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"en": "This dataset presents historical arable land data from northern Finland, covering an area within ±3 degrees of the Arctic Circle. This historical dataset offers valuable insights for detailed, field-level analysis of arable land use in the Finnish Arctic region.\n\nArable land was extracted using machine learning (Extreme Gradient Boosting) from scanned historical paper maps: [Finnish Basic maps from 1947 and 1979](http://urn.fi/urn:nbn:fi:att:59126695-74e3-4689-8835-7bfd38588f3c) at a scale of 1:20,000. The model achieved exceptional performance, with a Cohen's Kappa and Matthews Correlation Coefficient of 0.992. Class-wise performance for arable land detection was nearly perfect, with recall, precision, and F1 score of 1.0, 0.998, and 0.995, respectively. The data was extracted 2025-2026.\n\nThe code for the machine learning model can be found at https://github.com/anneliagren/HistoricalArableLandNorthFinland\n\n\nCoordinate system: ETRS89 / ETRS-TM35FIN (EPSG:3067)",
"fi": "Tämä aineisto esittää historiallista peltomaata Pohjois-Suomesta alueelta, joka sijaitsee ±3 asteen sisällä napapiiristä. Tämä historiallinen aineisto tarjoaa arvokasta tietoa yksityiskohtaiseen, peltolohkokohtaiseen peltomaan käytön analyysiin Suomen arktisella alueella.\n\nPeltomaa on erotettu koneoppimisen (Extreme Gradient Boosting) avulla skannatuista historiallisista paperikartoista: [peruskartat 1947–1979](http://urn.fi/urn:nbn:fi:att:59126695-74e3-4689-8835-7bfd38588f3c) mittakaavassa 1:20 000. Malli saavutti poikkeuksellisen suorituskyvyn: Cohenin kappa-arvo ja Matthewsin korrelaatiokerroin olivat 0,992. Luokkakohtainen suorituskyky peltomaan tunnistamisessa oli lähes täydellinen, ja recall-, precision- ja F1-arvot olivat vastaavasti 1,0, 0,998 ja 0,995. Aineisto laskettiin 2025-2026.\n\nKoneoppimismallin koodi: https://github.com/anneliagren/HistoricalArableLandNorthFinland\n\nKoordinaattijärjestelmä: ETRS89 / 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": "560abf95-2ee7-4b78-8992-5c70d5b73f4f",
"url": "http://www.yso.fi/onto/okm-tieteenala/ta415",
"in_scheme": "http://www.yso.fi/onto/okm-tieteenala/conceptscheme",
"pref_label": {
"en": "Other agricultural sciences",
"fi": "Muut maataloustieteet",
"sv": "Övriga jordbruksvetenskaper"
}
}
],
"infrastructure": [],
"issued": "2026-03-13",
"keyword": [
"Paituli"
],
"language": [],
"metadata_owner": {
"id": "e883ab57-f794-4692-8027-147f71a3eafd",
"organization": "csc.fi",
"admin_organization": "csc.fi"
},
"other_identifiers": [],
"persistent_identifier": "urn:nbn:fi:fd-2369597d-4781-3352-b128-4dd34583e32d",
"pid_generated_by_fairdata": true,
"projects": [],
"provenance": [],
"relation": [],
"remote_resources": [
{
"title": {
"en": "Historic arable Land for Northern Finland"
},
"use_category": {
"id": "ed5844fc-6309-4a8f-98bb-e18c1aec9fdc",
"url": "http://uri.suomi.fi/codelist/fairdata/use_category/code/outcome",
"in_scheme": "http://uri.suomi.fi/codelist/fairdata/use_category",
"pref_label": {
"en": "Outcome material",
"fi": "Tulosaineisto"
}
},
"download_url": "https://paituli.csc.fi/download.html?data_id=slu_old_fiedls_20m_1947_1979_tiff_euref"
},
{
"title": {
"en": "Files in folder view"
},
"use_category": {
"id": "ed5844fc-6309-4a8f-98bb-e18c1aec9fdc",
