cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.tbh7k7
title: Occurrence Download

description: A dataset containing 4021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Crocus nudiflorus Sm."
 ]
}

 The dataset includes 4021 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000394-250225085111116/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14923717
title: Table S1
DOI: 10.5281/zenodo.14923718
title: Table S1
DOI: 10.5281/zenodo.14639277
title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with deep-learning based Post-Processing Segmentation"

description: Title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation"

Authors: Simone Monaco, Luca Monaco, Daniele Apiletti

Description:This dataset supports the study presented in the paper "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation". The work focuses on improving daily quantitative precipitation forecasts and uncertainty estimates over the Piedmont and Aosta Valley regions in Italy by blending outputs from four Numerical Weather Prediction (NWP) models using uncertainty-aware deep learning methods and NWIOI observational data (Turco et al., 2013). NWPs forecasts can be obtained on request, observational data is provided in this repository. The NWPs include:



BOLAM-ISAC-CNR (Buzzi et al., 1994): data requests are possibile at ISAC-CNR, at this link

COSMO-2I (Baldauf et al., 2011): data requests are possible at the COSMO Consortium, at this link

COSMO-5M (Doms & Baldauf, 2018): data requests are possible at the COSMO Consortium, at this link

ECMWF-IFS-HRES (ECMWF, 2016): data download is possible at this link


Observational data from NWIOI serve as the ground truth for model training. The dataset contains 420 gridded precipitation events from 2018 to 2024.

Dataset contents:



obs.zip: NWIOI observed precipitation data (.csv format, one file per event)

domain_mask.csv: Binary mask (1 for grid points in the study area, 0 otherwise)

allevents_dates.csv: Classification of all events by type and intensity, used for n-fold cross-validation and dataset splits


Citations:



BOLAM-ISAC-CNR: Buzzi, A., Fantini, M., Malguzzi, P., & Nerozzi, F. (1994). Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorology and Atmospheric Physics, 53(3), 137–153. Springer.

COSMO-2I: Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Monthly Weather Review, 139(12), 3887–3905. American Meteorological Society.

COSMO-5M: Doms, G., & Baldauf, M. (2018). A Description of the Nonhydrostatic Regional COSMO Model. Part I: Dynamics and Numerics. COSMO Technical Report, Deutscher Wetterdienst.

ECMWF-IFS-HRES: ECMWF. (2016). IFS Documentation CY43R1. ECMWF Technical Documentation.

NWIOI: Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C., & Mercogliano, P. (2013). Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. Natural Hazards and Earth System Sciences, 13(6), 1457–1468.


Related Repository: https://github.com/simone7monaco/probabilistic-rainprediction
DOI: 10.5281/zenodo.14639278
title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with deep-learning based Post-Processing Segmentation"

description: Title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation"

Authors: Simone Monaco, Luca Monaco, Daniele Apiletti

Description:This dataset supports the study presented in the paper "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation". The work focuses on improving daily quantitative precipitation forecasts and uncertainty estimates over the Piedmont and Aosta Valley regions in Italy by blending outputs from four Numerical Weather Prediction (NWP) models using uncertainty-aware deep learning methods and NWIOI observational data (Turco et al., 2013). NWPs forecasts can be obtained on request, observational data is provided in this repository. The NWPs include:



BOLAM-ISAC-CNR (Buzzi et al., 1994): data requests are possibile at ISAC-CNR, at this link

COSMO-2I (Baldauf et al., 2011): data requests are possible at the COSMO Consortium, at this link

COSMO-5M (Doms & Baldauf, 2018): data requests are possible at the COSMO Consortium, at this link

ECMWF-IFS-HRES (ECMWF, 2016): data download is possible at this link


Observational data from NWIOI serve as the ground truth for model training. The dataset contains 420 gridded precipitation events from 2018 to 2024.

Dataset contents:



obs.zip: NWIOI observed precipitation data (.csv format, one file per event)

domain_mask.csv: Binary mask (1 for grid points in the study area, 0 otherwise)

allevents_dates.csv: Classification of all events by type and intensity, used for n-fold cross-validation and dataset splits


Citations:



BOLAM-ISAC-CNR: Buzzi, A., Fantini, M., Malguzzi, P., & Nerozzi, F. (1994). Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorology and Atmospheric Physics, 53(3), 137–153. Springer.

COSMO-2I: Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Monthly Weather Review, 139(12), 3887–3905. American Meteorological Society.

COSMO-5M: Doms, G., & Baldauf, M. (2018). A Description of the Nonhydrostatic Regional COSMO Model. Part I: Dynamics and Numerics. COSMO Technical Report, Deutscher Wetterdienst.

ECMWF-IFS-HRES: ECMWF. (2016). IFS Documentation CY43R1. ECMWF Technical Documentation.

NWIOI: Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C., & Mercogliano, P. (2013). Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. Natural Hazards and Earth System Sciences, 13(6), 1457–1468.


