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346 results for “ships”
Ship logs from Helmer Hanssen on PolarFront cruise 2022-05
<p><strong>PolarFront 2022-05 ship logs</strong></p> <p>Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.</p> <p>See Daase ed., 2022 for further details on study area and for a list of named stations.</p> <p><strong>References</strong><br> Daase M (ed.) (2022). <a href="https://doi.org/10.5281/zenodo.7128746">PolarFront May 2022 Cruise Report</a>. Zenodo. https://doi.org/10.5281/zenodo.7128746</p>
Intermediate data for: Environment and shipping drive eDNA beta-diversity among commercial ports
<p>Intermediate data generated by Paul Czechowski as part of MEC-22-0945.R1 using code stored at <a href="https://github.com/macrobiotus/ships_and_bugs">GitHub</a>, most recently release with <a href="https://doi.org/10.5281/zenodo.7600608">DOI: 10.5281/zenodo.7600608 </a> . Pre-print with linked final manuscript version available at BioRxiv via <a href="https://doi.org/10.1101/2021.10.07.463538">DOI: 10.1101/2021.10.07.463538</a>. Please refer to the published manuscript for a full list of available digital resources associated with this work.</p>
Dataset for 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'
<p>Dataset for ACP manuscript 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'. Further explanations on the datasets can be found in the 'README.txt' file. In case of any questions please contact: Philipp Eger (eger@bafg.de), Federal Institute of Hydrology.</p> <p>1_Timeseries_Worms</p> <p>Description: Time series of measured gaseous and particulate species for station "BRI" in Worms on 10 December 2021. </p> <p><br> 2_All_ship_peaks_Worms</p> <p>Description: All analyzed ship peaks fulfilling the quality check criteria. </p> <p><br> 3_Ship_contribution_Worms</p> <p>Description: Mean monthly contribution from shipping based on analyzed peaks. </p> <p><br> 4_Examples_Worms</p> <p>Description: Particle size distribution of five exemplary peaks (A-E).</p>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s
<p>This is the dataset created for the Journal of Glaciology paper "<i>Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s</i>" by Robert Headland, Nick Hughes and Jeremy Wilkinson (<a href="https://doi.org/10.1017/jog.2023.80">doi:10.1017/jog.2023.80</a>). We have endeavoured to make the data as accessible as possible by providing it in a range of formats.</p><p>Please see the README.pdf for a detailed description of the files, and the paper for the dataset. Version 1.1 contains additional reports from newspaper archives.</p>
Data on the fleas of house mice and ship rats in the Orongorongo Valley, New Zealand
<p>Data on autopsies and fleas of individual rats and mice snap-trapped in 80 quarterly 3-day trapping sessions over 1971-1991 in the Orongorongo Valley, Wellington, New Zealand. The fleas are preserved as slides in the Pilgrim Collection of Te Papa Tongarewa Museum of New Zealand, Wellington. The data are analysed in a forthcoming paper by Fitzgerald, Efford and Karl.</p> <p>'Autopsy data.csv' is the main spreadsheet.</p> <p>'Data format.csv' defines columns and codes.</p> <p>The autopsy data are a subset of those reported by Fitzgerald, Efford<br> and Karl (2004) and Efford, Fitzgerald, Karl and Berben (2006), both<br> with respect to the sampling period (trapping continued after May 1991,<br> but rats and mice were not searched for fleas) and data columns<br> (some reproductive fields have been omitted).</p> <p>The autopsy data may be read from the csv file into R (R Core Team 2023)<br> with the statement:</p> <p>autopsyF <- read.csv(file = 'Orongorongo fleas 1971-1991 Autopsy data.csv')</p> <p>The resulting dataframe has 31 columns and 3006 rows:</p> <p>names(autopsyF)<br> [1] "Autopsy" "Line" "Date" "Analysis.Year" "Session" <br> [6] "Day" "Season" "Trap" "Traptype" "Alive.dead" <br> [11] "Species" "Sex" "Age" "Weight" "TotLth" <br> [16] "TailLth" "Fleas" "LsegnisM" "LsegnisF" "LsegnisU" <br> [21] "Lsegnis" "NfascM" "NfascF" "NfascU" "Nfasc" <br> [26] "Vagina" "Lact" "Uterus" "EmbryosT" "ScarCond" <br> [31] "Testes" <br> <br> nrow(autopsyF)<br> [1] 3006</p> <p>For completeness, the autopsy data include 64 hosts from these trapping sessions that were<br> not searched for fleas (coded as Fleas = 2).</p> <p>Quarterly indices of host density were tabulated by Fitzgerald et al. (2004; mice) and<br> Efford et al. (2006; rats).</p> <p>References</p> <p>Efford MG, Fitzgerald BM, Karl BJ, Berben PH. 2006. Population dynamics<br> of the ship rat <em>Rattus rattus</em> L. in the Orongorongo Valley, New Zealand.<br> New Zealand Journal of Zoology 33:273-297.</p> <p>Fitzgerald BM, Efford MG, Karl BJ. 2004. Breeding of house mice and the<br> mast seeding of southern beeches in the Orongorongo Valley, New Zealand.<br> New Zealand Journal of Zoology 31:167-184.</p> <p>Fitzgerald BM, Efford MG, Karl BJ. 2023. The fleas of house mice (<em>Mus musculus</em>)<br> and ship rats (<em>Rattus rattus</em>) in forest of the Orongorongo Valley, New Zealand.<br> For submission to New Zealand Journal of Zoology.</p> <p>R Core Team 2023. R: A Language and Environment for Statistical Computing.<br> R Foundation for Statistical Computing, Vienna, Austria.<br> https://www.R-project.org/</p>
Invasive species and thermal squeeze: Distribution of two invasive predators and drivers of ship rat (Rattus rattus) invasion in mid-elevation Fuscospora forest
This data package is from a trapping network set up in Craigieburn Forest Park, New Zealand, in 2013. These are records of the stoats and rats caught in the traps each time the traps were checked by volunteers since 2013. Associated long term air temperature and seedfall data from the Craigieburn area is also provided. If the original trapping records (containing more mammalian catch information such as weasels and cats) are required please contact the data providers.
