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  • PANGAEA  (8)
  • Frontiers Media SA  (4)
  • 2020-2024  (12)
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  • 1
    Publikationsdatum: 2022-01-31
    Beschreibung: Examination of corals and reef-associated organisms which endure in extreme coral reef environments is challenging our understanding of the conditions that organisms can survive under. By studying individuals naturally adapted to unfavorable conditions, we begin to better understand the important traits required to survive rapid environmental and climate change. This Research Topic, comprising reviews, and original research articles, demonstrates the current state of knowledge regarding the diversity of extreme coral habitats, the species that have been studied, and the knowledge to-date on the mechanisms, traits and trade-offs that have facilitated survival.
    Schlagwort(e): GC1-1581 ; Q1-390 ; ocean acidification ; Climate Change ; Coral Bleaching ; Marginal ; Extreme ; fish ; ocean warming ; coral reef ; Environmental stress
    Sprache: Englisch
    Format: image/jpeg
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2024-04-05
    Beschreibung: With global populations expected to exceed 9.2 billion by 2050 and available land and water resources devoted to crop production dwindling, we face significant challenges to secure global food security. Only 12 plant species feed 80% of the world’s population, with just three crop species (wheat, rice and maize) accounting for food consumed by 50% of the global population. Annual losses to crop pests and pathogens are significant, thought to be equivalent to that required to feed a billion people, at a time when crop productivity has plateaued. With pesticide applications becoming increasingly unfeasible on cost, efficacy and environmental grounds, there is growing interest in exploiting plant resistance and tolerance traits for crop protection. Indeed, mankind has been selectively breeding plants for desirable traits for thousands of years. However, resistance and tolerance traits have not always been those most desired, and in many cases have been inadvertently lost during the domestication process: crops have been effectively ‘disarmed by domestication’. Moreover, mechanistic understanding of how resistance and tolerance traits operate is often incomplete, which makes identifying the right combination for crop protection difficult. We aimed to address this Research Topic by inviting authors to contribute their knowledge of appropriate resistance and tolerance traits, explore what is known about durability and breakdown of defensive traits and, finally, asking what are the prospects for exploiting these traits for crop protection. The research topic summarised in this book addresses some of the most important issues in the future sustainability of global crop production.
    Schlagwort(e): QK1-989 ; Q1-390 ; Integrated Pest Management ; crop protection ; Insect herbivore ; pathogen ; biological control ; global climate change ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences
    Sprache: Englisch
    Format: image/jpeg
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
    Publikationsdatum: 2024-04-05
    Beschreibung: Biometals such as copper, zinc and iron have key biological functions, however, aberrant metabolism can lead to detrimental effects on cell function and survival. These biometals have important roles in the brain, driving cellular respiration, antioxidant activity, intracellular signaling and many additional structural and enzymatic functions. There is now considerable evidence that abnormal biometal homeostasis is a key feature of many neurodegenerative diseases and may have an important role in the onset and progression of disorders such as Alzheimer’s, Parkinson’s, prion and motor neuron diseases. Recent studies also support biometal roles in a number of less common neurodegenerative disorders. The role of biometals in a growing list of brain disorders is supported by evidence from a wide range of sources including molecular genetics, biochemical studies and biometal imaging. These studies have spurred a growing interest in understanding the role of biometals in brain function and disease as well as the development of therapeutic approaches that may be able to restore the altered biometal chemistry of the brain. These approaches range from genetic manipulation of biometal transport to chelation of excess metals or delivery of metals where levels are deficient. A number of these approaches are offering promising results in cellular and animal models of neurodegeneration with successful translation to pre-clinical and clinical trials. At a time of aging populations and slow progress in development of neurotherapeutics to treat age-related neurodegenerative diseases, there is now a critical need to further our understanding of biometals in neurodegeneration. This issue covers a broad range of topics related to biometals and their role in neurodegeneration. It is hoped that this will inspire greater discussion and exchange of ideas in this crucial area of research and lead to positive outcomes for sufferers of these neurodegenerative diseases.
