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"en": "Number of population by statistical units. The statistical variables in the data are:\r\n\r\n- total population (vaesto),\r\n- share of the total population (vaesto_p)\r\n- number of men (miehet)\r\n- mens share of the population in an area (miehet_p)\r\n- number of women (naiset)\r\n- womens share (naiset_p)\r\n- those aged under 15: number (ika_0_14) and share (ika_0_14p),\r\n- those aged 15 to 64: number (ika_15_64) and share (ika_15_64p),\r\n- aged 65 or over: number (ika_65_)and share (ika_65_p).\r\n\r\nThe data is suitable for the examination of population distribution and making various regional analyses.\r\n\r\nFrom Statistics Finland WMS and WFS service also other statistical units are available: sub-regional units, regions, major regions, Regional State Administrative Agencies (AVI) and Centres for Economic Development, Transport and the Environment (ELY).\r\n\r\nThe data format has been changed to Shape at CSC. \r\n\r\nCoordinate Reference System: ETRS89 / ETRS-TM35FIN (EPSG:3067)",
"fi": "Väestön lukumäärätiedot kunnittain. Käytetty kuntajako on viitevuodesta seuraavana vuonna voimassa ollut kuntajako.\r\n\r\nAineiston tilastomuuttujia ovat: \r\n\r\n * vaesto - alueen väestömäärä yhteensä 31.12.2025 \r\n * vaesto_p - alueen väestömäärän osuus (koko maan väestöstä) 31.12.2025 \r\n * miehet - miesten lukumäärä 31.12.2025 \r\n * miehet_p - miesten lukumäärä (miehet), miesten osuus (alueen väestöstä) 31.12.2025 \r\n * naiset - naisten lukumäärä 31.12.2025 \r\n * naiset_p - naisten osuus 31.12.2025 \r\n * ika_0_14 - alle 15-vuotiaat 31.12.2025 \r\n * ika_0_14p - alle 15-vuotiaiden osuus 31.12.2025 \r\n * ika_15_64 - 15-64 -vuotiaat 31.12.2025 \r\n * ika_15_64p - 15-64 -vuotiaiden osuus 31.12.2025 \r\n * ika_65_ - 65 vuotta täyttäneet 31.12.2025 \r\n * ika_65_p - 65 vuotta täyttäneiden osuus 31.12.2025 \r\n\r\nAineisto soveltuu väestömäärien tarkasteluun ja erilaisten alueellisten analyysien tekemiseen. Tilastokeskuksen rajapinnalta on saatavilla sama aineisto myös muille tilastointialueille, mm. maakunnat, suuralueet, Aluehallintovirastot (AVI) ja Elinkeino-, liikenne- ja ympäristökeskukset (ELY).\r\n\r\nMuunnos Shape formaattiin on tehty CSC:llä. \r\n\r\nKoordinaattijärjestelmä: ETRS-TM35FIN (EPSG:3067)"
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"en": "Road traffic accidents involving personal injury known to the police in Finland and reported to Statistics Finland, which have co-ordinate data. \n\nThe data cover the following information: \nvvonn= year of the accident \nkkonn= month of the accident \nkello= time of the accident \nvakav= seriousness of the accident: \n 1=accident resulting in death \n 2= accident resulting in injury \n 3 = accident resulting in serious injury\nonntyyppi= type of accident: \n 0=Same direction of travel (going straight) \n 1=Same direction of travel (turning) \n 2=Opposite direction of travel (going straight) \n 3=Opposite direction of travel (turning) \n 4=Intersecting direction of travel (going straight) \n 5=Intersecting direction of travel (turning) \n 6=Pedestrian accident (on pedestrian crossing) \n 7=Pedestrian accident (elsewhere) \n 8=Running off the road \n 9=Other accident \nlkmhapa= number of passenger cars and vans in the accident \nlkmlaka= number of buses and lorries in the accident \nlkmjk= number of pedestrians in the accident \nlkmpp= number of cyclists in the accident \nlkmmo= number of mopeds in the accident \nlkmmp= number of motor cycles in the accident \nlkmmuukulk= number of other vehicles in the accident \n\nThe data format has been changed to Shape at CSC. \nCoordinate Reference System: ETRS89 / ETRS-TM35FIN (EPSG:3067)",
