cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6damimihc
title: IPCC DDC: MIROC MIROC6 model output prepared for CMIP6 DAMIP hist-CO2

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MIROC.MIROC6.hist-CO2' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.3886/icpsr37277.v1
title: Los Angeles Family and Neighborhood Survey (L.A.FANS), Los Angeles Neighborhood Services and Characteristics Data (L.A.NSC), 1980-2010
title: Version 1

description: This study, known as the Los Angeles Neighborhood Services and Characteristics data (L.A.NSC) includes data from governmental and business data sources on the characteristics of census tracts in Los Angeles County from 1980 to 2010. The unit of analysis is 1990 census tract (census tracts using 1990 census tract boundaries). The data also include crosswalks for census boundaries across census years. It is public use data. The L.A.NSC data were assembled by the L.A.FANS projects to be used with L.A.FANS survey data restricted data versions 2, 2.5, and 3 all of which contain 1990 census tract numbers which can be linked with data in this file. However, the L.A.NSC files includes ALL census tracts in Los Angeles County, not just the 65 tracts sampled by L.A.FANS. Thus, the L.A.NSC data can be analyzed on their own without linkage to L.A.FANS survey data and provide longitudinal data on the characteristics of Los Angeles census tracts over a 30 year period. Additional information on the data is available at the RAND website. See also: The L.A.NSC Database Users' Guides (The Los Angeles Neighborhood Services and Characteristics Database: Codebook and 2010 Neighborhood Services and Characteristics Database: NSC2).
description: These data include all census tracts in Los Angeles County.
description: Datasets: DS0: Study-Level Files DS1: LA_CORE Data File DS2: NSC_BUS Data File DS3: NSC_POP Data File DS4: NSC_POV Data File DS5: NSC_STF3 Data File DS6: NSC2_POP Data File DS7: NSC2_ACS Data File DS8: TRACTWGT10TO00 Data File DS9: TRACTWGT00TO90 Data File
description: Smallest Geographic Unit: Census tract
description: record abstractsL.A.FANS User Guides and Questionnaires are available to read or download from the L.A.FANS website. For additional information, please visit the L.A.FANS website.
DOI: 10.5067/aqua/modis/l3m/chl/2018
title: MODIS-Aqua Level 3 Mapped Chlorophyll Data Version R2018.0
DOI: 10.5281/zenodo.12697471
title: COVID-19-Impfungen in Deutschland

description: Die COVID-19-Impfung kann einen Wendepunkt in der Kontrolle der COVID-19-Pandemie darstellen und erfährt daher hohes Maß an öffentlicher Aufmerksamkeit. Einführung und Umsetzung der COVID-19-Impfung gehen mit besonderen Herausforderungen einher, die bei der Impfdatenerfassung zu berücksichtigen sind. In diesem Kontext ist es Ziel des Projekts 'Digitales Impfquoten-Monitoring' (DIM), tagesaktuell, bundesweit die Impfquote zu erfassen und folgend aufbereitet darzustellen, um zeitnah den Verlauf der COVID-19-Impfkampanne zu analysieren, bei Bedarf nach zusteuern, und logistisch bzw. organisatorische Konsequenzen zu ziehen.

Der durch das DIM-Projekt bereitgestellte Datensatz enthält Daten über den Verlauf der COVID-19 Impfungen in Deutschland. Die hier veröffentlichten Impfdaten aggregieren Daten aus drei Datenquellen:



Die DIM-Daten enthalten Angaben der Impfzentren, mobilen Impfteams, Krankenhäuser und der Betriebsärzte_innen, die über die DIM-Webanwendung übermittelt werden

Der täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Kassenärztliche Bundesvereinigung (KBV)

Der täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Privatärztliche Bundesvereinigung (PBV)
description: Am 30.06.2024 ist die COVID-19-Vorsorgeverordnung außer Kraft getreten. Mit dem Auslaufen der Verordnung wurden die Meldeportale für die niedergelassene Ärzteschaft zur zeitnahen Übermittlung durchgeführter COVID-19-Impfungen abgeschaltet.

