cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9d1kqgr
title: Tungsten skarn mineral resource assessment of the Great Basin region of western Nevada and eastern California - Geodatabase

description: A mineral resource assessment for tungsten, a critical mineral commodity (see 'Related External Resources' section below) for the United States, was carried out by the U.S. Geological Survey (USGS) for a portion of the Great Basin region, in western Nevada and eastern California, between latitudes 36N and 42N and longitudes 116W and 120W. This study (Lederer and others, in review) integrates data from several sources, including geologic, geochemical, geophysical, remote sensing, watershed analysis, and mining with recently developed grade and tonnage models, expert estimates, and software tools and analyses to generate probabilistic estimates of undiscovered tungsten skarn resources. The assessment was conducted following the 3-part assessment methodology developed by Singer and Menzie (2010, see 'Related External Resources' section below), which involved the delineation of permissive tracts, as well as the evaluation of interdisciplinary data that were then presented to a panel of experts, who made estimations that were then analyzed using economic filters. These data are presented in several formats: a GIS geodatabase, shapefiles, and tabular (csv) data. Several layers or individual files are derived or contain data from U.S. Geological Survey (USGS) and other existing, published sources, mainly the USGS National Geochemical Database (NGDB), USGS Mineral Resources Data System (MRDS), and the National Uranium Resource Evaluation (NURE) databases.
DOI: 10.5281/zenodo.10201157
title: Stronger oceanic CO2 sink in eddy-resolving simulations of global warming: simulations outputs

description: This repository contains 1) the air-sea CO2 flux and the Dissolved Inorganic Carbon (DIC) distribution in idealized simulations run at different resolutions 2) the terms of the DIC budget for each simulation integrated temporally on the all simulation and integrated spatially on different boxes of the domain. These data are used in the article "Stronger oceanic CO$_2$ sink in eddy-resolving simulations of global warming" published in Geophysical Research Letters for producing Figs. 2, 3, 4. Refer to this paper for details about the data.
DOI: 10.5519/qd.g7b3r8uk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6399 records)
DOI: 10.5281/zenodo.10076209
title: kj-kling/Kling-etal-People-Nature: Code, Results Outputs, & Datasets
title: v.1.0.0

description: This repository contains the (1) code, (2) results output, and (3) datasets used for a paper entitled "Provisioning services decline for both people and Critically Endangered wildlife in a rainforest transformation landscape."
This was a pre-release version and has since been updated (vpub.1.0.0)
Update includes calculation of marginal & conditional R2GLMM values for each model in analysis (in R markdown files) and expanded list of scientific names for study plant assemblage (in Appendix S1).
DOI: 10.5281/zenodo.7943054
title: kj-kling/Kling-etal-People-Nature: Kling et al, P&N Resubmission

description: This repository contains the (1) code, (2) results output, and (3) datasets used for a paper entitled "Provisioning services decline for both people and Critically Endangered wildlife in a rainforest transformation landscape."
DOI: 10.34740/kaggle/dsv/6432515
title: Tempering data for carbon and low alloy steels
DOI: 10.5281/zenodo.10278553
title: Boat sales Ontology
DOI: 10.5281/zenodo.10278554
title: Boat sales Ontology
DOI: 10.5066/p9pk0eh0
title: Landscape position-based habitat modeling for the Alabama Barrier Island feasibility assessment at Dauphin Island

description: A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, "medium" storminess and "high" storminess based on storm climatology data) and 4 sea-level scenarios (that is, a "low" increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).
DOI: 10.5066/p9rqphql
title: Data for the development of a new method for dynamically estimating exposure time for turbulent flow measurements

