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opencc-by-4.0Nov 2023View details →

Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)

Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland Creators: Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-Sphagnum- dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). Peat analyses The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).Stoichiometric calculationsThe O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). Related datasets used in the same publication are:Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in FinlandTurunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a> Data column description ID - Site identifiersite - undrained peatland (UDP) for all rowsncoord - North coordinate (latitude), degrees.depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.type - Site type classification according to the Finnish peatland site type system.origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.Cmol - Molar carbon concentration in the sampleHmol - Molar hydrogen concentration in the sampleNmol - Molar nitrogen concentration in the sampleOmol - Molar oxygen concentration in the sampleSmol - Molar sulphur concentration in the samplebd - Bulk density, kg/m3cox - C oxidation stateor - Oxidative ratiodu - Degree of unsaturationhc - H:C ratiocn - C:N ratiooc - O:C ratioReferencesMasiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. manuscript.

opencc-by-4.0Nov 2023View details →

Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland

Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in FinlandCreators: Larmola T, Anttila J, Alm J The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006).  The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). The site selection criteria were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. Peat analyses Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. Stoichiometric calculationsThe O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). Related datasets used in the same publication are:Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/10.5281/zenodo.10068486Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a> Data column descriptionID - Site identifiersite - Forestry-drained peatland (FDP) for all rowsncoord - North coordinate (latitude), degrees.depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.type - Site type classification according to the Finnish peatland site type system.origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.Cmol - Molar carbon concentration in the sampleHmol - Molar hydrogen concentration in the sampleNmol - Molar nitrogen concentration in the sampleOmol - Molar oxygen concentration in the sampleSmol - Molar sulphur concentration in the samplebd - Bulk density, kg/m3cox - C oxidation stateor - Oxidative ratiodu - Degree of unsaturationhc - H:C ratiocn - C:N ratiooc - O:C ration - Number of samples. 2 for averages from both depths, 1 for all other rows. ReferencesHotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. manuscript.

opencc-by-4.0May 2023View details →

Raw Particle Number Size-Distribution Data of twin-DMPS equipped with two CPCs for nanoparticle detection for SMEAR II station, Hyytiälä, Finland, Spring 2017

Raw size-Distribution data from twin-DMPS system (Aalto et al., 2001), where the nano-DMA (measuring up to 40 nm, short Hauke type DMA) is quipped with two detectors: a TSI 3776 and a modified Airmodus A20 (Kangasluoma et al., 2015) Data acquired during in March-May 2017 at the SMEAR II station in Hyytiälä, Finland. Data associated with the publication Stolzenburg, Laurila et al. (2023), Atmos. Meas. Techn., "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system" Files DMYYDDMM_A20.Dat contain the raw DMPS data, with YYMMDD indicating the day of the measurement. Data are provided alternating between data acquired with the nano-DMA and with the long-DMA, on a scan by scan basis. First line of each scan cycle (for both DMAs) always indicates the start and end times of the voltage scan. Second line gives the parameters related to the DMPS as given below: (sheath flow in [l per min], aerosol flow in [l per min], DMA inner electrode diameter in [m], DMA outer electrode diameter in [m], DMA classification length in [m], other parameters) Following lines give (for long-DMA): set voltage at DMA [in V], concentration measured by TSI3772 in [per cm3] (for nano_DMA): et voltage at DMA [in V], concentration measured by TSI 3776 in [per cm3], concentration measured by mod. Airmodus A20 in [per cm3] File dmps_data_format_specifier.text gives a conversion from voltage to diameter and indicates the measurement time at each voltage during the stepping of the DMPS. Needs to be used to convert measured concentrations in counts per set-interval. Files GR_J_overview.xlsx gives size-distribution derived quantities during that campaign. Header defines Date, Growth Rate and Formation Rate measured at different sizes [in nm] and by the two different CPCs connected to the nano-DMA. Growth rates in [nm per h], formation rate in [per cm3 per s]. Other data related to the campaign can be obtained from the corresponding author upon reasonable request. juha.kangasluoma@helsinki.fi References: Stolzenburg, Laurila et al. "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system", Atmos. Meas. Techn., in press, 2023 Aalto et al., "Physical characterization of aerosol particles during nucleation events", Tellus B, vol. 53, pp. 344-358, 2001 Kangasluoma et al., "Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters", Aerosol Sci. Techn., vol. 49, pp. 674-681, 2015

opencc-by-4.0Oct 2023View details →

Climate change velocity metrics calculated for three climate variables across Finland

