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"fi": "Ravintola-alalla on keskeinen rooli kestävän ruoan kulutuksen edistämisessä tarjoamalla kestäviä ateriavaihtoehtoja ja muokkaamalla kuluttajien mieltymyksiä. Edistääkseen ravintoloiden ilmastotyötä Luonnonvarakeskus (Luke) on tuottanut avoimen ilmastovaikutusdatan, joka kattaa yli 1200 raaka-ainetta. \nAineisto kattaa Suomen elintarvikkeiden koostumustietopankki Finelin sisältämien yleisten raaka-aineiden ilmastovaikutukset. Aineisto on tuotettu elinkaariarvioinnilla (Life Cycle Assessment, LCA), jolloin ilmastovaikutukset kattavat raaka-aineiden elinkaaren aikana syntyneet kasvihuonekaasupäästöt maatalouden tuotantopanosten valmistuksesta aina tukkukauppaan asti. Aineistossa on huomioitu sekä kotimainen että ulkomainen tuotanto, ja julkaistu datasetti sisältää näiden ilmastovaikutusten painotetun keskiarvon.\nSysteemin rajat kattavat vaikutukset kehdosta tukkukauppaan ja aineiston toiminnallinen yksikkö on 1 kilogramma tuotetta, sekä 100 grammaa tuotetta. Ilmastovaikutukset on arvioitu käyttämällä IPCC:n kuudennen arviointiraportin mukaisia GWP (100) -kertoimia. Aineisto koostuu kahdesta eri laatuisesta datasta; tässä hankkeessa lasketuista geneerisistä raaka-ainekohtaisista ilmastovaikutuksista (noin 500 tuotetta) sekä tämän aineiston pohjalta lasketuista tuoteryhmäkohtaisista keskiarvoista (noin 700 tuotetta). Datan laatu on ilmoitettu erikseen jokaisen tuotteen kohdalla.\nAineiston tunnistenumerot ja nimikkeistö on yhteensopiva Terveyden ja hyvinvoinnin laitoksen ylläpitämän elintarvikkeiden kansallisen koostumustietopankki Finelin kanssa. Tarkempi kuvaus aineiston tuotannon taustoista ja menetelmistä on julkaistu erillisessä raportissa. \n\n### Muuttujat\n\n| Muuttuja | Kuvaus |\n| ---- | ---- |\n| FOODID | Raaka-aineen tunnistenumero, vastaa Finelin (THL, 2019) tunnistenumeroa |\n| FOODNAME_FI | Raaka-aineen nimi suomeksi, vastaa Finelin (THL, 2019) nimeä |\n| FOODNAME_EN | Raaka-aineen nimi englanniksi, vastaa Finelin (THL, 2019) nimeä | \n| FOODNAME_SV | Raaka-aineen nimi ruotsiksi, vastaa Finelin (THL, 2019) nimeä | \n| FUCLASS | Ruoankäyttöluokka, vastaa Finelin (THL, 2019) luokkaa | \n| IGCLASS | Raaka-aineluokka, vastaa Finelin (THL, 2019) luokkaa | \n| FUCLASS_substitute | Korvaava ruoankäyttöluokan selite, jos kategorialla ei ole keskiarvoa, mitä on käytetty tilalla | \n| kgCO2-eq/kg | Raaka-aineen ilmastovaikutus ilmoitettuna hiilidioksidiekvivalenttikiloina kiloa raaka-ainetta kohden | \n| gCO2-eq/100 g | Raaka-aineen ilmastovaikutus ilmoitettuna hiilidioksidiekvivalenttigrammoina 100 grammaa raaka-ainetta kohden | \n| Data quality | Datan laatu, jossa Product tarkoittaa tuotekohtaista geneeristä ilmastovaikutusta ja Category tuoteryhmän ilmastovaikutuksen keskiarvoa | \n| Average_source | Kertoo perustuuko keskiarvo IGCLASS vai FUCLASS -luokitukseen |"
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"en": "The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.",
"fi": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>",
"und": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>"
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"en": "Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.",
"fi": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>",
"und": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>"
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"en": "General information:\r\nTitle of the Paper: \"Direct and Indirect Effects of Fungicides on Growth, Feeding, and Pigmentation of the Freshwater Detritivore Asellus aquaticus\"\r\nAuthors: Akshay Mohan, Blake Matthews, Katja Räsänen\r\nDate of Data Collection: July 2022 - October 2022\r\nFunder: University of Jyväskylä\r\nPublication Date: 15.10.2024\r\nDOI of the Associated Paper: 10.1016/j.ecoenv.2024.117017\r\n\r\nOverview of the Dataset:\r\nThis dataset comprises two primary files used in the analysis of fungicide effects on Asellus aquaticus:\r\nRangefinding.csv: The file contains results from a range-finding experiment where three fungicides were tested at different concentrations.\r\nTebuconazole-Expt.csv: The file ncludes results from the direct and indirect exposure experiment, where Asellus aquaticus was exposed to Tebuconazole through both water and diet.\r\n\r\n\r\nData Files and Structure:\r\nThe file “Rangefinding.csv” contains the results of the range-finding study. It has the following columns:\r\nNumber: Unique identifier for each entry.\r\nImage_name: Filename of the image taken for each isopod.