cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10196102
title: SAMI3 Density for 20200401

description: SAMI3 model density output for 20200401. 
This file was used as ionospheric truth in ANCHOR paper.
DOI: 10.5281/zenodo.10194844
title: Scripts for the publication "HONO production via the heterogeneous reaction between NO2 and diesel soot: role of environmentally persistent free radicals"

description: Access only for reviewers until manuscript acceptance.
DOI: 10.5281/zenodo.10194845
title: Scripts for the publication "HONO production via the heterogeneous reaction between NO2 and diesel soot: role of environmentally persistent free radicals"

description: Access only for reviewers until manuscript acceptance.
DOI: 10.5281/zenodo.10160453
title: Dataset and code for the publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT"

description: This is the code as well as the dataset for our publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT".
Please see the README.md for more information.
DOI: 10.5281/zenodo.10089708
title: Dataset and code for the publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT"

description: This is the code as well as the dataset for our publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT".
Please see the README.md for more information.
DOI: 10.5281/zenodo.10089707
title: Dataset and code for the publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT"

description: This is the code as well as the dataset for our publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT".
Please see the README.md for more information.
DOI: 10.6084/m9.figshare.24535018
title: The geography of metacommunities - Code and Data

description: In this study, we used simulation models to demonstrate that a conceptual framework centered on the dependency of species pool characteristics on landscape attributes is valuable for understanding the geography of metacommunity assembly, i.e., understanding variation in the relative importance of mechanisms that assemble different metacommunities distributed along broad-scale ecological gradients or across biogeographic regions. We used empirical data on moth metacommunities in a tropical (data from Maicher et al. 2019) and temperate mountain (Miller and Jones 2005) to provide empirical validation of some of the predictions derived from our simulation models.<br>
DOI: 10.3886/wy1c-yv16
title: Mongolia Water Supply: Design Report
DOI: 10.3886/zry3-4214
title: Mongolia Water Supply: Design Report
DOI: 10.6084/m9.figshare.24615036
title: Data of- QOL among patients on Anti-VEGF

description: The present data set is of the study on Quality of Life and financial burden among patients receiving anti-VEGF injection
DOI: 10.6084/m9.figshare.24615036.v1
title: Data of- QOL among patients on Anti-VEGF

description: The present data set is of the study on Quality of Life and financial burden among patients receiving anti-VEGF injection
DOI: 10.34740/kaggle/dsv/4930249
title: DeepRadar2022
DOI: 10.5281/zenodo.10196492
title: Constraining carbonate diagenesis using clumped isotope temperatures and U-Pb dating: a case study from the Gerze area in western central Tibet and implications for paleoelevation interpretations

description: Data S1: clumped isotope data of new samples; Data S2: clumped isotope data of standards; and Data S32: carbonate LA-ICPMS U-Pb data
DOI: 10.5281/zenodo.10196493
title: Constraining carbonate diagenesis using clumped isotope temperatures and U-Pb dating: a case study from the Gerze area in western central Tibet and implications for paleoelevation interpretations

description: Data S1: clumped isotope data of new samples; Data S2: clumped isotope data of standards; and Data S32: carbonate LA-ICPMS U-Pb data
DOI: 10.5281/zenodo.10196587
title: Simulated RO and Ionosonde data for 20200401

description: Simulated radio occultation and ionosonde data based on SAMI3 model. 
This data was used in ANCHOR paper.
DOI: 10.5281/zenodo.10196588
title: Simulated RO and Ionosonde data for 20200401

description: Simulated radio occultation and ionosonde data based on SAMI3 model. 
This data was used in ANCHOR paper.
DOI: 10.6084/m9.figshare.22292737.v1
title: yak_pangenome

description: We presented the first yak pan-genome obtained through the de novo assembly of 16 yak genomes. A total of 290Mb non-reference sequences and 504 novel genes were identified.
DOI: 10.25345/c5ng4h328
title: MassIVE MSV000093463 - Senegal Mangrove spcecies Raw data extract
title: senegal mangrove mangrove raw extract : ethanol and water
DOI: 10.5281/zenodo.10196649
title: ANCHOR DA results for 20200401 with simulated data

