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1,335 results for “Portugal”
Carbon and water fluxes in a cork oak woodland in Central Portugal
<p>The Data set contains eddy covariance measurements of carbon and water fluxes and ancillary measurements observed at a cork oak woodland (<em>Quercus suber </em>L.) in central Portugal. The climate is Mediterranean, with mild, wet winters and hot, dry summers.</p> <p>Data are available in the file data.csv (UTF-8 encoding), the description of the variables and units are available in the file meta.csv (UTF-8 encoding).</p> <p>Further description of the site, methods and data processing can be viewed in the files metadata.pdf and metadata.csv</p> <p> </p>
DISASTER database on hydro-geomorphologic disasters in Portugal
<p>In the last century, Portugal was affected by several natural disasters of hydrogeomorphologic origin that often caused high levels of destruction. However, data on past events related to floods and landslides were scattered. The DISASTER project created a consistent and validated hydro-geomorphologic database for Portugal, by creating, disseminating and exploiting a GIS database on disastrous floods and landslides for the period 1865–2010, further updated until 2020.</p> <p>Data collection was steered by the concept of disaster used within the DISASTER project. Therefore, any hydro-geomorphological case is stored in the database if the occurrence led to fatalities or injuries, and missing, evacuated or displaced people, independently of the number of people affected.</p> <p>The sources of information are 16 national, regional and local newspapers that implied the analysis of 145,344 individual newspapers. The hydro-geomorphologic occurrences were stored in a database containing two major parts: the characteristics of the hydro-geomorphologic case and the corresponding damages. We provide the main results of the DISASTER database for the public.</p> <p>Further details about the data collection and exploitation can be found in the following paper: </p> <p>Zêzere, J.L., Pereira, S., Tavares, A.O. <em>et al.</em> DISASTER: a GIS database on hydro-geomorphologic disasters in Portugal. <em>Natural Hazards</em> <strong>72</strong>, 503–532 (2014). https://doi.org/10.1007/s11069-013-1018-y</p> <p>We provide a shapefile with the location of the hydro-geomorphological hazard for the period 1865-2020 for mainland Portugal and additional details about the hydrogeomorphological hazard type and subtype, date of occcurrence, year, month, day, hour, georeferencing quality, source, source date, source type, page in the source, number of human damages (fatalities, injured, evaciated, displaced and missing people), district, municipality and parish.</p> <p>Also, we provide a word document with the database codes description.</p>
Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery
<div> <div> <div> <p>This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.</p> <p> </p> <p>For a description of the methodology see the article [link] and Github repo [link].</p> <p> </p> <h1>Repository content</h1> <h2>1. system-masks.zip</h2> <p>Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis.</p> <p><strong>CRS</strong> - EPSG:4326</p> <h2>2. manual-clean-up-masks.gpkg</h2> <p>Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not.</p> <p>File contains a single layer with 52 polygons and one variable.</p> <p><strong>Variables:</strong></p> <ul> <li>system [<em>string</em>] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> <p>Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes.</p> <p>File contains a single layer with 2448 points and 18 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the quadrat was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [<em>boolean</em>] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [<em>numerical</em>] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [<em>numerical</em>] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [<em>numerical</em>] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [<em>numerical</em>] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [<em>numerical</em>] - Surface reflectance values at 443 nm.</li> <li>blue [<em>numerical</em>] - Surface reflectance values at 490 nm.</li> <li>green_i [<em>numerical</em>] - Surface reflectance values at 531 nm.</li> <li>green [<em>numerical</em>] - Surface reflectance values at 565 nm.</li> <li>yellow [<em>numerical</em>] - Surface reflectance values at 610 nm.</li> <li>red [<em>numerical</em>] - Surface reflectance values at 665 nm.</li> <li>rededge [<em>numerical</em>] - Surface reflectance values at 705 nm.</li> <li>nir [<em>numerical</em>] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> <p>Comma-delimited file with information about vegetation sampling transects. Each row represents one transect.</p> <p>File contains 6 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [<em>string</em>] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>date [<em>date yyyy-mm-dd</em>] - Date of transect sampling.</li> <li>notes [<em>string</em>] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> <p>Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species.</p> <p>File contains a single layer with 2448 points and 32 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>distance_from_water <em>[integer]</em> - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> <p>Geotiff file with a single layer for predicted saltmarsh community. Values are:<br> - <em>no data</em> - Not saltmarsh<br> - <em>1</em> - Low saltmarsh<br> - <em>2</em> - Middle saltmarsh<br> - <em>3</em> - High saltmarsh</p> <p><strong>CRS</strong> - EPSG:32629</p> </div> </div> </div>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Portugal
