cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3237788
title: Global treecover persistence for 2014

description: Global tree cover persistence for the period 2001–2014  with loss (defined as any change below the 10% threshold of tree cover) subtracted from 1 km tree cover extent map for 2000.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.5281/zenodo.3237789
title: Global treecover persistence for 2014

description: Global tree cover persistence for the period 2001–2014  with loss (defined as any change below the 10% threshold of tree cover) subtracted from 1 km tree cover extent map for 2000.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.21227/eqc9-4320
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.5281/zenodo.3237457
title: Twitter pre-trained word vectors

description: Clean up of <strong>glove.twitter.27B.zip </strong>&lt;ODC Public Domain Dedication and Licence (PDDL) 1.0&gt;.

"2B tweets, 27B tokens, 1.2M vocab, uncased"

<strong>Changes from original</strong>


	Headers added to allow loading by gensim. [Added via scripts.glove2word2vec]
	Recompressed as individual gzip files [Instead of a combined zip].


These changes make the files easier to work with and increase compatibility.

<strong>Headers</strong>

Example of added header line


1193513 200


Header gives number of tokens and dimensions.

<strong>Statistics</strong>


	Entries: 1,193,513
	Token length (characters). Min:1, Max:140, Avg:6.73
	Number of words per token. Min:0, Max:17, Avg:1.00669200921984
	Tokens with more than one word: 4874 (0.41%)
	Twitter data collection date: <em>Unknown.</em><strong><em> </em></strong>


History:


	<strong>?? Aug 2014 </strong>— GloVe v.1.0 released
	<strong>16 Aug 2014 </strong>— Files first appear as headerless .txt.gz files, some files have mislabeled linked (via wayback machine)
	<strong>?? Oct 2015</strong> — GloVe v.1.2 released
	<strong>?? ??? ????</strong> — Files replaced with a .zip file
	<strong>03 June 2019 </strong>— (These files) Repackaged like original as .txt.gz, plus added headers for increased compatibility


Example of 17-word token:


 سكس_طيز_قحبه_عنيف_اغتصاب_سكسيه_فحل_زب_نيك_بنات_مكوه_شهوه_لحس_عنف_تومبوي_ليدي_سبورت


<strong>200d file:</strong>


	Normalized: no
	Values:  Min:-6.7986, Max:4.609, Avg:0.009065093
	Zero values (exactly zero): none
	Zero values (approx zero) per entry: Min:0 (0.00%), Max:2 of 200 (1.0%), Avg:0.00375865197949247 (0.00%)
DOI: 10.5281/zenodo.3237458
title: Twitter pre-trained word vectors

description: Clean up of <strong>glove.twitter.27B.zip </strong>&lt;ODC Public Domain Dedication and Licence (PDDL) 1.0&gt;.

"2B tweets, 27B tokens, 1.2M vocab, uncased"

<strong>Changes from original</strong>


	Headers added to allow loading by gensim. [Added via scripts.glove2word2vec]
	Recompressed as individual gzip files [Instead of a combined zip].


These changes make the files easier to work with and increase compatibility.

<strong>Headers</strong>

Example of added header line


1193513 200


Header gives number of tokens and dimensions.

<strong>Statistics</strong>


	Entries: 1,193,513
	Token length (characters). Min:1, Max:140, Avg:6.73
	Number of words per token. Min:0, Max:17, Avg:1.00669200921984
	Tokens with more than one word: 4874 (0.41%)
	Twitter data collection date: <em>Unknown.</em><strong><em> </em></strong>


History:


	<strong>?? Aug 2014 </strong>— GloVe v.1.0 released
	<strong>16 Aug 2014 </strong>— Files first appear as headerless .txt.gz files, some files have mislabeled linked (via wayback machine)
	<strong>?? Oct 2015</strong> — GloVe v.1.2 released
	<strong>?? ??? ????</strong> — Files replaced with a .zip file
	<strong>03 June 2019 </strong>— (These files) Repackaged like original as .txt.gz, plus added headers for increased compatibility


Example of 17-word token:


