Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Heritage Sites in Bani Walid, Libya

A dataset of 211 heritage sites in and around the city of Bani Walid, Libya used for the EAMENA Machine Learning Automated Change Detection Case Study.

opencc-zeroAug 2024View details →

National Checklists 2017: Libya Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Libya collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

National Checklists 2019: Libya Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Libya collected using effechecka and geonames polygons

opencc-by-4.0Aug 2021View details →

Molecular characterization and genetic diversity of four undescribed novel oleaginous Mortierella alpina strains from Libya

A large number of undiscovered fungal species still exist on earth, which can be useful for bioprospecting, particularly for single cell oil (SCO) production. Mortierella is one of the significant genera in this field and contains about hundred species. Moreover, M. alpina is the main single cell oil producer / arachidonic acid producer at commercial scale under this genus.

opencc-by-4.0Jan 2021View details →

qdgc Libya

QDGC tables delivered in geopackage file - - - - - - - - - - - - - - - - - - - - - - QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth. Within each geopackage file you will find a number of tables with these names: -tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc The attributes for each table are: qdgc Unique Quarter Degree Grid Cell reference string area_reference Country level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry Metadata -------- Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0 Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000) Licensing --------- Creative Commons Attribution 4.0 International Conditions ---------- Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Thankyou -------- The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them: - Tanzania Wildlife Research Institute - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey References ---------- * http://en.wikipedia.org/wiki/QDGC * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/ * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection * http://www.safe.com Ragnvald Larsen Trondheim 20th of January, 2021 ragnvald@mindland.com www.mindland.com

opencc-by-sa-4.0Dec 2023View details →

Infrastructure Climate Resilience Assessment Data Starter Kit for Libya

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. 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. 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. Hazards: <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> Exposure: <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> Contextual information: <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> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. 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. These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: <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> 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. 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). References <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>

opencc-by-4.0Dec 2023View details →

Transport Starter Data Kit: Historical socio-transport data for Libya

This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.

opencc-zeroAug 2024View details →

National Checklists: Libya 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>

Text-fig. 16. WUSC 4C 33, snout of Kubwachoerus khinzikebirus from Gebel Zelten, Libya. a: palatal view; b: anterior view; c: left lateral view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 16. WUSC 4C 33, snout of Kubwachoerus khinzikebirus from Gebel Zelten, Libya. a: palatal view; b: anterior view; c: left lateral view.

Text-fig. 14. ACH 6C 1, mandible of Kubwachoerus khinzikebirus from Gebel Zelten, Libya. a: oblique anterior view to show alveolus of right lower canine; b: oblique ventral view of symphysis; c: anterior view to show incisor and canine alveoli. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 14. ACH 6C 1, mandible of Kubwachoerus khinzikebirus from Gebel Zelten, Libya. a: oblique anterior view to show alveolus of right lower canine; b: oblique ventral view of symphysis; c: anterior view to show incisor and canine alveoli.

Text-fig. 12. CUWM 132, right M3/ of Libycochoerus massai from Moghara, Egypt. a: stereo occlusal views; b: lingual view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 12. CUWM 132, right M3/ of Libycochoerus massai from Moghara, Egypt. a: stereo occlusal views; b: lingual view.

Text-fig. 11. CGM 94-138, right mandible fragment containing p/4–m/3 of Libycochoerus massai from Moghara, Egypt. a: lingual view; b: stereo occlusal view; c: buccal view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 11. CGM 94-138, right mandible fragment containing p/4–m/3 of Libycochoerus massai from Moghara, Egypt. a: lingual view; b: stereo occlusal view; c: buccal view.

Text-fig. 13. Bivariate plots of teeth of Libycochoerus massai from Gebel Zelten (circles) and Moghara (+ sign). in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 13. Bivariate plots of teeth of Libycochoerus massai from Gebel Zelten (circles) and Moghara (+ sign).

Text-fig. 9. CUWM 360, right mandible fragment and associated m/3 of Diamantohyus africanus from Moghara, Egypt. a: stereo occlusal views; b: buccal view; c: lingual view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 9. CUWM 360, right mandible fragment and associated m/3 of Diamantohyus africanus from Moghara, Egypt. a: stereo occlusal views; b: buccal view; c: lingual view.

Text-fig. 8. CUWM 261, right mandible with damaged m/2–m/3 of Diamantohyus africanus from Moghara, Egypt. a: lingual view; b: stereo occlusal views; c: buccal view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 8. CUWM 261, right mandible with damaged m/2–m/3 of Diamantohyus africanus from Moghara, Egypt. a: lingual view; b: stereo occlusal views; c: buccal view.

Text-fig. 7. CUWM 239, left m/3 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 7. CUWM 239, left m/3 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view.

Text-fig. 5. CUWM 63, left mandible with d/4–m/2 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 5. CUWM 63, left mandible with d/4–m/2 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view.

Text-fig. 3. CUWM 134, right lower molar of Diamantohyus africanus from Moghara, Egypt. a: stereo occlusal view; b: lingual view; c: buccal view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 3. CUWM 134, right lower molar of Diamantohyus africanus from Moghara, Egypt. a: stereo occlusal view; b: lingual view; c: buccal view.

Text-fig. 6. CUWM 121, right mandible with m/3 of Diamantohyus africanus from Moghara, Egypt. a: lingual view; b: stereo occlusal views; c: buccal view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 6. CUWM 121, right mandible with m/3 of Diamantohyus africanus from Moghara, Egypt. a: lingual view; b: stereo occlusal views; c: buccal view.

Text-fig. 4. CUWM 106, left mandible with p/4–m/3 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view. in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 4. CUWM 106, left mandible with p/4–m/3 of Diamantohyus africanus from Moghara, Egypt. a: buccal view; b: stereo occlusal views; c: lingual view.