Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
586
datasets available to search
ShareScore release 0.9.0
Dataset results
586 results for “Finland”
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Finland
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Finnish Food Authority</p> <p>OCC-CHEMMON2021 – Finnish Food Authority</p> <p>OCC-CHEMMON2022 – Finnish Food Authority</p> <p>OCC-CHEMMON2023 – Finnish Food Authority; Finnish Customs</p> <p>OCC-CHEMMON2024 – Finnish Food Authority; Finnish Customs</p>
Complete PM Compositional Data Set from Kevo, Finland
<p>These are the complete set of chemical composition data from weekly samples collected in Kevo Finland from October 1964 to December 2010</p>
A Gridded Microclimate Dataset from a Sub-Arctic Biodiversity Hotspot in Finland
<p><strong>The dataset comprises 63 spatially continuous microclimate surfaces for the Kilpisjärvi region in northwestern Finland. The study region is a biodiversity hotspot for arctic-alpine flora and fauna and one of the most extensively investigated regions in Northern Europe. The data were gathered through a collaborative network of microclimate loggers, encompassing 430 measurement locations that comprehensively cover the 300 km2 landscape under study. We employed predominantly well-performing Random Forest models to project microclimate variables across the study area at a 3-metre spatial resolution.</strong></p>
Breeding male Ortolan Bunting observations from Finland 2000-2018
<p>Ortolan Bunting male territory observations from Finland from 2000-2018 and R-code used to analyse the association between population growth rate and various environmental and spatial variables. Data includes individual identification number for 238 singing groups (id), the year of the observation (year), number of observed territories within a singing group at the year of the observation (terri), number of observed territories during the previous year (terpre), proportion of agricultural land use within the singing group landscape (agri5), the mean daily precipitation (mm) (prec) and temperature (ºC) (temp) of the previous year summer, Shannon-Wiener diversity index of crop types within the singing group area (shang), proportion of crop types which provide bare ground in spring (bare), density of buildings (build), rivers (river), streams (stream) and roads (road), the municipality (municipality) and the region (region) where the singing group is located.</p>
National Checklists: Finland Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Finland
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Finland. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Finnish Food Authority</p> <p>VMPR_2022 – Finnish Food Authority</p> <p>VMPR_2021 – Finnish Food Authority</p> <p>VMPR_2020 – Finnish Food Authority</p> <p>VMPR_2019 – Finnish Food Authority</p> <p>VMPR_2018 – Finnish Food Authority</p> <p>VMPR_2017 – Finnish Food Authority</p>
Vehicular traffic count measurements at Tampere City, Finland, Updated version
<p>Inductive loop vehicular traffic count measurements from Tampere City, Finland</p> <p>15-minute resolution, 2 locations, 2011-2014</p> <p>Data utilized in "Origin-destination matrix estimation with a conditionally binomial model" by P.Kuusela, I. Norros, J. Kilpi and T. Räty</p> <p>See VehicularTrafficCountMeasurements_TampereFinland_2011-2014.rtf</p>
Long-term litter fall data series from 34 boreal forest stands in Finland
<p><strong>Introduction</strong></p> <p>Litter fall data were collected on a network of 34 forest sampling plots in Finland from late 1950s to 2010s. The data collection spanned different time periods in different sampling plots. The data have been used for studies on the flowering and seed crop of forest trees, air quality, and insect damage (see list of publications in the end of this document). They have been used to develop seed production and needle litter fall models for Scots pine (<em>Pinus sylvestris</em>) and Norway spruce (<em>Picea abies</em>), a branch litter model for pine, and total litter fall models used in greenhouse gas inventories.</p> <p><strong>Data collection</strong></p> <p>The litter fall collection was set up in mature, single species stands. The focal tree species include Scots pine (<em>Pinus sylvestris</em>), Norway spruce (<em>Picea abies</em>), Silver birch (<em>Betula pendula</em>), Downy birch (<em>Betula pubescens</em>), Grey alder (<em>Alnus</em> <em>incana</em>), European rowan (<em>Sorbus aucuparia</em>), European larch (<em>Larix decidua</em>), and Siberian larch (<em>Larix sibirica</em>). The sampling plots varied in shape and in size with a typical area of 0.1-0.25 ha. Between 6 and 30 litter collecting funnels were used per plot. The funnels were made of galvanized sheet metal and attached to cloth bags to collect the falling litter. The sampling sites were monitored for changes in conditions, such as natural disturbances, tree harvesting, forestry operations, or construction on the plot or in its immediate proximity (none observed).</p> <p>The litter samples were collected from the sampling plots, usually four to six times per year in spring to autumn. The samples were dried in room temperature (except male flowers in 1960s – 1970s, see note in Table 1) and stored in paper bags. The dried samples were sorted into litter fractions (Table 1). These fractions varied between tree species and between years to some extent. Cones and seeds were counted, and all other litter fractions were weighed to the nearest milligram.