cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6962632
title: Potential and realized distribution at 30m for Aleppo pine (Pinus halepensis) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (<em>Pinus halepensis, 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_pinus.halepensis_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>pinus.halepensis</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_pinus.halepensis_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_pinus.halepensis_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_pinus.halepensis_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_pinus.halepensis_<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.5882763
title: Potential and realized distribution at 30m for Aleppo pine (Pinus halepensis) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (<em>Pinus halepensis, 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_pinus.halepensis_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>pinus.halepensis</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_pinus.halepensis_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_pinus.halepensis_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_pinus.halepensis_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_pinus.halepensis_<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.6961187
title: Potential and realized distribution at 30m for Norway spruce (Picea abies) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (<em>Picea abies, </em>L. H. Karst.) 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_picea.abies_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>picea.abies</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_picea.abies_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_picea.abies_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_picea.abies_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_picea.abies_<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.5881699
title: Potential and realized distribution at 30m for Norway spruce (Picea abies) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (<em>Picea abies, </em>L. H. Karst.) 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_picea.abies_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>picea.abies</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_picea.abies_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_picea.abies_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_picea.abies_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_picea.abies_<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.6964167
title: Potential and realized distribution at 30m for Austrian pine (Pinus nigra) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Austrian pine (<em>Pinus nigra</em> J. F. Arnold) 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_pinus.nigra_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>pinus.nigra</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_pinus.nigra_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_pinus.nigra_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_pinus.nigra_<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.5883180
title: Potential and realized distribution at 30m for Austrian pine (Pinus nigra) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the Austrian pine (<em>Pinus nigra</em> J. F. Arnold) 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_pinus.nigra_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>pinus.nigra</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_pinus.nigra_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_pinus.nigra_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_pinus.nigra_<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.6964782
title: Dataset Topic modelling De Gids

description: Dataset accompanying 'Van Wissen, L. (2019). Topic Modelling “De Gids”: An Explorative Study into the Use of Topic Modelling on a Cultural Periodical. <em>Research Master Thesis, </em>Vrije Universiteit Amsterdam.' (included as pdf) <strong>Usage</strong><br> See the 'experiment_1', 'experiment_2', and 'experiment_3' folders in the 'notebooks' folder. These correspond to the three experiments described in the thesis. <strong>Corpus</strong><br> The corpus is not included in this repository because its licence does not allow for it, but part of it, if not all, is accessible through the website of the DBNL.
description: {"references": ["Van Wissen, L. (2019). Topic Modelling \"De Gids\": An Explorative Study into the Use of Topic Modelling on a Cultural Periodical.\u00a0Research Master Thesis, Vrije Universiteit Amsterdam."]}
DOI: 10.5281/zenodo.6964781
title: Dataset Topic modelling De Gids

description: Dataset accompanying 'Van Wissen, L. (2019). Topic Modelling “De Gids”: An Explorative Study into the Use of Topic Modelling on a Cultural Periodical. <em>Research Master Thesis, </em>Vrije Universiteit Amsterdam.' (included as pdf) <strong>Usage</strong><br> See the 'experiment_1', 'experiment_2', and 'experiment_3' folders in the 'notebooks' folder. These correspond to the three experiments described in the thesis. <strong>Corpus</strong><br> The corpus is not included in this repository because its licence does not allow for it, but part of it, if not all, is accessible through the website of the DBNL.
description: {"references": ["Van Wissen, L. (2019). Topic Modelling \"De Gids\": An Explorative Study into the Use of Topic Modelling on a Cultural Periodical.\u00a0Research Master Thesis, Vrije Universiteit Amsterdam."]}
DOI: 10.5281/zenodo.6859733
title: SPEAR_SST_data(August-December_initialization)

description: Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)
DOI: 10.5281/zenodo.6859732
title: SPEAR_SST_data(August-December_initialization)

description: Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)
DOI: 10.5281/zenodo.6966675
title: Ieper - De Meersen. Deel 1: Een kerk, kerkhof en abdij in de Ieperse binnenstad. Studie van de begravingen (appendices 1-4)

description: Appendices bij het hoofdstuk 'Pijpelink A. & Van de Vijver K. 2022: <em>Fysisch antropologisch onderzoek van 500 skeletten</em>', in 'De Groote & Ervynck A. (red.), <em>Ieper - De Meersen. Deel 1: Een kerk, kerkhof en abdij in de Ieperse binnenstad. Studie van de begravingen', </em>een onderzoeksrapport van het agentschap Onroerend Erfgoed: <strong>Appendix 1</strong>: Inventaris van de overzichtsfoto’s per individu. <strong>Appendix 2.0:</strong> Onderzoeksresultaten per individu algemeen (TF: tandvorming, DL: maximum lengte van de diafyse, EF: mate van fusie van de verschillende elementen, AS: veranderingen op het auriculair oppervlak van het bekken, PS: veranderingen op de schaambeenvoeg (<em>symphysis pubica</em>) van het bekken, DW: slijtage van het tandoppervlak, CS: sluiting van de schedelnaden, EP: entheseale letsels, PNBF: periosteale nieuwe botvorming). <strong>Appendix 2.1:</strong> Overzicht van de verschillende observaties voor de gebitsgezondheid. <strong>Appendix 2.2</strong>: Overzicht van de aanwijzingen voor mogelijke metabolische stoornissen. <strong>Appendix 2.3</strong>: Overzicht van pathologische veranderingen op en rond de gewrichten. <strong>Appendix 2.4:</strong> Overzicht van het voorkomen van entheseale veranderingen. <strong>Appendix 2.5:</strong> Overzicht van het voorkomen van traumatische letsels. <strong>Appendix 2.6:</strong> Overzicht van het voorkomen van periosteale nieuwe botvormingen en aanwijzingen voor mogelijke infectie. <strong>Appendix 2.7:</strong> Overzicht van het voorkomen van overige ziekteverschijnselen. <strong>Appendix 2.8</strong>: Overzicht van het voorkomen van anomalieën op de beenderen. <strong>Appendix 3:</strong> Tabel met vergelijkingen van de maximumlengte van het dijbeen en gestalteberekening voor mannelijke individuen uit verschillende middeleeuwse en post-middeleeuwse skeletpopulaties uit Vlaanderen en Nederland (o.b.v. De Groote <em>et al.</em> 2011: tabel 7, p. 173; en Maat 2003: tabel 2). <strong>Appendix 4:</strong> Overzicht van de kenmerken van de grafcontext per individu.