description: The mission of the U.S. Geological Survey (USGS) involves providing reliable, impartial, and timely information that is needed to understand the Nation?s water resource. New techniques that aid in achieving this mission are important, especially those that allow USGS to do so more accurately or cost-effectively. To this end, a new method for selecting the optimum exposure time for velocity and discharge measurements has been explored. These data were assembled to assist in the development and evaluation of this new method. Four kinds of time-series data are available and used for this purpose. They are: (1) model-derived synthetic velocities, (2) point-velocity measurements in laboratory flumes, (3) point-velocity measurements in streams, and (4) water velocity profile measurements in streams. The model-derived velocity data were obtained using methods described in Garcia and others (2005). Point-velocity flume measurement data were obtained using a Nortek 16 MHz acoustic Doppler velocimeter (ADV) for the purpose of characterizing turbulence in the flow in a flume. Point-velocity measurement data collected in the field were obtained using a SonTek Flowtracker ADV (1 MHz) and an OTT acoustic Doppler current meter or ADC (6 Mhz) as a part of routine mid-section discharge measurements. Water velocity profile measurements in streams were collected using SonTek and Teledyne RD Instruments Acoustic Doppler current profilers (ADCPs) during routine mid-section discharge measurements. The laboratory ADV data were collected, processed, and exported using the associated ADV software. The point-velocity field measurement data were reviewed by an experienced user and by means of built-in data-quality checks in the manufacturer software. ADCP water velocity profile measurements were collected and processed with the instrument manufacturer software. Data are provided in the zip file, 'DynamicExpTime.zip' which contains 4 types of times series data. The model-derived velocities are provided in a spreadsheet format. The ADV, ADC, Flowtracker, and ADCP data were exported from their native file formats and are provided in comma-separated value and ASCII text files. References Garcia, C. M., Cantero, M. I., Ni?o, Y., and Garcia, M. H. (2005). Turbulence measurements with Acoustic Doppler velocimeters: Journal of Hydraulic Engineering: v. 131 no. 12. [Also available at https://doi.org/10.1061/(asce)0733-9429(2005)131:12(1062).]
DOI: 10.7303/syn53128370
title: A Trem2R47H mouse model without cryptic splicing drives age- and disease-dependent tissue damage and synaptic loss in response to plaques
DOI: 10.25345/c5mw28r4x
title: MassIVE MSV000093588 - mRNA translation shapes the transcriptome through m6A mRNA decay
title: The digests were analyzed using a Thermo Fisher Scientific EASY-nLC 1200 coupled on-line to a Fusion Lumos mass spectrometer (Thermo Fisher Scientific). Buffer A (0.1% FA in water) and buffer B (0.1% FA in 80 % ACN) were used as mobile phases for gradient separation. A 75 um x 15 cm chromatography column (ReproSil-Pur C18-AQ, 3 um, Dr. Maisch GmbH, German) was packed in-house for peptide separation. Peptides were separated using a gradient of 5-40% buffer B over 30 min, 40-100% B over 10 min at a flow rate of 400 nL/min. The Fusion Lumos mass spectrometer was operated in a data independent acquisition (DIA) mode. MS1 scans were collected in the Orbitrap mass analyzer from 350-1400 m/z at 120K resolutions with the normalized automatic gain control (AGC) target set to 750 and a maximum ion accumulation time of 50 ms. The instrument was set to select precursors in 45 x 14 m/z wide windows with 1 m/z overlap from 350-975 m/z for HCD fragmentation. The MS/MS scans were collected in the orbitrap at 15K resolution. The normalized AGC setting was 6000 and the maximum ion accumulation time was 22 ms. Data were analyzed, searched, filtered, and assembled into protein quantification values using DIA-NN v1.8 128 with the following settings: protease: Trypsin/P, missed cleavages: 1, peptide length range 7-30, FDR: 1%, match between runs: enable, quantification strategy: Robust LC (high precision), cross-run normalization: RT-dependent. The spectral library was generated in DIA-NN using in silico deep-learning and RT prediction based on the mouse Uniprot database entries (8/7/2021).
DOI: 10.5281/zenodo.10278683
title: Qixian water temperature
DOI: 10.5281/zenodo.10278684
title: Qixian water temperature
DOI: 10.5066/p9ama3pl
title: Sediment Biogeochemistry and Subsequent Mercury Biomagnification in Wetland Food Webs of the San Francisco Bay, CA (ver. 2.0, December 2023)

description: Methylmercury (MeHg) is a globally pervasive contaminant that biomagnifies in food webs and can reach toxic concentrations in consumers at higher trophic levels, including wildlife and humans. The production of MeHg, and its subsequent entry and biomagnification in food webs, is governed by a complex suite of biogeochemical, physical, and ecological processes, resulting in spatial variation in the distribution of MeHg. To better understand the link between MeHg production in sediments and MeHg bioaccumulation in biota, we evaluated the effects of habitat biogeochemistry, food web structure, and diet composition on bioaccumulation in the wetland-obligate California black rail (Laterallus jamaicensis coturniculus) at three wetlands along the Petaluma River in northern San Francisco Bay, California, USA. We also characterized meso-scale spatial variation by assessing differences among marsh subhabitats in sediment biogeochemistry and MeHg concentrations of sediments, surface waters, and tissues of biota.