    Schlagwort(e): RC321-571 ; Q1-390 ; Brain ; neurodegenerative disease ; Neurons ; Metals ; Iron ; Copper ; Alzheimer's disease ; Zinc ; Manganese ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Sprache: Englisch
    Format: image/jpeg
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
    Publikationsdatum: 2023-02-07
    Beschreibung: This dataset includes organic carbon measurements on sediment samples collected in Bute Inlet (British Columbia, Canada) in October 2016 (cruise number PGC2016007) and October 2017 (cruise number PGC2017005) aboard the research vessel CCGS Vector. The cruise PGC2016007 took place between 7 October and 17 October 2016 and was led by Gwyn Lintern. The cruise PGC2017005 took place between 19 and 29 October and was led by Cooper Stacey. River samples were taken in the Homathko and Southgate rivers using Niskin bottles in the water column and a grab sampler in the river beds and the river deltas
    Schlagwort(e): Age, 14C AMS; Age, dated; Bottle, Niskin; Bute Inlet, British Columbia, Canada; Carbon, organic, total; DEPTH, sediment/rock; DEPTH, water; Environment; Event label; fjords; Grab; GRAB; Latitude of event; Longitude of event; NIS; organic carbon (OC); Percentile 50; Percentile 90; PGC-2017-005; PGC-2017-005_RB16; PGC-2017-005_RB22; PGC-2017-005_RB24; PGC-2017-005_RBL18; PGC-2017-005_RD12; PGC-2017-005_RD14; PGC-2017-005_RD6; PGC-2017-005_RD8; PGC-2017-005_RP11; PGC-2017-005_RP13; PGC-2017-005_RP15; PGC-2017-005_RP16; PGC-2017-005_RP17; PGC-2017-005_RP19; PGC-2017-005_RP7; PGC-2017-005_RP9; PGC-2017-005_RW23; PGC-2017-005_SS18; PGC-2017-005_SS20; River; sediment; submarine canyon; Vector; δ13C, organic carbon
    Materialart: Dataset
    Format: text/tab-separated-values, 118 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
    Publikationsdatum: 2023-02-07
    Beschreibung: This dataset includes organic carbon measurements on sediment samples collected in Bute Inlet (British Columbia, Canada) in October 2016 (cruise number PGC2016007) and October 2017 (cruise number PGC2017005) aboard the research vessel CCGS Vector. The cruise PGC2016007 took place between 7 October and 17 October 2016 and was led by Gwyn Lintern. The cruise PGC2017005 took place between 19 and 29 October and was led by Cooper Stacey. Marine sediment samples were collected in Bute Inlet using a box corer for the sandy samples in the submarine channel and a piston corer for the muddy samples in the overbanks and distal basin.
    Schlagwort(e): 1; 10; 11; 12; 13; 14; 15; 2; 3; 4; 5; 6; 7; 8; 9; Age, 14C AMS; Age, dated; BC; Box corer; Bute Inlet, British Columbia, Canada; Carbon, organic, total; Core; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Elevation of event; Event label; fjords; Latitude of event; Longitude of event; Method/Device of event; organic carbon (OC); PC; Percentile 50; Percentile 90; PGC-2016-003; PGC-2016-003_STN01; PGC-2016-007; PGC-2016-007_STN010; PGC-2016-007_STN014; PGC-2016-007_STN015; PGC-2016-007_STN019; PGC-2016-007_STN020; PGC-2016-007_STN021; PGC-2016-007_STN025; PGC-2016-007_STN026; PGC-2016-007_STN028; PGC-2016-007_STN029; PGC-2016-007_STN030; PGC-2016-007_STN031; PGC-2016-007_STN032; PGC-2016-007_STN036; PGC-2016-007_STN09; Piston corer; sediment; Sub-Environment; submarine canyon; Vector; δ13C, organic carbon
    Materialart: Dataset
    Format: text/tab-separated-values, 516 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 6
    Publikationsdatum: 2023-12-14
    Beschreibung: A suite of eight glassy rims and six crystalline interiors from pillowed basalts was collected from within the Mid-Atlantic Ridge rift valley between 25°N and 30°N during Trans-Atlantic Geotraverse (TAG) NOAA cruises using the R/V Discoverer. Radiochemical neutron activation analysis has been used to determine Tl, Rb. Cs. Co and Cr. Major element and S analyses of the glasses were determined by electron probe microanalysts of small polished chips of glass.
