cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.961123
title: Wideband integrated bioaerosol sensor (WIBS) hyper-fluorescent particle number concentrations measured in the Swiss container during MOSAiC 2019/2020

description: These datasets contain the total particle number concentrations of hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The WIBS measures the size, asymmetry and fluorescence of particles with an optical diameter of 0.5 – 20 µm. Detected particles are excited by two UV flashlamps at wavelengths of 280 and 370 nm and their emitted fluorescence is measured by two photomultipliers with bandwidths of 310 - 400 nm, and 420 - 650 nm. The WIBS counts excited particles at a maximum frequency of 125 Hz, which corresponds to a maximum concentration of 2.5*104 particles/L with a sample flow of 0.3 L/min. Excited particles were classified as fluorescent if their fluorescent intensity exceeded the background intensity by three standard deviations (3σ) and as hyper-fluorescent if the fluorescent intensity exceeded the background intensity by 9σ. Excited particles with a lower fluorescent intensity were considered to be non-fluorescent. The background fluorescence was determined by measuring the fluorescent signal in the measurement chamber in absence of particles. Background measurements were performed every 26 h. The combination of two excitation wavelengths and two detector wavebands allows the classification of fluorescent particles into seven types: A, B, C, AB, AC, BC, and ABC (Perring et al. (2015); Savage et al. (2017)). For further information about the instrumental setup, refer to Heutte et al. (Submitted).
DOI: 10.1594/pangaea.961122
title: Wideband integrated bioaerosol sensor (WIBS) fluorescent particle number concentrations measured in the Swiss container during MOSAiC 2019/2020

description: These datasets contain the total particle number concentrations of fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The WIBS measures the size, asymmetry and fluorescence of particles with an optical diameter of 0.5 – 20 µm. Detected particles are excited by two UV flashlamps at wavelengths of 280 and 370 nm and their emitted fluorescence is measured by two photomultipliers with bandwidths of 310 - 400 nm, and 420 - 650 nm. The WIBS counts excited particles at a maximum frequency of 125 Hz, which corresponds to a maximum concentration of 2.5*104 particles/L with a sample flow of 0.3 L/min. Excited particles were classified as fluorescent if their fluorescent intensity exceeded the background intensity by three standard deviations (3σ) and as hyper-fluorescent if the fluorescent intensity exceeded the background intensity by 9σ. Excited particles with a lower fluorescent intensity were considered to be non-fluorescent. The background fluorescence was determined by measuring the fluorescent signal in the measurement chamber in absence of particles. Background measurements were performed every 26 h. The combination of two excitation wavelengths and two detector wavebands allows the classification of fluorescent particles into seven types: A, B, C, AB, AC, BC, and ABC (Perring et al. (2015); Savage et al. (2017)). For further information about the instrumental setup, refer to Heutte et al. (Submitted).
DOI: 10.1594/pangaea.961125
title: Wideband integrated bioaerosol sensor (WIBS) excited particle normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020

description: These datasets contain normalized size distributions (dN/dlogDp) of excited particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset.
DOI: 10.1594/pangaea.961126
title: Wideband integrated bioaerosol sensor (WIBS) fluorescent particle normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020

description: These datasets contain normalized size distributions (dN/dlogDp) of fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset.
DOI: 10.1594/pangaea.961128
title: Wideband integrated bioaerosol sensor (WIBS) hyper-fluorescent particle normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020

description: These datasets contain normalized size distributions (dN/dlogDp) of hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset.
DOI: 10.1594/pangaea.952456
title: GoJelly databank: A compilation of jellyfish observation data and metadata

description: A compilation of jellyfish observations from various genera (Aurelia, Cyanea, Periphylla, Rhizostoma etc.) collected from 1790 to 2018. The area considered is the Northern Atlantic Ocean and sampling areas include the Mediterranean Sea, Celtic, Baltic and North Sea. This dataset is a collection of observations mostly retrieved from publications and therefore the sampling methods are various. The methods used include but are not limited to plankton net, bongo nets, collections from surface waters, trawls, hauls and buckets. Each observations listed in the dataset include the specific reference from which the data is collected from. This project was funded by the European Union's Horizon 2020 research and innovation programme (Grant agreement no. 774499) as part of GoJelly (work package 2: 'Driving mechanisms and predictions of jellyfish blooms')
DOI: 10.1594/pangaea.959782
title: Below-ground carbon stocks and soil properties in the central Lena River Delta, Kurungnakh and Samoylov Islands from 12 permafrost profiles sampled in 2018

