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.
67
datasets available to search
ShareScore release 0.9.0
Dataset results
67 results for “Scots pine”
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
Below-ground hydraulic constraints during drought-induced decline in Scots pine
<p>Dataset from the paper 'Below-ground hydraulic constraints during drought-induced decline in Scots pine'.</p> <p><strong>Files:</strong></p> <p>DOY refers to day of year 2012, idtree is tree identity and Class is defoliation class. Tree characteristics can be found in the supplementary materials of the paper. Variables are expressed in the same units as in the paper.</p> <p><em>WaterPotentials.csv</em> - water potential data (predawn, PD, midday MD, difference)</p> <p><em>SapFlowDeltaPResistbc.csv</em> - daily sap flow per unit leaf area (Jl_daily), delta pressure (deltaP), VPD,SWC, belowcrown resistance (r_bc).</p> <p><em>Resistbc_percent.csv</em> - below-crown resistance as a percentage of total tree resistance</p> <p> </p>
Detailed point cloud data on stem size and shape of Scots pine trees
<p>This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland.</p> <p>TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m<sup>2</sup>, 976 points/m<sup>2</sup>, and 1030 points/m<sup>2</sup> for study site 1, 2, and 3, respectively.</p> <p>The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a).</p> <p>The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space.</p> <p>Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b).</p> <p> </p> <p><strong>References</strong></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. <a href="https://doi.org/10.1101/2020.03.02.972521">https://doi.org/10.1101/2020.03.02.972521</a></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. <a href="https://doi.org/10.1101/2020.03.09.983973">https://doi.org/10.1101/2020.03.09.983973</a></p> <p>Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. <a href="https://doi.org/10.3390/agriculture8050070">https://doi.org/10.3390/agriculture8050070</a></p> <p>Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. <a href="https://doi.org/10.31223/osf.io/ybs7c">https://doi.org/10.31223/osf.io/ybs7c</a></p>
Data for investigating structural complexity of individual Scots pine trees
<p>Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (<em>Pinus sylvestris</em> L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it.</p> <p>Please see the data descriptor for more information on the data structure and its possibilities.</p> <p>Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.</p>
Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs, in field and laboratory.
<p>The manuscript entitled "Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs" used two set of data: Field data and Laboratory data</p> <p>##### 1. FIELD DATA:<br> We measured photosynthesis and non-structural carbohydrate (NSC) content in adult Scots pine (Pinus sylvestris L.), in boreal conditions at Hyytiaälä SMEAR II station in Sourthen Finland. In the folder "Field Data", you will find automatic CO2 exchange measurements by shoot chambers, dynamic parameters for the light response of photosynthesis, and needles´ non-structural carbohydrate content (NSC) in 2008, 2009 and 2015. See the readme file in the folder for further information.</p> <p> </p> <p>#### 2. LABORATORY DATA</p> <p>We measured the relationship between photosynthesis and non-structural carbohydrate (NSC) content under stable laboratory conditions in three shrubs species:<br> i) evergreen lingonberry (Vaccinium vitis-idaea L.),<br> ii) evergreen heather (Calluna vulgaris (L.) Hull) and<br> iii) deciduous bilberry (Vaccinium myrtillus L.).<br> The plants grew in chambers where we measured the CO2 gas exchange and estimated photosynthesis. After CO2 gas exhcnage measurements we sampled the leaves for NSC analyses. See the readme file in the folder for further information.</p> <p> </p> <p> </p> <p> </p>
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Potential and realized distribution at 30m for Scots pine (Pinus sylvestris) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Scots pine (<em>Pinus sylvestris, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.sylvestris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.sylvestris</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests
<p>This dataset includes all the data we collected at the first summer after biochar application in boreal forests. Our paper“ the effect of biochar on soil CO<sub>2</sub> efflux in boreal forests“ now is under review in Annals of Forest Science. Biochar prepared at two reaction temperatures was applied at three rates (including non-amended controls). During the first year after treatment, efflux increased with higher rates of biochar, but the reaction temperature had no effect. o explain char effects on efflux, soil moisture and temperature were also added to the model testing treatment effects. These environmental variables explained more of the variation in efflux and caused treatment to no longer have a significant effect. Based on this result, we concluded that soil temperature explains the effect of char on efflux.</p>
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests
<p>This dataset includes all the data we collected at the first summer after biochar application in boreal forests. Our paper“ the effect of biochar on soil CO<sub>2</sub> efflux in boreal forests“ now is under review in Annals of Forest Science. Biochar prepared at two reaction temperatures was applied at three rates (including non-amended controls). During the first year after treatment, efflux increased with higher rates of biochar, but the reaction temperature had no effect. o explain char effects on efflux, soil moisture and temperature were also added to the model testing treatment effects. These environmental variables explained more of the variation in efflux and caused treatment to no longer have a significant effect. Based on this result, we concluded that soil temperature explains the effect of char on efflux.</p>
Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><strong>Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential</strong></p> <p>Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273.</p> <p>Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)*<br> 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland<br> 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland.<br> 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland <br> 4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping</p> <p> </p> <p><strong>Study and data description</strong></p> <p>Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed.</p> <p>The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds. </p> <p>The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).</p> <p> </p> <p>Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are:</p> <ul> <li><strong>additional_info_scotspine</strong>: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li><strong>manuscript_figures:</strong> Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li><strong>matlab_scripts:</strong> Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li><strong>mri_scotspine</strong>: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li><strong>radiography_scotspine: </strong>Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>).</li> <li><strong>readme.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Please see the included readme.txt for further details.</p> <p> </p> <p>(Teemu Tuomainen, Jan 25, 2022)</p>
Data supporting "Solar radiation drives methane emissions from the shoots of Scots pine"
<p>Data supporting our New Phytologist publication "Solar radiation drives methane emissions from the shoots of Scots pine". </p>
Fig. 2 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 2. Logistic regression curve – Relationship between age of the Polish Scots pine stands (years) and mean individual biomass of carabids (mg)
Fig. 1 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 1. Logistic regression curve – Relationship between age of the Western German beech stands (years) and mean individual biomass of carabids (mg)
Fig. 7 in Differences Of The Bog And Dry Site Scots Pine Population Seedlings Germination And Early Growth
Fig. 7. Comparison of development stage when the terminal bud is formed between seedlings from bog site and dry site (mean development stage and standard error).
