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3,135 results for “spain”
SD4EO: AI-based synthetic satellite multispectral agricultural textures in Spain (Oct 2017 - Sep 2018)
<p>This dataset has been created as part of the deliverables for ESA’s <a title="https://eo4society.esa.int/projects/sd4eo/" href="https://eo4society.esa.int/projects/sd4eo/" target="_blank" rel="noopener">SD4EO project.</a> It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types:</p> <ul> <li> Barley.</li> <li> Wheat.</li> <li> Other grain leguminous.</li> <li> Peas.</li> <li> Fallow & Bare soil.</li> <li> Vetch.</li> <li> Alfalfa.</li> <li> Sunflower.</li> <li> Oats.</li> </ul> <p>The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset.</p> <p>The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season.</p> <p>In <em>this</em> dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area.</p> <p>Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data.</p> <p>File names are structured as follows:</p> <ul> <li>Prefix "HO" indicating the synthesis method</li> <li>The crop type name: <ul> <li>Barley</li> <li>Wheat</li> <li>OtherGrainLeguminous</li> <li>Peas</li> <li>FallowAndBareSoil</li> <li>Vetch</li> <li>Alfalfa</li> <li>Sunflower</li> <li>Oats</li> </ul> </li> <li>Year/Month/01 (representing the start of the month period)</li> <li>Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)</li> <li>Number of the synthesis variant</li> </ul> <p>The generation parameters for all images include:</p> <ul> <li>Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)</li> <li>4 levels of depth in the Steerable pyramid</li> <li>6 orientations in the Steerable pyramid</li> <li>14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.</li> <li>30 iterations</li> </ul> <p>A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses).</p> <p>Thus, the following files can be considered partially failed syntheses:</p> <ul> <li>HO_Alfalfa_20180801_768_1.nc</li> <li>HO_FallowAndBareSoil_20180101_768_3.nc</li> <li>HO_OtherGrainLeguminous_20171201_256_4.nc</li> <li>HO_Vetch_20180301_384_3.nc</li> </ul> <p>Files are encoded in the standardized net4CDF format [<a href="https://unidata.github.io/netcdf4-python/">link</a>], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period.</p> <p>The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size:</p> <ul> <li>'B02' 10m (Sentinel-2)</li> <li>'B03' 10m (Sentinel-2)</li> <li>'B04' 10m (Sentinel-2)</li> <li>'B08' 10m (Sentinel-2)</li> <li>'B05' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B06' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B07' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B11' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B12' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B8A' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'VH' also resampled to 10m (Sentinel-1)</li> </ul> <p>The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid.</p> <p>Additionally, the following metadata are stored as xarray attributes:</p> <ul> <li>"long_name": corresponding to the crop type name</li> <li>"date": the period of the original data used as the base image for synthesis</li> <li>"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia</li> <li>"synthetic_method": corresponds to the high-order constrained method</li> <li>"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast</li> </ul> <p>A total of:</p> <p><strong> 9</strong> types of crops x <strong>12</strong> months x <strong>6</strong> variants = <strong>648</strong> synthetized multispectral textures</p> <p>occupying <strong>34.5</strong>GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p> <p> </p>
Socio-political attitudes in Spain (2023)
<p>This dataset captures the responses of over 1500 participants in Spain to an original online survey.</p> <p>This online survey was designed by a group of experts in populism from Universidad Nacional de Educación a Distancia (UNED, Madrid), King's College London, Univerity of York, Universidad Diego Portales, Chile, Universidad Autónoma de Madrid (UAM) and University of Liverpool.</p> <p>The survey contains over a hundred items:</p> <ul> <li>Socio-demographic items: education, age, religion, gender, employment</li> <li>Populism items: including Akkerman et al.'s 2014 scale of populist attitudes, and a new items corresponding to a new multi-dimensional scale of populist attitudes (Olivas Osuna 2021; Olivas Osuna et al. forthcoming) (32 items)</li> <li>Items related to trust on institutions and media (9 items)</li> <li>Items related to satisfaction with the functioning of democracy, services and institutions (7 items)</li> <li>Authoritarian values (Feldman and Stenner 1997)</li> <li>Liberal democratic values (Zanotti and Rama 2021)</li> <li>Authoritarian personality indexes (Hibbing 2020)</li> <li>Conspiracy theories (3 items)</li> <li>Nationalism (5 items)</li> <li>Nativism (Young et al. 2019)</li> <li>Affective polarisation</li> <li>Support for political party (past vote and vote intention)</li> <li>Left-right ideological self-placement</li> <li>Other socio-political questions.</li> </ul> <p>Fieldwork was conducted by YouGov Spain in February 2023. This surveys was part of the following projects: <em>Populism and Borders: a Supply- and Demand-Side Comparative Analysis of Discourses and Attitudes (PBSDCA) </em>and <em>Principal Investigator Interdisciplinary Comparative Project on Populism and Secessionism (ICPPS).</em></p> <p>The uploaded files contain:</p> <ul> <li>Detail of survey results (.sav)</li> <li>Questionnaire (.doc)</li> <li>Summary of results (.xls)</li> <li>Fieldwork summary file (.pdf)</li> </ul>
Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)
<p>Dataset used to perform the following manuscripts: </p> <p>- Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. <em>JMIR mHealth and uHealth</em>, <em>4</em>(3), e112. http://doi.org/10.2196/mhealth.5716</p> <p>rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework. </p>
Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in <em>Pinus halepensis</em> Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Parameters included in the dataset: <br> Plot: plot identification in the SNFI and iuFOR networks.<br> Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis)<br> Slope: gradient in the plot in percentage.<br> Altitude: elevation of the plot in meters above the sea level<br> Latitude and Longitude: geographical coordinates of the plots in degrees<br> Density: number of trees per hectare in the plot<br> Dg: quadratic mean diameter in centimetersç<br> Hm: mean height in meters of the trees in the plot<br> H0; dominant height in meters of the trees in the plot<br> BA: basal area of the plot in square meters per hectare<br> SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands) <br> SQ: the site quality class<br> Age: average age in years of the trees in the plot<br> AW: soil available water in percentage<br> CO: soil coarse particles in percentage<br> Porosity: soil porosity in percentage<br> CLAY: clay content in soil in percentage<br> SILTUS: silt content in soil following the USDA criteria in percentage<br> SILTIS: silt content in soil following the International criteria, in percentage<br> SANDUS: sand content in soil following the USDA criteria, in percentage<br> SANDIS: sand content in soil following the International criteria, in percentage<br> OHT: organic horizon thickness in the plot in centimeters<br> ([C/N]L): the total carbon to total nitrogen ratio in the litter fraction of the organic horizon<br> ([C/N]FH): the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon <br> L: amount of litter fraction in the organic horizon in tons per hectare<br> FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare. <br> pH: soil pH value <br> CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934)<br> AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962)<br> TN: total N in soil in percentage<br> TOC/TN: total organic C to total N ratio in soil<br> Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945)<br> WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983)<br> Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972)<br> React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007)<br> Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954)<br> Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978)<br> EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> Sat: base saturation of soil in percentage <br> AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968)<br> AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987) <br> AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996)<br> AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971)<br> Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987)<br> Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952)<br> Cmin/TOC: mineralizable carbon to total organic carbon ratio <br> Cmic/TOC: microbial biomass carbon to total organic carbon ratio<br> qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil<br> FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute)<br> DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute)<br> AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute)<br> Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute)<br> Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O<sub>2</sub> produced per gram of soil and minute)<br> MAT: mean annual temperature in degrees centigrade (Ninyerola et al., 2005)<br> MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MMCM: mean maximum temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MTCM: mean temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> TP: total precipitation in millimeters (Ninyerola et al., 2005)<br> PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005)<br> PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> AHI: Annual Hydric Index (Thornthwaite, 1949)<br> Martonne: Martonne index (De-Martonne, 1926)<br> Lang: Lang index (Lang, 1919)</p> <p>Code -999.99 indicates missing values.</p>
DS_Wave_BiMEP: Wave resource at BiMEP (Spain)
<p>This Technical Note extends the first published dataset obtained from the TRIAXYS buoy deployed at BiMEP. It covers two periods: i) December 2016 to October 2017; ii) March 2018 to July 2018</p> <p>Sensor is located at 87 m water depth, 300 m up-wave of the OCEANTEC's Wave Energy Converter, MARMOK- A5 (43°28'12.19"N, 2°52'17.88"O). Statistical wave data are calculated from 20-min time series.</p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2017
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p> <p> </p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p>
H2020 PrimeFish National Level Competitiveness Iceland Norway Spain Vietnam Newfoundland
