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(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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                "fi": "Aineisto koostuu 71 muistelukirjoituksesta, joissa teemana on omaan ulkonäköön liittyvät lapsuuden ja nuoruuden kokemukset. Kirjoittajat muistelevat mm. ulkonäön ja kehonkuvan muutoksia eri elämänvaiheissa, ulkonäköpaineita, ulkonäön kommentointia, itsetuntoa, lihavuutta ja pukeutumista. Aineisto kerättiin vuonna 2015 tutkimukseen, jossa tarkastellaan ulkonäön koettuja vaikutuksia eri-ikäisten suomalaisten elämässä. Suuri enemmistö kirjoittajista on naisia. Kirjoittajien ikä vaihtelee 18 - 78-ikävuoden välillä. Kirjoitukset ovat vapaamuotoisia ja vaihtelevat pituudeltaan. Kirjoitusohjeissa vastaajaa pyydettiin kertomaan, millaisena hän on nähnyt ja näkee nykyään oman ulkomuotonsa ja miten ulkonäköasioita on käsitelty lapsuuden perheessä. Lisäksi kysyttiin, mikä vaikutus perheen suhtautumisella ulkonäköön on ollut kirjoittajan elämään silloin ja myöhemmin aikuisiällä. Edelleen vastaajalta tiedusteltiin, onko hän ottanut ulkonäön kommentoinnin puheeksi jälkikäteen kommentoijan kanssa. Lisäksi kysyttiin vielä, keskusteleeko hän ulkoisesta habituksesta eri ihmisten, kuten puolison kanssa. Erityisesti kirjoittajaa rohkaistiin tuomaan esille ulkonäköön liittyvien nuoruuden kokemusten ja kommenttien vaikutuksia omaan ulkonäkösuhteeseen. Kirjoituksissa käsiteltiin myös seuraavia aiheita: kauneuskäsityksiä, perhesuhteiden dynamiikkaa, koulukiusaamista, vaatteita, syömishäiriötä ja liikalihavuutta. Taustatietoina aineistossa on kirjoittajan ammatti, syntymävuosi, asuinmaakunta ja sukupuoli."
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                "fi": "Teemahaastatteluissa on tiedusteltu suomalaisten mielipiteitä liittyen Neuvostoliittoon sekä neuvostoliittolaisiin ja virolaisiin. Haastattelun aikana haastateltavalle näytettiin Neuvostoliittoon liittyviä kuvia, sanalistoja, tekstiotteita sekä lehtileikkeitä, joita haastateltavaa pyydettiin kommentoimaan. Kysymyksissä esiin nostetut teemat käsittelivät muun muassa politiikkaa, arkielämää, taloutta ja kulttuuria. Näkemyksiä kysytään paitsi neuvostoliittolaisista ihmisistä myös Neuvostoliitosta valtiona. Haastatteluissa on pyritty tietoisesti rikkomaan \"sopivan puhetavan normi\" esimerkiksi myötäilemällä haastateltavan puhetapaa sekä käyttämällä tämän kanssa samaa sanastoa. Haastatteluja on kerätty neljään eri ikäryhmään kuuluvilta suomalaisilta. Ikäryhmät jaksottuivat 15 vuoden välein siten, että haastateltavien syntymävuodet olivat 1923, 1938, 1953 ja 1968. Haastatellut olivat kotoisin neljältä eri paikkakunnalta, jotka sijaitsivat Uudenmaan läänissä, Hämeen läänissä, Oulun läänissä ja Vaasan läänissä. Aineisto on kerätty alunperin Pentti Raittilan lisensiaatin työtä varten. Kaikki haastattelut on tehty keväällä 1989."
