Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
274
datasets available to search
ShareScore release 0.7.1
Dataset results
274 results for “Baltic Sea”
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Spatiotemporal Dataset on Moon Jellyfish (Aurelia aurita) Incidental Observations in the Gulf of Riga and Eastern Gotland Basin, Baltic Sea
<p>This data article describes the occurrences of the moon jelly <em>Aurelia aurita </em>medusae in the Eastern Gotland basin and the Gulf of Riga (Baltic Sea) between 1998 and 2023. All data are incidental observations obtained during Latvian national monitoring cruises. </p>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
Meiofauna higher taxa abundance data from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide abundance data for meiofauna taxa determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.</p> <p>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. The organisms were extracted by decantation over a 32-μm sieve. The total number of individuals per taxon was counted and is presented as individuals per 10 cm<sup>2</sup>.</p> <p>In the framework of our monitoring, samples were primarily taken for a large-scale metabarcoding study on meiofauna communities. One core per station and sampling date was reserved for morphology-based community analyses. Here we present the results for the stations AH01, AH03, AH05, and PAP. We selected these stations because of their location at both ends and in the center of the impacted zone (AH01, AH03, AH05) and at the control site (PAP). The meiofauna (32–1000 µm) was mostly represented by Copepoda, Nematoda, Platyhelminthes, Gastrotricha, and some Annelida. We counted 27445 individuals in total, encompassing 10 higher taxa. We counted copepod nauplii separately due to their small body size. We defined the combined group “Plathyhelminthes+<em>Diurodrilus</em> sp.” because members of the annelid genus <em>Diurodrilus</em> sp. are not distinguishable from Platyhelminthes under the stereomicroscope.</p> <p>Here we present a Table on meiofauna higher taxa counts per 10cm<sup>2</sup> (as xlsx and tab-delimited file; including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The meiofauna abundance data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and metabarcoding analyses (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
Metabarcoding data (number of reads per operational taxonomic unit) from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide metabarcoding data (number of reads per operational taxonomic unit, OTU) determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 and 16 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.<br>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. <br>Three cores (2 cores at T0) per sampling date were taken for metabarcoding analyses. The organisms were extracted by decantation over a 32-μm sieve. Genomic DNA was extracted from the filters using the DNeasy PowerSoil pro kit (Qiagen). Realtime-PCR was performed to amplify V1&V2, two hypervariable regions of 18S rDNA gene. The sequencing run was performed using the MiSeq Reagent Nanokit v2 (250 cycles paired end) on an Illumina MiSeq platform at the DZMB Metabarcoding lab in Wilhelmshaven, Germany. High-resolution amplicon sequence variants (ASVs) were obtained and compared to the NCBI database to assign taxonomic information to each ASV. The target meiofauna ASVs were further classified into operational taxonomic units (OTUs) with a 3% cut-off threshold using the statistical software R.</p> <p>Here, we present two Tables (as xlsx and tab-delimited files):<br>(1) the taxonomic description of the 843 OTUs and their assigned ID number;<br>(2) the number of reads per OTU per sample (including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The metabarcoding data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and meiofauna abundances (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Forward-modelled reflectance from spring and summer Baltic Sea specific inherent optical properties
<p>An extensive dataset of remote-sensing reflectance (R<sub>rs</sub>, units sr<sup>-1</sup>) spectra based on forward modelling of mean concentration-specific inherent optical properties (SIOPs) for both spring and summer optical conditions in the open Baltic Sea. The spectra are modelled using Hydrolight 5.2 for a wide range of Chlorophyll-a (Chla), Coloured Dissolved Organic Matter (CDOM), and Total Suspended Matter (TSM) concentrations as well as solar and viewing angles. The primary aim of providing this supplementary dataset is to aid evaluation of remote sensing algorithms for the Baltic Sea in future studies.</p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci 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>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</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>