    Schlagwort(e): Aluminium oxide; Atlantic Ocean; Caesium; Calcium oxide; Chromium; Cobalt; Discoverer (1966); Dredge; DRG; Electron Probe Microanalysis (EPMA); Elevation of event; Event label; Geochemistry; Identification; Iron oxide, FeO; Latitude of event; Longitude of event; Magnesium oxide; manganese micronodule; manganese nodule; Manganese oxide; NOAA and MMS Marine Minerals Geochemical Database; NOAA-MMS; ocean; Potassium oxide; Radiochemical neutron activation analysis (RNAA); Rock type; Rubidium; Sample type; sediment; Silicon dioxide; Sodium oxide; Sulfur; T3-71-10C; T3-71-7A; T3-72-16; T3-72-17; T4-73-6; TAG1971; TAG1971-10C; TAG1971-7A; TAG1972; TAG1972-16; TAG1972-17; TAG1973; TAG1973-6A; Thallium; Titanium dioxide; Trans-Atlantic Geotraverse 1971; Trans-Atlantic Geotraverse 1972; Trans-Atlantic Geotraverse 1973
    Materialart: Dataset
    Format: text/tab-separated-values, 188 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
    Publikationsdatum: 2023-12-14
    Beschreibung: A suite of three palagonites from pillowed basalts collected from within the Mid-Atlantic Ridge rift valley between 25°N and 30°N wer analysed along with another suite of two hydrothermally altered basaltic breccias and four hydrothermal manganese crusts collected from the TAG hydrothermal field at 26°N on the Mid-Atlantic Ridge. These specimen were collected during Trans-Atlantic Geotraverse (TAG) NOAA cruises using the R/V Discoverer. Two more hydrogenous ferromanganese crusts wer also analysed. They were collected from the eastern extension of the Atlantis Fracture Zone aboard the R/V Kurchatov in 1975. Radiochemical neutron activation analysis has been used to determine Tl, Rb. Cs. Co and Cr. Iron, Mn, and Mg concentrations in the crystalline samples and Mn crusts have been determined by AAS. K was determined by flame photometry, and S in these samples (as well as five glasses) has been determined with a Leco Automatic Sulfur titrator.
    Schlagwort(e): AK20-T0-75-1A; Akademik Kurchatov; AKU20; Aluminium oxide; Atlantic Ocean; Atomic absorption spectrophotometry; Caesium; Chromium; Cobalt; Discoverer (1966); Dredge; Dredge, chain bag; DRG; DRG_C; Elevation of event; Event label; Flame photometry; Geochemistry; Identification; Iron oxide, FeO; Latitude of event; Leco Automatic Sulfur titrator; Longitude of event; Magnesium oxide; manganese micronodule; manganese nodule; Manganese oxide; NOAA and MMS Marine Minerals Geochemical Database; NOAA-MMS; ocean; Potassium oxide; Radiochemical neutron activation analysis (RNAA); Rock type; Rubidium; Sample type; sediment; Sulfur; T0-75-1A; T3-71D 148-2B; T3-72-17; T4-73-2A3; T4-73-6; TAG1971; TAG1971-2B; TAG1972; TAG1972-17; TAG1973; TAG1973-2A; TAG1973-6A; Thallium; Trans-Atlantic Geotraverse 1971; Trans-Atlantic Geotraverse 1972; Trans-Atlantic Geotraverse 1973
    Materialart: Dataset
    Format: text/tab-separated-values, 121 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 8
    Publikationsdatum: 2024-03-15
    Beschreibung: Experiments with coral fragments (i.e. nubbins) have shown that net calcification is depressed by elevated PCO2. Evaluating the implications of this finding requires scaling of results from nubbins to colonies, yet the experiments to codify this process have not been carried out. Building from our previous research demonstrating that net calcification of Pocillopora verrucosa (2–13 cm diameter) was unaffected by PCO2 (400 and 1000 µatm) and temperature (26.5 and 29.7°C), we sought generality to this outcome by testing how colony size modulates PCO2 and temperature sensitivity in a branching acroporid. Together, these taxa represent two of the dominant lineages of branching corals on Indo-Pacific coral reefs. Two trials conducted over 2 years tested the hypothesis that the seasonal range in seawater temperature (26.5 and 29.2°C) and a future PCO2 (1062 µatm versus an ambient level of 461 µatm) affect net calcification of an ecologically relevant size range (5–20 cm diameter) of colonies of Acropora hyacinthus. As for P. verrucosa, the effects of temperature and PCO2 on net calcification (mg day−1) of A. verrucosa were not statistically detectable. These results support the generality of a null outcome on net calcification of exposing intact colonies of branching corals to environmental conditions contrasting seasonal variation in temperature and predicted future variation in PCO2. While there is a need to expand beyond an experimental culture relying on coral nubbins as tractable replicates, rigorously responding to this need poses substantial ethical and logistical challenges.
