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274 results for “baltic sea”
Bathymetry data from detonation scars in the Fehmarnbelt, German Baltic Sea.
<p>The bathymetric data were collected on the 27<sup>th</sup> of June 2020 as underway research data on a 1.5 km track during the cruise EMB239 with the German research vessel Elisabeth Mann Borgese. The objective of the data acquisition was to survey seafloor scars resulting from the controlled detonation of ground mines. For data acquisition, the ship’s hull-mounted Sonic 2024 (R2Sonic Inc.) multibeam echosounder was used. The raw sonar data were loaded in Qimera v2.4.3 (Quality Positioning Services B.V.) and automatically processed to compute sounding footprint location under consideration of sound velocity, position, motion, and heading information. To make the data usable without any specific software, the georeferenced soundings were exported without any bathymetric data cleaning as comma-separated ASCII file in the coordinate reference system EPSG: 32632 - WGS84 / UTM zone 32N.</p> <p>For more details please refer to Papenmeier, S., Darr, A., Feldens, P. (in prep): Geomorphological data from detonation craters in the Fehmarnbelt, German Baltic Sea.</p>
October 2019 700 kHz multibeam echo sounder data used for Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats
<p>The raw data used for the study</p> <p>Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats</p> <p>by Schulze et al.; currently under review at Frontiers in Remote Sensing. </p> <p> </p> <p>Files are stored in the s7k-Format, and sorted by date of acquisition and frequency. 200 and 400 kHz data were manufacturer-calibrated. Correct absorption values have been applied duirng the export. Refer to the paper for further dataset information.</p> <p> </p> <p>This upload stores the 700 kHz data recorded in October 2019.</p>
Retracing Cyanobacteria Blooms in the Baltic Sea
<p>This repository is the data supplement to</p> <p>"Retracing Cyanobacteria Blooms in the Baltic Sea" by U. Löptien and H. Dietze 2022 in Nature Scientific Report doi:.</p> <p>The files named<strong> </strong><strong><a href="https://zenodo.org/api/files/6ae9260c-42d5-4496-aa26-348f4f898d60/backtrace_2010.mov">backtrace_20**.mov </a></strong><strong> </strong>feature visualizations of of lagrangian particles that were randomly seeded within blooms (indicated by cyan crosses) and outside blooms (indicated by grey circles). The trajectories of these particels are backtraced using output of the general ocean circulation model <a href="https://doi.org/10.5194/gmd-7-1713-2014">MOMBA</a>. The background color shading indicates surface mixed layer depth.</p> <p>The <a href="https://en.wikipedia.org/wiki/MATLAB">Matlab</a> data files named<strong> <a href="https://zenodo.org/api/files/6ae9260c-42d5-4496-aa26-348f4f898d60/trajectV03_8_2010.mat">trajectV03_8_20**.mat </a></strong> contain 2-D matrices DD (distance from coast), MD (surface mixed layer depth), SOLD (solar radiation), TD (temperature), XD (longitude), YD (latitude) with lines and columns corresponding to timesteps and lagrangian particles, respectively. Icy and Inocy are index vectors indicating which of the columns refer to blooming and "not blooming" particles, respectively. timY, timM, timH and timD refer to Year, Month, Day and Hour of respective lines of the 2-matrices, respectively.</p> <p>Don't hesitate to contact ulrike.loeptien@ifg.uni-kiel.de or heiner.dietze@ifg.uni-kiel.de in case of confusion.</p> <p> </p> <p> </p>
Baltic Sea stable isotope ecology meta-data collection
<p>Stable isotope analysis (SIA) has become a pivotal method in food web and ecological research, leading to the establishment of the research field "stable isotope ecology". We conducted the first systematic review of stable isotope studies in this field in the Baltic Sea macro-region (Eglite et al. 2022). The meta-data collection provided here includes the information extracted from all 164 studies identified in the systematic review across various dimensions (topic, space, time, taxonomic, and technical focus), but not primary stable isotope data. The first published version of this meta-data collection represents the status as of July 10, 2021, and was used to filter and extract meta-data to produce the figures and tables in the review by Eglite et al. (2022). The meta-data collection is a resource for both experienced isotope ecologists and newcomers to grasp and access all published Baltic Sea SIA work on any fundamental or applied research topic, sub-region, taxon, or trophic group of interest. It also represents an ideal foundation for an envisioned "Baltic Isobank" database of primary stable isotope data, following the vision outlined in Eglite et al. (2022). We will provide regular updates of the meta-data collection in the Dryad repository, based on new runs of the systematic review query and including any additions of research papers and corrections received from the stable isotope ecology community. For this purpose, we encourage researchers to inform us about newly published research papers employing stable isotopes in the Baltic Sea ecology field by sending an e-mail with the publication reference to baltic-isobank@geomar.de.</p>
