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What is the clinical course of patients hospitalised for COVID-19 treatment Ireland: a retrospective cohort study in Dublin's North Inner City (the 'Mater 100')
<p><strong>Background: </strong>Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While several cohorts from China have been described, there is little data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. <strong>To improve our understanding of this emerging infection we carried out </strong>a retrospective review of patient data to<strong> examine the clinical characteristics </strong>of patients admitted for COVID-19 hospital treatment.</p> <p><strong>Methods<strong>:</strong></strong> Demographic, clinical and laboratory data on the first 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records.</p> <p><strong>R<strong>esults:</strong></strong> Fifty-eight per cent were male, 63% were Irish nationals, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of five days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal findings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modified) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit - 14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11).</p> <p>C<strong>onclusion:</strong> Our findings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.</p>
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
Nodal Modulation of Tidal Constituents in the North Sea
<p>Harmonic analysis of sea surface elevation is commonly performed using the f-u correction procedure, which alters the analyzed tidal constituents, based on the longitude of the lunar ascending node at a 18.61 year frequency. Measurement analysis has led us to conclude, that common nodal modulation factors are inaccurate in the shallow regions of the North Sea, due to the friction-induced generation of overtides. For this reason we applied a numerical model to quantify the nodal modulation spatially. The hereby presented data set is therefore derived from model results and includes the nodal amplitude modulation and phase lag for the North Sea on a 7,500m raster as supplemental material to Hagen, et al. (2021, to appear). This data set may be used for the manual correction of nodal modulation in modeling applications, MSL analysis and any other application regarding harmonic analysis in the North Sea region.</p>
North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
Udayagiri, Madhya Pradesh. Ford over the River Bes below the north hill.
<p>Udayagiri, Madhya Pradesh. Ford over the River Bes below the north hill, prior to destruction. Low resolution format.</p>
Sri Ksetra, Bago, Myanmar. Fields north of Payama stūpa.
<p>Sri Ksetra, Bago, Myanmar. Fields north of Payama stūpa, looking toward cemetery site (pyutaik), February, 2017. Located approximately at 18°50'12"N 95°18'12"E.</p>
Modelled time series of CO2 in the vicinity of a seep in the North Sea.
<p>The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from nine different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. </p> <p>For details see: Ali, A., Frøysa, H. G., Avlesen, H., & Alendal, G. (2016). Simulating spatial and temporal varying CO 2signals from sources at the seafloor to help designing risk-based monitoring programs. <em>Journal of Geophysical Research-Oceans</em>, <em>121</em>(1), 745–757. http://doi.org/10.1002/2015JC011198.</p> <p>The data files contains time series of excess CO2 concentration, velocity components and time, and position in longitude and latitude. One file for each grid position, a total of 53x51 grid points with the seep in location (I,j)=(28,27). </p>
LifeWatch observatory data: phytoplankton annotated trainingset by FlowCam imaging in the Belgian Part of the North Sea
<h1>Training dataset </h1> <p>The images were collected in the framework of the Belgian Lifewatch Research Infrastructure. During multidisciplinary campaigns, a number of fixed stations in the Belgian Part of the North Sea (BPNS) are visited on a monthly (onshore stations) or seasonal (offshore stations) basis. Samples are taken using a 55µm mesh size Apstein net and fixed in Lugol's iodine solution. In the lab, the samples are processed using a VS-4 FlowCAM model at 4X magnification targeting a particle size range of 55-300µm. The identification of the image data is done with the use of a CNN and followed by a manual validation step. Since May 2017, this dataset has provided micro- and phytoplankton observations, mainly covering diatoms, dinoflagellates and cilliates, for the Belgian Part of the North Sea (BPNS).