    Schlagwort(e): Acropora hyacinthus; Alkalinity, total; Alkalinity, total, standard error; Animalia; Aragonite saturation state; Aragonite saturation state, standard error; Area; Benthic animals; Benthos; Bicarbonate ion; Calcification/Dissolution; Calcification rate; Calcite saturation state; Calculated using seacarb; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard error; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Cnidaria; Coast and continental shelf; Containers and aquaria (20-1000 L or 〈 1 m**2); Diameter; EXP; Experiment; Experiment duration; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Identification; Irradiance; Irradiance, standard error; Laboratory experiment; Moorea_north_shore; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Partial pressure of carbon dioxide (water) at sea surface temperature (wet air), standard error; pH; pH, standard error; Potentiometric; Potentiometric titration; Registration number of species; Replicates; Salinity; Salinity, standard error; Single species; Size; South Pacific; Species; Temperature; Temperature, water; Temperature, water, standard error; Treatment; Tropical; Type; Uniform resource locator/link to reference
    Materialart: Dataset
    Format: text/tab-separated-values, 1334 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 9
    Publikationsdatum: 2024-03-15
    Beschreibung: Coral reefs are threatened by ocean acidification (OA), which depresses net calcification of corals, calcified algae, and coral reef communities. These effects have been quantified for many organisms, but most experiments last weeks-to-months, and do not test for effects on community structure. Here, the effects of OA on back reef communities from Mo'orea, French Polynesia (17.492 S, 149.826 W), were tested from 12 November 2015 to 16 November 2016 in outdoor flumes maintained at mean pCO2 levels of 364 µatm, 564 µatm, 761 µatm, and 1067 µatm. The communities consisted of four corals and two calcified algae, with change in mass (Gnet, a combination of gross accretion and dissolution) and percent cover recorded monthly. For massive Porites and Montipora spp., Gnet differed among treatments, and at 1067 µatm (relative to ambient) was reduced and still positive; for Porolithon onkodes, all of which died, Gnet was negative at high pCO2, revealing dissolution (sample sizes were too small for analysis of Gnet for other taxa). Growth rates (% cover month−1) were unaffected by pCO2 for Montipora spp., P. rus, Pocillopora verrucosa, and Lithophyllum kotschyanum, but were depressed for massive Porites at 564 µatm. Multivariate community structure changed among seasons, and the variation under all elevated pCO2 treatments differed from that recorded at 364 µatm, and was greatest under 564 µatm and 761 µatm pCO2. Temporal variation in multivariate community structure could not be attributed solely to the effects of OA on the chemical and physical properties of seawater. Together, these results suggest that coral reef community structure may be more resilient to OA than suggested by the negative effects of high pCO2 on Gnet of their component organisms.
    Schlagwort(e): Alkalinity, total; Animalia; Aragonite saturation state; Area; Benthic animals; Benthos; Bicarbonate ion; Calcification/Dissolution; Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Cnidaria; Coast and continental shelf; Community composition and diversity; Containers and aquaria (20-1000 L or 〈 1 m**2); Dry mass; Entire community; EXP; Experiment; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Group; Identification; Laboratory experiment; Lithophyllum kotschyanum; Macroalgae; massive Porites; Month; Montipora sp.; Moorea_coral; Number; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; Plantae; Pocillopora verrucosa; Porites rus; Porolithon onkodes; Potentiometric; Potentiometric titration; Rhodophyta; Rocky-shore community; Salinity; Single species; South Pacific; Species; Temperature, water; Treatment: partial pressure of carbon dioxide; Tropical; Type of study; Year of sampling
    Materialart: Dataset
    Format: text/tab-separated-values, 48833 data points
    Standort Signatur Erwartet Verfügbarkeit
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  • 10
    Publikationsdatum: 2024-04-29
    Beschreibung: We created a 3D GNSS surface velocity field to estimate tectonic plate motion and test the effect of a set of 1D and 3D Glacial Isostatic Adjustment (GIA) models on tectonic plate motion estimates. The main motivation for creating a bespoke 3D velocity field is to include a larger number of GNSS sites in the GIA-affected areas of investigation, namely North America, Europe, and Antarctica. We created the GNSS surface velocity field using the daily network solutions submitted to the International GNSS Service (IGS) “repro2” data processing campaign, and other similarly processed GNSS solutions. We combined multiple epoch solutions into unique global epoch solutions of high stability. The GNSS solutions we used were processed with the latest available methods and models at the time: all the global and regional solutions adhere to IGS repro2 standards. Every network solution gives standard deviations of site position coordinates and the correlations between the network sites. We deconstrained and combined the global networks and aligned them to the most recent ITRF2014 reference frame on a daily level. Additionally, several regional network solutions were deconstrained and aligned to the unique global solutions. The process was performed using the Tanya reference frame combination software (Davies & Blewitt, 1997; doi:10.1029/2000JB900004) which we updated to facilitate changes in network combination method and ITRF realisation. This resulted in 57% reduction of the WRMS of the alignment post-fit residuals compared to the alignment to the previous ITRF2008 reference frame for an overlapping period. We estimated linear velocities from the time series of GNSS coordinates using the MIDAS trend estimator (Blewitt et al., 2016; doi:10.1002/2015JB012552). The sites selected through multiple steps of quality control constitute a final GNSS surface velocity field which we denote NCL20. This velocity field has horizontal uncertainties mostly within 0.5 mm/yr, and vertical uncertainties mostly within 1 mm/yr, which make it suitable for testing GIA models and estimating plate motion models.
