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167 results for “Lithuania”
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 1
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the first subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Lithuania
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2022_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2021_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2020_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2019_LT: State Food and Veterinary Service (VMVT)</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Lithuania
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
National Checklists 2017: Lithuania Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Lithuania collected using effechecka and geonames polygons
National Checklists 2019: Lithuania Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Lithuania collected using effechecka and geonames polygons
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #3
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 3</p> <p>https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_3.zip</p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 33.6 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #2
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 2</p> <p><a href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip/Hyper_2.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_2.zip</a></p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 23.4 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #1
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p><a title="Hyperspectral imaging dataset over Lithuanian forests " href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip</a></p> <p>Dataset consists of 6 flight lines filmed over a stated healthy forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 25.9 GB</p>
Social Accounting Matrix for Lithuania, 2017 (with disaggregated Rubber and Plastics activity)
<p>The dataset is based on doi: 10.5281/zenodo.5077893 but includes Rubber and plastics activity disaggregated to depict production of plastic bags with more details.</p>
Social Accounting Matrix for Lithuania, 2017 (with the shift to bioplastics in packing industry)
<p>The dataset is based on doi: 10.5281/zenodo.5077893 but includes bioplastics as the main input for the production of plastic sacks and bags.</p>
Social Accounting Matrix for Lithuania, 2017
<p>The dataset provides Lithuanian Social Accounting Matrix compilded using official data from Eurostat:</p> <p>•Supply table (naio_10_cp15)</p> <p>•Use table (naio_10_cp16)</p> <p>•Statistics on non-financial transactions (nasq_10_nf_tr)</p> <p>The matrix is balanced minimizing relative deviations from original values. It uses Eurostat's naming conventions. In addition, elements ACT1, ACT2, ACT3, ACT4, ACT5, CMD1, CMD2, CMD3, CMD4, CMD5, FC1, FC2 are introduced to enable disaggregations and descriptions of industrial transformations.</p>
Testing data from SUPER PV demo site in Vilnius (Lithuania)
<p>Sets of the data, collected with SuperPV MLPE boxes from the Lithuanian demo site during the period 2021/08/15 - 2021/08/30. Contain information of the PV modules parameters, as follows:</p> <p>"1122334455667788" - Nr.1 / 90-degree angle (vertical);<br> "FFFFFFFFFFFFFFFF" - Nr.2 / 90-degree angle (vertical);</p> <p>"3333333333333333" - Nr.3 / 45-degree angle;<br> "4444444444444444" - Nr.4 / 45-degree angle;</p> <p>Data files column names explanation:</p> <p>Uoc - open circuit voltage<br> Isc - short circuit current<br> Uin - voltage at maximum power point<br> Iin - current at maximum power point</p> <p>Temp - temperature<br> P - power</p>
Social Matrix for Lithuania, 2020 with disaggregated CPA_C22
<p>The dataset contains the Lithuanians social accounting matrix for 2020 with disaggregated CPA_C22. The FIGARO database’s 2022 edition (Eurostat (2022). ESA supply, use and input-output tables) is used to create product-by-product input-output table for the EU, while Eurostat’s data on non-financial transactions (a dataset called nasa_10_nf_tr , Eurostat (2022). Non-financial transactions - annual data) is used to cover the remaining parts of the social accounting matrix.</p> <p>The dataset includes a baseline (Reference) scenario without the simulation of sustainability practices and three scenarios simulating the substitution of plastic bags by paper bags (PaperBags), bioplastic bags (BioPlastics) and the reduction of the use of plastic bags (ConsReduction).</p> <p> </p> <p>This research was funded by the grant S-MIP-20-53 from the Research Council of Lithuania.</p>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Lithuania
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Lithuania
<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 and 2021, 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-CHEMMON2021 – National Food and Veterinary Risk Assessment Institute</p> <p>OCC-CHEMMON2022 – National Food and Veterinary Risk Assessment Institute</p> <p>OCC-CHEMMON2023 – National Food and Veterinary Risk Assessment Institute</p> <p>OCC-CHEMMON2024 – National Food and Veterinary Risk Assessment Institute</p>
Fig. 1 in Infestation With Ixodes Ricinus Ticks On Migrating Passerine Birds In Lithuania And Norway
Fig. 1 Molecular taxonomical identification of the I. ricinus by PCR assay. Lines 1 and 15 – 50 bp marker; Line 2 –negative control; Lines 2-13 –positive results: amplified 150 bp specific fragment for I. ricinus; Line 14 – positive control of I. ricinus (150 bp)
Fig. 1 in Birch Tree Response To Different Environmental And Meteorological Conditions In Lithuania
Fig. 1. Defoliation (%) of silver and downy birches growing in the sites of different humidity. Values are given as the mean±SE.
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Lithuania
<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 Lithuania. 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 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2022 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2021 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2020 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2019 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2018 – National Food and Veterinary Risk Assessment Institute</p> <p>VMPR_2017 – National Food and Veterinary Risk Assessment Institute</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.