cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4225/08/5701c885ab4fe
title: Multi-resolution Valley Bottom Flatness (MrVBF)

description: MrVBF is a topographic index designed to identify areas of deposited material at a range of scales based on the observations that valley bottoms are low and flat relative to their surroundings and that large valley bottoms are flatter than smaller ones. Zero values indicate erosional terrain with values 1 and larger indicating progressively larger areas of deposition. There is some evidence that MrVBF values correlate with depth of deposited material.

This collection includes 1 arc-second and 3 arc-second resolution versions of MrVBF.

The 3 arc-second resolution product was generated from the 1 arc-second MrVBF product and masked by the 3” water and ocean mask datasets.
DOI: 10.5067/aura/mls/data2516
title: MLS/Aura Level 2 Ozone (O3) Mixing Ratio V005
DOI: 10.5067/caliop/calipso/cal_lid_l2_pscmask-standard-v2-00
title: CALIPSO Lidar Level 2 Polar Stratospheric Clouds presents, composition, and optical properties, V2-00
DOI: 10.5067/cx2b9nw6fi27
title: OMPS-NPP L2 LP Aerosol Extinction Vertical Profile swath daily 3slit V2
DOI: 10.5067/hm00ohqbhktp
title: MERRA-2 tavg1_2d_adg_Nx: 2d,3-Hourly,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics (extended) V5.12.4
DOI: 10.5067/qbz6mg944hw0
title: MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4
DOI: 10.5281/zenodo.11127899
title: Characterizing Inter-Annual/Seasonal Dust Deposition and Removal on Mars Using Thermal Emission Imaging System (THEMIS) Infrared Data

description: Included in the zipped data file are two subfolders that contain the following:



KRC thermal model look up table data used in the work

Data for figures within the paper


Each subfolder includes a text file with helpful information for interpreting the data.
DOI: 10.5281/zenodo.11557287
title: Simulated Data for Reid and Marshall, 2024

description: Simulated data from Geant4 used to create the Figures in Reid and Marshall, 2024
DOI: 10.5281/zenodo.11657501
title: Supplemental Data: Rapid Automated Mapping of Clouds on Titan with Instance Segmentation

description: Supplemental data provided in conjuction with paper submitted to AGU JGR: Machine Learning and Computation.
DOI: 10.5281/zenodo.13486390
title: Reconstructed net primary productivity of marine sediment cores MD98-2152, BAR94-24, MD85-666, and MD98-2178
DOI: 10.5281/zenodo.13993647
title: Idealized Baroclinic Wave Test Results

description: Dataset containing results of an idealized baroclinic wave test simulation. Initial condition from Bouvier et al. (2023). Model from Bonev et al. (2023). 



Variables: t2m (two-meter temperature), tcwv (total column water vapor), msl (mean sea-level pressure), u10m (10 meter u-component of wind)

Times: timesteps 0-21 at 6 hour intervals

Lat/Lon: Global (721 lat x 1440 lon)
DOI: 10.5281/zenodo.13994330
title: ACE AMIP climatology

description: This dataset contains the seasonaly climatology from an AMIP run using ACE and PCMDI SST and sea ice forcings.
DOI: 10.5281/zenodo.14201597
title: Supporting material for Petricca et al. (2024), "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets

description: This archive contains the supplementary material for the paper "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets

Content of the dataset:



Synthetic gravity fields for Ariel and Titania generated in the study

SPICE kernels of the trajectory of the Uranus Orbiter and Probe designed at JPL


 

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Synthetic gravity fields

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The gravity fields are generated following the procedures described in Section 2.1.2 of the main paper. The hydrosphere thickness is assumed to be 190 km and 220 km for Ariel and Titania, respectively. The ocean density is fixed at 1050 kg/m^3. The syntethic topography is generated with pyshtools (Wieczorek and Meschede, 2018). The label of the file indicates the amplitude of the topography of each interface (ice shell or ocean floor) and the maximum degree of the spherical harmonics expansion. The files are formatted according to the Spherical Harmonics ASCII Data Record (SHADR) standard.