Related Repository: https://github.com/simone7monaco/probabilistic-rainprediction
DOI: 10.5281/zenodo.14923796
title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with deep-learning based Post-Processing Segmentation"

description: Title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation"

Authors: Simone Monaco, Luca Monaco, Daniele Apiletti

Description:This dataset supports the study presented in the paper "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation". The work focuses on improving daily quantitative precipitation forecasts and uncertainty estimates over the Piedmont and Aosta Valley regions in Italy by blending outputs from four Numerical Weather Prediction (NWP) models using uncertainty-aware deep learning methods and NWIOI observational data (Turco et al., 2013). NWPs forecasts can be obtained on request, observational data is provided in this repository. The NWPs include:



BOLAM-ISAC-CNR (Buzzi et al., 1994): data requests are possibile at ISAC-CNR, at this link

COSMO-2I (Baldauf et al., 2011): data requests are possible at the COSMO Consortium, at this link

COSMO-5M (Doms & Baldauf, 2018): data requests are possible at the COSMO Consortium, at this link

ECMWF-IFS-HRES (ECMWF, 2016): data download is possible at this link


Observational data from NWIOI serve as the ground truth for model training. The dataset contains 420 gridded precipitation events from 2018 to 2024.

Dataset contents:



obs.zip: NWIOI observed precipitation data (.csv format, one file per event)

domain_mask.csv: Binary mask (1 for grid points in the study area, 0 otherwise)

allevents_dates.csv: Classification of all events by type and intensity, used for n-fold cross-validation and dataset splits


Citations:



BOLAM-ISAC-CNR: Buzzi, A., Fantini, M., Malguzzi, P., & Nerozzi, F. (1994). Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorology and Atmospheric Physics, 53(3), 137–153. Springer.

COSMO-2I: Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Monthly Weather Review, 139(12), 3887–3905. American Meteorological Society.

COSMO-5M: Doms, G., & Baldauf, M. (2018). A Description of the Nonhydrostatic Regional COSMO Model. Part I: Dynamics and Numerics. COSMO Technical Report, Deutscher Wetterdienst.

ECMWF-IFS-HRES: ECMWF. (2016). IFS Documentation CY43R1. ECMWF Technical Documentation.

NWIOI: Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C., & Mercogliano, P. (2013). Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. Natural Hazards and Earth System Sciences, 13(6), 1457–1468.


Related Repository: https://github.com/simone7monaco/probabilistic-rainprediction
DOI: 10.34658/rdb.ksgmsj
title: Wet torrefaction of biomass waste into value-added liquid product (5-HMF) and high quality solid fuel (hydrochar) in a nitrogen atmosphere

description: In this research, we conducted a comprehensive investigation into the influence of reaction conditions on the WT process, evaluating its effects on the surface morphology and elemental composition of the resulting hydrochar, as well as on the formation of value-added liquid products, such as 5-hydroxymethylfurfural (5-HMF). During the course of our study, we utilized wood cellulose pulp residue (WCPR) as the feedstock and subjected it to WT in a nitrogen atmosphere. This process encompassed a temperature range of 180–260 °C, H2O/WCPR ratios ranging from 10 to 25, and reaction durations spanning from 15 to 60 min. Our findings unequivocally revealed that the reaction conditions during the WT of WCPR significantly influence the properties of the resulting hydrochar and the distribution of liquid products. Elemental and proximate analyses showed that as the reaction temperature and time increased during the WT of WCPR, the hydrochar composition experienced significant changes, including an increase in carbon content and a reduction in oxygen content.
DOI: 10.34658/rdb.ksgmsj/wruumv
title: Wet torrefaction of biomass waste into value-added liquid product (5-HMF) and high quality solid fuel (hydrochar) in a nitrogen atmosphere.opj

description: Data in OriginPro format.
DOI: 10.4228/zalf-xgw4-fcfw
title: Soil profile 'BDF_04'
DOI: 10.5281/zenodo.14923613
title: TRAINING DATASET FOR VibroSC-DAE

description: Sampling frequency: 80 Hz

Resolution: 0.125/4096
DOI: 10.5281/zenodo.14923614
title: TRAINING DATASET FOR VibroSC-DAE

description: Sampling frequency: 80 Hz

Resolution: 0.125/4096
DOI: 10.5281/zenodo.14823220
title: Survey Appropriateness of Information Flows in a Wearable context

description: This dataset contains survey responses from 500 Flemish participants evaluating the appropriateness of information flows in the context of wearable technology. The study employed a full-factorial vignette experiment to examine how data type (e.g., location, weight, health risk), sender (user vs. application), and purpose (functionalities, research, ads) impact perceived appropriateness. Additionally, privacy profiles were collected to analyse differences in privacy attitudes. The dataset is supplementary material to a journal article in Computers in Human Behavior Reports.
DOI: 10.5281/zenodo.14823221
title: Survey Appropriateness of Information Flows in a Wearable context

description: This dataset contains survey responses from 500 Flemish participants evaluating the appropriateness of information flows in the context of wearable technology. The study employed a full-factorial vignette experiment to examine how data type (e.g., location, weight, health risk), sender (user vs. application), and purpose (functionalities, research, ads) impact perceived appropriateness. Additionally, privacy profiles were collected to analyse differences in privacy attitudes. The dataset is supplementary material to a journal article in Computers in Human Behavior Reports.
DOI: 10.5281/zenodo.14923799
title: MoCEGS Project: Macro-level Power Data for European and Neighbouring Countries

description: Data Repository: MoCEGS Project

The data for each year in the MoCEGS project are in zipped file.
The names of the files are saved as the following context:
[Area]_[Starting Date DDMMYYYY]_[Ending Date DDMMYYYY]_[Project Name]


Within the zipped file, the following folder structure exist:
- capacity: This is 30-minute generation capacity (by energy source) data that is directly queried from ENTSOE API.
	- The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station.
	- The source of the energy is labelled as B[number]. Refer to the 'env.csv' file to get a reference to the source of generation.