Die am 09.07.2024 publizierten Daten repräsentieren den finalen Datenstand des Digitalen Impfquotenmonitorings. Die COVID-19-Impfungen werden künftig wie die anderen Routineimpfungen im Rahmen der KV-Impfsurveillance ausgewertet.
DOI: 10.5281/zenodo.13933259
title: Incorporating Hourly Convective Cloud data into Tropical Cyclone Rapid Intensification Forecasting with Machine Learning

description: These models were developed to predict both the probability of RI and the binary RI classification for tropical cyclones. They are a weighted average of probabilities derived from logistic regression, random forest, decision tree, and extremely randomized tree algorithms within the standard Python scikit-learn package. The uploaded files include the hyperparameters and weights for each individual machine learning model.
description: Note that we have only explored a limited number of tunable hyperparameters in the machine learning schemes as the primary aim of this study is to demonstrate the potential impact of integrating very deep convection into tropical cyclone rapid intensification forecasts using machine learning methods. For real‐time operations, the hyperparameters and weights for the individual machine learning schemes should be updated based on available samples prior to each hurricane season.
DOI: 10.5281/zenodo.14008245
title: Datasets for the article "Redox melting of garnet lherzolite: Generation of carbonated silicate and sulfide melts and implications for OIB formation from chalcophile and redox-sensitive elements"
DOI: 10.5281/zenodo.14249034
title: Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks

description: Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxECCT, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxECCT to SPAGxEmixCCT, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxECCT, SPAGxE+, and SPAGxEmixCCT to analyze time-to-event traits in UK Biobank. For the SPAGxECCT analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included.  For the SPAGxEmixCCT analyses, 338,044 individuals from all ancestries were included. SPAGxECCT, SPAGxE+, and SPAGxEmixCCT are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.
DOI: 10.5281/zenodo.14271782
title: An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021

description: Cultivated pastures have rapidly developed across the Tibetan Plateau over the past several decades, raising concerns about grassland degradation. Accordingly, considerable attention is focused on the protection of grassland ecosystems. However, the high-resolution spatial distribution of cultivated pastures on the Tibetan Plateau remains poorly understood, primarily due to the difficulty of discriminating cultivated pastures from non-cultivated pastures using remote sensing techniques. The absence of such information hinders efficient agricultural and livestock husbandry management, making it challenging to support ecological protection and restoration efforts. Here, we mapped the cultivated pastures on the Tibetan Plateau at a 30-m resolution for the years 1988 to 2021 using the Landsat data on the Google Earth Engine (GEE) cloud computing platform. We built a Random Forest (RF) binary classification model with inputs of the spectral-temporal metrics of Landsat images acquired in the growing season, as well as ancillary topographic data. The model was trained using carefully selected training samples and validated against 2,000 independent random reference points. The model achieved an overall accuracy of 97.05% ± 0.4% and an F1 spatial consistency score of 82.51% ± 14.22% (Precision: 90.04% ± 6.18%, Recall: 76.74% ± 9.91%), suggesting high confidence in mapping the distribution of cultivated pastures. We produced a dataset of cultivated pasture maps for the years from 1988 to 2021 for Qinghai Province and the Tibet Autonomous Region on the Tibetan Plateau, covering 77% of the plateau. To our knowledge, we are the first to map cultivated pastures on the Tibetan Plateau, and our RF binary classification approach holds promise in identifying cultivated pastures in other regions of the world, which could prove invaluable for scientists, policymakers, ecological conservation practitioners, and herdsmen.
DOI: 10.5281/zenodo.14522732
title: Model Results for 'Deep Learning Forecast of Semidiurnal Internal Tides from the Mariana Arc'

description: Model Results for ‘Deep Learning Forecast of Semidiurnal Internal Tides from the Mariana Arc’
DOI: 10.5281/zenodo.14522770
title: The training and testing data sets for 'Deep Learning Forecast of Semidiurnal Internal Tides from the Mariana Arc'