The header of each file contains: reference radius (km), GM (km^3 / s^2), uncertatinty in the GM (not used and set to zero), maximum degree l of the expansion, maximum order m of the expansion, normalization (0 for unnormalized, 1 for 4pi normalization), reference latitude, reference longitude

The columns contain: degree l, order m, coefficient C_lm, coefficient S_lm

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UOP trajectories

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The reference positions and velocities of the UOP were generated by Damon Landau (JPL) as part of an internal study at JPL. These initial positions and velocities were numerically integrated by Flavio Petricca (JPL) using the dynamical models described in the main paper. For this reason, the trajectories only cover +- 8 hours from closest approach with each moon and not the entire tour.

The ID of the spacecraft is set to -999. The simple text kernel provided here (id_name_map.txt) can be loaded in the kernel pool to associate the ID code with the SPICE names 'URANUS ORBITER PROBE' and 'UOP' for a more explicit and user-friendly access to the trajectories.
DOI: 10.5281/zenodo.14880189
title: Limits on the efficacy of wave-particle interaction on the energization and transport of atomic and molecular heavy ionospheric ions

description: This repository contains the simulation data presented in 2024JA033523R submitted to JGRSP.  Each zip file contains the cases presented in the study: 



point5-8.zip: Case A

1point7-8.zip: Case B

1point1-5.zip: Case C

1point7-5.zip: Case D


Each .zip folder contains



Ion distribution functions ('DistFunc_xx') from 1,000 to 4,000 km altitude

Ion density, velocity, and temperature: north_plots_iline0001.out

Energy source files: 'north_Energy' for PIC scheme and 'north_energy_low_altitude' for hydrodynamic scheme

production/loss: 'north_ion_source_loss'
DOI: 10.5281/zenodo.2575599
title: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA

description: <strong>Related paper containing detailed description</strong>: <strong>Moesinger et al. (2020)</strong> Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management. We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them. There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files. Currently there is an issue with opening the file using ESA SNAP. As an alternative Panoply can be used to quickly visualize the data. An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020. <strong>Please contact us if you have any questions, problems or suggestions for improvement!</strong> <strong>Files:</strong> "VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): VODCA C-band files, sorted into yearly folders "VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): VODCA X-band files, sorted into yearly folders "VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : VODCA Ku-band files, sorted into yearly folders "vodca_v01-0_K-band_2007-06-01.nc" sample file of the Ku-band product "ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid <strong>Variables of data in VODCA files:</strong> "VOD": Unitless, Vegetation Optical Depth of the respective band "sensor_flag": Bit-flag indicating which sensors contributed to each observation. Values: 1 = AMSR-E 2 = AMSR2 3 = SSM/I F8 4 = SSM/I F11 5 = SSM/I F13 6 = TMI 7 = WindSat "processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations Values: 0 = Everything is fine 10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz 11 = Sensor is scaled to matched TMI instead of AMSR-E 12 = Sensor scaled without temporally overlapping observations "time"/"lon"/"lat": Dimensions of the data.
DOI: 10.5281/zenodo.8063488
title: LGM simulations based on AWIESM

description: This dataset gives the raw model outputs from a pre-industrial (PI) and several Last Glacial Maximum (LGM) sensitivity simulations based on AWIESM. A more detailed description of the individual file is presented below: PI.tar.gz: model outputs for PI simulation. LGM-full-forced.tar.gz: model outputs for full forced LGM simulation. LGM-with-PI-greenhousegas.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the greenhouse gases are the same as for PI. LGM-with-PI-icesheets.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the ice sheets are the same as for PI. LGM-with-PI-orbital.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the orbital parameters are the same as for PI. LGM-with-PI-greenhousegas-and-icesheets.nctar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the greenhouse gases and ice sheets are the same as for PI. Each file contains the following Netcdf: AWI_moisture_budget.nc: decomposition of the anomalous net precipitation in relative to LGM (not present for LGM-full-forced) pr.nc: precipitation psl.nc: sea level pressure ps.nc: surface pressure q.nc: specific humidity tas.nc: surface air temperature ts.nc: surface temperature u.nc: u-wind at all levels v.nc: v-wind at all levels zg.nc: geopotential height
DOI: 10.5439/1813858
title: Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP)