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-81 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and *.tif.aux.xml) in the dataset: (a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.*(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*Note that velocity surfaces e and f include disappearing climate conditions.Summary of the study:Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.Calculation of climate change velocity metrics for the three climate variablesThe overall process of calculation of climate velocities included three main steps. (1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et al. 2020). The resulting topoclimate data effectively captured the physiographic effects of solar radiation and cold-air pooling.To produce gridded precipitation data, we applied global kriging interpolation to the data from 343 rain gauges from the ECA&D dataset. The interpolation was carried out using information on geographical location, topography (elevation and eastness index) and proximity to the sea and R package gstat. The eastness index was obtained from a sine-transforming aspect raster surface calculated from a 50 m × 50 m digital elevation model to capture the effect of prevailing westerly winds on the accumulated precipitation on windward slopes. The gridding was first run at a resolution of 500 × 500 m, whereafter gridded precipitation values were bilinearly interpolated into the same 50 × 50 m resolution as the air temperature data. Next, the three bioclimatic variables ((i) growing degree days (GDD, °C days) indicating the accumulated warmth during the growing season; (ii) mean January air temperature -  TJan, °C; (iii) climatic water balance - WAB, mm) were calculated for each 50 x 50 grid cell from the high-resolution gridded 1981–2010 ('baseline') climate data. Earlier research has demonstrated the ecological relevance of these three complementary variables which provide estimations of winter cold, seasonal warmth and moisture availability (Sykes et al. 1996; Luoto et al. 2006; Huntley et al. 2007, 2008). Following Carter et al. (1991), GDD was calculated as the effective temperature sum above the base temperature of 5 °C as follows:GDD5 = ∑ni (Ti - Tb),  if Ti -Tb > 5where Ti denotes the mean temperature at day i, Tb represents the base temperature, and n is the length of the summation period. However, because the daily air temperature data was not available, here the GDD was estimated using monthly data as in Araújo & Luoto (2007). The WAB is the difference between the total annual precipitation sum and the potential evapotranspiration (PET), which was estimated from the monthly air temperatures following Skov and Svenning (2004): PET = 58.93 × Tabove 0°C / 12(2) In the second step we developed data on future climates by using the climate projections from the ensemble of 23 global climate models (GCMs), derived from the Coupled Model Intercomparison Project phase 5 archives (Taylor et al. 2012). From these archives, we processed to predicted averaged changes in mean temperature and precipitation with respect to the baseline 1981–2010 for the years 2070–2099, and the three RCP scenarios (cf. Moss et al. 2010). As the Coupled Model Intercomparison Project phase 5 climate scenario data represent coarse-scale resolution data, we converted it to match our fine-resolution baseline climate data by interpolation. For this, the climate model data depicting the predicted change in mean temperatures and precipitation with respect to the baseline climate were bilinearly interpolated to the 50 × 50 m grid system, and the change predicted by the GCMs was added to the spatially detailed baseline climate data. After this, the bioclimatic variables were recalculated for each RCP scenario to allow the calculation of climate change velocities across the whole country and the Natura 2000 protected areas.(3) In the third step we developed climate change velocities for the three bioclimatic variables using the climate-analog approach (Hamann et al. 2015) where velocity is calculated by measuring the distance between present-day locations with certain climatic conditions and their future climate analogues, divided by the number of years between the two points in time. Thus, we calculated climate-analog velocities for the 50-m resolution grid climate data by measuring the distance between climatically similar grid cells for the present and future climates under RCP2.6, RCP4.5 and RCP8.5. Prior the actual climate-analog velocity measurements, the climate variable surfaces were converted from continuous values into classified variable surfaces. For this, we defined the boundary values for the variable classes so that the climatically matching grid cells had their within-class ranges as small as possible but, at the same time, avoided artefactual extreme precision. After a set of pilot reclassifications, the following within-class ranges were applied: GDD, within-class range 50 °C with 51 categories; TJan, within-class range 0.5 °C with 60 categories; WAB, within-class range 50 mm with 55 categories. Next, using the reclassified present-day and future climate surfaces the search of the minimum distances between grid cells with similar present-day and future GDD/TJan/WAB climates were executed. The search was carried out using the ArcGIS software (Desktop 10.5.1.) by employing the Euclidean distance function. The minimum distances measured for each 50-m grid cell were divided by the difference between the mean points in the two time slices, 1981–2010 and 2070–2099. The resulting 50-m resolution climate velocity surfaces for the three climate variables are provided in the zipped files included this data repository. In Heikkinen et al. (2020), these climate velocity data were employed in a series of subsequent analyses. For example, high-velocity areas ('velocity hotspots') of the three climate variables were visually compared with each other based on maps showing their 50-m resolution velocities across mainland Finland and the degree of overlap between the present-day range and projected future range of the three climate variables were investigated in each of the 5,068 Natura 2000 polygons included in the study.ReferencesAalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M., 2017. Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R., Kraft, N.J.B., 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16, 476-487.Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E., 2020. Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and the Environment 18, 288-297.Araujo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16.Batllori, E., Parisien, M.-A., Parks, S.A., Moritz, M.A., Miller, C., 2017. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Global Change Biology 23, 3219-3230.Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J., 2018. Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.Carter, T.R., Porter, J.H., Parry, M.L., 1991. Climatic warming and crop potential in Europe: Prospects and uncertainties. Global Environmental Change 1, 291-312.Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J.A., Mynsberge, A.R., Holden, Z.A., Schwartz, M.K., 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19, 241-251.Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., Hannah, L., 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19, 473-483.Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E., 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004. Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R., 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678.Huntley, B., Green, R.E., Collingham, Y.C., Willis, S.G., 2007. A climatic atlas of European breeding birds. Durham University, The RSPB and Lynx Edicions, Barcelona.Huntley, B., Collingham, Y.C., Willis, S.G., Green, R.E., 2008. Potential Impacts of Climatic Change on European Breeding Birds. Plos One 3.Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. International Journal of Climatology 29, 1182-1191.Lenoir, J., Hattab, T., Pierre, G., 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253-266.Luoto, M., Heikkinen, R.K., Pöyry, J., Saarinen, K., 2006. Determinants of biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 33, 1764-1778.Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747-756.R Development Core Team, 2011. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing).Skov, F., Svenning, J.-C., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.Sykes, M.T., Prentice, I.C., Cramer, W., 1996. A bioclimatic model for the potential distributions of north European tree species under present and future climates. Journal of Biogeography 23, 203-233.Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society Series B 73, 3-36.