Manually Annotated Drone Imagery (RGB) Dataset for automatic coastline delineation of Southern Baltic Sea, Poland with polyline annotations (0.1.1)
<p><strong>Overview:</strong></p> <p>The Manually Annotated Drone Imagery Dataset (MADRID) consists of hand annotated high resolution RGB images taken in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast in 2022-2023. All images were converted into a uniform format of 1440x2560 pixels, polyline annotated and set into file structure format suited for semantic segmentation tasks (See "Usage" notes below for more details).</p> <p>The raw images of our dataset were captured Zenmuse L1 Sensor (RGB) mounted on a DJI Matrice 300 RTK Drone. Total of 4895 images were captured, however the dataset contains 3876 images with each image annotated with coastline. The dataset only include images with coastlines that are visually identifiable with the human eye. For the annotations of the images, CVAT v2.13 open-source software was utilized.</p> <p><strong>Usage:</strong></p> <p>The compressed RAR file contains two folders train and test. Each folder contains the file that represents the date at which the image was captured in the format of (year, month, day), number of the image and the name of the drone utilized to capture the image. For example, DJI_20220111140051_0051_Zenmuse-L1-mission and DJI_20220111140105_0053_Zenmuse-L1-mission. Additionally, the test folder contains annotations (one per image) which are extracted from the original XML annotation file provided in the CVAT 1.1 image format.</p> <p>Archives were compressed using RAR compression. They can be decompressed in a terminal by opening and extracting Madrid_v0.1_Data.zip.</p> <p>The subset of the data with the name Madrid_subset_data.zip has been added which contains a small portion of train and test images for purpose of inspecting the dataset without downloading the entire dataset.</p> <p>The training images for both training data and testing data are structured as follows.</p> <pre><code>Train/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...<br><br>Test/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...</code></pre> <p> </p>
Swell hindcast data for the Baltic Sea
<p>Swell hindcast data from 1999 to 2018. These data are based on the simulation made by The Baltic Monitoring Forecasting Centres(BAL MFC) Production Unit at Finnish Meteorological Institute (FMI) for the Copernicus Marine Environment Monitoring Service (CMEMS) for the product BALTICSEA_REANALYSIS_WAV_003_015. However, it contains variables not available in the CMEMS products.</p> <p>These data are documented in the publication <strong> </strong> Björkqvist, J.-V., Pärt, S., Alari, V., Rikka, S., Lindgren, E., and Tuomi, L.: Swell hindcast statistics for the Baltic Sea, Ocean Sci. Discuss. [preprint], https://doi.org/10.5194/os-2021-62, in review, 2021. The figures in the publication can be reproduced using these data.</p> <p> </p> <p> </p>
Seasonal ecophysiology of Fucus vesiculosus (Phaeophyceae) in the Northern Baltic Sea
<p>This dataset contains ecophysiological measurements of brown algae <em>Fucus vesiculosus</em> and environmental variables, measured in different seasons in 2017 in the Northern Baltic Sea, SW Finland. More specifically, the measured parameters include in situ chlorophyll <em>a</em> fluorescence: F<sub>v</sub>/F<sub>m</sub>, maximum relative electron transport rate, and quantum yield of photochemistry. Carbon and nitrogen content, carbon:nitrogen ratio and chlorophyll <em>a</em> and <em>c</em> content were determined in the laboratory. Environmental parameters monitored include in situ irradiance, temperature, salinity, alkalinity, DIC, pCO<sub>2</sub>, HCO<sub>3</sub><sup>-</sup>, CO<sub>3</sub><sup>2-</sup>, CO<sub>2</sub>, seawater nitrogen (NO<sub>2</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup>) and phosphorus (PO<sub>4</sub><sup>3-</sup>). Measurements were conducted in February, May, July, September and November in two sites, with additional three sites sampled in July. Irradiance was measured with 1 meter depth intervals from the surface. Irradiance values in the data are irradiances at <em>F. vesiculosus</em> sampling depths in each site, estimated with linear regression from the irradiance and depth data. F<sub>v</sub>/F<sub>m</sub> values for sites Spikarna, Ekö and Björnholmen in July were imputed from other variables using R package "Amelia". </p>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
BSIOM Baltic Sea-Ice Ocean Model 1950-2022
