cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ddmvrdc4sc.2
title: Research evaluation information - MC-DIR

description: In order to share the data from the research carried out with some participants, this space was created. The evaluation of the research is carried out in relation to the MC-DIR method, which was created to specify the dynamic behaviour of the user in the face of recommendation systems.
DOI: 10.17632/ddmvrdc4sc.3
title: Research evaluation information - MC-DIR

description: In order to share the data from the research carried out with some participants, this space was created. The evaluation of the research is carried out in relation to the MC-DIR method, which was created to specify the dynamic behaviour of the user in the face of recommendation systems.
DOI: 10.17632/ddmvrdc4sc.4
title: Research evaluation information - CM-DIR

description: In order to share the data from the research carried out with some participants, this space was created. The evaluation of the research is carried out in relation to the CM-DIR method, which was created to specify the dynamic behaviour of the user in the face of recommendation systems.
DOI: 10.17632/g52k45nsmm.1
title: Response to John et al.’s comments of “Paroxetine is an effective treatment for refractory erythema of rosacea: Primary results from the Prospective Rosacea Refractory Erythema

description: VISIA images of the patients were displayed encouraging results following paroxetine therapy.
DOI: 10.6084/m9.figshare.24624603
title: 20231124_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24624603.v1
title: 20231124_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.17632/nc894fnrzr.1
title: Supplementary Files for A close look at dental caries and developmental defects of enamel in individuals with cerebral palsy: a systematic review and meta-analysis

description: Supplementary files for A close look at dental caries and developmental defects of enamel in individuals with cerebral palsy: a systematic review and meta-analysis
DOI: 10.17632/66p3c4xzkj.3
title: Parvimico materdei gen. et sp. nov.: A new platyrrhine from the Early Miocene of the Amazon Basin, Peru

description: SOM File S1. NEXUS-formatted file for phylogenetic analysis. (.nex file) SOM File S2. Nexus-formatted file with branch lengths for PGLS analysis. (.nex file) SOM File S3. R code for PGLS run. (R script) SOM Table S1. Extant platyrrhine frugivore mean upper first molar mesiodistal length, and sum of shearing (includes both real values and natural logs). (csv file) SOM Table S2. Molar shearing and mesiodistal lengths for platyrrhine specimens. (xlsx-formatted Excel spreadsheet) SOM Table S3. Procrustes-aligned data matrix and ancillary results from principal component analysis reported in the text. (xlsx-formatted Excel spreadsheet) SOM S1. Age determinations using U/Pb dating of detrital zircons. SOM S2. Characters and character states used in the phylogenetic analysis. SOM S3. Landmark Analyses: Additional Data and Results.
DOI: 10.6084/m9.figshare.24550993.v1
title: Sequences

description: Fasta files from references and generated sequences:excluded sequences = positive controls (already deposited sequences) and sequences below 50% length coveragesequences_for_submission = sequences with no gaps
DOI: 10.6084/m9.figshare.24550993
title: Sequences

description: Fasta files from references and generated sequences:excluded sequences = positive controls (already deposited sequences) and sequences below 50% length coveragesequences_for_submission = sequences with no gaps
DOI: 10.17632/3yz3mxsd52.1
title: Infant and young child feeding practices

description: Infant and young child feeding datasets
DOI: 10.17632/tr9py7hzj4.1
title: Video Dataset of Male Basic Balinese Dance Movement for Action Recognition

description: This dataset contains videos of eleven basic male Balinese dance movements demonstrated by four male dancers. These basic movements including, agem kanan, agem kiri, gandang-gandang, malpal, nayog, nepuk kampuh, oyod, piles, seledet, tapak sirang pada, and ulap-ulap. The total number of videos is 828. The data is divided into three directories, which are train, validation, and test.
DOI: 10.17632/5j49shkzzb.1
title: Pushing and pulling on a string? Inflation effects of expansionary and contractionary monetary policies when rates are negative

description: Replication files of the paper "Pushing and pulling on a string? Inflation effects of expansionary and contractionary monetary policies when rates are negative"
DOI: 10.17632/5j49shkzzb.2
title: Pushing and pulling on a string? Inflation effects of expansionary and contractionary monetary policies when rates are negative

description: Replication files of the paper "Pushing and pulling on a string? Inflationary effects of expansionary and contractionary monetary policies when rates are negative"
DOI: 10.57745/cjgedv
title: Septoria_linicola_SE14017.annotations-TE.tab

description: :unav
DOI: 10.57745/fx9uzj
title: Septoria_linicola_SE14017_TEconsensus.fasta

description: :unav
DOI: 10.57745/hlb4nf
title: Septoria_linicola_SE15195_TE.gff3

description: :unav
DOI: 10.57745/ehdurm
title: Septoria_linicola_SE14017_TE.gff3

description: :unav
DOI: 10.57745/lhacv4
title: Septoria_linicola_SE15195_TEconsensus.fasta

description: :unav
DOI: 10.57745/u63xq3
title: Septoria_linicola_SE15195.annotations-TE.tab

