue Applications

description: This dataset contains development-phase logs used to train and evaluate J48, Random Forest, and a simple probability-level hybrid classifier for a portable volatile-signature detector intended for urban search-and-rescue triage. Data were collected in tethered mode while the ESP32 firmware executed repeating intake–hold–purge cycles. During each hold window the device computed a per-hold feature vector and mirrored it to a host PC along with synchronized raw/derived channels and metadata.
DOI: 10.17632/5fp2mnsxsh
title: Xiaozhang Paste Alleviates Sepsis-Induced Gastrointestinal Dysfunction via Remodeling the Gut Microbiota and Restoring the VIPR2-Mediated Gut-Brain Axis

description: This dataset contains the underlying source data supporting the findings of the associated manuscript.
The files include:
Statistical Source Data: Raw numerical data for survival rates, histological scores, ELISA assays, qPCR results (Ct values and fold changes), and quantification of western blots, organized by corresponding figures (Fig 1 - Fig 5).
DOI: 10.17632/5fp2mnsxsh.2
title: Xiaozhang Paste Alleviates Sepsis-Induced Gastrointestinal Dysfunction via Remodeling the Gut Microbiota and Restoring the VIPR2-Mediated Gut-Brain Axis

description: This dataset contains the underlying source data supporting the findings of the associated manuscript.
The files include:
Statistical Source Data: Raw numerical data for survival rates, histological scores, ELISA assays, qPCR results (Ct values and fold changes), and quantification of western blots, organized by corresponding figures (Fig 1 - Fig 5).
DOI: 10.17632/zdnrtmyfgh
title: A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

description: This dataset is associated with an article bearing the same name ( https://doi.org/10.64898/2026.01.28.701823 ), describing the results of &gt;400 CRISPR/Cas9 chemical-genetic genome-wide knockout screens covering 305 different compounds and 5 conditions. The dataset also contains meta-data about each compound as well as the raw and/or processed data necessary to reproduce various analyses and figures from the article. The contents of each table and sub-table (tab) are described in the overview tab of each file. Table contents are briefly described below.
Table S1. Summary of the CGI dataset. (A) Sequenced samples. (B) Independent screens. (C) Compound details and verification. (D) NALM-6 gene expression. (E) CGIs detected.
Table S2. CRANKS scores.
Table S3. Known compound-target relationships.
Table S4. Combined CRANKS scores for two independent TRAIL screens.
Table S5. Jaccard similarity indices between screens in the CGI dataset. Pairs used in Fig. 2D are indicated by class.
Table S6. Compound-transporter CGI scores.
Table S7. (A) Compound dose-to-solubility ratios by screen. (B) Correlation coefficients of CRANKS scores with compound dose-to-solubility ratios. 
Table S8. Vemurafenib SSL hits and Gene Ontology annotations.
Table S9. Predicted basic pKa and logP for compounds represented in the screen similarity network.
Table S10. Mean genome-wide CRANKS score profile of the 28 screens in the phospholipidosis cluster.
Table S11. LipidTOX Red assay determinations. (A) Cell counts and average LipidTOX levels. (B) Compound classification.
Table S12. Average CRANKS scores for combined screens. (A) Nocodazole. (B) Active site mTOR inhibitors. (C) Rapamycin. (D) AICAR. (E) Pyridostatin and topoisomerase II inhibitors. (F) DHFR inhibitor screens. (G) PARP inhibitors. (H) CHEK1 inhibitors.
Table S13. Comparison of CGIs for 9 shared compounds in this dataset and the Olivieri et al (2020) dataset. 
Table S14. Data for assessment of compound synergy/antagonism, including luminescence readings, treatments by well and log2 Bliss scores determined by compound pair.
Table S15. (A) CRANKS2 scores. (B) Top 20,000 most correlated gene pairs.
Table S16. RNA-seq data. (A) FK-506 versus DMSO and read counts by gene. (B) PPP3CA knockout versus wild-type controls. (C) PPP3R1 knockout versus wild-type controls. (D) Transcription rates in PABIR1 knockout clones.
Table S17. List of DDR-associated genes.
Table S18. (A) Significant exon-level CGIs. (B) Exon classification and validation data.
Table S19. (A) Hypothetical gene CGIs with p-values &lt;0.002 or absolute scores ≥2.5. (B) Hypothetical gene classification and mass spectral counts.
Table S20. Correlation between gene CRANKS scores and population doublings.
DOI: 10.17632/zdnrtmyfgh.1
title: A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

