{"count":8487,"next":"https://metax.fairdata.fi/v3/datasets?format=json&limit=20&offset=120","previous":"https://metax.fairdata.fi/v3/datasets?format=json&limit=20&offset=80","results":[{"id":"61d2cc18-e9ce-49b2-baae-26621c8213c3","access_rights":{"id":"508fddda-3588-4e45-8bb2-611f88436605","license":[{"id":"35b18e72-3819-4ec6-a1be-14624f29d968","custom_url":"http://www.nic.funet.fi/index/geodata/tilastokeskus/Tilastokeskus_terms_of_use_2018.pdf","title":{"en":"Creative Commons Attribution 4.0 International (CC BY 4.0)","fi":"Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)","und":"Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)"},"url":"http://uri.suomi.fi/codelist/fairdata/license/code/CC-BY-4.0","in_scheme":"http://uri.suomi.fi/codelist/fairdata/license","pref_label":{"en":"Creative Commons Attribution 4.0 International (CC BY 4.0)","fi":"Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)"}}],"access_type":{"id":"d01ac02c-fc70-4c68-9434-8383cb693ff0","url":"http://uri.suomi.fi/codelist/fairdata/access_type/code/open","in_scheme":"http://uri.suomi.fi/codelist/fairdata/access_type","pref_label":{"en":"Open","fi":"Avoin"}},"restriction_grounds":[]},"actors":[{"id":"56dfd0c4-137e-426d-9cc2-e1a829e4b3f8","roles":["creator"],"organization":{"id":"32fcff23-7d2d-44b4-9283-a5a39c978648","pref_label":{"en":"Statistics Finland","fi":"Tilastokeskus","sv":"Statistikcentralen","und":"Tilastokeskus"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/02454911","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"56f86bdb-cf43-412f-949b-fff18cf44dda","roles":["publisher"],"organization":{"id":"cd0e53f1-8f0a-4972-b8b0-fde81326afb9","pref_label":{"en":"CSC – IT Center for Science","fi":"CSC - Tieteen tietotekniikan keskus Oy","sv":"CSC – IT Center for Science","und":"CSC - Tieteen tietotekniikan keskus Oy"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/09206320","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-att","description":{"en":"The number of population on the last day of the reference year (31 December) by age group. Data includes only inhabited grids. The statistical variables of the data are: Total population (vaesto), number of men (miehet) and women (naiset), under 15 year-olds (ika_0_14), 15-64 year-olds (ika_15_64), and aged over 65 (ika_65_). A grid identifier is expressed using a national identification code derived from the coordinates (idD_nro (num)) and INSPIREs standard identification code (grd_id (char)). The data also contains the coordinates of the lower left-hand corner of a grid (xkoord, ykoord (num)). The data describes the population distribution independent of administrative areas (such as municipal borders). The data is suitable for examination of population distribution and making various spatial analysis.\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 lukumäärä viitevuoden viimeisenä päivänä (31.12) ikäryhmittäin. Aineisto sisältää vain asutut ruudut. Aineiston tilastomuuttujia ovat: väestömäärä yhteensä (vaesto), miesten (miehet) ja naisten (naiset) lukumäärä, alle 15-vuotiaat (ika_0_14), 15-64 -vuotiaat (ika_15_64) ja 65 vuotta täyttäneet (ika_65_). Alle 10 asukkaan ruuduista ilmoitetaan vain väestömäärä. Ruudun tunniste ilmoitetaan koordinaateista johdetulla kansallisella identifiointitunnuksella (id_nro (num)) sekä INSPIRE:n standardilla identifiointitunnuksella (grd_id (char)). Aineisto sisältää myös ruudun vasemman alanurkan koordinaatit (xkoord, ykoord (num)). Aineisto kuvaa väestömäärää ruuduittain hallinnollisista aluejaoista (esim. kuntarajoista) riippumatta. Aineisto soveltuu väestöjakauman tarkasteluun ja erilaisten alueellisten analyysien tekemiseen.