cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10594875
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification.



genomad_db_v1.7.tar.gz: MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19.

genomad_hmm_v1.7.tar.gz: HMMER HMM models.

genomad_metadata_v1.7.tsv.gz: Metadata for all the 227,897 markers.

genomad_msa_v1.7.tar.gz: Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.14874465
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification.



genomad_db_v1.8.tar.gz: MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's MSL39.

genomad_hmm_v1.8.tar.gz: HMMER HMM models.

genomad_metadata_v1.8.tsv.gz: Metadata for all the 227,918 markers.

genomad_msa_v1.8.tar.gz: Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.14886553
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification.



genomad_db_v1.9.tar.gz: MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's MSL39.

genomad_hmm_v1.9.tar.gz: HMMER HMM models.

genomad_metadata_v1.9.tsv.gz: Metadata for all the 227,918 markers.

genomad_msa_v1.9.tar.gz: Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.6994741
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification.



genomad_db_v1.9.tar.gz: MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's MSL39.

genomad_hmm_v1.9.tar.gz: HMMER HMM models.

genomad_metadata_v1.9.tsv.gz: Metadata for all the 227,918 markers.

genomad_msa_v1.9.tar.gz: Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.6994742
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.0.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_metadata_v1.0.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.0.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.7084650
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.1.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_hmm_v1.1.tar.gz:</strong> HMMER HMM models. <strong>genomad_metadata_v1.1.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.1.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.7586412
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.2.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_hmm_v1.2.tar.gz:</strong> HMMER HMM models. <strong>genomad_metadata_v1.2.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.2.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.7793532
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.3.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_hmm_v1.3.tar.gz:</strong> HMMER HMM models. <strong>genomad_metadata_v1.3.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.3.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.8303752
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.4.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_hmm_v1.4.tar.gz:</strong> HMMER HMM models. <strong>genomad_metadata_v1.4.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.4.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.5281/zenodo.8339387
title: geNomad database

description: Marker database used by geNomad for virus and plasmid identification. <strong>genomad_db_v1.5.tar.gz:</strong> MMseqs2 profile database. Entries are decorated with taxonomy information from a custom taxdump based on ICTV's VMR number 19. <strong>genomad_hmm_v1.5.tar.gz:</strong> HMMER HMM models. <strong>genomad_metadata_v1.5.tsv.gz:</strong> Metadata for all the 227,897 markers. <strong>genomad_msa_v1.5.tar.gz:</strong> Multiple sequence alignments in FASTA format.
DOI: 10.15468/dl.52y46j
title: Occurrence Download

description: A dataset containing 221903 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "Geometry POLYGON((-72.57351 44.58993,-71.91684 44.56034,-71.97406 44.86856,-72.60169 44.89401,-72.57351 44.58993))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2007-2022"
 ]
}

 The dataset includes 221903 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000633-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dsd2xa
title: Occurrence Download

description: A dataset containing 8558 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Machlolophus spilonotus (Bonaparte, 1850)",
 "Year 2010-2020"
 ]
}

 The dataset includes 8558 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000645-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vhrjbn
title: Occurrence Download

description: A dataset containing 51508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Alcippe morrisonia Swinhoe, 1863",
 "Year 2010-2020"
 ]
}

 The dataset includes 51508 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000646-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11390559
title: Amphibole compositions record cold post-emplacement reequilibration in plutons

description: Amphibole compositions record cold post-emplacement reequilibration in plutons

Compiled and simulated data used in this study are included.

Description of the data and file structure

ds01_Whole_Rock_and_Temperatures_Pressures_Equilibrium_Melt_Compositions_Higgins2021.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature, pressure, and equilibrium melt compositions calculated by the method in Higgins et al. (2021).

ds02_Whole_Rock_and_Temperatures_Putirka2016_Pressures_Ridolfi2022_Equilibrium_Melt_Compositions_Zhang2017.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature (Putirka2016), pressure (Ridolfi2022), and equilibrium melt compositions calculated by the method in Zhang et al. (2017).

ds03_Whole_Rock_Composition_and_Temperatures_Pressures_Derived_from_Biotite_LiandZhang.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for biotite, and temperature and pressure calculated by the method in Li & Zhang (2022).

ds04_Simulation_Results_Using_Big_Dataset_as_Initial_Composition.csv.zip

    Simulated dataset using whole-rock compositions of GEOROC arc magmatic rocks as initial compositions calculated by Perple_X, including melt, solid, and whole-rock chemical compositions.

