cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ry7bjcx95x
title: Time series of nitrate, ammonium and phosphorus in low-salinity estuarine waters and streams along the coast of Gipuzkoa (Basque Country, Bay of Biscay).

description: Data are provided on molar concentration of nitrate, ammonium and total dissolved phosphorus (sum of organic and inorganic forms), together with salinity, for six estuaries in the south-east corner of the Bay of Biscay. These are the mesotidal estuaries of the Deba, Urola, Oria, Urumea, Oiartzun and Bidasoa rivers. In addition, the dataset includes the Endara stream, a tributary of the Bidasoa, and five other small rivers that also flow into the Basque coast: the Mijoa, Narrondo, Igara, Añorga and Molinao streams. The frequency of the nutrient data is approximately eight times per year for most of these systems, although the Oiartzun estuary and some of the streams present a smaller amount of data.

The data were gathered from the water quality control network of the Gipuzkoa Provincial Council (GPC), which started in 1986. The GPC network stablished 5−9 sampling stations along the longitudinal axe of the estuaries, from head to mouth, and one station in each of the streams. Samplings took place mostly during the morning and the tidal state was not considered to choose the dates. The water samples were collected just below the surface using a clean bucket and immediately, their salinity was measured by field sensors. In the laboratory, from 2011 on, nitrate was determined by ion chromatography with chemical suppression followed by conductivity detector; ammonium by indophenol blue colorimetry; and phosphorus by inductively coupled plasma mass spectrometry (ICP−MS) after filtration (0.45 µm) and nitric acid acidification.

This dataset mainly represents low salinity waters (≤1 PSU) because the analytical methods were not optimised for other matrices and, in the laboratory, the samples whose conductivity was higher than 1500 µS/cm were generally rejected. Therefore, the tidal state and the river runoff at the time of sampling largely influenced which estuarine stations were to be analysed for nutrients. The instrumentation and the detection limits varied little during long time periods (&gt;10 years), both in the field and in the laboratory.

We want to thank the staff of the GPC for their assistance with the field work and the Fraisoro Agro‑environmental Laboratory for performing the chemical analyses. The data processing was carried out by AZTI and funded by the GPC and by the Horizon Europe GES4SEAS project (grant agreement no. 101059877; www.ges4seas.eu).
DOI: 10.17632/ry7bjcx95x.1
title: Time series of nitrate, ammonium and phosphorus in low-salinity estuarine waters and streams along the coast of Gipuzkoa (Basque Country, Bay of Biscay).

description: Data are provided on molar concentration of nitrate, ammonium and total dissolved phosphorus (sum of organic and inorganic forms), together with salinity, for six estuaries in the south-east corner of the Bay of Biscay. These are the mesotidal estuaries of the Deba, Urola, Oria, Urumea, Oiartzun and Bidasoa rivers. In addition, the dataset includes the Endara stream, a tributary of the Bidasoa, and five other small rivers that also flow into the Basque coast: the Mijoa, Narrondo, Igara, Añorga and Molinao streams. The frequency of the nutrient data is approximately eight times per year for most of these systems, although the Oiartzun estuary and some of the streams present a smaller amount of data.

The data were gathered from the water quality control network of the Gipuzkoa Provincial Council (GPC), which started in 1986. The GPC network stablished 5−9 sampling stations along the longitudinal axe of the estuaries, from head to mouth, and one station in each of the streams. Samplings took place mostly during the morning and the tidal state was not considered to choose the dates. The water samples were collected just below the surface using a clean bucket and immediately, their salinity was measured by field sensors. In the laboratory, from 2011 on, nitrate was determined by ion chromatography with chemical suppression followed by conductivity detector; ammonium by indophenol blue colorimetry; and phosphorus by inductively coupled plasma mass spectrometry (ICP−MS) after filtration (0.45 µm) and nitric acid acidification.