opencc-by-4.0Jan 2021View details →

Water vapor isotope data from Pallas-Yllastunturi National Park, Finland (Winter 2017-18)

Calibrated water vapor isotope and mixing ratio data from Pallas-Yllastunturi National Park, Finland. Site Name: Sammaltunturi Station, Finland (Finnish Meteorological Institute)         Site Location:    67.973°N; 24.116°E          Site Elevation:    565 m above sea level             Instrumentation: Picarro L2130-i Isotope and Gas Concentration Analyser Parameters: δ18O water vapor, δ2H water vapor, deuterium (d)-excess water vapor, mixing ratio (5-minute averages) Date/Time start: 20/12/2017 05:45 EET Date/Time end: 31/03/2018 23:55 EET

opencc-by-4.0Sep 2023View details →

Historical Travel and Communications in Finland

This dataset contains a proof-of-concept GIS database of over 29,000 individual historical road polyline segments as a shapefile dataset, covering over 11,000 km2 in the western Finland from the city of Turku to northern parts of the province of Satakunta. These polylines capture the regional layout of the overland transport infrastructure of late nineteenth and early twentieth century Finland.

opencc-by-4.0Dec 2021View details →

Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland

Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript  "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland". Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2. The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.

opencc-by-4.0Mar 2023View details →

Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland

This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland" (Heikinheimo et al. 2023) and used in the research article "Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions" (Viinikka et al. 2023).  This repository contains data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.   Data description article:  Heikinheimo, V., Tiitu, M., & Viinikka, A. (2023). Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland. Data in Brief, 50, 109458. <a href="https://doi.org/10.1016/j.dib.2023.109458">https://doi.org/10.1016/j.dib.2023.109458</a> Related research article:  Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. Applied Geography, 157, 102973. <a href="https://doi.org/10.1016/j.apgeog.2023.102973">https://doi.org/10.1016/j.apgeog.2023.102973</a>

opencc-by-4.0Jun 2020View details →

Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland

This repository contains files that show optimal sites in Central Finland for the northern goshawk (Accipiter gentilis, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:   Björklund Heidia, Parkkinen Anssib, Hakkari Tomic, Heikkinen Risto K.d, Virkkala Raimod, Lensu Anssib (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .    a Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland b University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland c Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland d Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland   The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)).  Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .   Summary of the study Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (Picea abies, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization. The study species The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings. Developing Maxent models for the goshawk The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016. Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand. The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92). Maxent model outputs Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020). State of the optimal goshawk squares In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019. In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.

opencc-by-4.0Nov 2022View details →

Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Finland

This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001. Reporting authorities contributing to each data collection: <ul> <li>TSE_2023_FI - Finnish Food Authority (Evira)</li> <li>TSE_2022_FI - Finnish Food Authority (Evira)</li> <li>TSE_2021_FI - Finnish Food Authority (Evira)</li> <li>TSE_2020_FI - Finnish Food Authority (Evira)</li> </ul>

opencc-by-4.0Jan 2022View details →

Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland.

