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  • 2025-2025  (24)
  • 2020-2024  (36,701)
  • 2023  (36,701)
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  • 1
    Publication Date: 2024-06-01
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 2
    Publication Date: 2024-06-01
    Description: Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.
    Keywords: mangrove forests ; forest inventory ; monitoring ; habitat mapping ; UAV ; UAS ; artificial ; intelligence ; machine learning ; instance segmentation ; semantic segmentation ; above ground biomass ; carbon stock
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
    Publication Date: 2024-06-01
    Description: This study examined the metabolic response of juvenile turbot (Scophthalmus maximus) to diets with graded fishmeal (FM) replacement with plant, animal, and emerging protein sources (PLANT, PAP, and MIX) in comparison to a commercial-like diet (CTRL). The feeding experiment was carried out from April to July 2019 in the Centre for Aquaculture Research (ZAF) at the Alfred Wegener Institute for Polar and Marine research in Bremerhaven, Germany. The juvenile turbot (Scophthalmus maximus) were purchased from France Turbot (L'Épine, France) and acclimated to the recirculating aquaculture system (RAS) for 2 weeks prior to starting the 16 weeks experimental trial. To elucidate the effects of the protein sources and the level of FM replacement on the metabolic response of the fish, a 1H‐nuclear magnetic resonance (NMR) spectroscopy was used to assess the metabolic profiles of muscle and liver tissue after feeding the fish the experimental diets for 16 weeks. Feed, muscle, and liver samples were ground under liquid nitrogen and approx. 200–250 mg tissue was homogenized in 5x volume of ice‐cold 0.6 M perchloric acid (PCA) (w:v). After one cycle of 20 s at 6000 rpm and 3 °C, using Precellys 24 (Bertin Technologies, Montigny‐le‐Bretonneux, France), samples were sonicated for 2 min at 0 °C and 360 W (Branson Sonifier 450, FisherScientific, Schwerte, Germany). Homogenates of the experimental diets, muscle and liver tissues were centrifuged for 2 min at 0 °C and 16,000 g, and supernatants were neutralized with ice cold potassium hydroxide (KOH) and PCA to pH 7.0–7.5. To remove precipitated potassium, perchlorate samples were centrifuged again for 2 min at 0 °C and 16,000 g. The entire supernatant was transferred, shock‐ frozen in liquid nitrogen, and stored an −80 °C for later analysis. One‐dimensional 1H‐NMR spectra for feed and tissues extracts were acquired using a vertical 9.4 T wide bore magnet with Avance III HD (Bruker‐GmbH, Ettlingen, Germany) at 400.13 MHz with a 1.7 mm diameter triple tuned (1H‐13C‐15N) probe. Each spectrum was processed and analyzed with Chenomx NMR Suite 8.4 software (Chenomx Inc., Edmonton, Canada). Before analyzing, the spectra were corrected for phase, shim and baseline and calibrated to trymethylsilyl proprionate (TSP) signal (at 0.0 ppm).
