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Data for A method for assessment of the general circulation model quality using k-means clustering algorithm
No full text<p>The dataset consists of simulated and observed salinity/temperature data which were used in the manuscript "A method for assessment of the general circulation model quality using k-means clustering algorithm" submitted to Geoscientific Model Development. <br>The model simulation dataset is from long-term 3D circulation model simulation (Maljutenko and Raudsepp 2014, 2019). The observations are from the "Baltic Sea - Eutrophication and Acidity aggregated datasets 1902/2017 v2018" SMHI (2018). <br> <br>The files are in simple comma separated table format without headers. <br>The Dout-t_z_lat_lon_Smod_Sobs_Tmod_Tobs.csv file contains columns with following variables [units]: <br>Time [matlab datenum units], Vertical coordinate [m], latitude [oN], longitude [oE], model salinity [g/kg], observed salinity [g/kg], model temperature [oC], observed temperature [oC].</p><p>The Dout-t_z_lat_lon_dS_dT_K1_K2_K3_K4_K5_K6_K7_K8_K9.csv file contains columns with following variables [units]: <br>4 first columns are the same as in the previous file, salinity error [g/kg], temperature error [oC], columns 7-8 are integers showing the cluster to which the error pair is designated. </p><p>do_clust_valid_DataFig.m is a Matlab script which reads the two csv files (and optionally mask file Model_mask.mat), performs the clustering analysis and creates plots which are used in Manuscript. The script is organized into %% blocks which can be executed separately (default: ctrl+enter).</p><p>k-means function is used from the Matlab Statistics and Machine Learning Toolbox.</p><p>Additional software used in the do_clust_valid_DataFig.m:</p><p>Author's auxiliary formatting scripts script/<br>datetick_cst.m <br>do_fitfig.m <br>do_skipticks.m <br>do_skipticks_y.m</p><p>Colormaps are generated using cbrewer.m (Charles, 2021).<br>Moving average smoothing is performed using nanmoving_average.m (Aguilera, 2021).</p><p>Refferences:</p><p>Aguilera, C. A. V., 2021. moving_average v3.1 (Mar 2008) (https://www.mathworks.com/matlabcentral/fileexchange/12276-moving_average-v3-1-mar-2008), MATLAB Central File Exchange. Retrieved March 2, 2021.</p><p>Charles, 2021. cbrewer : colorbrewer schemes for Matlab (https://www.mathworks.com/matlabcentral/fileexchange/34087-cbrewer-colorbrewer-schemes-for-matlab), MATLAB Central File Exchange. Retrieved March 2, 2021.</p><p>Maljutenko, I., Raudsepp, U., 2019. Long-term mean, interannual and seasonal circulation in the Gulf of Finland—the wide salt wedge estuary or gulf type ROFI. Journal of Marine Systems, 195, pp.1-19. doi:10.1016/j.jmarsys.2019.03.004</p><p>Maljutenko, I., Raudsepp, U., 2014. Validation of GETM model simulated long-term salinity fields in the pathway of saltwater transport in response to the Major Baltic Inflows in the Baltic Sea. Measuring and Modeling of Multi-Scale Interactions in the Marine Environment - IEEE/OES Baltic International Symposium 2014, BALTIC 2014, 6887830. doi:10.1109/BALTIC.2014.6887830</p><p>SMHI 2018, Swedish Meteorological and Hydrological Institute (SMHI) (2018). Baltic Sea - Eutrophication and Acidity aggregated datasets 1902/2017 v2018. Aggregated datasets were generated in the framework of EMODnet Chemistry III, under the support of DG MARE Call for Tender EASME/EMFF/2016/006 - lot4. doi:10.6092/595D233C-3F8C-4497-8BD2-52725CEFF96B</p>
Bakalaureusetöös "Liivi lahe jääolude ajalis-ruumiline muutlikus" kasutatud andmed ja scriptid.
No full text<p>Manusesse on lisatud bakalaurusetöös "Liivi lahe jääolude ajlis-ruumiline muutlikus" kasutatud andmed ja scriptid. Originaal andmed on võetud Copernicus Marine kodulehelt.</p>
Designing microorganisms for the transition to bioeconomy
No full text<p>Oral presentation was delivered at the XV Science Conference of the School of Science, Tallinn University of Technology, on November 29, 2023, in Tallinn, Estonia.</p>
Plant inventory of GreenTwins project pilot areas in Tallinn and Helsinki
No full text<p>This dataset contains vegetation data collected and/or curated at the project's pilot areas in Tallinn, Estonia (59.438453 N, 24.736159 E), and Helsinki, Finland (60.199457 N, 24.978951 E). The project had two pilot areas in both cities: a large pilot area ranging from 400 ha (Tallinn) to 600 ha (Helsinki), used for testing the application Virtual Green Planner, and a small pilot area, ranging from 14.5 ha (Toompark, Tallinn) to 60.9 ha (Hermannin rantapuisto, Helsinki), used for testing the application Urban Tempo. Borders of the pilot areas are presented in an included file, and the datasets have their respective readme files for further information on the included parameters. Vegetation data of the Large pilot area, Tallinn, is not inlcuded in this package.</p><p>The data of Helsinki pilot areas and the Tallinn Toompark were collected as follows:</p><ul><li>Hermannin rantapuisto, Helsinki: Data on trees, shrubs and herbaceous plants were collected using a field inventory. The data contain a description of the fieldwork and the resulting raw and curated data. In the curated data, data layers are split into individual trees (point data) and plant cover data consisting of trees and herbaceous vegetation types (polygon data).