description: ANCHOR data assimilation results with simulated RO and simulated ionosonde data for 20200401 based on SAMI3 model, with PyIRI background.
DOI: 10.5281/zenodo.10196650
title: ANCHOR DA results for 20200401 with simulated data

description: ANCHOR data assimilation results with simulated RO and simulated ionosonde data for 20200401 based on SAMI3 model, with PyIRI background.
DOI: 10.5519/qd.82zpwu6m
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.6084/m9.figshare.22292737
title: yak_pangenome

description: We presented the first yak pan-genome obtained through the de novo assembly of 16 yak genomes. A total of 290Mb non-reference sequences and 504 novel genes were identified.
DOI: 10.6084/m9.figshare.22292737.v2
title: yak_pangenome

description: We presented the first yak pan-genome obtained through the de novo assembly of 16 yak genomes. A total of 290Mb non-reference sequences and 504 novel genes were identified.
DOI: 10.22002/3be4v-35679
title: Dataset: A time-efficient workflow to purify a library of alanine mutants of surface-exposed residues of the domain III of the envelope protein of dengue virus serotype 1

description: Characterization of purified preparations of Ala-mutants of the recombinant protein DIIIE1 using ESI-MS
DOI: 10.5281/zenodo.8392705
title: 18S rRNA V9 taxonomic classifier

description: 18S metabarcoding databases and naive-bayes classifier specific to the V9 region. Built from the PR2 v5.0.0 database using Qiime2 (version 2021.2).
Primers used:
1391f: GTACACACCGCCCGTC
EukBr: TGATCCTTCTGCAGGTTCACCTAC
File Description 
pr2_seqs.qza     |.   Full length PR2 v5.0.0 sequences
pr2_tax.qza        |    Taxa for PR2 v5.0.0 database
refseqs_V9.qza. |   Sequences for 18S V9 (primers 1391f, EukBrr), extracted from PR2 SSU v5.0.0, generated by qiime2-2021.2 (forward compatible)
classifier_V9.qza |  Unweighted (uniform) naive-bayes classifier for 18S V9 extracted from PR2 SSU v5.0.0, generated by qiime2-2021.2 
   
description: "This work was supported by award 21392 to the Northern Gulf Institute from NOAA/OAR Ocean Acidification Program."
DOI: 10.5281/zenodo.8392706
title: 18S rRNA V9 taxonomic classifier

description: 18S metabarcoding databases and naive-bayes classifier specific to the V9 region. Built from the PR2 v5.0.0 database using Qiime2 (version 2021.2).
Primers used:
1391f: GTACACACCGCCCGTC
EukBr: TGATCCTTCTGCAGGTTCACCTAC
File Description 
pr2_seqs.qza     |.   Full length PR2 v5.0.0 sequences
pr2_tax.qza        |    Taxa for PR2 v5.0.0 database
refseqs_V9.qza. |   Sequences for 18S V9 (primers 1391f, EukBrr), extracted from PR2 SSU v5.0.0, generated by qiime2-2021.2 (forward compatible)
classifier_V9.qza |  Unweighted (uniform) naive-bayes classifier for 18S V9 extracted from PR2 SSU v5.0.0, generated by qiime2-2021.2 
   
description: "This work was supported by award 21392 to the Northern Gulf Institute from NOAA/OAR Ocean Acidification Program."
DOI: 10.5281/zenodo.10196716
title: pRecipe masks

description: A data.table with 1036800 obs. of 20 variables:
lon
lat
basin_id
basin_name
biome_class
biome_short_class
country
country_short
elev_class
ipcc_continent
ipcc_region
ipcc_short_region
KG_class
KG_class_1
KG_class_2
KG_class_3
KG_class_1_name
land_cover_class
land_cover_short_class
land_mask
DOI: 10.5281/zenodo.8228785
title: pRecipe masks

description: A data.table with 1036800 obs. of 20 variables:
lon
lat
basin_id
basin_name
biome_class
biome_short_class
country
country_short
elev_class
ipcc_continent
ipcc_region
ipcc_short_region
KG_class
KG_class_1
KG_class_2
KG_class_3
KG_class_1_name
land_cover_class
land_cover_short_class
land_mask
DOI: 10.5281/zenodo.8228786
title: pRecipe masks