    Keywords: Acetate; Adenine; Adenosine diphosphate; Adenosine monophosphate; Adenosine triphosphate; Alanine; Analysis; Analysis date/time, experiment; Anserine; Arginine; Aspartate; betaine; Betaine; by-product; Carnitine; Choline; Creatine; Creatine phosphate; Creatinine; D-Glucose 6-phosphate; Dimethylamine; Dimethyl sulfone; Experiment; Experiment number; Formate; Fumarate; Glutamate; Glutamine; Glycine; Identification; insect meal; Isoleucine; Laboratory experiment; Lactate; Leucine; Location; Malonate; Material; Methionine; Method comment; N,N-Dimethylglycine; Nuclear magnetic resonance spectrometer (NMR), Bruker, Avance III HD 400; O-Phosphocholine; Proline; Sample, optional label/labor no; Sample ID; Sampling date/time, experiment; Sarcosine; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI); Succinate; Tank number; Taurine; Threonine; Time point, descriptive; TMAO; Treatment; Trimethylamine N-oxide; Type of study; Valine
    Type: Dataset
    Format: text/tab-separated-values, 6200 data points
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  • 4
    Publication Date: 2024-06-01
    Description: Presence-absence records for four cold-water coral (CWC) taxa (Enallopsammia rostrata, Acanella arbuscula, Metallogorgia spp. and Paramuricea spp.) were gathered to conduct distribution models on seamounts (Cadamosto, Nola, Senghor and Cabo Verde) of the Cabo Verde archipelago (NW Africa), covering a bathymetric range from 2100 to 750 m water depth. Data were extracted from video footage collected with Remotely Operated Vehicles during the M80/3 Meteor (2010) and the iMirabilis2 (2021) research expeditions. Video data from the iMirabilis2 expedition was analysed, quantitively, using the open-source software BIIGLE (Langenkämper et al. 2017). Observations from five continuous 1 to 2 km-long video transects between 2000 and 1400 m depth at Cadamosto Seamount were converted into presence-absence data points. Similar data were not available for the seamounts explored during M80/3 Meteor. However, all the available images and short video clips from that expedition were analysed to identify presence and absence points for each of the four target CWC taxa. All the available presence/absence data from the two expeditions was transformed into one point per grid cell of a 100 m resolution bathymetry grid, with the prevalence of the presence records over the absence records, in grid cells where both categories overlapped.
    Keywords: Atlantic Ocean; Binary Object; Binary Object (File Size); Binary Object (Media Type); Cabo Verde; Cadamosto Seamount, Cabo Verde; Cape Verde; cold-water coral; Cruise/expedition; DATE/TIME; Deep-sea; distribution modelling; Event label; File content; Genus; Horizontal datum; iAtlantic; iMirabilis2_Leg1; iMirabilis2_Leg1_24; iMirabilis2_Leg1_46; iMirabilis2_Leg1_55; iMirabilis2_Leg1_64; iMirabilis2_Leg1_75; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; LATITUDE; Latitude, northbound; Latitude, southbound; Location; LONGITUDE; Longitude, eastbound; Longitude, westbound; M80/3; M80/3_10; M80/3_100; M80/3_33; M80/3_35; M80/3_7; M80/3_84; Meteor (1986); Presence/absence; Remote operated vehicle; ROV; ROV Luso; Sarmiento de Gamboa; Species; Taxon/taxa, unique identification (Semantic URI); Taxon/taxa, unique identification (URI); UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vertical datum; VIDEO; Video camera
    Type: Dataset
    Format: text/tab-separated-values, 10855 data points
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  • 5
    Publication Date: 2024-05-31
    Description: In the present study, we compared mucus and gut-associated prokaryotic communities from seven nudibranch species with sediment and seawater from Thai coral reefs using high-throughput 16S rRNA gene sequencing. The nudibranch species were identified as Doriprismatica atromarginata (family Chromodorididae), Jorunna funebris (family Discodorididae), Phyllidiella nigra, Phyllidiella pustulosa, Phyllidia carlsonhoffi, Phyllidia elegans, and Phyllidia picta (all family Phyllidiidae). The most abundant bacterial phyla in the dataset were Proteobacteria, Tenericutes, Chloroflexi, Thaumarchaeota, and Cyanobacteria. Mucus and gut-associated communities differed from one another and from sediment and seawater communities. Host phylogeny was, furthermore, a significant predictor of differences in mucus and gut-associated prokaryotic community composition. With respect to higher taxon abundance, the order Rhizobiales (Proteobacteria) was more abundant in Phyllidia species (mucus and gut), whereas the order Mycoplasmatales (Tenericutes) was more abundant in D. atromarginata and J. funebris. Mucus samples were, furthermore, associated with greater abundances of certain phyla including Chloroflexi, Poribacteria, and Gemmatimonadetes, taxa considered to be indicators for high microbial abundance (HMA) sponge species. Overall, our results indicated that nudibranch microbiomes consisted of a number of abundant prokaryotic members with high sequence similarities to organisms previously detected in sponges.