<p>Daily temperature, dissolved oxygen and salinity concentration data from the <a name="_Hlk167710471"></a>Baltic Sea Ice Ocean Model (BSIOM) from 1950 to 2022. A detailed description of the equations and modifications made, necessary to adapt the model to the Baltic Sea, can be found in Lehmann et al. (see references below). The model is forced realistically using the ERA5 global re-analysis in the preliminary extension version back to 1950. The resolution of the original output from BSIOM is specified with vertical 60 levels, which enables to resolve the upper 100m by layers of 3 m thickness. The horizontal resolution of the model is 2.5km. The datasets here presented are divided into surface and bottom files. We calculated the sea surface temperature (SST) using the average values from the first three depth layers (upper nine meters), while the sea bottom temperature (SBT) was the average of the last three depth layers following the bathymetry of the Western Baltic Sea (lower nine meters). The values for dissolved oxygen and salinity concentration were calculated in the same manner. The data is spatially constrained to the area between 9° 45’ to 14° 45’ East and 53° 53’ to 56° 30’ North.</p> <p>In the datasets, the following data is available: </p> <ul> <li>Lat: latitude values in degrees (°)</li> <li>Long: longitude values in degrees (°)</li> <li>Depth: depth values (m). The surface file shows a constant value of 1.5, while the bottom file shows the maximum depth (m) for that pixel. To show the depth in reference to the sea-level reference (0 meters) the values should be multiplied by -1.</li> <li>temp: temperature (°C)</li> <li>SO: salinity (g/kg)</li> <li>O2: dissolved oxygen (ml/L-1)</li> <li>t: day of the year (YYYY-MM-DD)</li> <li>LongLat: string with the combination of longitute and latitude values (only for the bottom file)</li> <li>GridID: identification value for each set of coordinates (Long and Lat)</li> </ul>
Directional wave data collected by R/V Aranda in the Baltic Sea
<p>Data source: Finnish Meteorological Institute</p> <p>This is wave and meteorological data collected on board R/V Aranda in July 2015 in the Baltic Sea. Each netcdf-file contains the data and metadata from one station.</p> <p>The experimental setup is described in the paper: Björkqvist, J.-V., Pettersson, H., Drennan, W. M., and Kahma, K. K., 2019: A new inverse phase speed spectrum of nonlinear gravity wind waves, Journal of Geophysical Research: Oceans, 124, 6097–6119, DOI: 10.1029/2018JC014904</p>
Monitoring of the Vistula estuary into the Baltic Sea using Sentinel-2 data
<p>Senitnel-2 data and dedicated presentations were used during the Daily Animation. The aim of the exercise was to familiarize the participants with the structure of Senitnel-2 data and creating RGB compositions in SNAP and QGIS software.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-QGIS.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-SNAP.pdf</p> <p> </p>
Baltic Sea shipborne Hyperspectral Reflectance data from 2016
<p>Hyperspectral Remote-sensing reflectance data collected by the Finnish Environment Institute (SYKE) within the BONUS FerryScope project, analysed (quality checks and spectral filtering) at the Plymouth Marine Laboratory. Methods initially described in:</p> <p>The data were collected from merchant vessels Finnmaid (Finnlines) and Transpaper (Transatlantic). This data set is limited to records for the year 2016. </p> <p>Field data collection and processing:</p> <p>Simis, S.G.H., & Olsson, J. (2013). Unattended processing of shipborne hyperspectral reflectance measurements. Remote Sensing of Environment, 135, 202–212</p> <p>Data quality control: </p> <p>Qin, P., Simis, S.G.H., & Tilstone, G.H. (2017). Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC. Remote Sensing of Environment, 200, 263-280</p> <p> </p> <p>Data specification</p> <p>lat, lon - geographical latitude/longitude coordinates in decimal degrees</p> <p>time - timestamp (date+time) in UTC following ISO 8601 notation. </p> <p>(The location and time fields correspond to the start of a measurement)</p> <p>Rrs_001_3233 .... Rrs_193_9536 - Remote-sensing reflectance (Rrs, units 1/sr). The sequential numbering (1-193) denotes band number, the last term is wavelength x 10 in nm. For example Rrs_001_3233 is the 1st band centred at 323.3 nm. The wavebands approximate the native resolution of the 3-sensor system (TriOS Ramses ARC + ACC units) used to collect radiance and irradiance spectra of the sea surface and sky. </p> <p>Contributions:</p> <p>Stefan Simis, Jenni Attila, Mikko Kervinen, Kari Kallio, Sampsa Koponen, Sakari Väkevä maintained the in situ system.</p> <p>Stefan Simis developed the code to process the (ir)radiance data to Remote-sensing reflectance.</p> <p>Mikko Kervinen and Stefan Simis maintained the operational processing system</p> <p>Ping Qin analysed multi-year observation records and developed quality-control filters</p> <p>Silvia Pardo and Gavin Tilstone analysed the data against satellite sensor records. </p>
