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                "fi": "Peltokasvien pinta-alat kertoo viljelykäytössä olevien peltojen pinta-alat 1 km ruutuaineistona. Aineisto perustuu maataloustukien hallinnoinnissa käytettävään IACS-rekisteriin ja viljelijöiden sinne ilmoittamiin viljelysuunnitelmiin. Yksittäiset peltolohkot on yleistetty 1 km ruudulle ja tietoja ryhmitelty yleisempiin luokkiin. Luokan perässä on IACS-rekisterin luokat, jotka luokittelu sisältää:\n\nSyysvehnä – 1110 Syysvehnä; Syysspelttivehnä\n\nKevätvehnä – 1120 Kevätvehnä; 1142 Kevätspelttivehnä\n\nKevätruis – 1120 Kevätruis\n\nRuis - 1230 Syysruis\n\nRuisvehnä – 1210 Syysruisvehnä; 1211 Kevätruisvehnä\n\nMallasohra – 1320 Mallasohra\n\nMuu ohra – 1310 Rehuohra; 1330 Syysohra\n\nKaura – 1400 Kaura\n\nSeosvilja – 1545 Seoskasvusto (viljat); 1555 Seoskasvusto (vilja+öljykasvit)\n\nVihantavilja - 1601 Vihantavilja (ohra); 1602 Vihantavilja (kaura); 1603 Vihantavilja (vehnä); 1604 Vihantavilja (ruis); 1605 Vihantavilja (viljaseos)\n\nHerne – 2110 Ruokaherne; 2120 Rehuherne\n\nSeoskasvustot - 2170 Seoskasvusto (valkuaiskasvit+öljykasvit); 2175 Seos (herne/härkäpapu/makea lupiini/öljykasvit); 2180 Seos herne/härkäpapu/makea lupiini yli 50 %+viljaa; 2185 Seoskasvusto (valkuaiskasvit+vilja); 2195 Seoskasvusto (valkuaiskasvit)\n\nHärkäpapu - 2200 Härkäpapu\n\nPeruna - 3110 Ruokaperuna; 3120 Ruokateollisuusperuna; 3130 Tärkkelysperuna; 3150 Varhaisperuna (katteenalainen); 3160 Siemenperuna (sertifioidun siemenen tuotantoon); 3190 Tärkkelysperunan oma siemenlisäys\n\nSokerijuurikas - 3210 Sokerijuurikas, sokerintuotantoon; 3230 Sokerijuurikas, energiantuotantoon\n\nRypsi - 4110 Kevätrypsi; 4120 Syysrypsi\n\nRapsi - 4210 Kevätrapsi; 4220 Syysrapsi\n\nErikoiskasvit – 1700 Tattari; 1750 Kvinoa (kinua); 1800 Maissi; 1810 Sokerimaissi; 2300 Soijapapu; 2400 Virna; 2410 Mesikkä; 2420 Mailanen; 2430 Apila; 2440 Vuohenherne; 2450 Linssi; 2500 Makealupiini; 2510 Muut lupiinit; 4010 Tupakka; 4300 Auringonkukka; 4400 Humala; 4620 Kuitupellava; 4810 Siemenmausteet ja lääkekasvit (pl kumina, sinappi); 5824 Sinappi; 6060 Siirtonurmi; 9060 Hunajantuotantoon tarkoitettu kasvusto; 9061 Aitohunajakukka\n\nÖljykasvit - 4030 Ruistankio (Camelina, Kitupellava); 4390 Seoskasvusto (öljykasvit); 4530 Öljyhamppu; 4610 Öljypellava\n\nKuitu- ja energiakasvit - 4020 Kuitunokkonen; 4525 Kuituhamppu; 4904 Ruokohelpi (kuivike/rehu); 4905 Ruokohelpi (energia); 4913 Energiapuu, lyhytkiertoinen (haapa ja paju); 4914 Energiapuu, lyhytkiert. (hybriidihaapa ja poppeli); 4930 Ahdekaunokki, energiantuotantoon\n\nAvomaan vihannekset ja juurekset – 5101 Tarhaherne; 5102 Pensaspapu; 5103 Valko- eli keräkaali; 5104 Kiinankaali; 5105 Kukkakaali; 5106 Porkkana; 5108 Lanttu; 5109 Nauris; 5110 Mukulaselleri;5111 Palsternakka;5113 Sipulin pikkuistukkaat;5114 Purjo; 5115 Avomaankurkku; 5116 Kurpitsa; 5117 Pinaatti; 5119 Raparperi; 5120 Punajuurikas ja keltajuurikas; 5121 Ruokasipuli (sis. punasipuli ja jättisipuli); 5124 Punakaali; 5125 Savoijinkaali (kurttukaali); 5127 Ruusukaali; 5128 Parsakaali; 5129 Kyssäkaali; 5131 Lehtiselleri; 5133 Lamopinaatti; 5134 Retiisi; 5140 Salaatti (Lactuca-suku); 5141 Salaattisikurit (Cichorium-suku); 5142 Salaattifenkoli; 5143 Kesäkurpitsa; 5148 Lehtikaali; 5149 Tilli; 5150 Persilja; 5157 Valkosipuli; 5158 Piparjuuri; 5165 Maa-artisokka; 5173 Meloni; 5176 Latva-artisokka; 5198 Muut vihannekset; 5816 Parsa; 5831 Korianteri; 5845 Ratamot; 5846 Yrttikasvit alle 5 v. (ei tilli eikä persilja); 5847 Yrttikasvit väh. 5 v.; 9630 Kasvimaa\n\nMarjapensaat, hedelmäpuut ja mansikka - 5210 Omena; 5212 Pihlaja (marjantuotanto); 5213 Päärynä; 5220 Muut hedelmät; 5221 Luumu; 5222 Viinirypäle; 5301 Mustaherukka; 5302 Punaherukka; 