<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_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2022_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2021_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2020_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2019_PT: Portuguese National Authority for Animal Health (DGAV)</li> </ul>
Diplomados em Design em Portugal (2002-2020)
<p>Base de dados agrega informação sobre diplomados em design em Portugal (2002-2020), recolhidos a partir da Direção Geral da Estatística da Educação e Ciência (DGEEC). Contém informações detalhadas sobre o nome das escolas de design, a sua localização geográfica (por distrito e NUT II), cursos na área e diplomados (H/M), por ano. </p> <p>A base de dados é desenvolvido no âmbito do projeto Design Obs. Para um Observatório de Design em Portugal: Modelos, Instrumentos, Representação e Estratégias (<a href="http://www.designobs.pt/">www.designobs.pt</a>), cofinanciado pelo Programa Operacional Regional de Lisboa (LISBOA 2020) e o Programa Operacional Competitividade e Internacionalização (POCI-01-0145-FEDER-032445), através do Portugal 2020 e do Fundo Europeu de Desenvolvimento Regional (FEDER) e por fundos nacionais através da FCT – Fundação para a Ciência e a Tecnologia.</p>
Empresas e diplomados em Portugal - visão diacrónica 2010-2019 por NUT II
<p>Base de dados cruza informação sobre o número total de diplomados em Design em Portugal (somados a partir da lista criada em "Diplomados em Design em Portugal, 2002-2020"), com informação sobre empresas de Design em Portugal extraída a partir dos relatórios da Estatística da Cultura produzidos na última década. Contém informações sobre a totalidade das empresas com código 74.10 (atividade de Design), volume de negócio, empregados e custos com pessoal, por NUT II (Norte, Centro, Área Metropolitana de Lisboa, Alentejo, Algarve, Região Autónoma da Madeira e Região Autónoma dos Açores).</p> <p>Base de dados desenvolvida no âmbito do projeto Design Obs. Para um Observatório de Design em Portugal: Modelos, Instrumentos, Representação e Estratégias (<a href="http://www.designobs.pt/">www.designobs.pt</a>), cofinanciado pelo Programa Operacional Regional de Lisboa (LISBOA 2020) e o Programa Operacional Competitividade e Internacionalização (POCI-01-0145-FEDER-032445), através do Portugal 2020 e do Fundo Europeu de Desenvolvimento Regional (FEDER) e por fundos nacionais através da FCT – Fundação para a Ciência e a Tecnologia.</p>
Figure 9 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This if the figure 9 of paper with DOI 10.5894/rgci-n432.</p> <p>Dunes of Trafaria and Costa da Caparica. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>The original source can be found at Archive from Instituto para a Conservação da Natureza e Florestas (Portugal).</p> <p>Representation of the works made by the Forestry Services between 1884 and 1910. Reference to an area flooded by the ocean in 1905 [sementeira de 1905 inundada pelo mar] and areas where new sowings had to be done [resementeiras]. Notice the drainage systems [valla] and the fences [sébe] near the coastline to protect the plants.</p>
Figure 11 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This is the figure 11 of the article with DOI 10.5894/rgci-n432.</p> <p>Forests of Trafaria and Costa da Caparica in the 1930s-1940s. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>Original source can be found at the Archive of Instituto para a Conservação da Natureza e Florestas.</p> <p>In green, the existing forests. In pink, the Forestry Services areas given to other institutions or services for public uses. In yellow, the dunes to be afforested.</p> <p> </p>
Railway services operated with diesel multiple units in Spain and Portugal
<p>Identifier: DOI</p> <p>Creator: German Aerospace Center, Institute of Vehicle Concepts</p> <p>nameType: Organizantional</p> <p>Title: Railway services operated with diesel multiple units in Spain and Portugal.</p> <p>Publisher: Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Fahrzeugkonzepte.</p> <p>Publication Year: 2022</p> <p>ResourceType: Simulated Trajectories</p> <p>Subject: This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes.</p> <p>Date: 2022-02-10</p> <p>Description:<br> This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes. Methodology is described in relatedItem.</p> <p>FundingReference: FCH2Rail; Fuel Cell Hybrid Power Pack for Rail Applications; Grant Agreement Number: 101006633</p> <p>RelatedItem: "D1.1 - Report on line and use case based requirements" of the FCH2Rail project.