Water quality, phytoplankton, and zooplankton in the Sacramento Deep Water Ship Channel, CA
Drivers of phytoplankton and zooplankton dynamics vary spatially and temporally in estuaries due to variation in hydrodynamic exchange and residence time, complicating efforts to understand controls on food web productivity. We conducted approximately monthly (2012 – 2019; n = 74) longitudinal sampling at ten fixed stations along a freshwater tidal terminal channel in the San Francisco Estuary, California, characterized by seaward to landward gradients in water residence time, turbidity, nutrient concentrations, and plankton community composition. We used multivariate autoregressive state space (MARSS) models to quantify environmental (abiotic) and biotic controls on phytoplankton and mesozooplankton biomass. The importance of specific abiotic drivers (e.g. water temperature, turbidity, nutrients) and trophic interactions differed significantly among hydrodynamic exchange zones with different mean residence times. Abiotic drivers explained more variation in phytoplankton and zooplankton dynamics than a model including only trophic interactions, but individual phytoplankton-zooplankton interactions explained more variation than individual abiotic drivers. Interactions between zooplankton and phytoplankton were strongest in landward reaches with the longest residence times and the highest zooplankton biomass. Interactions between cryptophytes and both copepods and cladocerans were stronger than interactions between bacillariophytes (diatoms) and zooplankton taxa, despite contributing less biovolume in all but the most landward reaches. Our results demonstrate that trophic interactions and their relative strengths vary in a hydrodynamic context, contributing to food web heterogeneity within estuaries at spatial scales smaller than the freshwater to marine transition.
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea
<p>This data submission is connected to a scientific paper submitted to<br> Atmospheric Chemistry and Physics ("Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea" by Walden et al.). It consists of measurement results conducted beside the ship routs at the Baltic Sea near Helsinki, Finland. The gaseous and particle concentrations were measured along with the meteorological parameters, and the fluxes were calculated by the micrometeorological methods. The content of sulfur in the marine fuel, FSC, used by the passing ships was also calculated. We paid attention to calculate the uncertainties of the measurement results, both for the fluxes and for the FSC.</p> <p>The released data of:<br> 1. Gases, particles and met data (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles) as minute values. </p> <p>Data_ACP_Fig4_acbd.xlsx.</p> <p> <br> 2. Size distribution of nanoparticles (number concentration of nanoparticles at size class). Data_ACP_Fig6.xlsx</p> <p> <br> 3. Profiles of 30 min averages of gases, nanoparticles and meteorological parameters (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles), wind direction and wind speed, friction velocity, stability parameter and Monin-Obukhov length. Calculated values of atmospheric turbulence parameters and calculated fluxes of CO2 and nanoparticles by gradient and/or eddy covariance method.</p> <p>Data_ACP_Fig8_abcd_Fig9_abcd.xlsx<br> <br> 4. CO2 fluxes by Eddy covariance method from land based and sea based measurements. Concentration of CO2 in seawater and in air.</p> <p>Data_ACP_Fig10_ab.xlsxEngl</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
<p>The CloudTracks dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship tracks. More information about how the dataset was constructed may be found at <a href="http://github.com/stanfordmlgroup/CloudTracks">github.com/stanfordmlgroup/CloudTracks</a>. The file structure of the dataset is as follows:</p><p>CloudTracks/<br> full/<br> images/<br> (sample image name) mod2002121.1920D.png<br> jsons/<br> (sample json name) mod2002121.1920D.json</p><p>The naming convention is as follows:<br>mod2002121.1920D: the first 3 letters specify which of the sensors on the two MODIS satellites captured the image, mod for Terra and myd for Aqua. This is followed by a 4 digit year (2002) and a 3 digit day of the year (121). The following 4 digits specify the time of day (1920; 24 hour format in the UTC timezone), followed by D or N for Day or Night.</p><p>The 1,780 MODIS Terra and Aqua images were collected between 2002 and 2021 inclusive over various stratocumulus cloud regions (such as the East Pacific and East Atlantic) where ship tracks have commonly been observed. Each image has dimension 1354 x 2030 and a spatial resolution of 1km. Of the 36 bands collected by the instruments, we selected channels 1, 20, and 32 to capture useful physical properties of cloud formations.