5303 Valkoherukka; 5305 Karviainen; 5310 Vadelma ja mesivadelma; 5311 Mansikka; 5312 Mesimarja; 5313 Pensasmustikka; 5314 Marja-aronia; 5318 Saskatoon (Marjatuomipihlaja); 5319 Muut marjakasvit; 5410 Koristekasvit, alle 5 v.; 5420 Koristekasvit, 5 v. ja yli, jatkuva sato avomaalta; 5440 Leikkovihreä ja leikkohavu, alle 5 v. kasveista; 5441 Leikkovihreä ja leikkohavu, väh. 5 v. kasveista; 5442 Koristepaju punontatarkoitukseen, alle 5 v.; 5443 Koristepaju punontatarkoitukseen, 5 - 20 v.; 5451 Tyrni; 5452 Kirsikka; 5512 Taimitarhat, alle 5 v. marja-, hedelmä-, koristek.; 5537 Taimitarhat, väh. 5 v. marja-, hedelmä-, koristek.\n\nKumina - 5806 Kumina\n\nViherlannoitusnurmi - 6050 Viherlannoitusnurmi; 6051 Viherlannoitusnurmi (ei ympäristösitoumusta)\n\nViljelty laidun - 6112 1-vuotiset laidunnurmet; 6122 Monivuotiset laidunnurmet\n\nSäilörehunurmi – 6111 1-vuot. kuivaheinä-, säilörehu-, tuorerehunurmet; 6121 Moniv. kuivaheinä-, säilörehu- ja tuorerehunurmet; 6210 Pysyvä kuivah.,säilör., tuorer. (väh 5, alle10 v)\n\nLuonnonlaidun – 6220 Pysyvä laidunnurmi (väh 5, alle 10 v); 6300 Luonnonlaidun ja –niitty; 6303 Luonnonlaidun jolla korkea luontoarvo (Ahvenanmaa); 6304 Luonnonlaidun jolla kohd. toim.pit. (Ahvenanmaa); 6305 Luonnonlaidun kulttuurialueella (Ahvenanmaa); 6307 Muu luonnonlaidun (Ahvenanmaa); 6600 Metsälaidun; 6710 Hakamaa, avoin; 6720 Hakamaa, puustoinen; 9801 Erityistukisopimusala, pysyvä laidun\n\nSiemennurmi 6113 1-vuotiset siemennurmet; 6115 1-vuotinen siemennurmi, yksilajinen; 6123 Monivuotiset siemennurmet; 6125 Monivuotinen siemennurmi, yksilajinen; 6545 Englannin raiheinän siemen, valvottu tuotanto; 6546 Italianraiheinän (westerw.) siemen, valv. tuotanto; 6550 Ruokonadan siemen, valvottu tuotanto; 6561 Apilan siemen, valvottu tuotanto; 6562 Timotein siemen, valvottu tuotanto; 6565 Nurminadan siemen, valvottu tuotanto\n\nKesanto, viljelemätön, ym. erityisalat – 9101 20 v. erityistukisopimus, pelto; 9102 20 v. erityistukisopimus, muu ala; 9403 Sänkikesanto; 9404 Avokesanto; 9620 Tilapäisesti viljelemätön ala; 9700 Pysyvästi viljelemätön\n\nViherkesanto ja luonnonhoitopelto - 9405 Luonnonhoitopelto (nurmikasvit, väh. 2 v.); 9412 Viherkesanto (nurmi ja niitty); 9413 Viherkesanto (riista ja maisema); 9422 Monimuotoisuuspelto, riista; 9423\n\nMonimuotoisuuspelto, maisema; 9424 Monimuotoisuuspelto, niitty 1. ja 2. vuosi; 9806 Kurki-, hanhi- ja joutsenpelto (sopimus)\n\nSuojavyöhykkeet ja –kaistat - 9810 Suojavyöhykenurmi (sopimukset ennen 2015); 9811\n\nSuojavyöhyke (sitoumus alkaen 2015); 9820 Suojakaista\n\nPeltojen kokonaispinta-ala sisältää yllä lueteltujen luokkien lisäksi seuraavat IACS-luokat:\n1410 Syyskaura; 3330 Rehujuurikasvit; 3340 Rehukaali; 5534 Metsäpuiden taimitarhat pellolla; 5850 Saneerauskasvi (valkosinappi); 5851 Saneerauskasvi (öljyretikka); 5852 Saneerauskasvi (samettikukka); 5853 Saneerauskasviseos; 6401 Muut rehukasvit; 6402 Rehurapsi; 9802 Erityistukisopimusala, muu ala; 9803 Erityistukisopimusala, pelto; 9804 Erityistukisopimusala, metsämaa; 9805 Ympäristösopimusala, pysyvä nurmi; 9807 Ympäristösopimusala, muu ala; 9808 Ympäristösopimusala, metsämaa; 9812 Monivuotinen ympäristönurmi; 9830 Maisemapiirre"
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                            "en": "Jaruseviciute, V., Kiuru, N., & Silinskas, G. (2022). Teacher– and parent–child relationships and children’s adjustment behaviors in grade 1 : The role of temperament.. <i>Journal of Family Psychology</i>, <i>36</i>(8), 1363-1375. <a href=\"https://doi.org/10.1037/fam0001023\" target=\"_blank\">https://doi.org/10.1037/fam0001023</a>",