</p> <table> <caption>Table 1. Litter fractions and their codes. The code of the litter fraction is used in the data files.</caption> <thead> <tr> <th scope="col">Code</th> <th scope="col">Litter fraction</th> <th scope="col">Description / note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Male flowers</td> <td>In the 1960s – 1970s male flowers were dried in 105°C for 24 hours (Sarvas 1962, 1968).</td> </tr> <tr> <td>2</td> <td>Seeds</td> <td>Seed wings and seeds from species other than the focal one were included into the “other litter” fraction.</td> </tr> <tr> <td>3</td> <td>Female flowers</td> <td> </td> </tr> <tr> <td>4</td> <td>Needles</td> <td> </td> </tr> <tr> <td>5</td> <td>Insects and their faeces</td> <td> </td> </tr> <tr> <td>6</td> <td>Other litter</td> <td> </td> </tr> <tr> <td>7</td> <td>Lichens, branches, and tree bark</td> <td>In some years lichens, branches, and bark were combined in the same fraction, and in some they were separated in their own fractions (numbers 12-14 below).</td> </tr> <tr> <td>8</td> <td>Small cones (1 year)</td> <td>For pine stands, cones were separated into one-year old small cones and large cones. Cones were counted.</td> </tr> <tr> <td>9</td> <td>Cones</td> <td> </td> </tr> <tr> <td>10</td> <td>Cones and loose scales</td> <td>In some years loose scales were included in the same fraction as cones, and in some they were included into the “other litter” fraction.</td> </tr> <tr> <td>11</td> <td>Shifting dust</td> <td> </td> </tr> <tr> <td>12</td> <td>Branches</td> <td> </td> </tr> <tr> <td>13</td> <td>Lichens</td> <td> </td> </tr> <tr> <td>14</td> <td>Tree bark</td> <td> </td> </tr> <tr> <td>15</td> <td>Leaves</td> <td>For deciduous stands, leaves were separated into small (diameter < 1 cm) and large leaves (diameter > 1 cm), while for conifer stands all leaves were included into the “other litter” fraction.</td> </tr> <tr> <td>16</td> <td>Berries</td> <td> </td> </tr> </tbody> </table> <p>In addition to litter fall data, tree stand data were collected on most of the plots in some years. In the tree stand inventories, all trees in the sampling plot with diameter at breast height ≥ 7 cm were mapped, and all trees were counted and measured for diameter (at breast height and at 6 meters), total height, and height to first living branches. Stand basal area and dominant diameter and height were calculated. Stand age was estimated based on core samples from five trees outside but representative of the sample plot. Crown coverage was estimated with a Cajanus tube.</p> <p><strong>Description of the data files</strong></p> <p>The litter fall data is in eight csv-files, one per tree species. The files are named “Litter_Tree_species.csv”, for example “Litter_Betula_pendula.csv”.</p> <p>Variables (in columns) are consistent across the files (explained in Table 2), but note that there are varying numbers of columns between the tables in the files, as each litter collecting funnel has its own column and different maximum numbers of funnels were used in different sampling sites and tree species (see row “S1 – S30” in Table 2 for more details).</p> <table> <caption>Table 2. Variables included in the litter fall data files.</caption> <thead> <tr> <th scope="col">Variable name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Form</td> <td>Number identifying the original paper form.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Tree species code: 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch, 5 = Grey alder, 6 = Siberian larch, 7 = European larch, 8 = European rowan.</td> </tr> <tr> <td>LitterFraction</td> <td>Code for the litter fraction (1-16), explained in Table 1.</td> </tr> <tr> <td>Coefficient</td> <td>Coefficient used to transform the weight of the litter (g) to weight per square meter (g m<sup>-2</sup>). The coefficient is based on the number and area of the collection funnels.</td> </tr> <tr> <td>Date</td> <td>Date of sample collection.</td> </tr> <tr> <td>Period</td> <td>Variable used to define the time of data collection as calendar year or phenological year. The variable is based on the schedule of data collection in different years and on the focal tree species so that it corresponds to the species-specific litter fall schedule. For spruce, the peak needle fall is in the spring, so the calendar year is appropriate for describing the temporal variation in litter fall. For pine, the peak needle fall is in August–September, so a phenological year defined as July 1<sup>st</sup> – June 30<sup>th</sup> is appropriate for describing the temporal variation in litter fall. Period = -1 means the values in the row are allocated to the previous calendar year; period = 0 means the values are allocated to the current calendar year; and period = 1 means the values are allocated to the next calendar year.</td> </tr> <tr> <td>S1 – S30</td> <td>Columns S1 – S30 refer to litter collection funnels 1 – 30. The maximum number of funnels varies between tree species: for <em>silver birch</em> up to 30 funnels were used per plot, for downy birch up to 20 funnels, for spruce up to 10 funnels, for pine up to 15 funnels, for rowan 10 funnels, and for both larch species and alder 8 funnels. Missing values (NA) mean that the funnel was not used in the plot. Value -1 means that the funnel was used but the sample was missing. The values give the dry weight of the litter in mg.