description: This dataset is associated with an article bearing the same name ( https://doi.org/10.64898/2026.01.28.701823 ), describing the results of &gt;400 CRISPR/Cas9 chemical-genetic genome-wide knockout screens covering 305 different compounds and 5 conditions. The dataset also contains meta-data about each compound as well as the raw and/or processed data necessary to reproduce various analyses and figures from the article. The contents of each table and sub-table (tab) are described in the overview tab of each file. Table contents are briefly described below.
Table S1. Summary of the CGI dataset. (A) Sequenced samples. (B) Independent screens. (C) Compound details and verification. (D) NALM-6 gene expression. (E) CGIs detected.
Table S2. CRANKS scores.
Table S3. Known compound-target relationships.
Table S4. Combined CRANKS scores for two independent TRAIL screens.
Table S5. Jaccard similarity indices between screens in the CGI dataset. Pairs used in Fig. 2D are indicated by class.
Table S6. Compound-transporter CGI scores.
Table S7. (A) Compound dose-to-solubility ratios by screen. (B) Correlation coefficients of CRANKS scores with compound dose-to-solubility ratios. 
Table S8. Vemurafenib SSL hits and Gene Ontology annotations.
Table S9. Predicted basic pKa and logP for compounds represented in the screen similarity network.
Table S10. Mean genome-wide CRANKS score profile of the 28 screens in the phospholipidosis cluster.
Table S11. LipidTOX Red assay determinations. (A) Cell counts and average LipidTOX levels. (B) Compound classification.
Table S12. Average CRANKS scores for combined screens. (A) Nocodazole. (B) Active site mTOR inhibitors. (C) Rapamycin. (D) AICAR. (E) Pyridostatin and topoisomerase II inhibitors. (F) DHFR inhibitor screens. (G) PARP inhibitors. (H) CHEK1 inhibitors.
Table S13. Comparison of CGIs for 9 shared compounds in this dataset and the Olivieri et al (2020) dataset. 
Table S14. Data for assessment of compound synergy/antagonism, including luminescence readings, treatments by well and log2 Bliss scores determined by compound pair.
Table S15. (A) CRANKS2 scores. (B) Top 20,000 most correlated gene pairs.
Table S16. RNA-seq data. (A) FK-506 versus DMSO and read counts by gene. (B) PPP3CA knockout versus wild-type controls. (C) PPP3R1 knockout versus wild-type controls. (D) Transcription rates in PABIR1 knockout clones.
Table S17. List of DDR-associated genes.
Table S18. (A) Significant exon-level CGIs. (B) Exon classification and validation data.
Table S19. (A) Hypothetical gene CGIs with p-values &lt;0.002 or absolute scores ≥2.5. (B) Hypothetical gene classification and mass spectral counts.
Table S20. Correlation between gene CRANKS scores and population doublings.
DOI: 10.5281/zenodo.18545352
title: SuperDARN data in netCDF format (2021-Sep)

description: 2021-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


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



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




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



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18547397
title: Plate 3.11 Mean Snow Depths, 1983–2002
title: Tafel 3.11 Mittlere Schneehöhen 1983–2002
title: Planche 3.11 Hauteurs de neige moyennes, 1983–2002
title: Tavola 3.11 Altezze medie della neve 1983–2002