"fi": "Aineisto sisältää Suomessa poliisin tietoon tulleet ja Tilastokeskukselle ilmoittamat henkilövahinkoon johtaneet tieliikenteen onnettomuudet, joilla on koordinaattitiedot. \n\nAineisto sisältää seuraavat muuttujat:\nonnettomuuden tapahtumavuosi (VVONN) \nonnettomuuden tapahtumakuukausi (KKONN) \nonnettomuuden kellonaika (KELLO) \n\nonnettomuuden vakavuus (VAKAV): \n1=kuolemaan johtanut onnettomuus \n2=loukkaantumiseen johtanut onnettomuus \n3 = vakavaan loukkaantumiseen johtanut onnettomuus\n\nonnettomuustyyppi (ONNTYYPPI): \n0=Samat ajosuunnat (ajo suoraan) \n1=Samat ajosuunnat (ajo kääntyen) \n2= Vastakkaiset ajosuunnat (ajo suoraan) \n3=Vastakkaiset ajosuunnat (ajo kääntyen) \n4=Risteävät ajosuunnat (ajo suoraan) \n5=Risteävät ajosuunnat (ajo kääntyen) \n6=Jalankulkijaonnettomuus (suojatiellä) \n7=Jalankulkijaonnettomuus (muualla) \n8=Tieltä suistuminen \n9=Muu onnettomuus, \n\nhenkilöautojen ja pakettiautojen lukumäärä onnettomuudessa (LKMHAPA) \nlinja-autojen ja kuorma-autojen lukumäärä onnettomuudessa (LKMLAKA) \njalankulkijoiden lukumäärä onnettomuudessa (LKMJK) \npolkupyöräilijöiden lukumäärä onnettomuudessa (LKMPP) \nmopojen lukumäärä onnettomuudessa (LKMMO) \nmoottoripyörien lukumäärä onnettomuudessa (LKMMP) \nmuiden kulkuneuvojen lukumäärä onnettomuudessa (LKMMUUKULK) \n\nMuunnos Shape formaattiin on tehty CSC:llä. \nKoordinaattijärjestelmä: ETRS-TM35FIN (EPSG:3067)"
},
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},
{
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"url": "http://www.yso.fi/onto/okm-tieteenala/ta519",
"in_scheme": "http://www.yso.fi/onto/okm-tieteenala/conceptscheme",
"pref_label": {
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"fi": "Yhteiskuntamaantiede, talousmaantiede",
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}
}
],
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"issued": "2026-09-03",
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],
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"pref_label": {
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"fi": "ei kielellistä sisältöä",
"sv": "inget språkligt innehåll"
}
}
],
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Identity number, participant age, name, gender, contact information, education, native language, foreign language abilities, musical training, health, social relationships, social anxiety, general anxiety, and depression symptoms. Additional post-session questionnaires assessed the enjoyability of the learning and test phases, perceived help from the pair partner, and motivation for learning.\r\nFormats: Scanned questionnaires (.pdf), CSV tables (.csv).\r\nB) Psychophysiological and behavioural measures. 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Data and analysis documentation, experimental procedure documentation, stimulus-presentation scripts, and analysis scripts.\r\nFormats: Presentation program (.sce, .exp), plain text (.txt), Python scripts (.py), R scripts (.r), and Markdown (.md).\r\nWP2: Social Foreign Language Word Learning\r\nFinnish title: Sosiaalinen vieraan kielen sanojen oppiminen\r\nPreregistration: \r\nhttps://doi.org/10.17605/OSF.IO/TS5YD\r\nDescription\r\nWe investigated foreign-language word learning in a digital environment under three within-participant conditions: a Speaking Tutor condition (visual cues for speech available), a Silent Tutor condition (no visual cues for speech; speech presented through speakers), and a No Tutor condition (speech presented through speakers in a non-social situation without sensory cues). Participants learned 114 Mandarin Chinese object nouns, with 38 words assigned to each condition. Word assignment was randomised for each participant, and condition order was counterbalanced. Learning was assessed using a word–picture identification task, an oral translation task, and a listen-and-repeat pronunciation task. Behavioural responses during testing—accuracy, response time, and verbal responses—were collected and compared across conditions. EEG, ECG, and electrodermal activity were recorded to examine neural and autonomic responses during learning and testing.\r\nStatus: Data collection complete (N = 33). \r\nData\r\nAll data were collected from the participants.\r\nA) Background information (questionnaires). Identity number, participant age, name, gender, contact information, education, native and additional language background, language-use estimates, experience living abroad, musical background, BDI-II responses and total score, and BAI responses and total score. Additional custom ratings collected before and/or after each learning condition assessed alertness, motivation, perceived learning, and pleasantness.