</td> </tr> <tr> <td>Combined</td> <td>For some samples, litter collected by different funnels has been combined and the total weight is shown in column S1. In this column, 0 = values have not been combined, and 1 = values have been combined.</td> </tr> <tr> <td>TotalWeight</td> <td>Total weight of the litter (mg).</td> </tr> <tr> <td>TotalWeightArea</td> <td>Total weight of the litter per area (mg m<sup>-2</sup>). Value is same as TotalWeight × Coefficient.</td> </tr> <tr> <td>Note</td> <td>Note</td> </tr> </tbody> </table> <p>Tree stand data is in one csv-file, named “Tree_stand_data.csv”, and can be combined with the litter fall data based on the sample plot abbreviations (variable “PlotAbbr” in both litter fall data files and the tree stand data file). Note that there is no tree stand data available for all the same years as litter fall data. There is no tree stand data available at all for one downy birch site (abbreviation HEI568), one spruce site (NOO85), one pine site (HEI566), and the alder, rowan, and larch sites. Tree stand data variables are explained in Table 3.</p> <table> <caption>Table 3. Variables included in the tree stand data file.</caption> <tbody> <tr> <td>Variable name</td> <td>Description</td> </tr> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Dominant tree species. 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch.</td> </tr> <tr> <td>Age</td> <td>Stand age (years).</td> </tr> <tr> <td>SiteType</td> <td>Forest site type describing site productivity. 2 = xeric heath forest, 3 = sub-xeric heath forest, 4 = mesic heath forest, 5 = herb-rich heath forest. </td> </tr> <tr> <td>North</td> <td>North coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>East</td> <td>East coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>Elevation</td> <td>Elevation (m above sea level).</td> </tr> <tr> <td>N</td> <td>Stem number (ha<sup>-1</sup>).</td> </tr> <tr> <td>BA</td> <td>Basal area (m<sup>2</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>DomD</td> <td>Diameter (cm) of dominant trees.</td> </tr> <tr> <td>DomH</td> <td>Height (m) of dominant trees.</td> </tr> <tr> <td>CrownLength</td> <td>Crown length (m).</td> </tr> <tr> <td>V</td> <td>Stem volume (m<sup>3</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>CrownCover</td> <td>Crown coverage (%).</td> </tr> </tbody> </table> <p> </p> <p><strong>List of publications </strong></p> <p>Hilli, A., Hokkanen, T., Hyvönen, J. & Sutinen, M.-L. 2008. Long-term variation in Scots pine seed crop size and quality in northern Finland. Scandinavian Journal of Forest Research 23(5): 395-403. </p> <p>Koski, V. & Tallqvist, R. (1978). Results of long-time measurements of the quantity of flowering and seed crop of forest trees (in Finnish with English summary). Folia Forestalia, 364, 1-60. </p> <p>Kouki, J. & Hokkanen, T. 1992. Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors. Oecologia 89: 176-181. </p> <p>Lehtonen, A., Lindholm, M., Hokkanen, T., Salminen, H. & Jalkanen, R. 2008. Testing dependence between growth and needle litterfall in Scots pine - a case study in northern Finland. Tree Physiology 28(11): 1741-1749. </p> <p>Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K.T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling 180(2-3): 305-315. </p> <p>Leikola, M., Raulo, J. & Pukkala, T. (1982). Prediction of the variations of the seed crop of Scots pine and Norway spruce (in Finnish with English summary). Folia Forestalia, 537, 1-43. </p> <p>Niemistö, P., Hokkanen T. & Varama, M. 2004. Karikemäärän muutokset 1982–2001 ja puiden kunto lumi- ja hallamittariesiintymän vaivaamissa koivikoissa Noormarkussa. Metsätieteen aikakauskirja 1/2004: 21–41. </p> <p>Poikolainen, J. & Kuusinen, M. 2000. Abundance of epiphytic lichens in litterfall during 1967-1994. In: Forest condition in a changing environment - the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers / Ed. Mälkönen, E. Sivut: 171-172. </p> <p>Pukkala, T. 1987a. A model for predicting the seed crop of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 135-144. </p> <p>Pukkala, T. 1987b. Effect of seed production on the annual growth of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 145-158. </p> <p>Pukkala, T., Hokkanen, T. & Nikkanen, T. 2010. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fennica 44(4): 629-642. </p> <p>Ranta, E., Lindström, J., Kaitala, V., Crone, E., Lundberg, P., Hokkanen, T. & Kubin, E. 2010. Life history mediated responses to weather, phenology and large-scale population patterns. In: Hudson, I. L & Keatley, M. R. (eds.). Phenological Research. Springer, Dordrecht Heidelberg London New York, Netherlands. p. 321-338. </p> <p>Raulo, J. & Hokkanen, T. 1989. Litter fall of Alnus incana and Alnus glutinosa (in Finnish with English summary). Folia Forestalia 738. 25 s.</p> <p>Saarsalmi, A., Starr, M., Hokkanen, T., Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. 2007. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 242(2-3): 578-586. </p> <p>Sarvas, R. (1962). Investigations on the flowering and seed crop of Pinus Silvestris. Communicationes Instituti Forestalis Fenniae, 53, 1-198. </p> <p>Sarvas, R. 1968. Investigation on the flowering and seed crop of Picea abies. Communicationes Instituti Forestalis Fenniae 67.5. 84 pp. </p> <p>Starr, M., Saarsalmi, A., Hokkanen, T., Merilä, P. & Helmisaari, H.-S. 2005. Models of litterfall production for Scots pine (Pinus sylvestris L.) in Finland using stand, site and climate factors. Forest Ecology and Management 205: 215-225. </p> <p>Ťupek, B., Mäkipää, R., Heikkinen J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M. & Lehtonen, A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Environment Research 20: 283–304</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Dataset containing DTS-data used in Karttunen et al. "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland"