</p><p>The labels are found in the corresponding JSON files for each image. The following keys in the json are particularly important:</p><p>imagePath: the filename of the image.<br>shapes: the list of annotations corresponding to the image, where each element of the list is a dictionary corresponding to a single instance annotation. The dictionary has a key with value "shiptrack" or "uncertain" which is the label of the annotation and the corresponding value is a linestrip detailing the ship track path.</p><p>Further pre-processing details may be found at the GitHub link above. If you have any questions about the dataset, contact us at:<br><a href="mailto:mahmedch@stanford.edu">mahmedch@stanford.edu</a>, <a href="mailto:lynakim@stanford.edu">lynakim@stanford.edu</a>, <a href="mailto:jirvin16@cs.stanford.edu">jirvin16@cs.stanford.edu</a></p>
Figure 1. Ships from which the early Discovery Expedition was conducted. a in The Discovery Expedition sea cucumbers (Echinodermata: Holothuroidea)
Figure 1. Ships from which the early Discovery Expedition was conducted. a, Scott's RRS Discovery I (collected from 25 Sep 1925 to 1927); b, RRS Discovery II (collected for five voyages from 1929 to 1935, and a sixth voyage in 1950).
Streams of inland and sea going ships
Open the record for dataset details and reuse information.
Original figure plates for "First fossil species of ship-timber beetles (Coleoptera, Lymexylidae) from Eocene Rovno amber (Ukraine)"
<p>Original figure plates for "<strong>First fossil species of ship-timber beetles (Coleoptera, Lymexylidae) from Eocene Rovno amber (Ukraine)</strong>".</p> <p>Abstract. A new lymexylid fossil species, †<em>Raractocetus sverlilo</em> Nazarenko, Perkovsky & Yamamoto, sp. nov., is described from late Eocene Rovno amber in Ukraine. This new species is similar to species of the recent genera <em>Atractocerus</em> Palisot de Beauvois and <em>Raractocetus</em> Kurosawa in the ship-timber beetle subfamily Atractocerinae, but differs in pronotal and elytral features. Notably, the new species is one of the smallest atractocerines known to date. This is the first member of the family Lymexylidae found in Rovno amber. Our finding sheds further light on the paleodiversity of atractocerine beetles, highlighting a peculiar distribution during the Eocene. Only one extant atractocerine specimen has been reported from Europe (Greece), while three species from Eocene European amber forests with equable climate are known now, including two species from the otherwise tropical genus <em>Raractocetus</em>. Our finding of the<em> Raractocetus</em> beetle from Rovno amber is of significant biogeographically because it indicates the wide distribution of the genus in the Eocene European amber forests.</p>
Data on transit history and anti-fouling practices for ships arriving to the Canadian Arctic
<p>Ship biofouling is a major vector for the introduction and spread of harmful marine species globally, however, its importance in Arctic coastal ecosystems is understudied. The objective of this study was to provide insight regarding the extent of biofouling (i.e., percent cover, abundance, and species richness) on commercial ships operating in the Canadian Arctic. A questionnaire was used to collect information on transit history, anti-fouling practices, and self-reported estimates of biofouling extent from a sample of ships operating in the region during 2015 – 2016.</p>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Ship tracks detected using machine learning algorithm
<p>The filtered, vector ship tracks detected using the linked machine learning algorithm and derived from the linked segmentation masks. Each dataset contains the date and other related data for each shiptrack polygon. The `_geo` dataset contains the polygons on a lat/lon coordinate system while the other provides the polygons on the MODIS swath (pixel) indices.</p>
Lake St-Pierre ship wave study
<p>Database of model output and field measurements associated with Lac St-Pierre ship wave study. Please see the readme for details of the NC files contained in this repository.</p>
Sentinel2-Ship and SDGSAT-Ship
<p>The dataset includes two datasets, Sentinel2-Ship and SDGSAT-Ship, and two folders for training, validation, and testing datasets, respectively. The rotated boxes are labeled with long-side-defined annotation.</p>
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