AndreaBozzo/ceres-open-data-index huggingface dataset:
Habitat Suitability Model for Sabellaria spinulosa reefs in the UK: 2020

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Sabellaria spinulosa across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 300 m.

Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator.

The predictor variables used within the model include:

Depth to seabed
Slope of seabed
Light attenuation coefficient of photosynthetic active radiation (Kd(PAR))
Kinetic energy at the seabed due to currents
Kinetic energy at the seabed due to waves
Seabed Substrate
Mean of annual temperature at the seabed (over 30-year period)
Absolute minimum of seasonal salinity
Habitat Suitability Model for Zostera marina beds in the UK: 2020

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Zostera marina beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 30 0m.

Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator.

The predictor variables used within the model include:

Depth to seabed
Slope of seabed
PAR at the Seabed
Kinetic energy at the seabed due to currents
Kinetic energy at the seabed due to waves
Seabed Substrate
Mean of annual minima temperature at the seabed (over 30-year period)
Absolute minimum of seasonal salinity
EUSeaMap (2023) Broad-Scale Predictive Habitat Map for the Caspian - Substrate type (a habitat descriptor)

Classified seabed substrate types for European seas. Produced by EMODnet Seabed Habitats as an input layer for the 2023 EUSeaMap broad-scale habitat model for the Caspian region, based on a combination of EMODnet Geology seabed substrate products and biological substrates extracted form individual habitat maps from surveys around European seas. The extent of the mapped area includes the Caspian sea. The layer of seabed substrate was produced using data from EMODnet geology at the following scales: 1:10k, 1:500k, 1:1m, 1:5m (updated in 2023).

The seabed substrate include:
Rock and other hard substrata
Coarse substrate
Mixed sediment
Sand
Muddy Sand
Sandy Mud
Mud
Bottoms dominated by Mytilaster lineatus and Amphibalanus improvisus
Bottoms dominated by Dreissena grimmi

Biological substrates were included in the 2023 version of EUSeaMap to assist in the classification of biogenic habitats. A bespoke classification of benthic broad habitat types was developed as part of EMODnet Seabed Habitats.
EUSeaMap (2021) Broad-Scale Predictive Habitat Map - Energy class (a habitat descriptor)

Energy class layer produced by EMODnet Seabed Habitats as an input layer for the 2021 EUSeaMap broad-scale habitat model. The extent of the mapped area includes the Baltic Sea, and areas of the North Eastern Atlantic and Arctic extending from the Canary Islands in the south to Norway in the North. The map of energy classes was produced using underlying wave and current data and thresholds derived from statistical analyses or expert judgement on known conditions. This layer is the same as the input used in EUSeaMap 2019. 

A report on the methods used in the 2021 version of EUSeaMap (Vasquez et al., 2021) and reports on previous versions (v2016 and V2019) are linked in Online Resources.
EUSeaMap (2021) Broad-Scale Predictive Habitat Map - Confidence in classification of substrate type

Confidence in the classification of substrate type in the 2021 EUSeaMap broad-scale predictive habitat map.

Values are on a range from 1 (Low confidence) to 3 (High confidence).

Substrate type is one of the layers of information used to categorise physical habitat types in EUSeaMap; these layers of information are collectively known as 'habitat descriptors'. The substrate layer confidence was obtained from reclassification and standardisation of the confidence scores associated with each primary layer used to create the Substrate types layer.

A report on the methods used in the 2021 version of EUSeaMap (Vasquez et al., 2021) and reports on previous versions (v2016 and V2019) are linked in Online Resources.
EUSeaMap (2021) Broad-Scale Predictive Habitat Map - Confidence in classification of EUNIS 2007 habitat types

Confidence in the classification of the EUNIS 2007 habitat types in the EUSeaMap (2021) broad-scale predictive habitat map.

Values are 1 (low confidence), 2 (moderate confidence) or 3 (high confidence).

The final habitat type is classified by overlaying several layers of information; these layers of information are collectively known as 'habitat descriptors'. Habitat descriptors differ per region but include:
Biological zone
Energy class
Oxygen regime
Salinity regime
Seabed substrate
Riverine input

The confidence in the classification of the habitat type is taken as the minimum of the confidence in all of the relevant habitat descriptors at that location.