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                            "en": "Niskanen, Kujala, Kärkkäinen, Savolainen, Pyhäjärvi: Does the seed fall far from the tree? Weak fine-scale genetic structure in a continuous Scots pine population",
                            "fi": "Niskanen, Kujala, Kärkkäinen, Savolainen, Pyhäjärvi: Does the seed fall far from the tree? Weak fine-scale genetic structure in a continuous Scots pine population"
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                            "en": "Knowledge of fine-scale spatial genetic structure, i.e., the distribution of genetic diversity at short distances, is important in evolutionary research and in practical applications such as conservation and breeding programs. In trees, related individuals often grow close to each other due to limited seed and/or pollen dispersal. The extent of seed dispersal also limits the speed at which a tree species can spread to new areas. We studied the fine-scale spatial genetic structure of Scots pine (Pinus sylvestris) in two naturally regenerated sites located 20 km from each other in continuous south-eastern Finnish forest. We genotyped almost 500 adult trees for 150k SNPs using a custom made Affymetrix array. We detected some pairwise relatedness at short distances, but the average relatedness was low and decreased with increasing distance, as expected. Despite the clustering of related individuals, the sampling sites were not differentiated (FST = 0.0005). According to our results, Scots pine has a large neighborhood size (Nb = 1680– 3210), but a relatively short gene dispersal distance (σ = 36.5–71.3 m). Knowledge of Scots pine fine-scale spatial genetic structure can be used to define suitable sampling distances for evolutionary studies and practical applications. Detailed empirical estimates of dispersal are necessary both in studying post-glacial recolonization and predicting the response of forest trees to climate change.",
                            "fi": "<jats:title>Abstract</jats:title><jats:p>Knowledge of fine-scale spatial genetic structure,<jats:italic>i.e.</jats:italic>, the distribution of genetic diversity at short distances, is important in evolutionary research and in practical applications such as conservation and breeding programs. In trees, related individuals often grow close to each other due to limited seed and/or pollen dispersal. The extent of seed dispersal also limits the speed at which a tree species can spread to new areas.</jats:p><jats:p>We studied the fine-scale spatial genetic structure of Scots pine (<jats:italic>Pinus sylvestris</jats:italic>) in two naturally regenerated sites located 20 km from each other in continuous south-eastern Finnish forest. We genotyped almost 500 adult trees for 150k SNPs using a custom made Affymetrix array. We detected some pairwise relatedness at short distances, but the average relatedness was low and decreased with increasing distance, as expected.</jats:p><jats:p>Despite the clustering of related individuals, the sampling sites were not differentiated (<jats:italic>F<jats:sub>ST</jats:sub></jats:italic>= 0.0005). According to our results, Scots pine has a large neighborhood size (<jats:italic>Nb</jats:italic>= 1680– 3210), but a relatively short gene dispersal distance (<jats:italic>σ</jats:italic><jats:sub>g</jats:sub>= 36.5–71.3 m). Knowledge of Scots pine fine-scale spatial genetic structure can be used to define suitable sampling distances for evolutionary studies and practical applications. Detailed empirical estimates of dispersal are necessary both in studying post-glacial recolonization and predicting the response of forest trees to climate change.</jats:p>",
                            "und": "<jats:title>Abstract</jats:title><jats:p>Knowledge of fine-scale spatial genetic structure,<jats:italic>i.e.</jats:italic>, the distribution of genetic diversity at short distances, is important in evolutionary research and in practical applications such as conservation and breeding programs. In trees, related individuals often grow close to each other due to limited seed and/or pollen dispersal. The extent of seed dispersal also limits the speed at which a tree species can spread to new areas.</jats:p><jats:p>We studied the fine-scale spatial genetic structure of Scots pine (<jats:italic>Pinus sylvestris</jats:italic>) in two naturally regenerated sites located 20 km from each other in continuous south-eastern Finnish forest. We genotyped almost 500 adult trees for 150k SNPs using a custom made Affymetrix array. We detected some pairwise relatedness at short distances, but the average relatedness was low and decreased with increasing distance, as expected.</jats:p><jats:p>Despite the clustering of related individuals, the sampling sites were not differentiated (<jats:italic>F<jats:sub>ST</jats:sub></jats:italic>= 0.0005). According to our results, Scots pine has a large neighborhood size (<jats:italic>Nb</jats:italic>= 1680– 3210), but a relatively short gene dispersal distance (<jats:italic>σ</jats:italic><jats:sub>g</jats:sub>= 36.5–71.3 m). Knowledge of Scots pine fine-scale spatial genetic structure can be used to define suitable sampling distances for evolutionary studies and practical applications. Detailed empirical estimates of dispersal are necessary both in studying post-glacial recolonization and predicting the response of forest trees to climate change.</jats:p>"
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                "en": "Contains measurement data of air temperature for the manuscript \"Real-time measurements of micro-climatic temperature and relative humidity in the Finnish cities of Tampere, Helsinki and Rovaniemi\" by Kühn et al. (in preparation) of 30 measurement stations in Helsinki.\n\nTechnical Info:\n\nNB: Update on 7.4.2026: data file Helsinki\\_032026.dat added.\n\nThe measurements were conducted at a height of 3 m in the locations listed in Helsinki\\_Table1\\_New.txt during 12/2024-3/2026.\n\nEach measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf] Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.\n\nQuality check   \nThe temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Helsinki\\_Table1\\_New. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents:\n\nThe descriptions of the measurement stations are in Helsinki\\_Table1\\_New.txt.   \nColumns:   \n1. Station\\_code   \n2. Station\\_id   \n3. latitude   \n4. longitude   \n5. elevation above mean se alevel (m)   \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station)   \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)\n\nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Helsinki\\_YEAR.dat, with Celsius as Unit and missing value -999.   \nColumns:   \n1. Station\\_code   \n2. Timestamp(YMDHH24) UTC   \n3. Mean temperature of the previous hour   \n4. Minimum temperature of the previous hour   \n5. Maximum temperature of the previous hour   \n6. Standard deviation of the temperature measurements during the previous hour   \n7. Number of measurements during the previous hour (usually 12)   \n  \nReferences:\n\nAalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651   \nStewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022.   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. Zenodo. https://doi.org/10.5281/zenodo.6364593."
