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  • This dataset represents the Integrated biodiversity status assessment for pelagic habitats using the BEAT tool. Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological Quality Ratios (BQR) above 0.6 correspond to good status. Open sea areas were assessed based on the core indicators ‘Zooplankton mean size and total stock’ and ‘Chlorophyll-a’, as well as the pre-core indicator ‘Cyanobacterial bloom index’ . Coastal areas were assessed by national indicators. This dataset displays the result of the integrated biodiversity status in HELCOM Assessment unit Scale 4 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas and division of the coastal areas by WFD water types or water bodies). Attribute information: "BQR" = Biological Quality Ratio "Confidence" = Confidence of the assessment "HELCOM_ID" = Code of the HELCOM assessment unit "Country" = Country of coastal assessment unit/ open sea "Level2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the HELCOM scale 4 assessment unit "Area_km2" = Area of assessment unit "AULEVEL" = scale of the assessment unit "HID" = assessment unit ID by country "SAUID" = ID number for the spatial assessment unit "EcosystemC" = Ecosystem component assessed "Confiden_1" = Confidence of the assessment (0-1, higher values mean higher confidence) "Total_numb" = Number of indicators used in assessment "STATUS" = Integrated status category (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score, 0.8-1.0 = good (highest score))

  • The technical harvesting potential of logging residues and stumps from final fellings can be defined as the maximum potential procurement volume of these available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of logging residues and stumps have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). The technical harvesting potentials were estimated using the sample plots of the eleventh national forest inventory (NFI11) measured in the years 2009–2013. First, a large number of sound and sustainable management schedules for five consecutive ten-year periods were simulated for each sample plot using a large-scale Finnish forest planning system known as MELA (Siitonen et al. 1996; Redsven et al. 2013). MELA simulations consisted of natural processes and human actions. The ingrowth, growth, and mortality of trees were predicted based on a set of distance-independent tree-level statistical models (e.g. Hynynen et al. 2002) included in MELA and the simulation of the stand (sample plot)-level management actions was based on the current Finnish silvicultural guidelines (Äijälä et al. 2014) and the guidelines for harvesting of energy wood (Koistinen et al. 2016). Final fellings consisted of clear cutting, seed tree cutting, and shelter-wood cutting, but only the clear-cutting areas were utilized for energy wood harvesting. As both logging residues and stumps are byproducts of roundwood removals, the technical potentials of chips have to be linked with removals of industrial roundwood. Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustainable removals. The maximum sustainable removals were defined such that the net present value calculated with a 4% discount rate was maximized subject to non-declining periodic industrial roundwood and energy wood removals and net incomes, and subject to the saw log removal remaining at least at the level of the first period. There were no constraints concerning tree species selection, cutting methods, age classes, or the growth/drain ratio in order to efficiently utilize the dynamics of forest structure. The felling behaviour of the forest owners was not taken into account either. For the present situation in 2015, the removal of industrial roundwood was assumed to be the same as the average level in 2008–2012. Fourth, the technical harvesting potentials were derived by retention of 30% of the logging residues onsite (Koistinen et al. 2016) and respectively by retention of 16–18% of stump biomass (Muinonen et al. 2013). Next, the regional potentials were allocated to municipalities proportionally to their share of mature forests (MetINFO 2014). Subsequently, the municipality-level potentials were spread evenly on a raster grid at 1 km × 1 km resolution. Only grid cells on Forests Available for Wood Supply (FAWS) were considered in this operation. Here, FAWS was defined as follows: First, forest land was extracted from the Finnish Multi-Source National Forest Inventory (MS-NFI) 2013 data (Mäkisara et al. 2016). Second, restricted areas were excluded from forest land. The restricted areas consisted of nationally protected areas (e.g. nature parks, national parks, protection programme areas). References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P (2014) Metsänhoidon suositukset [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Hynynen J, Ojansuu R, Hökkä H, Salminen H, Siipilehto J, Haapala P (2002) Models for predicting the stand development – description of biological processes in MELA system. The Finnish Forest Research Institute Research Papers 835. Koistinen A, Luiro J, Vanhatalo K (2016) Metsänhoidon suositukset energiapuun korjuuseen, työopas [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. Koljonen T, Soimakallio S, Asikainen A, Lanki T, Anttila P, Hildén M, Honkatukia J, Karvosenoja N, Lehtilä A, Lehtonen H, Lindroos TJ, Regina K, Salminen O, Savolahti M, Siljander R (2017) Energia ja ilmastostrategian vaikutusarviot: Yhteenvetoraportti. [Impact assessments of the Energy and Climate strategy: The summary report.] Publications of the Government´s analysis, assessment and research activities 21/2017. Mäkisara K, Katila M, Peräsaari J, Tomppo E (2016) The Multi-Source National Forest Inventory of Finland – methods and results 2013. Natural resources and bioeconomy studies 10/2016. Muinonen E, Anttila P, Heinonen J, Mustonen J (2013) Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fenn 47. Redsven V, Hirvelä H, Härkönen K, Salminen O, Siitonen M (2013) MELA2012 Reference Manual. Finnish Forest Research Institute. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O, Teuri M (1996) MELA Handbook. Metsäntutkimuslaitoksen tiedonantoja 622. ISBN 951-40-1543-6.

  • The marine habitat type data concerns the modelling work carried out within the Finnish Inventory Programme for the Underwater Marine Environment (VELMU) in spring 2015. The task was done in cooperation between the Geological Survey of Finland (GTK) and Åbo Academi University (ÅA). The work included the modelling of the marine habitats included in the Annex 1 of the Habitats Directive: reefs (1170) and sandbanks, which are slightly covered by sea water all the time (1110). The aforementioned marine habitat types are specified on the basis of seabed substrate type and topographic form and they can overlap one another. The objective was to produce comprehensive maps of the occurrences of reefs and sandbanks throughout the entire marine area of Finland based on the best data available. The criteria to determine the marine habitats were discussed with the responsible bodies and the instructions (version 5.1), which include more precise criteria for determining marine habitat types than the Natura 2000 Habitats Manual (Airaksinen & Karttunen 2001), for a Natura 2000 inventory were utilised. On the basis of different criteria and test analyses, a decision was made to model the following entireties: - Potential rocky reefs - detail-scale sites that are likely to have reef occurrences. - Potential rocky reef environments - larger sites that are likely to have reef occurrences. - Potential sandbanks - detail-scale sites that are likely to have sandbank occurrences. - Potential sandbank environments - larger sites that are likely to have sandbank occurrences. The data concerning the marine habitats of restricted areas has been removed.

  • Seabed substrate 1:250 000 is one of the products produced in the EMODnet (European Marine Observation and Data network) Geology EU project. Project provided seabed geological material from the European maritime areas. The EMODnet Geology project (http://www.emodnet-geology.eu/) collects and harmonizes geological data from the European sea areas to support decision-making and sustainable marine spatial planning. The EMODnet Geology partnership has included 36 marine organizations from 30 countries. This data includes the EMODnet seabed substrate map at a scale of 1:250 000 from the Finnish marine areas. It is based on the data produced on a scale of 1:20 000 by the Geological Survey of Finland (GTK), which does not cover the whole Finnish marine area yet. The seabed substrate data will be updated with a new interpreted data on a yearly basis.The data has been harmonized and reclassified into five Folk substrate classes (mud, sandy clays, clayey sands, coarse sediments, mixed sediments) and bedrock. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. The data have been generalized into a target scale (1:250 000). The smallest smallest cartographic unit within the data is 0.3 km2 (30 hectares). Further information about the EMODnet-Geology project is available on the portal (http://www.emodnet-geology.eu/). Permission (AK15246) to publish the material was obtained from the Finnish Defence Office 28.07.2014

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    Lorry parking areas at E18 road in Finland. Data is in Esri shapefiles, in ETRS-TM35FIN coordinates. Data is maintained and provided by FTA (Finnish Transport Agency).

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    The GTK’s Mineral Deposit database contains all mineral deposits, occurrences and prospects in Finland. Structure of the new database was created in 2012 and it is based on global geostan-dards (GeoSciML and EarthResourceML) and classifications related to them. The database is in Oracle, data products are extracted from the primary database. During 2013 GTK’s separate mineral deposit databases (Au, Zn, Ni, PGE, U, Cu, Industrial minerals, FODD, old ore deposit database) were combined into a single entity. New database contains extensive amount of information about mineral occurrence feature along with its associated commodities, exploration activities, holding history, mineral resource and re-serve estimates, mining activity, production and geology (genetic type, host and wall rocks, min-erals, metamorphism, alteration, age, texture, structure etc.) Database will be updated whenever new data (e.g. resource estimate) is available or new deposit is found. Entries contain references to all published literature and other primary sources of data. Also figures (maps, cross sections, photographs etc.) can be linked to mineral deposit data. Data is based on all public information on the deposits available including published literature, archive reports, press releases, companies’ web pages, and interviews of exploration geologists. Database contains 33 linked tables with 216 data fields. Detailed description of the tables and fields can be found in separate document. (http://tupa/metaviite/MDD_FieldDescription.pdf) The data products extracted from the database are available on Mineral Deposits and Exploration map service (http://gtkdata.gtk.fi/MDaE/index.html) and from Hakku -service (http://hakku.gtk.fi).

  • Potential cumulative impacts on benthic habitats is based on the same method than <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9477be37-94a9-4201-824a-f079bc27d097" target="_blank">Baltic Sea Impact Index</a>, but is focused on physical pressures and benthic habitats. The dataset was created based on separate analysis for potential cumulative impacts on only the benthic habitats, as these are particularly affected by physical pressures. In this case the evaluation was based on pressure layers representing <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ea0ef0fa-0517-40a9-866a-ce22b8948c88" target="_blank">physical loss</a> and <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/05e325f3-bc30-44a0-8f0b-995464011c82" target="_blank">physical disturbance</a>, combined with information on the distribution of eight broad benthic habitat types and five habitat-forming species (<a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/363cb353-46da-43f4-9906-7324738fe2c3" target="_blank">Furcellaria lumbricalis</a>, <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/f9cc7b2c-4080-4b19-8c38-cac87955cb91" target="_blank">Mytilus edulis</a>, <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/264ed572-403c-43bd-9707-345de8b9503c" target="_blank"> Fucus sp.</a>, <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/822ddece-d96a-4036-9ad8-c4b599776eca" target="_blank">Charophytes</a> and <a href="http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ca327bb1-d3cb-46c2-8316-f5f62f889090" target="_blank">Zostera marina</a>). The potential cumulative impacts has been estimated based on currently best available data, but spatial and temporal gaps may occur in underlying datasets. Please scroll down to "Lineage" and visit <a href="http://stateofthebalticsea.helcom.fi/cumulative-impacts/" target="_blank">State of the Baltic Sea website</a> for more info.

  • Laser scanning data refers to three-dimensional point-like data depicting the ground and objects on the ground. Each point is provided with x, y and z coordinate information. Laser scanning data is collected i.a. for updating elevation models, creating 3D geometries of buildings, mapping flood risks, and collecting information about forest resources. Laser scanning data 0.5 p has been spaced out from Laser scanning data 5 p's density of 5 p/m² to a density of 0.5 p/m². Laser scanning data 0.5 p is open data, and from 2020 onwards, it will be collected according to the national laser scanning programme (in Finnish): https://www.maanmittauslaitos.fi/maastotiedonyllapito. For the time being, it is available only from certain parts of Finland. Laser scanning data 0.5 p is available mainly automatically classified from the NLS Open data file download service: https://www.maanmittauslaitos.fi/en/e-services/open-data-file-download-service. More information (in Finnish): https://www.maanmittauslaitos.fi/laserkeilausaineistot.

  • This dataset contains integrated eutrophication status assessment 2011-2016. The assessment is done using the HEAT 3.0 by combining assessment unit-specific results from various indicators by three MSFD criteria groups (C1: Nutrient levels, C2: Direct effect, C3: Indirect effect). The assessment is done on HELCOM Assessment Unit level 4: HELCOM Subbasins with coastal WFD water type or water bodies. The HEAT 3.0 has been applied for open sea assessment units using HELCOM core indicators and for coastal areas using national WFD indicators. In case of Denmark, the WFD results were used directly, displaying different classification as obtained from HEAT. For more information about the methodology, see the State of the Baltic Sea report and HELCOM Eutrophication assessment manual. Attribute information: "HELCOM_ID": ID of the HELCOM Level 4 Assessment unit "Country": Country/ Opensea "level_2": Name of the HELCOM Level 2 Assessment unit "Name": Name of the HELCOM Level 4 Assessment unit "Area_km2": Area of assessment unit "C1_N": MSFD criteria 1, number of indicators used for calculating Eutrophication Ratio (ER) "C1_ER": MSFD Criteria 1, ER "C1_SCORE": MSFD Criteria 1, Confidence of ER "C2_N": MSFD Criteria 2, number of indicators used for calculating ER "C2_ER": MSFD Criteria 2, ER "C2_SCORE": MSFD Criteria 2, Confidence of ER "C3_N": MSFD Criteria 3, number of indicators used for calculating ER "C3_ER": MSFD Criteria 3, ER "C3_SCORE": Criteria 3, Confidence of ER "N": Number of criteria used for calculating overall ER "ER": Overall ER "SCORE": Status confidence "STATUS": Status classification (Good (classes 0-0.5 & 0.5-1.0), Not Good (classes 1.0-1.5, 1.5-2.0 & >2.0), Not assessed) "CONFIDENCE": Final confidence class (< 50% = low, 50-74 % = Moderate, = 75 % = High) "AULEVEL": Level of assessment units

  • The database consists of three components: "Published age determination”, ”Published Sm-Nd isotope data" and "Pb isotope data on galena". The "Published age determination" database is based on age determinations, which comprise predominantly U-Pb zircon data produced at the Geological Survey of Finland since 1960’s. For igneous rocks the age register contains radiometric ages mostly interpreted as primary ages. The information given consists of location data, rock type, method, mineral analyzed, age results, comments and references. "Published Sm-Nd isotope data" comprise Sm-Nd data procuded at GTK since 1981, which mostly are used to constrain the origin of crust. "Pb isotope data on galena" gives results produced at GTK since 1970's, and include also previously unpublished data.