\r\n\r\nMuunnos Shape formaattiin on tehty CSC:llä. \r\n\r\nKoordinaattijärjestelmä: ETRS-TM35FIN (EPSG:3067)"},"field_of_science":[{"id":"7334f2e9-1aae-4b87-9088-8226211b06c9","url":"http://www.yso.fi/onto/okm-tieteenala/ta1171","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Geosciences","fi":"Geotieteet","sv":"Geovetenskaper"}},{"id":"27ad92ae-71a5-4cf1-896c-fbbdecf2289d","url":"http://www.yso.fi/onto/okm-tieteenala/ta519","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Social and economic geography","fi":"Yhteiskuntamaantiede, talousmaantiede","sv":"Socialgeografi och ekonomisk geografi"}}],"infrastructure":[],"issued":"2026-09-03","keyword":["Paituli"],"language":[{"id":"a6d1131d-161e-4830-bd82-06a3940fc097","url":"http://lexvo.org/id/iso639-3/zxx","in_scheme":"http://lexvo.org/id/","pref_label":{"en":"Not 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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)"},"field_of_science":[{"id":"7334f2e9-1aae-4b87-9088-8226211b06c9","url":"http://www.yso.fi/onto/okm-tieteenala/ta1171","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Geosciences","fi":"Geotieteet","sv":"Geovetenskaper"}},{"id":"27ad92ae-71a5-4cf1-896c-fbbdecf2289d","url":"http://www.yso.fi/onto/okm-tieteenala/ta519","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Social and economic geography","fi":"Yhteiskuntamaantiede, talousmaantiede","sv":"Socialgeografi och ekonomisk 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Social learning effects were examined by assigning participants to paired (n = 22) or individual (n = 20) training conditions.\r\nStatus: Data collection complete. Report published.\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 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. Electroencephalography (EEG), electrocardiogram (heart-rate variability), skin conductance response (SCR), reaction times and accuracy in behavioural tests, audio recordings of participants' pronunciation, accelerometer activity from the participant's vocal cord (neck), and video recordings of the sessions.\r\nEstimated data volume: approximately 215 GB.\r\nAnalysis software: BrainVision Analyzer, MNE-Python, SPSS, R, JASP, and custom Python and R scripts.\r\nFormats: Raw EEG in BrainVision format (.eeg, .vhdr, .vmrk), plain-text logs (.txt, .log), PsychoPy session files (.psydat), CSV tables (.csv), Word documents (.docx), Excel spreadsheets (.xlsx), SPSS data files (.sav), raw audio recordings (.mp3), edited audio recordings (.wav), raw video recordings (.mp4), and preprocessed MNE-Python data (.fif).\r\nC) Hearing screening. Hearing ability was tested for each participant with an audiometer in the laboratory.\r\nD) Documentation. 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. 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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). 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The genetic data is generated by paired-end ddRAD sequencing. Although the raw sequences (.fastq) are now publicly available in NCBI (project accession PRJNA1231510) and the Wolbachia  sequences (.fasta) are deposited in Barcode of Life Data System (DS-POLYEROS), all the other relevant data used to generate the results for the manuscripts are deposited here. This dataset included input data, scripts and their corresponding output for 11 different main analyses. The aim of this study is to to (1) investigate evolutionary relationships, (2) assess levels of admixture and (3) Wolbachia infection, as well as (4) species boundaries in the European representatives of the P. eros species group.\n\nThe specimens were collected within European continent. As most of these butterflies fly above 800m a.s.l., many of the specimens were collected in high mountains, with some exceptions that came from steppe in Ukraine. All the specimen were deposited in a public research collection at the Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain. All the genetic materials were processed at the University of Oulu, Finland, the DNA extracts and bioinformatics data are stored at the University of Oulu (physical DNA samples) and CSC (electronic and bioinformatics data).\n\nAll bioinformatics workflow was carried out within the CSC computer cluster, Puhti - discontinued since August 2026 and replaced by the new cluster named Roihu, the configuration of the scripts and environment is optimised for system used in Puhti, but theoratically suitable for all other UNIX system. The computer cluster under CSC uses SLURM to manage the cluster, and therefore, most of the shell scripts are tailor for SLURM system.