Sharing/Access information

Data was derived from the following sources:

*   GEOROC: [https://georoc.mpch-mainz.gwdg.de//georoc/](https://georoc.mpch-mainz.gwdg.de//georoc/)

Code/Software

The code for analyzing the compiled data, calculating the geothermobarometer, and generating and analyzing the simulated data can be found here:[https://github.com/Geokeke/HornblendeReequilibration](https://github.com/Geokeke/HornblendeReequilibration)
DOI: 10.5281/zenodo.11390558
title: Amphibole compositions record cold post-emplacement reequilibration in plutons

description: Amphibole compositions record cold post-emplacement reequilibration in plutons

Compiled and simulated data used in this study are included.

Description of the data and file structure

ds01_Whole_Rock_and_Temperatures_Pressures_Equilibrium_Melt_Compositions_Higgins2021.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature, pressure, and equilibrium melt compositions calculated by the method in Higgins et al. (2021).

ds02_Whole_Rock_and_Temperatures_Putirka2016_Pressures_Ridolfi2022_Equilibrium_Melt_Compositions_Zhang2017.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature (Putirka2016), pressure (Ridolfi2022), and equilibrium melt compositions calculated by the method in Zhang et al. (2017).

ds03_Whole_Rock_Composition_and_Temperatures_Pressures_Derived_from_Biotite_LiandZhang.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for biotite, and temperature and pressure calculated by the method in Li & Zhang (2022).

ds04_Simulation_Results_Using_Big_Dataset_as_Initial_Composition.csv.zip

    Simulated dataset using whole-rock compositions of GEOROC arc magmatic rocks as initial compositions calculated by Perple_X, including melt, solid, and whole-rock chemical compositions.

Sharing/Access information

Data was derived from the following sources:

*   GEOROC: [https://georoc.mpch-mainz.gwdg.de//georoc/](https://georoc.mpch-mainz.gwdg.de//georoc/)

Code/Software

The code for analyzing the compiled data, calculating the geothermobarometer, and generating and analyzing the simulated data can be found here:[https://github.com/Geokeke/HornblendeReequilibration](https://github.com/Geokeke/HornblendeReequilibration)
DOI: 10.5281/zenodo.14889279
title: Amphibole compositions record cold post-emplacement reequilibration in plutons

description: Amphibole compositions record cold post-emplacement reequilibration in plutons

Compiled and simulated data used in this study are included.

Description of the data and file structure

ds01_Whole_Rock_and_Temperatures_Pressures_Equilibrium_Melt_Compositions_Higgins2021.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature, pressure, and equilibrium melt compositions calculated by the method in Higgins et al. (2021).

ds02_Whole_Rock_and_Temperatures_Putirka2016_Pressures_Ridolfi2022_Equilibrium_Melt_Compositions_Zhang2017.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for hornblende, and temperature (Putirka2016), pressure (Ridolfi2022), and equilibrium melt compositions calculated by the method in Zhang et al. (2017).

ds03_Whole_Rock_Composition_and_Temperatures_Pressures_Derived_from_Biotite_LiandZhang.csv.zip

    Includes whole-rock compositional data for magmatic rocks (volcanic and intrusive) compiled from the GEOROC database and other published data, chemical compositional data for biotite, and temperature and pressure calculated by the method in Li & Zhang (2022).

ds04_Simulation_Results_Using_Big_Dataset_as_Initial_Composition.csv.zip

    Simulated dataset using whole-rock compositions of GEOROC arc magmatic rocks as initial compositions calculated by Perple_X, including melt, solid, and whole-rock chemical compositions.