Confidence values are also available for each habitat descriptor and input data layer.

Detailed information on the modelling process is found in the EMODnet Seabed Habitats technical reports and its appendices (links in Resources). 

Created by the EMODnet Seabed Habitats project consortium.
Credit: Licensed under CC-BY 4.0 from the European Marine Observation and Data Network (EMODnet) Seabed Habitats initiative (www.emodnet-seabedhabitats.eu), funded by the European Commission.

It is important to note that a habitat type confidence score is only relevant to that particular level of the classification system. For example, a cell of A3.1 high energy infralittoral rock with ÔÇÿlowÔÇÖ energy class confidence, ÔÇÿmoderateÔÇÖ biozone confidence and ÔÇÿhighÔÇÖ substrate type confidence would have an overall ÔÇÿlowÔÇÖ confidence. However, moving up the hierarchy to EUNIS level two (A3 infralittoral rock) removes the energy class; therefore, the confidence of the EUNIS level two habitat type would only consider the ÔÇÿmoderateÔÇÖ biozone confidence and ÔÇÿhighÔÇÖ substrate type confidence, resulting in an overall ÔÇÿmoderateÔÇÖ confidence.
Black Sea - Danube Delta - DIVAnd 4D seasonal analysis of Water body silicate 1970/2022 v2025

Water body silicate - Seasonal Climatology for NW Black Sea (region in front of Danube Delta months) for the period 1970-2019 on the domain: Lon E28.5-30.5°E, Lat N43.7-45.6°N. 
Season definition: winter: December-February, - spring: March-May, - summer: June-August, - autumn: September-November.
Units: umol/l.
Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network.
Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.13, using GEBCO 30sec topography for the spatial connectivity of water masses. Horizontal correlation length of 47.73km and vertical correlation length of 45m were applied. 
The horizontal resolution of the produced DIVAnd maps grids is 0.01 degrees.
The vertical resolution is 17 depth levels: 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0 m.  
The error variance (epsilon2): 1 
An anamorphosis transformation was applied to the data: threshold value=200.
The mean value was used as background analysis field.
Quality control of the observations was applied using the interpolated field: QCMETHOD=3 and visual inspection (Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis after thoroughly visual inspection).
Modelled distribution map of Kophobelemnon stelliferum assemblage in the North East Atlantic in 2021

Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021.

Citation: Rix J.; Graves K.; Piechaud N.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Kophobelemnon stelliferum assemblage in the North East Atlantic in 2021. https://marineinfo.org/doc/dataset/8251
Modelled distribution map of Lophelia Reef and Pheronema aggregations in the North East Atlantic in 2022

Species distribution models (Maxent) predicting the distribution of two Vulnerable Marine Ecosystems (VME): the reef-forming Scleractinian coral Desmophyllum pertusum and the aggregations forming Hexactinellid sponge Pheronema carpenteri. Both of these species are VME indicator taxa and form habitat that enhance deep-sea diversity (Ross and Howell, 2013). Maps of the likely distribution of the habitat formed by these two species will enable efficient Marine Spatial planning to facilitate their conservation. This work was performed at the University of Plymouth in 2021. A GIS layer is provided for each species.

Citation: Bridges, A.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Lophelia Reef and Pheronema aggregations in the North East Atlantic in 2022. https://marineinfo.org/doc/dataset/8213
Modelled distribution map of Solenosmilia variabilis reef in the North East Atlantic in 2021

Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Solenosmilia variabilis reef assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021.

Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Solenosmilia variabilis reef in the Northeast Atlantic in 2021. https://marineinfo.org/doc/dataset/8255
Modelled distribution map of mixed cold-water coral communities in the North East Atlantic in 2021

Species distribution models (Random Forest) predicting the distribution of mixed cold-water coral community (Coral Garden) assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. 

Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of mixed cold-water coral community in the Northeast Atlantic in 2021. https://marineinfo.org/doc/dataset/8254
Greater North Sea and Celtic Seas - Contaminants aggregated datasets 1970/2024 v2026

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on potential hazardous substances, despite the fact that some data might not be related to pollution (e.g. collected by deep corer). Temperature, salinity and additional parameters are included when available. It covers the Greater North Sea and Celtic Seas. Data were harmonised and validated by the 'Aarhus University, Department of Bioscience, Marine Ecology Roskilde' in Denmark.
The dataset contains water (profiles), sediment (profiles and timeseries) and biota (profiles and timeseries). The temporal coverage is 1970–2022 for water measurements, 1973–2022 for sediment measurements and 1979–2022 for biota measurements.
Regional datasets concerning contaminants are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4 ). Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/ .
The harmonised dataset can be downloaded as as an ODV spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported into ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ). In addition, the same dataset is offered also as a txt file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms into subcomponents (substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications (e.g. LibreOffice Calc).
Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer 2009 (UIG), Blue mussel beds in the Wadden Sea of Lower Saxony 2009

Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer 2009. Im Rahmen des Miesmuschel Managementplans wurden j├ñhrliche Kartierungen der eulitoralen Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer durchgef├╝hrt. Die Erfassung erfolgte mittels stereoskopischer Auswertung von Luftbildern. Die Befliegungen fanden im Fr├╝hjahr bzw. Sommer fl├ñchendeckend statt. (Deutsch), Blue mussel beds in the Wadden Sea of Lower Saxony 2009. The intertidal blue mussel beds were determined yearly in the scope of the blue mussel management plan. Aerial surveys were carried out and aerial photographs were analysed by using a stereoscope to detect the mussel beds. (Englisch)
Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer 2007 (UIG), Blue mussel beds in the Wadden Sea of Lower Saxony 2007

Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer 2007. Im Rahmen des Miesmuschel Managementplans wurden j├ñhrliche Kartierungen der eulitoralen Miesmuschelb├ñnke im Nieders├ñchsischen Wattenmeer durchgef├╝hrt. Die Erfassung erfolgte mittels stereoskopischer Auswertung von Luftbildern. Die Befliegungen fanden im Fr├╝hjahr bzw. Sommer fl├ñchendeckend statt. (Deutsch), Blue mussel beds in the Wadden Sea of Lower Saxony 2007. The intertidal blue mussel beds were determined yearly in the scope of the blue mussel management plan. Aerial surveys were carried out and aerial photographs were analysed by using a stereoscope to detect the mussel beds. (Englisch)
European and Caribbean Intertidal Habitats (2025)

This dataset provides a map of global intertidal habitats from European and Caribbean regions.
			The map is produced from Murray et al. (2022) High-resolution global maps of tidal flat ecosystems from 1984 to 2019 dataset, which provides the global distribution of intertidal habitats. We masked the Caribbean region based on Caribbean study area extent and European region based on EMODnet Bathymetry Coastline dataset, which was buffered by 30 kilometers in order to maintain the intertidal areas deeper inland.
Natural geography in shore areas

This dataset provides data collected during the Natural Geography In Shore Areas (NaGISA) quantitative global nearshore census.
The data is coming in (i.e. the dataset is continually being added to and should not be considered complete) from a growing set of globally distributed standard transects from the high inter-tidal zone to the depth of 20m, most of which will be repeated, and many of which are being uploading in stages (i.e. taxonomic detail will improve with time). Target habitats are globally distributed algal/ hard bottom and sea-grass/ soft bottom communities. For each study site, replicate samples are collected at high, mid and low inter-tidal and 1, 5 and 10m sub-tidal depth (where possible at 15 and 20m). The NaGISA protocols (which are standardized) include: (1) Passive sampling, photography and observational techniques, estimates of percent cover of colonial invertebrates and rhizoidal macro algae and counts of algal stripes and solitary fauna within quadrates. (2) Active sampling, core samples of sea grass beds, and careful removal of organisms from small quadrants within macro algal sites. (3) Assessment, measurements of surface and bottom seawater temperature and a visual classification of substrata. For more details please see the project website http://www.nagisa.coml.org/. Please note that all enquires made to NaGISA about this dataset should make complete reference to the field number (s) listed in the NaGISA OBIS dataset.
MICROBIS Database (version 1) - European data

This dataset contains the European data of MICROBIS, a database of marine microbial biota.
This dataset contains the European data of MICROBIS.