These data support the following publication:
Hall, L.A., Woo, I., Marvin-DiPasquale, M., Tsao, D.C., Krabbenhoft, D.P., Takekawa, J.Y. and De La Cruz, S.E., 2019. Disentangling the effects of habitat biogeochemistry, food web structure, and diet composition on mercury bioaccumulation in a wetland bird. Environmental Pollution, p.113280. https://doi.org/10.1016/j.envpol.2019.113280

Hall, L.A., Woo, I., Marvin-DiPasquale, M., Takekawa, J.Y., Krabbenhoft, D.P., Yee, D., Grenier, L. and De La Cruz, S.E., 2023. Linking Mesoscale Spatial Variation in Methylmercury Production to Bioaccumulation in Tidal Marsh Food Webs. Environmental Science & Technology. https://doi.org/10.1021/acs.est.3c04907
DOI: 10.6084/m9.figshare.24760296
title: Supplementary Material 231206.docx

description: A systematic review synthesized 26 music-based language enhancement studies and described the effectiveness of intervention characteristics in improving the language skills of preschoolers, both with and without language delay
DOI: 10.6084/m9.figshare.24760296.v1
title: Supplementary Material 231206.docx

description: A systematic review synthesized 26 music-based language enhancement studies and described the effectiveness of intervention characteristics in improving the language skills of preschoolers, both with and without language delay
DOI: 10.5281/zenodo.8134199
title: Red Sea surface temperature

description: Daily sea-surface temperature observations from the years 1985 to 2015 (inclusive), for 16703 regularly-spaced locations across the Red Sea; see Donlon et al. (2012) for details.
 
These data have been analysed by Hazra and Huser (2021), Simpson and Wadsworth (2021), Simpson et al. (2023), and Sainsbury-Dale et al. (2023), among others.
 
## References
- Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W. (2012). The operational sea surface temperature and sea ice analysis (OSTIA) system. *Remote Sensing of Environment*, 116:140–158.
- Hazra, A. and Huser, R. (2021). Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model. *Annals of Applied Statistics*, 15:572–596.
- Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023) Likelihood-free parameter estimation with neural Bayes estimators. doi:10.1080/00031305.2023.2249522.
- Simpson, E. S., Opitz, T., and Wadsworth, J. L. (2023). High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields. *Extremes*, to appear.
- Simpson, E. S. and Wadsworth, J. L. (2021). Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures. *Spatial Statistics*, 41(100482).
description: Denote the number of days for which data was recorded by *r* = 16703. A number of objects are stored in this data set:
 
- `data` is an *r* x *n* matrix with rows corresponding to days and columns corresponding to spatial location.
 
- `loc` is a 2 x *n* matrix of spatial locations.
 
- `time`, `day`, `month`, and `year` are *r*-vectors whose *i*th elements respectively give the time, day, month, and year that `data[i, ]` was observed.
DOI: 10.5281/zenodo.8134200
title: Red Sea surface temperature

description: Daily sea-surface temperature observations from the years 1985 to 2015 (inclusive), for 16703 regularly-spaced locations across the Red Sea; see Donlon et al. (2012) for details.
 
These data have been analysed by Hazra and Huser (2021), Simpson and Wadsworth (2021), Simpson et al. (2023), and Sainsbury-Dale et al. (2023), among others.
 
## References
- Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W. (2012). The operational sea surface temperature and sea ice analysis (OSTIA) system. *Remote Sensing of Environment*, 116:140–158.
- Hazra, A. and Huser, R. (2021). Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model. *Annals of Applied Statistics*, 15:572–596.
- Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023) Likelihood-free parameter estimation with neural Bayes estimators. doi:10.1080/00031305.2023.2249522.
- Simpson, E. S., Opitz, T., and Wadsworth, J. L. (2023). High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields. *Extremes*, to appear.
- Simpson, E. S. and Wadsworth, J. L. (2021). Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures. *Spatial Statistics*, 41(100482).
description: Denote the number of days for which data was recorded by *r* = 16703. A number of objects are stored in this data set:
 
- `data` is an *r* x *n* matrix with rows corresponding to days and columns corresponding to spatial location.
 
- `loc` is a 2 x *n* matrix of spatial locations.
 