</p> <p>Related Item can be found on the project website (https://www.fch2rail.eu/en/projects/fch2rail) and/or in Cordis (https://cordis.europa.eu/project/id/101006633/results)</p> <p><br> This dataset comprises following attributes:</p> <p>service:<br> First and last station of the railway service.</p> <p>length:<br> Length of the railway service in km.</p> <p>electrified_length:<br> Length of electrified sections in km.</p> <p>not_electrified_length:<br> Length of not electrified sections in km.</p> <p>electrification_degree:<br> Electrfiicatioin degree in %.</p> <p>longest_autonomy:<br> Longest not electrified section in km.</p> <p>first_station_electrified:<br> Binary of first station is electrified with catenary.</p> <p>last_station_electrified:<br> Binary of last statin is electrified with catenary.</p> <p>elevation_first_station:<br> Elevation of first station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>elevation_last_station:<br> Elevation of last station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>daily_trips:<br> Count of daily trips on the service.</p> <p>vehicle:<br> Vehicle type used on this service.</p> <p>stop_number:<br> Numver of stops at stations throughout a trip.</p> <p>trip_time:<br> Trip duration in hours.</p> <p>gauge:<br> Railway gauge in mm.</p> <p>type:<br> Railway vehicle type. Mainline Loc = Mainline Locomotive, MU Iber. gauge = Multiple unit on iberian gauge, MU Feve Gauge = multiple unit on feve gauge.</p> <p>avg_stop_distance:<br> Average stop distance in km.</p> <p>avg_speed:<br> Average velocity in km/h.</p> <p>daily_autonomy:<br> Cumulated distance under not electrified sections in km.</p> <p>annual_train_km:<br> Train kilometers per year.</p> <p>annual_train_km_wo_catenary:<br> Train kilometers per year not under catenary. </p> <p> </p>
CoMix social contact data (Portugal)
<p>CoMix social contact data for Portugal.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Baltazar Nunes at the National Health Institute Doutor Ricardo Jorge (INSA).</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 - Portugal
<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>
Magnetic, gravity and seismicity data for the Monchique intrusion and surroundings (SW Portugal, SW Iberia)
<p>This dataset contains the following data:</p> <p> </p> <p><strong>1.</strong> Magnetic anomaly data (processed line data) acquired by drone-borne magnetometer for the Monchique area (.dat file)</p> <p><strong>2.</strong> Magnetic and gravity anomaly maps for the Monchique area in SW Portugal, SW Iberia:</p> <ul> <li>Magnetic anomaly (.tif and .grd files)</li> <li>Reduced to the pole (RTP) magnetic anomaly (.tif and .grd files)</li> <li>Free air gravity anomaly (.tif and .grd files)</li> <li>Complete Bouguer gravity anomaly, after terrain correction (.tif and .grd files)</li> </ul> <p><strong>3.</strong> Seimicity data:</p> <ul> <li>Relocated earthquakes that occurred between 01/01/2007 and 01/07/2023 in the Monchique area (.xlsx file)</li> <li>Focal mechanisms (moment tensor inversion solutions) of earthquakes occurred in the Monchique area (.xlsx file)</li> </ul> <p> </p> <p>For all details on data collection and processing please refer to:</p> <p>Neres, M., Camargo, G., Soares, A., Custódio, S., Bos, M., Vales, D., & Terrinha, P. (2024). Monchique alkaline magmatic intrusion (SW Iberia): Geophysical modeling and relationship with active seismicity and hydrothermalism. <em>Tectonophysics</em>. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.tecto.2024.230426" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.tecto.2024.230426</a></p> <p> </p>
National Checklists 2017: Portugal 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 Portugal collected using effechecka and geonames polygons
National Checklists 2019: Portugal 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 Portugal collected using effechecka and geonames polygons
A dataset of physicochemical water parameters from Lima and Douro estuaries (Northwest Portugal)
<p>This dataset provides physicochemical water data collected during five sampling campaigns at two temperate North Atlantic estuaries, the Lima and Douro, in northern Portugal, between 2021 and 2022. The dataset is described in the <em>Data in Brief </em>article “Physicochemical dataset from Lima and Douro estuaries (Northwest Portugal)”. It includes: a) in-situ vertical profiles of water parameters (temperature, salinity, pH, turbidity, dissolved oxygen), acquired with a multiparameter probe along the water column, from the surface to the bottom, at 8 stations in the Lima estuary (during ebb and flood tides) and 11 stations in the Douro estuary (during flood tide); and b) chemical analyses of nutrients, chlorophyll <em>a</em>, total particulate matter, particulate organic matter, and metal concentrations from 5 representative stations in each estuary. Additional dissolved metal data are provided for the Lima estuary.</p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Extract from the glazed tile heritage database of Covilhã-Portugal (12 elements)
