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FIGURE 2 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 2. Zoroaster fulgens (Zoroasteridae) Feeding Observations. A. Gulf of Mexico. B. Puerto Rico, 2018 m. C. Key West Scarp, 1154 m.

FIGURE 1 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 1. Neomorphaster margaritaceus (Stichasteridae) A. Southeast of the Azores, North Atlantic, 2098 m. B. Retriever Seamount, North Atlantic, feeding posture. C. USNM E18509, Bahamas, 614m showing papular pores. D. Oral plate showing pedicellariae. E. Gut contents showing foraminifera tests.

FIGURE 10 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 10. Cladaster rudis (Goniasteridae) A. In situ. B. Abactinal C. Abactinal surface with pedicellariae. D. Actinal E. Adambulacral furrow.

FIGURE 7 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 7. Bathyceramaster spp. in situ. (Goniasteridae) A. and B. Same individual. Bathyceramaster sp. 1. B shows different angle highlighting abactinal surface. Sp. 1. feeding on cladorhizid sponge, North Bermuda Tritop, 2578 m. C. Sp. 2. feeding on encrusting organism, Yakutat seamount, 2096 m.

FIGURE 12. Corallivorous Goniasteridae. A in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 12. Corallivorous Goniasteridae. A. Evoplosoma scorpio (Goniasteridae), Kurchatov Ridge, 1725 m. B. Floriaster maya (Goniasteridae), Key West Scarp, 1112 m.

FIGURE 8 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 8. Circeaster americanus (Goniasteridae) New Observations, East Atlantic. A. Kuchatov Ridge, 1756 m. B. Kuchatov Ridge, 1658 m.

FIGURE 9 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 9. Circeaster americanus (Goniasteridae) A. Cachalote Guyote, 1462 m. B. Million Mounds (east coast of Florida), 816 m.

FIGURE 6. Bathyceramaster kelliottae n in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic

FIGURE 6. Bathyceramaster kelliottae n. sp. (Goniasteridae) A. Abactinal. B. Abactinal plates, closeup. C. In situ image. D. Abactinal-superomarginal edge. E. Actinal. F. Actinal view, oral region, large bivalve pedicellariae. G. Actinal viewadambulacral plates, large bivalve pedicellariae. Scale: A, E=10.0 mm, B,F,G=2.0 mm, D=3.0 mm,

opencc-by-4.0Nov 2023View details →

Supplementary Data for Manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'

This file contains the supplementary data for the manuscript  'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'

opencc-by-4.0Mar 2024View details →

CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023

Contains processed data to create all figures and tables from the publication Diamond et al. (2024) CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters [Full reference available on publication]   Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.   F1/ f1a_obs_ts.csv: Timeseries of 1979-2023 SIE f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly. f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.   F2/ F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations. F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble. F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.   F3/ Data for all figures is already provided elsewhere: F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv. F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and  5--95% ranges of SIE variability datasets in F2/F2_a.csv   ~ SF1/ SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages. SF2/ sf2_all.csv: SIE decadal means and ranges for all model simulations. sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin. SF3/ SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations. SF3_simulations.csv: SIE variability datasets for all model simulations SF4/ F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble. F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble. F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble. F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test. SF5/ Data for both SF5a and b are already provided in F1/f1b_all_instances.csv. SF6/ SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping) SF7/ Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv. SF7_all.csv: as for SF6_all.csv, but instead using subset of models.

opencc-by-4.0Apr 2024View details →

Replication package for: "Observed Patterns of Free-Floating Car-Sharing Use"

Replication packet for “Observed Patterns of Free-Floating Car-Sharing Use.” By Natalia Fabra, Catarina Pintassilgo, and Mateus Souza. SERIEs: Journal of the Spanish Economic Association. The code in this replication packet generates all the tables and figures from the paper. The car-sharing trips data were obtained under confidentiality agreements thus cannot be publicly shared. To obtain permissions to access the car-sharing data, replicators may contact the authors. All other data are publicly available and included in the packet.