<p>This record contains DTS-data used in the following study:</p> <p>Karttunen et al. (2021): Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland, submitted to AMTD</p> <p> </p> <p>DTS_highfreq_SMEARIII_Karttunen_et_al.zip contains continuous high frequency potential temperature profiles measured along the SMEAR III 31-metre tall mast. See more information on the data in the netCDF-file attributes and on the measurement setup in the related manuscript.</p> <p>DTS_statistics_SMEARIII_Karttunen_et_al.nc contains profiles for the turbulence temperature statistics calculated from the continuous DTS potential temperature profiles.See more information in the netCDF-file attributes and the related manuscript.</p> <p> </p>
CoMix social contact data (Finland)
<p>CoMix social contact data for Finland.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Kari Auranen at the Finnish Institute for Health and Welfare (THL).</p>
Data from: Modeling the drivers of eutrophication in Finland with a machine learning approach
<p>The dataset contains data on characteristics of 1547 Finnish EU Water Framework Directive monitored lakes and their catchments from years 2016-2019.</p> <p> </p> <p><strong>Usage notes</strong></p> <p>The zip-file contains catchment and lake characteristics data to support the main analysis code in Github (link) (‘Analyses and visualization’) which produces results, figures and tables presented in the study ‘Modeling the drivers of eutrophication in Finland with a machine learning approach’. Detailed information about the data can be found in the ‘README.docx’ file.</p>
Chamber flux data (CH4 , CO2) from Finland (1991 -1993) originally used in Nykänen et al. GBC, 12(1), 53 - 69, 1988,
<p>Chamber flux (CH4 , CO2), temperature, water table and vegetaion data from Särkkä (62° 48'; 30° 58') and Lakkasuo (61° 47'; 24°16') from Finland (1991 -1993). These are natural and drained site data few decades after drainage nearby each other. </p> <p>This data was originally used in following articles: </p> <table> <tbody> <tr> <td>Chamber measuremets CH4, CO2 </td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Alm J. and Silvola J. 1995. Changes in fluxes of carbon dioxide, methane and nitrous oxide due to forest drainage of mire sites of different trophy. Plant and Soil 168/169: 571-577.</td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Crill P. and Silvola J. 1993. Effect of a lowered water table on nitrous oxide fluxes from northern peatlands. Nature 366 (4): 51-53.</td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Crill P. and Silvola J. 1993. The effect of changing water table on methane fluxes at two Finnish mire sites. Suo Mires and Peat 43: 237-240.</td> </tr> <tr> <td>Nykänen H., Alm J., Lang K., Silvola J. and Martikainen P.J. 1995. Emissions of CH4, N2O and CO2 from a virgin fen and a fen drained for grassland in Finland. Journal of Biogeography 22: 351-357.</td> </tr> <tr> <td>Descriptions: Nykänen H., Alm J., Silvola J. and Tolonen K., Martikainen P.J. 1998. Methane fluxes on boreal peatlands of different fertility and the effect of long-term experimental lowering of the water table on flux rates. Global Biogeochemical Cycles 12: 53-69.</td> </tr> </tbody> </table>
Story Map of the Technical Research Centre of Finland (VTT)
<p>The Co-Change Lab at the Technical Research Centre of Finland (VTT) focuses on co-designing and implementing VTT’s sustainability programme. This work is a collaborative process that involves VTT’s sustainability responsibility researchers and the nominated responsibility task force.</p>
Fine-root biomass production, sedge root, sedge leaf, and moss shoot decomposition, soil water-table level, and temperature data from two sedge fens in Finland
<p>Dataset including fine-root biomass production, mass loss of sedge (<em>Carex rostrata</em>) roots and leaves, and moss (<em>Sphagnum</em> <em>fallax</em>) shoots, along with environmental data (soil water-table level, air temperature, soil temperature at 5 cm, and soil temperature at 15 cm) from two sedge fens located in southern Finland (Lakkasuo, Orivesi, 61°48' N 24°19'E) and northern Finland (Lompolojänkkä, Kittilä, 68°N 24°12'E). Data are from a climate change experiment, where warming was induced with open top chambers (OTCs) and drying with shallow ditching. Data are from years 2011-2013.</p>
Asiantuntijakysely kaupunkibiotooppien monimuotoisuudesta ja kaupunkibiotooppikartta Suomen pääkaupunkiseudulla / Expert Questionnaire Results Regarding Biodiversity of Urban Biotopes and an Urban Biotope Map in Helsinki Metropolitan Area, Finland
