cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10469859
title: Dataset for AspGap: Augmented Stellar Parameters and Abundances for 37 million RGB stars from Gaia XP

description: AspGap is a novel data-driven model to perform regressions with the XP spectra, augmented by transfer learning from high-resolution APOGEE spectra. Using a training set of stellar labels from APOGEE, we demonstrate that AspGap can deliver effective temperature, surface gravity, metallicity, and alpha abundance to accuracies of ~ 1%, 0.15 dex, 0.07 dex, and 0.03 dex, respectively. See the paper for more details.

In version 4, JDrgb_37m.hdf5 is the compiled catalog of 37 million RGB stars,

JDrgb_14m_hasrv.hdf5 is the catalog inculdes radial velocity (RV) measurements by Gaia. 

JDrgb_12m_v3_3_6dBJ.hdf5 is left from version 3.3, with RV, and distances are obtained by cross-matching with Bailer-Jones+ (2021) for user-friendly access (12 million). The AspGap labels are introduced in our paper, and the 6-D coordinates (x, y, z, vx, vy, vz) are calculated using astropy.coord.galactocentric_frame_defaults.set('v4.0')

The following code provides a recommended way to easily read the catalog:

import vaex

cat = vaex.open("JDrgb_12m_v3_3_6dBJ.hdf5")

# if you work with astropy.table
tab_astropy = cat.to_astropy_table()

# or pd.DataFrame ...
tab_df = cat.to_pandas_df()

 

 
description: This project was developed in part at the 2023 Gaia XPloration, hosted by the Institute of Astronomy, Cambridge University.
Thanks to Yuting Wang (NAOC) for helping cross-match the catalog.
DOI: 10.5281/zenodo.7879066
title: Dataset for AspGap: Augmented Stellar Parameters and Abundances for 37 million RGB stars from Gaia XP

description: AspGap is a novel data-driven model to perform regressions with the XP spectra, augmented by transfer learning from high-resolution APOGEE spectra. Using a training set of stellar labels from APOGEE, we demonstrate that AspGap can deliver effective temperature, surface gravity, metallicity, and alpha abundance to accuracies of ~ 1%, 0.15 dex, 0.07 dex, and 0.03 dex, respectively. See the paper for more details.

In version 4, JDrgb_37m.hdf5 is the compiled catalog of 37 million RGB stars,

JDrgb_14m_hasrv.hdf5 is the catalog inculdes radial velocity (RV) measurements by Gaia. 

JDrgb_12m_v3_3_6dBJ.hdf5 is left from version 3.3, with RV, and distances are obtained by cross-matching with Bailer-Jones+ (2021) for user-friendly access (12 million). The AspGap labels are introduced in our paper, and the 6-D coordinates (x, y, z, vx, vy, vz) are calculated using astropy.coord.galactocentric_frame_defaults.set('v4.0')

The following code provides a recommended way to easily read the catalog:

import vaex

cat = vaex.open("JDrgb_12m_v3_3_6dBJ.hdf5")

# if you work with astropy.table
tab_astropy = cat.to_astropy_table()

# or pd.DataFrame ...
tab_df = cat.to_pandas_df()

 

 
description: This project was developed in part at the 2023 Gaia XPloration, hosted by the Institute of Astronomy, Cambridge University.
Thanks to Yuting Wang (NAOC) for helping cross-match the catalog.
DOI: 10.5281/zenodo.7879067
title: AspGap: Augmented Stellar Parameters and Abundances for 24 million RGB stars from Gaia XP

description: AspGap is a novel data-driven model to perform regressions with the XP spectra, augmented by transfer learning from high-resolution APOGEE spectra. Using a training set of stellar labels from APOGEE, we demonstrate that AspGap can deliver effective temperature, surface gravity, metallicity, and alpha abundance to accuracies of ~ 1%, 0.15 dex, 0.07 dex, and 0.024 dex, respectively.
DOI: 10.5281/zenodo.8002699
title: AspGap: Augmented Stellar Parameters and Abundances for 24 million RGB stars from Gaia XP

description: AspGap is a novel data-driven model to perform regressions with the XP spectra, augmented by transfer learning from high-resolution APOGEE spectra. Using a training set of stellar labels from APOGEE, we demonstrate that AspGap can deliver effective temperature, surface gravity, metallicity, and alpha abundance to accuracies of ~ 1%, 0.15 dex, 0.07 dex, and 0.024 dex, respectively.
DOI: 10.5281/zenodo.8377308
title: AspGap: Augmented Stellar Parameters and Abundances for 24 million RGB stars from Gaia XP