"url": "http://uri.suomi.fi/codelist/fairdata/use_category/code/outcome",
"in_scheme": "http://uri.suomi.fi/codelist/fairdata/use_category",
"pref_label": {
"en": "Outcome material",
"fi": "Tulosaineisto"
}
},
"access_url": "https://www.nic.funet.fi/index/geodata/slu/arable_land"
}
],
"spatial": [
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"fi": "Aluevaalitutkimus 2022 kartoittaa suomalaisten mielipiteitä aluevaaleista ja hyvinvointialueista. Kyselyssä selvitettiin vastaajien poliittista osallistumista, poliittisia asenteita, puolueiden kannatusta, ehdokas- ja puoluevalintoja sekä äänestysaktiivisuutta. Vaalitutkijoiden kokoama aineisto sisältää kysymyksiä myös Suomen tulevaisuuden näkymistä. Aluevaalitutkimuksen toteutuksesta vastasi Suomen kansallinen vaalitutkimuskonsortio, joka koostuu useiden eri yliopistojen vaali- ja demokratiatutkijoista. Ensiksi vastaajalta kysyttiin kiinnostuksesta politiikkaan sekä tyytyväisyydestä demokratian toimivuuteen Suomessa. Tämän jälkeen tiedusteltiin, minkä verran vastaaja seurasi aluevaaleja eri tiedotusvälineistä ja kuinka usein hän keskustelee politiikasta muiden ihmisten kanssa. Asenneväittämin kartoitettiin vastaajan mielipiteitä äänestämisestä, demokratiasta ja poliittisen päätöksenteon muodoista. Tyytyväisyyttä poliittiseen järjestelmään kartoitettiin kysymällä, kuinka paljon vastaaja luottaa eri tahoihin, kuten hallitukseen, puolueisiin, kunnanvaltuustoon, tiedotusvälineisiin, terveyden ja hyvinvoinnin laitokseen ja aluehallintovirastoon. Seuraavaksi vastaajan mielipiteitä kartoitettiin erilaisin aluevaaleihin ja lähidemokratiaan liittyvin väittämin. Vastaajan tuli myös asettaa tärkeysjärjestykseen se, ketä aluevaltuutetun tulisi ensisijaisesti edustaa. Lisäksi tiedusteltiin aluevaaleihin liittyvän tiedon saatavuudesta. Seuraavana teemana olivat puolueet. Vastaajalta tiedusteltiin, kokeeko hän olevansa lähellä jotakin poliittista puoluetta, ja jos kokee, niin mitä. Lisäksi kysyttiin mielipidettä eri poliittisista puolueista. Vastaajaa pyydettiin myös sijoittamaan itsensä vasemmisto-oikeisto-akselille sekä arvoliberaali-arvokonservatiivi-asteikolle. Suomen tulevaisuuden suuntaa kartoitettiin asenneväittämillä esimerkiksi monikulttuurisuudesta, kristillisistä arvoista, ilmastonmuutoksen torjunnasta, valtion säätelystä ja verotuksesta. Seuraavaksi vastaajalta kysyttiin aluevaaleissa äänestämisestä. Vastaajalta kysyttiin äänestämättä jättämisestä ja sen itsestään selvyydestä. Vaaleissa äänestäneiltä kysyttiin tarkemmin, minkä puolueen ehdokasta vastaaja äänesti, oliko ehdokas omassa kunnassa asuva ja missä vaiheessa hän teki äänestyspäätöksen. Lopuksi kysyttiin hyvinvointialueisiin liittyviä kysymyksiä. Vastaajalta kysyttiin, pitäisikö julkisia varoja käyttää terveydenhuoltoon enemmän, saman verran vai vähemmän kuin nykyisin. Lisäksi esitettiin useita väittämiä liittyen esimerkiksi hyvinvointialueiden ja oman kotikunnan tarjoamiin palveluihin, verotukseen ja asiakasmaksuihin. Taustatietoja aineistossa ovat vastaajan syntymävuosi, sukupuoli, hyvinvointialue, koulutusaste, siviilisääty, ammattiasema, työskentelysektori, kotitalouden bruttovuositulot, paikkakuntatyyppi, asuntotyyppi, ensisijainen tapa käyttää terveyspalveluja, puoluekanta, äidinkieli, kotona käytettävät kielet, kuuluminen etniseen vähemmistöryhmään, kotitalouden henkilömäärä ja lasten määrä."