</p> <p> </p> <p>This dataset comprises a trainings datasplit of 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. The goal of this dataset is to be able to facilitate model training, here we have organized the data into a standard split, with 80% allocated for training, 10% for validation, and another 10% for testing purposes. This dataset structure ensures a balanced representation and supports scientific rigor in subsequent analyses.</p> <h1>Technical details </h1> <h2>Data preprocessing </h2> <p>Raw FlowCam output data is fully processed using in-house datapipelines, the VisualSpreadsheet software is only used for data acquisition during the lab run of the sample. Raw images and binary images are never saved during the FlowCam run, we only work on the image collages saved at the end of the run. Single images are cut from these collages using each image coordinates width and height pulled from the .lst file using in-house python code. The background of the images is not removed. These images are then predicted and annotated in-house at VLIZ.</p> <h2>Data splitting </h2> <p>The training dataset is 80% used for training, 10% for validation and 10% for prediction. </p> <h2>Classes, labels and annotations</h2> <p>The dataset comprises 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. Taxonomic coverage of the dataset comprises mainly of diatoms, dinoflagellates and cilliates, but to a lesser extent also zooplankton and other protists.</p> <h2>Parameters </h2> <p>The images are read using cv2.imread and the values are used as parameters.</p> <p><strong>Metadata Parameter Descriptions:</strong></p> <ul> <li> <p><strong>image_path</strong>: The relative path to the image file showing the plankton or particle, usually structured by taxon or project folder.</p> </li> <li> <p><strong>sample_datetime</strong>: The exact date and time (in YYYY-MM-DD HH:MM:SS.sss format) when the sample was acquired using the imaging instrument.</p> </li> <li> <p><strong>flowcam_version</strong>: The software or hardware version of the FlowCAM instrument used to capture the image and process the sample.</p> </li> <li> <p><strong>station</strong>: The sampling location code where the image was taken. These station codes typically refer to predefined geographic or monitoring points in the field.</p> </li> <li> <p><strong>accepted_label</strong>: The final validated taxonomic label (e.g., genus or species) assigned to the organism or particle in the image, often based on expert review.</p> </li> <li> <p><strong>accepted_aphia_id</strong>: The unique identifier (AphiaID) corresponding to the accepted taxonomic label in the <strong>World Register of Marine Species (WoRMS)</strong> database, which ensures standardized taxonomic reference.</p> </li> <li> <p><strong>original_reference_id</strong>: A unique identifier (often a UUID) assigned to the image or sample in the original classification system (e.g., EcoTaxa), useful for traceability and linking back to the source record.</p> </li> </ul> <h2>Data sources </h2> <p>Images are collected during the monthly monitoring of phytoplankton communities in the Belgian Part of the North Sea during the LifeWatch multidisciplinary campaigns by FlowCam VS-4 benchmodel (Fluid Imaging Technologies, Yarmouth, Maine, U.S.A.).</p> <h2>Data quality </h2> <p>All images are predicted and subsequently manually validated to ensure the quality of the trainingset.</p> <h2>Image resolution </h2> <p>The size range imaged is 55-300µm. Images are acquired using a Sony XCD SC90 digital gray-scale camera. Images are during training of CNN resized to 100px by 100px.</p> <h2>Spatial coverage </h2> <p>The data comes from a number of fixed stations in the Belgian Part of the North Sea (BPNS). </p> <p>Nine stations onshore are visited monthly:</p> <table> <tbody> <tr> <td><strong>Station</strong></td> <td><strong>Longitude</strong></td> <td><strong>Latitude</strong></td> </tr> <tr> <td>130</td> <td>2.90535</td> <td>51.27055</td> </tr> <tr> <td>780</td> <td>3.057283</td> <td>51.471367</td> </tr> <tr> <td>330</td> <td>2.809083</td> <td>51.434117</td> </tr> <tr> <td>230</td> <td>2.85035</td> <td>51.308683</td> </tr> <tr> <td>710</td> <td>3.138283</td> <td>51.441217</td> </tr> <tr> <td>215</td> <td>2.61075</td> <td>51.274867</td> </tr> <tr> <td>ZG02</td> <td>2.500717</td> <td>51.33515</td> </tr> <tr> <td>120</td> <td>2.702483</td> <td>51.186083</td> </tr> <tr> <td>700</td> <td>3.221017</td> <td>51.377</td> </tr> </tbody> </table> <p>Eight additional offshore stations are visited seasonally:</p> <table> <tbody> <tr> <td><strong>Station</strong></td> <td><strong>Longitude</strong></td> <td><strong>Latitude</strong></td> </tr> <tr> <td>LW01</td> <td>2.256</td> <td>51.568667</td> </tr> <tr> <td>LW02</td> <td>2.556</td> <td>51.8</td> </tr> <tr> <td>435</td> <td>2.790333</td> <td>51.580667</td> </tr> <tr> <td>W07bis</td> <td>3.012517</td> <td>51.588033</td> </tr> <tr> <td>W08</td> <td>2.35</td> <td>51.458333</td> </tr> <tr> <td>W09</td> <td>2.7</td> <td>51.75</td> </tr> <tr> <td>W10</td> <td>2.416667</td> <td>51.683333</td> </tr> <tr> <td>421</td> <td>2.45</td> <td>51.4805</td> </tr> </tbody> </table> <h2> </h2> <h2>Temporal coverage </h2> <p>The monitoring was initiated in May 2017 and has been running continuously every month.</p> <h2>Contact information </h2> <p>For technical questions about training dataset, you can contact wout.decrop@vliz.be. </p> <p> </p> <p> </p> <p> </p> <p> </p>