\n\n########## Dataset Structure ##########\n\nThe data is structured and sorted into analyses corresponding to the methods descripted in the manuscript, each of the analyses/tools was named in the format of '\"xxx_toolsname_version\". Within each directory, there will be three subdirectories: (1) Input, (2) Output and (3) script. All input data with symbolic link is stored as a file a \"SYM_\" tag. Please run the \"setup_symlink.sh\" before checking the data to restore the symbolic links. 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It is designed to let readers inspect the event list, station-specific valid-event support, event-window Schumann resonance measurements, ICME-overlap flags used for the main figures and Table S1 in the manuscript.\n\nEvent definition:\nCandidate onsets were identified from a 3-day moving median of solar-wind speed, where `Vsw >= 500 km/s` and the increase relative to two days earlier was at least `80 km/s`, with a minimum 20-day separation between accepted onsets.\n\nThese events are described as threshold-defined HSS/CIR proxy onsets, not as a formal external HSS/CIR catalog.\n\nTime windows:\nAll event-window tables use:\n- event window: days -15 to +20 relative to onset\n- baseline window: days  -15 to -5 relative to onset\n- response window: days  0 to +1 relative to onset\n\nThe daily time stamp is represented as a UTC date. The response values used in the manuscript are baseline-subtracted values, stored as `delta_from_baseline`.\n\nValidity flags:\n\nThe event-window SR tables preserve all 173 onsets and provide inclusion flags:\n- `included_in_final_station_composite`: event/station/component satisfies the manuscript baseline and response validity rules.\n- `included_in_matched_support_106`: event is valid for weighted-average AVG F1 at HRN, IVA, and ROV.\n- `included_in_final_hrn_lorentz_composite`: HRN Lorentz-fit event/component/variable satisfies the Figure 1 validity rules.\n\nThe validity rule is at least 6 finite baseline days in days `-15` to `-5` and at least one finite response value in days `0` to `+1`.\n\nJosé Tacza acknowledges funding from the National Science Centre, Poland (Grant 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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. 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.\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. 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Kervinen","external_identifier":"0009-0000-3779-6389"},"organization":{"id":"56addf29-2163-45c6-8e44-b3c0a3c604f5","pref_label":{"en":"Finnish Environment Institute","fi":"Suomen ympäristökeskus","sv":"Finlands miljöcentral","und":"Suomen ympäristökeskus"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/7020017","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"07855c2c-8976-4b45-89c2-049038cae5aa","roles":["publisher","rights_holder"],"organization":{"id":"56addf29-2163-45c6-8e44-b3c0a3c604f5","pref_label":{"en":"Finnish Environment Institute","fi":"Suomen ympäristökeskus","sv":"Finlands miljöcentral","und":"Suomen ympäristökeskus"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/7020017","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-ida","description":{"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"},"field_of_science":[{"id":"7e12a692-26de-4e3c-b28c-64f8a32ef40a","url":"http://www.yso.fi/onto/okm-tieteenala/ta114","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Physical sciences","fi":"Fysiikka","sv":"Fysik"}},{"id":"99add68c-0c1f-4248-ad68-d0b57d89c984","url":"http://www.yso.fi/onto/okm-tieteenala/ta1172","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Environmental sciences","fi":"Ympäristötiede","sv":"Miljövetenskap"}}],"infrastructure":[],"issued":"2025-10-14","keyword":["vegetation phenology","boreal zone","green-up","phenology"],"language":[{"id":"ec748146-3403-4a7f-adfe-bdbb1b889372","url":"http://lexvo.org/id/iso639-3/eng","in_scheme":"http://lexvo.org/id/","pref_label":{"en":"English","fi":"englanti","sv":"engelska"}}],"metadata_owner":{"id":"3f28aba8-9c9a-41f7-843a-818e4d6d5083","organization":"syke.fi","admin_organization":"syke.fi"},"other_identifiers":[],"persistent_identifier":"doi:10.48488/depf-r646","pid_generated_by_fairdata":false,"projects":[{"id":"9d9b11a0-6585-492e-a5a7-6d0bfef3358e","title":{"en":"Monimet - 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