Dataset includes increment core data from Lettosuo drained peatland forest site. The study site locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores were analysed for the ring widths for dominant and suppressed Norway spruce trees, and for the ring δ13C values from suppressed Norway spruce trees. Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Academy of Finland.    Sampling for increment cores was done during October 2020 for sample trees (10 in total, of which 5 were suppressed trees from thinned area and 5 suppressed trees from control area) and additional sampling was conducted for annual diameter increment for 3 tree groups to increase sample size for diameter growth (suppressed trees in thinned area [n=20], dominant trees in thinned area [n=22] and suppressed trees in control area[n=20]) during March 2021.    Tree ring carbon isotope data   Laser ablation IRMS method was applied in the Stable Isotope Laboratory of Luke (SILL) to quantify δ13C values in 10 increment cores for the time period 2010–2020, following principles of Schulze et al. (2004) and described in Lehtonen et al (manuscript). Up to 11 evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual variation of δ13C of the samples.    (1) File: Lettosuo_d13C.xls File includes d13C measurements   Data column description below for isotope data:    id stands for tree id [id includes tree identity, year and also spot number] year is the year of the tree ring nr is an index for data  tree indicates tree identity "C" for control and "H" for harvest treatment indicates the treatment of the sampling area (control / harvest) d13C gives the measured d13C value based on the LA-IRMS measurements season indicates whether observation originated from the earlywood (EW) or latewood (LW) period, where 1 is EW and 2 is LW   Tree ring width measurements   In addition to the δ13C values, also the ring widths were measured. Here, also additional dominant trees were measured.    (3) Files: controlRW.csv dominantRW.csv thinningRW.csv   Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area.    In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).   References:   Lehtonen A, Leppä K, Sahlstedt E, Schiestl-Aalto P, Heikkinen J, Young G, Korkiakoski M, Peltoniemi M, Rinne-Garmston K, Sarkkola S, Lohila A, Mäkipää R (manuscript). Fast recovery of Norway spruce trees after thinning from above on a drained peatland forest site.   Korkiakoski M, Ojanen P, Penttilä T, Minkkinen K, Sarkkola S, Rainne J, Laurila T, Lohila A (2020) Impact of partial harvest on CH4 and N2O balances of a drained boreal peatland forest. Agric For Meteorol 295:108168.   Schulze B, Wirth C, Linke P, Brand WA, Kuhlmann I, Horna V, Schulze E-D (2004) Laser ablation-combustion-GC-IRMS--a new method for online analysis of intra-annual variation of 13C in tree rings. Tree Physiol 24:1193–1201.

opencc-by-4.0Sep 2021View details →

FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 1: Essays, Finland.

Version 1.1. Updated from https://zenodo.org/record/5517595 Changes: added .csv copy of the dataset. Clarified the README below, and added name of publishing journal. No other changes. Added a FEDORA project README below.   Description of dataset: This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article Students’ technological images of the future: implications for science and technology education, accepted to be published in European Journal of Futures Research. As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article. 385 such excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. The numbering was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A-C). The dataset is intended for providing transparency, but it may also be used for further research. Assistance may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience. To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred. The related research article gives a fuller description of the dataset and analysis. Please contact the corresponding author for more information.   --   <a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:   README Data Set Title: “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland" Data Set Author/s: Antti Laherto, Tapio Rasa, (University of Helsinki) Data Set Contact Person/s: Tapio Rasa (University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi; Data Set License: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Publication Year: 2021 Project Info: FEDORA (Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty , funded by European Union, Horizon 2020 Programme. Grant Agreement num. 872841, www.fedora-project.eu)   Data set Contents The data set consists of: One spreadsheet file, provided in two alternative formats (CSV and XLSX). Students_images_of_technological_futures_DATA_Zenodo_csv.csv Students_images_of_technological_futures_DATA_Zenodo_xlsx.xlsx   Data set Documentation Given above this README, on the ZENODO repository. https://zenodo.org/record/6397196

opencc-by-4.0Jun 2022View details →

Synthetic 3D PPGIS Data _ Turku - Finland

3D PPGIS data generated synthetically in Turku, Finland. Data is created in an approximately 2 km2 area near center. 150X150 m grid cells were used for data generation.