opencc-by-4.0Apr 2024View details →

Team Presentation Observations Spring 2023

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →

Data for a preliminary observational study on the characteristics of surface turbulent fluxes over the South China Sea Islands

data

opencc-by-4.0Aug 2023View details →

Bottle-calibrated dissolved oxygen profiles from yearly turn-around cruises for the Ocean Observations Initiative (OOI) Irminger Sea Array 2014-2022

This dataset contains bottle-calibrated dissolved oxygen (DO) profiles collected from Conductivity Temperature Depth (CTD) casts on turn-around cruises performed yearly to maintain the Ocean Observations Initiative (OOI) Global Irminger Sea Array (60.46°N, 38.44°W). DO profiles were used in conjunction with oxygen bottle measurements (Winklers) to produce a post-cruise oxygen-calibrated CTD product for scientific use. Bottle-calibrated CTD salinity products were used to produce post-cruise oxygen-calibrated CTD profiles starting in 2018 (Year 5). This document contains overviews of CTD data collection and processing and post-processing oxygen sensor calibration method. Reports for each cruise include a summary of relevant cruise events, oxygen sensor calibration results, and issues/problems associated with oxygen data collected on each cruise. This dataset has been created for end-users that require field-calibrated oxygen data products that are currently not provided by OOI through its standard data dissemination.

Case stuides of ground-based remote sensing observations from Cabauw, NL, as obtained during the ACCEPT campaign in 2014.

Datasets of Mira-35 NMRA and Mira-35 MBR4 cloud radars, PollyXT multiwavelength polarization lidar, and the corresponding Cloudnet categorization data for the case-study periods of 3 November and 7 November 2014. A detailed description of each dataset is provided below. 1. 20141103_cesar_categorize.nc <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 3, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> 2. 20141107_cesar_categorize.nc <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 7, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> 3. 20141103_2000.pdm <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 20:15 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> 4. 20141107_0915.pdm <ul> <li>This dataset was collected on November 7, 2014, between 09:15 and 09:30 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> 5. 20141107_0945.pdm <ul> <li>This dataset was collected on November 7, 2014, between 09:45 and 10:00 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> 6. 20141107_0800.mmclx <ul> <li>This dataset was collected on November 7, 2014, between 08:00 and 10:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the sixth and eighth scans were utilized to calculate wind speed and direction for the time intervals 09:15–09:30 UTC and 09:45–10:00 UTC, respectively.</li> </ul> 7. 20141103_2000.mmclx <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 22:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the first scan were utilized to calculate wind speed and direction for the time intervals 20:00–2015 UTC.</li> </ul> 8. 20141103_cesar_pollyxt.nc <ul> <li>This dataset was collected on November 03, 2014, between 08:00 and 10:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul> 9. 20141107_cesar_pollyxt.nc <ul> <li>This dataset was collected on November 07, 2014, between 20:00 and 21:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul>

Data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy

Supporting data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy. Raw and processed NMR data and fitting codes. See individual README.txt in each zip file for details.

High-speed video observation of needles formed during the leader stage in a positive cloud-to-ground flash

The data supports the manuscript entitled “High-speed video observation of needles formed during the leader stage in a positive cloud-to-ground flash”. The high-speed video data files of the positive cloud-to-ground lightning of *.cihx and *.avi can be opened by Photron Camera Control software (Photron Fastcam Viewer) that can be download from this link: https://photron.com/photron-support/ In addition, MATLAB can open the *.mat file (electric field change data), and Microsoft Excel can open the *.xlsx file (Guangdong-Hong Kong-Macao Lightning Location data. The data can be used freely for scientific purposes with the appropriate citation.

IECDT AI for Earth Observation

Open the record for dataset details and reuse information.

Quantifying the Different Sources of REE in the Deep Philippine Sea: Insights from Numerical Models and Field Observations

The data within this database includes station location information, sampling depth, and salinity data, temperature data, AOU data, as well as dissolved rare earth elements in both seawater and porewater.

Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites

The datasets include: <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.