description: The training and testing data sets for ‘Deep Learning Forecast of Semidiurnal Internal Tides from the Mariana Arc’
DOI: 10.5281/zenodo.14610115
title: Regulation of Mammalian Cellular Metabolism by Endogenous Cyanide Production
title: Cyanide as an endogenous mammalian regulator

description: Small, gaseous molecules – such as nitric oxide (NO), carbon monoxide (CO) and hydrogen sulfide (H2S) are produced as signalling molecules in mammalian cells. Here, we show that low concentrations of cyanide are generated endogenously in various mammalian tissues and cells. We detect cyanide in several cellular compartments of human cells and in various tissues and the blood of mice. Cyanide production is stimulated by glycine, occurs at the low pH of lysosomes and requires peroxidase activity. When generated at a specific rate, cyanide exerts stimulatory effects on mitochondrial bioenergetics, cell metabolism, and cell proliferation, but impairs cellular bioenergetics at high concentrations. Cyanide can modify cysteine residues via protein S-cyanylation, which is detectable basally in cells and mice, and increases in response to glycine. Low-dose cyanide supplementation exhibits cytoprotective effects in hypoxia and reoxygenation models in vitro and in vivo. Conversely, pathologically elevated cyanide production in nonketotic hyperglycinemia is detrimental to cells. Our findings indicate that cyanide should be considered part of the same group of endogenous mammalian regulatory gasotransmitters as NO, CO and H2S.
DOI: 10.5281/zenodo.14652432
title: Simulating 2D lattice gauge theories on a qudit quantum computer

description: Datasets to research paper titled "Simulating 2D lattice gauge theories on a qudit quantum computer" to be published.https://arxiv.org/abs/2310.12110All files provided as CSV, see README.txt for details on the individual datasets.
DOI: 10.5281/zenodo.14830176
title: Supplemental information of "Improvement of a Single-Moment Cloud Microphysics Scheme Consistent with Dual-Polarization Radar"

description: This dataset contains parameters  for Equation 17, Equation 20, and Table 3, as presented in the manuscript "Improvement of a Single-Moment Cloud Microphysics Scheme Consistent with Dual-Polarization Radar".
DOI: 10.5281/zenodo.4017659
title: Dataset of BattLeDIM: Battle of the Leakage Detection and Isolation Methods

description: Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may lead to water losses. Typically, these water losses are due to background leakages and pipe bursts which may occur anywhere within the distribution network. Background leakages are normally difficult to detect due to their small size, whereas pipe bursts are easier to locate as they are of larger size and may appear on the surface. The early detection and localization of some leakage event is extremely important, as this would reduce the time required for accommodating the event and therefore reducing the risk of further infrastructure degradation, contamination events and consumer complaints. In previous years, a number of methodologies have been proposed to detect and isolate the location of leakage events using various types of sensor measurements. These methods were commonly evaluated on private commercial datasets, and as a result, it is not possible to objectively compare these methods in their ability to detect and isolate leaks. In the past year, a leakage detection dataset has been proposed, LeakDB, based on benchmark networks and created using the WNTR tool, using pressure-driven demands and realistic leakage modelling. Inspired by the “BATtle of the Attack Detection ALgorithms” (BATADAL), which focused on the detection of cyber-physical attacks, our team decided to organize a similar “battle” focusing on leakage events. The Battle of the Leakage Detection and Isolation Methods (BattLeDIM), aims at objectively comparing the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within water distribution networks. Participants may use different types of tools and methods, including (but not limited to) engineering judgement, machine learning, statistical methods, signal processing, and model-based fault diagnosis approaches.
DOI: 10.5281/zenodo.7746425
title: Monterey Bay, Monterey Peninsula, and Santa Cruz Directional Spectra

description: Directional spectra estimated from SOFAR Ocean Spotter buoy and Nortek Signature 1000 acoustic doppler current profilers. 

Data used in the manuscript "Shoreline and Bottom-Influenced Sea-Swell Wave Reflection Across Sandy and Rocky Shores" by Collins, MacMahan, Thornton, Benbow and Jessen.

