AndreaBozzo/ceres-open-data-index huggingface dataset:
Density of Loggerhead Turtles in the Mediterranean Sea

Loggerhead turtles (Caretta caretta) are a globally vulnerable species of marine turtle. The Mediterranean Sea subpopulation, which ranges throughout the entire Mediterranean basin, is listed as least concern by the International Union for the Conservation of Nature (IUCN), but experiences significant threats in the region including bycatch in fisheries, climate change, coastal development, and marine pollution. Broad-scale patterns of distribution and abundance can provide regional managers a tool to effectively conserve and manage this species at basin and sub-basin scales.
In 2020, a spatial density model estimating loggerhead density, abundance, and distribution across the Mediterranean Sea was generated as a long-term annual average. The model was adjusted for availability bias using dive data from loggerhead turtles tagged with time depth recorders from multiple regions within the Mediterranean Sea. Geographic extrapolation in areas near surveys was undertaken with caution. Mean abundance for the long-term average model was estimated as 1,201,845 (CV 0.22). This estimate represents the first basin-wide estimate of abundance for this species in the Mediterranean not based on demographic models. This work was funded by the U.S. Navy to assist with assessing risk to protected marine species resulting from military readiness activities.The data are an annual estimated density of loggerhead turtles, expressed as the number of individuals per square kilometer, within the Mediterranean Sea. The study area was gridded into 5x5 kilometer blocks, the resolution at which density modeling was performed. The study area encompasses all major subregions of the Mediterranean Sea, except the Sea of Marmara, which loggerhead turtles do not frequent. The DENSITY attribute contains the estimated density (individuals per square kilometer) and the UNCERTAINTY attribute contains the estimated coefficient of variation (CV) for the density estimate. The density estimates represent the mean throughout year, averaged for the period 2003-2018. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation. Density was not estimated for nearshore locations and inshore waters as they were often not covered by surveys or lacked reliable remoted sensed covariate coverage. See Sparks and DiMatteo (2020) for more information about the modeling process. The metadata documentation has more information regarding individual attributes.The shoreline and other geographic features were delinated by NAVFAC from a database provided by the National Geospatial-Intelligence Agency (NGA).
Spatial interpolation of Calanus finmarchicus and Calanus helgolandicus observations in the North Sea

Spatial interpolation of Calanus finmarchicus and Calanus helgolandicus observations in the North Sea using the DIVAnd software tool. The objective of this project is twofold: (1) Create gridded maps of Calanus finmarchicus and Calanus helgolandicus abundances and (2) Develop and apply a multivariate approach in the interpolation method.
Molluscs from two rocky shores (infralittoral zone) of the North coast of Crete, collected for the NaGISA project 2007-2008

This dataset contains occurrence records of infralittoral molluscs from two infralittoral rocky shore sampling sites in northern Crete (Greece). Samples were collected for a monitoring project of infralittoral rocky shores in Crete, in the framework of the NaGISA project (Natural Geography in Shore Areas, http://www.coml.org/projects/natural-geography-shore-areas-nagisa).
The two sampling sites are characterised by a continuous hard bottom habitat with dense algal coverage and a moderate wave exposure and are not impacted by detectable anthropogenic activity. At each site, five random replicate units were collected from each depth of 1m, 5m, 10m, 15m and 20m by placing a plexiglas frame (25x25 cm) on the substrate and scraping the framed area completely. The samples were then collected with an underwater suction device working with pressure from a diving bottle. Samples were washed through a 0.5 mm mesh sieve and fixed in 99% ethanol. In the laboratory, all samples were identified to the most precise taxonomic level possible, using the most recent literature for the phylum. The pollychaetes records are available as a separate dataset in the dataset "Polychaetes from two subtidal rocky shores of the North coast of Crete, collected for the NaGISA project 2007-2008" available at http://ipt.medobis.eu/resource?r=nagisa_species_2007_2008.
280_181024-HRDTM-Alboran-60m

This composite Digital Terrain Model (cDTM) is a high resolution bathymetry dataset that covers SHAKE,TOPOMED-GASSIS and MELCOR cruises. Multibeam data acquired in 2011, 2012 and 2015 on board the R/V Sarmiento de Gamboa and R/V García del Cid with and Atlas Hydrosweep DS-3 and Elac Seabeam 1050D echo-sounders.
Grey Seal Distribution Polygons