<p>Extract from the tile heritage database of Covilhã - Portugal: geographic and alphanumeric data of 12 buildings with facades fully or partially covered with tiles, of heritage interest (registered in the SIPA), located in the municipality of Covilhã.</p> <p>The information is related to the buildings and tile compositions on its facades.</p> <p>Building data</p> <p>Building location: country, region, district, municipality, parish, street and house number, Lon WGS84, Lat WGS84, X ETRS89, Y ETRS89, Z elevation. SIPA: designation, identification number, link to SIPA data. Building identification: if it does not exist, an ID must be assigned. Protection and conditioning: world heritage, built heritage, benefits from a protection zone, in the process of classification, in study, non-existent. Public interest, national monument, municipal interest. Property type: public, private. Ownership: ownership information. Case number at municipal services: number ID. Typology: residence, industrial, commercial, religious, panel or other. Occupation: property occupation. Architectural style: dominant, secondary. Other decorative elements than tiles: if existing. General building condition (exterior): good, reasonable, bad, doom. Interventions chronology: if existing. Main moments of design, application, conservation, remodeling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p> <p>Glazed tile data</p> <p>Tile location: facade, inside building. Tile identification: ID number. Construction period: main construction periods. Local context: brief description of the glazed tile object surroundings, in its geophysical, historical, and socio-cultural aspects. Classification 1: coating, ceramic panel, toponymical plate or other. Classification 2: facade, patio, external staircase, balcony, wall, wall panel, small religious panel, caption. Classification 3: pattern, repeat composition, figurative, ornamental, or loose figure. Iconography: religion, biblical, magic, mysticism, nature, human being – humanity, society - civilization – culture, history, ancient history, literature, classical mythology, abstract ideas and concepts, abstract - non-figurative art. Monochrome or polychromatic: monochrome, polychromatic. Objective and succinct tile description: From general to particular: building, space, glazed tile, and section. Material (ceramic product): faience, stone dust, stoneware, porcelain, terracotta. Tile decoration technique: information on tile manufacture, divided into conformation, decoration and application. Composition dimensions: height x width (meters). Tile dimensions: height x width (centimetres). Tile condition: good, reasonable, bad. Authorship: company, artist, painter or other. Authorship nationality: country. Interventions chronology: if existing. Main moments of design, application, conservation, remodelling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p>
South Portugal Live Fuel Moisture Content (LFMC) dataset
<p>This dataset contains a collection of biweekly LFMC field samples collected between April 2022 and July 2023 over the Alentejo region, South Portugal.<br><br>Metadata:</p> <p>Coordinate Reference System: ETRS_1989_Portugal_TM06 - EPSG 3763<br>File Format: ESRI shapefile<br>Column Fields:</p> <ul> <li>FID - Internal ID</li> <li>Ponto - control point for backup purposes.</li> <li>LFMC - Live fuel moisture content (LFMC) in percentage.</li> <li>DATE - Sample date. AREA - in-situ field name.</li> <li>POINT_X - Longitude.</li> <li>POINT_Y - Latitude.</li> <li>POINT_Z - Altitude.</li> </ul> <p>Fundings:</p> <p>Filippe Santos was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2022.11960.BD).<br>This research was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020).</p>
Fig. 2 in Schizorhynchia Meixner, 1928 (Platyhelminthes, Rhabdocoela) of the Iberian Peninsula, with a description of four new species from Portugal
Fig. 2. Copulatory organ of species of Proschizorhynchus Meixner, 1928. A. Proschizorhynchus algarvensis sp. nov., holotype (HU 615). B–C. P. arnautsae sp. nov. B. Holotype (HU 616). C. A specimen from the reference collection of Hasselt University (HU X.1.35). D–E. P. troglodytus sp. nov. D. Holotype (HU 617). E. A specimen from the reference collection of Hasselt University (HU X.1.40). F–G. P. pectinatus l'Hardy, 1965 (HU X.1.43). F. Copulatory organ. G. Sclerotised spermatic duct. H. P. reniformis Brunet, 1970 (HU X.1.45). Scale bars: A, F = 50 µm; B–E, G–H = 20 µm.
BRAMS-SFIRE outputs to South of Portugal
<p>The BRAMS-SFIRE (Menezes, 2015) consists of a mesoscale atmospheric model, the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS (Freitas et al., 2009; 2017)), coupled with the Spread Fire model (SFIRE) (Mandel et al., 2009; 2011). Here we presented the outputs of the simulations of this model for a domains of south of Portugal in some locations, namely, in Alcácer do Sal, Reguengos de Monsaraz and in Ossa mountain range. The data are grouped in netcdf simulations data and scripts GRADS, to images visualization of this outputs, for some ambient situation with forestry fire and without forestry fire, simulated for day 7th August 2006 (Ossa mountain range, around 7.54°W, 38.73°N), for day 4th July 2007 (Reguengos de Monsaraz, approximately 7.59°W, 38.37°N) and for day 28th July 2010 (Alcácer do Sal, around 8.69°W, 38.35°N).</p>
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