description: :unav
DOI: 10.57745/vixhzz
title: Readme.txt

description: :unav
DOI: 10.5525/gla.researchdata.1524
title: Fluorescence Lifetime Hong-Ou-Mandel Sensing

description: Repository contains the raw data, and code required to process it.
DOI: 10.57745/7jribf
title: Botrytis_cinerea_Sl3_TEconsensus.fasta

description: :unav
DOI: 10.57745/feqnjm
title: Botrytis_cinerea_Vv3_TEconsensus.fasta

description: :unav
DOI: 10.57745/mf62xz
title: Botrytis_cinerea_Vv3_Sl3.annotations-TE.xlsx

description: :unav
DOI: 10.57745/ysrz6h
title: Botrytis_cinerea_Sl3_TE.gff3

description: :unav
DOI: 10.57745/urufok
title: Readme.txt

description: :unav
DOI: 10.57745/y4fpyy
title: Botrytis_cinerea_Vv3_TE.gff3

description: :unav
DOI: 10.6084/m9.figshare.24624609
title: multiple sequence alignments for trees

description: Multiple sequence alignments for trees from publication "<b>A tale of caution: How endogenous viral elements affect virus discovery in transcriptomic data"</b>
DOI: 10.6084/m9.figshare.24624609.v1
title: multiple sequence alignments for trees

description: Multiple sequence alignments for trees from publication "<b>A tale of caution: How endogenous viral elements affect virus discovery in transcriptomic data"</b>
DOI: 10.25405/data.ncl.24552007
title: Simulation of diffusion mass transport in amperometric gas sensors.

description: Simulation and closed form approximations of diffusional mass transport in commercial amperometric gas sensors.
DOI: 10.25405/data.ncl.24552007.v1
title: Simulation of diffusion mass transport in amperometric gas sensors.

description: Simulation and closed form approximations of diffusional mass transport in commercial amperometric gas sensors.
DOI: 10.6084/m9.figshare.24624576
title: Script input files

description: All input files for scripts from publication "<b>A tale of caution: How endogenous viral elements affect virus discovery in transcriptomic data"</b>
DOI: 10.5281/zenodo.10066111
title: Party primaries in Spain

description: Original dataset comprising over 300 selection processes of candidates and leaders in Spain from 1991 to 2021 at both the national and regional level.
DOI: 10.5281/zenodo.10066112
title: Party primaries in Spain

description: Original dataset comprising over 300 selection processes of candidates and leaders in Spain from 1991 to 2021 at both the national and regional level.
DOI: 10.5281/zenodo.10201235
title: Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."

description: CAR36 dataset
These data correspond to the 1-year (2019) simulation from the regional ocean system CAR36. These daily hindcasts have been used in the study presented in the paper submitted in GMD editor and entitled:  "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described.
The uploaded files are in netcdf format:

CAR36_daily_SSH_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Height 
CAR36_daily_SST_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Temperature
CAR36_daily_SSU_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Current Speed (zonal component)
CAR36_daily_SSV_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Current Speed (meridian component)
All data are projected on the native model tripolar ORCA grid in 1/36° horizontal resolution.
NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average. 
CAR36 software
The NEMO_CAR36.tar file gathers the NEMO code configuration of the CAR36 model. This code follows the same license than NEMO one : CeCILL. A file named "License_CeCILL.txt" reminds the details of this license in the NEMO_CAR36.tar file.NB.: This model have been renamed CAR36 (English acronym) for the paper instead of ARCAN36 (French initial acronym). In the provided NEMO code, the name ARCAN36 is still used.
DOI: 10.5281/zenodo.8386854
title: Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."

description: CAR36 dataset
These data correspond to the 1-year (2019) simulation from the regional ocean system CAR36. These daily hindcasts have been used in the study presented in the paper submitted in GMD editor and entitled:  "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described.
The uploaded files are in netcdf format:

CAR36_daily_SSH_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Height 
CAR36_daily_SST_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Temperature
CAR36_daily_SSU_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Current Speed (zonal component)
CAR36_daily_SSV_20190102-20191224.nc = 1-year daily hindcasts of Sea Surface Current Speed (meridian component)
All data are projected on the native model tripolar ORCA grid in 1/36° horizontal resolution.
NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average. 
CAR36 software
The NEMO_CAR36.tar file gathers the NEMO code configuration of the CAR36 model. This code follows the same license than NEMO one : CeCILL. A file named "License_CeCILL.txt" reminds the details of this license in the NEMO_CAR36.tar file.NB.: This model have been renamed CAR36 (English acronym) for the paper instead of ARCAN36 (French initial acronym). In the provided NEMO code, the name ARCAN36 is still used.
DOI: 10.5281/zenodo.8386855
title: Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."