description: Large parts of Switzerland are covered by a solid layer of snow for many months. The thickness of this snow cover depends on various factors such as season or altitude, which means that it can vary considerably within a short distance and between one region and another.The depth of the snow cover is measured and analysed at around 300 stations. These spot measurements are then extrapolated to larger areas to estimate the depth of snow in places where no measurements are available. Maps of snow depth are an important basis for avalanche warnings, although they are also important for tourism in relation to snow reliability, for setting up hydrological models and for nivo-climatic studies.
description: Grosse Gebiete der Schweiz liegen während Monaten unter einer geschlossenen Schneedecke. Die Mächtigkeit dieser Schneedecke hängt von verschiedenen Faktoren wie Jahreszeit oder Höhe über Meer ab; sie ist deshalb lokal wie regional sehr unterschiedlich.An rund 300 Stationen wird die Schneedecke gemessen und untersucht. Anhand dieser punktuellen Messungen können flächendeckende Aussagen gemacht werden, welche Abschätzungen der Schneehöhen an Stellen ohne Messungen ermöglichen. Schneehöhenkarten bilden eine wichtige Grundlage für die Lawinenwarnung; sie sind aber auch für touristische Aussagen bezüglich Schneesicherheit, für hydrologische Abflussmodellierungen und für schneeklimatologische Untersuchungen wichtig.
description: De vastes zones de la Suisse demeurent durant des mois sous une couche de neige persistante. L’épaisseur de cette couche de neige dépend de divers facteurs comme la saison ou l’altitude; elle varie donc considérablement au niveau local ainsi que régional.Environ 300 stations permettent de mesurer et d’étudier la couche de neige. Ces mesures ponctuelles peuvent ensuite être extrapolées pour estimer la hauteur de neige aux endroits où elle n’a pas été relevée. Les cartes des hauteurs de neige servent de base pour l’alerte aux avalanches, mais elles sont aussi importantes pour le tourisme (informations sur l’enneigement), pour les modélisations hydrologiques des écoulements et pour les recherches menées en climatologie nivale.
description: Ampi territori della Svizzera restano per mesi sotto una solida coltre di neve il cui spessore dipende da vari fattori, come la stagione o l’altitudine, differenziandosi così parecchio sia localmente che regionalmente.Il manto nevoso è misurato e analizzato presso circa 300 stazioni. Questi rilevamenti puntuali consentono di eseguire valutazioni sui territori interessati, permettendo di fare delle stime in aree sprovviste di misure. Le mappe delle altezze nevose sono fondamentali per la segnalazione del pericolo di valanghe, ma servono anche per considerazioni sulla neve in ambito turistico, per la generazione di modelli idrologici di deflusso e per indagini nivoclimatologiche.
DOI: 10.5281/zenodo.18547396
title: Plate 3.11 Mean Snow Depths, 1983–2002
title: Tafel 3.11 Mittlere Schneehöhen 1983–2002
title: Planche 3.11 Hauteurs de neige moyennes, 1983–2002
title: Tavola 3.11 Altezze medie della neve 1983–2002