This dataset mainly represents low salinity waters (≤1 PSU) because the analytical methods were not optimised for other matrices and, in the laboratory, the samples whose conductivity was higher than 1500 µS/cm were generally rejected. Therefore, the tidal state and the river runoff at the time of sampling largely influenced which estuarine stations were to be analysed for nutrients. The instrumentation and the detection limits varied little during long time periods (&gt;10 years), both in the field and in the laboratory.

We want to thank the staff of the GPC for their assistance with the field work and the Fraisoro Agro‑environmental Laboratory for performing the chemical analyses. The data processing was carried out by AZTI and funded by the GPC and by the Horizon Europe GES4SEAS project (grant agreement no. 101059877; www.ges4seas.eu).
DOI: 10.17632/p4bffrpr8k
title: Identification and validation of Perioperative anesthesia -associated dignostic and druggable frameworks for Deep vein thrombosis patients and its associations with Kawasaki disease: insights from machine learning and multi-omics

description: Background: Perioperative anesthesia(PA) drugs for deep vein thrombosis(DVE) patients can significantly affect their rehabilitation. Indeed, DVE is a major manifestation of Kawasaki disease(KD) patients. Hence, research targeting investigation of the role of PA in KD-associated DVE can provide novel insights into KD with DVE management. 
Methods: By combination of 3 DVE patient bulk profiles(GSE118259, GSE19151 and GSE48000) with perioperative anesthesia-related drug target(PARDTGs) and integrative bioinformatic analysis(Limma and machine learning framework), we identified hub differentially expressed PARDTGs for DVE patients. The dignostic performance and corresponding molecular and immune features were also estimated among DVE patients. Furthermore, natural compounds for alleviation of DVE targeting hub PARDTGs were also enriched by CTD database and validated by molecular docking. In addition, differentially expressed PARDTGs can guide the risk stratification for DVE patients via consensus clustering. Besides, KD peripheral blood single-cell profile(GSE254657) was utilized, and hub differentially expressed PARDTGs functional performance in investigation of association between DVT and patients were estimated in spatial and temporal manners.
DOI: 10.17632/p4bffrpr8k.1
title: Identification and validation of Perioperative anesthesia -associated dignostic and druggable frameworks for Deep vein thrombosis patients and its associations with Kawasaki disease: insights from machine learning and multi-omics

description: Background: Perioperative anesthesia(PA) drugs for deep vein thrombosis(DVE) patients can significantly affect their rehabilitation. Indeed, DVE is a major manifestation of Kawasaki disease(KD) patients. Hence, research targeting investigation of the role of PA in KD-associated DVE can provide novel insights into KD with DVE management. 
Methods: By combination of 3 DVE patient bulk profiles(GSE118259, GSE19151 and GSE48000) with perioperative anesthesia-related drug target(PARDTGs) and integrative bioinformatic analysis(Limma and machine learning framework), we identified hub differentially expressed PARDTGs for DVE patients. The dignostic performance and corresponding molecular and immune features were also estimated among DVE patients. Furthermore, natural compounds for alleviation of DVE targeting hub PARDTGs were also enriched by CTD database and validated by molecular docking. In addition, differentially expressed PARDTGs can guide the risk stratification for DVE patients via consensus clustering. Besides, KD peripheral blood single-cell profile(GSE254657) was utilized, and hub differentially expressed PARDTGs functional performance in investigation of association between DVT and patients were estimated in spatial and temporal manners.
DOI: 10.17632/n98vmbxxxc.1
title: QTNano - Catalytic Performance of Single and Double Metal MXenes for the Hydrogen Evolution Reaction, JPCC, X (2025)

description: Raw data.
DOI: 10.15468/dl.hqsfk2
title: Occurrence Download

description: A dataset containing 4271 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Centrarchus macropterus"
 ]
}