Sharing/Access information

Data was derived from the following sources:

*   GEOROC: [https://georoc.mpch-mainz.gwdg.de//georoc/](https://georoc.mpch-mainz.gwdg.de//georoc/)

Code/Software

The code for analyzing the compiled data, calculating the geothermobarometer, and generating and analyzing the simulated data can be found here:[https://github.com/Geokeke/HornblendeReequilibration](https://github.com/Geokeke/HornblendeReequilibration)
DOI: 10.17632/kmxyfwj8g6.1
title: Phototherapy for cutaneous mastocytosis: A systematic review

description: Supplemental material for "Phototherapy for cutaneous mastocytosis: A systematic review"
DOI: 10.17632/h7vbmkc4wn
title: Tl isotope data for "Experimental verification of nuclear field shift effect using thallium isotopes"

description: Data for the manuscript:
Lu. S, Payne. J, Evans. D, Kelly. J, Switalska. E, De Los Reyes. M. "Experimental verification of nuclear field shift effect using thallium isotopes". 2025. submitted to GCA.
DOI: 10.17632/h7vbmkc4wn.1
title: Tl isotope data for "Experimental verification of nuclear field shift effect using thallium isotopes"

description: Data for the manuscript:
Lu. S, Payne. J, Evans. D, Kelly. J, Switalska. E, De Los Reyes. M. "Experimental verification of nuclear field shift effect using thallium isotopes". 2025. submitted to GCA.
DOI: 10.17632/w67yn3fhw7.1
title: Spanish Cultural Sites & Facilities

description: The Spanish Cultural Sites & Facilities dataset provides geospatial and descriptive information about cultural facilities across Spain, including theaters, museums, libraries, cultural centers, and other cultural venues. The dataset consists of 24,577 records and 28 attributes, capturing key details about each facility, such as its name, classification, location, contact information, and associated institutions.

This dataset is valuable for spatial analysis, urban planning, cultural heritage management, and public policy research. It enables researchers, policymakers, and cultural organizations to analyze the distribution, accessibility, and characteristics of cultural facilities across Spain.

The dataset is stored in GeoPackage (.gpkg) format, ensuring efficient geospatial data storage and compatibility with GIS software such as QGIS, ArcGIS, and other geospatial tools.

This dataset has been developed by Econcult, the Research Group on Cultural Economics at the University of Valencia, through the collection, cleaning, and processing of data from multiple sources of information.
DOI: 10.6084/m9.figshare.28439688
title: DDE_Find.zip from DDE-Find: learning delay differential equations from noisy, limited data

description: Delay differential equations (DDEs) are a class of differential equations that can model diverse scientific phenomena. However, identifying the parameters, especially the time delay, that make a DDE’s predictions match experimental results can be challenging. We introduce DDE-Find, a data-driven framework for learning a DDE’s parameters, time delay and initial condition function. DDE-Find uses an adjoint-based approach to efficiently compute the gradient of a loss function with respect to the model parameters. We motivate and rigorously prove an expression for the gradients of the loss using the adjoint. DDE-Find builds upon recent developments in learning DDEs from data and delivers the first complete framework for learning DDEs from data. Through a series of numerical experiments, we demonstrate that DDE-Find can learn DDEs from noisy, limited data.
DOI: 10.6084/m9.figshare.28439688.v1
title: DDE_Find.zip from DDE-Find: learning delay differential equations from noisy, limited data

description: Delay differential equations (DDEs) are a class of differential equations that can model diverse scientific phenomena. However, identifying the parameters, especially the time delay, that make a DDE’s predictions match experimental results can be challenging. We introduce DDE-Find, a data-driven framework for learning a DDE’s parameters, time delay and initial condition function. DDE-Find uses an adjoint-based approach to efficiently compute the gradient of a loss function with respect to the model parameters. We motivate and rigorously prove an expression for the gradients of the loss using the adjoint. DDE-Find builds upon recent developments in learning DDEs from data and delivers the first complete framework for learning DDEs from data. Through a series of numerical experiments, we demonstrate that DDE-Find can learn DDEs from noisy, limited data.
DOI: 10.17632/29txvz235y.1
title: Prediction of the best alternative for cloud network services delivering using fractals variables and super-efficiency DEA models for time-series data comparison