These grid data were derived from National Parks and Wildlife Service cetacean surveys within the Irish MSFD area and the EEA-10km GRID. The grid shows the current distribution of Harbour seal (Phoca vitulina) and Grey seal (Halichoreus grypus) in Irish coastal and marine waters. The data were collected for the purposes of 2019 reporting under Article 17 of the EU Habitats Directive.
Polygon shapefile showing the 10km grid cell-based current distribution of the Annex II seal species. The following data sources were used to determine their distribution: Morris, C. D. and Duck C. D. (2019) Aerial thermal imaging survey of seals in Ireland, 2017-2018. Report (unpublished) for the National Parks and Wildlife Service of the Department of Culture, Heritage and the Gaeltacht. NPWS (2018) Unpublished data collected during local site surveillance and regional monitoring of Ireland's seal populations along the east-southeast, west-southeast and northwest coasts between 2009 and 2018. Supporting Data: Data collected during the IWDG ISCOPE I and II projects, the IWDG/GMIT Marine Mammals and Megafauna in Irish waters project, the IWDG Ferry Surveys Programme and the IWDG casual and effort-based sightings scheme between January 2005 and January 2011. A single marine mammal observer (or up to three observers, in the case of IWDG ferry surveys) conducted a visual survey effort from research vessels, naval service vessels and commercial ro-ro ferries between 2005 and 2011. Survey effort was conducted either from the ship’s bridge, the monkey island (the roof of the bridge) or from the crow’s nest (R.V. Celtic Explorer). Using an angle board and distances were estimated with the aid of a range-finding stick (Heinemann 1981). Environment data were recorded every 15 / 20 minutes using Logger 2000 software (IFAW 2000). Sightings were also recorded using Logger 2000. Automated position data were obtained through a laptop computer linked to a USB GPS receiver. Survey effort was conducted up to Beaufort sea-state six and in moderate to good visibility. As these were surveys onboard vessels of opportunity, the surveys were conducted in passing mode and cetaceans sighted were not approached. Sightings were identified to species level where possible, with species identifications being graded as definite, probable or possible. Where species identification could not be confirmed, sightings were downgraded (e.g. unidentified dolphin / unidentified whale / unidentified beaked whale etc.) according to criteria established for the IWDG’s cetacean sightings database (IWDG 2013). Observer effort focused on a 90-degree arc ahead of the ship; however, sightings located up to 90 degrees to port and starboard were included. Surveyors scanned the area by eye and using binoculars (typically 10X40 or 8X50). Bearings to sightings were measured.
Harbour Seal Distribution

These grid data were derived from National Parks and Wildlife Service cetacean surveys within the Irish MSFD area and the EEA-10km GRID. The grid shows the current distribution of Harbour seal (Phoca vitulina) in Irish coastal and marine waters. The data were collected for the purposes of 2019 reporting under Article 17 of the EU Habitats Directive.
Polygon shapefile showing the 10km grid cell-based current distribution of the Annex II seal species. The following data sources were used to determine their distribution: Morris, C. D. and Duck C. D. (2019) Aerial thermal imaging survey of seals in Ireland, 2017-2018. Report (unpublished) for the National Parks and Wildlife Service of the Department of Culture, Heritage and the Gaeltacht. NPWS (2018) Unpublished data collected during local site surveillance and regional monitoring of Ireland's seal populations along the east-southeast, west-southeast and northwest coasts between 2009 and 2018. Supporting Data: Data collected during the IWDG ISCOPE I and II projects, the IWDG/GMIT Marine Mammals and Megafauna in Irish waters project, the IWDG Ferry Surveys Programme and the IWDG casual and effort-based sightings scheme between January 2005 and January 2011. A single marine mammal observer (or up to three observers, in the case of IWDG ferry surveys) conducted a visual survey effort from research vessels, naval service vessels and commercial ro-ro ferries between 2005 and 2011. Survey effort was conducted either from the ship’s bridge, the monkey island (the roof of the bridge) or from the crow’s nest (R.V. Celtic Explorer). Using an angle board and distances were estimated with the aid of a range-finding stick (Heinemann 1981). Environment data were recorded every 15 / 20 minutes using Logger 2000 software (IFAW 2000). Sightings were also recorded using Logger 2000. Automated position data were obtained through a laptop computer linked to a USB GPS receiver. Survey effort was conducted up to Beaufort sea-state six and in moderate to good visibility. As these were surveys onboard vessels of opportunity, the surveys were conducted in passing mode and cetaceans sighted were not approached. Sightings were identified to species level where possible, with species identifications being graded as definite, probable or possible. Where species identification could not be confirmed, sightings were downgraded (e.g. unidentified dolphin / unidentified whale / unidentified beaked whale etc.) according to criteria established for the IWDG’s cetacean sightings database (IWDG 2013). Observer effort focused on a 90-degree arc ahead of the ship; however, sightings located up to 90 degrees to port and starboard were included. Surveyors scanned the area by eye and using binoculars (typically 10X40 or 8X50). Bearings to sightings were measured.
Gridded abundance maps of phytoplankton diversity and toxicity around the French coast

