cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7689733
title: Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling: Supplementary Model Files

description: Supplementary code and model files for the manuscript entitled "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling". OlDoinyoLengai_code_and_models.zip contains all necessary Matlab code, functions, input and output files for the GNSS, InSAR, and joint inversions presented in our manuscript necessary to reproduce the results. dMODELS is an open source code developed by the United States Geological Survey. The originally published program is available here: https://pubs.usgs.gov/tm/13/b1/. With this manuscript we are providing an update to dMODELS that includes improved graphics and joint inversion capabilities for both InSAR and GNSS data.
description: This work was funded by the National Science Foundation (NSF) grant number EAR-1943681, Virginia Tech, Korean Institute of Geosciences and Minerals (KIGAM), and Ardhi University. Funding for this work also came from USAID via the Volcano Disaster Assistance Program and from the U.S. Geological Survey (USGS) Volcano Hazards Program.This material is based on services provided by the GAGE Facility, operated by UNAVCO, Inc., with support from the National Science Foundation, the National Aeronautics and Space Administration, and the U.S. Geological Survey under NSF Cooperative Agreement EAR-1724794. We acknowledge and thank Alaska Satellite Facility for making InSAR data freely available and TZVOLCANO GNSS data sets available through the UNAVCO data archive.
DOI: 10.5281/zenodo.7718257
title: Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling: Supplementary Model Files

description: Supplementary code and model files for the manuscript entitled "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling". OlDoinyoLengai_code_and_models.zip contains all necessary Matlab code, functions, input and output files for the GNSS, InSAR, and joint inversions presented in our manuscript necessary to reproduce the results. dMODELS is an open source code developed by the United States Geological Survey. The originally published program is available here: https://pubs.usgs.gov/tm/13/b1/ and the revised software archived here will also be available through the USGS website code.usgs.gov/vsc/publications/OlDoinyoLengai or by contacting Maurizio Battaglia. With this manuscript we are providing an update to dMODELS that includes improved graphics and joint inversion capabilities for both InSAR and GNSS data.
description: This work was funded by the National Science Foundation (NSF) grant number EAR-1943681, Virginia Tech, Korean Institute of Geosciences and Minerals (KIGAM), and Ardhi University. Funding for this work also came from USAID via the Volcano Disaster Assistance Program and from the U.S. Geological Survey (USGS) Volcano Hazards Program.This material is based on services provided by the GAGE Facility, operated by UNAVCO, Inc., with support from the National Science Foundation, the National Aeronautics and Space Administration, and the U.S. Geological Survey under NSF Cooperative Agreement EAR-1724794. We acknowledge and thank Alaska Satellite Facility for making InSAR data freely available and TZVOLCANO GNSS data sets available through the UNAVCO data archive.
DOI: 10.5281/zenodo.7897255
title: Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling: Supplementary Model Files

description: Supplementary code and model files for the manuscript entitled "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling". OlDoinyoLengai_code_and_models.zip contains all necessary Matlab code, functions, input and output files for the GNSS, InSAR, and joint inversions presented in our manuscript necessary to reproduce the results. dMODELS is an open source code developed by the United States Geological Survey. The originally published program is available here: https://pubs.usgs.gov/tm/13/b1/ and the revised software archived here will also be available through the USGS website code.usgs.gov/vsc/publications/OlDoinyoLengai or by contacting Maurizio Battaglia. With this manuscript we are providing an update to dMODELS that includes improved graphics and joint inversion capabilities for both InSAR and GNSS data.
description: This work was funded by the National Science Foundation (NSF) grant number EAR-1943681, Virginia Tech, Korean Institute of Geosciences and Minerals (KIGAM), and Ardhi University. Funding for this work also came from USAID via the Volcano Disaster Assistance Program and from the U.S. Geological Survey (USGS) Volcano Hazards Program.This material is based on services provided by the GAGE Facility, operated by UNAVCO, Inc., with support from the National Science Foundation, the National Aeronautics and Space Administration, and the U.S. Geological Survey under NSF Cooperative Agreement EAR-1724794. We acknowledge and thank Alaska Satellite Facility for making InSAR data freely available and TZVOLCANO GNSS data sets available through the UNAVCO data archive.
DOI: 10.6084/m9.figshare.22766321
title: Supplementary A: GBS Sample Data from Integrating kelp genomic analyses and geological data to reveal ancient earthquake impacts

