cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.tqp8hd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alcides musculus Fairmaire, 1902"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045475-241126133413365/datasets/export for details.
DOI: 10.15468/dl.cnxcav
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Musculus sorgenfreii Anderson, 1967"
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045477-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w265tf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cleonus musculus Fåhrs., 1842"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045478-241126133413365/datasets/export for details.
DOI: 10.15468/dl.weu4uk
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Blepharisma musculus Ehrenberg"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045479-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bmdy8r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tentaxus musculus (Fibiger, 2011)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045480-241126133413365/datasets/export for details.
DOI: 10.15468/dl.t9kwmv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vartiania musculus Rothschild, 1912"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045481-241126133413365/datasets/export for details.
DOI: 10.15468/dl.bnvnwr
title: Occurrence Download

description: A dataset containing 1356319 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Branta leucopsis (Bechstein, 1803)"
 ]
}

 The dataset includes 1356319 records from 643 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045476-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tb665y
title: Occurrence Download

description: A dataset containing 129 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Musculus viridulus (H.Adams, 1871)"
 ]
}

 The dataset includes 129 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045482-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t245k2
title: Occurrence Download

description: A dataset containing 1649 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Musculus lateralis (Say, 1822)"
 ]
}

 The dataset includes 1649 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045483-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dpc54s
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Branta leucopsis x bernicla"
 ]
}

 The dataset includes 47 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045484-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14534821
title: Data and model code - Unlocking Solar and Hydrogen Potentials: A Comparative Analysis of Solar Tracking Systems for South Africa's Energy Transition

description: Data, model, and results for the study "Unlocking Solar and Hydrogen Potentials: A Comparative Analysis of Solar Tracking Systems for South Africa's Energy Transition"
DOI: 10.5281/zenodo.14534822
title: Data and model code - Unlocking Solar and Hydrogen Potentials: A Comparative Analysis of Solar Tracking Systems for South Africa's Energy Transition

description: Data, model, and results for the study "Unlocking Solar and Hydrogen Potentials: A Comparative Analysis of Solar Tracking Systems for South Africa's Energy Transition"
DOI: 10.6084/m9.figshare.28077779
title: Mapeamento Geológico da SPRM e Litologia do Geoparque Quarta Colônia.

description: Dados espaciais sobre o mapeamento geológico da SPRM e Litologia do Geoparque Quarta Colônia.
DOI: 10.6084/m9.figshare.28077779.v1
title: Mapeamento Geológico da SPRM e Litologia do Geoparque Quarta Colônia.

description: Dados espaciais sobre o mapeamento geológico da SPRM e Litologia do Geoparque Quarta Colônia.
DOI: 10.15468/dl.pjs2rj
title: Occurrence Download

description: A dataset containing 2598 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((65 20,135 20,135 60,65 60,65 20))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Pinus koraiensis Siebold & Zucc., Larix olgensis A.Henry, Larix principis-rupprechtii Mayr, Pinus tabuliformis Carrière)"
 ]
}

 The dataset includes 2598 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045485-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28077836
title: Principais estradas do Geoparque Quarta Colônia.

description: Dados espaciais sobre as principais estradas do território do Geoparque Quarta Colônia.
DOI: 10.6084/m9.figshare.28077836.v1
title: Principais estradas do Geoparque Quarta Colônia.

description: Dados espaciais sobre as principais estradas do território do Geoparque Quarta Colônia.
DOI: 10.6084/m9.figshare.28077839
title: Unidades Geomorfológicas do Geoparque Quarta Colônia.

description: Unidades Geomorfológicas do Geoparque Quarta Colônia.
DOI: 10.6084/m9.figshare.28077839.v1
title: Unidades Geomorfológicas do Geoparque Quarta Colônia.