 The dataset includes 4271 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058886-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gkhjh4v8tg
title: Dataset of Chilean Oak micropyrolysis over Zn and Ga supported on natural zeolite catalyst in oxygen-depleted (He) and reductive (H₂) atmospheres

description: Biomass pyrolysis has promising potential as an alternative to fossil-based compounds, but the high content of oxygenates in the liquid product limits its application on a large scale. To address this issue, alternatives have been proposed, such as the use of a catalyst and changes in the reaction atmosphere to reduce the oxygenate content of the liquid fraction. However, the challenges lie in understanding the reactions that occur during the process using experimental data to propose robust and accurate reaction schemes.
Here, we present analytical pyrolysis data (Py-GC/MS) for Chilean oak (Nothofagus obliqua) torrefied and non-torrefied under four different conditions: Torr1 (523 K, 30 min), Torr2 (573 K, 15 min), Torr3 (573 K, 30 min), and Torr4 (523 K, 15 min). The tests were carried out in oxygen-depleted (He) and reductive (H₂) atmospheres over a catalyst based on natural Chilean zeolite loaded with 2 wt.% or 5 wt.% Zn or Ga. The catalysts were prepared via ion exchange, first with ammonium sulphate to remove the compensation cations from the zeolite, and then with precursor metal salts (nitrates) for metal loading. The data provided includes characterization of both untreated and torrefied biomass, as well as catalysts. Thermogravimetric analyses were also performed on the biomass samples at different heating rates to determine the triplet that controls their decomposition.
The data collection also includes catalytic pyrolysis tests using various oaks (torrefied and non-torrefied) in He and H₂ atmospheres against the catalysts. Py-GC-MS experiments provide detailed information on the composition of condensable pyrolysis gases, which is useful for industrial scale-up proposals that utilize machine learning or multivariate statistics, as well as a description of reaction mechanisms via computational chemistry. The data is organized into three main folders, which contain the dataset in XLS and CSV formats, where the folders that include them have the same name according to the file type. The third folder contains machine-readable metadata files, which allow for the correct handling of the data provided. In this folder, you will locate files that link the names of individual files to the experimental conditions, column headers to the units, and compounds identified with their respective chemical formulas. On the other hand, the folders containing the data are organized as follows (both folders have the same organization):
Folder 1. Raw materials characterization-Oak (untreated and torrefied)
Folder 2. Characterisation of catalysts
Folder 3. Thermogravimetric analysis at different heating rates
Folder 4. Non-catalytic Pyrolysis and hydropyrolysis
Folder 5. Catalytic Pyrolysis and hydropyrolysis
DOI: 10.17632/gkhjh4v8tg.2
title: Dataset of Chilean Oak micropyrolysis over Zn and Ga supported on natural zeolite catalyst in oxygen-depleted (He) and reductive (H₂) atmospheres