description: Arctic warming increases the degradation of permafrost soils but little is known about floodplain soils and other permafrost soils in the permafrost region. This dataset present soil properties from twelve analyzed cores located in the northeastern Siberian Lena River Delta within the continuous permafrost zone in northern Yakutia, Russia that were sampled in 2018. The cores represent intact yedoma, yedoma thaw slumps, and floodplain profiles. The soil coring and sampling was carried out in August 2018 for a total of 30 soil cores, 35 soil profiles, and 341 sediment samples. First, vegetation and other characteristics of the plots were described. Then, the active layer soils were excavated, described, and sampled with a fixed volume cylinder (250 cm³). Then the permafrost layers were sampled with a modified, snow, ice, and permafrost (SIPRE) auger to a depth of 1 m (core diameter of 7.62 cm) and visually described in their characteristics. Each core was divided into subsamples with 5-10 cm length according to its facies horizons, transported frozen to Alfred Wegener Institute in Potsdam, and stored at -20 °C until analysis. A subset of these sediment samples (n=105 from 12 cores) were later analyzed in the laboratory for physical and chemical properties; this data is presented here. The analysis from these samples includes: water content, dry bulk density, gravimetric ice content, pH, conductivity, total organic carbon content, total inorganic carbon content, total carbon content, total nitrogen content, carbon density, TOC storage, and TN storage. Selected samples were further analyzed for radiocarbon and grain size analysis.
DOI: 10.1594/pangaea.959728
title: Gridded pan-Arctic total neutral atmospheric 10-m drag coefficient estimates derived from ICESat-2 ATL07 sea ice height data (Version 2)

description: This data-set contains average drag coefficient estimates for the whole of the Arctic (resampled onto a 25 km polar stereographic grid) for each month from 11.2018 to 06.2022. The total drag coefficients are referenced to a height of 10 meters and a neutrally stratified atmosphere is assumed. The total drag is the sum of open water drag scaled with (1-A) where A is sea ice concentration (Spreen et al., 2008), drag due to floe edges incorporated via a constant derived from parameterization (Lüpkes et al., 2012) and scaled with A(1-A), sea ice skin drag scaled with A, and sea ice form drag due to obstacles (e.g. ridges) computed from sea ice feature averages (Garbrecht et al., 2002) derived from ICESat-2 ATL07 sea ice heights (for all 25 km grid cells filled by ICESat-2 ATL07 data) (Kwok et al., 2021). Each individual component is also given as a separate variable in the data-set. In addition, the sea ice feature averages (10-km average obstacle height and obstacle spacing) used to derive form drag due to obstacles is also gridded and included in the data files. Obstacles below a threshold value of 20 cm and those that do not fulfill the Rayleigh Criterion (wherein if the trough between two peaks is smaller than the higher of two crests, only the higher one is considered) are omitted. Lastly, we scale up the ICESat-2 ATL07-derived form drag coefficients via a regression derived from comparing them to drag coefficients derived from topographic data collected during the 04.2019 NASA airborne Operation IceBridge ICESat-2 under-flights (Studinger, 2013). This is to deal with the sampling issues associated with resolution differences.The current updated version corrects for unrealistic negative ICESat-2 ATL07-derived obstacle form drag coefficients by fixing the y-intercept of the regression used to scale up the values to zero.
description: The data files are in NetCDF4 format and contain drag coefficients from obstacles derived from ICESat-2 ATL07 (drag_IS2), drag coefficients due to open water (draw_ow), drag coefficients from floe edges (drag_fe), sea ice skin drag coefficients (drag_s) and total drag coefficients (drag_TOT; the sum of the previous 4). It also contains the data distribution (data_dist), longitude (lon), latitude (lat), average obstacle heights (H_e) and obstacle spacings (x_e). All data variables are gridded onto a 25 km polar stereographic grid and are thus stored as matrices. Longitude and latitude matrices contain one extra row and column for python pcolormesh(shading='flat') compatibility.
DOI: 10.1594/pangaea.950713
title: Stiffness measurements for direct-shear apparatuses

description: Under a wide range of conditions, we directly measure the stiffness in the shear loading direction of five direct-shear apparatuses employed in the Marine Geotechnics laboratory at MARUM, University of Bremen. For the stiffness measurements, a steel blank simulating a rock sample with dimensions of 25 mm height and 25 mm diameter was placed in the sample cell and loaded in the same manner as geologic samples. A vertical load was applied to the top face of the steel cylinder corresponding to normal stresses (σn) 1, 4, 7, 10 or 14 MPa. For G5, additional normal stresses of 20, 30, and 48 MPa were tested. At each normal stress, stiffness was measured for both a loading and unloading phase. For each normal stress, the range of shear stresses tested was designed to nearly match the range of the applied normal stress; for example, for G2 at 10 MPa normal stress (Figure 4), the sample was loaded over a shear stress range of 1-9 MPa. We designed the shear loads in our measurements to cover a range of equivalent friction coefficients, so that an exact apparatus stiffness value could be matched to any particular geologic sample.
DOI: 10.1594/pangaea.961508
title: Thermal performance assay of a plankton community from Helgoland Roads for an indoor mesocosm experiment in March 2022