Gridded abundance maps representing Dinophysis species, phytoplankton taxon richness and algal toxicity along the French coast. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by IFREMER (REPHY - réseau de surveillance du phytoplancton. Institut Français pour la Recherche et l'exploitation de la mer, Nantes, France.).
Several Dinophysis species produce toxins and can cause gastrointestinal illness and Diarrhetic Shellfish Poisoning (DSP), even at low cell densities. A algal toxicity ratio was calculated by dividing the concentration of harmful planktonic cells by the sum of the harmful and the harmless species. Algal toxins from harmful algal blooms (HABs) can cause shellfish poisoning in humans and be of danger to live stocks in coastal water (Descriptor 5 Eutrophication). Phytoplankton organisms are responsible for about 45% of annual global photosynthesis. They are as such vital for the functioning of marine food webs (considered in MSFD descriptor 4) and are the main drivers of the global biogeochemical cycles.
Gridded abundance maps of marine mammals around the Azores

Gridded abundance maps of marine mammals (sperm whale, short beaked common dolphin, common bottlenose dolphin, Atllantic spotted dolphin)  around the Azores. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by the Azores Fisheries Observation Programme (POPA).
This dataset is based on:
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2014): POPA- Fisheries Observer Program of the Azores: Marine mammal sightings in the Azores between 2004 and 2013 recorded by Biosphere Expedition. http://dx.doi.org/10.14284/21
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2016): POPA- Fisheries Observer Program of the Azores: Marine mammal sightings in the Azores tuna fishery from 1998 on: during navigation or search mode. http://dx.doi.org/10.14284/19
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2014): POPA- Fisheries Observer Program of the Azores: Marine mammal sightings in the Azores tuna fishery from 1998 to 2013: during the capture of live bait. http://dx.doi.org/10.14284/18
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2014): POPA- Fisheries Observer Program of the Azores: Marine mammal sightings in the Azores tuna fishery from 1998 to 2013: while fishing. http://dx.doi.org/10.14284/16
Phytoplankton community analysis in the Northern Adriatic

This data product is an R Shiny application that discloses the data collected by the National Institute of Oceanography and Experimental Geophysics (OGS) in the North Adriatic-Gulf of Trieste LTER. A time series has been built of observations on the species composition of the plankton. The application shows the evolution over time of abundance of major groups of species, as well as the most frequent species (or other taxonomic units) in the dataset. There is also a multivariate representation based on a PCA of abundances of the most frequent species, which shows the seasonal (monthly) fluctuations and the long-term (yearly) trend, and the contribution of each individual species to the temporal evolution of the community.
Summary presence/absence maps of macro-endobenthos in the greater North Sea, based on nearly 100,000 samples from 65 assembled monitoring data sets

The large databases of EMODNET Biology only store confirmed presences of species. However, when mapping species distribution, it is also important where the species did not occur: there is at least as much information in absences as in presences. Inferring absences from presence-only databases is difficult and always involves some guesswork. In this product we have used as much meta-information as possible to guide us in inferring absences.
Gridded abundance maps of marine birds from the North Sea

Gridded abundance maps of marine birds from the North Sea. All the bird species of this dataset are indicator species for Descriptor 1 Biodiversity of the MSFD for the North East Atlantic. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by JNCC (Joint Nature Conservation Committee):

Dunn, T. 2012. JNCC seabird distribution and abundance data (all trips) from ESAS database. Data downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/427).
Gridded abundance maps of diatoms and dinoflagellates from the North Atlantic and North Sea

Gridded abundance maps representing diatoms and dinoflagellates in the North Atlantic and North Sea between 1958 and 2012. Phytoplankton organisms are responsible for about 45% of annual global photosynthesis. They are as such vital for the functioning of marine food webs (considered in MSFD descriptor 4) and are the main drivers of the global biogeochemical cycles. This product was developed with DIVA (Data-Interpolating Variational Analysis) and the scale represents log-transformed abundance. Data was provided by SAHFOS (Continuous Plankton Recorder (CPR) data (phytoplankton) from the Sir Alister Hardy Foundation for Ocean Science (SAHFOS)).
Gridded abundance maps of commercial fish species from the North Sea

Gridded abundance maps of commercial fish species. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by ICES (IBTS dataset - Fish trawl survey: ICES North Sea International Bottom Trawl Survey for commercial fish species. ICES Database of trawl surveys (DATRAS). The International Council for the Exploration of the Sea, Copenhagen. 2010. Online source: http://ecosystemdata.ices.dk.). The maps show clearly the dramatic stock depletion of the last decades in the North Sea. Scale: log-transformed CPUE.
Gridded abundance maps of microorganisms from the North Sea

These products are a series of gridded abundance maps of microorganisms in the North Sea from 2007-2008 at four different depth ranges. Micro-organisms are vital for the functioning of marine food webs (considered in MSFD descriptor 4) and are the main drivers of the global biogeochemical cycles. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by NIOZ (C. Brussaard - Brandsma, J.; Martínez Martínez, J.; Slagter, H.A.; Evans, C.; Brussaard, C.P.D. (2013). Microbial biogeography of the North Sea during summer. Biogeochemistry 113(1-3): 119-136. dx.doi.org/10.1007/s10533-012-9783-3 ).
Gridded abundance map of the loggerhead sea turtle Caretta Caretta around the Azores

Gridded abundance map of Caretta caretta around the Azores between 1998 and 2002. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by the Azores Fisheries Observation Programme (POPA).
This dataset is based on:
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2014): POPA- Fisheries Observer Program of the Azores: Sea turtle sightings in the Azores between 2004 and 2013 recorded by Biosphere Expedition. http://dx.doi.org/10.14284/14
Machete, M.; Institute of Marine Research (IMAR), Portugal; Department of Oceanography and Fisheries, University of the Azores (DOP/UAC), Portugal; (2016): POPA- Fisheries Observer Program of the Azores: Turtle sightings in the Azores tuna fishery from 2000 on: during navigation or search mode. http://dx.doi.org/10.14284/13
Gridded abundance maps of marine mammals from the North Sea

Gridded abundance maps of seal species. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by JNCC (Joint Nature Conservation Committee): 
Dunn, T. 2012. JNCC seabird distribution and abundance data (all trips) from ESAS database. Data downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/427).
Distribution of fish living modes in European seas

This series of products displays the distributions of the main functional types of benthic and bentho-pelagic fish species derived from a multivariate analysis of eight life history traits. Species data from the International Bottom Trawl Survey data base was combined with a compiled list of life history traits. A multivariate analysis of the life history traits revealed four main living modes. Interpolated maps of the relative distribution of the four living modes were generated in DIVAnd, the n-dimensional Data Interpolating Variational Analysis tool.
The first and second groups comprise small species with a short life cycle. Species from group one differ from those from group two by a reduced fecundity compensated by either internal incubation or benthic embryonic development that increase juvenile survival, whereas species from group two are characterised by higher fecundity and juvenile mortality (unattended pelagic eggs). By contrast, groups three and four comprise large and long-lived species. Species from these groups also differ in fecundity, higher in group three, and juvenile survival, higher in group four. Group four is exclusively composed of elasmobranchs (rays, squates and sharks) which either internally incubate a few offspring that they release as adult-miniatures or release large eggs in strongly protective cases fixed on the sea floor.

DIVAnd version 2.1.0 was used to generate the interpolated maps: 

Alexander Barth. (2018, September 3). Gher-ulg/DIVAnd.jl: v2.1.0 (Version v2.1.0). Zenodo. Http://doi.org/10.5281/zenodo.1407912
Neural network modelling of Baltic zooplankton abundances

This data product is a series of gridded abundance maps for 40 zooplankton species from 2007 to 2013 in the Baltic Sea, based on a neural network analysis. As input data a combination of EMODnet Biology datasets were used, together with the environmental variables dissolved oxygen, salinity, temperature, chlorophyll concentration bathymetry and the distance from coast. Additionally the position (latitude and longitude) and the year are provided to the neural network. DIVAnd (n-dimensional Data-Interpolating Variational Analysis) and the neural network library Knet were used in this analysis.
North Sea DIVA 4D analysis of Water_body_dissolved_oxygen_concentration

This gridded product visualizes 1960 - 2014 water body dissolved oxygen concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November).

It is produced as a Diva 4D analysis, version 4.6.11: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field.

Every step of the time dimension corresponds to a 10-year moving average for each season.

The depth dimension allows visualizing the gridded field at various depths.
Data product numerical abundance of benthic macroinvertebrates in North Sea and Baltic Sea

The data product on benthic living modes (Beauchard, 2018), was based on an extensive compilation of data on benthic abundance and biomass.  However, this dataset was only present as a data file, without the underlying scripts to reproduce the result. With the present data product, we correct this procedural gap. This dataset differs in details from the file underlying the data product on living modes. Datasets were selected that were sufficiently similar in methods for  sampling (either boxcore or grab), sampled surface (in the order of 0.1  square meter, although the exact value is variable - it can be found  back in the data files) and sieves (1 mm and 0.5 mm sieves were  included). For all datasets, abundance was either used directly from the given abundance in the dataset, or calculated from the given counts and area sampled.