FIN Aineiston tarkoituksena on: -Identifioida tie- ja rata-alueet, joiden varrella esiintyy uhanalaisia ja silmälläpidettäviä lajeja -Identifioida tie- ja rata-alueet, joiden varrella esiintyy hyviä elinvoimaisia niittyindikaattorilajeja (hyönteisten mesi- ja ravintokasveja) -Identifioida tie- ja rata-alueet, joiden varrella esiintyy suojelualueita -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua uhanalaisten lajien lisäksi -> Löytää herkät alueet ja paikallistaa vieraslajien uhka Tieto esitetään 1 kilometrin ruuduissa. Aineistosta on julkaistu kaksi erillistä versiota. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Avoin versio, jonka lajitietoa on karkeistettu mahdollisista herkistä lajeista johtuen. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) ja sitä saa käyttää lisenssiehtojen mukaisesti -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Alkuperäinen karkeistamaton versio. Tämä versio on vain viranomaiskäyttöön eikä kyseistä aineistoa saa jakaa Aineistosta on tehty tarkempi menetelmäkuvaus https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf sekä muuttujaseloste https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx ENG The purpose of the material is to: -Identify road and rail areas that have nearby observations of endangered and near threatened species -Identify road and rail areas with good meadow indicator plant species -Identify road and rail areas along which there are protected areas -Identify the road and rail areas along which there are observations of Lupinus polyphyllus or Rosa rugosa observations -Identify the road and rail areas along which there are Lupinus polyphyllus or Rosa rugosa observations in addition to sensitive species -> Finds sensitive areas and identify the overall threat of alien species The data is presented in 1-kilometer square grid cells. There are two separate versions of the data. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Open access version, in which its species-related parts have been simplified due to data restriction issues. The material belongs to Syke's open materials (CC BY 4.0) and may be used in accordance with the license terms. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Original version. This version is only for official use and the material in question may not be shared. A more precise description about the data procedures can be found from (In Finnish) https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf Furthermore, all the variables in the data are explained in this bilingual variable description https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx This dataset was updated with the newest species observations on 10/2023 and 11/2024 Process code for this can be found from https://github.com/PossibleSolutions/VierasVayla_SpeciesUpdate

KUVAUS: Karttatason kohteet ovat peräisin LUMO-asukaskyselystä marraskuulta 2024. Aineisto on kerätty Fiilis-karttakyselyllä (Ilmasto- ja ympäristöpolitiikan yksikkö). Kysely oli osa lumo-ohjelman päivityksen vuorovaikutusprosessia. Vastaajaa pyydettiin merkitsemään kartalle pisteitä tai alueita, joissa on havainnut 1) myönteisiä muutoksia tai 2) kielteisiä muutoksia luonnon monimuotoisuudessa viimeisen neljän vuoden aikana. Kartalle sai myös merkitä pisteitä tai alueita, joissa olisi halukas itse toimimaan luonnon monimuotoisuuden parantamiseksi. Kyselyn vastaajamäärä oli 570 hlö. Kyselyyn pystyi vastaamaan joko suomeksi tai englanniksi. Vastaajien anonyymit taustatiedot on tarvittaessa saatavilla datan yhteyshenkilöltä. KATTAVUUS: Tampere YLLÄPITO: Kyseessä on poikkileikkausaineisto (Aineisto ei päivity). KOORDINAATTIJÄRJESTELMÄ: Aineisto tallennetaan ETRS-GK24 (EPSG:3878) tasokoordinaattijärjestelmässä. GEOMETRIA: vektori (pisteitä ja alueita) SAATAVUUS: Aineisto on katsottavissa kirjautuneille käyttäjille Oskari-karttapalvelussa. AINEISTOSTA VASTAAVA TAHO: Tampereen kaupunki, Ilmasto- ja ympäristöpolitiikan yksikkö

The Regional Stream Water Geochemical Mapping data set gives information on the elemental concentrations in organic sediments of small headwater streams. The samples have been taken from small headwater streams (catchment area under 30 km2) in the late summer of 1990. Sampling has been repeated for about every fourth point during the years 1995, 2000 and 2006. The number of samples was 1162 in 1990 (at a density of one sample / 300 km2), 286 in 1995, 286 in 2000 and 249 in 2006. The data set covers the whole of Finland. Stream water samples have also been taken at the same time. Sampling, processing and analysis methods have been described in the Geochemical Atlas of Finland, Part 3, p. 27 - 30 (Lahermo et. al 1996). Field observations, coordinates and element concentrations determined from samples have been made into a database, in which each record represents one sample point. The data for each sampling year have been recorded on different tables. The method of analysis is referred to with a four-character method code. The codes are as follows: 503H = mercury determination using the cold vapour method 503P = nitric acid extraction in a microwave oven, measurement with ICP-AES 503M = nitric acid extraction in a microwave oven, measurement with ICP-MS 820L = carbon, hydrogen and nitrogen determination with a LECO analyser. The element concentration data include a numerical concentration value (as mg kg-1 or ppm) and possibly a check mark. The concentration is recorded as a variable, which has a name that comprises the chemical symbol for the element and the code for the method of analysis. For example AS_503M is arsenic (As) concentration, which is determined with the ICP-MS method (503M). The next variable has a check mark, for example AS_503MT. If the numerical value following the check mark is ‘>’ or '‘<’ then the number recorded in the concentration field is the determination limit of the chemical analytical method used and the actual concentration is less than this value. If the check mark is an exclamation mark (!), the analytical result is smaller than the determination limit of the analytical method use but the (unreliable) value obtained with the measuring instrument has been entered in the database. There is no data are if the check mark is a 'x'. The original purpose of the Regional Stream Water Geochemical Mapping data set was national general geochemical mapping and the basic assessment of environmental state. Other uses are, for example, the assessment of changes in environmental state and determination of the baseline concentrations of surface water as part of the evaluation of the chemical state of catchment areas in accordance with the Water Framework Directive of the 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 1:10 000 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/).

