cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.20393/dfe92be5-69a1-47a4-a60e-f3da8e85cd16
title: CoClime - Probability of phytoplankton (Alexandrium species) presence, Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium spp. presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of Alexandrium spp. presence in the future (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_probability/ ). This dataset is used for displaying present time prediction of Alexandrium spp. probability of presence on the Shiny app.
DOI: 10.20393/e277dfb3-260d-418b-b320-cd1cb0131bf6
title: CoClime - Abundance of phytoplankton (Dinophysis acuminata), Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of D. acuminata abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of D. acuminata in the future (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuminata_abundance/). This dataset is used for displaying future prediction of D. acuminata abundance on the Shiny app.
DOI: 10.5281/zenodo.8189455
title: Identifying Interdisciplinary Research in Research Projects

description: The dataset is based on the paper: Hoang-Son Pham, Bram Vancraeynest, Hanne Poelmans, Sadia Vancauwenbergh and Amr Ali-Eldin ‘’Identifying Interdisciplinary Research in Research Projects’’, Scientometrics (Accepted). The dataset includes: 1. FRIS_project_2019: a list of project metadata extracted from FRIS portal 2. VODS_disciplines: a list of VODS disciplines 3. Distance matrix: Distance matrix * Kindly refer to our paper for more details on the dataset.
DOI: 10.5281/zenodo.8189454
title: Identifying Interdisciplinary Research in Research Projects

description: The dataset is based on the paper: Hoang-Son Pham, Bram Vancraeynest, Hanne Poelmans, Sadia Vancauwenbergh and Amr Ali-Eldin ‘’Identifying Interdisciplinary Research in Research Projects’’, Scientometrics (Accepted). The dataset includes: 1. FRIS_project_2019: a list of project metadata extracted from FRIS portal 2. VODS_disciplines: a list of VODS disciplines 3. Distance matrix: Distance matrix * Kindly refer to our paper for more details on the dataset.
DOI: 10.20393/9f282b4d-53ae-4c7d-9764-1bf3bcc05001
title: CoClime - Probability of phytoplankton (Karenia mikimotoi) presence, Southwest Ireland - present time prediction (1997 - 2016)

description: This is the output of GBM (Gradient Boosting Model) analyses of K. mikimotoi presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of K. mikimotoi presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/K_mikimotoi_probability/ ). This dataset is used for displaying present time prediction of K. mikimotoi probability of presence on the Shiny app.
DOI: 10.20393/08b29b37-d8ee-4783-b742-3d9f6887ad78
title: CoClime - Abundance of phytoplankton (Dinophysis acuta), Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of D. acuta, abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of D. acuta in present time (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuta_abundance/). This dataset is used for displaying present time prediction of D. acuta abundance on the Shiny app.
DOI: 10.20393/1c866c93-5d6d-436d-925e-ab3fd20c0b6e
title: CoClime - Probability of phytoplankton (“Pseudo-nitzschia seriata” complex ) presence, Southwest Ireland - present time prediction (1997 - 2016)

description: This is the output of GBM (Gradient Boosting Model) analyses of P. seriata presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of P. seriata presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/P_seriata_probability/). This dataset is used for displaying present time prediction of P. seriata probability of presence on the Shiny app.
DOI: 10.25345/c5kh0f88s
title: MassIVE MSV000092535 - Cancer-associated Histone H3 N-terminal arginine mutations disrupt PRC2 activity and impair differentiation
title: Cancer-associated Histone H3 N-terminal arginine mutations disrupt PRC2 activity and impair differentiation. Middle down analysis of histone H3 WT or mutant
DOI: 10.21410/7e4/c6bc8l
title: Sociologie Politique de l'Insécurité durant les élections Présidentielles de 2022 - vague 2 (ELIPSS 2022)

description: L’insécurité est un thème récurrent dans les sociétés démocratiques modernes. Elle réapparait à intervalles plus ou moins réguliers dans le débat public en fonction de l’actualité. Il est notamment quelques évènements politiques durant lesquels les opinions sur l’insécurité se polarisent fréquemment : c’est le cas des élections présidentielles. Aussi cet événement est particulièrement propice à l’étude de ces relations, c'est pourquoi l'enquête "Sociologie Politique de l'Insécurité durant les élections Présidentielles de 2022" (SPIP2022) a pour objectif de comprendre comment les relations entre les comportements politiques, les expériences de l’insécurité (peurs et atteintes) et les opinions sur la délinquance s’organisent et évoluent avant, pendant et après les élections présidentielles de 2022. Le questionnaire SPIP2022 sur le sentiment d'insécurité et la victimation sera soumis à trois reprises aux panélistes ELIPSS (décembre 2021, avril 2022 et octobre 2022) et permettra de répondre aux 4 objectifs suivant : - comprendre le rapport politiques/insécurités ; - comprendre l’évolution des configurations insécurités/politiques et la remémoration des victimations ; - améliorer les enquêtes sur l’insécurité et la victimation : combiner les apports des enquêtes françaises et anglosaxonnes ; - assurer la jonction entre les enquêtes CVS Insee et SSMSI.
DOI: 10.20393/0af86238-cd9f-4bec-9769-252c66a72336
title: CoClime - Abundance of phytoplankton (Karenia mikimotoi), Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of K. mikimotoi, abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of K. mikimotoi in present time (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/K_mikimotoi_abundance/). This dataset is used for displaying present time prediction of K. mikimotoi abundance on the Shiny app.
DOI: 10.20393/5d518141-3d97-45d0-8566-c381e98440e3
title: CoClime - Probability of phytoplankton (Dinophysis acuminata) presence, Southwest Ireland - future prediction (2017 - 2035)
title: CoClime - Probability of phytoplankton (Dinophysis acuminata) presence, Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of D. acuminata presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of D. acuminata presence in future projection (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuminata_probability/). This dataset is used for displaying future prediction of D. acuminata probability of presence on the Shiny app.
DOI: 10.20393/457cd3e4-c235-4591-81cb-317a31bd4c18
title: CoClime - Abundance of phytoplankton (Alexandrium species), Southwest Ireland - present time prediction (1997 - 2016)

