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92 results for “Andalusia”
GoNEXUS SEF application: Andalusia practical case study
<p>Data for the figures in the journal article:</p> <p>Gonzalez-Rosell A, Arfa I and Blanco M (2023) Introducing GoNEXUS SEF: a solutions evaluation framework for the joint governance of water, energy, and food resources. Sustainability Science. https://doi.org/10.1007/s11625-023-01324-1</p> <p>Figure 3. Generation of new quantitative evidence: Percentage of variation of indicators between the water price scenario (WP) and baseline scenario (BS) for the year 2030. Source: Own elaboration based on the participatory SDM results (González-Rosell et al., 2020).</p> <p>Figure 4. Cross-impact matrix of the indicator system and solution from Table 2 for the water pricing in the Andalusia case study. Source: Own elaboration.</p> <p>Figure 5. Analysis of the degree of distribution of the network system based on the cross-impact matrix in Figure 4. Source: Own elaboration.</p> <p>Figure 6. (a) Full network graph: links between 16 indicators and WP solution based on the cross-impact matrix. (b) Tree network graph of the total influence of the WP solution at the first and second order based on the cross-impact matrix. Colour scale as in</p> <p>Figure 8. Synergies (green) and trade-off (red) of the WP solution on policy objectives and nexus objectives in Andalusia. Source: Own elaboration.</p> <p> </p>
Campaign content analysis on X for the Castilla y León and Andalusia 2022 regional elections
<p>This dataset refers to the replication data of the article titled: "Could you teach new tricks to old dogs? An analysis of online communication of political leaders in Spanish regional elections", to be published in Frontiers in Political Sciece, after reviewing process.</p>
Figs 6 –10 in Two new micropterous species of the genus Sunius from Andalusia (Spain) (Coleoptera: Staphylinidae: Paederinae)
Figs 6 –10. Sunius lauxaensis sp. nov.,J. 6 – habitus in dorsal view; 7 – forebody; 8 –sternite VIII; 9 – aedeagus in lateral view; 10 – aedeagus in ventral view.
Figs 11–12 in Two new micropterous species of the genus Sunius from Andalusia (Spain) (Coleoptera: Staphylinidae: Paederinae)
Figs 11–12. Habitat of Sunius in Sierra Gorda de Loja. 11 – type locality of Sunius tricoliensis sp. nov.; 12 – type locality of Sunius lauxaensis sp. nov.
Figs 1–5 in Two new micropterous species of the genus Sunius from Andalusia (Spain) (Coleoptera: Staphylinidae: Paederinae)
Figs 1–5. Sunius tricoliensis sp. nov.J. 1– habitus in dorsal view; 2 – forebody; 3 – sternite VIII; 4 – aedeagus in lateral view; 5 – aedeagus in ventral view.
Plate XVI, Figs G.1–G.6 – Type G (Spain, Andalusia, River Castril). G.1, adult ♀, head and pronotum; G.2, adult ♀, head with ocelli; G.3, adult ♂, sclerotized apex of aedeagus; G.4, adult ♂, with everted aedeagus and aedeagal tube (photo by José Manuel Tierno de Figueroa); G.5, adult ♂, sclerotized apex of aedeagus; G.6, adult ♂, mesal field with sensilla and hemitergal lobes, dorsal view. in Steps towards a revision of the Perla bipunctata Pictet, 1833 species complex (Plecoptera: Perlidae)
Plate XVI, Figs G.1–G.6 – Type G (Spain, Andalusia, River Castril). G.1, adult ♀, head and pronotum; G.2, adult ♀, head with ocelli; G.3, adult ♂, sclerotized apex of aedeagus; G.4, adult ♂, with everted aedeagus and aedeagal tube (photo by José Manuel Tierno de Figueroa); G.5, adult ♂, sclerotized apex of aedeagus; G.6, adult ♂, mesal field with sensilla and hemitergal lobes, dorsal view.
