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971 results for “romania”
RoCliB - Bias corrected CORDEX RCM dataset over Romania
<p>This dataset contains a set of four climate variables from 10 General Circulation Models (GCMs), dynamically downscaled in the EURO-CORDEX initiative by several Regional Climate Models (RCMs) and adjusted (bias-corrected) over Romania for the period 1971–2100. The climate models data were obtained from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a>. Two climate change scenarios were selected, namely the moderate (RCP4.5) and business-as-usual scenario (RCP8.5). The multivariate bias correction by the N-dimensional probability density method (MBCn) was used to bias correct the RCMs outputs [1], using as reference the ROCADA gridded dataset [2].</p> <p>Characteristic:</p> <ul> <li><strong>Climate variables</strong>: air temperature (tasAdjust - Celsius degree), maximum air temperature (tasmaxAdjust - Celsius degree), minimum air temperature (tasminAdjust - Celsius degree) and precipitation (prAdjust - mm)</li> <li><strong>Bias-correction method:</strong> multivariate bias correction (N-pdft)</li> <li><strong>The reference period used for bias correction: </strong>1971-2005</li> <li><strong>The observational dataset used as a reference for bias correction: </strong>ROCADAv1</li> <li><strong>Temporal resolution:</strong> daily</li> <li><strong>Temporal extent</strong>: <ul> <li>Historical: 1971-2005;</li> <li>RCP4.5 and RCP8.5: 2006-2100.</li> </ul> </li> <li><strong>Spatial resolution:</strong> 0.1 degrees (~10km)</li> <li><strong>Spatial extent:</strong> from 20.1 to 29.8°E and 43.5 to 48.4°N</li> <li><strong>File format: n</strong>etCDF, CF-1.4-compliant format using netCDF4 compression</li> <li><strong>Coordinate system: </strong>WGS 1984 (EPSG:4326)</li> <li><strong>Naming conventions: </strong><em>variablename</em>_ROU-11_<em>cmip5experiment</em>_<em>globalmodel</em>_<em>run</em>_r<em>egionalmodel</em>_<em>rcmversionid</em>_<em>timefrequency</em>_<em>starttime-endtime</em><em>.</em>nc</li> <li><strong>RMCs</strong> (Institution or working group, RCM Model, GCM Institute, GCM Driving): <ul> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 CNRM-CERFACSCNRM-CM5</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E CNRM-CERFACS CNRM-CM5</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4CNRM-CERFACS CNRM-CM5</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 ICHECEC-EARTH</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4I CHECEC-EARTH</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E ICHECEC-EARTH</li> <li>Danish Meteorological Institute (DMI) HIRHAM5 ICHECEC-EARTH</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 MPI-MMPI-ESM-LR</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4 MPI-MMPI-ESM-LR</li> <li>Climate Service Center Germany (GERICS) REMO2015 NCC NorESM1-M</li> </ul> </li> </ul> <p><strong>The terms of use</strong> for RoCliB datasets are the same as those from the original EURO-CORDEX simulations obtained from ESGF servers: <a href="https://is-enes-data.github.io/cordex_terms_of_use.pdf">https://is-enes-data.github.io/cordex_terms_of_use.pdf</a>.</p> <p><strong>To access and visualize</strong> relevant facts and statistics about climate change based on the RoCliB datasets use <a href="http://suscap.meteoromania.ro/en/roclib">http://suscap.meteoromania.ro/en/roclib</a>.</p> <p><strong>Acknowledgement</strong><br> This work was supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III. We also acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5.</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Romania
<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_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2022_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2021_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2020_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2019_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> </ul>
CROSSBOW HLU2-UC4-TC4 Simulated curtailments the area of Tariverde, Romania
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Tariverde and the simulation of 30 limitations applied on random days</p>
CROSSBOW HLU2-UC4-TC5 Day ahead energy Price for the demonstration period in Romania
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Tariverde and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Croatia at the time the demonstration was held</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Romania
<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>
Raw luminescence data for samples from Abri 122/1200 (Vârghiș Gorges, Romania)
<p>The files contain the raw luminescence data used to calculate the equivalent doses and ages cited in the study by Schmidt et al.: Evidence for the oldest Middle Palaeolithic cave occupation in the Romanian Carpathians.</p> <p>The .seq files contain the measurement parameters, while the .binx files contain the results (.binx files can be read by the Analyst software thta can be downloaded for free here: https://users.aber.ac.uk/ggd/).</p> <p>DRT: Dose recovery test</p> <p>PHP: Preheat plateau test</p> <p> </p> <p>The .csv files contain all parameters used to calculate the final ages, which were derived by using the software DRAC (Durcan et al., 2015). The two scenarios considering the shielding of the cave overburden for calculation of the cosmic dose rate refer to the two different .csv files, according to their name.</p> <p> </p>
Archetypes of climate change adaptation among large-scale arable farmers in southern Romania