description: Detached buoyant kelp can disperse thousands of kilometres at sea and can colonize newly available shores in the wake of disturbances that wipe out competitors. Localized earthquake uplift can cause extirpation of intertidal kelp populations followed by recolonization. Sources of recolonizing kelp can be detectable in genomic structure of contemporary populations. Our field observations combined with LiDAR mapping identified a previously unrecognized zone of uplifted rocky coastline in a region that is slowly subsiding. Intertidal kelp (<i>Durvillaea antarctica</i>) on the uplifted section of coast is genetically distinctive from nearby populations, with genomic signatures most similar to that of kelp 300 km to the south. Genetic divergence between these locations suggests reproductive isolation for thousands of years. Combined geological and genetic data suggest that this uplift event occurred during one of four major earthquakes between 6000 and 2000 years ago, with one of the younger events most likely. Extirpation of the pre-existing kelp required sudden uplift of approximately 2 metres, precluding several small incremental uplift events. Our results show the power of integrating biological (genomic) analyses with geological data to understand ancient geological processes and their ecological impacts.
DOI: 10.6084/m9.figshare.22766321.v1
title: Supplementary A: GBS Sample Data from Integrating kelp genomic analyses and geological data to reveal ancient earthquake impacts

description: Detached buoyant kelp can disperse thousands of kilometres at sea and can colonize newly available shores in the wake of disturbances that wipe out competitors. Localized earthquake uplift can cause extirpation of intertidal kelp populations followed by recolonization. Sources of recolonizing kelp can be detectable in genomic structure of contemporary populations. Our field observations combined with LiDAR mapping identified a previously unrecognized zone of uplifted rocky coastline in a region that is slowly subsiding. Intertidal kelp (<i>Durvillaea antarctica</i>) on the uplifted section of coast is genetically distinctive from nearby populations, with genomic signatures most similar to that of kelp 300 km to the south. Genetic divergence between these locations suggests reproductive isolation for thousands of years. Combined geological and genetic data suggest that this uplift event occurred during one of four major earthquakes between 6000 and 2000 years ago, with one of the younger events most likely. Extirpation of the pre-existing kelp required sudden uplift of approximately 2 metres, precluding several small incremental uplift events. Our results show the power of integrating biological (genomic) analyses with geological data to understand ancient geological processes and their ecological impacts.
DOI: 10.6084/m9.figshare.21084829.v23
title: List of native-alien populations in South Africa

description: Native-alien population is defined as a population that is within a country to which the species is native and founded by individuals moved by direct human agency (or substantial indirect human agency) over a biogeographical barrier to an area beyond the species’ native range (Nelufule et al. 2022). This dataset followed the data structure used for the species list of South Africa’s second national status report on biological invasions ‘The status of biological invasions and their management in South Africa in 2019' (Zengeya and Wilson 2020). This represents the first inventory of native-alien populations in South Africa, and is a step towards a greater understanding of native-alien populations and the biosecurity threat they pose. Reference Nelufule T, Robertson MP, Wilson JRU, Faulkner KT (2022) Native-alien populations—an apparent oxymoron that requires specific conservation attention. <em>NeoBiota</em> 74: 57–74, https://doi.org/10.3897/neobiota.74.81671 Zengeya, T. A. & Wilson, J. R. U. (eds.). The status of biological invasions and their management in South Africa in 2019. pp.71. South African National Biodiversity Institute, Kirstenbosch and DSI-NRFCentre of Excellence for Invasion Biology, Stellenbosch. (2020).
DOI: 10.23701/sr.0001
title: EPOS horizontal strain rates – preliminary version