Plate XVII, Figs G.10–G.15 – Type G (Spain, Andalusia, River Castril). G.10, ♀ nymph, head, pronotum and mesonotum; G11, tergites of nymph; G.12, tergites of nymph, lateral view; G.13, femora of nymph; G.14, paraprocts of nymph, ventral view; G.15, basal mediodorsal setation on cerci of nymph. in Steps towards a revision of the Perla bipunctata Pictet, 1833 species complex (Plecoptera: Perlidae)
Plate XVII, Figs G.10–G.15 – Type G (Spain, Andalusia, River Castril). G.10, ♀ nymph, head, pronotum and mesonotum; G11, tergites of nymph; G.12, tergites of nymph, lateral view; G.13, femora of nymph; G.14, paraprocts of nymph, ventral view; G.15, basal mediodorsal setation on cerci of nymph.
Fig. 1 in Stenolemus novaki Horváth, 1888 (Hemiptera: Reduviidae) in the autonomous communities of Andalusia and Madrid (Spain)
Fig. 1.- Specimen of Stenolemus novaki Horváth, 1888, near Miraflores de la Sierra, Madrid. (Photo: Kristian Leahy).
Dry perennial herbaceous community data of Castril, Santiago and Pontones high-altitude rangelands in Andalusia (Spain)
<p>Data generated from vegetation monitoring in Castril, Santiago and Pontones rangelands situated in Sierra de Segura and Sierra de Castril in North-Eastern Andalusia (Spain).</p> <p>These data and R Script are linked to the article <em>How transhumance and pastoral commons shape plant community structure and composition. </em></p> <p>The plant community sampled belongs to <em>Festuco hystricis-Ononidetea striatae</em> class, consisting of <em>Coronillo minimae-Astragaletum nummularioidis</em> and <em>Seseli granatensis-Festucetum hystricis</em> plant associations. Plant data community consists in 72 vegetation transects. For further details see the article or contact S.A. Parra (santiago.parra-bulacio@etu.univ-amu.fr).</p> <p>File “Read_me.txt” details the databases and R script published.</p>
Normalized Difference Vegetation Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p><br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector)</p> <p>AoI: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndvi</p> <p> </p>
Normalized Multi-band Drought Index for Andalusia Region based on MODIS
<p><strong>Normalized Multi-band Drought Index </strong>(NMDI) was calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. </p> <p> </p> <p>NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.(Lingli Wang, 2007)</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_nmdi</p> <p> </p>
Normalized Difference Water Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Water Index</strong> (NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. </p> <p>The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001).</p> <p><a href="https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf">https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndwi</p>
The Sediment Thickness Model for Andalusia (STMA) data, intermediate data and scripts
<p>This dataset contains the Sediment Thickness Model for Andalusia (South of Spain), the GroundHog files and the consulting scripts linked to the paper "Thickness model of Andalusian's nearshore and coastal inland topography " under review in <i>Journal of Marine Science and Engineering</i>.</p><p>The following ZIP files can be found here:</p><p>===============<br><strong>STMA.zip</strong></p><p>A dataset of 108 ESRI ASCII grid files group by province (Almeria, Cadiz, Granada, Huelva and Malaga) and by physiographic zone (18). Each zone has six different files named as:</p><p>PZZ_V_T_S.asc</p><p>where,</p><p>P: It is the first letter of the province (e.g. A for Almeria, C for Cadiz…)</p><p>ZZ: number of physiographic zone from a minimum number of 2 to 6 in each province. Each province was divided by different overlapping rectangles following on the orientation of the coastline, the shape of the continental shelf, river intersections, capes, the main sediment type, and the level of influence from atmospheric and maritime weathering agents, primarily.