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 6
    Publication Date: 2024-05-31
    Description: n the deep ocean, whale falls (deceased whales that sink to the seafloor) act as a boost of productivity in this otherwise generally food-limited setting, nourishing organisms from sharks to microbes during the various stages of their decomposition. Annelid worms are habitual colonizers of whale falls, with new species regularly reported from these settings and their systematics helping to resolve biogeographic patterns among deep-sea organic fall environments. During a 2017 expedition of the Australian research vessel RV Investigator to sample bathyal to abyssal communities off Australia’s east coast, a natural whale fall was opportunistically trawled at ~1000 m depth. In this study, we provide detailed taxonomic descriptions of the annelids associated with this whale-fall community, using both morphological and molecular techniques. From this material we describe nine new species from five families (Dorvilleidae: Ophryotrocha dahlgreni sp. nov. Ophryotrocha hanneloreae sp. nov., Ophryotrocha ravarae sp. nov.; Hesionidae: Vrijenhoekia timoharai sp. nov.; Nereididae: Neanthes adriangloveri sp. nov., Neanthes visicete sp. nov.; Orbiniidae: Orbiniella jamesi sp. nov.), including two belonging to the bone-eating genus Osedax (Siboglinidae: Osedax waadjum sp. nov., Osedax byronbayensis sp. nov.) that are the first to be described from Australian waters. We further provide systematic accounts for 10 taxa within the Ampharetidae, Amphinomidae, Microphthalmidae, Nereididae, Orbiniidae, Phyllodocidae, Protodrilidae, Sphaerodoridae and Phascolosomatidae. Our investigations uncover unique occurrences and for the first time enable the evaluation of biogeographic links between Australian whale falls and others in the western Pacific as well as worldwide.
    Keywords: polychaete ; chemosynthesis ; organic fall ; bathyal ; Bathymodiolinae ; Pacific Ocean
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 7
    Publication Date: 2024-05-31
    Description: About 40 samples are collected from the upper 250 cm of the sediment core GeoB 10053-7 offshore Java, covering the past 5,000 years (Mohtadi et al., 2011; doi:10.1038/ngeo1209). The average sample resolution is around 120 years. Here, we use markers for low intensity fires and soil erosion to reconstruct human activities in East Java (Indonesia) over the last 5,000 years. We use the accumulation rate of branched glycerol dialkyl glycerol tetraethers (brGDGTs), markers for soil-derived organic matter, to indicate levels of soil erosion in the catchment region. We also use the accumulation rate of levoglucosan to indicate past fire use in the catchment. Independent hydroclimate reconstruction that are not influenced by human activities is compared in order to differentiate the impact of human activities vs. hydroclimate on soil erosion in the catchment area. Specifically, the stable hydrogen isotope composition (δD) of leaf wax n-alkanes reflect changes in the monsoonal rainfall intensity in the catchment. In addition, the stable carbon isotope composition (δ13C) of leaf wax n-alkanes derived from our sediment core reflects the relative abundance of regional C3 versus C4 vegetation.
    Keywords: Center for Marine Environmental Sciences; leaf waxes; levoglucosan; Marine Sediment Core; MARUM
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 8
    Publication Date: 2024-05-31
    Keywords: Accumulation rate, levoglucosan; AGE; Calculated; Center for Marine Environmental Sciences; Comment; DEPTH, sediment/rock; GeoB10053-7; Gravity corer (Kiel type); leaf waxes; levoglucosan; Levoglucosan; Levoglucosan according to Schreuder et al. 2018; Marine Sediment Core; MARUM; PABESIA; SL; SO184/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 95 data points
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  • 9
    Publication Date: 2024-05-31
    Description: Temperature, salinity, and pH, along with copepod traits were measured inter-daily (i.e., on average each 4 days) in an upwelling and temperate estuary in the coastal Southeast Pacific. The purpose of the study was to evaluate the phenotypic plasticity of local copepod populations to natural extreme low pH conditions. Temperature and salinity were measured with CTD (Ocean Seven 305 Plus in the estuary system, and SeaBird SBE19 Plus in the upwelling location). Samples for pH measurements were collected with an oceanographic bottle. pH was measured potentiometrically, and calibrated with Tris buffer at 25 °C. Adult females of the copepod species Acartia tonsa (Copepoda, Calanoidea) were sampled with a WP2 plankton net. Cephalothorax length was measured under a stereomicroscope. Egg production was estimated individually over 24 h incubation.