Baltic Sea Region Land Cover Plus - Training and Validation data
<p>Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: <a href="https://doi.org/10.5281/zenodo.10653871" target="_blank" rel="noopener">Dataset link</a></p> <ul> <li><strong>landcover_training_data_2006_2018.gpkg</strong>: Points data of consistent land cover from 2006 to 2018</li> <li><strong>crop_training_data_{year}.gpkg</strong>: Points data of crop types derived from <a href="https://doi.org/10.1038/s41597-023-02517-0">EuroCrop dataset </a>in particular year (2019, 2021, 2023)</li> <li><strong>landcover_validation_{year}.gpkg</strong>: Points data of validation data derived from <a href="https://doi.org/10.1038/s41597-020-00675-z">LUCAS points </a>in particular year (2009, 2012, 2015, 2018)</li> <li><strong>Metadata.pdf</strong>: Information of land cover code in each dataset</li> </ul> <p>Version notes:</p> <p>Version 2: Correcting the validation data 2018 and Metadata file</p> <p>Version 1: Original upload</p>
Impact of persistently high sea surface temperatures on the rhizobiomes of Zostera marina in a Baltic Sea benthocosms
Open the record for dataset details and reuse information.
Marine seismic multichannel data collected in the Pomeranian Bay and around Rügen (southern Baltic Sea) by University of Hamburg
<p>The dataset includes multi-channel seismic data collected during various marine student training cruises. The cruises were organized and led by the Institute of Geophysics at the University of Hamburg.</p> <p>The seismic sources were GI-Guns or Mini-GI-Guns from the company SODERA. The data was recorded with various analog streamer systems. All data are poststack time-migrated with suppressed seafloor multiples.</p> <p>All data are in SEG-Y format with CDP-coordinates at standard byte positions (UTM33)</p> <table> <tbody> <tr> <td> <p>Vessel</p> </td> <td> <p>Cruise-ID</p> </td> <td> <p>Year</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL225</p> </td> <td> <p>2003</p> </td> </tr> <tr> <td> <p>RV HEINCKE</p> </td> <td> <p>HE217</p> </td> <td> <p>2004</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL263</p> </td> <td> <p>2005</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL562</p> </td> <td> <p>2021</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL582</p> </td> <td> <p>2022</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL605</p> </td> <td> <p>2023</p> </td> </tr> </tbody> </table>
Dataset for Interannual and seasonal variability of the air-sea CO2 exchange at Utö in the coastal region of the Baltic Sea
<p>Uto Atmospheric and Marine Research Station<br>Finnish Meteorological Institute and Finnish Environment Institute</p> <p>Data Jan 2017 - Dec 2021</p> <p> </p> <p>Data used in:<br>Honkanen, M., Aurela, M., Hatakka, J., Haraguchi, L., <br>Kielosto, S., Mäkelä, T., Seppälä, J., Siiriä, S.-M., <br>Stenbäck, K., Tuovinen, J.-P., Ylöstalo, P., and Laakso, L.: <br>Interannual and seasonal variability of the air-sea CO2 exchange at Utö in the coastal region of the Baltic Sea, <br>EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-628, 2024.</p> <p> </p> <p>This research has been supported by the Research Council of Finland project SEASINK (Evolving carbon sinks and sources in coastal<br>seas – will ecosystem response temper or aggravate climate change? project nos. 317297 and 317298), and the JERICO-NEXT and JERICO-S3 projects which have received funding from the European Union Horizon 2020 Research and Innovation Program under grant agreement nos. 654410 and 871153, respectively.</p>
Fig. 9 in Abundance And Seasonal Migration Of Gulls (Laridae) On The Lithuanian Baltic Sea Coast
Fig. 9. Gull number in the flocks during spring migration on the seacoast of Lithuania
Fig. 6 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 6. Trend of changes in ice-free period (in days) on Lake Onega and Lake Peipsi for 1960- 2015.