HG_OOSTENDE - Herring gulls (Larus argentatus, Laridae) breeding at the southern North Sea coast (Belgium)
<p><em>HG_OOSTENDE - Herring gulls (Larus argentatus, Laridae) breeding at the southern North Sea coast (Belgium)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>HG_OOSTENDE</strong>, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, <a href="http://www.uva-bits.nl">http://www.uva-bits.nl</a>). The study was operational from 2013 until 2022. In total 60 individuals of European herring gull (<em>Larus argentatus</em>) have been tagged in or near their breeding area at the southern North Sea coast (Ostend and Zeebrugge in Belgium), mainly to study their habitat use. Data are periodically uploaded from the UvA-BiTS database to Movebank and from there archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>). No new data are expected.</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study986040562">986040562</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/10054230/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>HG_OOSTENDE-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>HG_OOSTENDE-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> <li><strong>HG_OOSTENDE-acceleration-yyyy.csv.gz</strong>: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by VLIZ and INBO funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch.</p>
Stable isotope data of a community of mid‑trophic level epipelagic species in the North Atlantic
<p>A total of 495 individuals from a guild of 14 species (fishes, squids and crustaceans) were sampled along the west and south Iberia (Portuguese coast) aboard a research vessel during 2014, 2016 and 2017. Two seasonal oceanographic surveys were conducted each year to sample pelagic mid-trophic level species during spring and autumn. Specimens were captured with trawl hauls, and information on date, time, position of capture (latitude and longitude) and depth (m) were recorded. The body mass (g) and total length (in fishes; mm), mantle length (in squids; mm) or carapace width (in crustaceans; mm) were recorded for each individual. Stable isotopes of carbon (δ13C) and nitrogen (δ15N) were analysed in the muscle of each individual. Lipids were removed from samples by successive rinses in a 2:1 chloroform–methanol solution. More details of the methodology can be found in:</p> <p>Ceia FR, Cherel Y, Silva AV, Garrido S, Angélico MM, da Silva JM, Laranjeiro MI, Ramos JA (2023) Drivers of niche partitioning in a community of mid-trophic level epipelagic species in the North Atlantic. Hydrobiologia, 850: 1583–1599. (DOI:10.1007/s10750-023-05160-3).</p>
The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3
<p>The North Pacific Eukaryotic Gene Catalog consolidates eukaryotic metatranscriptome data from three latitudinal transects of the North Pacific transition zone and one cruise in the subtropical gyre. Metatranscriptomes were gathered from latitudinally-resolved surface samples, and diel-resolved temporal studies, with samples taken in triplicate or duplicate and collected on 0.2-100 μm, 0.2-3 μm, and 3 μm-100 or 200 μm size fractions. These metatranscriptome data were <em>de novo</em> assembled into 175 independent assemblies, totalling 182 million clustered nucleotide contigs. Assemblies were annotated by taxonomy and function. This catalog provides assembled environmental contigs, their translated peptide sequences, and their taxonomic and functional annotations with the aim of facilitating continued discoveries about the molecular ecology of microbial eukaryotes in the North Pacific.<br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.