</p><p>V: the model version</p><p>T: Type of sediment, Consolidated (C) and Unconsolidated (U)</p><p>S: Grain size, Fine (F), Sand (S) and Gravel (G)</p><p>e.g. the file A01_1_C_F.asc is the province of Almeria, zone 01, version 1, Consolidated sediment and Fine fraction.</p><p>All files have a projection file (EPSG 25830) with the same name but a *.prj extension and a auxiliary file (*.asc.aux.xml) with additional information about the projection used. </p><p>===============<br><strong>Script.zip</strong></p><p>Specifically, three Pyqgis scripts and three Model Qgis: </p><ul><li><i>1_Add_STMA_by_province_and_zone.py</i>: to add the STMA to QGIS grouping the layers by province and zones.</li><li><i>2_Point_value_STMA.py:</i> to add the STMA value to a point sample layer.</li><li>3_Zonal_statistics_STMA.py: to calculate the zonal statistics of STMA in a polygon area.</li><li>1_Volume_STMA.model3: Calculate the volume of each material (six raster layer) in one zone.</li><li>2_Volume_STMA_Clip.model3: Calculate the volume of each material in a clip area using a polygon layer.</li><li>3_Zonal_statistics_STMA.model3: Zonal statistics of the STMA in a polygon layer. It is a model version of the last pyqgis script 3_Zonal_statistics_STMA.py.</li></ul><p>The first two models could be used in a batch processing if the user needs information of more than one zone.</p><p>===============<br><strong>GroundHog.zip</strong></p><p>Five folders, one for each province, with information to open the models in Groundhog software.</p><p> </p>
Mosque-Cathedral of Cordoba, Andalusia, Spain
Cathedral of Our Lady of the Assumption, Córdoba, Andalusia, Spain Source: Objaverse 1.0 / Sketchfab
cultural heritage elements in Andalusia and demographic data sorted by urban category
<p>Dataset concerning cultural heritage elements and demographic data in Andalusia at the municipality level, as they appear on SIMA (Sistema de Información Multiterritorial de Andalucía), March 2023. They have been sorted by urban category, depending on the number of inhabitants of each municipality: less than 1.000 inhabitants, 1.000 to 10.000 inhabitants, 10.000 to 100.000 inhabitants, and more than 100.000 inhabitants.</p>
GIS output and layers for the physiologically based demographic modeling analysis of interacting olive and olive fly in Andalusia
<p>GIS output and layers for the physiologically based demographic modeling analysis of interacting olive and olive fly in Andalusia performed under the MED-GOLD project within the Horizon 2020 framework program of the European Union. This version includes the file eurocordex_HadGEM2_ES_RCA4_2071-2100_rcp8.5.zip that was inadvertently not included in previous versions.</p>
Assessment of the Humanisation of Care in Hospitals in Andalusia-Spain
ClinicalTrials.gov study NCT06174844. IPD Sharing: NO. Countries: 1. Publications: 1.
FIGURE 8 in Dalytyphloplanida (Platyhelminthes: Rhabdocoela) from Andalusia, Spain, with the description of four new species
FIGURE 8. Microdalyellia armigera (Schmidt, 1861) Gieysztor, 1938a (A) Stylet. Gieysztoria cuspidata (Schmidt, 1861) Ruebush & Hayes, 1939 (B) Stylet (arrows indicate digitiform protuberances). Gieysztoria macrovariata (Weise, 1942) Ruebush & Hayes, 1939 (C) Stylet. Gieysztoria beltrani (Gieysztor, 1931) Ruebush & Hayes, 1939 (D) Stylet.
FIGURE 1 in Dalytyphloplanida (Platyhelminthes: Rhabdocoela) from Andalusia, Spain, with the description of four new species
FIGURE 1. Parapharyngiella involucrum Willems et al., 2005 (A) Habitus from a live animal. (B) Bursal canal. (C) Stylet. (D) Bursal appendage.
FIGURE 2 in Dalytyphloplanida (Platyhelminthes: Rhabdocoela) from Andalusia, Spain, with the description of four new species
FIGURE 2. Trigonostomum penicillatum (Schmidt, 1857) Micoletzky, 1910 (A) Stylet from the whole mount. (B) Bursal appendage from a live animal. Promesostoma maculosum Ax, 1956 (C) Stylet and copulatory organ from a live animal. Kytorhynchidarum sp. (D) Head region. Phaenocora sp.? Ehrenberg, 1836 (E) Live animal. Strongylostoma elongatum Hofsten, 1907 (F) Copulatory organ from a live animal.
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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.