Fig. 2 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 2. Phytoplankton biomass structure in the Lake Onega for August 1976-2010.
Fig. 1 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 1. Map of lakes with indication of sampling sites.
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014.
GeoTIFF Dataset for: Land-to-sea mapping of the glacial erosion unconformity reveals evolution of the Jasmund Glacitectonic Complex East of Rügen Island (SW Baltic Sea)
<p>This dataset comprises two GeoTIFF files, both with a WGS84 UTM 33 N (EPSG: 32633) projection. The erosional unconformity has a grid size of 100 x 100 metres, while the moraine or raft feature was gridded using a grid size of 20 x 20 metres. The two files were created using marine-multichannel seismic data from the Hübscher et al. (2024) dataset (<a href="https://deref-gmx.net/mail/client/7eMD7NnHVnY/dereferrer/?redirectUrl=https%3A%2F%2Fzenodo.org%2Fdoi%2F10.5281%2Fzenodo.11242567" target="_blank" rel="noopener">10.5281/zenodo.11242567</a>). The dataset was employed in the preparation of Paper by the same authors (<a href="https://doi.org/10.1029/2024GL111603">https://doi.org/10.1029/2024GL111603</a>).</p>
Fig. 1 in A global checklist of the parasites of the harbor porpoise Phocoena phocoena, a critically-endangered species, including new findings from the Baltic Sea
Fig. 1. The harbor porpoise parasites load (number of species/number of individuals).
LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80
<p>The datafile contains two sheets: HTM and MCA, corresponding to geochemical data from the Holocene Thermal Maximum and Medieval Climate Anomaly intervals, respectively, of a sediment core from the Baltic Sea (site F80, 58°00.00N, 19°53.81E, water depth 191m, Fårö Deep, collected during the HYPER/COMBINE cruise of R/V Aranda, May/June 2009). In each sheet, columns A-J contain Laser Ablation (LA)-ICP-MS line scan data of element ratios in resin-embedded sediment (Mo/Al, Fe/Al and Br/P) presented in the time domain (Age in years BP). Dating of the sediment core is described in the accompanying manuscript and references therein. These profiles are presented in three forms: Raw= raw data resampled to 1 year resolution; Det= detrended and normalized to unit variance; Gau; Gaussian bandpass filter at a period of 20-100 years. Columns L-S contain time-series analysis results of the detrended, normalized elemental ratios in period domain, including power spectra of each ratio (Blackman-Tukey window, columns M-O) and cross-spectral analysis (Blackman-Tukey window, bandwidth 5 years) of Mo/Al vs Br/P (columns P-Q) and Mo/Al vs Fe/Al (columns R-S), respectively. All analyses were performed in Analyseries 1.1.1 (Paillard et al., 1996). Figures containing the data have been submitted as part of a manuscript to Geophysical Research Letters (Jilbert et al., forthcoming),</p> <p> </p> <p>Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. <em>Eos, Transactions American Geophysical Union</em>,<em> 77</em>(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a></p> <p>Jilbert, T., Gustafsson, B.G., Veldhuijzen, S., Reed, D.C., van Helmond, N.A.G.M., Hermans, M., & Slomp, C.P (forthcoming). Iron-phosphorus feedbacks drive multidecadal oscillations in Baltic Sea hypoxia. Submitted to <em>Geophysical Research Letters</em></p>
Generated data for "Limited ventilation of the central Baltic Sea due to elevated oxygen consumption" paper