\r\nFormats: Scanned questionnaires (.pdf), CSV tables (.csv).\r\nB) Psychophysiological and behavioural measures. Electroencephalography (EEG), electrocardiogram (heart-rate variability), skin conductance response (SCR), reaction times and accuracy in behavioural tests, audio recordings of participants' pronunciation, and accelerometer activity from the participant's vocal cord (neck).\r\nEstimated data volume: approximately 61 GB.\r\nAnalysis software: MNE-Python, R, and custom Python and R scripts.\r\nFormats: Raw EEG in BrainVision format (.eeg, .vhdr, .vmrk), Presentation/PsychoPy logs (.log, .dat), CSV tables (.csv), plain-text logs (.txt), raw audio recordings (.mp3), edited audio recordings (.wav), and preprocessed MNE-Python data (.fif).\r\nC) Documentation. Preregistration, experimental procedure documentation, stimulus-presentation scripts, data and analysis documentation, and analysis scripts.\r\nFormats: Word documents (.docx), PDF (.pdf), plain text (.txt), Presentation program (.sce, .exp), Markdown (.md), Python scripts (.py), R scripts (.R), and CSV tables (.csv).\r\nWP3: Social Interaction Effect on Foreign Language Learning in a 3D-VR Environment\r\nPreregistration: \r\nhttps://doi.org/10.17605/OSF.IO/YX37T\r\nDescription\r\nWe investigated the effect of a virtual avatar's social presence on learning, memory, and the neural processes underlying Arabic word acquisition in 25 adult native Finnish speakers with no prior knowledge of Arabic. Participants attended a single experimental visit during which they learned Arabic vocabulary in a virtual reality (VR) environment. The visit consisted of two 30-minute VR sessions, separated by a short break: one session included a virtual avatar, whereas the other was conducted without the avatar. At the beginning of the visit, informed consent was obtained and demographic and general participant information were collected. During the VR learning and memorisation tasks, neural activity (continuous EEG recording), heart rate, and interaction data were continuously recorded. Following each VR session, participants completed behavioural assessments, and semantic event-related potentials (ERPs) were measured. At the end of the visit, participants completed a battery of questionnaires.\r\nStatus: Data collection complete (n = 28)\r\nData\r\nAll data were collected from the participants.\r\nA) Background information (questionnaires). Participant age, gender, contact information, visual acuity, coffee and nicotine intake, sleep quality, educational level, native language, foreign language abilities, musical training, arousal levels, and fatigue assessments. Additional questionnaires assessed participants' experiences during the measurements, including the VR experience, impressions of learning with a virtual avatar, and general feedback (how enjoyable the learning and test phases were, how helpful the avatar was, motivation for learning), as well as a questionnaire on eye and body symptoms to evaluate fatigue within the virtual space. Full name, phone number, address, and social security number were also collected for the tax reporting of the gift card.\r\nFormats: Plain text (.txt).\r\nB) Psychophysiological and behavioural measures. Electroencephalography (EEG), electrocardiogram (heart-rate variability), reaction times and accuracy in behavioural tests, audio recordings of participants' pronunciation during the VR sessions, and log files (transcripts) of the conversation and interaction with the avatar and the VR environment.\r\nEstimated data volume: approximately 200 GB.\r\nAnalysis software: Python, Excel, SPSS, R, and custom Python scripts.\r\nFormats: Raw EEG in BrainVision format (.eeg, .vhdr, .vmrk), plain-text logs (.txt), CSV tables (.csv), Word documents (.docx), Excel spreadsheets (.xlsx), audio recordings (.mp3, .wav), SPSS data files (.sav), and PDF (.pdf).\r\nC) Documentation. Data and analysis documentation, stimulus-presentation scripts, and analysis scripts.\r\nFormats: Plain text (.txt), PDF (.pdf), Presentation data files (.dat), Python scripts (.py), preprocessed MNE-Python data (.fif), image files (.png), CSV tables (.csv), and SPSS data files (.sav)."