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                "en": "Contains measurement data of air temperature for the manuscript \"Real-time measurements of micro-climatic temperature and relative humidity in the Finnish cities of Tampere, Helsinki and Rovaniemi\" by Kühn et al. (in preparation) of 23 measurement stations in Tampere.\n\nTechnical Info:\n\nNB: Update on 7.4.2026: data file Tampere\\_202603.dat added.\n\nThe measurements were conducted at a height of 3 m in the locations listed in Tampere\\_Table1\\_New.txt during 07/2023-3/2026.\n\nEach measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf] Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.\n\nQuality check\n\nThe temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Table 1. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents:   \nThe descriptions of the measurement stations are in Tampere\\_Table1\\_New.txt.   \nColumns   \n1. Station\\_code   \n2. Station\\_id   \n3. latitude   \n4. longitude   \n5. elevation above mean se alevel (m)   \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station)   \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)   \n  \nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Tampere\\_YEAR.dat, with Celsius as Unit and missing value -999.   \nColumns:   \n1. Station\\_code   \n2. Timestamp(YMDHH24) UTC   \n3. Mean temperature of the previous hour   \n4. Minimum temperature of the previous hour   \n5. Maximum temperature of the previous hour   \n6. Standard deviation of the temperature measurements during the previous hour   \n7. Number of measurements during the previous hour (usually 12)   \n  \nReferences:\n\nAalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651 Stewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022.   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. Zenodo. https://doi.org/10.5281/zenodo.6364593."
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            "description": {
                "en": "The study charted the respondents' political attitudes, voting behaviour and views on the 2023 parliamentary elections. The dataset includes data from six rounds of polling with the same respondents. The data were collected as part of the Citizens' Opinion panel, which is part of The Finnish Research Infrastructure for Public Opinion (FIRIPO). First, the respondent's voting behaviour was surveyed by asking, for example, whether they planned to vote in the 2023 parliamentary elections, and if so, which party they planned to vote for. Those who indicated that they would not be voting were asking about their reasons for doing so (e.g. haven't found a suitable party or candidate, have inadequate information to make a voting decision). Political participation was examined with questions about interest in politics, forms of political participation engaged in (e.g. donating money to a candidate, participating in a peaceful demonstration, participating in a political party's campaign activities), and participation in citizens' initiatives. Next, political attitudes were investigated with a series of statements (e.g. Finnish Members of Parliament are competent at their jobs, I trust my own abilities to take part in politics, by voting ordinary people can have an impact on political decision-making). The respondents' views on current political issues charted with a series of statements (e.g. immigration is mostly a good thing for Finland, Finland should be much more active in the fight against climate change, Russia is a security threat to Finland, public services must be cut to balance the Finnish economy), and they were asked to assess how important various topics in politics (e.g. taxation, minority rights, social and health care, national security and defence) were for them. The respondents were also asked whether they would for or against Finland's NATO membership if they had the chance. Voting choice was surveyed by asking the respondents which factors (e.g. the party's values, the party's activities during the previous parliamentary term) had impacted their choice of political party in the 2023 parliamentary elections. The factors which impacted the respondents' choice of candidate in parliamentary elections (e.g. candidate's trustworthiness, candidate's previous political experience) were also examined. Additionally, the respondents were asked which political parties they would like to see in government after the 2023 parliamentary elections and which political parties they hoped would not be part of the government. Opinions were investigated on each political party in the Finnish parliament and the chairpersons of those parties. The respondents were also asked how they would feel about having a close friend or a colleague that supported each of the political parties in the Finnish parliament, and how they would react if their son or daughter married a supporter of each of the parties in the Finnish parliament. General attitudes towards the supporters of each political party in the Finnish parliaments were charted. Trust in various institutions (e.g. the European Union, the Finnish President, political parties) was measured and satisfaction in democracy in Finland and in the EU was investigated. Satisfaction with the Cabinet led by Prime Minister Sanna