description: Strain rates calculated with STRAINTOOL (https://dsolab.github.io/StrainTool/) from the EPOS velocity field EPND D2100 provided by the EUREF Permanent Network Densification (Kenyeres et al., 2019), see https://epnd.sgo-penc.hu/, for more information.
DOI: 10.23701/sr.0002
title: EPOS horizontal strain rates – intermediate version

description: Strain rates calculated with STRAINTOOL (https://dsolab.github.io/StrainTool/) from the EPOS velocity field EPND D2200 provided by the EUREF Permanent Network Densification (Kenyeres et al., 2019), see https://epnd.sgo-penc.hu/, for more information.
DOI: 10.6084/m9.figshare.22766339
title: Data from Towards the generation of gnotobiotic larvae as a tool to investigate the influence of the microbiome on the development of the amphibian immune system

description: Data used in figures and analysis for this study
DOI: 10.6084/m9.figshare.22766339.v1
title: Data from Towards the generation of gnotobiotic larvae as a tool to investigate the influence of the microbiome on the development of the amphibian immune system

description: Data used in figures and analysis for this study
DOI: 10.5281/zenodo.7897289
title: Data for "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023)

description: Data used in the paper "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023). All code for the paper can be found at https://github.com/epiforecasts/uk-crowd-forecasting-challenge.
DOI: 10.5281/zenodo.7897288
title: Data for "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023)

description: Data used in the paper "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023). All code for the paper can be found at https://github.com/epiforecasts/uk-crowd-forecasting-challenge.
DOI: 10.5281/zenodo.7871012
title: Keeping Up to Date With P4Runtime: An Analysis of Data Plane Updates on P4 Switches

description: This dataset contains the measurement scripts and the results of their execution for the paper “Keeping up to Date with P4Runtime: An Analysis of Data Plane Updates on P4 Switches”. For post-processing, the script <code>post-processing.py</code> was used. Its execution results in CSV suitable for inclusion in tikz figures as included in the paper. In the paper, the following experiment runs are considered: experiment runs figures <code>2022-08-19_11-59-49_801994</code> 01, 05, 09 5 <code>2022-08-19_21-19-07_490218</code> 01, 05, 09 6 <code>2022-08-20_10-28-26_900864</code> 1 4, 5 <code>2023-01-28_16-54-28_462097</code> 0 4 <code>2023-01-28_17-51-17_645739</code> 1 4 <code>2023-01-28_19-15-05_255922</code> 0 4 <code>2023-01-28_20-10-43_259639</code> 1 4 <code>2023-01-29_17-50-08_687660</code> 1 4 <em>Note</em>: Only a subset of capture CSV <em>not</em> discussed in the paper are included, to reduce the dataset size limit.
DOI: 10.5281/zenodo.7871013
title: Keeping Up to Date With P4Runtime: An Analysis of Data Plane Updates on P4 Switches

description: This dataset contains the measurement scripts and the results of their execution for the paper “Keeping up to Date with P4Runtime: An Analysis of Data Plane Updates on P4 Switches”. For post-processing, the script <code>post-processing.py</code> was used. Its execution results in CSV suitable for inclusion in tikz figures as included in the paper. In the paper, the following experiment runs are considered: experiment runs figures <code>2022-08-19_11-59-49_801994</code> 01, 05, 09 5 <code>2022-08-19_21-19-07_490218</code> 01, 05, 09 6 <code>2022-08-20_10-28-26_900864</code> 1 4, 5 <code>2023-01-28_16-54-28_462097</code> 0 4 <code>2023-01-28_17-51-17_645739</code> 1 4 <code>2023-01-28_19-15-05_255922</code> 0 4 <code>2023-01-28_20-10-43_259639</code> 1 4 <code>2023-01-29_17-50-08_687660</code> 1 4 <em>Note</em>: Only a subset of capture CSV <em>not</em> discussed in the paper are included, to reduce the dataset size limit.
DOI: 10.5281/zenodo.7896819
title: Keeping Up to Date With P4Runtime: An Analysis of Data Plane Updates on P4 Switches