description: A data.table with 1036800 obs. of 16 variables: lon lat basin_id biome_class biome_short_class country country_short elev_class KG_class KG_class_1 KG_class_2 KG_class_3 KG_class_1_name land_cover_class land_cover_short_class land_mask
DOI: 10.5281/zenodo.8374531
title: pRecipe masks

description: A data.table with 1036800 obs. of 16 variables: lon lat basin_id biome_class biome_short_class country country_short elev_class KG_class KG_class_1 KG_class_2 KG_class_3 KG_class_1_name land_cover_class land_cover_short_class land_mask
DOI: 10.5281/zenodo.10065648
title: FTIR MS spectra of SRNOM after one-electron oxidations
DOI: 10.5281/zenodo.10065647
title: FT-ICR MS spectra of SRNOM after one-electron oxidations
DOI: 10.5281/zenodo.10066860
title: FT-ICR MS spectra of SRNOM after one-electron oxidations
DOI: 10.5281/zenodo.10097551
title: FT-ICR MS spectra of SRNOM after one-electron oxidations
DOI: 10.5281/zenodo.10196715
title: FT-ICR MS spectra of SRNOM after one-electron oxidations
DOI: 10.7284/153192
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AR70-B using a Hawaii UHDAS instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153193
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR70-B using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153204
title: Flowmeter data collected during research cruise AR70-B using a Thiel Thiel Flowmeter instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153182
title: Fluorometer data collected during research cruise AR70-B using a WETLabs WETStar instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153188
title: GNSS (Global Navigation Satellite System) data collected during research cruise AR70-B using a CCTech C-Nav 3050 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153203
title: GNSS (Global Navigation Satellite System) data collected during research cruise AR70-B using a Kongsberg DPS112 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153201
title: INS (Inertial Navigation System) data collected during research cruise AR70-B using a Applanix POS/MV-320 V5 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153194
title: Meteorological Station data collected during research cruise AR70-B using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153195
title: Meteorological Station data collected during research cruise AR70-B using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153197
title: Multibeam Echosounder data collected during research cruise AR70-B using a Kongsberg EM710 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153213
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise AR70-B using a GeneralOceanics 8050 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153190
title: Radiometer data collected during research cruise AR70-B using a Eppley RAD instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153191
title: Radiometer data collected during research cruise AR70-B using a Biospherical QSR-2150 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153187
title: Singlebeam Echosounder data collected during research cruise AR70-B using a Knudsen 3260 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153206
title: Speedlog data collected during research cruise AR70-B using a Furuno DS-60 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153199
title: Splitbeam Echosounder data collected during research cruise AR70-B using a Kongsberg EK80 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153208
title: SSV (Sound Speed Velocimeter) data collected during research cruise AR70-B using a Valeport Thru-Hull SVS instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153196
title: Water Temperature data collected during research cruise AR70-B using a SeaBird SBE-48 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153209
title: Transmissometer data collected during research cruise AR70-B using a SeaBird C-Star instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153211
title: Transmissometer data collected during research cruise AR70-B using a SeaBird C-Star instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/153183
title: TSG (Thermosalinograph) data collected during research cruise AR70-B using a SeaBird SBE-45 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.10196722
title: Positive effects of public breeding on U.S. rice yields under future climate scenarios

description: This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "Positive effects of public breeding on U.S. rice yields under future climate scenarios". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm.


This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205.
DOI: 10.5281/zenodo.8040082
title: Positive effects of public breeding on U.S. rice yields under future climate scenarios

description: This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "Positive effects of public breeding on U.S. rice yields under future climate scenarios". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm.


This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205.
DOI: 10.5281/zenodo.8040083
title: Positive effects of public breeding on U.S. rice yields under future climate scenarios

description: This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "<strong>Positive effects of public breeding on U.S. rice yields under future climate scenarios</strong>". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm. This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205.
DOI: 10.5281/zenodo.8346660
title: Positive effects of public breeding on U.S. rice yields under future climate scenarios

description: This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "<strong>Positive effects of public breeding on U.S. rice yields under future climate scenarios</strong>". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm. This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205.
DOI: 10.5281/zenodo.10196806
title: 133_dark_kinase_proximity_data
DOI: 10.5281/zenodo.10196807
title: 133_dark_kinase_proximity_data