<p>(in English below)</p> <p><strong>ASIANTUNTIJAKYSELY KAUPUNKIBIOTOOPPIEN MONIMUOTOISUUDESTA JA KAUPUNKIBIOTOOPPIKARTTA SUOMEN PÄÄKAUPUNKISEUDULLA</strong></p> <p><strong>Tausta ja tavoitteet</strong></p> <p>Luonnonsuojelu on keskittynyt perinteisesti Suomessa tiettyihin lajeihin ja luontotyyppeihin, mikä ei välttämättä kuvaa erilaisten kaupunkiympäristöjen ekologisia arvoja kattavasti. Lisäksi tulisi huomioida eliöyhteisöjä, jotka turvaavat kestävän ja monimuotoisen ekosysteemien toiminnallisuuden.</p> <p>Tämän asiantuntijakyselyn tavoitteena oli selvittää, miten erilaiset kaupunkibiotoopit tukevat erilaisia ekologisten yhteisöjen monimuotoisuutta kuvaavia tekijöitä eri eliöryhmillä. Nämä <em>monimuotoisuuden laatutekijät</em> kuvaavat yhdessä biotooppien roolia kaupunkiluonnon monimuotoisuudessa, toiminnallisuudessa ja täten epäsuorasti myös mm. ekosysteemipalvelujen tarjoamisessa. Kyselyn tuloksia voidaan hyödyntää ekologisten arvojen turvaamiseksi paremmin osana kaupunkisuunnittelua tai kaupunkiluonnon monimuotoisuuden kattavan turvaamisen perustana. Kysely keskittyi pääkaupunkiseudun (Helsinki, Espoo, Vantaa, Kauniainen) biotooppeihin. Lisäksi asiantuntijat antoivat arvionsa biotooppipisteytyksen sovellettavuudesta muualla Suomessa.</p> <p>Kysely on kuvattu Terra-lehdessä (Jalkanen & Vierikko 2022) sekä Jalkanen ym. (2020).</p> <p><strong>Menetelmät</strong></p> <p><em>Kaupunkibiotooppien pisteytys</em></p> <p>Aineisto kerättiin internetkyselyllä 5.10.-21.11.2016 välisenä aikana. Kysely lähetettiin 38 paikalliselle lajiasiantuntijalle (Luonnontieteellisessä keskusmuseossa, Helsingin yliopistossa, Suomen ympäristökeskuksessa, ympäristökonsulttiyrityksissä ja luontojärjestöissä), joista 24 osallistui kyselyyn.</p> <p>Mukana olleet asiantuntijat (suluissa heidän lajiryhmänsä):</p> <ul> <li>Heidi Björklund (Linnut)</li> <li>Tea von Bonsdorff (Sienet, muut kuin käävät)</li> <li>Eero Haapanen (Nisäkkäät, muut kuin lepakot)</li> <li>Nina Hagner-Wahlsten (Lepakot)</li> <li>Jari Kaitila (Perhoset)</li> <li>Jarkko Korhonen (Sienet, muut kuin käävät)</li> <li>Jaakko Kullberg (Perhoset)</li> <li>Eeva-Maria Kyheröinen (Lepakot)</li> <li>Esa Lammi (Putkilokasvit)</li> <li>Riku Lumiaro (Nisäkkäät, muut kuin lepakot)</li> <li>Sampsa Malmberg (Kovakuoriaiset)</li> <li>Ilpo Mannerkoski (Kovakuoriaiset)</li> <li>Olli Manninen (Käävät)</li> <li>Heikka Marttila-Tornio (Matelijat & sammakkoeläimet)</li> <li>Juho Paukkunen (Pistiäiset)</li> <li>Terhi Ryttäri (Putkilokasvit)</li> <li>Jarmo Saarikivi (Matelijat & sammakkoeläimet)</li> <li>Hannu Sarvanne (Linnut)</li> <li>Keijo Savola (Käävät)</li> <li>Ilkka Teräs (Pistiäiset)</li> <li>Stephen Venn (Kovakuoriaiset)</li> <li>Tarmo Virtanen (Perhoset)</li> <li>Terhi Wermundsen (Lepakot)</li> <li>Rauno Yrjölä (Linnut)</li> </ul> <p>Asiantuntijat pisteyttivät kyselyssä 68 biotooppia sen mukaan, kuinka ne tukevat heidän lajiryhmiensä eri ominaisuustekijöitä. Jokainen biotooppi arvioitiin erikseen kunkin tekijän näkökulmasta. Pisteet annettiin 5-portaisella asteikolla (0–4; 0 alin). Asiantuntijoita ohjeistettiin miettimäään koko vuodenaikaiskiertoa (arvioimaan biotooppien merkitystä siis myös esim. talvehtimisen kannalta). Kysely perustui Vierikon ym. (2014) biotooppiluokitteluun seuraavin muutoksin:</p> <ul> <li>Metsäbiotoopit jaettiin kahteen ikäluokkaan (30–100-vuotiaat ja yli 100-vuotiaat metsät)</li> <li>Piha-alueet jaettiin päällystettyihin ja maavaraisiin</li> <li>Tiiviiden pientaloalueiden ja townhouse-alueiden pihat lisättiin omana biotooppinaan</li> <li>Kalliolaet, -rinteet ja -seinämät sekä kivikot yhdistettiin samaksi biotoopiksi (paljaat kalliopinnat)</li> <li>Uimarannat, kanaalit ja rantaterassit yhdistettiin samaksi biotoopiksi (rakennetut rannat)</li> <li>Kiviseinät ja lintuluodot poistettiin</li> <li>Viherseinät lisättiin omana biotooppinaan</li> </ul> <p>Biotooppien merkitystä kysyttiin seuraavissa eliöyhteisöjen monimuotoisuuden laatua kuvaavissa kategorioissa:</p> <ol> <li>Lajirikkaus</li> <li>Vaatelias lajisto</li> <li>Biomassa</li> <li>Runsaus</li> <li>Tasaisuus</li> <li>Uniikkius</li> <li>Seudullinen edustavuus</li> <li>Herkkyys ihmissyntyisiä häiriöitä kohtaan</li> <li>Kytkeytyvyys</li> </ol> <p>Asiantuntijat arvioivat lisäksi omien vastaustensa luotettavuutta, erikseen jokaisen tekijän kohdalla. Tässä aineistossa biotooppien pisteet on painotettu nousevan painokertoimen mukaan, jotta luotettavat vastaukset korostuvat epäluotettavia voimakkaammin. Luotettavuuskertoimet ovat 0, 1, 2, 4 ja 8, mitkä tarkoittavat “erittäin epäluotettavia”, “epäluotettavia”, “jonkin verran epäluotettavia”, ”melko luotettavia” ja ”erittäin luotettavia” vastauksia.</p> <p> <em>Biotooppipisteiden sovellettavuus muualla Suomessa</em></p> <p>Kyselyn jälkeen asiantuntijat arvioivat työpajassa, kuinka hyvin heidän vastauksiaan voi soveltaa muissa suomalaisissa kaupungeissa. Työpaja pidettiin 29.11.2016. Tarkka kysymyksenasettelu oli:</p> <p><em>Tämä kysely on laadittu Etelä-Suomen ja erityisesti pääkaupunkiseudun (Helsinki, Espoo, Vantaa, Kauniainen) näkökulmasta. Kuinka hyvin kyselyn tulokset kuvaavat eliöryhmäsi lajistoa muiden Suomen maakuntien kaupungeissa? Vastaa asteikolla 0–10 (0: tämän kyselyn tuloksia ei voi soveltaa lainkaan ko. maakunnan kaupunkeihin, 10: kyselyn tulokset soveltuvat erittäin hyvin ko. maakunnan kaupunkiluontoon). Vastaa myös, kuinka luotettavina vastauksiasi voidaan pitää asteikolla 0-3 (0: hyvin epäluotettavina, 3: hyvin luotettavina). <strong>Vastaa oman eliöryhmäsi näkökulmasta.</strong></em></p> <p>Tässä aineistossa vastaukset näytetään alkuperäisinä, eli luotettavia vastauksia ei korosteta erikseen kuten biotooppipisteytyksessä.