Find research datasets worth reusing

opencc-zeroAug 2024View details →

National Checklists 2019: 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 details.A list of species from Finland collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

Water Body Checklists: Gulf of Finland Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Gulf of Finland using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

opencc-by-4.0Aug 2024View details →

User survey data of learning environment eTKMY3, Turku School of Economics, Finland.

User survey data of learning environment (eTKMY3) of an introduction to statistics course, Turku School of Economics.  Variables question_11_row_1                  Starting eTKMY3 was difficult question_11_row_3                  eTKMY3 is a successful system question_11_row_4                  I can manage my studying and exercises easily question_11_row_5                  Navigation was easy question_11_row_7                  eTKMY3 was complex question_12_row_8                  individual starting values of most exercises as a good way to promote independent working Observations: Students of Turku School of Economics taking the course "TKMY3 Introduction of Statistics", Spring 2024.

opencc-by-4.0Dec 2022View details →

Soil gas concentration and flux data from two peatland forest harvesting experiments in Southern Finland

The dataset contains data analyzed in a scientific manuscript Peltoniemi et al., Soil CO2, CH4 and N2O concentrations and fluxes in peatland forests are associated with water table level - implications of selection harvesting on soil emissions, in review. The data was collected from two peatland forests. The sites Lettosuo (60.63° N, 23.95° E ) and Paroninkorpi (61.01°N, 24.75°E) locate in southern Finland, and they are fertile drained spruce mires. Both sites have unharvested control and selection harvested treatments. The dataset contains CO2, CH4, N2O and O2 concentrations analyzed by gas chromatograph from soil gas samples collected with silicon rubber tubes inserted into soil at different depths over the year 2018-2020. The dataset additionally contains soil gas flux measurements in 2020 with a portable gas analyzer. Air temperature and water table level were measured at both sites, rainfall in nearby meteorological stations, and soil redox potential at one of the sites (Paroninkorpi). More information about the research sites and data is available in the manuscript, and Laurila et al., 2020 (<a href="http://urn.fi/URN:ISBN:978-952-380-191-2">http://urn.fi/URN:ISBN:978-952-380-191-2</a>).

opencc-by-4.0Feb 2023View details →

Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).

Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode). Accessing the GOLDENAI GUI, please refer to the following link (login required): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a>

opencc-by-4.0Feb 2023View details →

Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland

A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.

opencc-by-4.0May 2021View details →

FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 3: Interviews, Finland.

Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing. As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper. To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character > marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context. 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, Elina Palmgren (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). Future_technology_agency_DATA_CSV.csv Future_technology_agency_DATA_XLSX.xlsx   Data set Documentation Given above this README, on the ZENODO repository. https://zenodo.org/record/4734161

opencc-by-4.0Aug 2023View details →

Regional greenhouse gas net emission intensities by land cover category in Finland

The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing. Data description Data format. The data are distributed as GeoTiff raster files, which can be read using most GIS-software. Units and definitions. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO2 considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO2-eq m-2) based on the 100-year global warming potential as reported in the IPCC 5th assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or no data, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO2 or N for N2O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023). Data processing. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022). Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m2 with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see Rivers below). The buffer was created without caps so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts. For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities. Thematic layers Cropland. CO2 emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017). Data files: <ul> <li>Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code></li> <li>Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code></li> </ul> Forests. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2015</a>) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025. CO2 emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO2 emissions due to peat and litter decomposition, the soil emissions include the CH4 and N2O emissions. The net emissions due to CO2, CH4 and N2O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type). Data files: <ul> <li>Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code></li> <li>Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code></li> </ul> Lakes. Emissions of CO2 and CH4 were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO2 evasion (Kortelainen et al. 2006), CH4 diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH4 emissions due to the macrophytes Phragmites australis and Equisetum fluviatile (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute. Data files: <ul> <li>Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code></li> </ul> Rivers. CO2 emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers < 5 m wide as linear features. For rivers < 5m wide, an average width of 3.5 m was assumed. Data files: <ul> <li>Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code></li> </ul> Undrained mires. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH4 (Minkkinen and Ojanen 2013), CO2 (Sallantaus 1994 , Turunen et al. 2002), and N2O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH4 and from N2O peatland. Peatland sites were extracted from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2019</a>; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013). Data files: <ul> <li>Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code></li> </ul>

opencc-by-4.0Dec 2022View details →

The long term series of characteristics of floods that happened to 12 rivers in Finland and northern Russia.

The dataset of flood’s characteristics (annual and spring): the volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date were estimated for each year from the daily series of water discharges observed at the hydrometric sites. To define the dates of spring flood begin and end we applied the semi-empirical method given in Shevnina (2013). The yearly maximum water discharges have been obtained in Gudmundsson et al. (2018) for the period until 2017; this dataset gives a good agreement in the estimations for the overlapping periods. The series of volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date are given in the dataset supplementing the study submitted to the Water resource research journal (<a href="https://agupubs.onlinelibrary.wiley.com/journal/19447973">https://agupubs.onlinelibrary.wiley.com/journal/19447973</a> ).  The daily series of water river discharges at the sites located in Finland were extracted  from (a) the Global runoff database <a href="https://portal.grdc.bafg.de/">https://portal.grdc.bafg.de/</a> (for the period from beginning of the observations to 2017); (b) the archive of the Finnish Environmental Institute <a href="https://www.syke.fi/">https://www.syke.fi</a> (for the period  2018–2020) and these series can be obtained after its representatives’ permission from the author. The daily series of water discharges at the sites located in the Russian Federation were extracted from (a) the yearly hydrological books published by the State Hydrological Institute <a href="http://www.hydrology.ru/en">http://www.hydrology.ru/en</a>  (for the period from the beginning of observation to 2007); (b) the automated information system for state monitoring of water bodies <a href="https://gmvo.skniivh.ru/">https://gmvo.skniivh.ru/</a> (for the period 2008–2020) and these series are available from its web-site after a registration.   The dataset consists of the CSV/TXT files, each file contains the long term series of the characteristics listed in the header: "year", "DFB" (date when a spring flooding period begins, day of year, DOY),"DFE" (date when the spring flooding period ends, DOY),"Length" (length of the spring flooding period, days), "DFMax" (date when the yearly maximum water discharge is recorded, DOY), "Qmax" (the yearly maximum water discharge, cubic m per second), "FRD" (the volume of spring flood expressed in mm per flooding period), "YRD" (volume of annual flow, expressed in mm per year),"Ftype" (the source of annual flood equaling to 1 of the yearly maximum water discharge is recorded in the spring flooding period or 0 if it is not).  The dataset was obtained in the study funded by the Academy of Finland under the contract number 317999. It will become freely available once the manuscript is published.  References Gudmundsson, L., Do, H. X., Leonard, M., & Westra, S. (2018), The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018. Shevnina E. (2013), Method to calculate characteristics of spring flood from daily water discharges, Problems of the Arctic and Antarctic, 1(95), pp. 12-21. In Russian

