cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5896665
title: Potential and realized distribution at 30m for Goat willow (Salix caprea) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the goat willow (<em>Salix caprea, 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_salix.caprea_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>salix.caprea</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_salix.caprea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_salix.caprea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_salix.caprea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_salix.caprea_<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.6967371
title: Potential and realized distribution at 30m for Cork oak (Quercus suber) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the cork oak (<em>Quercus suber, 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_quercus.suber_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>quercus.suber</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_quercus.suber_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.suber_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.suber_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.suber_<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.5896620
title: Potential and realized distribution at 30m for Cork oak (Quercus suber) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the cork oak (<em>Quercus suber, 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_quercus.suber_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>quercus.suber</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_quercus.suber_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.suber_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.suber_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.suber_<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.5816815
title: Supplement materials - An appraisal of Ulva (Ulvophyceae, Chlorophyta) taxonomy

description: Sequence alignments, supplement figures and tables for the manuscript "An appraisal of <em>Ulva </em>(Ulvophyceae, Chlorophyta) taxonomy"
DOI: 10.5281/zenodo.5816814
title: Supplement materials - An appraisal of Ulva (Ulvophyceae, Chlorophyta) taxonomy

description: Sequence alignments, supplement figures and tables for the manuscript "An appraisal of <em>Ulva </em>(Ulvophyceae, Chlorophyta) taxonomy"
DOI: 10.6084/m9.figshare.20442630
title: gingko_biloba_spades_01.fasta.gz

description: ginkgo biloba genome
DOI: 10.6084/m9.figshare.20442630.v1
title: gingko_biloba_spades_01.fasta.gz

description: ginkgo biloba genome
DOI: 10.5281/zenodo.6967765
title: Política, Twitter y Covid-19. La percepción de los turistas extranjeros durante la desescalada en España en la primavera de 2021

description: This file contains the job database described below. The traditional journalism of the written press of the day and the linear generalist television news have lost their monopoly as generators of public opinion. The communicative and social relevance of social networks is indisputable, even more so in circumstances such as those provoked by the Covid-19 election period for the Community of Madrid. We focus on two issues of great relevance in networks in this period: the image of tourism and policy makers on Twitter and in Spain between March and May 2021. The results of the analyses carried out with computational and corpus tools reveal a very heterogeneous constellation of participants, as well as that the pandemic and its management was, in general, used as an electoral weapon through the arrival of tourism in the middle of the pandemic in the capital. The analyses of key words and their associations show a vision of tourism with a clear electoral intention, where all the general interest was subordinated to the battle for Madrid.
description: Papers data: The traditional journalism of the written press of the day and the linear generalist television news have lost their monopoly as generators of public opinion. The communicative and social relevance of social networks is indisputable, even more so in circumstances such as those provoked by the Covid-19 election period for the Community of Madrid. We focus on two issues of great relevance in networks in this period: the image of tourism and policy makers on Twitter and in Spain between March and May 2021. The results of the analyses carried out with computational and corpus tools reveal a very heterogeneous constellation of participants, as well as that the pandemic and its management was, in general, used as an electoral weapon through the arrival of tourism in the middle of the pandemic in the capital. The analyses of key words and their associations show a vision of tourism with a clear electoral intention, where all the general interest was subordinated to the battle for Madrid.
DOI: 10.5281/zenodo.6967764
title: Política, Twitter y Covid-19. La percepción de los turistas extranjeros durante la desescalada en España en la primavera de 2021

description: This file contains the job database described below. The traditional journalism of the written press of the day and the linear generalist television news have lost their monopoly as generators of public opinion. The communicative and social relevance of social networks is indisputable, even more so in circumstances such as those provoked by the Covid-19 election period for the Community of Madrid. We focus on two issues of great relevance in networks in this period: the image of tourism and policy makers on Twitter and in Spain between March and May 2021. The results of the analyses carried out with computational and corpus tools reveal a very heterogeneous constellation of participants, as well as that the pandemic and its management was, in general, used as an electoral weapon through the arrival of tourism in the middle of the pandemic in the capital. The analyses of key words and their associations show a vision of tourism with a clear electoral intention, where all the general interest was subordinated to the battle for Madrid.
description: Papers data: The traditional journalism of the written press of the day and the linear generalist television news have lost their monopoly as generators of public opinion. The communicative and social relevance of social networks is indisputable, even more so in circumstances such as those provoked by the Covid-19 election period for the Community of Madrid. We focus on two issues of great relevance in networks in this period: the image of tourism and policy makers on Twitter and in Spain between March and May 2021. The results of the analyses carried out with computational and corpus tools reveal a very heterogeneous constellation of participants, as well as that the pandemic and its management was, in general, used as an electoral weapon through the arrival of tourism in the middle of the pandemic in the capital. The analyses of key words and their associations show a vision of tourism with a clear electoral intention, where all the general interest was subordinated to the battle for Madrid.
DOI: 10.5281/zenodo.5813675
title: Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders

description: This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z
DOI: 10.5281/zenodo.5813674
title: Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders

description: This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z
DOI: 10.6084/m9.figshare.20436201.v1
title: imgtofmri_analysis_data