description: AspGap is a novel data-driven model to perform regressions with the XP spectra, augmented by transfer learning from high-resolution APOGEE spectra. Using a training set of stellar labels from APOGEE, we demonstrate that AspGap can deliver effective temperature, surface gravity, metallicity, and alpha abundance to accuracies of ~ 1%, 0.15 dex, 0.07 dex, and 0.03 dex, respectively. See the paper for more details. In version 3.2, the catalog includes radial velocity measurements by Gaia (~ 12 million), and distances are obtained by cross-matching with Bailer-Jones+ (2021) for user-friendly access. The AspGap labels are introduced in our paper, and the 6-D coordinates (x, y, z, vx, vy, vz) are calculated using astropy.coord.galactocentric_frame_defaults.set('v4.0') The following code provides a recommended way to easily read the catalog: <pre><code class="language-python">import vaex cat = vaex.open("JDrgb_12m_v3_3_6dBJ.hdf5") # if you work with astropy.table tab_astropy = cat.to_astropy_table() # or pd.DataFrame ... tab_df = cat.to_pandas_df()</code></pre>
description: This project was developed in part at the 2023 Gaia XPloration, hosted by the Institute of Astronomy, Cambridge University. Thanks to Yuting Wang (NAOC) for helping cross-match the catalog.
DOI: 10.6084/m9.figshare.24963831
title: Supplementary materials: The cost–effectiveness of oral semaglutide versus empagliflozin in Type 2 diabetes in Denmark

description: <b>These are peer-reviewed supplementary material for the article ‘</b><b>The cost–effectiveness of oral semaglutide versus empagliflozin in Type 2 diabetes in Denmark’ </b><b>published in the </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Table S1</b>: Baseline Characteristics.<b>Table S2</b>: Treatment Effects – First Line.<b>Table S3</b>: Adverse Events.<b>Table S4</b>: Treatment effects for second/third line treatments (treatment policy estimand).<b>Table S5</b>: Proportion of patients on preventive medication.<b>Table S6</b>: Screening and patient management proportions.<b>Table S7</b>: Sensitivity and specificity of tests.<b>Table S8</b>: Annual treatment costs applied (DKK, AIP 2020, [20]).<b>Table S9</b>: Cost Inputs for the CDM (Costs inflated to 2020 values, DKK).<b>Table S10</b>: Utilities used in CDM.<b>Table S11</b>: Scenario analysis results.<b>Table S12</b>: Breakdown of costs (DKK, per average patient).<b>Summary: </b><b>Aim:</b> To evaluate the cost–effectiveness of oral semaglutide+metformin versus empagliflozin+metformin in people with Type 2 diabetes uncontrolled on msetformin alone. <b>Materials and methods:</b> The IQVIA Core Diabetes Model was populated with efficacy data from a head-to-head study between oral semaglutide+metformin and empagliflozin+metformin. Danish costs and quality-of-life data were sourced from literature. Price per day was Danish Krone (DKK) 25.53 for oral semaglutide and DKK11.40 for empagliflozin. Discounting was fixed at 4%. Scenario and sensitivity analyses were performed. <b>Results:</b> Over a lifetime, Core Diabetes Model projected 8.78 and 8.75 quality-adjusted life-years and a total cost of DKK 447,633 and DKK 387,786, thereby generating an incremental cost–effectiveness ratio of DKK 1,930,548 for oral semaglutide+metformin versus empagliflozin+metformin. Scenario and sensitivity analyses showed the robustness of the outcomes. Duration of treatment with oral semaglutide is the key driver of the analyses. <b>Conclusion:</b> Oral semaglutide+metformin seems not cost effective versus empagliflozin+metformin in patients uncontrolled on metformin in Denmark.
DOI: 10.6084/m9.figshare.24963831.v1
title: Supplementary materials: The cost–effectiveness of oral semaglutide versus empagliflozin in Type 2 diabetes in Denmark