description: This dataset contains the measurement scripts and the results of their execution for the paper “Keeping up to Date with P4Runtime: An Analysis of Data Plane Updates on P4 Switches”. For post-processing, the script <code>post-processing.py</code> was used. Its execution results in CSV suitable for inclusion in tikz figures as included in the paper. In the paper, the following experiment runs are considered: experiment runs figures <code>2022-08-19_11-59-49_801994</code> 01, 05, 09 5 <code>2022-08-19_21-19-07_490218</code> 01, 05, 09 6 <code>2022-08-20_10-28-26_900864</code> 1 4, 5 <code>2023-01-28_16-54-28_462097</code> 0 4 <code>2023-01-28_17-51-17_645739</code> 1 4 <code>2023-01-28_19-15-05_255922</code> 0 4 <code>2023-01-28_20-10-43_259639</code> 1 4 <code>2023-01-29_17-50-08_687660</code> 1 4 <em>Note</em>: Only a subset of capture CSV <em>not</em> discussed in the paper are included, to reduce the dataset size limit.
DOI: 10.34740/kaggle/dsv/5603605
title: Balochi Language to English Translation Dataset
DOI: 10.6084/m9.figshare.22766372
title: submission-code-rsi-nodata.zip;Main_FIles_(Colored-Text).zip;Main_Files_(FinalVersion).zip from Steering herds away from dangers in dynamic environments

description: Zip file containing Python code to reproduce the experiments.;Zip file containing LaTeX source files (tex code and figures) where edited text is highlighted in red (corresponding to reviewers' comments).;Zip file containing LaTeX source files (tex code and figures) of the 'clean' version of the new manuscript that incorporates the changes made, but does not highlight them
DOI: 10.6084/m9.figshare.22766372.v1
title: submission-code-rsi-nodata.zip;Main_FIles_(Colored-Text).zip;Main_Files_(FinalVersion).zip from Steering herds away from dangers in dynamic environments

description: Zip file containing Python code to reproduce the experiments.;Zip file containing LaTeX source files (tex code and figures) where edited text is highlighted in red (corresponding to reviewers' comments).;Zip file containing LaTeX source files (tex code and figures) of the 'clean' version of the new manuscript that incorporates the changes made, but does not highlight them
DOI: 10.7281/t1/pxcqcc/1uouae
title: 03_forcing_swrad.nc

description: This file contains the downwelling shortwave radiation input data used to force MOM5 for the sward perturbation simulation.
DOI: 10.7281/t1/pxcqcc/d97n52
title: 02_forcing_precip.nc

description: This file contains the precipitation input data used to force MOM5 for the precip perturbation simulation.
DOI: 10.7281/t1/pxcqcc/hrqf7d
title: 05_control_output_timeseries_data.nc

description: This file contains the output data from the MOM5 control simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/yoxlbh
title: 01_forcing_wind.nc

description: This file contains the wind input data used to force MOM5 for the wind perturbation simulation.
DOI: 10.7281/t1/pxcqcc/ccl0xz
title: 10_all_output_timeseries_data.nc

description: This file contains the output data from the MOM5 simulation perturbing all fields used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/hya3qh
title: 08_swrad_output_timeseries_data.nc

description: This file contains the output data from the MOM5 downwelling shortwave radiation perturbation simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/rgkqen
title: 06_wind_output_timeseries_data.nc

description: This file contains the output data from the MOM5 wind perturbation simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/ykkfkp
title: 12_control_output_profile_data.nc

description: This file contains the output data from the MOM5 control simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/zd3tdb
title: 11_control_output_surface_data.nc

description: This file contains the output data from the MOM5 control simulation used to create the surface plots in the publication.
DOI: 10.7281/t1/pxcqcc/zej2q5
title: 07_precip_output_timeseries_data.nc