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"en": "Digiroad is a national roads and streets database. It is designed to support different kind of transport systems and it is updated by municipalities (attribute data) and National Land Survey (geometry). Digiroad contains all streets and public, private and forest roads. Digiroad includes data about road names and numbers, road width and cover, number of lines, speed limits, bridges and tunnels, parking houses and areas, forbidden turns, addresses etc. \n\nDigiroad 2022 is available both in Shape and GeoPackage formats, in Shape format the data has been divided ti files according to counties.\n\nDigiroad homepage, where you can also download the latest Digiroad data: https://www.digiroad.fi\n\nCoordinate Reference System: ETRS89 / ETRS-TM35FIN (EPSG:3067)",
"fi": "Digiroad on kansallinen tie- ja katutietoaineisto, joka sisältää teiden ja katujen geometriatiedot sekä liikkumisen suunnittelua palvelevat ominaisuustiedot. Digiroad kattaa kaikki Suomen maantiet, kadut ja yksityistiet n. 430 000 km. Digiroad on kokoava aineisto joka saa tietonsa Väyläviraston Tierekisteristä, Maanmittauslaitoksen Maastotietokannasta, kuntien katutietojärjestelmistä sekä mm. HSL:n tietojärjestelmistä.\n\nTieverkko muodostuu henkilöautolla ajettavista teistä, lautoista ja losseista sekä kevyenliikenteen väylistä (n. 13 000 km). Digiroadin liikkumisen suunnittelua palvelevia ominaisuustietoja ovat mm. tien toiminnallinen luokka, tieto liikennevirran suunnasta, nimet ja osoitteet, tie- ja katuverkon käyttörajoitukset (suljettu yhteys, kääntymisrajoitukset, paino-, korkeus-, pituus- ja leveysrajoitus), tie- ja katuverkon muut ominaisuustiedot (nopeusrajoitus, leveys, päällystetieto, pysäkit, tieto silta / alikulku / tunneli kohteesta, tasoristeykset) sekä käyttäjiä tukevat palvelukohteet (joukkoliikenne- ja taksiasemat, sairaalat ja ensiapupisteet, ajoneuvon lastaus junaan tai laivaan, pysäköintialueet).\n\nDigiroad 2022 aineisto on saatavilla Shape ja GeoPackage muodossa, Shape aineisto on jaettu tiedostoihin maakuntarajojen mukaan.\n\nDigiroadin kotisivu, josta on saatavilla aina mm. uusin versio aineistosta: https://www.digiroad.fi\n\nKoordinaattijärjestelmä: ETRS-TM35FIN (EPSG:3067)"
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"en": "The data set consists of yearly maps of the start of the vegetation active period in deciduous vegetation(Day of Year), which is defined as the day when deciduous trees unfold new leaves in spring. It is also often referred to as the green-up or greening day. The data set was derived from time series of the Normalized Difference Water Index (NDWI)calculated from Moderate Resolution Imaging Spetroradiometer (MODIS) satellite observation.\n\nData processing\n\nMODIS Level 1B data were calibrated to top-of-atmosphere reflectances and projected to a geographic latitude/longitude grid (datum WGS-84) using the software envimon by Technical Research Centre of Finland (VTT).\n\nThe Normalized Difference Water Index (NDWI, Gao et al.1996) was calculated from near-infrared (MODIS band 2) and mid-infrared (MODIS band 6)reflectance. 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Remote Sensing of Environment, 101, 52-62. https://doi.org/10.1016/j.rse.2005.11.012\n\nBöttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C., Kolari, P., Arslan, A., & Pulliainen, J. (2016). Evaluating biosphere model estimates of the start of the vegetation active season in boreal forests by satellite observations. Remote Sensing, 8, 580. https://doi.org/10.3390/rs8070580\n\nAdditional information: \n\nDetermination of phenological dates in boreal regions using normalized difference water index. https://doi.org/10.1016/j.rse.2005.03.011\n\nEvaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. https://doi.org/10.3390/rs8070580\n\nQuality control\n\nThe product was compared to the visual observation of the bud break of birch from the phenological network of the Natural Resource Institute of Finland (Luke). Satellite observations were highly correlated with the ground observations of birch bud break. The accuracy was 7 days for the period 2001-2015 based on 84 site-years. The bias was negligible (0.4 days).