- flow: This is the 30-minute power flow data that is directly queried from ENTSOE API. (This is an optional query)
	- The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.

- load: This is the 30-minute power load (consumption) that is directly queried from ENTSOE API.
	- The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station.

- price: This is the 30-minute power price that is directly queried from ENTSOE API.
	- The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.
DOI: 10.5281/zenodo.14923800
title: MoCEGS Project: Macro-level Power Data for European and Neighbouring Countries

description: Data Repository: MoCEGS Project

The data for each year in the MoCEGS project are in zipped file.
The names of the files are saved as the following context:
[Area]_[Starting Date DDMMYYYY]_[Ending Date DDMMYYYY]_[Project Name]


Within the zipped file, the following folder structure exist:
- capacity: This is 30-minute generation capacity (by energy source) data that is directly queried from ENTSOE API.
	- The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station.
	- The source of the energy is labelled as B[number]. Refer to the 'env.csv' file to get a reference to the source of generation.

- flow: This is the 30-minute power flow data that is directly queried from ENTSOE API. (This is an optional query)
	- The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.

- load: This is the 30-minute power load (consumption) that is directly queried from ENTSOE API.
	- The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station.

- price: This is the 30-minute power price that is directly queried from ENTSOE API.
	- The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.
DOI: 10.5281/zenodo.14916810
title: Protocol for Invisible in the Smart City

description: This document contains the protocol for the research on which the article "Invisible in the Smart City" was based.
DOI: 10.5281/zenodo.14916811
title: Protocol for Invisible in the Smart City

description: This document contains the protocol for the research on which the article "Invisible in the Smart City" was based.
DOI: 10.15468/dl.kwdfe9
title: Occurrence Download

description: A dataset containing 26810 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pulicaria vulgaris Gaertn."
 ]
}

 The dataset includes 26810 records from 491 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000399-250225085111116/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e2mpjg
title: Occurrence Download

description: A dataset containing 888 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Iguana iguana iguana"
 ]
}

 The dataset includes 888 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006673-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.akbq5z
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "Geometry POLYGON((-104.48944 17.1553,-100.00065 17.24691,-100.53297 24.6262,-109.80272 28.4231,-104.48944 17.1553))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vaccinium stenophyllum Steud."
 ]
}

 The dataset includes 176 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006675-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgtrhg
title: Occurrence Download

description: A dataset containing 227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is United States of America",
 "Issue is Geodetic datum assumed WGS84",
 "OccurrenceStatus is Present",
 "TaxonKey is Pieris rapae (Linnaeus, 1758)"
 ]
}

 The dataset includes 227 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006674-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.79szq2
title: Occurrence Download

description: A dataset containing 37193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-02-08",
 "EventDate is less than or equal to 2025-03-10",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 37193 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006676-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qgjk6r
title: Occurrence Download

description: A dataset containing 15718951 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is International Barcode of Life project (iBOL)"
 ]
}

 The dataset includes 15718951 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006670-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t528zf
title: Occurrence Download

description: A dataset containing 24662 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Albizia julibrissin Durazz.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 24662 records from 366 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006656-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pbzhg7
title: Occurrence Download

description: A dataset containing 361573 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis Linnaeus, 1758"
 ]
}

 The dataset includes 361573 records from 883 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006652-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kmb99f
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-75.63091 6.1437,-75.59619 6.14859,-75.56357 6.1588,-75.54711 6.17783,-75.53679 6.20229,-75.52578 6.25322,-75.53477 6.30181,-75.52217 6.35093,-75.56808 6.37103,-75.61154 6.31625,-75.62851 6.24399,-75.62208 6.2031,-75.63772 6.17321,-75.66592 6.16549,-75.63091 6.1437))",
 "TaxonKey is Cerdocyon thous (Linnaeus, 1766)"
 ]
}

 The dataset's 1 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006655-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wcd3bm
title: Occurrence Download

description: A dataset containing 361573 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis Linnaeus, 1758"
 ]
}

 The dataset includes 361573 records from 883 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006665-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ag6nhu
title: Occurrence Download

description: A dataset containing 4049 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tlalocohyla smithii (Boulenger, 1902)"
 ]
}

 The dataset includes 4049 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121734-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.szc87x
title: Occurrence Download

description: A dataset containing 242825 species occurrences available in GBIF matching the query:
{
 "PublishingCountry" : [
 "is Iran (Islamic Republic of)"
 ]
}

 The dataset includes 242825 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121739-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.