description: Unidades Geomorfológicas do Geoparque Quarta Colônia.
DOI: 10.15468/dl.angngu
title: Occurrence Download

description: A dataset containing 11208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Medicago sativa L., Zephyranthes candida Herb., Zephyranthes carinata Herb., Solanum pseudocapsicum L., Paspalum dilatatum Poir., Euphorbia maculata L., Tropaeolum majus L., Cardamine flexuosa With.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((73.66 4.82,135.08 4.82,135.08 53.55,73.66 53.55,73.66 4.82))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11208 records from 149 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045486-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28077863
title: Uso e Cobertura da Terra 2023 do Geoparque Quarta Colônia

description: Dados espaciais sobre o uso e cobertura da terra de 2023.
DOI: 10.6084/m9.figshare.28077863.v1
title: Uso e Cobertura da Terra 2023

description: Dados espaciais sobre o uso e cobertura da terra de 2023.
DOI: 10.6084/m9.figshare.28077863.v2
title: Uso e Cobertura da Terra 2023 do Geoparque Quarta Colônia

description: Dados espaciais sobre o uso e cobertura da terra de 2023.
DOI: 10.5281/zenodo.8011419
title: Virtual Viewing Experiences (Application)

description: How to simulate hands-on media practices in a virtual environment using 3D-represented cultural heritage objects in such a way that the material characteristics of the objects and their usages become part of the virtual and immersive user experience? In this project, we have explored this question by focusing on two media historical objects: the Kinora motion picture viewer (ca. 1907) and the Pathé Baby 9.5mm film projector (ca. 1924) as early-twentieth-century technologies of home cinema. The application development was carried out in four stages: (1) digitization, (2) conceptualization, (3) contextualization, and (4) digital storytelling. The project was developed within the framework of a research internship at the Open University of the Netherlands in collaboration with Maastricht University, the Netherlands. It was conducted in support of the project CRAFTED: Enrich and promote traditional and contemporary crafts, co-financed by the Connecting Europe Facility of the European Commission. Video demonstration of the application: https://www.youtube.com/watch?v=8UnoPSDZCiw.
DOI: 10.5281/zenodo.14540664
title: Analysis of bulk soil indicators, ultrasonic fractionation with EA-IRMS, and thermogravimetry data

description: This dataset provides comprehensive information on soil properties and analysis methods, comprising the following files:





bulk_soil.xlsx



Sheet 1 ("bulk_soil_data"): Contains bulk soil indicators for 10 sites.

Sheet 2 ("bulk_soil_indicators"): Provides detailed explanations of abbreviations and the units used in Sheet 1.





fraction_data.xlsx



Includes data from ultrasonic dispersion combined with subsequent wet sieving, as well as elemental analysis and isotope ratio mass spectrometry (EA-IRMS).





site_description.xlsx



Details the selected sites, including mean annual precipitation and temperature (based on Hiebl et al., 2011), soil texture, and GPS coordinates. Note: GPS coordinates are anonymized and correspond to the center of a nearby town rather than the exact field location.





TML_bulk.xlsx



Contains mass loss data of bulk soil determined through thermogravimetry.





TML_fraction_Hartl.xlsx



Provides thermogravimetric mass loss (TML) data for fractions of a specific site, measured at 1°C intervals or as cumulative mass loss within 10°C steps.




For a detailed methodological explanation, refer to Wieser et al. (2024) and Wieser et al. (2025 in review).
DOI: 10.5281/zenodo.14540779
title: amarginops_platus_spades_01.fasta.gz
DOI: 10.5281/zenodo.14540780
title: amarginops_platus_spades_01.fasta.gz
DOI: 10.5281/zenodo.10607130
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), published in the Journal of the World Aquaculture Society.

Some material was originally created by Celeste Venolia in March 2018-December 2019 for Venolia et al. (2020).



cold_validation.R contains the code to validate DEB kelp model against data from Trømso, Norway

venolia_validation.R contains the code to validate the model against the data from Rhode Island, USA, reproducing the analysis from the original paper and producing new estimates with the newly-estimated temperature parameters

SolveR_R.R includes the function that is used to solve for the specific growth rate (r)

KelpDEB_model.R includes the deSolve-structured function used to run the model


Please use the most recent version (3.1) so that all data/code needed to run the analyses are present.
DOI: 10.5281/zenodo.10607131
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), submitted to the Journal of the World Aquaculture Society.
DOI: 10.5281/zenodo.11117496
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), submitted to the Journal of the World Aquaculture Society.
DOI: 10.5281/zenodo.11117502
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), submitted to the Journal of the World Aquaculture Society.
DOI: 10.5281/zenodo.11117511
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), submitted to the Journal of the World Aquaculture Society.
DOI: 10.5281/zenodo.11117526
title: Data and code to accompany sugar kelp DEB thermal response manuscript

description: Data and R code to accompany the manuscript "Improving growth models of cultivated sugar kelp (Saccharina latissima) by accounting for intraspecific variation in thermal tolerance" (Krasnow et al., 2024), published in the Journal of the World Aquaculture Society.