description: These data correspond to the <strong>1-year (2019)</strong> simulation from the regional ocean system CAR36. These <strong>daily hindcasts</strong> have been used in the study presented in the paper submitted in GMD editor and entitled: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described. <br> The uploaded files are in <strong>netcdf</strong> format: <em>CAR36_daily_SSH_20190102-20191224.nc</em> = 1-year daily hindcasts of <strong>Sea Surface Height </strong> <em>CAR36_daily_SST_20190102-20191224.nc </em>= 1-year daily hindcasts of <strong>Sea Surface Temperature</strong> <em>CAR36_daily_SSU_20190102-20191224.nc</em> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (zonal component)</strong> <em>CAR36_daily_SSV_20190102-20191224.nc</em> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (meridian component)</strong> All data are projected on the native model tripolar<strong> ORCA grid</strong> <strong>in 1/36° </strong>horizontal resolution. NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average.
DOI: 10.5281/zenodo.8158579
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: [updated July 2023] This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; preprint: https://doi.org/10.31223/X5SD3T). The data include three csv files: <strong>'Sengupta_Lazarus_REC_1990_2020_v02.csv' </strong>– annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed]. To produce the smoothed data, the raw data are passed through a Savitzky–Golay filter. <strong>'Sengupta_Lazarus_REC_TEU_2011_2020_v02.csv'</strong> – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], and TEU (millions), collated from archived Lloyd's List reports. <strong>'Sengupta_Lazarus_REC_TEU_totals_v02.csv'</strong> – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021). <strong>'RECLAIM_port_trajectories_v7.ipynb' </strong>– Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.) The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation These data and code are also available here: https://github.com/edlazarus/Seaports
description: {"references": ["Sengupta, D., Choi, Y. R., Tian, B., Brown, S., Meadows, M., Hackney, C. R., et al. (2023). Mapping 21st century global coastal land reclamation. Earth's Future, 11, e2022EF002927. https://doi.org/10.1029/2022EF002927", "Verschuur, J., Koks, E. E., Li, S. et al. (2023). Multi-hazard risk to global port infrastructure and resulting trade and logistics losses. Commun Earth Environ 4, 5. https://doi.org/10.1038/s43247-022-00656-7"]}
DOI: 10.5281/zenodo.8399137
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: [updated October 2023] This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; preprint: https://doi.org/10.31223/X5SD3T). The data include four csv files: <strong>'Sengupta_Lazarus_REC_1990_2020_v05.csv' </strong>– annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed]. To produce the smoothed data, the raw data are passed through a Savitzky–Golay filter. <strong>'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv'</strong> – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], and TEU (millions), collated from archived Lloyd's List reports. <strong>'Sengupta_Lazarus_REC_TEU_totals_v04.csv'</strong> – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021). <strong>'Sengupta_Lazarus_2023_ports_excluded.csv'</strong> – contains list of 34 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020. <strong>'RECLAIM_port_trajectories_v11.ipynb' </strong>– Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.) The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Seaport_reclamation These data and code are also available here: https://github.com/edlazarus/Seaports
description: {"references": ["Sengupta, D., Choi, Y. R., Tian, B., Brown, S., Meadows, M., Hackney, C. R., et al. (2023). Mapping 21st century global coastal land reclamation. Earth's Future, 11, e2022EF002927. https://doi.org/10.1029/2022EF002927", "Verschuur, J., Koks, E. E., Li, S. et al. (2023). Multi-hazard risk to global port infrastructure and resulting trade and logistics losses. Commun Earth Environ 4, 5. https://doi.org/10.1038/s43247-022-00656-7"]}
DOI: 10.5281/zenodo.10201300
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: [updated November 2023]
This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; https://doi.org/10.1038/s43247-023-01110-y).
This repository includes three .csv files, one .xlsx file, and a .ipynb file:
'Sengupta_Lazarus_REC_1990_2020_v05.csv' – annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, and Reclaimed area (km^2) [raw measurement]. 
'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv' – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and TEU (millions), collated from archived Lloyd's List reports.
'Sengupta_Lazarus_REC_TEU_totals_v04.csv' – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021).
'Sengupta_Lazarus_2023_ports_excluded_v2.xlsx' – contains list of 35 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.
'RECLAIM_port_trajectories_v11.ipynb' – Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.)
The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Seaport_reclamation
These data and code are also available here: https://github.com/edlazarus/Seaports
DOI: 10.5281/zenodo.7674075
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: [updated November 2023]
This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; https://doi.org/10.1038/s43247-023-01110-y).
This repository includes three .csv files, one .xlsx file, and a .ipynb file:
'Sengupta_Lazarus_REC_1990_2020_v05.csv' – annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, and Reclaimed area (km^2) [raw measurement]. 
'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv' – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and TEU (millions), collated from archived Lloyd's List reports.
'Sengupta_Lazarus_REC_TEU_totals_v04.csv' – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021).
'Sengupta_Lazarus_2023_ports_excluded_v2.xlsx' – contains list of 35 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.
'RECLAIM_port_trajectories_v11.ipynb' – Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.)
The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Seaport_reclamation
These data and code are also available here: https://github.com/edlazarus/Seaports