cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20103890.v1
title: 202713590189_R08C01_Grn

description: Green IDAT file for ISB 3450 Nairi/Nadira
DOI: 10.6084/m9.figshare.20103899.v1
title: 202713590172_R07C01_Red

description: Red IDAT file for ISB 1773 Oliver
DOI: 10.6084/m9.figshare.20103896
title: 202713590189_R06C01_Red

description: Red IDAT file for ISB 1387 Dumplin/Fern
DOI: 10.6084/m9.figshare.20103893.v1
title: 202713590189_R03C01_Grn

description: Green IDAT file for ISB 1980 Bella
DOI: 10.6084/m9.figshare.20103896.v1
title: 202713590189_R06C01_Red

description: Red IDAT file for ISB 1387 Dumplin/Fern
DOI: 10.6084/m9.figshare.20103905.v1
title: 202713590099_R04C01_Red

description: Red IDAT file for ISB 3351 Menari
DOI: 10.6084/m9.figshare.20103902.v1
title: 202713590189_R05C01_Red

description: Red IDAT file for ISB 3572 Batari
DOI: 10.6084/m9.figshare.20103908.v1
title: 202713590198_R01C01_Red

description: Red IDAT file for ISB 3619 Max
DOI: 10.6084/m9.figshare.20103908
title: 202713590198_R01C01_Red

description: Red IDAT file for ISB 3619 Max
DOI: 10.6084/m9.figshare.20103911
title: 202713590099_R04C01_Grn

description: Green IDAT file for ISB 3351 Menari
DOI: 10.6084/m9.figshare.20103911.v1
title: 202713590099_R04C01_Grn

description: Green IDAT file for ISB 3351 Menari
DOI: 10.6084/m9.figshare.20103899
title: 202713590172_R07C01_Red

description: Red IDAT file for ISB 1773 Oliver
DOI: 10.6084/m9.figshare.20103902
title: 202713590189_R05C01_Red

description: Red IDAT file for ISB 3572 Batari
DOI: 10.6084/m9.figshare.20103905
title: 202713590099_R04C01_Red

description: Red IDAT file for ISB 3351 Menari
DOI: 10.34943/be7ac98a-fd10-4a8e-b3ce-f4bf94940ccf
title: Baynes Sound Junction Box Deployed 2022-07-20

description: The Signal Combiner Unit V2 07 was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Junction Box. Junction Boxes supply power and communications to deployed instruments. Junction boxes have a number of serial and ethernet ports, including 400V ethernet ports that enable connections to other junction boxes and high-voltage instruments. Junction boxes can convert high voltages to lower voltages (15V, 24V or 48V) required by many instruments. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.6956944
title: Potential and realized distribution at 30m for the European beech (Fagus sylvatica) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the European beech (<em>Fagus sylvatica, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_fagus.sylvatica_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>fagus.sylvatica</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_fagus.sylvatica_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_fagus.sylvatica_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_fagus.sylvatica_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_fagus.sylvatica_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.5877786
title: Potential and realized distribution at 30m for the European beech (Fagus sylvatica) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the European beech (<em>Fagus sylvatica, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_fagus.sylvatica_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>fagus.sylvatica</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_fagus.sylvatica_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_fagus.sylvatica_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_fagus.sylvatica_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_fagus.sylvatica_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5258/soton/d2188
title: TinyOps: ImageNet Scale Deep Learning on Microcontrollers

description: Data obtained in TinyOps: ImageNet Scale Deep Learning on Microcontrollers research. To support a paper to be presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2022
DOI: 10.17909/k8h7-s556
title: Data used in Wilson et al. 2022: Testing Lyman α emission line reconstruction routines at multiple velocities in one system.

description: COS and STIS spectral observations of the Post Common Envelope Binary EG UMa. The STIS observations cover the Lyman alpha line and are phase-resolved to the binary orbital period. Note that dataset OEHUA2010 requires a custom extraction as described in the paper.
DOI: 10.5281/zenodo.6953790
title: Potential and realized distribution at 30m for Silver fir (Abies alba) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the silver fir (<em>Abies alba, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_abies.alba_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>abies.alba</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_abies.alba_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_abies.alba_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_abies.alba_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_abies.alba_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.5873412
title: Potential and realized distribution at 30m for Silver fir (Abies alba) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the silver fir (<em>Abies alba, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_abies.alba_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>abies.alba</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_abies.alba_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_abies.alba_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_abies.alba_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_abies.alba_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.6084/m9.figshare.20439216
title: DATA- indentation failure load of FRP composite facesheet polymer foam core sandwich beams

description: Raw data includes sandwich beam flexural tests, FRP composite facesheet tensile tests, and polymer foam compression tests.
DOI: 10.5281/zenodo.6951628
title: Potential and realized distribution at 30m for Sweet chestnut (Castanea sativa) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the sweet chestnut (<em>Castanea sativa, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_castanea.sativa_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>castanea.sativa</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_castanea.sativa_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_castanea.sativa_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_castanea.sativa_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_castanea.sativa_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.5873917
title: Potential and realized distribution at 30m for Sweet chestnut (Castanea sativa) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the sweet chestnut (<em>Castanea sativa, Mill.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_castanea.sativa_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>castanea.sativa</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_castanea.sativa_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_castanea.sativa_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_castanea.sativa_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_castanea.sativa_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.6966803
title: WGS sample sheet

description: WGS sample sheet
DOI: 10.5281/zenodo.6966802
title: WGS sample sheet

description: WGS sample sheet
DOI: 10.5517/ccdc.csd.cc1z7xrw
title: CCDC 1824869: Experimental Crystal Structure Determination

description: Related Article: María Frutos, Mar Gómez-Gallego, Elena A. Giner, Miguel A. Sierra, Carmen Ramírez de Arellano|2018|Dalton Trans.|47|9975|doi:10.1039/C8DT02296H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zhgwt
title: CCDC 1832127: Experimental Crystal Structure Determination

description: Related Article: Evan A. Doud,  Michael S. Inkpen,  Giacomo Lovat,  Enrique Montes,  Daniel W. Paley,  Michael L. Steigerwald,  Héctor Vázquez,  Latha Venkataraman,  Xavier Roy|2018|J.Am.Chem.Soc.|140|8944|doi:10.1021/jacs.8b05184
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zhgxv
title: CCDC 1832128: Experimental Crystal Structure Determination

description: Related Article: Evan A. Doud,  Michael S. Inkpen,  Giacomo Lovat,  Enrique Montes,  Daniel W. Paley,  Michael L. Steigerwald,  Héctor Vázquez,  Latha Venkataraman,  Xavier Roy|2018|J.Am.Chem.Soc.|140|8944|doi:10.1021/jacs.8b05184
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.