description: <b>These are peer-reviewed supplementary material for the article ‘</b><b>The cost–effectiveness of oral semaglutide versus empagliflozin in Type 2 diabetes in Denmark’ </b><b>published in the </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Table S1</b>: Baseline Characteristics.<b>Table S2</b>: Treatment Effects – First Line.<b>Table S3</b>: Adverse Events.<b>Table S4</b>: Treatment effects for second/third line treatments (treatment policy estimand).<b>Table S5</b>: Proportion of patients on preventive medication.<b>Table S6</b>: Screening and patient management proportions.<b>Table S7</b>: Sensitivity and specificity of tests.<b>Table S8</b>: Annual treatment costs applied (DKK, AIP 2020, [20]).<b>Table S9</b>: Cost Inputs for the CDM (Costs inflated to 2020 values, DKK).<b>Table S10</b>: Utilities used in CDM.<b>Table S11</b>: Scenario analysis results.<b>Table S12</b>: Breakdown of costs (DKK, per average patient).<b>Summary: </b><b>Aim:</b> To evaluate the cost–effectiveness of oral semaglutide+metformin versus empagliflozin+metformin in people with Type 2 diabetes uncontrolled on msetformin alone. <b>Materials and methods:</b> The IQVIA Core Diabetes Model was populated with efficacy data from a head-to-head study between oral semaglutide+metformin and empagliflozin+metformin. Danish costs and quality-of-life data were sourced from literature. Price per day was Danish Krone (DKK) 25.53 for oral semaglutide and DKK11.40 for empagliflozin. Discounting was fixed at 4%. Scenario and sensitivity analyses were performed. <b>Results:</b> Over a lifetime, Core Diabetes Model projected 8.78 and 8.75 quality-adjusted life-years and a total cost of DKK 447,633 and DKK 387,786, thereby generating an incremental cost–effectiveness ratio of DKK 1,930,548 for oral semaglutide+metformin versus empagliflozin+metformin. Scenario and sensitivity analyses showed the robustness of the outcomes. Duration of treatment with oral semaglutide is the key driver of the analyses. <b>Conclusion:</b> Oral semaglutide+metformin seems not cost effective versus empagliflozin+metformin in patients uncontrolled on metformin in Denmark.
DOI: 10.5281/zenodo.10471946
title: Calculated seismic intensities for the paper "Contrasting sedimentary and long-lasting geochemical imprints of seismic shaking in a small, isolated lake basin (Klopeiner See, Eastern European Alps)"

description: This table provides the data used and the intensities calculated for the paper "Contrasting sedimentary and long-lasting geochemical imprints of seismic shaking in a small, isolated lake basin (Klopeiner See, Eastern European Alps)"
DOI: 10.5281/zenodo.10471947
title: Calculated seismic intensities for the paper "Contrasting sedimentary and long-lasting geochemical imprints of seismic shaking in a small, isolated lake basin (Klopeiner See, Eastern European Alps)"

description: This table provides the data used and the intensities calculated for the paper "Contrasting sedimentary and long-lasting geochemical imprints of seismic shaking in a small, isolated lake basin (Klopeiner See, Eastern European Alps)"
DOI: 10.6084/m9.figshare.24963837.v1
title: oct4

description: oct4 cut&tag in wt vs senp1n556a
DOI: 10.6084/m9.figshare.24963837
title: oct4

description: oct4 cut&tag in wt vs senp1n556a
DOI: 10.5281/zenodo.10093574
title: GLORY Example Dataset

description: Example dataset for the GLORY model.
DOI: 10.5281/zenodo.10093575
title: GLORY Example Dataset

description: Example dataset for the GLORY model.
DOI: 10.5281/zenodo.10452783
title: GLORY Example Dataset

description: Example dataset for the GLORY model.
DOI: 10.5281/zenodo.10471982
title: GLORY Example Dataset

description: Example dataset for the GLORY model.
DOI: 10.5281/zenodo.10470743
title: Example datasets for AlphaCRV

description: Example datasets for AlphaCRV: A Pipeline for Identifying Accurate Binder Topologies in Mass-Modeling with AlphaFold. With these datasets you can replicate two of the three examples shown in the paper, following along with the Jupyter notebooks in the GitHub repository at https://github.com/strubelab/AlphaCRV. Description:



AVRPia.fasta, AVRPik.fasta, SKP1.fasta: Sequences of the bait molecules for the three examples.

AVRPia_binders.fasta, AVRPik_binders.fasta, SKP1_binders.fasta: Sequences of the binder molecules for the three examples.

