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Fig. 7. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 7. Distribution maps. A–B. Nanonycteris veldkampii (Jentink, 1888). C–D. Megaloglossus woermanni Pagenstecher, 1885. E–F. Myonycteris angolensis (Bocage, 1898). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 14. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 14. Distribution maps. A–B. Lavia frons (E. Geoffroy St.-Hilaire, 1810). C–D. Rhinolophus alcyone Temminck, 1852. E–F. Rhinolophus blasii Peters, 1867. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 11. Distribution maps. A–B. Hipposideros abae J.A. Allen, 1917. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 11. Distribution maps. A–B. Hipposideros abae J.A. Allen, 1917. C–D. Hipposideros beatus (K. Anderson,1906). E–F. Hipposideros caffer (Sundevall, 1846). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 6. Distribution maps. A–B. Hypsignathus monstrosus H. Allen, 1862. C–D. Micropteropus intermedius Hayman, 1963. E–F in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 6. Distribution maps. A–B. Hypsignathus monstrosus H. Allen, 1862. C–D. Micropteropus intermedius Hayman, 1963. E–F. Micropteropus pusillus (Peters, 1867). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 4. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 4. Distribution maps. A–B. Epomophorus labiatus (Temminck, 1837). C–D. Epomophorus minimus Claessen & De Vree, 1991. E–F. Epomophorus minor Dobson, 1880. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 8. Distribution maps. A–B. Myonycteris relicta Bergmans, 1980. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 8. Distribution maps. A–B. Myonycteris relicta Bergmans, 1980. C–D. Myonycteris torquata (Dobson, 1878). E–F. Plerotes anchietae (Seabra, 1900). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 13. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 13. Distribution maps. A–B. Macronycteris gigas (Wagner, 1845). C–D. Macronycteris vittatus (Peters, 1852). E–F. Cardioderma cor (Peters, 1872). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 9. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 9. Distribution maps. A–B. Rousettus aegyptiacus (E. Geoffroy St.-Hilaire, 1810). C–D. Casinycteris argynnis Thomas, 1910. E–F. Scotonycteris bergmansi Hassanin et al., 2015. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 3. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 3. Distribution maps. A–B. Eidolon helvum (Kerr, 1792). C–D. Epomophorus anselli Bergmans & Van Strien, 2004. E–F. Epomophorus crypturus Peters, 1852. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 12. Distribution maps. A–B. Hipposideros camerunensis Eisentraut, 1956. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 12. Distribution maps. A–B. Hipposideros camerunensis Eisentraut, 1956. C–D. Hipposideros fuliginosus (Temminck, 1853). E–F. Hipposideros ruber (Noack, 1893). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
Mapping of protein domains to exons
<p>Database table maps transcripts and exons to their isoforms and Pfam domains. </p> <p>Originally generated from Ensembl Biomart </p> <p>If you use the database, please cite:</p> <p>Zakaria Louadi, Kevin Yuan, Alexander Gress, Olga Tsoy, Olga V Kalinina, Jan Baumbach, Tim Kacprowski*, Markus List*. <a href="https://doi.org/10.1093/nar/gkaa768">DIGGER: exploring the functional role of alternative splicing in protein interactions</a>, Nucleic Acids Research.<br> * Joint last authors.</p> <p>Source code of DIGGER: <a href="https://github.com/louadi/DIGGER">https://github.com/louadi/DIGGER</a></p>
The geologic map of Sinus Iridum, and the geologic units in this work
<p>There are three documents here. </p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/The%20geologic%20map%20of%20Sinus%20Irudum.jpg">The geologic map of Sinus Irudum.jpg</a> shows the geologic map we did. </p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/geounits.zip">geounits.zip</a> shows the geologic units in our work.</p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/Crater%20counting%20files%20and%20the%20results.zip">Crater counting files and the results.zip</a> is the crater counting files and the results in this work.</p>
Secondary studies in the academic context: A systematic mapping and survey
<p>An online version of the survey executed in the paper "Secondary studies in the academic context: A systematic mapping and survey".</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Data from: Comparing methods for mapping global parasite diversity
Aim Parasites are a major component of global ecosystems, yet spatial variation in parasite diversity is poorly known, largely because their occurrence data are limited and thus difficult to interpret. Using a recently compiled database of parasite occurrences, we compare different models which we use to infer parasite geographic ranges and parasite species richness across the globe. Innovation To date, most studies exploring spatial patterns of parasite diversity assumed, with little validation, that the geographic range of a parasite species can be represented by the collective geographic range of its host species. Our study compares this assumption with a suite of other methods to infer parasite distribution from parasite occurrence data (e.g. based on data density, ecoregions and climatic conditions). We highlight diversity hotspots identified by the various methods and compare the effects of sampling intensities in different regions, a crucial factor of observed parasite diversity. Main conclusions The type of model used to infer parasite distributions affects estimates of both total species richness and spatial patterns of hotspots of parasite richness. Overall, the models based on reported occurrences share similar areas of high parasite richness that tends to be biased towards areas of high sampling effort. In contrast, the model based on host distributions showed hotspots of parasite diversity which are biased towards areas of high host species richness. Accounting for sampling effort could only help to reconcile the outcome from the different models in some regions. Further, the non-saturated species accumulation curves even for the best studied regions of the world such as Europe and North America as a call for further sampling effort and development of effective analytic tools that can provide robust accounts of global parasite diversity.
Map 5 in On the taxonomy and zoogeography of some West Palaearctic Quedius species, with a focus on the East Mediterranean and the species allied to Quedius umbrinus and Q. nivicola (Coleoptera: Staphylinidae: Staphylininae)
Map 5: Distributions of Quedius problematicus (black circles: revised records; white circles: literature records) and Q. petraensis (black triangle) in the Middle East.
Map 3 in A revision of Othiini. XX. Two new species of Othius, the first record of the genus from Myanmar, and additional records (Coleoptera: Staphylinidae: Staphylininae)
Map 3: Distribution of Othius grandis (black circles) in the Caucasus region, based on examined records. White circle: doubtful locality "Kirovabad" (= Gandza).
Map 1 in A revision of Othiini. XX. Two new species of Othius, the first record of the genus from Myanmar, and additional records (Coleoptera: Staphylinidae: Staphylininae)
Map 1: Distribution of Othius stenocephalus (black circles) in the Caucasus region, based on examined records. White star: type locality "Helenendorf".
Map 1 in Six new species of Chinecallicerus from China, with a new synonymy (Coleoptera: Staphylinidae: Aleocharinae: Geostibini)
Map 1: Distributions of Chinecallicerus species in China: C. wrasei (black diamond); C. schuelkei (white circle); C. laevigatus and C. serratus (both black circle); C. subater (white diamond); C. reticulatus (black star); C. pinnatus (black triangle); C. grandicollis (grey star); C. carinatus (white triangle); C. orbiculatus (white star); C. transversus (black square); C. granulosissimus (white square); C. trituberculatus (grey triangle).
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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.