<p>This data are essential for reproducing the figures from Naumov et al. "Limited ventilation of the central Baltic Sea due to elevated oxygen consumption" paper. Each archive is named after one of the ten figures and includes the data necessary for that specific figure. Some data are used in more than one figure. In some cases, performing a particular type of analysis with the given data (linear regression, for instance) is necessary to fully reproduce the figure.</p>
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
Bio-optical observations of the Baltic Sea and coastal areas, 2008-2012
<p>This a dataset of optical-biogeochemical measurement results was collected during 2008-2012 as part of spring and summer cruises with R/V Aranda as well as from flow-through water samples taken with the Ferrybox system on M/S Finnmaid. The majority of observations were made in the Gulf of Finland, Baltic Proper, Archipelago Sea, and Gulf of Bothnia in the Baltic Sea. A number of riverine and inshore observations are also included. The data collection is owned by the Finnish Environment Institute SYKE and made available under a CC-BY-NC licence. </p> <p>Detail on methods and protocols are provided in the following papers </p> <ul> <li>Simis, Stefan GH; Ylöstalo, Pasi; Kallio, Kari Y; Spilling, Kristian; Kutser, Tiitt. 2017. Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea. PLoS One 12(4), e0173357. https://doi.org/10.1371/journal.pone.0173357</li> <li>Ylöstalo, Pasi; Seppälä, Jukka; Kaitala, Seppo; Maunula, Petri; Simis, Stefan. 2016. Loadings of dissolved organic matter and nutrients from the Neva River into the Gulf of Finland–Biogeochemical composition and spatial distribution within the salinity gradient. Marine Chemistry 186, 58-71. https://doi.org/10.1016/j.marchem.2016.07.004</li> </ul> <p>A large number of individuals took part in these bio-optical research cruises over the years. The authors of this dataset are particularly grateful to the contributions by international visitors, students and volunteers taking part in one or more cruises, as well as crew and support staff operating the research vessel and ship-of-opportunity. </p> <p>Variables included in the dataset include: </p> <table> <tbody> <tr> <td>Column name</td> <td>unit/format</td> <td>Description</td> </tr> <tr> <td>Secchi</td> <td>m</td> <td>Secchi disk depth</td> </tr> <tr> <td>AirTemp(38)</td> <td>°C, 01H</td> <td>Air temperature from ship weather channel 38, 1-h average</td> </tr> <tr> <td>SeaTemp(42)</td> <td>°C, 01H</td> <td>Sea temperature from ship weather channel 42, 1-h average</td> </tr> <tr> <td>WindSpeed(92)</td> <td>m/s, 10M</td> <td>Wind speed from ship weather channel 92, 10-min average</td> </tr> <tr> <td>WindDir(96)</td> <td>°, 10M</td> <td>Wind direction from ship weather channel 96, 10-min average</td> </tr> <tr> <td>Salinity(104)</td> <td>PSU, 01H</td> <td>Salinity from ship weather channel 104, 1-h average</td> </tr> <tr> <td>Rel.humid(54)</td> <td>%, 01H</td> <td>Relative humidity from ship weather channel 54, 1-h average</td> </tr> <tr> <td>Chla</td> <td>mg/m3</td> <td>Chlorophyll-a concentration (cold ethanol extraction and calibrated fluorescence)</td> </tr> <tr> <td>TSM_avg</td> <td>mg/L</td> <td>Total Suspended Matter Dry Weight, Average</td> </tr> <tr> <td>OSM_avg</td> <td>mg/L</td> <td>Dry weight of Organic fraction of TSM, Average</td> </tr> <tr> <td>ISM_avg</td> <td>mg/L</td> <td>Dry weight of Inorganic fraction of TSM, Average</td> </tr> <tr> <td>DOC_avg</td> <td>µM</td> <td>Dissolved Organic Carbon concentration, Average</td> </tr> <tr> <td>TDN_avg</td> <td>µM</td> <td>Total Dissolved Nitrogen concentration, Average</td> </tr> <tr> <td>NH4</td> <td>µM</td> <td>Ammonium concentration</td> </tr> <tr> <td>NO32</td> <td>µM</td> <td>Nitrate-Nitrate concentration</td> </tr> <tr> <td>NO2</td> <td>µM</td> <td>Nitrite