</p> <p><strong>Aineistot:</strong></p> <p>Aineistot ovat suomeksi (etuliite ”FIN”) ja englanniksi (”ENG”). Aineisto sisältää:</p> <ul> <li>Monimuotoisuuden laatutekijöiden luonnehdinnat ja pisteytysohjeet (.pdf)</li> <li>Kaupunkibiotooppien luonnehdinnat (.pdf)</li> <li>Kaupunkibiotooppien pisteytys taulukkona (.xlsx)</li> <li>Taulukko vastausten sovellettavuudesta muualla Suomessa (.xlsx)</li> </ul> <p>Taulukkotiedostoissa monimuotoisuuden laatutekijän tai eliöryhmän perässä oleva numero viittaa asiantuntijaan (esim. lintuja koskevissa sarakkeissa ”Lajirikkaus 1” ja ”Vaateliaat lajit 1” viittaavat saman lintuasiantuntijan vastauksiin). Asiantuntijoiden vastaukset on listattu satunnaisjärjestyksessä. Kaikki asiantuntijat ovat suostuneet heidän vastaustensa ja nimiensä julkaisemiseen kirjallisesti.</p> <p><strong>KAUPUNKIBIOTOOPPIKARTTA</strong></p> <p>Kansiossa "FIN_Kaupunkibiotooppikartta" on rasterimuotoinen kartta pääkaupunkiseudun kaupunkibiotoopeista paikkatietomuodossa. Kartta on luotu 2021 eri paikkatietolähteistä (ks. Jalkanen ym. 2020). Kaupunkibiotooppikartta mahdollistaa esimerkiksi monimuotoisuusarvojen tarkastelun pääkaupunkiseudulla yhdessä asiantuntijakyselyn tulosten kanssa. HUOM! Karttaa ei ole tarkoitettu sellaisenaan suunnittelukäyttöön. Minkäänlaisia takuita tulosten oikeellisuudesta, virheettömyydestä tai käytettävyydestä ei myönnetä.</p> <p>Kansiossa on seuraavat tiedostot:</p> <ul> <li>Kaupunkibiotooppikartta (.tif) (CRS: EPSG 3902)</li> <li>Kaupunkibiotooppikartan soluarvojen selitykset (.xlsx). Kaupunkibiotooppikartta käsittää 53 biotooppia/maanpeiteluokkaa, eli kaikkia asiantuntijakyselyn biotooppeja ei ole pystytty koostamaan kartalle.</li> <li>Kaupunkibiotooppikartan koostamisen ja lähtöaineistojen kuvaus (.pdf)</li> </ul> <p><strong>Kiitokset: </strong>Kiitämme Silviya Korpiloa, Susanna Lehvävirtaa ja Stephen Venniä avusta englanninnosten kanssa. </p> <p><strong>Viittausohje:</strong> Jalkanen, J. & Vierikko, K. (2022) Asiantuntijakysely kaupunkibiotooppien monimuotoisuudesta sekä kaupunkibiotooppikartta Suomen pääkaupunkiseudulla [Aineisto] https://doi.org/10.5281/zenodo.6563190</p> <p>Aineistoon tulee viitata käytettäessä.</p> <p><strong>Viitteet: </strong></p> <ul> <li>Jalkanen, J. & Vierikko, K. (2022) Viheralueiden elonkirjo – Asiantuntijakysely ja luonnon monimuotoisuuden laatumittaristo kaupunkisuunnittelun tueksi. <em>Terra</em> 134: 207–223. https://doi.org/10.30677/terra.120163</li> <li>Jalkanen, J., Vierikko, K. & Moilanen, A. (2020) Spatial prioritization for urban Biodiversity Quality using biotope maps and expert opinion. <em>Urban Forestry & Urban Greening</em> 49: 126586. https://doi.org/10.1016/j.ufug.2020.126586.</li> <li>Vierikko, K., Niemelä, J., Salminen, J., Jalkanen, J. & Tamminen, N. 2014: Helsingin kestävä viherrakenne –Miten turvata kestävä viherrakenne ja kaupunkiluonnon monimuotoisuus tiivistyvässä kaupunkirakenteessa. Helsingin kaupunkisuunnitteluviraston yleissuunnitteluosaston selvityksiä 2014:27. Helsinki. 132 s.</li> </ul> <p> </p> <p><strong>EXPERT QUESTIONNAIRE RESULTS REGARDING BIODIVERSITY OF URBAN BIOTOPES AND AN URBAN BIOTOPE MAP IN HELSINKI METROPOLITAN AREA, FINLAND</strong></p> <p><strong>Background and aim:</strong></p> <p>Finnish biodiversity conservation has traditionally focused on certain species and biotopes, which does not necessarily describe urban areas’ ecological values in a comprehensive manner. In addition, focus should be put on ecological communities that enable resilient and diverse ecosystem functioning.</p> <p>The aim of this questionnaire was to determine how different urban biotopes support different biodiversity quality attributes of ecological communities of different higher taxonomic groups. Together these attributes describe biotopes’ support for sustainable urban ecosystem functioning and, thus, indirectly for ecosystem services provisioning. The results can be used to better preserve ecological values in urban planning and as a basis for comprehensive urban biodiversity conservation. The questionnaire focused on biotopes found in the Helsinki Metropolitan Area (HMA; municipal cities of Helsinki, Espoo, Vantaa, and Kauniainen), Southern Finland. In addition, the applicability of the scoring elsewhere in Finnish cities was evaluated.</p> <p>The questionnaire is described in Terra (Jalkanen & Vierikko 2022; in Finnish with English abstract) and in Jalkanen et al. (2020).</p> <p><strong>Methods:</strong></p> <p><em>Scoring of urban biotopes</em></p> <p>The data was collected using an online questionnaire during 5.10.-21.11.2016. It was sent to 38 local taxonomic experts (from Finnish Museum of Natural History, University of Helsinki, Finnish Environment Institute, environmental consultant firms, and local environmental NGOs), out of which 24 replied.</p> <p>Experts who participated were (their taxon):</p> <ul> <li>Heidi Björklund (Birds)</li> <li>Tea von Bonsdorff (Fungi, other than polypores)</li> <li>Eero Haapanen (Mammals, other than bats)</li> <li>Nina Hagner-Wahlsten (Bats)</li> <li>Jari Kaitila (Butterflies)</li> <li>Jarkko Korhonen (Fungi, other than polypores)</li> <li>Jaakko Kullberg (Butterflies)</li> <li>Eeva-Maria Kyheröinen (Bats)</li> <li>Esa Lammi (Vascular plants)</li> <li>Riku Lumiaro (Mammals, other than bats)</li> <li>Sampsa Malmberg (Beetles)</li> <li>Ilpo Mannerkoski (Beetles)</li> <li>Olli Manninen (Polypores)</li> <li>Heikka Marttila-Tornio (Herpetofauna)</li> <li>Juho Paukkunen (Hymenoptera)</li> <li>Terhi Ryttäri (Vascular Plants)</li> <li>Jarmo Saarikivi (Herpetofauna)</li> <li>Hannu Sarvanne (Birds)</li> <li>Keijo Savola (Polypores)</li> <li>Ilkka Teräs (Hymenoptera)</li> <li>Stephen Venn (Beetles)</li> <li>Tarmo Virtanen (Butterflies)</li> <li>Terhi Wermundsen (Bats)</li> <li>Rauno Yrjölä (Birds)</li> </ul> <p>In the questionnaire, the experts scored 68 local urban biotopes in terms of how well they support different biodiversity attributes of their taxonomic group. Each biotope was scored separately for every attribute. Scores were given on a 5-rank scale (0–4; 0 being the lowest). Experts were advised to consider all seasons (e.g., to consider the importance of the biotope also for wintering). We used the biotope classification from the expert questionnaire by Vierikko et al. (2014) with the following modifications:</p> <ul> <li>Forest biotopes were further divided into two age classes (30-100 y. and over 100 y.)