Fig. 5 in Differences Of The Bog And Dry Site Scots Pine Population Seedlings Germination And Early Growth
Fig. 5. Dry site seedlings early growth stages change during the first season till terminal bud formation.
Fig. 6 in Differences Of The Bog And Dry Site Scots Pine Population Seedlings Germination And Early Growth
Fig. 6. Bog site seedlings early growth stages change during the first season till terminal bud formation.
Fig. 3 in Variation In Cone And Seed Morphology Traits Among The Mitochondrial Dna Haplotypes Of Scots Pine (Pinus Sylvestris L.)
Fig. 3. Dependence of seed number per cone on cone length for the type A and type B mitotypes of Scots pine. Individual cone values are shown.
Soil and understory CO2 respiration, CH4, and N2O fluxes, tree biomass and litter, and soil carbon stock after a long-term N fertilization of a Scots pine forest in Finland
<p>Data of forest soil respiration, soil and undestory respiration, CH4, and N2O fluxes, soil temperature and volumetric water content (Data_Karstula_GHG_temp.swc.csv), continuous soil temperature and moisture data (Data_Karstula_measured_temperature_2021_2023.csv, Data_Karstula_measured_moisture_2021_2023.csv), forest biomass and litter (Data_Karstula_total_biomass_litter.csv, Data_Karstula_measured_litter_2021_2023.csv), and soil C stocks (Data_Karstula_soc.csv) from the boreal Scots pine forest site Karstula after a long-term N fertilization in Finland (62°54'43.343"N; 24°34'16.021"E).</p> <p>The dataset is used for the publication "Tupek et al. : <strong>Lower sensitivity of microbial respiration to soil moisture after long-term N fertilization increases soil carbon retention in a Scots pine forest</strong>. 2024".</p>
Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer
<p><strong>The dataset comprises nuclear microsatellite data (PCR products lengths) used in the paper "Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer".</strong></p> <p><strong>The pdf file includes the list of populations.</strong></p> <p>Abstract: Scots pine (<em>Pinus sylvestris</em> L.) seems to be a species of low conservation priority because it has a very wide Eurasian distribution and plays a leading role in many forest tree breeding programs. Nevertheless, considering its economic value, long breeding history, range fragmentation, and increased mortality, which is also projected in the future, it requires a more detailed description of its genetic resources. Our goal was to compare patterns of genetic variation found in biparentally inherited nuclear DNA with previous research carried out with mitochondrial and chloroplast DNA due to their different modes of transmission. We analysed the genetic variation and relationships of 60 populations across the distribution of Scots pine in Eurasia (1,262 individuals) using a set of nuclear DNA markers. We confirmed the high genetic variation and low genetic differentiation of Scots pine spanning large geographical areas. Nevertheless, there was a clear division between European and Asian gene pools. The genetic variation of Asian populations was lower than in Europe. Spain, Turkey, and the Apennines constituted separate gene pools, the latter showing the lowest values of all genetic variation parameters. The analyses showed that most populations experienced genetic bottlenecks in the distant past. Ongoing admixture was found in Fennoscandia. Our results suggest a much simpler recolonization history of the Asian than European part of the Scots pine distribution, with migration from limited sources and possible founder effects. Eastern European stands seem to have descended from the Urals refugium. It appears that Central Europe and Fennoscandia share at least one glacial refugium in the Balkans and migrants from higher latitudes, as well as from southeastern regions. The low genetic structure between Central Europe and Fennoscandia, along with their high genetic admixture, may result at least partially from past human activities related to the transfer of germplasm in the 19<sup>th</sup> and early 20<sup>th</sup> centuries. In light of ongoing climate changes and projected range shifts of Scots pine, conservation strategies are especially needed for marginal and isolated stands of this species. Genetic research should also be complemented in parts of the species distribution that have thus far been poorly studied.</p>
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.