    Schlagwort(e): 1LSU_GNSS; 1NSU_GNSS; 1ULM_GNSS; AB04_GNSS; AB08_GNSS; AB12_GNSS; AC58_GNSS; ACOR_GNSS; ACP1_GNSS; ACP6_GNSS; ACSO_GNSS; ACUM_GNSS; ADE1_GNSS; ADIS_GNSS; ADRI_GNSS; AJAC_GNSS; AL30_GNSS; AL40_GNSS; AL50_GNSS; AL60_GNSS; AL70_GNSS; AL90_GNSS; ALCI_GNSS; ALES_GNSS; ALGO_GNSS; ALIC_GNSS; ALRT_GNSS; AMC2_GNSS; ANDO_GNSS; ANG1_GNSS; ANP5_GNSS; ANTO_GNSS; AOML_GNSS; AOPR_GNSS; ARBT_GNSS; ARCM_GNSS; ARFY_GNSS; ARGI_GNSS; ARHP_GNSS; ARHR_GNSS; ARJM_GNSS; ARP3_GNSS; ARPG_GNSS; ARTU_GNSS; ASC1_GNSS; ASCG_GNSS; ASHV_GNSS; ASUB_GNSS; AUCK_GNSS; AUDR_GNSS; AUS5_GNSS; AUTN_GNSS; AVCA_GNSS; AXPV_GNSS; BACA_GNSS; BACK_GNSS; BACO_GNSS; BADH_GNSS; BAHR_GNSS; BAIA_GNSS; BAIE_GNSS; BAKE_GNSS; BAN2_GNSS; BARH_GNSS; BARN_GNSS; BAUS_GNSS; BAYR_GNSS; BBYS_GNSS; BCLN_GNSS; BELE_GNSS; BELF_GNSS; BELL_GNSS; BENN_GNSS; BET1_GNSS; BIAZ_GNSS; BIL5_GNSS; BISK_GNSS; BJCO_GNSS; BJU0_GNSS; BLA1_GNSS; BNDY_GNSS; BNFY_GNSS; BOD3_GNSS; BOGI_GNSS; BOMJ_GNSS; BOR1_GNSS; BORJ_GNSS; BORK_GNSS; BORR_GNSS; BPDL_GNSS; BRAZ_GNSS; BRFT_GNSS; BRGS_GNSS; BRIP_GNSS; BRMF_GNSS; BRMU_GNSS; BRST_GNSS; BRTW_GNSS; BRU5_GNSS; BRUS_GNSS; BSCN_GNSS; BSMK_GNSS; BUDP_GNSS; BUE1_GNSS; BUMS_GNSS; BURI_GNSS; BVHS_GNSS; BYDG_GNSS; CACE_GNSS; CAEN_GNSS; CAGL_GNSS; CAGS_GNSS; CALU_GNSS; CANT_GNSS; CAPF_GNSS; CARM_GNSS; CAS1_GNSS; CASB_GNSS; CASC_GNSS; CASP_GNSS; CAYU_GNSS; CBMD_GNSS; CBSB_GNSS; CCV5_GNSS; CEBR_GNSS; CEDU_GNSS; CEFE_GNSS; CFRM_GNSS; CGGN_GNSS; CHA1_GNSS; CHAN_GNSS; CHAT_GNSS; CHB5_GNSS; CHIZ_GNSS; CHL1_GNSS; CHPI_GNSS; CHR1_GNSS; CHT1_GNSS; CHTI_GNSS; CHUR_GNSS; CJTR_GNSS; CKIS_GNSS; CLIB_GNSS; CLK5_GNSS; CLRK_GNSS; CN13_GNSS; CN14_GNSS; CN15_GNSS; CN16_GNSS; CN23_GNSS; CN24_GNSS; CN28_GNSS; CN29_GNSS; CN33_GNSS; CN34_GNSS; CN35_GNSS; CN41_GNSS; CN46_GNSS; CN53_GNSS; CNC0_GNSS; CNIV_GNSS; CNMR_GNSS; COLA_GNSS; CONO_GNSS; CORB_GNSS; CORC_GNSS; COTE_GNSS; COVG_GNSS; COVX_GNSS; CPAR_GNSS; CRAK_GNSS; CRAO_GNSS; CRDI_GNSS; CRST_GNSS; CTAB_GNSS; CTBR_GNSS; CTGU_GNSS; CTPU_GNSS; CTWN_GNSS; CUIB_GNSS; CUSV_GNSS; CVMS_GNSS; DAKR_GNSS; DANE_GNSS; DARE_