description: This dataset is based on active measurements obeying RFC 2544 and RFC 6815, forming 150 tunneled virtual networks to be compared. The virtual networks were assembled using the VXLAN protocol in an IaaS within DevStack (ALL-IN-ONE) running on a VM in VirtualBox on an Ubuntu 20.4 operating system of a computer with an Intel i7 7500U processor, 2.9GHz clock, and 16GB of RAM. OpenStack-Neutron with virtual switch SDN (OpenvSwitch) was used to interconnect the VMs under analysis. The benchmarking tools iperf, VBoxManage, and lscpu (to capture the temperature of each core) were used for active measurements. In the experiments, two Guest VMs were utilized, with one serving as a traffic generator (TG) and the other as a device under test (DUT), but the DUT runs within a Docker container acting as a server. In the DUT, we varied the number of vCPUs (1,2,4), the amount of vRAM (512MB, 1GB, 2GB, 4GB, and 8GB), and the fifteen Linux TCP congestion control algorithms (BIC, BBR, CDG, CUBIC, DCTCP, ILLINOIS, HYBLA, HTCP, LP, NV, VEGAS, VENO, SCALABLE, WESTWOOD, and YEAH) making up the 150 decision-making units (DMU). The virtual networks (DMU) were ranked using the super-efficiency model of data envelopment analysis (DEA) with variable return to scale and input-oriented. The decision variables listed for ranking were: a) inputs – X1) TCP bandwidth fractal dimension; X2) core0 fractal dimension, X3) core1 fractal dimension, X4) core0 temperature average, and X5) core1 temperature average, and b) outputs– Y1) core 0 hurst, Y2) core 1 Hurst, Y3) TCP bandwidth average, and Y4) TCP bandwidth Hurst. All data per DMU were separated by a folder containing their respective time series. Thus, we conclude that the correct DMU was chosen to provide optimal virtual network services over time. All scripts used to extract decision variables are available in this dataset. Calculating fractal dimensions was madogram and the Hurst parameter was calculated using R/S.
DOI: 10.17632/29txvz235y.2
title: Prediction of the best alternative for cloud network services delivering by using fractals variables and super-efficiency DEA models for time-series data comparison

description: This dataset is based on active measurements obeying RFC 2544 and RFC 6815, forming 150 tunneled virtual networks to be compared. The virtual networks were assembled using the VXLAN protocol in an IaaS within DevStack (ALL-IN-ONE) running on a VM in VirtualBox on an Ubuntu 20.4 operating system of a computer with an Intel i7 7500U processor, 2.9GHz clock, and 16GB of RAM. OpenStack-Neutron with virtual switch SDN (OpenvSwitch) was used to interconnect the VMs under analysis. The benchmarking tools iperf, VBoxManage, and lscpu (to capture the temperature of each core) were used for active measurements. In the experiments, two Guest VMs were utilized, with one serving as a traffic generator (TG) and the other as a device under test (DUT), but the DUT runs within a Docker container acting as a server. In the DUT, we varied the number of vCPUs (1,2,4), the amount of vRAM (512MB, 1GB, 2GB, 4GB, and 8GB), and the fifteen Linux TCP congestion control algorithms (BIC, BBR, CDG, CUBIC, DCTCP, ILLINOIS, HYBLA, HTCP, LP, NV, VEGAS, VENO, SCALABLE, WESTWOOD, and YEAH) making up the 150 decision-making units (DMU). The virtual networks (DMU) were ranked using the super-efficiency model of data envelopment analysis (DEA) with variable return to scale and input-oriented. The decision variables listed for ranking were: a) inputs – X1) TCP bandwidth fractal dimension; X2) core0 fractal dimension, X3) core1 fractal dimension, X4) core0 temperature average, and X5) core1 temperature average, and b) outputs– Y1) core 0 hurst, Y2) core 1 Hurst, Y3) TCP bandwidth average, and Y4) TCP bandwidth Hurst. All data per DMU were separated by a folder containing their respective time series. Thus, we conclude that the correct DMU was chosen to provide optimal virtual network services over time. All scripts used to extract decision variables are available in this dataset. Calculating fractal dimensions was madogram and the Hurst parameter was calculated using R/S.
DOI: 10.17632/zshy6vbx37.1
title: Cutaneous Melanoma Trends and Disparities in Florida: A Decade of Incidence, Mortality, and Workforce Data

description: Supplementary Figure 1. Cutaneous Melanoma Age-adjusted Incidence Trends (2012-2021)
Supplementary Figure 2. Cutaneous Melanoma Age-adjusted Mortality Trends (2013-2022)
Journal of the American Academy of Dermatology
DOI: 10.17632/ctytmbbn29.1
title: The effect of seasonal variation in common inter-tidal UK macroalgae wrack extracts on model plant seedling growth and soil respirometry

description: Data relating to submitted manuscript "The effect of seasonal variation in common inter-tidal UK macroalgae wrack extracts on model plant seedling growth and soil respirometry"