description: The objective of Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP) is to provide the vertical distribution of cloud condensation nuclei (CCN) number concentrations to better represent aerosol indirect effects in climate models. The RNCCN VAP allows to collect a large number of independent samples of vertical CCN measurements at various supersaturation values from the ground. This helps to avoid the high cost of aircraft and increases the temporal distribution of retrieved CCN in the planetary boundary layer. The VAP method is based on Ghan and Collins (2004) and Ghan et al. (2006) proposed parameterizations that retrieve the vertical profiles of CCN directly from the ground level CCN, humidification factor, and in-situ lidar measurements. Further, the VAP accuracy is improved by using the quality controlled data that ensures that the boundary layer is well mixed.
DOI: 10.25430/researchdata.cab.unipd.it.00001538
title: Headwater streams control the non-perennial fraction of the global river network - CODES

description: This collection is composed by the codes written in MATLAB to model the catchment- and network-scale functions (PhiC.m and PhiN.m), contained in folders "1 - Catchemnt-scale temporariness" and "2 - Network-scale temporariness". This collection comprehends also MATLAB codes written to estimate the Wetness Index, collected in folder "3 - Wetness Index". The "readme.txt" file contains detailed information regarding the content of each folder.
DOI: 10.13099/vip21000-03-0
title: Tissue Properties Database V3.0

description: References
description: Log file of changes
description: Density, Heat Capacity, Thermal Conductivity, Heat Transfer Rate, Heat Generation Rate, Dielectric Properties, Speed of Sound, Non-Linearity, Attenuation Constant, T1 relaxation times, T2 relaxation times
DOI: 10.17189/1414242
title: MAVEN Insitu Key Parameters Data Collection

description: A collection of MAVEN key parameters from the in situ instruments on MAVEN: NGIMS, EUV, LPW, MAG, SEP, STATIC, SWEA, and SWIA. Instrument data is derived directly from Level 2 data. Ephemeris information is derived using SPICE libraries and kernels provided by MAVEN/NAV team and Lockheed-Martin.
DOI: 10.17632/2rnnz5nz74.2
title: Data on OCT and Fundus Images

description: The dataset is comprised of 50 OCT and Fundus images which included health and glaucomatous images. Annotation of Cup to disc ratio (CDR) was performed by four ophthalmologist are provided in excel file.
DOI: 10.17632/btchxktzyw.6
title: October 2023 data-update for "Updated science-wide author databases of standardized citation indicators"

description: Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2022 and single recent year data pertain to citations received during calendar year 2022. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918). Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a
DOI: 10.17632/j8bsyyxzvn.1
title: Free water in healthy aging

description: Brain aging is associated with cerebrovascular changes and related microstructural pathology, which can be examined by measures of diffusion tensor imaging (DTI) such as fractional anisotropy or mean diffusivity. However, it was suggested that small vessel disease (SVD) is mainly driven by increases in extracellular free water (FW) rather than alterations of white matter fibre organization. The aim of this study was to examine the association of FW, FW-uncorrected and FW-corrected DTI measures with aging, and to determine if there are differences in FW and DTI measures between individuals with incipient signs of small vessel disease (iSVD) and elderly controls (CON). 

We examined 188 age-matched individuals (mean age = 70.14, SD = 8.44; 59.6% female) with incipient signs of SVD and CON of the Austrian Stroke Prevention Family Study. Individuals with iSVD were defined by white matter hyperintensities (WMH) scores ≥ 2, presence of lacunes and/or microbleeds, whereas CON did not show any signs of lacunes, microbleeds and exhibited WMH scores &lt; 2. We assessed the association of FW across the entire normal appearing white matter (NAWM), FW-uncorrected and FW-corrected DTI with age. To test if FW mediates the association of FW-uncorrected DTI measures and age, we applied simple mediation models. Lastly, we examined differences in FW and FW-corrected DTI parameters between groups.

FW in NAWM and FW-uncorrected DTI measures in NAWM were significantly associated with age in both the total sample (rrange = -.24 - .44) and subgroups (rrange = -.36 - .48). For the correlation between FW-corrected DTI parameters and age, predominantly non-significant results were observed. Mediation analyses revealed that the association between FW-uncorrected DTI measures and age is mediated by FW in the total sample as well as in both subgroups. Differences between individuals with iSVD and CON were identified for FW in NAWM and FW-uncorrected DTI, but not for FW-corrected DTI measures.