<p>Supplementary material belonging to the publication.</p> <p>Two files:</p> <p>1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020.</p> <p>2. PDF with interview guideline</p>
National Checklists 2017: Romania 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 Romania collected using effechecka and geonames polygons
National Checklists 2019: Romania 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 Romania collected using effechecka and geonames polygons
Danube Delta (Romania) - NEVERMORE Climate Dataset
<p>The dataset consist of the historical and climate projection (CMIP6) for gridded atmospheric variables and the climate hazards/extreme events alongside the return values (likelihood) of hazards/extreme events. The dataset was developed during NEVERMORE project as part of WP3 from CMCC and NCSRD.</p>
PLOS Open Science Indicators & Zotero Romania Metadata
<p>Matched metadata from PLOS Open Science Indicators and Zotero export</p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Spatial models of topsoil properties in Romania using digital soil mapping techniques
<p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:</p> <p>Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, <em>Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning</em><strong>, PLOS ONE</strong>, 2023</p> <p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).</p> <p>The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).</p> <p>The soil variables are classified as follows:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Classes</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>2</strong></p> </td> <td> <p><strong>3</strong></p> </td> <td> <p><strong>4</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><em>pH</em></p> </td> <td> <p>≤ 5</p> <p>(strongly acid)</p> </td> <td> <p>5.1 – 5.8 (moderately acid)</p> </td> <td> <p>5.9 – 6.8</p> <p>(weakly acid)</p> </td> <td> <p>6.9 – 7.2</p> <p>(neutral)</p> </td> <td> <p>7.3 – 8.4</p> <p>(weakly alkaline)</p> </td> <td> <p>8.5 – 8.8 (moderately alkaline)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>EC (mS m<sup>-1</sup>)</em></p> </td> <td> <p>≤ 12.75</p> </td> <td> <p>12.76 – 16.49</p> </td> <td> <p>16.50 – 20.04</p> </td> <td> <p>20.05 – 24.18</p> </td> <td> <p>24.19 – 29.11</p> </td> <td> <p>29.12 – 35.23</p> </td> <td> <p>≤ 35.24</p> </td> </tr> <tr> <td> <p><em>OC (g kg<sup>-1</sup>)</em></p> </td> <td> <p>< 7.5</p> <p>(very low)</p> </td> <td> <p>7.5 – 17.4</p> <p>(low)</p> </td> <td> <p>17.4 – 37.8 (moderate)</p> </td> <td> <p>37.8 – 61.0</p> <p>(high)</p> </td> <td> <p>> 61</p> <p>(very high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>CaCO<sub>3</sub></em></p> <p><em>(g kg<sup>-1</sup>)</em></p> </td> <td> <p>0</p> <p>(no carbonates)</p> </td> <td> <p>1 – 10</p> <p>(low)</p> </td> <td> <p>11 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 80</p> <p>(medium 2)</p> </td> <td> <p>81 – 107</p> <p>(medium 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>P (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>< 4</p> <p>(extremely low)</p> </td> <td> <p>4 – 8</p> <p>(very low)</p> </td> <td> <p>8 – 18</p> <p>(low)</p> </td> <td> <p>18 – 36</p> <p>(medium)</p> </td> <td> <p>36 – 72</p> <p>(high)</p> </td> <td> <p>> 72</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>N (g kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 1</p> <p>(very low)</p> </td> <td> <p>1.1 – 1.4</p> <p>(low)</p> </td> <td> <p>1.5 – 2.0</p> <p>(medium 1)</p> </td> <td> <p>2.1 – 2.7</p> <p>(medium 2)</p> </td> <td> <p>2.8 – 6.0</p> <p>(high)</p> </td> <td> <p>> 6</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>K (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 40 *</p> <p>(extremely low)</p> </td> <td> <p>41 – 65 *</p> <p>(very low)</p> </td> <td> <p>66 – 130</p> <p>(low)</p> </td> <td> <p>131 – 200 (medium)</p> </td> <td> <p>201 – 300</p> <p>(high)</p> </td> <td> <p>> 300</p> <p> (very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Clay (%)</em></p> </td> <td> <p>≤ 25</p> <p> (low 1)</p> </td> <td> <p>26 – 32</p> <p>(low 2)</p> </td> <td> <p>33 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 45</p> <p>(medium 2)</p> </td> <td> <p>≥ 46</p> <p> (high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Silt (%)</em></p> </td> <td> <p>< 25</p> <p>(medium 1)</p> </td> <td> <p>25 – 32</p> <p>(medium 2)</p> </td> <td> <p>33 – 40</p> <p>(high 1)</p> </td> <td> <p>41 – 50</p> <p>(high 2)</p> </td> <td> <p>> 50</p> <p>(high 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Sand (%)</em></p> </td> <td> <p>< 15</p> <p>(low 1)</p> </td> <td> <p>15 – 25</p> <p>(low 2)</p> </td> <td> <p>26 – 35</p> <p>(low 3)</p> </td> <td> <p>36 – 56</p> <p>(medium)</p> </td> <td> <p>> 56</p> <p>(high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>* classes not present on the Romanian territory</p> <p> </p> <p> </p> <p> </p>
Fig. 5 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 5. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov. A. Holotype (ZMA V.Pl. 7264.1), microphotograph of gregarine protozoans in a gut diverticulum. B. Specimen from the Movile Cave, microphotograph of a nematode infecting the bulb of the adenodactyl (ZMA V.Pl. 7265.2). C. Holotype, microphotograph of the mass of sperm inside the copulatory bursa with the rod-like structures. D. Specimen from the Limanu well, microphotograph of the mass of sperm inside the copulatory bursa, with a multilayered concentric organization of the circular structures (ZMA V.Pl. 7269.1).