Some material was originally created by Celeste Venolia in March 2018-December 2019 for Venolia et al. (2020).



cold_validation.R contains the code to validate DEB kelp model against data from Trømso, Norway

venolia_validation.R contains the code to validate the model against the data from Rhode Island, USA, reproducing the analysis from the original paper and producing new estimates with the newly-estimated temperature parameters

SolveR_R.R includes the function that is used to solve for the specific growth rate (r)

KelpDEB_model.R includes the deSolve-structured function used to run the model


Please use the most recent version (3.1) so that all data/code needed to run the analyses are present.
DOI: 10.15468/dl.5d6dpw
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is 南投縣鹿谷鄉清秀堤段生態檢核補充調查"
 ]
}

 The dataset includes 105 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045487-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13165124
title: Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"

description: this dataset contain: 



2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA

2m temperature observaton data, obs_t2m

24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip

24H precipitation data, in raw_data_r24.zip

source code (in python)


 

how to use it: 

1. prepare data and python environment    1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data    1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva    1.3 need cartopy to draw picture FigS01. 

2. try the 2m temperature blending methods <optional>    2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/    2.2 run the Station_FCST_MMWB.py in python environment 

3. try the 24h QPF multi blending methods <optional>    3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/    3.2 run the Station_FCST_MMWB_r24.py in python environment

4. draw figures    4.1 run Fig01.py in python environment     4.2 run Fig02.py in python environment     4.3 run Fig03.py in python environment     4.4 run FigA01.py in python environment     4.5 run FigS01.py in python environment
DOI: 10.5281/zenodo.13165125
title: Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"

description: this dataset contain: 



de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA

observaton data, obs_t2m

source code (in python)
DOI: 10.5281/zenodo.14525169
title: Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"

description: this dataset contain: 



de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA

observaton data, obs_t2m

source code (in python)


 

how to use it: 

1. prepare data and python environment    1.1 if you want to run Station_FCST_MMWB.py, please download the station forecast and observation data    1.2 unzip the python-src-v2.zip    1.3 need meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva    1.4 need cartopy to draw picture FigS01. 

2. try the multi blending method <optional>    2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/    2.2 run the Station_FCST_MMWB.py in python environment 

3. draw figures    3.1 run Fig01.py in python environment     3.2 run Fig02.py in python environment     3.3 run Fig03.py in python environment     3.4 run FigA01.py in python environment     3.5 run FigS01.py in python environment
DOI: 10.5281/zenodo.14540805
title: aspidura_guentheri_spades_01.fasta.gz
DOI: 10.5281/zenodo.14540806
title: aspidura_guentheri_spades_01.fasta.gz
DOI: 10.15468/dl.3kq2ua
title: Occurrence Download

description: A dataset listing the 105 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is 南投縣鹿谷鄉清秀堤段生態檢核補充調查"
 ]
}

 The dataset's 105 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045488-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14540644
title: High-resolution projections of extreme temperatures, heatwaves and heat exposure in Southeast Asia

description: Projections of extreme heat in SEA.zip is the dataset of projected future extreme temperatures, heatwaves and exposure in Southeast Asia using a regional climate model SINGV-RCM, which dynamcially downscaled from six selected CMIP6 models

python notebooks are used to plot the dataset
DOI: 10.5281/zenodo.14540645
title: High-resolution projections of extreme temperatures, heatwaves and heat exposure in Southeast Asia

description: Projections of extreme heat in SEA.zip is the dataset of projected future extreme temperatures, heatwaves and exposure in Southeast Asia using a regional climate model SINGV-RCM, which dynamcially downscaled from six selected CMIP6 models

python notebooks are used to plot the dataset
DOI: 10.5281/zenodo.14540819
title: High-resolution projections of extreme temperatures, heatwaves and heat exposure in Southeast Asia