Marin, the activities of opposition parties after the 2019 parliamentary elections, the government's approach to the war in Ukraine, and the government's approach to the COVID-19 pandemic were surveyed. The respondents were asked whether various areas (e.g. defence, care of the elderly, employment, taxation, minority rights) had improved or worsened since the 2019 parliamentary elections and whether they believed that the changes in those areas were due to the government's actions or caused by other reasons. The respondents were asked how suitable they felt that different political systems (e.g. a democratic political system, a strong leader who does not have to care about the parliament or elections) were as a form of government in Finland. Views on democracy were studied by asking how essential certain factors (e.g. women have the same rights as men, the people elect their leaders in free elections, the media are free to criticise the government) were for democracy. Additionally, the respondents were asked for their opinions on various proposals concerning the future direction of Finland (e.g. Finland should accept fewer refugees, Finland should have a smaller public sector, Finland should have lower taxation even if it weakens public services). Background variables included the respondent's age, gender, highest level of education, economic activity and occupational status, mother tongue, electoral area, and municipality type.",
                "fi": "Aineisto sisältää kuudella kyselykierroksella samoilta vastaajilta kerättyjä mielipidemittauksia ja eduskuntavaaleihin liittyviä näkemyksiä. Aineisto on kerätty osana Kansalaismielipidepaneelia, joka kuuluu kansalliseen yleisen mielipiteen mittaamisen infrastruktuuriin (The Finnish Research Infrastructure for Public Opinion, FIRIPO). Alussa kartoitettiin vastaajan äänestyskäyttäytymistä, eli aikooko vastaaja äänestää, ja jos aikoo, niin mitä puoluetta on suunnitellut äänestävänsä. Vastaajilta kysyttiin mielipiteitä kansalaisaloitteista, ja heille esitettiin erilaisia väittämiä kansanedustajista ja ajankohtaisista poliittisista kysymyksistä. Vastaajia pyydettiin arvioimaan poliittisia puolueita ja niiden puheenjohtajia. Kysyttiin, mitkä asiat vaikuttivat puolueen ja ehdokkaan valintaan, tai toisaalta miksi vastaaja päätti jättää äänestämättä. Vastaajat kertoivat, millaisiin yhteiskunnallisen osallistumisen muotoihin he ovat ottaneet osaa. Vastaajilta kysyttiin luottamuksesta erilaisia poliittisia puolueita kohtaan. Taustamuuttujia aineistossa ovat vastaajan ikä, sukupuoli, koulutustaso, elämäntilanne, äidinkieli, vaalialue ja kuntaluokitus."
            },
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                        "pref_label": {
                            "en": "Restriced access for research based on contract",
                            "fi": "Saatavuutta rajoitettu sopimuksen perusteella vain tutkimuskäyttöön",
                            "sv": "Begränsad åtkomst på bas av kontrakt ändast för forskningsändamål"
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                            "en": "Tampere University",
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                                "en": "Tampere University",
                                "fi": "Tampereen yliopisto",
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                                "und": "Tampereen yliopisto"
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            "description": {
                "en": "The study focused on the significance of gender in teaching and the impact of gender on teachers' pedagogical choices. Finnish primary and lower secondary school teachers were asked to write about their thoughts on the subject in their own words. In the writing invitation, the researcher listed some specific questions that the participants could use in formulating their thoughts. These questions concerned how teachers thought gender showed in everyday school life, whether gender produced inequality in school, how and what kind of inequality, what the potential participants thought of feminism, how they promoted gender equality in teaching, whether they thought gender roles supported the students' sexual identity, and whether they thought it was necessary to pay attention to gender or think about the significance of gender issues in teaching. Background variables included gender, age group and whether the participant worked as a class teacher or a subject teacher.",
                "fi": "Aineisto sisältää opettajien kirjoituksia sukupuolen merkityksestä opetustyössä. Vastaajia pyydettiin kirjoittamaan vapaita tekstejä annetusta aiheesta. Osallistujille annettiin myös apukysymyksiä. Apukysymyksissä kysyttiin esimerkiksi sitä, miten sukupuoli vastaajan mielestä näkyy kouluarjessa ja tuottaako sukupuoli eriarvoisuutta koulussa. Lisäksi ohjeistettiin pohtimaan sitä, edistääkö vastaaja jollakin tavalla itse sukupuolista tasa-arvoa opetustyössään, tai kokeeko hän ylipäätään tarpeelliseksi huomioida tai pohtia sukupuolen merkitystä omassa opetuksessaan. Yhdessä apukysymyksessä pyydettiin myös kertomaan omista näkemyksistään liittyen feminismiin. Taustatietona kysyttiin sukupuolta, ikäryhmää sekä sitä, toimiiko luokanopettajana vai aineenopettajana."