description: Large parts of Switzerland are covered by a solid layer of snow for many months. The thickness of this snow cover depends on various factors such as season or altitude, which means that it can vary considerably within a short distance and between one region and another.The depth of the snow cover is measured and analysed at around 300 stations. These spot measurements are then extrapolated to larger areas to estimate the depth of snow in places where no measurements are available. Maps of snow depth are an important basis for avalanche warnings, although they are also important for tourism in relation to snow reliability, for setting up hydrological models and for nivo-climatic studies.
description: Grosse Gebiete der Schweiz liegen während Monaten unter einer geschlossenen Schneedecke. Die Mächtigkeit dieser Schneedecke hängt von verschiedenen Faktoren wie Jahreszeit oder Höhe über Meer ab; sie ist deshalb lokal wie regional sehr unterschiedlich.An rund 300 Stationen wird die Schneedecke gemessen und untersucht. Anhand dieser punktuellen Messungen können flächendeckende Aussagen gemacht werden, welche Abschätzungen der Schneehöhen an Stellen ohne Messungen ermöglichen. Schneehöhenkarten bilden eine wichtige Grundlage für die Lawinenwarnung; sie sind aber auch für touristische Aussagen bezüglich Schneesicherheit, für hydrologische Abflussmodellierungen und für schneeklimatologische Untersuchungen wichtig.
description: De vastes zones de la Suisse demeurent durant des mois sous une couche de neige persistante. L’épaisseur de cette couche de neige dépend de divers facteurs comme la saison ou l’altitude; elle varie donc considérablement au niveau local ainsi que régional.Environ 300 stations permettent de mesurer et d’étudier la couche de neige. Ces mesures ponctuelles peuvent ensuite être extrapolées pour estimer la hauteur de neige aux endroits où elle n’a pas été relevée. Les cartes des hauteurs de neige servent de base pour l’alerte aux avalanches, mais elles sont aussi importantes pour le tourisme (informations sur l’enneigement), pour les modélisations hydrologiques des écoulements et pour les recherches menées en climatologie nivale.
description: Ampi territori della Svizzera restano per mesi sotto una solida coltre di neve il cui spessore dipende da vari fattori, come la stagione o l’altitudine, differenziandosi così parecchio sia localmente che regionalmente.Il manto nevoso è misurato e analizzato presso circa 300 stazioni. Questi rilevamenti puntuali consentono di eseguire valutazioni sui territori interessati, permettendo di fare delle stime in aree sprovviste di misure. Le mappe delle altezze nevose sono fondamentali per la segnalazione del pericolo di valanghe, ma servono anche per considerazioni sulla neve in ambito turistico, per la generazione di modelli idrologici di deflusso e per indagini nivoclimatologiche.
DOI: 10.5281/zenodo.7613364
title: SuperDARN data in netCDF format (2021-Sep)

description: 2021-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


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



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




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



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.17632/yt6pvk8s5d.1
title: QTNano - Tuning hydrogen adsorption through synergy in non-noble bimetallic substrates, International Journal of Hydrogen Energy, 212, 153692, (2026)

description: Raw data of the published paper - https://doi.org/10.1016/j.ijhydene.2026.153692 Cite this: Int. J. Hydrogen Energy 212, 153692 (2026)
DOI: 10.17632/mx35mhb5td
title: Metabolite Sensing in Yeast Cells with SABRE-Hyperpolarized [1-13C]Pyruvate

description: This dataset supports the study of [1-¹³C]pyruvate as a hyperpolarized magnetic resonance probe for investigating metabolism in yeast. It includes raw and processed NMR data from SABRE hyperpolarization experiments, spectral time series from live-cell measurements, and kinetic model outputs characterizing metabolic conversion pathways. These data enable visualization and quantification of pyruvate metabolism in real time and provide a foundation for further analysis of pyruvate flux in biological systems.
DOI: 10.17632/mx35mhb5td.1
title: Metabolite Sensing in Yeast Cells with SABRE-Hyperpolarized [1-13C]Pyruvate

description: This dataset supports the study of [1-¹³C]pyruvate as a hyperpolarized magnetic resonance probe for investigating metabolism in yeast. It includes raw and processed NMR data from SABRE hyperpolarization experiments, spectral time series from live-cell measurements, and kinetic model outputs characterizing metabolic conversion pathways. These data enable visualization and quantification of pyruvate metabolism in real time and provide a foundation for further analysis of pyruvate flux in biological systems.
DOI: 10.17632/p336chf7f5
title: In situ surface soil water content and precipitation dataset from a preserved Caatinga area in the Brazilian semi-arid region