description: Biomass pyrolysis has promising potential as an alternative to fossil-based compounds, but the high content of oxygenates in the liquid product limits its application on a large scale. To address this issue, alternatives have been proposed, such as the use of a catalyst and changes in the reaction atmosphere to reduce the oxygenate content of the liquid fraction. However, the challenges lie in understanding the reactions that occur during the process using experimental data to propose robust and accurate reaction schemes.
Here, we present analytical pyrolysis data (Py-GC/MS) for Chilean oak (Nothofagus obliqua) torrefied and non-torrefied under four different conditions: Torr1 (523 K, 30 min), Torr2 (573 K, 15 min), Torr3 (573 K, 30 min), and Torr4 (523 K, 15 min). The tests were carried out in oxygen-depleted (He) and reductive (H₂) atmospheres over a catalyst based on natural Chilean zeolite loaded with 2 wt.% or 5 wt.% Zn or Ga. The catalysts were prepared via ion exchange, first with ammonium sulphate to remove the compensation cations from the zeolite, and then with precursor metal salts (nitrates) for metal loading. The data provided includes characterization of both untreated and torrefied biomass, as well as catalysts. Thermogravimetric analyses were also performed on the biomass samples at different heating rates to determine the triplet that controls their decomposition.
The data collection also includes catalytic pyrolysis tests using various oaks (torrefied and non-torrefied) in He and H₂ atmospheres against the catalysts. Py-GC-MS experiments provide detailed information on the composition of condensable pyrolysis gases, which is useful for industrial scale-up proposals that utilize machine learning or multivariate statistics, as well as a description of reaction mechanisms via computational chemistry. The data is organized into three main folders, which contain the dataset in XLS and CSV formats, where the folders that include them have the same name according to the file type. The third folder contains machine-readable metadata files, which allow for the correct handling of the data provided. In this folder, you will locate files that link the names of individual files to the experimental conditions, column headers to the units, and compounds identified with their respective chemical formulas. On the other hand, the folders containing the data are organized as follows (both folders have the same organization):
Folder 1. Raw materials characterization-Oak (untreated and torrefied)
Folder 2. Characterisation of catalysts
Folder 3. Thermogravimetric analysis at different heating rates
Folder 4. Non-catalytic Pyrolysis and hydropyrolysis
Folder 5. Catalytic Pyrolysis and hydropyrolysis
DOI: 10.15468/dl.vtsu8z
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ophiophagus hannah"
 ]
}

 The dataset includes 67 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058887-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/h4zbs27tkr
title: TTS/V2V Audio Deepfake Dataset

description: This dataset was created in 2025 and consists of 643 synthetic audio samples generated using the Minimax.io platform. It includes both text-to-speech (TTS) and voice-to-voice (V2V) synthetic speech. Specifically, the dataset contains 603 TTS audios and 40 V2V audios. The material spans two languages—Spanish and English—and includes 302 female voices, with the remaining samples corresponding to male voices.

This dataset enables multiple downstream applications, including:
(1) the development and training of models for synthetic-audio detection,
(2) the external benchmarking of audio-deepfake classification systems, and
(3) the assessment of model robustness by introducing adversarial or signal-level perturbations to the audio samples.
DOI: 10.17632/h4zbs27tkr.2
title: TTS/V2V Audio Deepfake Dataset

description: This dataset was created in 2025 and consists of 643 synthetic audio samples generated using the Minimax.io platform. It includes both text-to-speech (TTS) and voice-to-voice (V2V) synthetic speech. Specifically, the dataset contains 603 TTS audios and 40 V2V audios. The material spans two languages—Spanish and English—and includes 302 female voices, with the remaining samples corresponding to male voices.

This dataset enables multiple downstream applications, including:
(1) the development and training of models for synthetic-audio detection,
(2) the external benchmarking of audio-deepfake classification systems, and
(3) the assessment of model robustness by introducing adversarial or signal-level perturbations to the audio samples.
DOI: 10.17632/gc7z7p349z.1
title: Cotton production in Mali

description: This dataset contains annual macro-agricultural and economic data used to analyze the resilience of the Malian cotton sector to climate variability, global price fluctuations, exchange-rate movements, and subsidy policies over the period 1990–2023. The data support a Structural Vector Autoregression (SVAR) analysis examining the dynamic transmission of external and domestic shocks to real cotton producer income per hectare.

The dataset includes five core variables:
(i) real cotton income per hectare (deflated using Mali’s rural CPI);
(ii) annual rainfall as a proxy for climatic conditions;
(iii) the Cotlook A Index as a measure of world cotton prices;
(iv) the FCFA/USD exchange rate capturing macro-monetary exposure; and
(v) input subsidies per hectare reflecting agricultural policy support.