opencc-by-4.0Sep 2022View details →

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Finland

This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of: <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>

opencc-by-4.0Apr 2018View details →

RAKSILA 3D. Laser scanning survey of the street fronts and green areas in Raksila, Oulu (FINLAND)

The video shows the preliminary results of the laser scanner survey of Raksila district in Oulu, Finland. Raksila is an important historical trace in the development of the urban planning of the city of Oulu. The district of Raksila is mainly a well-preserved residential Neighborhood characterized by a strong typicality.The general plan consists of a regular structure and a system of street fronts on the road are ordered and in an homogeneous profile. Despite this, Raksila still has no detailed and updated guidelines capable of managing all different types of interventions allowed (renovation, restoration, repair actions, possible modifications). For this reason, a laser scanner survey and detailed documentation have been created, through which all the elements and characteristics of the place have been defined and collected in sort of atlas and inventory reports. This new documentation is going to constitute the base for the definition of new guidelines, a practical support and analysis for future interventions that can be carried out in total respect of this heritage. This topic is inserted as case study for developing the Research Project n. 746215 entitled "Preserving Wooden Heritage". The project is financed by the European Commission with an Individual Marie S. Curie Fellowship assigned to PostDoctoral Researcher Sara Porzilli, who is working at the University of Oulu - Finland.

opencc-by-4.0Oct 2019View details →

Wave and wind data from the Helsinki archipelago and Gulf of Finland

Data source: Finnish Meteorological Institute This is wave and meteorological data collected in the Helsinki archipelago and GoF durin 2012-2018. Each file contains data and metadata for one location. The Gulf of Finland (GoF) site has a separate file for all integrated data (WaveData_GoF_integrated.nc), while separate files (WaveData_GoF_spectra_2016a.nc etc) exist for the spectra. This is because both a DWR Mk-III and DWR4/ACM wave buoy was used, and they have different sampling frequencies. Coinciding wind data is embedded in each file. The data are described in the publication "The wave spectrum in archipelagos", Ocean Science, 2019, DOI: 10.5194/os-15-1469-2019

opencc-by-4.0Dec 2019View details →

Digital Inventory of the Wooden Neighborhood of Raksila in Oulu, FInland

The material concerns the investigation made on the case study of Raksila, wooden historic neighborhood in Oulu, Finland. For each building it has been elaborated a census card by fixing the information on a sketch book, the analysis has included several specific aspects: dates of construction, function, materials used for the facades, structure, colours of the facades, typologies of frames and analysis of the open areas. For the analysis of Raksila two different types of card have been designed: one type for the residential buildings and another one (more simplified) for the description of the services located around the neighbourhood. The recognition on field represent a fundamental part in the elaboration of the census card, the operator needs to make an appreciation of the main aspects by producing lists of different typologies connected to a same category, i.e. elaborate different lists of values. Categories and list of values represent the main elements in the formulation of the census card. When the analysis is finished the entire categories analysed with the related lists need to be transfer to a digital card by using FileMaker software. This programme gives the possibility to create detailed digital databases by including specific data sets. After the recognition phase and the elaboration of the card in digital form, the operator can start the acquisition of the information on field. Each building is identified with a specific code that can be elaborated by the operator or, as in this case, by using the existing codification system (for the analysis of the buildings in Raksila it has been used the same codification elaborated by the Municipality of Oulu in order to deliver to them an updated material easily consulted). When the acquisition of the information is finished the result is a digital archive which can be navigated and used for different types of purposes. Moreover, the software gives the possibility to recover all those cards, which have the same information or group of information. In the final part, they have been then put in mutual dialogue through the support of a 3D model realized on the base of the laser scanner. This step produced thematic maps and interesting consideration for the understanding of the place with its intrinsic dynamics.

opencc-by-4.0Dec 2019View details →

3D Laser scanning survey of the Rural Farmhouse of Lamminaho in Vaala, FInland

The video shows the results of the laser scanning survey of Lamminaho. The project represents one of the case study chosen for performing the PresWoodenHeritage Marie Curie Project. The survey has been elaborated by using different types of laser scanners and it has been supported by Mitta Company.

opencc-zeroAug 2024View details →

National Checklists 2017: Finland Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Finland collected using effechecka and geonames polygons