This dataset contains points of information describing the location and size of spills of mineral oil observed during aerial surveillance flights by HELCOM Contracting Parties during 1998-2023. The data covers detections from fixed-wing aircraft only. Since 2014 Contracting Parties have also reported spills of other substances and unknown substances. The purpose of the regional aerial surveillance is to detect spills of oil and other harmful substances and thus prevent violations of the existing regulations on prevention of pollution from ships. Such illegal spills are a form of pollution which threatens the marine environment of the Baltic Sea area. Further information on detected spills in the Baltic Sea area and HELCOM aerial surveillance activities can be found at http://www.helcom.fi/baltic-sea-trends/maritime/illegal-spills/ and https://helcom.fi/action-areas/response-to-spills/aerial-surveillance/ The dataset contains the following information: Country Year Spill_ID = A unique code which will enable each individual spill to be individually identified FlightType = The type of flight the detection was made during: National = "N", CEPCO = "C", Super CEPCO = "SC", Tour d’Horizon = “TDH” Date = The date of the detection (dd.mm.yyyy) Time_UTC = The time of the detection in UTC (hh:mm) Wind_speed = The wind speed at the time of the detection (m/s) Wind_direc = The wind direction in degrees at the time of the detection (degrees) Latitude = The latitude of the detection (decimal degrees, WGS84) Longitude = The longitude of the detection (decimal degrees, WGS84) Length__km = The length of the detection (km) Width__km = The width of the detection (km) Area__km2_ = The area of the detection (km2) Spill_cat = Spill/pollution category: Mineral Oil = “Oil", Other Substance = "Other substance" , "Unknown substance" = “Unknown” EstimVol_m = If Spill_cat="Oil", then estimated min. volume of oil spill. Volume of the detection confirmed/observed as mineral oil as calculated using the Bonn Agreement Oil Appearance Code using the lower figure (BAOAC minimum) in m3. Vol_Category = Category of the detection: <0,1m3 = “1”, <0,1-1m3 = “2”, 1-10 m3 = “3”, 10-100 m3 = “4”, >100 m3 = “5” Type_substance = If Spill_cat="Other substance" or "Unknown. Product name or type of OS or GAR substances that could be identified (in case of known polluter, or via visual identification - cf. BAOAC Atlas). - Examples for OS: vegetable oils (palm oil sun flower oil, soya oil etc.), fish oil, molasses, various chemicals (methanol, biodiesels/FAME, toluene, paraffines etc.); Examples of GAR: solid cargo residues (e.g. coal residues), plastics, fish nets, … OR "Unknown" (in case the type of substance could not be identified) Polluter = Type of polluter source: Offshore Installation = “Rig”, Vessel = “Ship”, Other Polluter or source (e.g. land based source) = “Other”, Unknown = “Unknwon” (in case of an “orphan” spill that cannot be linked to a polluter) Remarks = Any additional information to inform on particular situations Description of marine litter sightings

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.

This dataset represents the integrated assessment of hazardous substances in the Baltic Sea in 2011-2016, assessed using the CHASE tool (https://github.com/helcomsecretariat/CHASE-integration-tool). The integration is based on hazardous substances core indicators covering concentrations of hazardous substances. This dataset displays the result of the assessment in HELCOM Assessment unit Level 3 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and offshore areas). Attribute information: "HELCOM_ID" = ID of the HELCOM scale 3 assessment unit "country" = Country/ opensea "level_3" = Name of the HELCOM scale 3 assessment unit "area_km2 = Area of the HELCOM scale 3 assessment unit "AULEVEL" = Scale of the assessment units "coastal" = Code of scale 3 HELCOM assessment unit "Input" = Contamination ratio of the assessment unit (Higher score indicates higher contamination) "Confidence" = Confidence of the assessment (Low/ Moderate/ High/ Not assessed) "Status" = Status value for the assessment (= 1.0: Low contamination score, > 1.0: High contaminantion score)