 سكس_طيز_قحبه_عنيف_اغتصاب_سكسيه_فحل_زب_نيك_بنات_مكوه_شهوه_لحس_عنف_تومبوي_ليدي_سبورت


<strong>200d file:</strong>


	Normalized: no
	Values:  Min:-6.7986, Max:4.609, Avg:0.009065093
	Zero values (exactly zero): none
	Zero values (approx zero) per entry: Min:0 (0.00%), Max:2 of 200 (1.0%), Avg:0.00375865197949247 (0.00%)
DOI: 10.21227/7fp2-jh22
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.21227/maph-rs70
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.21227/bwc5-g387
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.5061/dryad.307cm0h/1
title: Dryad Item 10.5061/DRYAD.307CM0H/1

description: (:tba)
DOI: 10.5061/dryad.307cm0h/2
title: Dryad Item 10.5061/DRYAD.307CM0H/2

description: (:tba)
DOI: 10.5281/zenodo.3237790
title: Projection of potential future tree cover persistence for 2029 based on the global model

description: Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.5281/zenodo.3237791
title: Projection of potential future tree cover persistence for 2029 based on the global model

description: Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.5281/zenodo.3237795
title: Global model results for transition potential

description: Global model results including areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition. Values are unitless and range from 0–1.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.5281/zenodo.3237796
title: Global model results for transition potential

description: Global model results including areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition. Values are unitless and range from 0–1.
description: "The data use terms for the input datasets are as follows:
-https://opendatacommons.org/licenses/odbl/1.0/
-CI's creative commons license (downscaled climate data)
1km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Use the following credit when these data are displayed:
Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 Km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (January 10, 2019): 14. https://doi.org/10.3390/land8010014."
DOI: 10.21227/rahx-cd38
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.5519/qd.rl6cb88a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (75 records)
DOI: 10.17632/jcfwd5sg4w.1
title: Data for: Methylmercury Sorption onto Engineered Materials

description: Sorption data for methylmercury sorption onto engineered sorbents
DOI: 10.21227/seqa-4t29
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.21227/v81n-np21
title: Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

description: Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remotesensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in ourexperiments a dataset containing Cannabis Sativa crops, spottedby police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used fortraining and testing the methods, highlighting the promising useof this solution to aid police operations in detecting and collectingevidence of such sensitive content properly.
DOI: 10.26133/nea6
title: Kepler Stellar Properties Table

description: The Kepler Stellar interactive table contains parameters for all targets observed by Kepler for the purpose of finding transiting planets. Individual tables include the values used for that specific processing, including the Q1-12, Q1-16, Q1-17 DR 24, and Q1-17 DR 25 pipeline runs. One additional table, the Q1-17 DR 25 Supplemental Stellar, includes values provided by the Kepler Stellar Properties Working Group (SPWG) independent of any pipeline processing in order to report their most current stellar values.

This dataset or service is made available by the NASA Exoplanet Science Institute at IPAC, which is operated by the California Institute of Technology under contract with the National Aeronautics and Space Administration.
DOI: 10.5519/qd.d7b7br5l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (304 records)
DOI: 10.25835/0064330
title: Finite size data for the q-deformed OSp(3|2) superspin chain

description: Bethe ansatz data (roots, finite size energies, scaling dimensions and conformal spin) for low energy states of the integrable $U_q[OSp(3|2)]$ superspin chain.\r\n\r\nsee Holger Frahm, Konstantin Hobu\xdf, M\xe1rcio J. Martins: "On the critical behaviour of the integrable $q$-deformed $OSp(3|2)$ superspin chain", Nucl. Phys. B __946__ (2019) 114697, arXiv:1906.00655
DOI: 10.26133/nea7
title: UKIRT Microlensing Survey

description: Light curves from the ongoing UKIRT microlensing survey made available with help from the UKIRT Microlensing Team.The Wide Field Camera (WFCAM) on the United Kingdom Infrared Telescope (UKIRT), located at Mauna Kea Observatory, has been used to conduct microlensing surveys with a variety of science goals.

This dataset or service is made available by the NASA Exoplanet Science Institute at IPAC, which is operated by the California Institute of Technology under contract with the National Aeronautics and Space Administration.