\n\nThe product is based on the NDWI which is sensitive to surface water and the water content of vegetation. Standing water e.g. in wetland areas can therefore have an effect on the detection of the vegetation greening. The satellite product has not been validated for wetland areas.\n\nA main source of uncertainty in the detection method is frequent cloud cover. The temporal resolution of the NDWI time series is one day, but missing observations due to cloud cover lead to gaps. For years 2001–2012 for the period April-July, depending on the location, in average 34%-73% of daily observations were missing. 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Report on EO products and comparison with in situ data, Monimet project deliverable,http://monimet.fmi.fi/index.php?style=warm&page=Deliverables&p=2 (accessed 27.07.2017).\n\nAdditional information:\n\nEvaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. https://doi.org/10.3390/rs8070580\n\nResource content descriptions:\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nDataset for download consists of yearly files for the period 2001-2022(GEOTIFFS) for the start of the vegetation active period of deciduous vegetation. https://www.syke.fi/en/environmental-data/downloadable-spatial-datasets#start-of-vegetation-period-in-coniferous-and-deciduous-forests-phenology\n\nWMS interface. https://paikkatiedot.ymparisto.fi/geoserver/syke_kasvukaudenalkulehtimetsissa/wms\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nReference\nBöttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C., Kolari, P., Arslan, A., & Pulliainen, J. (2016). Evaluating biosphere model estimates of the start of the vegetation active season in boreal forests by satellite observations. Remote Sensing, 8, 580, https://doi.org/10.3390/rs8070580"
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"en": "The data set consists of yearly maps of the start of the vegetation active period in deciduous vegetation(Day of Year), which is defined as the day when deciduous trees unfold new leaves in spring. It is also often referred to as the green-up or greening day. The data set was derived from time series of the Normalized Difference Water Index (NDWI)calculated from Moderate Resolution Imaging Spetroradiometer (MODIS) satellite observation.\n\nData processing\n\nModerate Resolution Imaging Spectrometer (MODIS) Terra Level 1B (1 km, 500 m),version 5 (V005) data were manually selected from the Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC) for the period 2001-2008. For years 2009 - 2014 data were obtained from the satellite receiving station of the Finnish Meteorological Institute (FMI)in Sodankylä, Finland and gap-filled with data from LAADS DAAC. For years 2015 onwards version 6 (V006) data were obtained from LAADS DAAC. Terra Level 1B version 6.1 (V061) was used starting from 2022 onwards.\n\nMODIS Level 1B data were calibrated to top-of-atmosphere reflectances and projected to a geographic latitude/longitude grid (datum WGS-84) using the software envimon by Technical Research Centre of Finland (VTT).\n\nThe Normalized Difference Water Index (NDWI, Gao et al.1996) was calculated from near-infrared (MODIS band 2) and mid-infrared (MODIS band 6)reflectance. Cloud covered observations were removed using an automatic cloud masking algorithm by the Finnish Environment Institute.\n\nDaily NDWI time series were averaged and smoothed for MODIS pixels with vegetation cover at a spatial grid resolution of 0.05 x 0.05 degrees.\nThe day of the start of vegetation active period (VAP) was determined from NDWI time series applying the method by Delbart et al.(2005) and further described in Böttcher et al. (2016).