cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1zhgyw
title: CCDC 1832129: 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.cc1zk5f4
title: CCDC 1833756: Experimental Crystal Structure Determination

description: Related Article: M. Concepción Gimeno, Alice Johnson, Isabel Marzo|2018|Chem.-Eur.J.|24|11693|doi:10.1002/chem.201801600
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.cc1zk5g5
title: CCDC 1833757: Experimental Crystal Structure Determination

description: Related Article: M. Concepción Gimeno, Alice Johnson, Isabel Marzo|2018|Chem.-Eur.J.|24|11693|doi:10.1002/chem.201801600
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.cc273grl
title: CCDC 2058919: Experimental Crystal Structure Determination

description: Related Article: Cyril A. Theulier, Yago García-Rodeja, Sonia Mallet-Ladeira, Karinne Miqueu, Ghenwa Bouhadir, Didier Bourissou|2021|Organometallics|40|2409|doi:10.1021/acs.organomet.1c00364
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.cc2744q8
title: CCDC 2059569: Experimental Crystal Structure Determination

description: Related Article: Cyril A. Theulier, Yago García-Rodeja, Sonia Mallet-Ladeira, Karinne Miqueu, Ghenwa Bouhadir, Didier Bourissou|2021|Organometallics|40|2409|doi:10.1021/acs.organomet.1c00364
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.cc2744r9
title: CCDC 2059570: Experimental Crystal Structure Determination

description: Related Article: Cyril A. Theulier, Yago García-Rodeja, Sonia Mallet-Ladeira, Karinne Miqueu, Ghenwa Bouhadir, Didier Bourissou|2021|Organometallics|40|2409|doi:10.1021/acs.organomet.1c00364
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.cc27qbmy
title: CCDC 2077050: Experimental Crystal Structure Determination

description: Related Article: Qun Zhao, Guangrong Meng, Guangchen Li, Carol Flach, Richard Mendelsohn, Roger Lalancette, Roman Szostak, Michal Szostak|2021|Chemical Science|12|10583|doi:10.1039/D1SC02619D
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.cc27ys22
title: CCDC 2084194: Experimental Crystal Structure Determination

description: Related Article: Sailajah Gukathasan, Sean Parkin, Esther P. Black, Samuel G. Awuah|2021|Inorg.Chem.|60|14582|doi:10.1021/acs.inorgchem.1c01517
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.cc27ys44
title: CCDC 2084196: Experimental Crystal Structure Determination

description: Related Article: Sailajah Gukathasan, Sean Parkin, Esther P. Black, Samuel G. Awuah|2021|Inorg.Chem.|60|14582|doi:10.1021/acs.inorgchem.1c01517
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: 10.5258/soton/d2122
title: Dataset for: Investigation of Factors Affecting the Stability of Compounds formed by Isovalent Substitution in Layered Oxychalcogenides, Leading to Identification of Ba3Sc2O5Cu2Se2, Ba3Y2O5Cu2S2, Ba3Sc2O5Ag2Se2 and Ba3In2O5Ag2Se2

description: Data to support publication in Journal of Materials Chemistry C. "Investigation of Factors Affecting the Stability of Compounds formed by Isovalent Substitution in Layered Oxychalcogenides, Leading to Identification of Ba3Sc2O5Cu2Se2, Ba3Y2O5Cu2S2, Ba3Sc2O5Ag2Se2 and Ba3In2O5Ag2Se2" DOI 10.1039/D0TA06629J
DOI: 10.34943/745e6320-a0e1-4127-b01d-7e30f56c3d0e
title: Baynes Sound Oxygen Sensor Deployed 2022-07-20

description: The Sea-Bird SBE 63 Dissolved Oxygen Sensor 632517 was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Oxygen Sensor. Oxygen sensors measure dissolved oxygen concentration in seawater. 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.6951687
title: Potential and realized distribution at 30m for Common hazel (Corylus avellana) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the common hazel (<em>Corylus avellana, L.</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_corylus.avellana_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>corylus.avellana</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_corylus.avellana_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_corylus.avellana_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_corylus.avellana_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_corylus.avellana_<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.5874796
title: Potential and realized distribution at 30m for Common hazel (Corylus avellana) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the common hazel (<em>Corylus avellana, L.</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_corylus.avellana_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>corylus.avellana</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_corylus.avellana_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_corylus.avellana_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_corylus.avellana_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_corylus.avellana_<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.6966816
title: Sample Sheet QuantSeq

description: Sample sheet QuantSeq
DOI: 10.5281/zenodo.6966815
title: Sample Sheet QuantSeq

description: Sample sheet QuantSeq
DOI: 10.34943/6c316f20-7fab-4acf-8aad-6c11aac80802
title: Baynes Sound Carbon Dioxide Sensor Deployed 2022-07-20

description: The Pro-Oceanus CO2-Pro CV (SN 40-718-75) was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Carbon Dioxide Sensor. Carbon Dioxide sensors monitor CO2 levels in the ocean. CO2 sensors measure the partial pressure, or the concentration of dissolved CO2 in seawater by a variety of techniques. 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: 10.34943/addd0aa5-09ec-4db6-a21a-32242e160b6a
title: Baynes Sound Conductivity Temperature Depth Deployed 2022-07-20

description: The Sea-Bird SeaCAT SBE19plus V2 5047 was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. 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: 10.34943/84ee12c7-eaed-4acf-9bc6-454f51cc2b1e
title: Baynes Sound Carbon Dioxide Sensor Deployed 2022-07-20

description: The Pro-Oceanus CO2-Pro CV (SN 36-321-75) was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Carbon Dioxide Sensor. Carbon Dioxide sensors monitor CO2 levels in the ocean. CO2 sensors measure the partial pressure, or the concentration of dissolved CO2 in seawater by a variety of techniques. 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: 10.34943/2bdb5539-b20d-4818-bd18-17fae10a5aaa
title: Baynes Sound Carbon Dioxide Sensor Deployed 2022-07-20

description: The Pro-Oceanus CO2-Pro CV (SN 36-323-75) was deployed on 2022-07-20 at Baynes Sound. Baynes Sound is located between Denman Island and Vancouver Island. This device is a Carbon Dioxide Sensor. Carbon Dioxide sensors monitor CO2 levels in the ocean. CO2 sensors measure the partial pressure, or the concentration of dissolved CO2 in seawater by a variety of techniques. 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.6966822
title: Sample Sheet TapeStation

description: Sample Sheet TapeStation
DOI: 10.5281/zenodo.6966821
title: Sample Sheet TapeStation

description: Sample Sheet TapeStation
DOI: 10.34943/128641c5-97f4-4981-9f67-822ee4053ef9
title: Baynes Sound Junction Box Deployed 2022-07-20

description: The Signal Combiner Unit V2 09 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.6960111
title: Potential and realized distribution at 30m for Olive tree (Olea europaea) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the olive tree (<em>Olea europaea, L.</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_olea.europaea_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>olea.europaea</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_olea.europaea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_olea.europaea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_olea.europaea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_olea.europaea_<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.5879371
title: Potential and realized distribution at 30m for Olive tree (Olea europaea) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the olive tree (<em>Olea europaea, L.</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_olea.europaea_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>olea.europaea</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_olea.europaea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_olea.europaea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_olea.europaea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_olea.europaea_<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).