- `time`, `day`, `month`, and `year` are *r*-vectors whose *i*th elements respectively give the time, day, month, and year that `data[i, ]` was observed.
DOI: 10.34740/kaggle/dsv/7142076
title: BBC News
DOI: 10.25345/c5h708b2b
title: MassIVE MSV000093589 - Jugiones A-D: Antibacterial xanthoquinodins from Australian soil-derived Penicillium shearii CMB-STF067
title: Chemical investigations of Penicillium sp. CMB-STF067 is based on both the antibacterial property of its extract and the Global Natural Product Social (GNPS) molecular networking analysis of 176 soil-associated fungi, guided the isolation of 4-new xanthoquinodins, jugiones A-D.
DOI: 10.34740/kaggle/dsv/7142130
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.5281/zenodo.10278719
title: Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)

description: The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census, originally downloaded from the Australian Bureau of Statistics (ABS).
This data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.
Sainsbury-Dale et al. (2023) used a subset of this data set in a study on poverty levels in an area of  (NSW) surrounding Sydney.  
 
References
Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023) "Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK", Journal of Statistical Software, to appear.
DOI: 10.5281/zenodo.10278720
title: Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)

description: The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census, originally downloaded from the Australian Bureau of Statistics (ABS).
This data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.
Sainsbury-Dale et al. (2023) used a subset of this data set in a study on poverty levels in an area of  (NSW) surrounding Sydney.  
 
References
Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023) "Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK", Journal of Statistical Software, to appear.
DOI: 10.5281/zenodo.10278721
title: A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells

description: Annotated WES VCF file and phenotype file used in Gene burden analysis of Naaldijk et al., "A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells", 2023.
DOI: 10.5281/zenodo.10278722
title: A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells

description: Annotated WES VCF file and phenotype file used in Gene burden analysis of Naaldijk et al., "A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells", 2023.
DOI: 10.5281/zenodo.6818396
title: 2D synthetic hydrothermal models

description: 2D hydrothermal models of 1 km × 1 km in the horizontal and depth directions, with a grid size of 50 × 50, created by TOUGH2 software. The 2D models were used to evaluate physics-informed neural networks in the following paper:
Ishitsuka, K. and Lin, W. (2023) Physics-informed neural network for inverse modeling of natural-state geothermal systems, Applied Energy, 337, 120855. https://doi.org/10.1016/j.apenergy.2023.120855
https://www.sciencedirect.com/science/article/pii/S0306261923002192
In the files, "x" and "z" indicate horizontal and vertical coordinates in meter. The columns of "pres", "temp" and "log10PER" indicate pressure, temperature and logarithm of permeability, respectively.
DOI: 10.5281/zenodo.6818397
title: 2D synthetic hydrothermal models

description: 2D hydrothermal models of 1 km × 1 km in the horizontal and depth directions, with a grid size of 50 × 50, created by TOUGH2 software. The 2D models were used to evaluate physics-informed neural networks in the following paper:
Ishitsuka, K. and Lin, W. (2023) Physics-informed neural network for inverse modeling of natural-state geothermal systems, Applied Energy, 337, 120855. https://doi.org/10.1016/j.apenergy.2023.120855
https://www.sciencedirect.com/science/article/pii/S0306261923002192
In the files, "x" and "z" indicate horizontal and vertical coordinates in meter. The columns of "pres", "temp" and "log10PER" indicate pressure, temperature and logarithm of permeability, respectively.
DOI: 10.5281/zenodo.10278748
title: Crime in Chicago: 2001 to 2019

description: A complete list of crimes committed in Chicago between the years 2001 – 2019 (inclusive), and was studied by Sainsbury-Dale et al. (2023). The data were provided by the Chicago Police Department and originally downloaded from the now retired open data source website, Plenario.
Before pre-processing, the data set contained 7,138,725 observations. However, 68,904 observations did not have a location recorded and were removed. A further 163 observations were removed as they were recorded at coordinates (36.619446395, -91.686565684), which is on the border of Missouri and Arkansas (certainly not in Chicago, Illinois). This left 7,069,658 valid observations. 
The data contains the following fields:

id : An identifier unique to each crime.
case_number : The case number of each crime.
date : The date and time at which each crime took place.
block : The neighbourhood block at which each crime occurred.
primary_type : A factor indicating the type of each crime (e.g., burgulary, theft, etc.).
description : A brief description of each crime.
location_description : A brief description of the location at which each crime occurred.
arrest : Logical indicating whether or not an arrest was made for each associated crime.
district : The district at which each crime occurred.
community_area : The community_area at which each crime occurred.
fbi_code : Federal Bureau of Investigation (FBI) code of each crime.
year : The year in which the crime occurred.
longitude : Latitude location of each.
latitute : Longitude location of each crime.
location : Latitude and Longitude (in that order) of each crime.
 
References 
Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023). Modelling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK. Journal of Statistical Software, to appear.