</li> <li>Yards were further divided into sealed and bare yards</li> <li>Densely-built residential gardens & townhouse gardens were added as a new biotope</li> <li>Outcrops, ledges, and rocky grounds were combined to one biotope (bare rocks)</li> <li>Beaches, canals, and terraced embankments were combined to one biotope (artificial shores)</li> <li>Stone walls and bird-colonized islets were excluded</li> <li>Green walls were added as a new biotope</li> </ul> <p>The relevance of biotopes was evaluated for the following Biodiversity Quality attributes that describe or relate to the diversity of urban ecological communities:</p> <ol> <li>Species richness</li> <li>Habitat specialist species</li> <li>Biomass</li> <li>Abundance</li> <li>Evenness</li> <li>Uniqueness</li> <li>Regional representativeness</li> <li>Sensitivity towards anthropogenic disturbance</li> <li>Connectivity</li> </ol> <p>In addition, experts gave overall self-evaluated confidence rates for their answers concerning each attribute. In the data, biotopes’ scores are weighted by an increasing confidence coefficient in order to emphasize confident answers over non-confident ones. Confidence coefficients are 0, 1, 2, 4, and 8 that refer to ‘very unconfident’, ‘unconfident’, ‘somewhat unconfident’, ‘somewhat confident’, and ‘very confident’ answers, respectively.</p> <p><em>Applicability of the urban biotope scores elsewhere in Finland</em></p> <p>After the questionnaire, experts assessed how applicable their answers are elsewhere in Finland. This task was done in an expert workshop on 29.11.2016. The exact question, given to the experts, was:</p> <p><em>This questionnaire is designed from Southern Finnish, and especially from the Helsinki Metropolitan area (cities of Helsinki, Espoo, Vantaa, Kauniainen) perspective. How well do the questionnaire results describe the species assemblages of your taxon found in cities in other Finnish provinces? Answer on a scale of 0-10 (0: the results of this questionnaire are completely inapplicable to the cities in the given province, 10: the results of the questionnaire apply very well to the urban nature of the given province). Also evaluate the confidence of your answers at the scale of 0-3 (0: very unconfident, 3: very confident). <strong>Answer from the point of view of your own taxon.</strong></em></p> <p>In the data, the expert answers are reported as they were given, i.e., confident answers are not emphasized over non-confident ones like with biotope scores.</p> <p><strong>Data:</strong></p> <p>Data is provided in Finnish (files starting with ‘FIN’) and in English (‘ENG’). This data set includes:</p> <ul> <li>Biodiversity Quality attribute descriptions and scoring instructions (.pdf)</li> <li>Urban biotope descriptions (.pdf)</li> <li>Table of urban biotope scores (.xlsx)</li> <li>Table of the applicability of biotope scores elsewhere in Finland (.xlsx)</li> </ul> <p>In the tables, the Arabic number after the attribute or taxon name refers to the corresponding expert (i.e., under birds (‘Richness 1’ and ‘Specialist species 1’ refer to the answers of the same bird expert). Experts’ answers are listed in random order. All experts have agreed on publication of their answers and names using written informed consent.</p> <p><strong>URBAN BIOTOPE MAP</strong></p> <p>The folder ’ENG_UrbanBiotopeMap’ includes a raster-type GIS layer of urban biotopes in the Helsinki Metropolitan area. The map has been compiled from several GIS sources (see Jalkanen et al. 2020). The urban biotope map allows for example spatial analyses of biodiversity values together with the expert questionnaire results. OBS! The map is not meant for planning purposes. No warrant about the correctness, flawlessness, or feasibility of the results is given.</p> <p>The folder includes the following files:</p> <ul> <li>The urban biotope map (.tif) (CRS: EPSG 3902)</li> <li>The explanation of the cell values of the urban biotope map (.xlsx). The urban biotope map comprises of 53 different biotopes/land cover types, i.e., every biotope asked in the questionnaire are not found in the map.</li> </ul> <p><strong>Acknowledgements:</strong> We thank Silviya Korpilo, Susanna Lehvävirta, and Stephen Venn for help with the English translations.</p> <p><strong>How to cite: </strong>Jalkanen, J. & Vierikko, K. (2022) Expert questionnaire results regarding biodiversity of urban biotopes and an urban biotope map in Helsinki Metropolitan Area, Finland [Data set] https://doi.org/10.5281/zenodo.6563190</p> <p>Data should be properly cited when used.