cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.893679
title: CICE output from Global Ocean Forecast System (GOFS) 3.1 for 2018031412

description: INITIALIZATION: 2018031412FORECASTS:12-hrly out to 7.5 days (t180)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892694
title: CICE output from Navy Earth System Model (NESM) for 2018071712

description: INITIALIZATION: 2018071712FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892696
title: CICE output from Navy Earth System Model (NESM) for 2018072212

description: INITIALIZATION: 2018072212FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.817327
title: SADA and DESYRE DSSs descriptive classification criteria (main issues related to DSS's)

description: Brownfield rehabilitation is an essential step for sustainable land-use planning and management in the European Union. In brownfield regeneration processes, the legacy contamination plays a significant role, firstly because of the persistent contaminants in soil or groundwater which extends the existing hazards and risks well into the future; and secondly, problems from historical contamination are often more difficult to manage than contamination caused by new activities. Due to the complexity associated with the management of brownfield site rehabilitation, Decision Support Systems (DSSs) have been developed to support problem holders and stakeholders in the decision-making process encompassing all phases of the rehabilitation. This paper presents a comparative study between two DSSs, namely SADA (Spatial Analysis and Decision Assistance) and DESYRE (Decision Support System for the Requalification of Contaminated Sites), with the main objective of showing the benefits of using DSSs to introduce and process data and then to disseminate results to different stakeholders involved in the decision-making process. For this purpose, a former car manufacturing plant located in the Brasov area, Central Romania, contaminated chiefly by heavy metals and total petroleum hydrocarbons, has been selected as a case study to apply the two examined DSSs. Major results presented here concern the analysis of the functionalities of the two DSSs in order to identify similarities, differences and complementarities and, thus, to provide an indication of the most suitable integration options.
description: Supplement to: Stezar, Ileana Codruta; Pizzol, Lisa; Critto, Andrea; Ozunu, Alexandru; Marcomini, Antonio (2013): Comparison of risk-based decision-support systems for brownfield site rehabilitation: DESYRE and SADA applied to a Romanian case study. Journal of Environmental Management, 131, 383-393
DOI: 10.1594/pangaea.892356
title: CICE output from Navy Earth System Model (NESM) for 2018062512

description: INITIALIZATION: 2018062512FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.818067
title: (Table 9) Geochemistry of selected glass at DSDP Site 65-483
DOI: 10.1594/pangaea.890727
title: CICE output from Navy Earth System Model (NESM) for 2018052712

description: INITIALIZATION: 2018052712FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892358
title: CICE output from Navy Earth System Model (NESM) for 2018063012

description: INITIALIZATION: 2018063012FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892361
title: CICE output from Navy Earth System Model (NESM) for 2018070312

description: INITIALIZATION: 2018070312FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.817290
title: Meteorological synoptical observations from station Lindenberg (2007-01)
DOI: 10.1594/pangaea.890161
title: VECTRI simulations of malaria incidence in the highlands of Kenya, accounting for climate, model and initial condition uncertainty using a genetic algorithm framework

description: The output is simulated cases per 1000 population for a highland location in the Kenyan highlands. Each file represents a single ensemble member integration of the model in the final generation of the genetic algorithm calibration method outlined in the paper. The global meta data gives the set of parameters used to define this run, as well as the exact version of the model code used (ref. http://www.gitlab.com/tompkins/vectri).
description: Supplement to: Tompkins, Adrian Mark; Thomson, Madeleine C (2018): Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors. PLoS ONE, 13(9), e0200638
DOI: 10.1594/pangaea.611263
title: Abundance of bacteria in the water column at station M1_CTD251a in the Persian Gulf (Appendix)
DOI: 10.1594/pangaea.496953
title: Physical oceanography from BT during cruise M909I00084B_1984
DOI: 10.1594/pangaea.818020
title: (Table 4) Composition of fresh and altered glass from a pillow margin and the base of a massive flow at DSDP Hole 65-483B
DOI: 10.1594/pangaea.890728
title: CICE output from Navy Earth System Model (NESM) for 2018052812

description: INITIALIZATION: 2018052812FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892395
title: CICE output from Global Ocean Forecast System (GOFS) 3.1 for 2018070112