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>
Mapping present and future predicted distribution patterns for a meso-grazer guild in the Baltic Sea
<p>Baltic Sea communities consisting of key and endemic species are threatened by climate change. Using Ecological niche modelling, we map predicted distribution patterns under recent and future climate change scenarios (2050) for a food-web consisting of a guild of meso-grazers (Idotea spp.), their host algae (Fucus vesiculosus and F. radicans) and their fish predator (Gasterosteus aculeatus). Brackish water species depend on two important abiotic factors: temperature and salinity. We assess which of these environmental factors determines the distribution limits of the grazers in the Baltic Sea today. For species in a semi-enclosed sea area such as the Baltic Sea, climate-induced changes may lead to dramatic food-web effects. We assess the consequences of the predicted climate-induced habitat range changes for this unique Baltic community.<br /> </p>
BSRLC+: An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
<p><strong>(NEW) </strong>Baltic Sea Region Land Cover <em>Urban</em> (BSRLC-U) focusing on urban built-up types now available: <a href="https://zenodo.org/records/17347941">https://zenodo.org/records/17347941 </a></p> <p><strong>Baltic Sea Region Land Cover <em>Plus </em>(BSRLC+) </strong>is annual land cover mapping (30 m) dataset in Europe from 2000 to 2022. The maps contain detailed information of 18 land cover (LC) types, including 9 crop types and 2 peat bog types.</p> <p>Input data : Optical multi-temporal remote sensing imageries (Landsat 5 (TM) / 7 (ETM+) / 8 (OLI) / 9 (OLI+) and Sentinel 2 (A / B ) from 2000 to 2022. Data is processed to surface reflectance and tiled into datacube structure using <a href="https://doi.org/10.3390/rs11091124">Framework for Operational Radiometric Correction for Environmental monitoring - FORCE.</a></p> <p>Mapping method: Maps are produced using data encoding and deep learning classification according to <a href="https://doi.org/10.1016/j.jag.2024.103867">Pham et al. 2024</a></p> <p>Validation: Maps have been rigorously validated using independent in-situ data <a href="https://doi.org/10.1038/s41597-020-00675-z">The Land Use/Cover Area frame Survey (LUCAS)</a>. </p> <p>Traing data and validation data are available: <a href="https://zenodo.org/records/11073291">https://zenodo.org/records/11073291</a></p> <p>This dataset contains:</p> <ul> <li><strong>00_preview.png</strong>: Preview map (2022) of the Baltic Sea region</li> <li><strong>BSRLC_{year}.tif</strong>: Annual map data (30 m) in GeoTIFF format (projection ETRS89 / EPSG:3035)</li> <li><strong>BSRLC_legend.xlss</strong>: Land cover codes and class names</li> <li><strong>BSRLC_qgis_style.qml</strong>: Map style to be used in QGIS</li> <li><strong>BSRLC_arcgis_style.lyrx</strong>: Map style to be used in ArcGIS</li> </ul> <p>Land cover codes (can also be found in <strong>BSRLC_legend.xlss</strong>):</p> <ul> <li>1: Built-up</li> <li>2: Bareland</li> <li>3: Water</li> <li>4: Shrubland</li> <li>5: Broadleaf forest</li> <li>6: Coniferous forest</li> <li>7: Wetland marsh</li> <li>8: Exploited peat bog</li> <li>9: Unexploited peat bog</li> <li>10: Wheat</li> <li>11: Barley</li> <li>12: Rye</li> <li>13: Oat</li> <li>14: Maize</li> <li>15: Seed crops</li> <li>16: Root crops</li> <li>17: Pulses, vegetable</li> <li>18: Grassland</li> <li>255: Nodata</li> </ul> <p> </p> <p><strong>Publication (please cite this publication if you are using the dataset):</strong></p> <ul> <li>Pham, V.-D., de Waard, F., Thiel, F., Bobertz, B., Hellmann, C., Nguyen, D.-V., Beer, F., Arasumani, M., Schwieder, M., Hartleib, J., Frantz, D., & van der Linden, S. (2024). An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022. <em>Scientific Data, 11</em>, 1242, <a href="https://doi.org/10.1038/s41597-024-04062-w">https://doi.org/10.1038/s41597-024-04062-w</a></li> </ul> <p> </p> <p><strong>Other related publications:</strong></p> <ul> <li><em>Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867, <a href="https://doi.org/10.1016/j.jag.2024.103867">https://doi.org/10.1016/j.jag.2024.103867</a></em></li> <li><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a></em></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This datatset is created in the frame of the Interdisciplinary Research Center for the Baltic Sea Region Research (IFZO) of University of Greifswald, Germany, and the research project Fragmented Transformations, which is funded by the German Federal Ministry of Education and Research (FKZ 01UC2102). </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.