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            "bibliographic_citation": "Silinskas, Gintautas; Raižienė, Saulė. <i>Datasets of the project \"Get Involved! Transition to Grade 1\"</i>. V. 31.8.2019. University of Jyväskylä.",
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                "en": "The project “Get involved! Transition to Grade 1”\r\nDataset originates from the longitudinal research project “Get Involved! Transition to Grade 1” investigated the role of parents and teachers in the development of children’s academic achievement, motivation, and behaviour during the critical transition from preschool to primary school. The study aimed to provide information on the role of parents’ and teachers’ instructional support and affect in children’s outcomes which is crucial in promoting learning among students in school. The project sought to test a comprehensive model of child development in the early phase of schooling, considering both parental and teachers influences, as well as children’s evocative influence on their interpersonal environment. It examined the longitudinal relations between parents’ and teachers’ instructional support and emotions, and children’s outcomes across the transition from preschool to primary school. The study also aimed to identify cases in which such support had the most favourable outcomes for children’s achievement, motivation, and behaviour, and to determine the mechanisms by which those outcomes emerge. \r\nData collection took place in three waves:\r\n\r\na) Preschool, spring 2017 (T1)\r\n\r\nb) Grade 1, fall 2017 (T2)\r\n\r\nc) Grade 1, spring 2018 (T3)  \r\n\r\nThe longitudinal study was conducted in Lithuania and involved children, parents/guardians and teachers. The data collection included a combination of child assessments, parent and teacher questionnaires.\r\n\r\nThe following datasets were gathered covering multiple time points (T1–T3):\r\na) Child Assessment Data:\r\nThis dataset included small group assessments of children’s vocabulary, letter knowledge, reading and counting skills, as well as motivation, self-regulation abilities and so on. The dataset also incorporated psychologist observations of the child's behaviour during testing sessions, including task persistence, student behaviour during tasks, student well-being during testing and so on.\r\n\r\nb) Parent/Guardian Questionnaire Data: \r\nThis dataset was collected from parents or guardians. It has information on parents’ involvement in academic-related activities (e.g., homework), their emotional and cognitive perceptions regarding their children, their relationship with the child and so on. Additionally, parents/guardians provided information about their collaboration with preschool institution.\r\n\r\nc) Teacher General Questionnaire Data:\r\nTeachers completed a questionnaire about themselves and their class. This questionnaire provided data on teaching styles, self-confidence, working environment, homework practices and collaboration with elementary schools, as well as the overall improvement in students’ reading and math skills. \r\n\r\nd) Teacher Individual Questionnaire Data: \r\nTeachers filled out another questionnaire about each participating student. This dataset included information on the teacher–child relationship, child’s behaviour, collaboration with the child’s parents and so on.\r\n\r\nThe number of participants was as follow:\r\na) At T1 (Preschool, spring 2017): 229 children, 245 parents/guardians, and 18 teachers.\r\n\r\nb) At T2 (Grade 1, fall 2017): 337 children, 349 parents, and 24 teachers.\r\n\r\nc) At T3 (Grade 3, spring 2018): children 341, parents 323, and 25 teachers.\r\n\r\nThe project was funded by the Research Council of Finland and conducted in cooperation with Mykolas Romeris University and Lithuanian schools. The study provides a comprehensive longitudinal view of how interactions between home and school contexts contribute to children’s academic development during the transition from preschool to primary school.\r\n\r\nMore information about participants, data collection methods and variables can be found in “Get Involved! Transition to Grade 1” codebook (published separately)."