    Keywords: Acartia tonsa, cephalotorax length; Acartia tonsa, egg production rate per female; Antofagasta_upw; Calculated; Calculated according to Vargas and González (2004); Calculated using the CO2sys_v3.0 software (Pierrot et al. 2021); Carbon, organic, particulate; Carbon chemistry; Coastal variability; copepods; CTD; CTD/Rosette; CTD-RO; DATE/TIME; Depth, bathymetric; DEPTH, water; Event label; extreme events; gene flow; Group; Habitat; Latitude of event; Longitude of event; pH; phenotypic plasticity; Salinity; Southeast Pacific; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI); Stereo microscope, Leica Microsystems, EZ4 HD; Temperate and subtropical systems; Temperature, water; Valdivia_est; Year of sampling
    Type: Dataset
    Format: text/tab-separated-values, 1914 data points
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  • 10
    Publication Date: 2024-05-31
    Description: The following data set contains particulate absorption, particulate attenuation, and particulate backscatter data from an optical inline system. Auxiliary data from the R/V Sikuliaq's existing underway system has also been attached, this includes standard shipboard physical oceanographic and meteorological data. The data was collected continuously during the cruises following previous work by the authors and IOCCG protocols (Burt et al., 2018 and IOCCG, 2019). The data has been binned to one-minute intervals to match with the existing underway data. The data was collected in the Northern Gulf of Alaska (NGA), as part of the expansion of the Long-Term Ecological Research (LTER) program in the Gulf. The data was collected using an ACS and BB3-eco triplet, on cruise SKQ202012s in the NGA.
    Keywords: Absorption coefficient, 402 nm; Absorption coefficient, 404 nm; Absorption coefficient, 406 nm; Absorption coefficient, 408 nm; Absorption coefficient, 410 nm; Absorption coefficient, 412 nm; Absorption coefficient, 414 nm; Absorption coefficient, 416 nm; Absorption coefficient, 418 nm; Absorption coefficient, 420 nm; Absorption coefficient, 422 nm; Absorption coefficient, 424 nm; Absorption coefficient, 426 nm; Absorption coefficient, 428 nm; Absorption coefficient, 430 nm; Absorption coefficient, 432 nm; Absorption coefficient, 434 nm; Absorption coefficient, 436 nm; Absorption coefficient, 438 nm; Absorption coefficient, 440 nm; Absorption coefficient, 442 nm; Absorption coefficient, 444 nm; Absorption coefficient, 446 nm; Absorption coefficient, 448 nm; Absorption coefficient, 450 nm; Absorption coefficient, 452 nm; Absorption coefficient, 454 nm; Absorption coefficient, 456 nm; Absorption coefficient, 458 nm; Absorption coefficient, 460 nm; Absorption coefficient, 462 nm; Absorption coefficient, 464 nm; Absorption coefficient, 466 nm; Absorption coefficient, 468 nm; Absorption coefficient, 470 nm; Absorption coefficient, 472 nm; Absorption coefficient, 474 nm; Absorption coefficient, 476 nm; Absorption coefficient, 478 nm; Absorption coefficient, 480 nm; Absorption coefficient, 482 nm; Absorption coefficient, 484 nm; Absorption coefficient, 486 nm; Absorption coefficient, 488 nm; Absorption coefficient, 490 nm; Absorption coefficient, 492 nm; Absorption coefficient, 494 nm; Absorption coefficient, 496 nm; Absorption coefficient, 