cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5141704
title: Normalized Difference Water Index for Douro Valley based on MODIS

description: <strong>Normalized Difference Water Index </strong>(NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001). https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp3_douro_MOD09A1_ndwi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141726
title: Normalized Multi-band Drought Index for Douro Valley based on MODIS

description: <strong>Normalized Multi-band Drought Index</strong> (NMDI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.( Lingli Wang, 2007) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021 Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp3_douro_MOD09A1_nmdi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141703
title: Normalized Difference Water Index for Douro Valley based on MODIS

description: <strong>Normalized Difference Water Index </strong>(NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001). https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp3_douro_MOD09A1_ndwi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141674
title: Normalized Difference Vegetation Index for Douro Valley based on MODIS

description: <strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation. Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wpe_douro_MOD09A1_ndvi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141673
title: Normalized Difference Vegetation Index for Douro Valley based on MODIS

description: <strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation. Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wpe_douro_MOD09A1_ndvi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141360
title: Normalized Multi-band Drought Index for Andalusia Region based on MODIS

description: <strong>Normalized Multi-band Drought Index </strong>(NMDI) was calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.(Lingli Wang, 2007) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021 Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_nmdi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141359
title: Normalized Multi-band Drought Index for Andalusia Region based on MODIS

description: <strong>Normalized Multi-band Drought Index </strong>(NMDI) was calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.(Lingli Wang, 2007) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021 Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_nmdi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141218
title: Normalized Difference Water Index for Andalusia Region based on MODIS

description: <strong>Normalized Difference Water Index</strong> (NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001). https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_ndwi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5141217
title: Normalized Difference Water Index for Andalusia Region based on MODIS

description: <strong>Normalized Difference Water Index</strong> (NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001). https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector) Aoi: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_ndwi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5139712
title: Normalized Difference Vegetation Index for Andalusia Region based on MODIS

description: <strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation. <br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector) AoI: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_ndvi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.5281/zenodo.5139711
title: Normalized Difference Vegetation Index for Andalusia Region based on MODIS

description: <strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation. <br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description. spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842 To easily manage the data, each file follow name structure: YYYYMMDD_medgold_workpackage_AoI_sensor_index. YYYYMMDD: Imagery acquisition date medgold: Project name sensor: sensor name workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector) AoI: Andalusia, Douro Valley. Index: NDVI, NMDI, NDWI Example: 20000218_medgold_wp2_andalusia_MOD09A1_ndvi
description: Datasets computed by GMV Aerospace and Defense, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467 Disclaimer: the producer of this dataset is not responsible of the results due to errors in the source dataset (sensor failures, clouds, reflectance calculations, etc.)
DOI: 10.6084/m9.figshare.16843489
title: Elemental data and age information from U1339D

description: All ICP-MS data from U1339D, with dates for each layer
DOI: 10.6084/m9.figshare.16843489.v1
title: Elemental data and age information from U1339D

description: All ICP-MS data from U1339D, with dates for each layer
DOI: 10.5281/zenodo.5588379
title: WMT'16 Biomedical Translation Task - Scielo parallel datasets - GMA alignment files

description: Aligment files using the GMA tool (https://nlp.cs.nyu.edu/GMA/) for the parallel data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html). The parallel data is available here: https://zenodo.org/record/5588265
DOI: 10.5281/zenodo.5588378
title: WMT'16 Biomedical Translation Task - Scielo parallel datasets - GMA alignment files

description: Aligment files using the GMA tool (https://nlp.cs.nyu.edu/GMA/) for the parallel data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html). The parallel data is available here: https://zenodo.org/record/5588265
DOI: 10.6084/m9.figshare.16843210
title: Disgust sensitivity increases in early pregnancy as a reponse to high pathogen risk

description: The sample was collected in the General University Hospital in Prague (Dept. of Obstetrics and Gynecology) during their prenatal medical check-ups between the 11<sup>th</sup> and 14<sup>th</sup> gestational weeks. Data include the variable Covid-19 pandemic (1=before, 2=during). Between November 2017 and November 2019 was collected the sample before the COVID-19 pandemic. Between 20<sup>th</sup> March and 10<sup>th</sup> December 2020, i.e. after the COVID-19 outbreak in the Czech Republic, we carried out the next wave of data collection.
DOI: 10.6084/m9.figshare.16843210.v1
title: Disgust sensitivity increases in early pregnancy as a reponse to high pathogen risk

description: The sample was collected in the General University Hospital in Prague (Dept. of Obstetrics and Gynecology) during their prenatal medical check-ups between the 11<sup>th</sup> and 14<sup>th</sup> gestational weeks. Data include the variable Covid-19 pandemic (1=before, 2=during). Between November 2017 and November 2019 was collected the sample before the COVID-19 pandemic. Between 20<sup>th</sup> March and 10<sup>th</sup> December 2020, i.e. after the COVID-19 outbreak in the Czech Republic, we carried out the next wave of data collection.
DOI: 10.5281/zenodo.4567427
title: Rheology of sliding leaflets in coarse-grained DSPC lipid bilayers accompanying files

description: Molecular dynamics configuration files accompanying the manuscript arXiv:2007.05784, published in Physical Review E.
DOI: 10.5281/zenodo.4567426
title: Rheology of sliding leaflets in coarse-grained DSPC lipid bilayers accompanying files

description: Molecular dynamics configuration files accompanying the manuscript arXiv:2007.05784, published in Physical Review E.
DOI: 10.6084/m9.figshare.13851581
title: Functional annotation, Illumina and PacBio transcript models

description: Functional annotation of merged structural annotations from short-read and long-read pipelines and six tissues.Structural annotations (transcript models) were merged with the TAMA tool tama_merge.py, fasta sequences extracted with Bedtools, protein-coding potential inferred with CPC2, open reading frames (ORFs) predicted and translated into amino acid sequences with the TAMA tool tama_orf_seeker.py, putative protein-coding sequences identified in the UniProt/UniRef50 database using blastp, results filtered and final annotation built with the TAMA tools tama_orf_blastp_parser.py and tama_cds_regions_bed_add.py, respectively.tama_merge.py options: ${-a 100 -z 100 -d merge_dup -f filelist.txt}filelist.txt settings, short-reads: "no_cap 2,1,2"filelist.txt settings, long-reads: "capped 1,2,1"bedtools options: ${getfasta -name -split -s}blastp options: ${-evalue 1e-10}<br>