description: This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of Alexandrium in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_abundance/). This dataset is used for displaying present time prediction of Alexandrium abundance on the Shiny app.
DOI: 10.20393/4aa90e6a-4a82-4f33-ae11-902e3f4edc5f
title: CoClime - Probability of phytoplankton (Karenia mikimotoi) presence, Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of K. mikimotoi presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of K. mikimotoi presence in the future (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/K_mikimotoi_probability/ ). This dataset is used for displaying future prediction of K. mikimotoi probability of presence on the Shiny app.
DOI: 10.20393/6d945fa4-8db7-4f2c-816e-19ac44b4705c
title: CoClime - Probability of phytoplankton (Dinophysis acuta) presence, Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of D. acuta presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of D. acuta presence infuture (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuata_probability/). This dataset is used for displaying future prediction of D. acuta probability of presence on the Shiny app.
DOI: 10.48432/nhdkdm
title: LiDAR survey on 510 hectares in Jaraguá do Sul and Massaranduba , Santa Catarina, Brasil in 2017

description: The LiDAR data Projeto Massaranduba (MMA_A01_2017_LiDAR) refers to the survey carried out in the Jaraguá do Sul and Massaranduba munipalities, Santa Catarina state, Brazil. The data were collected under the project Sustainable Landscapes, a project supported by the United States Agency for International Development (USAID) and US Department of State. The United States Forest Service working in collaboration with theBrazilian Agricultural Research Corporation (Embrapa) have made possible the provision of high accuracy LiDAR data aiming at developing new methods and generating knowledge in the field. This dataset contains: a. Classified LAS formatted point cloud data – compressed LAS files: LAZ format (vendor delivered); b. Digital Terrain Model (vendor delivered); c. Map of LiDAR coverage area and block boundaries;
DOI: 10.20393/179b9e88-8d6b-4987-8417-4aff48597dde
title: CoClime - Abundance of phytoplankton (Alexandrium species), Southwest Ireland - future prediction (2017 - 2035)

description: This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of Alexandrium in the future (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_abundance/). This dataset is used for displaying present time prediction of Alexandrium abundance on the Shiny app.
DOI: 10.48331/scielo.dgwml6/jmktxh
title: publicatons and sdgs - scielo 25-chbc20230727.pdf

description: :unav
DOI: 10.48331/scielo.dgwml6
title: Using bibliometric analysis to assess research targeting the U.N. SDGs

description: Presentation by Carlos Henrique de Brito Cruz at the SciELO 25 Years Seminar: Scholarly communication and the UN Sustainable Development Goals. Event page: https://25.scielo.org/en/seminars/cepal/
DOI: 10.20393/00da8edf-8c44-4b0f-a3b3-b0c4b233fb30
title: CoClime - Probability of phytoplankton (Dinophysis acuta) presence, Southwest Ireland - present time prediction (1997 - 2016)

description: This is the output of GBM (Gradient Boosting Model) analyses of D. acuta presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of D. acuta presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuata_probability/). This dataset is used for displaying present time prediction of D. acuta probability of presence on the Shiny app.
DOI: 10.20393/5aae82ce-3ff4-4d85-b8c7-07085f2cc904
title: CoClime - Abundance of phytoplankton (Karenia mikimotoi), Southwest Ireland - present time prediction (1997 - 2016)

description: This is the output of GBM (Gradient Boosting Model) analyses of K. mikimotoi, abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of K. mikimotoi in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/K_mikimotoi_abundance/). This dataset is used for displaying present time prediction of K. mikimotoi abundance on the Shiny app.
DOI: 10.5281/zenodo.8189737
title: nodopelta_heminoda_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8189736
title: nodopelta_heminoda_spades_01.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc2bm9cn
title: CCDC 2163501: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.cc2bm9dp
title: CCDC 2163502: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.cc2bm9fq
title: CCDC 2163503: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.cc2bm9lw
title: CCDC 2163508: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.cc2bm9ny
title: CCDC 2163510: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.cc2bm9pz
title: CCDC 2163511: Experimental Crystal Structure Determination

description: Related Article: Miaomiao Zhu, Zhengqi Chai, Tianyu Li, Junnian Wei, Ze-Jie Lv, Wen-Xiong Zhang|2023|Inorg.Chem.Front.|10|4569|doi:10.1039/D3QI00946G
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.