<p>The data set contains data on individual indicators of national seafood competitiveness. Data are collected as part of the EU H2020 project PrimeFish (grant no 635761). The analysis follows the general framework of the annual World Economic Forum Competitiveness Report. Indicators are taken from three sources; directly from the World Economic Forum report, survey among national experts and hard data such as stock sizes and wages. All data are numeric, and on a 1-7 scale. Data were collected from the World Economic Forum 2017 competitiveness report and surveys and hard data collected in 2017.</p> <p> </p> <p>Indicators are grouped in several catergories, that again are grouped in higher level categories, ultimately yielding a single competitiveness indicator for the seafood sector.</p>
(PRE) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria
<p>The dataset has been created from obtaining answers from 303 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Questionnaire.pdf</strong>). It is composed by several factors which are explained in different TXT files. All these factors are contained in a "<strong>all_code_final_zenodo.xlsx</strong>" along with their answers by participants. In the following a short descrition of each TXT which explian the dataset is provided.:</p> <p> * <strong>socio-economic_description_not_dependent_of_work</strong><br> - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents.</p> <p> * <strong>socio-economic_description_dependent_of_work</strong><br> - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such environment.</p> <p> * <strong>actions-at-work</strong><br> - Are a set of attributes which describe certain practices of employees in relation to energy efficiency.</p> <p> * <strong>persuasive_strategies</strong><br> - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Phychological Persuasive principles which are also explained in the file.</p> <p> * <strong>all_attributes_together</strong><br> - All the variables together without distinction of the environment where they are applicable.</p> <p>Finally, plots from every construct or attribute are provided in a zip file (<strong>plots_descriptive_analysis_per_city.zip</strong>) which contains the plots uploaded in "PNG" extension</p>
Dataset of Horizon scanning to identify invasion risk of ornamental plants marketed in Spain
<p>Full dataset for the research entitled "Horizon scanning to identify invasion risk of ornamental plants marketed in Spain". We classified non-native species into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain, and their potential environmental and socioeconomic impacts.</p>
National Checklists 2017: Spain 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 Spain collected using effechecka and geonames polygons
National Checklists 2019: Spain 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 Spain collected using effechecka and geonames polygons
Portable-XRF data of coastal sand for the Ria de Vigo (Spain): March/September 2023
<p>Document compiling the X-Ray Fluorescence results, done on a Bruker S1 Titan X-Ray Fluorescence Analyser, of samples collected in the Ria de Vigo, in March and September 2023, on GEOexploration mode, with a 90-second beam. The multi-element average values of the tree analyses were calculated.</p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
Vaccine Attitudes Examination (VAX) scale dataset in Spain
<p>This dataset contains data collected between November 15, 2021, and March 15, 2022. Demographic variables, data on vaccinated people, reasons for not getting vaccinated, and responses to items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels have been written in English to facilitate their understanding.<br>Data and codebooks are provided in csv format, following the FAIR principles.<br>Three files are provided:<br>1. VAX data, with the data related to sample characteristics and the answers to the questionnaire items.<br>2. Database codebook of variables, with information of the labels of the variables of the VAX data file.<br>3. Variables values codebook, with the labels of the values of the variables in the VAX data file.</p>
Murcia region (Spain) - 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>
Spain fuel prices
<p>This dataset provides insight into:</p> <ul> <li>All fuel types available at each petrol station</li> <li>Covers all petrol stations of Spain</li> <li>Informs fuel price evolution along wk46, 2022</li> </ul>
Ryanair: All december flight departures from Spain and their average prices
<p>In this dataset, we collected the average prices of all flight departures in Spain projected in December by Ryanair. Although we are uploading only raw data, this dataset can be useful to study the connectivity of Spain, and its accessibility by the people (comparing frequencies and prices in different airports).</p> <p>We searched for the December period to focus on the relative fluctuations of prices in holidays versus the rest of the month. Also, the prices may change between holidays lengths in days, and the day of departure. Specifically, we searched for 2 to 4 days of vacation, and departures from Thursday, Friday and Saturday. We only searched for the average prices of two adults because Ryanair prices are averages per person.</p> <p>All in all, this dataset has 11 attributes and 9794 rows.</p>
Renting prices in Spain
<p>This dataset contains in a .csv format the data about the houses in rent in the cities of Madrid, Barcelona, Valencia, Sevilla, Bilbao and Tenerife, at the time of the 22nd November 2022. The source code to obtain the data can be found in <a href="https://github.com/adrianvallsc/webscraping_housing">https://github.com/adrianvallsc/webscraping_housing</a></p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
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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.