</li><li>Large pilot area, Helsinki: The base data on trees were obtained from the City of Helsinki open data repositories: The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/helsingin-kaupungin-puurekisteri">Urban tree database of the City of Helsinki</a> dataset is Helsingin kaupunkiympäristön toimiala / Maankäyttö ja kaupunkirakenne / Kaupunkitila ja maisemasuunnittelu. The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/helsingin-laserkeilausaineistot">Lidar datasets of the city of Helsinki</a> dataset is Helsingin kaupunkiympäristön toimiala / Kaupunkimittauspalvelut. The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/paakaupunkiseudun-ortoilmakuvat">Orthophotos of the Helsinki metropolitan area</a> dataset is Helsingin seudun ympäristöpalvelut HSY. The datasets have been downloaded from <a href="https://hri.fi/">Helsinki Region Infoshare</a> service under the license <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</a>. A multi-algorithm approach (Yao, C. Fabritius, H., Fricker, P. & F. Dembski: Multi-Algorithm-Based Urban Tree Information Extraction and Its Applications in Urban Planning. A manuscript in revision) was used to identify individual tree locations and species form these data.</li><li>Toompark, Tallinn: Data on herbaceous plants were obtained in the form of green area polygons by the City of Tallinn, assigned to different vegetation types by local experts in botany in collaboration with the City of Tallinn. Tree data were extracted algorithmically using the same method as for Helsinki from Estonia Land Board Orthophotos (<a href="https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html">https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html</a>) and LIDAR (<a href="https://geoportaal.maaamet.ee/est/Ruumiandmed/Korgusandmed/Aerolaserskaneerimise-korguspunktid-p499.html">https://geoportaal.maaamet.ee/est/Ruumiandmed/Korgusandmed/Aerolaserskaneerimise-korguspunktid-p499.html</a>) data. Tree species were determined by field investigation.</li></ul>
Plant inventory of GreenTwins project pilot areas in Tallinn and Helsinki
No full text<p>This dataset contains vegetation data collected and/or curated at the project's pilot areas in Tallinn, Estonia (59.438453 N, 24.736159 E), and Helsinki, Finland (60.199457 N, 24.978951 E). The project had two pilot areas in both cities: a large pilot area ranging from 400 ha (Tallinn) to 600 ha (Helsinki), used for testing the application Virtual Green Planner, and a small pilot area, ranging from 14.5 ha (Toompark, Tallinn) to 60.9 ha (Hermannin rantapuisto, Helsinki), used for testing the application Urban Tempo. Borders of the pilot areas are presented in an included file, and the datasets have their respective readme files for further information on the included parameters. Vegetation data of the Large pilot area, Tallinn, is not inlcuded in this package.</p><p>The data of Helsinki pilot areas and the Tallinn Toompark were collected as follows:</p><ul><li>Hermannin rantapuisto, Helsinki: Data on trees, shrubs and herbaceous plants were collected using a field inventory. The data contain a description of the fieldwork and the resulting raw and curated data. In the curated data, data layers are split into individual trees (point data) and plant cover data consisting of trees and herbaceous vegetation types (polygon data).</li><li>Large pilot area, Helsinki: The base data on trees were obtained from the City of Helsinki open data repositories: The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/helsingin-kaupungin-puurekisteri">Urban tree database of the City of Helsinki</a> dataset is Helsingin kaupunkiympäristön toimiala / Maankäyttö ja kaupunkirakenne / Kaupunkitila ja maisemasuunnittelu. The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/helsingin-laserkeilausaineistot">Lidar datasets of the city of Helsinki</a> dataset is Helsingin kaupunkiympäristön toimiala / Kaupunkimittauspalvelut. The maintainer of the <a href="https://hri.fi/data/en_GB/dataset/paakaupunkiseudun-ortoilmakuvat">Orthophotos of the Helsinki metropolitan area</a> dataset is Helsingin seudun ympäristöpalvelut HSY. The datasets have been downloaded from <a href="https://hri.fi/">Helsinki Region Infoshare</a> service under the license <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</a>. A multi-algorithm approach (Yao, C. Fabritius, H., Fricker, P. & F. Dembski: Multi-Algorithm-Based Urban Tree Information Extraction and Its Applications in Urban Planning. A manuscript in revision) was used to identify individual tree locations and species form these data.</li><li>Toompark, Tallinn: Data on herbaceous plants were obtained in the form of green area polygons by the City of Tallinn, assigned to different vegetation types by local experts in botany in collaboration with the City of Tallinn. Tree data were extracted algorithmically using the same method as for Helsinki from Estonia Land Board Orthophotos (<a href="https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html">https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html</a>) and LIDAR (<a href="https://geoportaal.maaamet.ee/est/Ruumiandmed/Korgusandmed/Aerolaserskaneerimise-korguspunktid-p499.html">https://geoportaal.maaamet.ee/est/Ruumiandmed/Korgusandmed/Aerolaserskaneerimise-korguspunktid-p499.html</a>) data. Tree species were determined by field investigation.</li></ul>
AvaLinn Smart City Planning Hub Design and Brand Identity
No full text<p>AvaLinn Smart City Planning Hub Design and Brand Identity was done in the framework of the FinEst pilot project entitled <a href="https://www.finestcentre.eu/greentwins">GreenTwins.</a></p><p>The interior design and brand identity were developed by BOB & DOKO in collaboration with the City of Tallinn and TalTech / FinEst Centre.</p><p>The data set contains:</p><ul><li>Interior design visualisations;</li><li>Interior design drawings;</li><li>Corporate visual identity (CVI).</li></ul>
Green Twins - latest version of Urban tempo client
No full textoai:data.taltech.ee:jh4t4-8e797<p>This is the latest version of the urban tempo app client, beta14.31.5.2023</p>