GNSS; DAVM_GNSS; DEAR_GNSS; DEFI_GNSS; DEGE_GNSS; DELM_GNSS; DENE_GNSS; DENT_GNSS; DEVI_GNSS; DGLS_GNSS; DNRC_GNSS; DOBS_GNSS; DOMS_GNSS; DOUR_GNSS; DREM_GNSS; DRV5_GNSS; DSL1_GNSS; DUBO_GNSS; DUM1_GNSS; DUPT_GNSS; EBRE_GNSS; ECSD_GNSS; EDOC_GNSS; EGLT_GNSS; EIJS_GNSS; ELEN_GNSS; ENG1_GNSS; ENIS_GNSS; ENTZ_GNSS; EPRT_GNSS; ESCO1_GNSS; ESCU_GNSS; EUR2_GNSS; EUSK_GNSS; EVPA_GNSS; EXU0_GNSS; FALL_GNSS; FFMJ_GNSS; FIE0_GNSS; FLIN_GNSS; FLIU_GNSS; FLM5_GNSS; FLRS_GNSS; FONP_GNSS; FOYL_GNSS; FREE_GNSS; FREI_GNSS; FRKN_GNSS; FTP4_GNSS; FUNC_GNSS; GAAT_GNSS; GABR_GNSS; GACC_GNSS; GACL_GNSS; GACR_GNSS; GAIA_GNSS; GAIT_GNSS; GAL1_GNSS; GANP_GNSS; GARF_GNSS; GAST_GNSS; GCEA_GNSS; GDMA_GNSS; Glacial Isostatic Adjustment (GIA) model; GLPM_GNSS; GLPS_GNSS; GLSV_GNSS; GMSD_GNSS; GNSS; GNSS Receiver; GNVL_GNSS; GODE_GNSS; GOGA_GNSS; GOPM_GNSS; GOUG_GNSS; GRAS_GNSS; GRE0_GNSS; GRIS_GNSS; GRN0_GNSS; GRTN_GNSS; GTK0_GNSS; GUAM_GNSS; GUAX_GNSS; GUIP_GNSS; GUUG_GNSS; GWWL_GNSS; HAAG_GNSS; HAC6_GNSS; HAG6_GNSS; HALY_GNSS; HAMM_GNSS; HAMP_GNSS; HARK_GNSS; HASM_GNSS; HBCH_GNSS; HBRK_GNSS; HCES_GNSS; HDIL_GNSS; HELG_GNSS; HERS_GNSS; HILB_GNSS; HILO_GNSS; HIPT_GNSS; HJOR_GNSS; HKLO_GNSS; HLFX_GNSS; HNLC_GNSS; HNPT_GNSS; HNUS_GNSS; HOB2_GNSS; HOBU_GNSS; HOE2_GNSS; HOLM_GNSS; HONS_GNSS; horizontal GIA; HOS0_GNSS; HOUM_GNSS; HOUS_GNSS; HOWE_GNSS; HOWN_GNSS; HRMM_GNSS; HRST_GNSS; HUGO_GNSS; HYDE_GNSS; IBIZ_GNSS; ICT1_GNSS; IGEO_GNSS; IGGY_GNSS; IISC_GNSS; ILDX_GNSS; ILHA_GNSS; ILSA_GNSS; ILUC_GNSS; IMBT_GNSS; IMPZ_GNSS; INAB_GNSS; INES1_GNSS; INGG_GNSS; INVM_GNSS; INWN_GNSS; IQAL_GNSS; IQUI_GNSS; IRBE_GNSS; IRKM_GNSS; ISCO_GNSS; ISPA_GNSS; IZAN_GNSS; JAB2_GNSS; JCT1_GNSS; JFNG_GNSS; JFWS_GNSS; JOEN_GNSS; JONM_GNSS; JOZE_GNSS; JXVL_GNSS; KAR0_GNSS; KARL_GNSS; KARR_GNSS; KAT1_GNSS; KAUS_GNSS; KELY_GNSS; KERM_GNSS; KEVO_GNSS; KEW5_GNSS; KHAJ_GNSS; KHAR_GNSS; KIRI_GNSS; KIRM_GNSS; KIRU_GNSS; KIVE_GNSS; KJUN_GNSS; KLOP_GNSS; KMOR_GNSS; KNGS_GNSS; KNS5_GNSS; KNTN_GNSS; KOK1_GNSS; KOKM_GNSS; KOSG_GNSS; KOUC_GNSS; KOUG_GNSS; KOUR_GNSS; KRA0_GNSS; KRSS_GNSS; KRTV_GNSS; KST5_GNSS; KSTU_GNSS; KSU1_GNSS; KULU_GNSS; KUN0_GNSS; KUNZ_GNSS; KURE_GNSS; KUUJ_GNSS; KUUS_GNSS; KUWT_GNSS; KVTX_GNSS; KWJ1_GNSS; KWST_GNSS; KYBO_GNSS; KYMH_GNSS; KYTB_GNSS; KYTC_GNSS; KYTD_GNSS; KYTE_GNSS; KYTG_GNSS; KYTH_GNSS; KYTK_GNSS; KYTL_GNSS; KYW1_GNSS; KZN2_GNSS; LAMA_GNSS; LAMT_GNSS; LANS_GNSS; LATITUDE; LCDT_GNSS; LCHS_GNSS; LCKM_GNSS; LCSB_GNSS; LEBA_GNSS; LEES_GNSS; LEIJ_GNSS; LEK0_GNSS; LEON_GNSS; LESV_GNSS; LHCL_GNSS; LHUE_GNSS; LIL2_GNSS; LKHU_GNSS; LLIV_GNSS; LMNO_GNSS; LODZ_GNSS; LOFS_GNSS; LONGITUDE; LOVM_GNSS; LPAL_GNSS; LPGS_GNSS; LPIL_GNSS; LPLY_GNSS; LROC_GNSS; LSBN_GNSS; LSUA_GNSS; LWN0_GNSS; LWX1_GNSS; LYCO_GNSS; LYNS_GNSS; LYRS_GNSS; MACC_GNSS; MADM_GNSS; MADO_GNSS; MAG0_GNSS; MAIR_GNSS; MAJU_GNSS; MALD_GNSS; MALL_GNSS; MAN2_GNSS; MAPA_GNSS; MAR6_GNSS; MARJ_GNSS; MARN_GNSS; MARS_GNSS; MAS1_GNSS; MAUI_GNSS; MAW1_GNSS; MAYZ_GNSS; MCAR_GNSS; MCD5_GNSS; MCIL_GNSS; MCM4_GNSS; MCN1_GNSS; MCNE_GNSS; MCTY_GNSS; MDOR_GNSS; MDR6_GNSS; MDVJ_GNSS; MET6_GNSS; MET7_GNSS; METG_GNSS; MFLD_GNSS; MIAR_GNSS; MICW_GNSS; MIDS_GNSS; MIGD_GNSS; MIHO_GNSS; MIHT_GNSS; MIIR_GNSS; MIKL_GNSS; MIL1_GNSS; MIMN_GNSS; MIMQ_GNSS; MIN0_GNSS; MINI_GNSS; MIPR_GNSS; MIST_GNSS; MKEA_GNSS; MLF1_GNSS; MLVL_GNSS; MNBD_GNSS; MNBE_GNSS; MNCA_GNSS; MNDN_GNSS; MNGR_GNSS; MNJC_GNSS; MNP1_GNSS; MNPL_GNSS; MNRM_GNSS; MNRT_GNSS; MNRV_GNSS; MNSC_GNSS; MNTF_GNSS; MNVI_GNSS; MOAL_GNSS; MOB1_GNSS; MOBS_GNSS; MOED_GNSS; MOEL_GNSS; MOGF_GNSS; MOPN_GNSS; MORP_GNSS; MOVB_GNSS; MPLA_GNSS; MPLE_GNSS; MRO1_GNSS; MRRN_GNSS; MSB5_GNSS; MSHT_GNSS; MSKU_GNSS; MSNA_GNSS; MSPK_GNSS; MSSC_GNSS; MSYZ_GNSS; MTMS_GNSS; MTNT_GNSS; MTY2_GNSS; NAIN_GNSS; NAMA_GNSS; NAPL_GNSS; NAS0_GNSS; NAUR_GNSS; NAUS_GNSS; NBR6_GNSS; NCDU_GNSS; NCGO_GNSS; NCJA_GNSS; NCPO_GNSS; NCSW_GNSS; NCWH_GNSS; NCWI_GNSS; NDMB_GNSS; NEDR_GNSS; NEGI_GNSS; NEIA_GNSS; NESC_GNSS; NEWL_GNSS; NHUN_GNSS; NIST_GNSS; NIUM_GNSS; NJCM_GNSS; NJHC_GNSS; NJI2_GNSS; NJOC_GNSS; NJTW_GNSS; NKLG_GNSS; NLIB_GNSS; NMKM_GNSS; NNOR_GNSS; NOR0_GNSS; NOR1_GNSS; NOR3_GNSS; NOUM_GNSS; NPLD_