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143 results for “Malta”
Geophysical data from offshore Malta
<p>Geophysical data accompanying scientific paper on freshened groundwater offshore the Maltese Islands.</p>
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Malta
<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_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2022_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2021_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2020_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2019_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> </ul>
National Checklists 2017: Malta 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 Malta collected using effechecka and geonames polygons
National Checklists 2019: Malta 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 Malta collected using effechecka and geonames polygons
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Malta
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> </ul>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Malta
<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 the ‘<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 – Ministry for Health</p> <p>OCC-CHEMMON2021 – Ministry for Health</p> <p>OCC-CHEMMON2022 – Ministry for Health</p> <p>OCC-CHEMMON2023 – Ministry for Health</p> <p>OCC-CHEMMON2024 – Ministry for Health</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Malta
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
National Checklists: Malta Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Malta
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Malta. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Ministry for Agriculture, Fisheries and Animal Rights</p> <p>VMPR_2022 – Ministry for Agriculture, Fisheries and Animal Rights</p> <p>VMPR_2021 – Ministry for Agriculture, Fisheries and Animal Rights</p> <p>VMPR_2020 – Ministry for Agriculture, Fisheries and Animal Rights</p> <p>VMPR_2018 – Ministry for the Environment, Sustainable Development and Climate Change</p> <p>VMPR_2017 – Ministry for the Environment, Sustainable Development and Climate Change</p>
Figs 80–81 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 80–81. Geographic distribution of the four taxa treated in this study. Note that the shallow water area (light grey) between Sicily and the Maltese Islands roughly corresponded to a land bridge during glaciations.
Figs 64–74 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 64–74. Specimens of Temnothorax vivianoi Schifani, Alicata & Prebus sp. nov. in lateral (left), dorsal (center) and head view (right). Photos by Enrico Schifani, available onwww.antweb.org, specimen identifiers in parentheses. 64–66. Holotype worker from Monte Pellegrino (Palermo Mountains, Sicily) (ANTWEB1041551). 67–69. Paratype worker from Monte Pellegrino (Palermo Mountains, Sicily) (ANTWEB1041552). 70–72. Paratype queen from Monte Pellegrino (Palermo Mountains, Sicily) (ANTWEB1041553). 73–74. Damaged male from Palermo Mountains (Sicily) (ANTWEB1041554). Scale bars = 0.5 mm.
Figs 75–78 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 75–78. Morphometric differentiation of the four taxa treated in this study based on the worker caste. 75. Principal component analysis of all nine morphometric characters (excluding indices). 76. Scatter plot of eye size index (EYE/CS) against cephalic length index (CL/CW). 77. Scatter plot of absolute spines length (SPST) against cephalic size (CS). 78. Scatter plot of spine length index (SPST/ CL) against scape length index (SL/CS).
Fig. 79. Maximum likelihood phylogeny inferred with IQTREE ver. 2.1.2 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Fig. 79. Maximum likelihood phylogeny inferred with IQTREE ver. 2.1.2. The major clades found in Prebus (2017) are highlighted, and the focal species of the current study (all within the 'Palearctic clade IV') are evidenced as in Figs 75–78. Maximum likelihood bootstrap support for all nodes are 100, except where indicated.
Figs 46–63 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 46–63. Specimens of Temnothorax poldii Alicata, Schifani & Prebus sp. nov. in lateral (left), dorsal (center) and head view (right). Photos by Enrico Schifani, available on www.antweb.org, specimen identifiers in parentheses. 46–48. Holotype worker from Monte Arso (Etna, Sicily) (ANTWEB1041545). 49–51. Worker from Vallone Madonne degli Angeli (Madonie, Sicily) (ANTWEB1041546). 52– 54. Paratype worker from Monte Ruvolo (Etna, Sicily) (ANTWEB1041547). 55–57. Worker from Monte Manfrè (Etna, Sicily) (ANTWEB1041548), with ergatogynes characters in the mesosoma. 58– 60. Paratype queen from Monte Arso (Etna, Sicily) (ANTWEB1041549). 61–63. Paratype male from Monte Ruvolo (Etna, Sicily) (ANTWEB1041550). Scale bars = 0.5 mm.
Figs 34–45 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 34–45. Specimens of Temnothorax marae Alicata, Schifani & Prebus sp. nov. in lateral (left), dorsal (center) and head view (right). Photos by Enrico Schifani, available on www.antweb.org, specimen identifiers in parentheses. 34–36. Holotype worker from Vendicari (Hyblaean Sicily) (ANTWEB1041541). 37–39. Paratype worker from Vendicari (Hyblaean Sicily) (ANTWEB1041542). 40–42. Queen from Bosco di Santo Pietro (Hyblaean Sicily) (ANTWEB1041543), note that the lateral image was edited to compensate for the fact that the petiole and gaster plus postpetiole were accidentally disarticulated. 43–45. Male from Bosco di Santo Pietro (Hyblaean Sicily) (ANTWEB1041544). Scale bars = 0.5 mm.
Figs 19–33 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 19–33. Specimens of Temnothorax lagrecai (Baroni Urbani, 1964) in lateral (left), dorsal (center) and head view (right). Photos available on www.antweb.org, specimen identifiers in parentheses. 19– 21. Worker from Monte Pellegrino (Palermo mountains, Sicily) (ANTWEB1041536), photo by Enrico Schifani. 22–24. Paratype worker from Bosco di Santo Pietro (Hyblaean Sicily) (ANTWEB1041537), photo by Elia Nalini. 25–27. Worker from Bosco di Linera (Etna, Sicily) (ANTWEB1041538), photo by Enrico Schifani. 28–30. Queen from Bosco di Santo Pietro (type locality, Hyblaean Sicily) (ANTWEB1041539), photo by Enrico Schifani. 31–33. Male from Bosco di Santo Pietro (type locality, Hyblaean Sicily) (ANTWEB1041540), photo by Enrico Schifani. Scale bars = 0.5 mm.
Figs 13–15 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 13–15.Head sculpture variation in workers of Temnothorax Mayr, 1861, photos fromwww.antweb.org, specimen identifiers in parentheses. 13. T. alienus Schulz, Heinze & Pusch, 2007, type specimen from Italy (ANTWEB1041297), photo by Roland Schultz, weak longitudinal striae on a background of dense alveolate sculpture. 14. T. flavicornis (Emery, 1870), type specimen from Italy (CASENT0904761), photo by Will Ericson, strong longitudinal striae with a shiny background. 15. T. tebessae (Forel, 1890), type specimen from Algeria (CASENT0909034), photo by Will Ericson, extremely weak and few longitudinal striae on a shiny background.
Fig. 82 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Fig. 82. Spatial characterization of the sites where the four species treated in this study were found.
Figs 16–18 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Figs 16–18. Morphometric characters recorded from workers of Temnothorax Mayr, 1861 in this study. The background image is a paratype worker of T. lagrecai (Baroni Urbani, 1964) from the Natural History Museum of Vienna, photos by Anna Pal available from www.antweb.org, specimen identifier CASENT0919741. 16. Head view. 17. Lateral profile view. 18. Dorsal view of the mesosoma.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.