\r\nPlate_number: Identifier for the experimental plate used.\r\nFungicide: The type of fungicide used (e.g., Tebuconazole).\r\nConcentration: Fungicide concentration (µg/L).\r\nInitial_size, Final_size: Measured body length in cm (using FIJI v1.54d) of individuals before and after exposure.\r\nGrowth_rate: Calculated growth over the study.\r\nInitial_leaf_area, Final_leaf_area: Measured area of leaf discs in mm2 (using FIJI v1.54d) before and after exposure.\r\nFeeding_rate: Rate of leaf consumption during the study.\r\nSurvival: Binary (1 = alive, 0 = dead).\r\n\r\n\r\nThe file “Tebuconazole final data mod.csv” contains results from the direct and indirect exposure study. It has the following columns:\r\nNumber: Unique identifier for each entry.\r\nImage_name: Filename of the image taken for each isopod.\r\nPlate_number: Identifier for the experimental plate used.\r\nTreatment: Combination of diet and exposure.\r\nDiet: Specifies the diet treatment conditions.\r\nExposure: Specifies the exposure conditions.\r\nInitial_area, Final_area: Measured body area in mm2 (using Phenopype v3.3.4) of individuals before and after exposure.\r\nGrowth_rate: Calculated growth over the study period.\r\nInitial_leaf_area, Final_leaf_area: Measured area of leaf discs in mm2 (using FIJI v1.54d) before and after exposure.\r\nFeeding_rate: Rate of leaf consumption during the study.\r\nPigmentation_Initial, Pigmentation_final: Measured pigmentation values (using Phenopype v3.3.4) before and after exposure.\r\nPigmentation_rate: Change in pigmentation measured over the study period.\r\nSurvival, Moulting: Binary indicators of survival and molting.\r\n\r\nMethodology:\r\nData Collection:\r\nIn both experiments, individual Asellus aquaticus were exposed to varying concentrations of fungicides, and the response variables were measured weekly using digital photographs and analyzed using image analysis softwares.\r\n\r\nData Processing:\r\nGrowth rates were calculated based on the differences between initial and final sizes, and feeding rates were based on leaf area consumed. Pigmentation was measured using grayscale values.\r\n\r\nAccess and Licensing:\r\nThe dataset is licensed under [CC BY 4.0], allowing reuse with proper attribution.\r\nDOI for the dataset will be available after publication on JYX.\r\n\r\nContact Information:\r\nFor further details or inquiries, contact:\r\nAkshay Mohan - akmohank@jyu.fi\r\nBlake Matthews - blake.matthews@eawag.ch\r\nKatja Räsänen - katja.j.rasanen@jyu.fi"
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"en": "In this table we present the coordinates all-sky cameras of The Fireball Recovery and Inter Planetary Observation Network (FRIPON) (Colas et al., 2020) installed over the Romanian territory with the use of the Meteorite Orbits Reconstruction by Optical Imaging (MOROI) network (Nedelcu et al. 2018). Those cameras were installed over the course of the year 2021.\n\nThe detection of meteors is made with the use of fish-eye lens with the focal length of 1.25 mm and the detector Sony ICX445.\n\nDetections made by the FRIPON-MOROI cameras over the time period January 2021 - April 2022 are characterised in (Boaca et al. 2022) (https://iopscience.iop.org/article/10.3847/1538-4357/ac8542 ).\n\nIf using this data, please reference the above publication. \n\nThe online presentation of the paper can be found on the AAS youtube channel\n\n(https://www.youtube.com/watch?v=Wx186QB5ZVE).\n\n**Notes**\n\nThe work of IB and MB was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2019-0784, within PNCDI III. The work of AN was supported by a grant of the Ministry of National Education and Scientific Research, PNIII-P2-1214/25.10.2021, program no. 36SOL/2021.\n\n**References**\n\nColas, F., Zanda, B., Bouley, S., Jeanne, S., Malgoyre, A., Birlan, M., Blanpain, C., Gattacceca, J., Jorda, L., Lecubin, J., and et al. (385 more) (2020). FRIPON: a worldwide network to track incoming meteoroids. Astronomy and Astrophysics, 644:A53.\nNedelcu, D. A., Birlan, M., Turcu, V., Boaca, I., Badescu, O., Gornea, A., Sonka, A. B., Blagoi, O., Danescu, C., and Paraschiv, P. (2018). Meteorites Orbits Reconstruction by Optical Imaging (MOROI) Network. Romanian Astronomical Journal, 28(1):57–65.\nBoaca, I., Gritsevich M., Birlan M., Nedelcu, A., Boaca, T., Colas, F., Malgoyre, A., Zanda, B., Vernazza, P. (2022). Characterization of the Fireballs Detected by All-sky Cameras in Romania, The Astrophysical Journal, 936, 150."
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