description: To choose the treatment temperatures for an indoor mesocosm temperature experiment at the ICBM in Wilhelmshaven (https://doi.pangaea.de/10.1594/PANGAEA.961155), a thermal performance curve assay was performed from the 8th of March until the 16th of March. It was started one day after filling the mesocosms with seawater from Helgoland Roads (https://deims.org/1e96ef9b-0915-4661-849f-b3a72f5aa9b1) by randomly spreading pooled sample water in 50 ml culture flasks across ten temperatures (3 °C to 30 °C in 3 °C steps) in triplicates. Their fluorescence (395/680 Excitation/Emission) was measured daily using a SYNERGY H1 microplate reader (BioTek, Winooski, Vermont, USA).
description: For more information about the research site Helgoland Roads visit https://deims.org/1e96ef9b-0915-4661-849f-b3a72f5aa9b1
DOI: 10.1594/pangaea.961272
title: Bait consumption by benthic fauna investigated by baited camera lander deployments on the Cabo Verde Abyssal Plain

description: A total of eight deployments of an autonomous baited camera lander were conducted at the Cabo Verde Abyssal Plain (tropical East Atlantic, Lat. 14.72, Lon. -25.19, Water depth ~4200 m) using either Atlantic mackerel (Scomber scombrus, n=4) or Patagonian squid (Doryteuthis gahi, n=4) bait, to photograph organisms attracted to the bait over roughly 24 hours. The deployments took place during the iMirabilis2 campaign in August 2021 from the research vessel Sarmiento de Gamboa. This data set provides information on the consumption of the different baits by abyssal fauna assessed as the reduction in wet weight over the course of the deployment.
description: Time of lander arrival on seafloor can be back-calculated from 'Time at seafloor' and 'Time of lander release from seafloor'.
DOI: 10.1594/pangaea.961314
title: Image annotation data from baited camera lander deployments on the Cabo Verde Abyssal Plain

description: A total of eight deployments of an autonomous baited camera lander were conducted at the Cabo Verde Abyssal Plain (tropical East Atlantic, Lat. 14.72, Lon. -25.19, Water depth ~4200 m) using either Atlantic mackerel (Scomber scombrus, n=4) or Patagonian squid (Doryteuthis gahi, n=4) bait, to photograph organisms attracted to the bait over roughly 24 hours. The deployments took place during the iMirabilis2 campaign in August 2021 from the research vessel Sarmiento de Gamboa. A deep-sea time lapse camera system with an oblique view of the bait plate (12 cm x 45 cm) and surroundings took a picture every 150 seconds. The bar attached to the bait plate is 6 cm wide. The camera was located about 120 cm above the seafloor with an oblique view of 40 degrees (assuming straight down in 0 degrees). Annotations were performed in BIIGLE software (Langenkämper et al. 2017) on every second photograph, providing the morphospecies group label (or 'No ID' if to morphospecies level was not possible) and the taxonomic hierarchy to a level of best confidence for each annotation. Annotations were rectangular in shape, enclosing each individual so that the centre of the annotation was roughly the centre of mass, and the points of each rectangle corner are provided in pixels (x,y) where the lower left corner of the picture is 0,0. Images were 6000 pixels in width and 4000 pixels in height.
description: Taxonomic hierarchy of annotation: Morphospecies code with three letter and two numbers (XXX_00, for example COR_01) correspond to an identified morphospecies (see morphospecies). A morphospecies code is not always present, in that case the hierarchy shows the identification to a more general taxonomic detail. The morphospecies code is not always the end of the taxonomic hierarchy, sometimes an extra distinction was made (e.g. juvenile? or a colour). Organisms with an 'Unknown' annotation were thought to be an organism but no taxonomic identification could be made. If an individual could not be assigned to a morphospecies this value is set to 'No ID' and information on the best taxonomic detail can be found in the taxonomic hierarchy column.
DOI: 10.1594/pangaea.961319
title: ADCP data measured during baited camera lander deployments on the Cabo Verde Abyssal Plain

description: A total of eight deployments of an autonomous baited camera lander were conducted at the Cabo Verde Abyssal Plain (tropical East Atlantic, Lat. 14.72, Lon. -25.19; Water depth ~4200 m) using either Atlantic mackerel (Scomber scombrus, n=4) or Patagonian squid (Doryteuthis gahi, n=4) bait, to photograph organisms attracted to the bait over roughly 24 hours. The deployments took place during the iMirabilis2 campaign in August 2021 from the research vessel Sarmiento de Gamboa. A deep-sea time lapse camera system with an oblique view of the bait plate and surroundings took a picture every 2.5 min. An ADCP (Aquadopp 6000 m, Nortek) mounted on top of the lander frame took a reading of near bottom currents (~2 meter above bottom) every second. This dataset includes the data downloaded from the ADCP current meter.
DOI: 10.1594/pangaea.961134
title: Cloud Condensation Nuclei (CCN) concentrations at 0.2% supersaturation level measured in the Swiss container during MOSAiC 2019/2020

description: This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).