Fig. 3 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 3. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov., holotype (ZMA V.Pl. 7264.1). A. Sagittal reconstruction of the female copulatory apparatus (anterior to the left). B. Sagittal reconstruction of the male copulatory apparatus (anterior to the left). Only the terminal portions of the spermiducal vesicles are drawn. C. Sagittal reconstruction of the male copulatory apparatus with the cervix-like protrusion (anterior to the left).
Fig. 2 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 2. Dendrocoelum obstinatum Stocchino & Sluys, sp. nov. Sketch of the ventral view of a preserved (Bouin's fluid) specimen from Movile Cave.
Fig. 1 in A new species of freshwater flatworm (Platyhelminthes, Tricladida, Dendrocoelidae) inhabiting a chemoautotrophic groundwater ecosystem in Romania
Fig. 1. Geographic distribution of freshwater planarians of the genus Dendrocoelum recorded from Romania. ▲: unspecified locality of D. lacteum Müller, 1774. Rectangular inset (bottom) corresponds with enlarged area, showing the collection sites of D. obstinatum Stocchino & Sluys, sp. nov. indicated by blue stars (see also Table 1).
Figure 1 in Winter-active wolf spiders (Araneae: Lycosidae) in thermal habitats from western Romania
Figure 1. Map of the surveyed localities with thermal habitats in western Romania (1, Moneasa; 2, Ciocaia; 3, Roşiori; 4, Roşiori/Tămăşeu; 5, Săcuieni I; 6, Săcuieni II; 7, Curtici; 8, Socodor; 9, Chiribiş; 10, Chişlaz; 11, Livada de Bihor; 12, Mădăras; 13, Răbăgani; 14, Sânnicolau de Munte; 15, Tărian; 16, Acâş; 17, Beltiug; 18, Mihăieni; 19, Chiraleu; 20, Oradea; 21, Săcuieni III; 22, Tămăşeu; AR = Arad county, BH = Bihor county, SM = Satu Mare county).
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Romania
<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 – National Sanitary Veterinary and Food Safety Authority</p> <p>OCC-CHEMMON2021 – National Sanitary Veterinary and Food Safety Authority</p> <p>OCC-CHEMMON2022 – National Sanitary Veterinary and Food Safety Authority</p> <p>OCC-CHEMMON2023 – National Sanitary Veterinary and Food Safety Authority</p> <p>OCC-CHEMMON2024 – National Sanitary Veterinary and Food Safety Authority</p>
UCLARIS – urban thermo-hygrometric gridded dataset for Iasi city, Romania
<h4>This dataset contains 6 daily gridded climate variables derived from the measurements made during 10 years at 11 screen-level monitoring points for air temperature (T) and relative humidity (RH), distributed over the city of Iași, a medium-sized city in north-eastern Romania. Additionally, T and RH data from 3 air quality monitoring points of the Environmental Protection Agency (EPA) [1], and from the single National Meteorological Administration (NMA) official weather station of Iasi were used. The monitoring points cover the entire urban area of Iasi, sampling the most important local climate zones inside the city. The data were firstly quality controlled and homogenized using the CLIMATOL package [2], and afterwards the spatial distribution was obtained through residual kriging method with the digital elevation model (DEM) as predictor [3]. </h4><p><strong>Climate variables: </strong>Maximum air temperature – <strong>Tmax</strong>; Mean air temperature – <strong>Tavg</strong>; Minimum air temperature - <strong>Tmin</strong>; Maximum relative humidity - <strong>RHmax</strong>; Mean relative humidity - <strong>RHavg</strong>; Minimum relative humidity – <strong>RHmin</strong>.<strong> </strong></p><p><strong>Spatial extent:</strong> from 27.44167 to 27.84167 °E and 47.05833 to 47.25833 °N</p><p><strong>Temporal resolution</strong>: daily </p><p><strong>Temporal coverage</strong>: 2013/01/01 – 2022/12/31</p><p><strong>Spatial resolution</strong>: 0.008°</p><p><strong>File format: </strong>netCDF, CF-1.4-compliant format using netCDF4 compression</p><p><strong>Coordinate system: </strong>WGS 84 (EPSG: 4326)</p><p><strong>Other Institutions: </strong>National Meteorological Administration of Romania, Environmental Protection Agency of Romania</p><p><strong>Acknowledgement:</strong> This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2021-0882, within PNCDI III.</p><p><strong>References:</strong></p><p>[1] https://www.calitateaer.ro/</p><p>[2] Guijaro, J., 2023. Package "Climatol", CRAN, https://climatol.eu/</p><p>[3] European Digital Elevation Model (https://www.eea.europa.eu/en/datahub/datahubitem-view/d08852bc-7b5f-4835-a776-08362e2fbf4b)</p><p> </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.