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                            "fi": "<jats:p>Abstract. Wildfires present severe threats to various aspects of ecosystems, human settlements, and the environment. Early detection plays a critical role in minimizing the destructive consequences of wildfires. This study introduces an innovative approach for smoke-based wildfire detection in Boreal forests by combining the YOLO V5 algorithm and transfer learning. YOLO V5 is renowned for its real-time performance and accuracy in object detection. Given the scarcity of labelled smoke images specific to wildfire scenes, transfer learning techniques are employed to address this limitation. Initially, the generalisability of smoke as an object is examined by utilising wildfire data collected from diverse environments for fine-tuning and testing purposes in Boreal forest scenarios. Subsequently, Boreal forest fire data is employed for training and fine-tuning to achieve high detection accuracy and explore benchmarks for effective local training data. This approach minimises extensive manual labelling efforts while enhancing the accuracy of smoke-based wildfire detection in Boreal forest environments. Experimental results validate the efficacy of the proposed approach. The combined YOLO V5 and transfer learning framework demonstrates a high detection accuracy, making it a promising solution for automated wildfire detection systems. Implementing this methodology can potentially enhance early detection and response to wildfires in Boreal forest regions, thereby contributing to improved disaster management and mitigation strategies.\n                    </jats:p>",
                            "und": "<jats:p>Abstract. Wildfires present severe threats to various aspects of ecosystems, human settlements, and the environment. Early detection plays a critical role in minimizing the destructive consequences of wildfires. This study introduces an innovative approach for smoke-based wildfire detection in Boreal forests by combining the YOLO V5 algorithm and transfer learning. YOLO V5 is renowned for its real-time performance and accuracy in object detection. Given the scarcity of labelled smoke images specific to wildfire scenes, transfer learning techniques are employed to address this limitation. Initially, the generalisability of smoke as an object is examined by utilising wildfire data collected from diverse environments for fine-tuning and testing purposes in Boreal forest scenarios. Subsequently, Boreal forest fire data is employed for training and fine-tuning to achieve high detection accuracy and explore benchmarks for effective local training data. This approach minimises extensive manual labelling efforts while enhancing the accuracy of smoke-based wildfire detection in Boreal forest environments. Experimental results validate the efficacy of the proposed approach. The combined YOLO V5 and transfer learning framework demonstrates a high detection accuracy, making it a promising solution for automated wildfire detection systems. Implementing this methodology can potentially enhance early detection and response to wildfires in Boreal forest regions, thereby contributing to improved disaster management and mitigation strategies.\n                    </jats:p>"
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