description: INITIALIZATION: 2018070112FORECASTS:12-hrly out to 7.5 days (t180)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice"
DOI: 10.1594/pangaea.494630
title: Physical oceanography during cruise F58SDX9602C
DOI: 10.1594/pangaea.892644
title: Areas-of-Interest from OpenStreetMap (Switzerland)

description: This dataset contains Areas-of-Interest (AOI) derived solely from OpenStreetMap data as geospatial data (polygons) covering Switzerland.The objective of Areas-of-Interest (AOI) is to convey visual information to a user (map reader or tourist) where there are areas are of "high interest" regarding facilities like shopping, eating, accomodation, sightseeing or leisure.The use of this file comprises touristic applications as well as urban planning and location-allocation analysis and site selection. The preferred spatial resolution of this dataset is between 10 and 2 meters on the ground (zoom levels from 16 to 14).A main input of AOI are Points-of-Interest (POI). Typical POI are restaurants, bars, shops or museums. While POI are mostly punctual, the geometry of AOI is of type area or polygon. In fact, AOI can be also based on the street network and potentially on more information like human location tracks. Google introduced 2016 AOI in their map products and visualized it as orange shades. Google is reportedly using human location tracks to derive information of "high activity" but did not disclose the algorithms behind their AOI layer.The goal of the research behind this dataset at hand was to produce AOI with a reproducible process which is based on open data, specifically POI and pedestrian routing data from the OpenStreetMap crowdsourcing project. The AOI are defined here as "Urban area at city or neighbourhood level with a high concentration of Points-of-Interests (POI) and typically located along a street of high spatial importance". Roughly five processing steps are used to generate these AOI: (1) filtering relevant POI (taking POI from OpenStreetMap as input), (2) spatially clustering selected POI using the DBSCAN algorithm, (3) creating areas using concave hull algorithm, (4) extend the resulting areas with a certain spatial buffer based on a network centrality algorithm (taking routes as input), (5) sanitizing the AOI e.g. by removing water areas and eliminating sliver polygons.
DOI: 10.1594/pangaea.815921
title: (Table S3b) Major element compositions of ODP Hole 181-1123B tephras

description: All standard deviations = 2sd.
DOI: 10.1594/pangaea.890729
title: CICE output from Navy Earth System Model (NESM) for 2018052912

description: INITIALIZATION: 2018052912FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.893381
title: CICE output from Navy Earth System Model (NESM) for 2018022512

description: INITIALIZATION: 2018022512FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.892658
title: How niche mismatches impair our ability to predict potential invasions

description: Accurately anticipating potential invasions is a crucial step to deal with invasive species. The most commonly used method to identify suitable areas for invasion is to build Ecological Niche Models (ENMs); nevertheless, the accuracy of these models might be highly overestimated. We explored how biogeographical, biological and methodological factors might affect our ability to identify suitable areas for invasions. We created virtual species to control and explore disagreements between the fundamental and the available niche in the native region. First, we explored how differences in species characteristics (climatic tolerance and dispersal capacity) might hinder our ability to predict invasions with ENMs. We also evaluated how different algorithms behave under those differences. Furthermore, we measured how prediction accuracy varies throughout the world, by evaluating the degree of niche mismatch in each zoogeographic region. In general, ENMs predictions were more accurate for species with broad tolerance, while species with narrow tolerance had high levels of overprediction. We were able to distinguish algorithms by how much they under or overestimate species fundamental niche. Some zoogeographic regions are indeed more error-prone, as levels of climatic incompleteness and fundamental niche representation within the distribution vary throughout the world. We demonstrate that predicting potential invasions with ENMs might incur in wrongful niche estimation, therefore, missing potential locations for invasion. Biological factors such as species tolerance and dispersal capacity must be considered when discussing model uncertainty. Besides, algorithms had different predictions, with high levels of under or over prediction. Therefore, when it comes to invasions predictions, we must be meticulous, considering that the fundamental niche might not be adequately expressed within the native region.
description: Virtual species data used in the paper: "How niche mismatches impair our ability to predict potential invasions". Due to the large files size, data here consists of occurrence used for model fitting and the tables for niche and geographical analysis.
description: Supplement to: Andrade, André; Velazco, Santiago José Elíaz; De Marco Júnior, Paulo (submitted): How well can we predict global invasions. Global Ecology and Biogeography
DOI: 10.1594/pangaea.887844
title: Population trends of seabirds in Stinker Point, Elephant Island, South Shetlands - link to shapefiles