opencc-by-4.0Oct 2023View details →

HPD experiments -- DryDemag cryostat, Low-temperature laboratory, Aalto University, Finland, 2018

Data related to NMR experients in superfluid 3He-B in DryDemag cryostat in 2018.The main part is oscilloscope signals and spectrograms of HPD (homogeneously precessing domain) where we have found spatially-localised oscillation modes. We attribute some of these modes to oscillations of theta-solitons in HPD.Files:data2018_dd_hpd_1.tgz -- All data except oscilloscope signals (2.0Gb).data2018_dd_hpd_2.tgz  -- Oscilloscope signals (5.9Gb), archive is splitted into three parts with split(1) program.

opencc-by-4.0Jan 2020View details →

Education statistics 1970 - 2012 province of Uusimaa, Finland

Excel worksheet for project internal use. (http://tilastokeskus.fi/meta/til/kjarj.html TARGET=_blank) Kuvaus  (http://tilastokeskus.fi/til/kjarj/kas.html TARGET=_blank) Käsitteet määritelmät  (http://tilastokeskus.fi/til/kjarj/laa.html TARGET=_blank)  Laatuseloste

opencc-by-4.0Jan 2020View details →

Suomen tunnetut ukonvaajat / Thunderbolts in Finland

Artikkelin "Ukonvaajojen monet kasvot –  Luokittelu- ja tulkintakysymyksiä" aineisto kokonaisuudessaan. Supplementary data of the paper "The Many Faces of Thunderbolts – Questions of Classification and Interpretation" (in Finnish). Julkaistu / Published in: Harjula, J., Immonen, V. & Ruohonen, J. 2019: Puukenkien kopinaa. Henrik Asplundin juhlakirja. (Karhunhammas 19). Turku: Turun yliopiston arkeologia. (pp. 345-381)

opencc-by-4.0Dec 2020View details →

AI results complementing the 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 2019, 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-zeroOct 2016View details →

LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.

INTRO This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (http://fi.okfn.org/projects/biodiversity-map/) LICENCE The files listed below are all released to the public domain under a CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/) FILE DESCRIPTIONS FILE 1: background.zip Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland. FILE 2: conservation.lambdas This file contains fitted parameters for the maxent model. For more information, check maxent documentation at https://www.cs.princeton.edu/~schapire/maxent/ FILE 3: conserved_swd.csv Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv FILE 4: grass_create_forest_rasters_from_las.sh A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7. FILE 5: lidar_coverage.png A map showing the extent of LiDAR data available for Central Finland when we did the analyses. FILE 6: maxent_model_run_product.sh A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland. FILE 7: projection_product.csv The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area". FILE 8: README A file with a description of the dataset in human-readable form. VALIDATION FILES The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints. The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste). FILE 9: validation_felled.csv A comma separated list of those points that were not measured because they were felled. FILE 10: validation_gps_results_2016-09-07.csv A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above. FILE 11: validation_lying_deadwood_transects_2016-08-30.csv A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk. FILE 12: validation_relascope_2016-08-30.csv Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately. MORE INFORMATION For more in-depth descritions of the files, read the file named README. For some auxilliary files and information, check our old hackathon repository on github: https://github.com/Koalha/bdm_hackathon

Dataset for "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland"

This is the dataset (ver. 2017.02.13) for the manuscript "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland" submitted to the journal Atmospheric Chemistry and Physics.

Figure 4. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610

Figure 4. - Euphydryas maturna habitat in a commercial, thinned pine-dominated forest with ca. 30-year old trees, and in a clear-cut edge. This kind of forest habitat is probably suitable after thinning for several years, but longer than spruce-dominated forests (Fig. 3). Also, edge habitats in these relatively dry habitats overgrow somewhat slower than in moister edges (Fig. 2).

Figure 5. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610

Figure 5. - Powerline habitat of Euphydryas maturna. Vegetation under powerlines is kept open continuously, so powerline habitats may function both as breeding places and dispersal corridors.

Figure 3. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610

Figure 3. - Euphydryas maturna habitat in a commercial, thinned spruce-dominated forest. Such habitats are probably suitable after thinning for several years.

Figure 2. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610

Figure 2. - Clear-cut edge habitat of Euphydryas maturna. Clear-cut edges typically remain suitable for breeding for some years only until they become overgrown by tall grasses and tree seedlings.

Figure 1. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610

Figure 1. - Larval web of Euphydryas maturna on Melampyrum sylvaticum in Sipoo, S Finland (November 2nd, 2014).