</p> <div> <p>This dataset repository is associated with a codebase and documentation repository:<br><a href="https://github.com/armbrustlab/NPac_euk_gene_catalog" target="_blank" rel="noopener">https://github.com/armbrustlab/NPac_euk_gene_catalog</a><br>Please see this code repository for additional data and project updates<br><br>Translated and processed protein sequences and their annotations are available in this repository: <br><a href="../doi/10.5281/zenodo.10472589">https://zenodo.org/doi/10.5281/zenodo.10472589</a><br><br>99% identity clustered nucleotide sequences and kallisto enumerations are available here:<br><a href="../doi/10.5281/zenodo.10570448">https://zenodo.org/doi/10.5281/zenodo.10570448</a></p> </div> <div> <p>File contents: this repository contains five .tar.gz compressed tarballs with raw de novo Trinity assemblies of poly-A selected metatranscriptomes from the Gradients 1 through 3 cruises, and a plain-text file with the custom spike-in mRNA standards (CustomStandardSequences.txt)</p> </div> <div> <p><strong><br>Gradients1.KOK1606.PA.assemblies.tar.gz</strong><br>- Link to <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G1PA" target="_blank" rel="noopener">G1PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KOK1606" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KOK1606</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G1PA.process_short_reads.sh" target="_blank" rel="noopener">G1PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G1PA.trinity_assemblies.sh" target="_blank" rel="noopener">G1PA.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>Gradients2.MGL1704.PA.assemblies.tar.gz</strong><br>- Link to <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G2PA" target="_blank" rel="noopener">G2PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/MGL1704" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/MGL1704</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G2PA.process_short_reads.sh" target="_blank" rel="noopener">G2PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G2PA.trinity_assemblies.sh" target="_blank" rel="noopener">G2PA.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>Gradients3.KM1906.PA.assemblies.tar.gz</strong><br>- Link go <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G3PA" target="_blank" rel="noopener">G3PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1906" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1906</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_UW.process_short_reads.sh" target="_blank" rel="noopener">G3PA_UW.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_UW.trinity_assemblies.sh" target="_blank" rel="noopener">G3PA_UW.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>G3_diel.KM1906.PA.assemblies.tar.gz</strong><br>- Link go <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G3PA" target="_blank" rel="noopener">G3PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1906" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1906</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_diel.process_short_reads.sh" target="_blank" rel="noopener">G3PA_diel.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_diel.trinity_assemblies.sh" target="_blank" rel="noopener">G3PA_diel.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>CustomStandardSequences.txt<br></strong>- Plain-text FASTA file with the spike-in standards used during mRNA extraction and sequencing prep<br>- Link to publication of spike-in standards methods: <a href="https://www.nature.com/articles/s41564-019-0507-5" target="_blank" rel="noopener">https://www.nature.com/articles/s41564-019-0507-5</a></p> </div> <div> <p>The 2015 SCOPE Diel metatranscriptome raw assemblies have been released in a previous Zenodo repository, and are not included again in this deposition. We provide the links to the Diel1 resources here:<br>- Diel1 raw metatranscriptome assembly Zenodo repository: <a href="../records/5009803" target="_blank" rel="noopener">https://zenodo.org/records/5009803</a><br>- Dataset DOI: <a href="https://doi.org/10.5281/zenodo.5009803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5009803</a><br>- Associated publication: <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2021.682651/full" target="_blank" rel="noopener">https://www.frontiersin.org/articles/10.3389/fmicb.2021.682651/full</a><br>- Codebase: <a href="https://github.com/armbrustlab/diel_eukaryotes" target="_blank" rel="noopener">https://github.com/armbrustlab/diel_eukaryotes</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1513" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1513</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/D1PA.process_short_reads.sh" target="_blank" rel="noopener">D1PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/D1PA.trinity_assemblies.sh" target="_blank" rel="noopener">D1PA.trinity_assemblies.sh</a></p> </div> <p><br><br></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"
<p>This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. <strong>Please refer to the README.md file</strong> for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility. </p> <p><strong>Ethics Approval</strong></p> <p>This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.</p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
LifeWatch observatory data: phytoplankton annotated image library by FlowCam imaging for the Belgian part of the North Sea.