498 nm; Absorption coefficient, 500 nm; Absorption coefficient, 502 nm; Absorption coefficient, 504 nm; Absorption coefficient, 506 nm; Absorption coefficient, 508 nm; Absorption coefficient, 510 nm; Absorption coefficient, 512 nm; Absorption coefficient, 514 nm; Absorption coefficient, 516 nm; Absorption coefficient, 518 nm; Absorption coefficient, 520 nm; Absorption coefficient, 522 nm; Absorption coefficient, 524 nm; Absorption coefficient, 526 nm; Absorption coefficient, 528 nm; Absorption coefficient, 530 nm; Absorption coefficient, 532 nm; Absorption coefficient, 534 nm; Absorption coefficient, 536 nm; Absorption coefficient, 538 nm; Absorption coefficient, 540 nm; Absorption coefficient, 542 nm; Absorption coefficient, 544 nm; Absorption coefficient, 546 nm; Absorption coefficient, 548 nm; Absorption coefficient, 550 nm; Absorption coefficient, 552 nm; Absorption coefficient, 554 nm; Absorption coefficient, 556 nm; Absorption coefficient, 558 nm; Absorption coefficient, 560 nm; Absorption coefficient, 562 nm; Absorption coefficient, 564 nm; Absorption coefficient, 566 nm; Absorption coefficient, 568 nm; Absorption coefficient, 570 nm; Absorption coefficient, 572 nm; Absorption coefficient, 574 nm; Absorption coefficient, 576 nm; Absorption coefficient, 578 nm; Absorption coefficient, 580 nm; Absorption coefficient, 582 nm; Absorption coefficient, 584 nm; Absorption coefficient, 586 nm; Absorption coefficient, 588 nm; Absorption coefficient, 590 nm; Absorption coefficient, 592 nm; Absorption coefficient, 594 nm; Absorption coefficient, 596 nm; Absorption coefficient, 598 nm; Absorption coefficient, 600 nm; Absorption coefficient, 602 nm; Absorption coefficient, 604 nm; Absorption coefficient, 606 nm; Absorption coefficient, 608 nm; Absorption coefficient, 610 nm; Absorption coefficient, 612 nm; Absorption coefficient, 614 nm; Absorption coefficient, 616 nm; Absorption coefficient, 618 nm; Absorption coefficient, 620 nm; Absorption coefficient, 622 nm; Absorption coefficient, 624 nm; Absorption coefficient, 626 nm; Absorption coefficient, 628 nm; Absorption coefficient, 630 nm; Absorption coefficient, 632 nm; Absorption coefficient, 634 nm; Absorption coefficient, 636 nm; Absorption coefficient, 638 nm; Absorption coefficient, 640 nm; Absorption coefficient, 642 nm; Absorption coefficient, 644 nm; Absorption coefficient, 646 nm; Absorption coefficient, 648 nm; Absorption coefficient, 650 nm; Absorption coefficient, 652 nm; Absorption coefficient, 654 nm; Absorption coefficient, 656 nm; Absorption coefficient, 658 nm; Absorption coefficient, 660 nm; Absorption coefficient, 662 nm; Absorption coefficient, 664 nm; Absorption coefficient, 666 nm; Absorption coefficient, 668 nm; Absorption coefficient, 670 nm; Absorption coefficient, 672 nm; Absorption coefficient, 674 nm; Absorption coefficient, 676 nm; Absorption coefficient, 678 nm; Absorption coefficient, 680 nm; Absorption coefficient, 682 nm; Absorption coefficient, 684 nm; Absorption coefficient, 686 nm; Absorption coefficient, 688 nm; Absorption coefficient, 690 nm; Absorption coefficient, 692 nm; Absorption coefficient, 694 nm; Absorption coefficient, 696 nm; Absorption coefficient, 698 nm; Absorption coefficient, 700 nm; Absorption coefficient, 702 nm; Absorption