</p> <p><strong>References:</strong></p> <ul> <li>Jalkanen, J. & Vierikko, K. (2022) Viheralueiden elonkirjo – Asiantuntijakysely ja luonnon monimuotoisuuden laatumittaristo kaupunkisuunnittelun tueksi (Biodiversity in urban green spaces: Expert questionnaire about urban Biodiversity Quality to support urban planning) . <em>Terra</em> 134: 207–223. https://doi.org/10.30677/terra.120163 [In Finnish with English abstract.]</li> <li>Jalkanen, J., Vierikko, K. & Moilanen, A. (2020) Spatial prioritization for urban Biodiversity Quality using biotope maps and expert opinion. <em>Urban Forestry & Urban Greening</em> 49: 126586. https://doi.org/10.1016/j.ufug.2020.126586.</li> <li>Vierikko, K., Salminen, J., Niemelä J., Jalkanen, J. & Tamminen, N. 2014: Sustainable green infrastructure of Helsinki – urban ecological research report and recommendations for the Helsinki master plan 2050. Strategic Planning Office of the City Planning Department of the City of Helsinki. Research report. [In Finnish with English abstract.] Available in: <a href="https://www.hel.fi/hel2/ksv/julkaisut/yos_2014-27.pdf">https://www.hel.fi/hel2/ksv/julkaisut/yos_2014-27.pdf</a> (Cited 16.1.2018). 132 pp.</li> </ul> <p> </p>
Extinction Rebellion Finland (Elokapina) Post-Repression Twitter Activity and Network Patterns
<p><strong>Overview</strong></p> <p>This repository contains two time series data sets of activity levels and network patterns on Finnish climate Twitter for the period of July 18, 2020 to February 11, 2022. First, there is a day-level data set (574 days) containing Twitter activity levels of climate activists and non-activists. Second, there is a week-level data set (81 weeks) containing estimated coefficients for various network effects from exponential random graph models (ERGM) fit to retweet networks.</p> <p>For details, including definitions of activist/non-activist and the ERGM specifications, please see the referenced work: "Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism", detailed below.</p> <p> </p> <p><strong>Data Set Details</strong></p> <p>`raw_daily_counts.csv` contains the following variables at the day level.</p> <ul> <li><em>date</em>: from 2020-07-25 to 2022-02-11</li> <li><em>activity_activist</em>: the daily activity count of activist users on Finnish climate Twitter</li> <li><em>activity_nonactivist</em>: the daily activity count of non-activist users on Finnish climate Twitter</li> <li><em>users_activist</em>: the daily count of activist users on Finnish climate Twitter</li> <li><em>users_nonactivist</em>: the daily count of non-activist users on Finnish climate Twitter</li> </ul> <p>`weekly_ERGM_coefficients.csv` contains a subset of estimated coefficients from ERGMs of weekly retweet networks from Finnish climate Twitter. The data set contains the following variables (coefficients), not all of which are from the same model specification.</p> <ul> <li><em>week</em>: starting with week 2 (2020-07-25 to 2020-07-31) and ending with week 82 (2022-02-05 to 2022-02-11)</li> <li><em>political_activity_activist</em>: activists retweeting politically relevant users</li> <li><em>political_activity_nonactivist</em>: non-activists retweeting politically relevant users</li> <li><em>gwidegree0_activist</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork</li> <li><em>gwidegree0_nonactivist</em>: geometrically weighted in-degree (decay = 0) for the non-activist subnetwork</li> <li><em>gwidegree0_activistsub_thresh3</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh4</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh5</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh6</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh7</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh8</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh9</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> </ul> <p> </p> <p><strong>References</strong></p> <p>Please reference the original study when using this data set.<br>Savolainen, Sonja, Ville P. Saarinen, and Ted Hsuan Yun Chen. 2024. “Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism.” <a href="https://doi.org/10.31235/osf.io/p4yvk" target="_blank" rel="noopener">doi:10.31235/osf.io/p4yvk</a>.</p>
ENERGISE Living Lab country report - Finland
<p>ENERGISE Living Labs (ELLs) employ practice-based approaches to reduce energy use in households while co-creating knowledge on why energy-intensive practices are performed and how they depend on the context in which they are performed. Altogether 16 living labs were implemented in eight European countries in 2018.</p> <p>The Finnish ELLs were implemented in two different sites. ELL1 for individual households took place in a Porvoo region, about 50 kilometres from Helsinki and engaged 19 households living mainly in single-family homes. ELL2 (with collective elements) was implemented in Merihaka, urban district close to Helsinki city centre, and 18 households living in apartment buildings in Merihaka joined the ELL challenges and community activities.</p>
Dataset to: Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland
<p>This repository contains the datasets used in the study 'Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland'. Detailed information and technical aspects of the data can be found in the publication.</p>
Fig. 9 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland
Fig. 9. Size differences between infected and non-infected cormorants. The black line represents the median length (a and b) and median weight (c). The grey box represents the middle 50% of the data (n = 65).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.