Code used to generate the directional spectra from the Nortek Signature 1000 ADCP is available at: https://github.com/patrick-aus/Bayesian-Directional-Tilted-ADCP
DOI: 10.5285/aced40d7cb964f23a0fd3e85772f2d48
title: ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.0

description: This v2.0 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude-longitude grid. It combines the orbit data from the Advanced High Resolution Radiometer (AVHRR) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.
DOI: 10.5285/b25d4a6174de4ac78000d034f500a268
title: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0

description: This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.
DOI: 10.5439/1131339
title: ndropmfrsr

description: This is for the NDROP VAP, and has been revised to point to instrument code ndropmfrsr (Dec 2020).
DOI: 10.5439/1595321
title: Balloon-borne sounding system (BBSS), WNPN output data

description: ARM - Balloon-borne sounding system (BBSS) WNPN output data, sondewnpn.b1
DOI: 10.6073/pasta/1c5c63320541b9a65da3a94df04c80f4
title: Baltimore forest patch point-sample overstory and understory vegetation, 2018-2020

description: Urban forest patches, including woodland interiors and bounding edge habitat, result from secondary succession and fragmentation of more extensive forested landscapes in the eastern USA. Management regimes, surrounding land use, and successional processes lead to distinct environments and contribute to local and regional heterogeneity. However, many woodlands are degraded due to frequent disturbance, aggressive exotic species, and heavy browsing, which stress canopies, reduce regeneration, and may reduce ecosystem services. Effective management requires rapid, repeatable assessment of forest composition, structure, and condition at the scale of local decision-making. We present and apply a protocol for characterizing urban woodlands that generates new insight into the status of urban woodlands and baseline data for change detection over time. Samples of overstory composition, ground cover, surface soil measurements, and the Schumacher Vine Encroachment Index were collected at 845 points across each of 47 patches across Baltimore, Maryland. Simple citywide summaries allowed characterization of Baltimore’s urban overstories as overwhelmingly native, though dominated by a range of successional conditions. By contrast, we found that ground layers were predominantly exotic, with abundant invasives or ruderal native species benefitting from disturbed conditions. Seven overstory types were distinguished, the majority under threat from aggressive vines. Most soils showed little evidence of compaction, but variable organic content. Distributed data allowed crosspatch comparison as well as within-patch analyses along edge-to-interior gradients. Species diversity, nativity, and overstory basal area all increased towards woodland interiors, whereas soil compaction and vine encroachment decreased. Structural and compositional shifts in both overstory and ground layer species revealed indicators of edge (15.2-18.7 m) and interior (&gt; 41.5 m) conditions, as well as evidence of transitional zones with distinct patterns of biodiversity. Despite high levels of fragmentation and disturbance that challenge municipal land managers operating with limited resources, rapid, low-cost sampling enabled comparison across multiple scales, encouraging repeated sampling and adaptive response to changing forest conditions. Qualitative and quantitative analysis as well as specific examples illustrated the generic utility of the protocol for a range of applications and its ability to produce new insight enabling management action and informed conservation planning.
 These data are further analyzed in:
 Baker, M.E., I. Yesilonis, L. Templeton, B. Shobe, J. Bos, N.F. Sonti, K. Lautar. 2025. Distributed urban forest patch characterization detects edge effects and patterns of ecosystem diversity for monitoring and management. Ecosphere, in press.
DOI: 10.6073/pasta/cdee03ef7b17c2a4027a4a8b33c5b09b
title: Exported particulate carbon and nitrogen measurements from 4-day sediment trap deployments in the CCE region, 2007 - 2019 (ongoing).

description: Sediment traps are used to measure particulate export flux of organic matter from the euphotic zone at various depths (up to 100), so to better understand what flows through the water column regarding food chains in the CCE. Tubes are filled with dense seawater to create a density gradient, and are attached to a wire connected to a drifting float for up to 4 days. Samples from the recovered array of the trap material (fecal matter and other sinking particles - zooplankton removed) are filtered onto precombusted filters for analyses of particulate carbon and nitrogen flux rates for each cycle in the CCE Process cruises (since 2007, ongoing)