description: This dataset contains in a zipfile the shapefiles for the seabird breeding colonies at Stinker Point (Elephant Island, Maritime Antarctic Peninsula), the elevation of Stinker Point and the Elephant Island contour. All shapefiles are projected in WGS 1984 World Mercator projected coordinate system.
description: Supplement to: Petry, Maria Virginia; Valls, Fernanda C L; Petersen, Elisa; Finger, Julia V G; Krüger, Lucas (2018): Population trends of seabirds at Stinker Point, Elephant Island, Maritime Antarctica. Antarctic Science, 30(4), 220-226
DOI: 10.1594/pangaea.892686
title: Mapping and Modeling the Breeding Habitat of the Western Atlantic Red Knot, Calidris canutus rufa, at Local and Regional Scales

description: The Western Atlantic population of Red Knot (Calidris canutus rufa) has undergone dramatic declines in recent decades and conservation biologists have sought to improve knowledge about the species' ecology in an effort to understand these declines. One major information gap has been the lack of a detailed understanding of range and habitat use during the breeding season, when the species is distributed sparsely across the Canadian Arctic. Airborne radio-telemetry surveys of Red Knots tagged in Delaware Bay, New Jersey were conducted across the south and central Canadian Arctic, from Victoria Island in the west to Baffin Island in the east. Intensive field surveys were conducted on Southampton Island, Nunavut over successive summer field seasons to locate nesting Red Knots and record characteristics of their nesting habitat. Maximum entropy modeling (Maxent) and geographic information system (GIS) data on environmental characteristics were used to predict Red Knot habitat suitability at two spatial scales: of nesting site location suitability at the local scale across Southampton Island, and of breeding habitat suitability (i.e., both nesting and foraging habitat) at a broader, regional scale across the south and central Canadian Arctic.
DOI: 10.1594/pangaea.887261
title: Output from a global scavenging and circulation ocean model of thorium-230 and protactinium-231 (ProThorP 0.1)

description: This archive contains all output from the PISCES-v2 model, containing 24 carbon, nitrate, phosphate and iron tracers. The model is extended with models of lithogenic particles and thorium-230 and protactinium-231, adding eight additional tracers, namely small and big lithogenic particles, and dissolved, small and big Th-230 and Pa-231 (referred to as ProThorP). Thus the model contains 32 different tracers (ptrc) for two different simulations, as well as diagnostic data (diad). The model is executed as part of the global ocean general cirulation model NEMO in the ORCA2 configuration (2° x 2° cos(phi) x 31 layers).
description: [NEMO]: https://www.nemo-ocean.eu/[PISCES]: https://doi.org/10.5194/gmd-8-2465-2015[ProThorP]: https://doi.org/10.5281/zenodo.1009064
description: Supplement to: van Hulten, Marco M P; Dutay, Jean-Claude; Roy-Barman, Matthieu (2018): A global scavenging and circulation ocean model of thorium-230 and protactinium-231 with improved particle dynamics (NEMO–ProThorP 0.1). Geoscientific Model Development, 11(9), 3537-3556
DOI: 10.1594/pangaea.892703
title: CICE output from Global Ocean Forecast System (GOFS) 3.1 for 2018070812

description: INITIALIZATION: 2018070812FORECASTS:12-hrly out to 7.5 days (t180)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;
DOI: 10.1594/pangaea.893382
title: CICE output from Navy Earth System Model (NESM) for 2018022612

description: INITIALIZATION: 2018022612FORECASTS:daily at 00Z out to 45 days (t1068)GRIDS:ARCu0.08 -- poleward of 40NANTu0.08 -- poleward of 49.52SPARAMETERS:hi:standard_name = "sea_ice_thickness" ;hs:standard_name = "surface_snow_thickness" ;Tsfc:standard_name = "surface_temperature_where_sea_ice" ;aice:standard_name = "sea_ice_area_fraction" ;uvel:standard_name = "eastward_sea_ice_velocity" ;vvel:standard_name = "northward_sea_ice_velocity" ;sst:standard_name = "sea_water_temperature" ;uocn:standard_name = "eastward_sea_water_velocity" ;vocn:standard_name = "northward_sea_water_velocity" ;Tair:standard_name = "air_temperature" ;strength:standard_name = "compressive_strength_of_sea_ice" ;