            },
            "field_of_science": [
                {
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                    "url": "http://www.yso.fi/onto/okm-tieteenala/ta5",
                    "in_scheme": "http://www.yso.fi/onto/okm-tieteenala/conceptscheme",
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                        "en": "Social sciences",
                        "fi": "YHTEISKUNTATIETEET",
                        "sv": "Samhällsvetenskaper"
                    }
                }
            ],
            "infrastructure": [],
            "issued": "2018-12-17",
            "keyword": [
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                "sukupuoliroolit",
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                "opetus",
                "opettajat",
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                "peruskoulu",
                "sukupuolineutraalius",
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                "gender",
                "gender role",
                "lower secondary education",
                "primary education",
                "schoolteachers",
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                "teaching",
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                "Sukupuoli ja sukupuoliroolit",
                "Compulsory and pre-school education",
                "Gender and gender roles"
            ],
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                    "notation": "https://doi.org/10.60686/t-fsd3241",
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                "fi": "Tuotantoeläinten lannalla tarkoitetaan eläinten nestemäisen ja kiinteän ulosteen eli virtsan ja sonnan sekä kuivikkeiden ja/tai pesuvesien seosta. Lannan koostumukseen vaikuttaa eläimen saama ravinto, sillä lantaan erittyy suurin osa niistä aineista, joita eläin ei pysty käyttämään kasvuunsa ja elintoimintoihinsa. Lanta sisältää paljon ravinteita, kuten typpeä ja fosforia, sekä orgaanista ainesta. Lannan ominaisuudet riippuvat käytetystä kuivikkeesta sekä lannan käsittelyn ja mahdollisen prosessoinnin ratkaisuista. Lantaa levitetään lannoitteena suoraan peltoon, mutta siitä on mahdollista tuottaa prosessoimalla myös esimerkiksi biokaasua ja uudenlaisia lannoitevalmisteita. Lannan levitystä peltoon säätelee EU:n nitraattidirektiivi (91/676/ ETY) ja sen pohjalta laadittu kansallinen lainsäädäntö (VnA 1250/2014, muut. 220/2015, 435/2015, 1261/2015). \nLantapaikkatietoaineisto on muodostettu yhdistämällä Luken ja SYKEn Normilanta -järjestelmän [1, 2, 3] tuottamat eläinkohtaiset tiedot lannan määrästä ja ominaisuuksista eläinten määrätietoihin. Näin saadut kuntatason lannan määrä- ja ominaisuustiedot on viety 1 km ruutuaineistoksi levittämällä ne tasaisesti ruutuun kunnan alueella. Esitetyt eläinluokat ovat maitokarja, lihakarja, lihasiat, emakot ja porsaat, munituskanat, muu siipikarja, lampaat ja vuohet sekä hevoset ja ponit. \nNaudoille ja sioille esitetään lietelannat, kuivat lannat summattuna (sis. kuivalanta, kuivikelanta, kuivikepohjalanta) ja erilliskerätty virtsa. Muille eläinluokille esitetään kuivat lannat. Lantatieto esitetään sekä juuri eläinsuojasta kerätylle että varastoidulle lannalle, sillä niiden ominaisuudet eroavat mm. lannankäsittelyn aikana tapahtuvien hiilen ja typen hävikkien vuoksi. Lantatiedoista on valmiiksi vähennetty laiduntavien eläinten keskimääräinen laitumelle jäävä lanta. \nLampaiden ja vuohien lanta on laskettu vuohien erityskertoimilla lampaiden ja vuohien lukumääriin perustuen. Lampaiden lantatietoja tarkennetaan, kun Normilanta -järjestelmän kehittäminen etenee. Hevosten lantamäärä perustuu Hippoksen hevosenomistajarekisterin perusteella muodostettuun arvioon hevosten kunnittaisista määristä (rekisteri kertoo hevosten omistajien osoitteet, mutta ei hevosten sijaintia). Muiden lantatietojen lähtötietona käytetyt eläinmäärät on saatu Ruokaviraston ylläpitämästä eläinrekisteristä ja summattu kuntatasolle."
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