description: This file contains the output data from the MOM5 precipitation perturbation simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/c6lcet
title: 14_wind_output_profile_data.nc

description: This file contains the output data from the MOM5 wind perturbation simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/czbeu0
title: 09_lwcre_output_timeseries_data.nc

description: This file contains the output data from the MOM5 lwcre simulation used to create the timeseries plots in the publication.
DOI: 10.7281/t1/pxcqcc/nbo9vi
title: 13_wind_output_surface_data.nc

description: This file contains the output data from the MOM5 wind perturbation simulation used to create the surface plots in the publication.
DOI: 10.7281/t1/pxcqcc/xq0uch
title: 04_forcing_lwcre.nc

description: This file contains the downwelling longwave cloud radiative effect input data used to force MOM5 for the lwcre perturbation simulation.
DOI: 10.25345/c5bg2hm3x
title: MassIVE MSV000091858 - OSMAC - Untargeted metabolomics of Streptomyces sp. 11-1-2
title: OSMAC approach in plant pathogenic Streptomyces sp. 11-1-2
DOI: 10.5281/zenodo.7834043
title: Data for the study of Posidonia microbial nitrogen metabolism manuscript

description: These are data related to the study of the seagrass microbiome and function. <em>Posidonia oceanica</em>, an endemic seagrass species that dominates the Mediterranean Sea, achieves high abundances in seawater with relatively low concentrations of dissolved inorganic nitrogen. Here we test whether microbial metabolisms associated with <em>P. oceanica</em> and surrounding seawater enhance seagrass access to nitrogen. Using stable isotope enrichments of intact seagrass with amino acids, we show that ammonification by free-living and seagrass-associated microbes produce ammonium that is likely used by seagrass and surrounding particulate organic matter. Metagenomic analysis of the epiphytic biofilm on the blades and rhizomes support the ubiquity of microbial ammonification genes in this system. Further, we leveraged the presence of natural carbon dioxide vents and show that microbial ammonification was reduced at lower pH. The presence of <em>P. oceanica</em> enhanced the uptake of nitrogen by water column particulate organic matter, increasing carbon fixation by a factor of 8.6 to 17.4 with the greatest effect at CO<sub>2</sub> vent sites. Seagrass and its microbial associates thus enhanced water column productivity and were a locus for nutrient cycling.
DOI: 10.5281/zenodo.7834042
title: Data for the study of Posidonia microbial nitrogen metabolism manuscript

description: These are data related to the study of the seagrass microbiome and function. <em>Posidonia oceanica</em>, an endemic seagrass species that dominates the Mediterranean Sea, achieves high abundances in seawater with relatively low concentrations of dissolved inorganic nitrogen. Here we test whether microbial metabolisms associated with <em>P. oceanica</em> and surrounding seawater enhance seagrass access to nitrogen. Using stable isotope enrichments of intact seagrass with amino acids, we show that ammonification by free-living and seagrass-associated microbes produce ammonium that is likely used by seagrass and surrounding particulate organic matter. Metagenomic analysis of the epiphytic biofilm on the blades and rhizomes support the ubiquity of microbial ammonification genes in this system. Further, we leveraged the presence of natural carbon dioxide vents and show that microbial ammonification was reduced at lower pH. The presence of <em>P. oceanica</em> enhanced the uptake of nitrogen by water column particulate organic matter, increasing carbon fixation by a factor of 8.6 to 17.4 with the greatest effect at CO<sub>2</sub> vent sites. Seagrass and its microbial associates thus enhanced water column productivity and were a locus for nutrient cycling.
DOI: 10.7281/t1/k3onn5/r2i1x5
title: Macromolecule data original folder zhang katz macromolecules 2018.rar

description: Raw data supporting each figure in the manuscript. Measured data points producing plots of morphology and electronic properties.
DOI: 10.5281/zenodo.7197150
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any of this reproduction package for independent work, we recommend you cite the following papers:<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7197506
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any of this reproduction package for independent work, we recommend you cite the following papers:<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md