<p>In the framework of the Lifewatch marine observatory a number of fixed stations in the Belgian Part of the North Sea (BPNS) are sampled for phytoplankton monitoring. Samples are processed using a VS-4 FlowCAM model at 4X magnification, size range imaged is 55-300µm. The identification of the image data is done with the use of a classifier and followed by a manual validation step. These dataset comprises the full annotated image dataset which can be sampled for training of convolutional neural networks.</p>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Plausible 2050 offshore wind locations in the North Sea
<p>This dataset contains a set of zones and points representing plausible locations for offshore wind farms and individual turbines to have been built in the North Sea by the years 2030, 2040, and 2050, based off the national ambitions announced up to summer 2024.</p> <p>This version (version 3) is a major revision. Many wind farm zones and most turbines have moved. Column names have changed. See readme.pdf for further information and a changelog.</p> <p>A full description of how these coordinates were arrived at is currently under development as a journal article, and once it is available this readme will be updated to link to it. Check the "latest version" link in Zenodo to see if this has already happened.</p> <p>If using this version, please cite the dataset directly using the title and authors above and DOI 10.5281/zenodo.14222865</p> <p>Please do not use "OSW zones.png" for serious work; use the underlying data instead. The image is included so as to give a useful preview in Zenodo.</p>
Dataset for 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea'
<p>This dataset complements the paper 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea' accepted <span>for publication in Journal of Geophysical Research - Earth Surface</span>.</p> <p> </p> <p>This dataset contains Digital Terrain Maps (DTMs) of specific areas offshore from Dunkirk, on the northern coast of France, opening to the Southern Bight of the North Sea. These areas host marine dunes (sand waves) that have been numerically investigated in this research to identify the parameters influencing their migration. The openTELEMAC system (version v8p4) can be downloaded from <a href="https://opentelemac.org/">https://opentelemac.org/</a>. The model development, calibration and validation is described in Durand (2024).</p> <p> </p> <p>The site-specific data collected by France Energies Marines (2021) are currently proprietary. To protect these data, DTMs of bathymetric changes are provided, calculated as the difference in metres between the final and initial seabed levels. Negative values indicate lowering of the seabed (erosion) and positive values indicate rising (accretion).</p> <p>The initial and final periods are:</p> <ul> <li>S1: 17-Nov-2019</li> <li>S2: 17-Mar-2020</li> <li>S5: 5-Dec-2020</li> </ul> <p>The DTMs are provided for two areas (refer to paper for locations):</p> <ul> <li>Tile #1</li> <li>Tile #3</li> </ul> <p> </p> <p>Included in the dataset are observations, Case I model output (without wind and atmospheric pressure), and Case II model output (with wind and atmospheric pressure).</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - North Macedonia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2022_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2021_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2020_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2019_MK: Food and Veterinary Agency (FVA)</li> </ul>
Extent of past PSRs in the north and south polar regions of the Moon
<p>Extent of perennially shadowed regions (PSRs) obtained by applying ray-tracing to LOLA shape models with various values of the maximum solar declination.</p> <p>See README.TXT for a technical description of the data files.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.