GNSS; NPRI_GNSS; NRCM_GNSS; NRIL_GNSS; NRL1_GNSS; NRMD_GNSS; NTUS_GNSS; NYBH_GNSS; NYBT_GNSS; NYCL_GNSS; NYCP_GNSS; NYDV_GNSS; NYFD_GNSS; NYFS_GNSS; NYFV_GNSS; NYHC_GNSS; NYHM_GNSS; NYHS_GNSS; NYIR_GNSS; NYLV_GNSS; NYMD_GNSS; NYML_GNSS; NYNS_GNSS; NYON_GNSS; NYPD_GNSS; NYPF_GNSS; NYRB_GNSS; NYST_GNSS; NYWL_GNSS; NYWT_GNSS; OAKH_GNSS; ODS5_GNSS; OHAS_GNSS; OHFA_GNSS; OHHU_GNSS; OHLI_GNSS; OHMO_GNSS; OHMR_GNSS; OHPR_GNSS; OKAN_GNSS; OKAR_GNSS; OKBF_GNSS; OKCB_GNSS; OKCL_GNSS; OKDT_GNSS; OKGM_GNSS; OKHV_GNSS; OKMA_GNSS; OKOM_GNSS; OLKI_GNSS; OMH5_GNSS; ONSM_GNSS; OPMT_GNSS; ORMD_GNSS; OSKM_GNSS; OSLS_GNSS; OSPA_GNSS; OST0_GNSS; OUAG_GNSS; OULU_GNSS; OVE0_GNSS; P032_GNSS; P033_GNSS; P037_GNSS; P038_GNSS; P039_GNSS; P040_GNSS; P042_GNSS; P043_GNSS; P044_GNSS; P049_GNSS; P050_GNSS; P051_GNSS; P052_GNSS; P053_GNSS; P054_GNSS; P055_GNSS; P070_GNSS; P728_GNSS; P775_GNSS; P776_GNSS; P777_GNSS; P778_GNSS; P779_GNSS; P780_GNSS; P802_GNSS; P803_GNSS; P807_GNSS; P817_GNSS; PAAP_GNSS; PAFU_GNSS; PALK_GNSS; PAMS_GNSS; PAPC_GNSS; PARK_GNSS; PARY_GNSS; PASA_GNSS; PASS_GNSS; PATN_GNSS; PATT_GNSS; PBCH_GNSS; PBRM_GNSS; PECE_GNSS; PICL_GNSS; PIGT_GNSS; PIRT_GNSS; PKTN_GNSS; plate motion model; PLTC_GNSS; PNBM_GNSS; PNGM_GNSS; PNR6_GNSS; POAL_GNSS; POHN_GNSS; POLV_GNSS; POR2_GNSS; POTS_GNSS; POUS_GNSS; POVE_GNSS; PRCO_GNSS; PRDS_GNSS; PREI_GNSS; PREM_GNSS; PRPT_GNSS; PSU1_GNSS; PTBB_GNSS; PTGV_GNSS; PTIR_GNSS; PUB5_GNSS; PUIN_GNSS; PULK_GNSS; PUO1_GNSS; PUYV_GNSS; PWEL_GNSS; QAQ1_GNSS; QIKI_GNSS; RAMG_GNSS; RAMO_GNSS; RANT_GNSS; RAT0_GNSS; RBAY_GNSS; RCMV_GNSS; RECF_GNSS; REDU_GNSS; REDZ_GNSS; Reference frame; RESO_GNSS; REUN_GNSS; RG13_GNSS; RG15_GNSS; RG16_GNSS; RG17_GNSS; RG18_GNSS; RG19_GNSS; RG23_GNSS; RG24_GNSS; RIC1_GNSS; RIGA_GNSS; RIO1_GNSS; RIOJ_GNSS; RIS5_GNSS; RLAP_GNSS; RMBO_GNSS; ROB4_GNSS; ROBN_GNSS; ROMU_GNSS; ROSS_GNSS; ROTH_GNSS; RWSN_GNSS;
    Materialart: Dataset
    Format: text/tab-separated-values, 6755 data points
    Standort Signatur Erwartet Verfügbarkeit
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