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"en": "BACKGROUND\nWe studied the molecular mechanisms of biosynthesized cyanogenic toxicity in Heliconius erato by studying associations of toxicity levels (concentrations of biosynthesized cyanogenic glucoside compounds linamarin and lotaustralin measured with nuclear magnetic resonance, i.e. NMR) with patterns of whole-genome gene expression. Individuals originating from a laboratory stock were sampled for quantification of chemical compounds using NMR data, and their RNA samples sequenced with whole-transcriptome sequencing (RNASeq). The NMR data could also be used to analyze the complete metabolome or other compounds of interest. Additionally, to test energetic trade-offs and related differential RNA expression, measures of resting- and flight metabolic rates of the study individuals were acquired using flow-through respirometry.\n\nSTUDY QUESTIONS\n1.\tWhat are the physiological/biochemical processes that are directly associated with biosynthesized cyanogenic toxicity?\n2.\tWhat are the physiological/biochemical processes that are indirectly associated through e.g. depletion of energy/resources from biosynthesis of toxins? For this, we additionally acquired measures of resting- and flight metabolic rates of the study individuals using flow-through respirometry.\n3.\tAre there physiological mechanisms that may more directly link toxicity and flight, e.g. recycling cyanogenic glucosides to release nitrogenous compounds that could provide energy for flight?\n\nSAMPLE COLLECTION AND PREPARATION\n\nSample collection and RNA extraction: All individuals were sampled within three days following emergence, were unmated and unfed. One-half of the thorax was flash-frozen in liquid nitrogen and stored in -80°C. RNA was extracted from thorax tissue samples (one quarter of thorax) with Bioline TRIsure protocol, and re-dissolved in RNA-free water. DNA was removed with Ambion DNA-free kit. RNA concentration was analysed with Qubit RNA HS kit, RNA purity was assessed with Nanodrop and integrity with Agilent 2100 (using aliquots of a subset of 12 samples). RIN value is non-applicable for Lepidoptera RNA, but visual inspection proves the sample integrity if high (there is negligible degradation of the RNA product. Extracted RNA samples were further processed and sequenced by Novogene.\n\nRNA quantification and qualification: RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer (Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).\n\nLibrary preparation for Transcriptome sequencing: A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using random hexamer primer and M MuLV Reverse Transcriptase(RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~200 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.\nClustering and sequencing (Novogene Experimental Department): The clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq PE Cluster Kit cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated.\n\nQuantification of cyanogenic compounds linamarin and lotaustralin using nuclear magnetic resonance (NMR): The biosynthesized cyanogenic glucoside compounds linamarin and lotaustralin were identified and quantified from the H. erato samples using nuclear magnetic resonance (NMR). For details of the NMR method, see Mattila et al. 2021 (https://doi.org/10.7717/peerj.11523)\n\nMetabolic rates: Measures of resting- and flight metabolic rates of the study individuals were acquired using flow-through respirometry (see Niitepõld et al. 2009, https://doi.org/10.1890/08-1498.1) For the measurement, the individual was placed in a 1-L transparent respirometry chamber, through which CO2-free dry air was pumped at the rate of 1.04 L/min. After acclimatization in the darkened measurement chamber for ~30 min, resting metabolic rate (RMR) was measured during one minute of complete immobility. The individual was then stimulated to fly for 10 min in the exposed chamber, and the highest peak value in the CO2 emission rate during the flight was used as a measure of flight metabolic rate (FMR). This measure is expected to represent the maximal flight performance of an individual butterfly. Metabolic rate generally scales positively with body mass, and in intraspecific comparisons, this effect should be accounted for (e.g., Kleiber 1947). To remove the effect of body mass on FMR and enable the examination of mass-independent FMR differences, the residual from a linear model of FMR against body mass was used, calculated separately for females and males. In addition to body mass, measurement temperature and time of day of measurement were controlled for.\n\n \nDESCRIPTIONS OF THE RNASeq DATASETS:\n\nRNASeq dataset 1: \n•\tMain question: Genes associated with cyanogen biosynthesis and cyanogen-flight metabolic rate trade-off\n•\tSequenced samples: 54 H. erato (27 males, 27 females) with phenotype data of 1) biosynthesized cyanogenic toxicity (based on NMR analysis), 2) resting- and flight metabolic rates.\n•\tTwo sequencing runs: run 1 and run 2. Additional run 2 was done because of low coverage in some samples in run 1. Preliminary choice of higher quality sequencing run has been made per sample, provided in the associated phenotype data file.\n•\tRaw data of RNASeq dataset 1, sequencing run 1, found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset1_54 samples sequencing run 1”\n•\tRaw data of RNASeq dataset 1, sequencing run 2, found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset1_54 samples sequencing run 2 additional sequencing”\n\nRNASeq dataset 2:\n•\tMain question: Verification of cyanogen biosynthesis-associated differentially expressed genes and testing family effect\n•\tSequenced samples: 37 H. erato females across 8 full-sibling families with phenotype data of biosynthesized cyanogenic toxicity (based on NMR analysis).\n•\tRaw sequence data of RNASeq dataset 2 found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset2_37 females across 8 full-sibling families”\n\n\nDESCRIPTIONS OF ASSOCIATED DATASETS:\n\nIndividual and phenotype data: Basic information of study individuals, including origin, sex, mass, eclosion date and values of measured phenotypes.\n•\tDatafile names: \no\t“Heliconius erato cyanogenic glucoside toxicity RNASeq_dataset1_phenotypes.csv”\no\t“Heliconius erato cyanogenic glucoside toxicity RNASeq_dataset2_phenotypes.csv”\n\nNMR data: \n•\tProcessed NMR data found in datafile named: “Heliconius erato cyanogenic glucoside concentration data from NMR.csv”\n•\tRaw nuclear magnetic resonance data of 904 H. erato individuals found in data folder named: “Heliconius erato NMR data”\n•\tThese data also include NMR data of individuals sequenced for RNASeq datasets 1 and 2."
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