\n\nThe yearly maps were filtered with a median filter to remove spurious outliers and fill spatial gaps and the product was re-projected to EUREF-FIN (ETRS-TM35FIN). Lakes were masked.\n\n\nDelbart, N., Le Toan, T., Kergoat, L., & Fedotova, V. (2006). Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004). Remote Sensing of Environment, 101, 52-62. https://doi.org/10.1016/j.rse.2005.11.012\n\nBöttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C., Kolari, P., Arslan, A., & Pulliainen, J. (2016). Evaluating biosphere model estimates of the start of the vegetation active season in boreal forests by satellite observations. Remote Sensing, 8, 580. https://doi.org/10.3390/rs8070580\n\nAdditional information: \n\nDetermination of phenological dates in boreal regions using normalized difference water index. https://doi.org/10.1016/j.rse.2005.03.011\n\nEvaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. https://doi.org/10.3390/rs8070580\n\nQuality control\n\nThe product was compared to the visual observation of the bud break of birch from the phenological network of the Natural Resource Institute of Finland (Luke). Satellite observations were highly correlated with the ground observations of birch bud break. The accuracy was 7 days for the period 2001-2015 based on 84 site-years. The bias was negligible (0.4 days).\n\nThe product is based on the NDWI which is sensitive to surface water and the water content of vegetation. Standing water e.g. in wetland areas can therefore have an effect on the detection of the vegetation greening. The satellite product has not been validated for wetland areas.\n\nA main source of uncertainty in the detection method is frequent cloud cover. The temporal resolution of the NDWI time series is one day, but missing observations due to cloud cover lead to gaps. For years 2001–2012 for the period April-July, depending on the location, in average 34%-73% of daily observations were missing. NDWI time series with gaps longer than two weeks during the spring vegetation development were discarded.\n\nBöttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C., Kolari, P., Arslan, A., & Pulliainen, J. (2016). Evaluating biosphere model estimates of the start of the vegetation active season in boreal forests by satellite observations. Remote Sensing, 8, 580. https://doi.org/10.3390/rs8070580\n\nBöttcher, K., Kervinen, M., Keto, V., Luojus, K., Manninen, T., Metsämäki, S., & Rautiainen, K. (2017). Report on EO products and comparison with in situ data, Monimet project deliverable, http://monimet.fmi.fi/index.php?style=warm&page=Deliverables&p=2 (accessed 27.07.2017).\n\nAdditional information:\n\nEvaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. https://doi.org/10.3390/rs8070580\n\nResource content descriptions:\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nDataset for download consists of yearly files for the period 2001-2022 (GEOTIFFS) for the start of the vegetation active period of deciduous vegetation. https://www.syke.fi/en/environmental-data/downloadable-spatial-datasets#start-of-vegetation-period-in-coniferous-and-deciduous-forests-phenology\n\nWMS interface. https://paikkatiedot.ymparisto.fi/geoserver/syke_kasvukaudenalkulehtimetsissa/wms\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nWeb map application presenting yearly maps and averages of the start of vegetation active period for coniferous forests and for deciduous vegetation during 2001-2022 in Finland. http://syke.maps.arcgis.com/apps/webappviewer/index.html?id=a446e987496b4d8794b307e882da718a\n\nReference\nBöttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C., Kolari, P., Arslan, A., & Pulliainen, J. (2016). Evaluating biosphere model estimates of the start of the vegetation active season in boreal forests by satellite observations. Remote Sensing, 8, 580, https://doi.org/10.3390/rs8070580"
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