The MICROBIS database serves legacy, lipidomic, and pyrosequencing data and associated contextual data collected as part of the ICoMM (International Census of Marine Microbes) Census of Marine Life Ocean Realm Project. Legacy data include geospatial and environmental data collected from environmental sequencing surveys prior to the initiation of the ICoMM effort. MICROBIS links to lipid structures (via Lipid Maps) and a mass spectrometry library that contains lipid data derived from microbes from modern and ancient environments.
Historical zooplankton records from the Black Sea

Historical zooplankton records from the Black Sea
Dataset contains zooplankton records collected by Institute of Biology of the Southern Seas, NAS Ukraine. Samples were taken during different scientific cruises and small coastal expeditions. Only biogeographical records are provided to OBIS.The dataset also contains numbers per samples, abundance, biomass and can be obtained by request from Plankton Department of Institute of Biology of the Southern Seas, NAS Ukraine
A comparison of benthic biodiversity in the North Sea, English Channel and Celtic Seas - Epifauna

Comparison dataset of Animalia in the North Sea, the English Channel and the Celtic Sea.
Samples were obtained by MAFF research vessels from 69 stations between 1992 and 1996.

A standard 2 m Lowesoft beam trawl with a 3 mm mesh codend liner was deployed for 5-10 min across each station at a speed of about 0.5 m/s. The "start" (locking of winch following seabed contact) and "end" (commencement of hauling) positions were recorded. Tow length averaged about 400 m, but varied substantially (s.d. = 290) depending on tidal current velocity and wind strength at the time of sampling. On retrieval of the trawl, an estimate of sample volume was made, along with a summary of the contents, noting especially the presence of stones, rock, etc. The sample was then sorted on deck over a 5 mm mesh sieve. Most specimens were identified and enumerated at sea. Any problematic specimens were preserved in formalin for identification on land.

The presence of infaunal organisms arising from the fouling of soft sediments was noted, as were occurences of pelagic species such as jellyfish. However, these records were excluded from the final compilation of the data. The former included most polychaete worms (with the exception of encrusting forms such as Sabellaria and Filograna, and surface-dwellling specimens within the family Aphroditidae), bivalve molluscs (excepting taxa with a surface-dwelling habitat such as Chlamys and Modiolus), and burrowing echinoderms such as Echinocardium and Amphiura.
EMODnet Human Activities, Cultural Heritage, UNESCO World Heritage Convention

This dataset provides the location of coastal sites from the UNESCO World Heritage Convention list. It includes Unseco sites considered as coastal according to the definition of costal areas provided by the European Commission: coastal areas are local administrative units (LAUs) that are bordering or close to a coastline. A coastline is defined as the line where land and water surfaces meet (border each other). Coastal areas have at least 50% of their surface area within a distance of 10km from the coastline. The dataset includes the name of the sites, its code in the Unesco site database, the distance to coast, whether it is a cultural or natural site and a short description from the Unesco. It is available for viewing and download on the EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities).
Baltic Sea - Eutrophication and Acidity aggregated datasets 1902/2025 v2026

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on eutrophication and acidity, and covers the Baltic Sea. Data were aggregated and quality controlled by the "Swedish Meteorological and Hydrological Institute (SMHI)" in Sweden.
ITS-90 water temperature and water body salinity variables have also been included ('as are') to complete the eutrophication and acidity data. If you use these variables for calculations, please refer to SeaDataNet for the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets .
Regional datasets concerning eutrophication and acidity are automatically harvested, and the resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.13120/8xm0-5m67 ). Parameter names are based on P35 vocabulary, which relates to EMODnet Chemistry aggregated parameter names and is available at: https://vocab.nerc.ac.uk/search_nvs/P35/ .
When not present in original data, water body nitrate plus nitrite was calculated by summing all nitrate and nitrite parameters. The same procedure was applied for water body dissolved inorganic nitrogen (DIN), which was calculated by summing all nitrate, nitrite, and ammonium parameters. Concentrations per unit mass were converted to a unit volume using a constant density of 1.025 kg/L.
The aggregated dataset can also be downloaded as an ODV collection and spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ).