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 EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The partnership includes 39 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at various scales from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This dataset includes EMODnet seabed substrate maps at a scale of 1:10 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. Further information about the EMODnet Geology project is available on the portal (http://www.emodnet-geology.eu/).

NLS-FI INSPIRE View Service for Geographical Names Theme is an INSPIRE compliant Web Map Service. It contains the following harmonized INSPIRE map layers: NamedPlace. The service is based on the Geographic Names Register of the National Land Survey of Finland. The dataset is administrated by the National Land Survey of Finland.

The EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The partnership includes 39 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at various scales from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This dataset includes EMODnet seabed substrate maps at a scale of 1:45 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. Further information about the EMODnet Geology project is available on the portal (http://www.emodnet-geology.eu/).

The EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The partnership includes 39 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at various scales from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This dataset includes EMODnet seabed substrate maps at a scale of 1:50 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. Further information about the EMODnet Geology project is available on the portal (http://www.emodnet-geology.eu/).

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

This dataset represents the Integrated biodiversity status assessment for benthic 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. The assessment in open sea areas was based on the core indicators ‘State of the soft-bottom macrofauna community’ and ‘Oxygen debt’. Coastal areas were assessed by national indicators, and may hence not be directly comparable with each other. This dataset displays the result of the integrated biodiverity 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" = id of the HELCOM assessment unit "country" = name of the country / opensea "level_2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the coastal assessment unit on scale 4 "AULEVEL" = scale of the assessment units "type_descr" = Name of the HELCOM scale 4 assessment unit "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 "Area_km2" = Area of assessment unit (km2) "Confiden_1" = Confidence level of the assessment (scores < 0.5 = low, 0.5 - 0.75 = intermediate, > 0.75 = high) "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))

NLS-FI INSPIRE Download Service (WFS) for Buildings/Polygon is an INSPIRE compliant direct access Web Feature Service. It contains the following INSPIRE feature types: Building The service is based on the NLS-FI INSPIRE Buildings Theme Dataset. The dataset is administrated by the National Land Survey of Finland. The service contains all features from the dataset that are modelled as polygons.

The map compiles seabed samples since 1985 onwards. The data includes geographic data and metadata related to each sample, mainly based on the data produced by the Geological Survey of Finland