cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gt5cyu
title: Occurrence Download

description: A dataset containing 46232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-4.99868 36.29338,-3.70385 36.29338,-3.70385 37.00213,-4.99868 37.00213,-4.99868 36.29338))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 46232 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090839-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.439xnw
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Silene chalcedonica (L.) E.H.L.Krause",
 "Country is China",
 "Year is greater than or equal to 1900",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090847-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10911323
title: Data for "Friction in Rolling a Cylinder on or Under a Viscoelastic Substrate with Adhesion"

description: This dataset contains all the data generated for the publication

[1] R. Nazari, A. Papangelo, M. Ciavarella, Friction in Rolling a Cylinder on or Under a Viscoelastic Substrate with Adhesion, Tribology Letters, (2024), DOI: 10.1007/s11249-024-01849-1 

The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "dataRolling.mat" is the following:

%%%%%%%%%%%%%

clc

clearvars

 

close all

 

% load workspace containing data



load('dataRolling.mat') 

 

% Plot the curve number 2 of Figure 7

 

figure, hold on

curve_number = 2;

plot(Fig7.x(curve_number).x,Fig7.y(curve_number).y)

 

%%%%%%%%%%%%%
DOI: 10.5281/zenodo.10911324
title: Data for "Friction in Rolling a Cylinder on or Under a Viscoelastic Substrate with Adhesion"

description: This dataset contains all the data generated for the publication

[1] R. Nazari, A. Papangelo, M. Ciavarella, Friction in Rolling a Cylinder on or Under a Viscoelastic Substrate with Adhesion, Tribology Letters, (2024), DOI: 10.1007/s11249-024-01849-1 

The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "dataRolling.mat" is the following:

%%%%%%%%%%%%%

clc

clearvars

 

close all

 

% load workspace containing data



load('dataRolling.mat') 

 

% Plot the curve number 2 of Figure 7

 

figure, hold on

curve_number = 2;

plot(Fig7.x(curve_number).x,Fig7.y(curve_number).y)

 

%%%%%%%%%%%%%
DOI: 10.15468/dl.crjt3j
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pyrrosia kinabaluensis Hovenkamp"
 ]
}

 The dataset includes 49 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090848-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yasrp
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Viola variegata Fisch. ex Link",
 "Country is China",
 "Year is greater than or equal to 1900",
 "HasCoordinate is true"
 ]
}

 The dataset includes 191 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090849-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rkqghm
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ranunculus obtusidentiformis (Julin) Ericsson"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090852-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hjhbfk
title: Occurrence Download

description: A dataset containing 86949 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-5.50973 36.08028,-3.2836 36.5432,-3.46825 37.16704,-4.28895 37.42818,-5.42766 37.12615,-5.50973 36.08028))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 86949 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090766-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rsqj6h
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Restio tuberculatus Pillans"
 ]
}

 The dataset includes 66 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090853-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jvx3n8
title: Occurrence Download

description: A dataset containing 385 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhododendron crinigerum Franch."
 ]
}

 The dataset includes 385 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090851-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5bn82g
title: Occurrence Download

description: A dataset containing 18571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-4.77901 36.47064,-4.11736 36.47064,-4.11736 36.92796,-4.77901 36.92796,-4.77901 36.47064))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 18571 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090846-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w5sm57
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhynchosia calvescens Meikle"
 ]
}

 The dataset includes 39 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090850-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10822547
title: WaterFutures/BoN2024: BWDF input data and results
DOI: 10.5281/zenodo.10822548
title: WaterFutures/BoN2024: BWDF input data and results
DOI: 10.15468/dl.hbgjuk
title: Occurrence Download

description: A dataset containing 1211 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-4.55552 36.6303,-4.42128 36.6303,-4.42128 36.71456,-4.55552 36.71456,-4.55552 36.6303))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 1211 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0090855-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10654173
title: Quick scan 'Locations for highest-potential greenhouse development in the world'

description: Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.

This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.

This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.

The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. 

Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.

Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:•    Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.•    Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.•    Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.

The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below.
DOI: 10.5281/zenodo.10654174
title: Quick scan 'Locations for highest-potential greenhouse development in the world'

description: Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.

This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.

This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.

The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. 

Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.

Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:•    Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.•    Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.•    Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.

The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below.
DOI: 10.5281/zenodo.10716895
title: Quick scan 'Locations for highest-potential greenhouse development in the world'

description: Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.

This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.

This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.

The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. 

Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.

Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:•    Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.•    Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.•    Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.

The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below.
DOI: 10.5281/zenodo.10721264
title: Quick scan 'Locations for highest-potential greenhouse development in the world'

description: Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.

This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.

This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.

The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. 

Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.

Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:•    Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.•    Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.•    Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.

The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below.
DOI: 10.5281/zenodo.10686118
title: A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images

description: CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.

You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on https://github.com/huangleinxidimejd/CWLD_Model.

Training details

The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:



'train_batch_size': 4,

'val_batch_size': 4,

'train_crop_size': 512,

'val_crop_size': 512,

'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data

'Epoch Times': 200,

'gpu': correct,

'weight_decay': 5E-4,

'Momentum': 0.9,

'print_freq': 100,

'predict_step': 5,


usage



After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.

Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.

The utils package provides a large number of data processing tools to use.

The trained model can be predicted from a EvalSeg.py file.
DOI: 10.5281/zenodo.8333887
title: A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images

description: CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.

You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on https://github.com/huangleinxidimejd/CWLD_Model.

Training details

The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:



'train_batch_size': 4,

'val_batch_size': 4,

'train_crop_size': 512,

'val_crop_size': 512,

'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data

'Epoch Times': 200,

'gpu': correct,

'weight_decay': 5E-4,

'Momentum': 0.9,

'print_freq': 100,

'predict_step': 5,


usage



After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.

Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.

The utils package provides a large number of data processing tools to use.

The trained model can be predicted from a EvalSeg.py file.