description: Projections of extreme heat in SEA.zip is the dataset of projected future extreme temperatures, heatwaves and exposure in Southeast Asia using a regional climate model SINGV-RCM, which dynamcially downscaled from six selected CMIP6 models

python notebooks are used to plot the dataset
DOI: 10.5281/zenodo.14525670
title: On the Usage of PANS for the CFD Simulation of Wind Turbines

description: DATASET INFORMATION

- Dataset containing the CFD simulations presented in the conference paper "On the Usage of PANS for the CFD Simulation of Wind Turbines", DOI: 10.23967/eccomas.2024.001.

- Simulations performed in Navigator HPC cluster, from the LAC laboratory of Universidade de Coimbra, under the project "Medium and High-Fidelity Aerodynamic Models for Wind Farm Power Optimization" (2022.15647.CPCA.A2), funded by FCCN and RNCA.

- For any inquires, please contact Tiago Gomes.

WIND TURBINE GEOMETRY

- DTU10MW Model-Scale, from the UNAFLOW project experimental campaign in Politecnico di Milano's wind tunnel.

- As described in "UNAFLOW: a holistic wind tunnel experiment about the aerodynamic response of floating wind turbines under imposed surge motion", DOI: 10.5194/wes-6-1169-2021

SIMULATIONS DESCRIPTION

- CFD solver ReFRESCO 2023.1.0

- Unstructured hexahedral meshes generated using Hexpress:    - G1 - 16.7M cells    - G2 - 21.9M cells    - G3 - 59.5M cells

- Turbulence models tested:    - RANS - k-omega SST 2003    - DDES - k-omega SST 2003    - PANS - k-omega SST 2003 (fk tested of 25%, 50% and 75%)

- Inputs:    - controls.xml (ReFRESCO input parameters)

- Outputs:    - Residuals    - Courant number    - Forces on surfaces of the turbine    - Moments on surfaces of the turbine    - y+    - Linear solver iterations    - Velocity monitors in the wake (replicating UNAFLOWs hot-wires)    - Volumetric flow data in CGNS format (only for RANS case with grid G2 due to space limitations of the repository)

    Note: _unstd monitors include information for each non-linear iteration.
DOI: 10.5281/zenodo.14525671
title: On the Usage of PANS for the CFD Simulation of Wind Turbines

description: DATASET INFORMATION

- Dataset containing the CFD simulations presented in the conference paper "On the Usage of PANS for the CFD Simulation of Wind Turbines", DOI: 10.23967/eccomas.2024.001.

- Simulations performed in Navigator HPC cluster, from the LAC laboratory of Universidade de Coimbra, under the project "Medium and High-Fidelity Aerodynamic Models for Wind Farm Power Optimization" (2022.15647.CPCA.A2), funded by FCCN and RNCA.

- For any inquires, please contact Tiago Gomes.

WIND TURBINE GEOMETRY

- DTU10MW Model-Scale, from the UNAFLOW project experimental campaign in Politecnico di Milano's wind tunnel.

- As described in "UNAFLOW: a holistic wind tunnel experiment about the aerodynamic response of floating wind turbines under imposed surge motion", DOI: 10.5194/wes-6-1169-2021

SIMULATIONS DESCRIPTION

- CFD solver ReFRESCO 2023.1.0

- Unstructured hexahedral meshes generated using Hexpress:    - G1 - 16.7M cells    - G2 - 21.9M cells    - G3 - 59.5M cells

- Turbulence models tested:    - RANS - k-omega SST 2003    - DDES - k-omega SST 2003    - PANS - k-omega SST 2003 (fk tested of 25%, 50% and 75%)

- Inputs:    - controls.xml (ReFRESCO input parameters)

- Outputs:    - Residuals    - Courant number    - Forces on surfaces of the turbine    - Moments on surfaces of the turbine    - y+    - Linear solver iterations    - Velocity monitors in the wake (replicating UNAFLOWs hot-wires)    - Volumetric flow data in CGNS format (only for RANS case with grid G2 due to space limitations of the repository)

    Note: _unstd monitors include information for each non-linear iteration.