AVRPia_vs_rice_models.tar.lzma, AVRPik_vs_rice_models.tar.lzma: Compressed archives of the AlphaFold-Multimer models for the AVRPia and AVRPik examples. The 712 complexes for SKP1 are more than 100GB in size, so those can be provided upon request. To decompress the .tar.lzma archives use the following two commands on each:


unlzma AVRPia_vs_rice_models.tar.lzma
tar -xvf AVRPia_vs_rice_models.tar



AVRPia_vs_rice_clusters.tar.gz, AVRPik_vs_rice_clusters.tar.gz, SKP1_vs_rice_clusters.tar.gz: Compressed archives with the results from running AlphaCRV on the three examples presented in the paper. To decompress these archives use the following command on each:


tar -xvzf AVRPia_vs_rice_clusters.tar.gz
DOI: 10.5281/zenodo.10470744
title: Example datasets for AlphaCRV

description: Example datasets for AlphaCRV: A Pipeline for Identifying Accurate Binder Topologies in Mass-Modeling with AlphaFold. With these datasets you can replicate two of the three examples shown in the paper, following along with the Jupyter notebooks in the GitHub repository at https://github.com/strubelab/AlphaCRV. Description:



AVRPia.fasta, AVRPik.fasta, SKP1.fasta: Sequences of the bait molecules for the three examples.

AVRPia_binders.fasta, AVRPik_binders.fasta, SKP1_binders.fasta: Sequences of the binder molecules for the three examples.

AVRPia_vs_rice_models.tar.lzma, AVRPik_vs_rice_models.tar.lzma: Compressed archives of the AlphaFold-Multimer models for the AVRPia and AVRPik examples. The 712 complexes for SKP1 are more than 100GB in size, so those can be provided upon request. To decompress the .tar.lzma archives use the following two commands on each:


unlzma AVRPia_vs_rice_models.tar.lzma
tar -xvf AVRPia_vs_rice_models.tar



AVRPia_vs_rice_clusters.tar.gz, AVRPik_vs_rice_clusters.tar.gz, SKP1_vs_rice_clusters.tar.gz: Compressed archives with the results from running AlphaCRV on the three examples presented in the paper. To decompress these archives use the following command on each:


tar -xvzf AVRPia_vs_rice_clusters.tar.gz
DOI: 10.5519/qd.kv2kjwd7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (162 records)
DOI: 10.34740/kaggle/dsv/7365042
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.10471755
title: Activity coefficients for ideal mixtures (Hexane/Heptane, Benzene/Toluene, and 1-Propanol/Isopropanol) and non-ideal mixtures (Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water) determined with Aspen Plus® v12.

description: Comparison of the profile of activity coefficients for the mixtures Hexane/Heptane, Benzene/Toluene, 1-Porpanol/Isopropanol, Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water.

 

The mixtures Hexane/Heptane, Benzene/Toluene, and 1-Porpanol/Isopropanol are generally regarded as ideal considering that their behavior can be predicted accurately with Raoult's Law. The activity coefficient was expected to be around 1 for both components of each mixture and this was confirmed by retrieving the data from Aspen Plus® v12, using the chemicals template and the non-random two-liquid model.

 

The activity coefficients of the mixtures Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water were predicted with the chemicals template and the non-random two-liquid model, and the electrolyte template and the electrolyte non-random two-liquid property method. This latter approach was more suitable for aqueous solutions.

 

In addition to the files of the simulation is Aspen Plus® v12, the MS Excel file and Origin plot were included to summarize the results.
DOI: 10.5281/zenodo.10471756
title: Activity coefficients for ideal mixtures (Hexane/Heptane, Benzene/Toluene, and 1-Propanol/Isopropanol) and non-ideal mixtures (Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water) determined with Aspen Plus® v12.

description: Comparison of the profile of activity coefficients for the mixtures Hexane/Heptane, Benzene/Toluene, 1-Porpanol/Isopropanol, Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water.

 

The mixtures Hexane/Heptane, Benzene/Toluene, and 1-Porpanol/Isopropanol are generally regarded as ideal considering that their behavior can be predicted accurately with Raoult's Law. The activity coefficient was expected to be around 1 for both components of each mixture and this was confirmed by retrieving the data from Aspen Plus® v12, using the chemicals template and the non-random two-liquid model.

 

The activity coefficients of the mixtures Ethanol/Water, Ammonia/Water, and Carbon Dioxide/Water were predicted with the chemicals template and the non-random two-liquid model, and the electrolyte template and the electrolyte non-random two-liquid property method. This latter approach was more suitable for aqueous solutions.

 

In addition to the files of the simulation is Aspen Plus® v12, the MS Excel file and Origin plot were included to summarize the results.
DOI: 10.25345/c54t6fd7f
title: MassIVE MSV000093792 - GNPS Florida Keys Surface Water Dec 2020
title: Surface seawater from FL Keys Dec 2020 processed by PPL and analyzed by LC-Oribtrap for untargeted metabolomics. Raw files were converted with MSconvert.
DOI: 10.5281/zenodo.10472013
title: Nebivolol in Psoriasis induced mice by imiquimod
DOI: 10.5281/zenodo.10472014
title: Nebivolol in Psoriasis induced mice by imiquimod
DOI: 10.34740/kaggle/dsv/7364984
title: Linear Equation in 2 Variables
DOI: 10.5281/zenodo.10471910
title: GM12878_sample_HiC_data_features_and_IMR90_trained_ChIPr_HiC_model

description: Sample input features for GM12878 cell line Hi-C data used for prediction of Hi-C interactions.