description: This dataset provides continuous time series of in-situ soil moisture and precipitation collected over a six-year period (2015–2020) in a preserved area of the Caatinga biome, located in the Brazilian semiarid region at the Embrapa Semiárido experimental field in Petrolina, Pernambuco, Brazil. The dataset contains essential hydrological observations for investigating ecohydrological processes in seasonally dry tropical ecosystems. Soil moisture was measured in situ at a depth of 5 cm, representing volumetric soil water content (m³/m³). Measurements include daily averaged values and two fixed daily observations recorded at 6:00 AM and 18:00 local time. Precipitation data correspond to daily accumulated rainfall (mm), measured using a rain gauge installed within the same experimental area to ensure spatial consistency between variables. Data are provided in a single CSV (comma separated values) file structured according to international formatting standards: comma-separated fields, decimal point, and dates in ISO 8601 format (YYYY-MM-DD). Each row represents one day of observation. Althought the dataset was primarily developed to support the validation of satellite-derived soil moisture products, its applicability extends to multiple research fields, including hydrology, remote sensing, ecology, hydrological modeling, climate change studies, and water resource management in semiarid environments. By making these measurements publicy available, this dataset contributes to reducing the scarcity of long term in situ observations from preserved Caatinga areas, a biome that remains underrepresented in global hydrological databases.
DOI: 10.17632/p336chf7f5.1
title: In situ surface soil water content and precipitation dataset from a preserved Caatinga area in the Brazilian semi-arid region

description: This dataset provides continuous time series of in-situ soil moisture and precipitation collected over a six-year period (2015–2020) in a preserved area of the Caatinga biome, located in the Brazilian semiarid region at the Embrapa Semiárido experimental field in Petrolina, Pernambuco, Brazil. The dataset contains essential hydrological observations for investigating ecohydrological processes in seasonally dry tropical ecosystems. Soil moisture was measured in situ at a depth of 5 cm, representing volumetric soil water content (m³/m³). Measurements include daily averaged values and two fixed daily observations recorded at 6:00 AM and 18:00 local time. Precipitation data correspond to daily accumulated rainfall (mm), measured using a rain gauge installed within the same experimental area to ensure spatial consistency between variables. Data are provided in a single CSV (comma separated values) file structured according to international formatting standards: comma-separated fields, decimal point, and dates in ISO 8601 format (YYYY-MM-DD). Each row represents one day of observation. Althought the dataset was primarily developed to support the validation of satellite-derived soil moisture products, its applicability extends to multiple research fields, including hydrology, remote sensing, ecology, hydrological modeling, climate change studies, and water resource management in semiarid environments. By making these measurements publicy available, this dataset contributes to reducing the scarcity of long term in situ observations from preserved Caatinga areas, a biome that remains underrepresented in global hydrological databases.
DOI: 10.17632/2ryw3hpb6v
title: Dataset and codes for the paper "No-reference quality assessment of dermoscopic images using minimal expert supervision"

description: Dataset and Source Code for DermaIQA: No-Reference Quality Assessment of Dermoscopic Images

This repository contains:

Test Dataset
- 150 high-quality dermoscopic images
- 150 low-quality dermoscopic images

Inference Code
- Python implementation of DermaIQA
- Inference pipeline for quality assessment
- Requirements and dependencies
- Usage examples (inference.py)

Associated with the paper:
Ferraris A., Branciforti F., Meiburger K., Veronese F., Zavattaro E., Savoia P., and Salvi M., "No-reference quality assessment of dermoscopic images using minimal expert supervision", Applied Sciences, 2026.

Note: The training code and dataset generation pipeline are available upon reasonable request to the corresponding author.
DOI: 10.17632/2ryw3hpb6v.1
title: Dataset and codes for the paper "No-reference quality assessment of dermoscopic images using minimal expert supervision"

description: Dataset and Source Code for DermaIQA: No-Reference Quality Assessment of Dermoscopic Images

This repository contains:

Test Dataset
- 150 high-quality dermoscopic images
- 150 low-quality dermoscopic images

Inference Code
- Python implementation of DermaIQA
- Inference pipeline for quality assessment
- Requirements and dependencies
- Usage examples (inference.py)

Associated with the paper:
Ferraris A., Branciforti F., Meiburger K., Veronese F., Zavattaro E., Savoia P., and Salvi M., "No-reference quality assessment of dermoscopic images using minimal expert supervision", Applied Sciences, 2026.

Note: The training code and dataset generation pipeline are available upon reasonable request to the corresponding author.
DOI: 10.34740/kaggle/ds/9443930
title: Fallout New Vegas dataset