The association between FW-uncorrected diffusion measures and age appears to be driven by FW. Even in subjects with subtle, clinically silent, cerebral white matter changes, significant increases in FW could be observed. Therefore, FW might act as a sensitive marker at earliest, clinically silent, signs of SVD.
DOI: 10.17632/nstvb42skt.1
title: Synergistic Impacts of Diurnal Warm Layer and Internal Tides on Springtime Restratification in the South China Sea

description: This dataset include the float measurements in the scientific analysis. This data is gridded in the vertical resolution of 2 m and temporal resolution of 30 min. For the details about the variables, please contact the authors.
DOI: 10.17632/ys6hjz6994.1
title: Database of Water indicators and Pesticides Concentrations in the Kakia-Esamburmbur Catchment, Narok County, Kenya

description: This Excel file contains the database for the article titled "Use of Water Quality Index and Pesticides-Related Human Health Risk Assessment for Evaluating the Surface Water Sustainability Status of the Kakia-Esamburmbur Agricultural Watershed, Narok County, Kenya", organized across 2 sheets It contains all the 12 water physical, chemical and biological indicators and concentrations of 9 pesticides associated with antibiotic resistance genes (ARGs) obtained through field and laboratory works at 10 sample sites (in triplicate).
DOI: 10.18112/openneuro.ds000221.v1.0.0
title: MPI-Leipzig_Mind-Brain-Body
DOI: 10.18739/a2sb3x05g
title: Measurements of ice mass balance and temperature from autonomous Seasonal Ice Mass Balance buoys in the Arctic Ocean, 2019-2020.

description: The dataset contains measurements made from four autonomous Seasonal Ice Mass Balance (SIMB) buoys. The buoys were deployed in the fall of 2019 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network at sites L1, L2, L3, and at the Central Observatory. They were collocated with platforms making observations of the atmosphere and ocean. Each buoy measured position, barometric pressure, air temperature, and a vertical profile of temperature from the air, through the snow, through the ice, and into the upper ocean. An above ice downward looking sounder tracked the position of the surface and an underwater upward looking sounder tracked the position of the ice bottom. Sensors were placed every 0.02 meters (m) on the vertical temperature string. Measurements were made every four hours and results were transmitted via Iridium. There is one csv file for each SIMB.
DOI: 10.21227/fkap-fr63
title: Electric Vehicle Charging Fleet one Year Recordings

description: These measurements were taken at the point of common coupling using the power quality analyzer PQ-Box 200, where about 30 EV chargers are installed and exploited by the utility. For this reason, this data set only considers the charging behavior of the vehicles employed by the enterprise, namely the Renault Kangoo ZE and Renault Zoe. The period under consideration starts on 5.11.2018 and ends on 07.01.2020. Because of the large amount of data, values with a time interval of 10mins are extracted and used in this data set.
DOI: 10.24432/c5js49
title: Beijing PM2.5
DOI: 10.25850/nioz/7b.b.1f
title: Calibrated CTD data from OSNAP Pelagia cruise 64PE473 in July-Aug 2020

description: The goals of the research carried out during the cruise were to deploy surface drifters on the east Greenland shelf to investigate the export of freshwater towards deep convection areas, to service the five NIOZ Overturning of the Subpolar North Atlantic Program (OSNAP) moorings in the Irminger Sea located on the western flank of the Mid Atlantic Ridge, recover two lander deployed on the ridge by Ifremer in 2018 and to establish the hydrography along the OSNAP line (co-located with AR7E) to allow the study of inter-annual hydrographic variability. The data set published here comprises of the 67 CTD stations surveyed during the cruise. The cruise started and ended on Texel. After passing the Pentland Firth, a more or less direct line was steamed towards Cape Farewell, An initial CTD cast was done during the transit, for testing and training purposes, at 59? 36.71? N, 29? 31.34? W, on 18 July 2020. The first science station of the section was done just off Cape Farewell, at 60? 02.52? N, 43? 02.67? W, on 20 July 2020. The last station was done west of Rockall island, at 57? 34.90? N, 14? 36.87? W, on 11 August 2020, from where the Pelagia steamed back to Texel. Many of the CTD stations were taking near moorings along the OSNAP line, to facilitate use for mooring calibration. On the CTD stations, a total of 318 water samples were collected for the salinity calibration and 70 for oxygen calibration. Water samples were taken in layers with weak vertical gradients only. All samples have been analysed at NIOZ and final CTD data available here have been corrected for biases found against these samples.