Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

qdgc Burundi

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 Burundi

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 Burundi

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: Burundi 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>

opencc-by-4.0May 2024View details →

Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country

The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia. The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate. Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>) The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level. When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a>>. The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a>> A simplified version of the map can be found on Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

Fig. 5. A in On the genus Pachygnatha (Araneae, Tetragnathidae) in the Albertine Rift of Burundi, with the description of three new species

Fig. 5. A. Pachygnatha intermedia sp. nov. Female genitalia, cleared, ventral view. B. Pachygnatha ventricosa sp. nov. Female genitalia, cleared, ventral view. Scale bars = 0.1 mm.

Fig. 2 in On the genus Pachygnatha (Araneae, Tetragnathidae) in the Albertine Rift of Burundi, with the description of three new species

Fig. 2. Pachygnatha bispiralis sp. nov. A. Male left palp, dorsal view. B. Male palp, retrolateral view. C. Male right chelicera, frontal view. D. Male right chelicera, ventral view. Scale bars = 0.1 mm.

Fig. 1. A–C in On the genus Pachygnatha (Araneae, Tetragnathidae) in the Albertine Rift of Burundi, with the description of three new species

Fig. 1. A–C. Pachygnatha bispiralis sp. nov. A. Male habitus, dorsal view. B. Male habitus, ventral view. C. Male habitus, lateral view. D–I. Pachygnatha intermedia sp. nov. D. Male habitus, dorsal view. E. Male habitus, ventral view. F. Male habitus, lateral view. G. Female habitus, dorsal view. H. Female habitus, ventral view. I. Female habitus, lateral view. J–O. Pachygnatha ventricosa sp. nov. J. Male habitus, dorsal view. K. Male habitus, ventral view. L. Male habitus, lateral view. M. Female habitus, dorsal view. N. Female habitus, ventral view. O. Female habitus, lateral view. Scale bars = 2 mm.

Fig. 3 in On the genus Pachygnatha (Araneae, Tetragnathidae) in the Albertine Rift of Burundi, with the description of three new species

Fig. 3. Pachygnatha intermedia sp. nov. A. Male palp, ventral view. B. Male palp, retrolateral view. C. Male chelicera, frontal view. D. Male chelicera, ventral view. E. Female chelicera, ventral view. F. Female chelicera, frontal view. G. Female abdomen, ventral view. H. Female external genitalia, ventral view. Scale bars: A–F, H = 0.1 mm; G = 1 mm.

Fig. 4 in On the genus Pachygnatha (Araneae, Tetragnathidae) in the Albertine Rift of Burundi, with the description of three new species

Fig. 4. Pachygnatha ventricosa sp. nov. A. Male palp, ventral view. B. Male palp, retrolateral view. C. Male chelicera, frontal view. D. Male chelicera, ventral view. E. Female chelicera, ventral view. F. Female chelicera, frontal view. G. Female abdomen, ventral view. H. Female external genitalia, ventral view. Scale bars: AF, H = 0.1 mm; G = 1 mm.

Figure 1 in Potential of indigenous plants seed extracts of Anisophyllea boehmii and Aframomum sanguineum from Burundi to protect against oil oxidation

Figure 1. Reduction of the oxidation of the oil subjected to the sun by the extract of A. sanguinum (CS: control sample).

Figure 4 in Potential of indigenous plants seed extracts of Anisophyllea boehmii and Aframomum sanguineum from Burundi to protect against oil oxidation

Figure 4. Reduction of oxidation of oil subjected to 180°C by the extract of A. sanguineum (CS: control sample).

Figure 3 in Potential of indigenous plants seed extracts of Anisophyllea boehmii and Aframomum sanguineum from Burundi to protect against oil oxidation

Figure 3. Reduction of oxidation of oil subjected to 180°C by the extract of A. boehmii (CS: control sample).

Figure 2 in Potential of indigenous plants seed extracts of Anisophyllea boehmii and Aframomum sanguineum from Burundi to protect against oil oxidation

Figure 2. Reduction of the oxidation of oil subjected to the sun by the extract of A.sanguineum (CS: control sample).

Fig. 1 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 1. Study localities across Kenya and Burundi. Locations with the representatives of the genus Stenomacrus Forster, 1869 found in samples marked with red.

Fig. 4 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 4. Stenomacrus glabratus sp. nov., holotype, ♀ (ICIPE). A. Lateral view of habitus. B. Frontal view of face. C. Lateral view of head and mesosoma. D. Dorsal view of head and mesoscutum. E. Wings. F. Dorsal view of propodeum and metasomal tergites 1–2. Scale bars: A = 0.5 mm; B–F = 0.1 mm.

Fig. 8 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 8. Stenomacrus valvator sp. nov. A, C, E-I. Holotype, ♀ (ICIPE). D. Paratype, ♀ (ICIPE). B, J. Paratype, ♂ (ICIPE). A–B. Lateral view of habitus. C. Frontal view of face. D. Dorsal view of mandible: absence of inner tooth arrowed with red. E. Lateral view of head and mesosoma. F. Dorsal view of head and mesoscutum. G. Dorsal view of propodeum. H. Wings. I. Dorsal view of metasomal tergites 1–2. J. Dorsal view of propodeum and metasomal tergites 1–2. Scale bars: A–B = 0.5 mm; C–J = 0.1 mm.

Fig. 7 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 7. Stenomacrus scutellaris sp. nov., holotype, ♀ (ICIPE). A. Lateral view of habitus. B. Frontal view of face. C. Lateral view of head and mesosoma. D. Dorsal view of mesosoma. Red arrow indicates basally arched scutellum. E. Wings. F. Dorsal view of head. G. Dorsal view of first metasomal tergite. H. Dorsal view of second metasomal tergite. Scale bars: A = 0.5 mm; B–H = 0.1 mm.

Fig. 6 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 6. Stenomacrus pronotalis sp. nov., holotype, ♀ (ICIPE). A. Lateral view of habitus. B. Frontal view of face. C. Lateral view of head and mesosoma. D. Dorsal view of head and mesoscutum. E. Frontal view of epicnemium. Red arrow indicates a relatively short epicnemial carina. F. Wings. G. Dorsal view of propodeum and metasomal tergites 1–2. Scale bars: A = 0.5 mm; B–G = 0.1 mm.

Fig. 3 in Review of the genus Stenomacrus Förster, 1869 (Hymenoptera: Ichneumonidae: Orthocentrinae) from Kenya and Burundi: a first step to understanding the diversity of the genus in the Afrotropics

Fig. 3. Stenomacrus communis sp. nov., ♀♀ (ICIPE). A–G. Holotype. H. Paratype. A. Lateral view of habitus. B. Frontal view of face. C. Lateral view of head and mesosoma. D. Dorsal view of head and mesoscutum. E. Dorsal view of propodeum. F. Wings. G–H. Dorsal view of metasomal tergites 1–2. Scale bars: A = 0.5 mm; B–H = 0.1 mm.