coefficient, 704 nm; Absorption coefficient, 706 nm; Absorption coefficient, 708 nm; Absorption coefficient, 710 nm; Absorption coefficient, 712 nm; Absorption coefficient, 714 nm; Absorption coefficient, 716 nm; Absorption coefficient, 718 nm; Absorption coefficient, 720 nm; Absorption coefficient, 722 nm; Absorption coefficient, 724 nm; Absorption coefficient, 726 nm; Absorption coefficient, 728 nm; Absorption coefficient, 730 nm; Absorption coefficient, 732 nm; Absorption coefficient, 734 nm; Absorption coefficient, 736 nm; Absorption coefficient, 738 nm; According to Graff et al. (2015); ACS; Attenuation coefficient, 402 nm; Attenuation coefficient, 404 nm; Attenuation coefficient, 406 nm; Attenuation coefficient, 408 nm; Attenuation coefficient, 410 nm; Attenuation coefficient, 412 nm; Attenuation coefficient, 414 nm; Attenuation coefficient, 416 nm; Attenuation coefficient, 418 nm; Attenuation coefficient, 420 nm; Attenuation coefficient, 422 nm; Attenuation coefficient, 424 nm; Attenuation coefficient, 426 nm; Attenuation coefficient, 428 nm; Attenuation coefficient, 430 nm; Attenuation coefficient, 432 nm; Attenuation coefficient, 434 nm; Attenuation coefficient, 436 nm; Attenuation coefficient, 438 nm; Attenuation coefficient, 440 nm; Attenuation coefficient, 442 nm; Attenuation coefficient, 444 nm; Attenuation coefficient, 446 nm; Attenuation coefficient, 448 nm; Attenuation coefficient, 450 nm; Attenuation coefficient, 452 nm; Attenuation coefficient, 454 nm; Attenuation coefficient, 456 nm; Attenuation coefficient, 458 nm; Attenuation coefficient, 460 nm; Attenuation coefficient, 462 nm; Attenuation coefficient, 464 nm; Attenuation coefficient, 466 nm; Attenuation coefficient, 468 nm; Attenuation coefficient, 470 nm; Attenuation coefficient, 472 nm; Attenuation coefficient, 474 nm; Attenuation coefficient, 476 nm; Attenuation coefficient, 478 nm; Attenuation coefficient, 480 nm; Attenuation coefficient, 482 nm; Attenuation coefficient, 484 nm; Attenuation coefficient, 486 nm; Attenuation coefficient, 488 nm; Attenuation coefficient, 490 nm; Attenuation coefficient, 492 nm; Attenuation coefficient, 494 nm; Attenuation coefficient, 496 nm; Attenuation coefficient, 498 nm; Attenuation coefficient, 500 nm; Attenuation coefficient, 502 nm; Attenuation coefficient, 504 nm; Attenuation coefficient, 506 nm; Attenuation coefficient, 508 nm; Attenuation coefficient, 510 nm; Attenuation coefficient, 512 nm; Attenuation coefficient, 514 nm; Attenuation coefficient, 516 nm; Attenuation coefficient, 518 nm; Attenuation coefficient, 520 nm; Attenuation coefficient, 522 nm; Attenuation coefficient, 524 nm; Attenuation coefficient, 526 nm; Attenuation coefficient, 528 nm; Attenuation coefficient, 530 nm; Attenuation coefficient, 532 nm; Attenuation coefficient, 534 nm; Attenuation coefficient, 536 nm; Attenuation coefficient, 538 nm; Attenuation coefficient, 540 nm; Attenuation coefficient, 542 nm; Attenuation coefficient, 544 nm; Attenuation coefficient, 546 nm; Attenuation coefficient, 548 nm; Attenuation coefficient, 550 nm; Attenuation coefficient, 552 nm; Attenuation coefficient, 554 nm; Attenuation
    Type: Dataset
    Format: text/tab-separated-values, 3646149 data points
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