description: Data folder including preprocessed fMRI data for analyses associated with the jupyter notebook tutorial for imgtofmri.py. Includes subfolders for analyses associated with the Twinset prediction validation analysis, as well as a folder for the Partly Cloudy analysis.
DOI: 10.6084/m9.figshare.20407221
title: Diabetes dataset

description: diabetes dataset
DOI: 10.6084/m9.figshare.20436201
title: imgtofmri_analysis_data

description: Data folder including preprocessed fMRI data for analyses associated with the jupyter notebook tutorial for imgtofmri.py. Includes subfolders for analyses associated with the Twinset prediction validation analysis, as well as a folder for the Partly Cloudy analysis.
DOI: 10.6084/m9.figshare.20436201.v2
title: imgtofmri_analysis_data

description: Data folder including preprocessed fMRI data for analyses associated with the jupyter notebook tutorial for imgtofmri.py. Includes subfolders for analyses associated with the Twinset prediction validation analysis, as well as a folder for the Partly Cloudy analysis.
DOI: 10.5517/ccdc.csd.cc25x90f
title: CCDC 2023184: Experimental Crystal Structure Determination

description: Related Article: Yuval Zertal, Matthew Yong, Avishai Levi, Sagi Sevilia, Alex Tsoglin, Galit Parvari, Levi Gottlieb, Yoav Eichen|2022|ACSAppl.Polym.Mater.|4|4928|doi:10.1021/acsapm.2c00499
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.cc28k99g
title: CCDC 2101995: Experimental Crystal Structure Determination

description: Related Article: Kevin George, Pavithra Elavarasan, Shanmugam Ponnusamy, Kannadasan Sathananthan|2022|ACS Omega|7|20605|doi:10.1021/acsomega.2c00674
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.cc29l5dh
title: CCDC 2132626: Experimental Crystal Structure Determination

description: Related Article: Ricardo Molina Betancourt, Phannarath Phansavath, Virginie Ratovelomanana-Vidal|2022|Molecules|27|995|doi:10.3390/molecules27030995
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.cc2byq34
title: CCDC 2173506: Experimental Crystal Structure Determination

description: Related Article: Yuval Zertal, Matthew Yong, Avishai Levi, Sagi Sevilia, Alex Tsoglin, Galit Parvari, Levi Gottlieb, Yoav Eichen|2022|ACSAppl.Polym.Mater.|4|4928|doi:10.1021/acsapm.2c00499
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.cc2bzgld
title: CCDC 2174234: Experimental Crystal Structure Determination

description: Related Article: Yuval Zertal, Matthew Yong, Avishai Levi, Sagi Sevilia, Alex Tsoglin, Galit Parvari, Levi Gottlieb, Yoav Eichen|2022|ACSAppl.Polym.Mater.|4|4928|doi:10.1021/acsapm.2c00499
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.cc2c1bb3
title: CCDC 2176024: Experimental Crystal Structure Determination

description: Related Article: Dominik Vogt, Roland Fröhlich, Rudolf Aumann, Constantin G. Daniliuc |2022|CSD Communication|||
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.cc2c1csl
title: CCDC 2176069: Experimental Crystal Structure Determination

description: Related Article: Dominik Vogt, Roland Fröhlich, Rudolf Aumann, Constantin G. Daniliuc |2022|CSD Communication|||
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.cc2c1czs
title: CCDC 2176075: Experimental Crystal Structure Determination

description: Related Article: Dominik Vogt, Roland Fröhlich, Rudolf Aumann, Constantin G. Daniliuc |2022|CSD Communication|||
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: 10.6084/m9.figshare.13310194.v1
title: Additional file 7 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 7: Table S3. Differentially abundant metabolites between AhRKO and WT mice fed HFD at week 8.
DOI: 10.6084/m9.figshare.13310137
title: Additional file 11 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 11: Table S7. Differentially abundant metabolites between AhRKO and WT mice fed HFD at week 26.
DOI: 10.6084/m9.figshare.13310200
title: Additional file 9 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 9: Table S5. Differentially abundant metabolites between AhRKO mice fed HFD and LFD at week 8.
DOI: 10.6084/m9.figshare.13310149
title: Additional file 13 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 13: Table S9. Differentially abundant metabolites between AhRKO mice fed HFD and LFD at week 26.
DOI: 10.6084/m9.figshare.13310200.v1
title: Additional file 9 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 9: Table S5. Differentially abundant metabolites between AhRKO mice fed HFD and LFD at week 8.
DOI: 10.6084/m9.figshare.13310131.v1
title: Additional file 10 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 10: Table S6. Differentially abundant metabolites between WT mice fed HFD and LFD at week 8.
DOI: 10.6084/m9.figshare.13310137.v1
title: Additional file 11 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 11: Table S7. Differentially abundant metabolites between AhRKO and WT mice fed HFD at week 26.
DOI: 10.6084/m9.figshare.13310143.v1
title: Additional file 12 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 12: Table S8. Differentially abundant metabolites between AhRKO and WT mice fed LFD at week 26.
DOI: 10.6084/m9.figshare.13310194
title: Additional file 7 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 7: Table S3. Differentially abundant metabolites between AhRKO and WT mice fed HFD at week 8.
DOI: 10.6084/m9.figshare.13310131
title: Additional file 10 of Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

description: Additional file 10: Table S6. Differentially abundant metabolites between WT mice fed HFD and LFD at week 8.