concentration</td> </tr> <tr> <td>PO4</td> <td>µM</td> <td>Phosphate concentration</td> </tr> <tr> <td>SiO4</td> <td>µM</td> <td>Silicate concentration</td> </tr> <tr> <td>TN</td> <td>µM</td> <td>Total nitrogen concentration</td> </tr> <tr> <td>TP</td> <td>µM</td> <td>Total phosphorous concentration</td> </tr> <tr> <td>pH</td> <td>pH</td> <td>pH value</td> </tr> <tr> <td>Temp_CTD</td> <td>°C</td> <td>Water temperature measured by Seabird CTD on sampling rosette</td> </tr> <tr> <td>Salinity_CTD</td> <td>SSU</td> <td>Salinity measured by Seabird CTD on sampling rosette</td> </tr> <tr> <td>POC</td> <td>µM</td> <td>Particulate Organic Carbon concentration, Average</td> </tr> <tr> <td>PON</td> <td>µM</td> <td>Particulate Organic Nitrogen concentration, Average</td> </tr> <tr> <td>POP</td> <td>µM</td> <td>Particulate Organic Phosphorus concentration, Average (30.973762 g/Mol)</td> </tr> <tr> <td>Turbidity</td> <td>PSU</td> <td>Turbidity</td> </tr> <tr> <td>aCDOM</td> <td>m^-1</td> <td>spectral absorption coefficient of coloured dissolved organic matter</td> </tr> <tr> <td>CloudCover</td> <td>0-1</td> <td>Fraction (0-1) of cloud cover assesed from photos taken in the field.</td> </tr> <tr> <td>Kd</td> <td>m^-1</td> <td>spectral Vertical diffuse downwelling irradiance coefficient</td> </tr> <tr> <td>a_nap</td> <td>m^-1</td> <td>spectral absorption coefficient by non-pigmented fraction of suspended matter</td> </tr> <tr> <td>a_tsm</td> <td>m^-1</td> <td>spectral absorption coefficient by suspened matter</td> </tr> <tr> <td>R0</td> <td>-</td> <td>spectral Subsurface Irradiance Reflectance</td> </tr> <tr> <td>pigments</td> <td>mg/m3</td> <td>Chlorophyll and other pigments extracted and quantified using a combination of calibrated fluorometry and HPLC</td> </tr> </tbody> </table>
Data for: High focus on threatened species and habitats may undermine biodiversity conservation: evidence from the northern Baltic Sea
<p><span>Conservation policies and environmental impact assessments commonly target threatened species and habitats. Nevertheless, macroecological research provides reasons why also common species should be considered. </span><span>We investigate the consequences of focusing solely on legally protected species and habitats in a spatial conservation planning context using a comprehensive, benthic marine dataset from the northern Baltic Sea</span><span>. </span><span>Using spatial prioritization and surrogacy analysis, we </span><span>show that the common approach in conservation planning, where legally listed threatened species and habitats are the focus of conservation efforts, could lead to poor outcomes for common species (and therefore biodiversity as a whole), allowing them to decline in the future.</span> <span> </span><span>If conservation efforts were aimed solely at threatened species, common species would experience a loss of 62% coverage. In contrast, if conservation plans were based only on common species, threatened species would suffer a loss of 1%.</span><span> Threatened species are rare and their ecological niches distinct, making them poor surrogates for biodiversity. The best results are achieved by unified planning for all species and habitats. The minimal step towards acknowledging common species in conservation planning would be the inclusion of the richness of common species, complemented by information on indicator species or species of high importance for ecosystem functioning. The trade-off between planning for rare and common species should be evaluated, to minimize losses to biodiversity. </span></p>
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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.