All monetary variables are expressed in real terms, and logarithmic transformations are applied where appropriate to facilitate elasticity interpretation and reduce heteroskedasticity. The dataset is designed to enable replication of the empirical results presented in the associated manuscript and to support further research on income dynamics, price transmission, and policy effectiveness in export-oriented agricultural systems in Sub-Saharan Africa.
DOI: 10.15468/dl.5tpx36
title: Occurrence Download

description: A dataset containing 334 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.5_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 334 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058888-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.usnfny
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solecurtus afroccidentalis)",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058889-251120083545085/datasets/export for details.
DOI: 10.5285/1d2b498e981f4bcfb46c1f3c5923b122
title: GloSAT model experiments : UKESM1-1-LL simulations covering the period 1750-2014 - variables and derived variables analysed in the paper Ballinger et al (2025)

description: The Global Surface Air Temperature (GloSAT) model simulations were run on UK supercomputing platforms ARCHER2 and MONSooN using the UKESM1-1-LL UK Earth System Model described in Mulcahy, et al 2023 (https://doi.org/10.5194/gmd-16-1569-2023).
The simulations were run under the NERC funded project GloSAT. NE/S015647/2

The experiments and model set up are all described in:
Ballinger, A., Schurer, A., Hegerl, G., Dittus, A., Hawkins, E., Cornes, R., Kent, E., Marshall, L., Morice, C., Osborn, T., and Rayner, N.A. Rumbold, S.:(2025) Importance of beginning industrial-era climate simulations in the eighteenth century, Environmental Research Letters, 650, accepted.

This data set contains the variables and derived variables analysed in the paper Ballinger et al (2025)
These variables are taken from the 3 new model experiments described in this publication, which are

6 ensembles with all forcings 1750-2014, described as historical1750
4 ensembles with anthropogenic only forcing, 1750-1910, described as historicalANT1750
1 preindustrial control simulation run using 1750 constant forcing described as piControl1750

The 4 variables which have been archived here in netcdf format were those used in the publication:
tas - surface air temperature
thetao - ocean temperature
AMOC - Atlantic meridional streamflow
SurfaceTypes - vegetation types
DOI: 10.5285/4612fd14-7054-618c-e063-7086abc02b80
title: Highlight seabed images taken by ROV in the UK-1 area during expedition JC257 in the Clarion-Clipperton Zone (Pacific Ocean, 2024)

description: A collection of benthic still images was obtained using an oblique-looking camera mounted on the UK ISIS Remotely Operated Vehicle (ROV), deployed from the RRS James Cook during cruise JC257 in the abyssal plain (~4200 m depth) of the Clarion-Clipperton Zone, Pacific Ocean, in 2024. The images were obtained in an area under contract to UK Seabed Resources for exploration for deep-sea minerals through the International Seabed Authority. The expedition was independent from UK Seabed Resources. At the time of the expedition there had been no test mining in the area, so the images show natural baseline conditions. In some cases, there is equipment visible on the seafloor deployed during JC257 or on previous expeditions to the area. The images show the seabed covered by polymetallic nodules. The data were collected by scientists from the National Oceanography Centre, Southampton, UK as part of the NERC-funded Seabed Mining And Resilience To EXperimental impact (SMARTEX) project (NE/T003537/1).
DOI: 10.15468/dl.vegs3t
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Phyllonorycter hostis"
 ]
}

 The dataset includes 170 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058890-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/41869
title: Chandra X-ray Observatory ObsId 41869
DOI: 10.17026/ar/9zvo2i
title: PAN-00161244 - terra sigillata plate type Drag. 18 (SG)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00161244&lt;/p&gt;
description: Modified: 2025-11-03T11:35:31+01:00
DOI: 10.5281/zenodo.17815755
title: Mallorca Extreme Events: Influence of four different ground covers on flooding, Andratx basin, Mallorca.

description: A new open dataset analysing flood events in Mallorca with the influecne of different land covers and rainfall events. Data was collected in the Sa Font de la Vila Catchment, Andraxt, Mallorca, and used in MAHLERAN (Model for Assessing Hillslope-Landscape Erosion, Runoff And Nutrients. The dataset includes primarry data collected in the field in raw and processed form, secondary data used in the modelling and the inputs and outputs used in the model.