And IMR90 GB-ChIPr trained model used for prediction
DOI: 10.5281/zenodo.10471911
title: GM12878_sample_HiC_data_features_and_IMR90_trained_ChIPr_HiC_model

description: Sample input features for GM12878 cell line Hi-C data used for prediction of Hi-C interactions.

And IMR90 GB-ChIPr trained model used for prediction
DOI: 10.5281/zenodo.10471305
title: Enterococcus faecalis - Centrifuge Plasmid Database
DOI: 10.5281/zenodo.10471306
title: Enterococcus faecalis - Centrifuge Plasmid Database
DOI: 10.5281/zenodo.10472051
title: Enterococcus faecium - Centrifuge Plasmid Database
DOI: 10.5281/zenodo.10472050
title: Enterococcus faecium - Centrifuge Plasmid Database
DOI: 10.5281/zenodo.10472015
title: Citizens living in localities close to two petrochemical complexes (Castellón and South of Tarragona)

description: The dataset is made up with responses from citizens living in localites close to two petrochemical sites in Spain (Castellón and South of Tarragona).  We received 1732 valid responses by means of simple random sampling.
DOI: 10.5281/zenodo.10472016
title: Citizens living in localities close to two petrochemical complexes (Castellón and South of Tarragona)

description: The dataset is made up with responses from citizens living in localites close to two petrochemical sites in Spain (Castellón and South of Tarragona).  We received 1732 valid responses by means of simple random sampling.
DOI: 10.5517/ccdc.csd.cc2h5bnp
title: CCDC 2299042: Experimental Crystal Structure Determination

description: Related Article: Moritz J. Classen, Bilal Kicin, Vincent A. P. Ruf, Alexander Hamminger, Loélie Ribadeau-Dumas, Willi M. Amberg, Erick M. Carreira|2023|J.Am.Chem.Soc.|145|27225|doi:10.1021/jacs.3c11000
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.cc2h5bpq
title: CCDC 2299043: Experimental Crystal Structure Determination

description: Related Article: Moritz J. Classen, Bilal Kicin, Vincent A. P. Ruf, Alexander Hamminger, Loélie Ribadeau-Dumas, Willi M. Amberg, Erick M. Carreira|2023|J.Am.Chem.Soc.|145|27225|doi:10.1021/jacs.3c11000
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.cc2h5bqr
title: CCDC 2299044: Experimental Crystal Structure Determination

description: Related Article: Moritz J. Classen, Bilal Kicin, Vincent A. P. Ruf, Alexander Hamminger, Loélie Ribadeau-Dumas, Willi M. Amberg, Erick M. Carreira|2023|J.Am.Chem.Soc.|145|27225|doi:10.1021/jacs.3c11000
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.cc2h5brs
title: CCDC 2299045: Experimental Crystal Structure Determination

description: Related Article: Moritz J. Classen, Bilal Kicin, Vincent A. P. Ruf, Alexander Hamminger, Loélie Ribadeau-Dumas, Willi M. Amberg, Erick M. Carreira|2023|J.Am.Chem.Soc.|145|27225|doi:10.1021/jacs.3c11000
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.cc2h5f15
title: CCDC 2299116: Experimental Crystal Structure Determination

description: Related Article: Subhradeep Kar, Prabhat K. Maharana, Tharmalingam Punniyamurthy, Vishal Trivedi|2023|Org.Lett.|25|8850|doi:10.1021/acs.orglett.3c03547
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.cc2h9241
title: CCDC 2302622: Experimental Crystal Structure Determination

description: Related Article: Riedel, Sophie, Gerwig, Maik, Gerlach, Daniela, Brendler, Erica, Gericke, Robert, Kroke, Edwin, Wagler, Jörg|2023|Inorganics|11|473|doi:10.3390/inorganics11120473
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.cc2h9252
title: CCDC 2302623: Experimental Crystal Structure Determination

description: Related Article: Riedel, Sophie, Gerwig, Maik, Gerlach, Daniela, Brendler, Erica, Gericke, Robert, Kroke, Edwin, Wagler, Jörg|2023|Inorganics|11|473|doi:10.3390/inorganics11120473
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.