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Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica)
<p>We provide in this dataset the complete records from the top 120 m of two ice cores drilled on two adjacent ice rises (Hammarryggen and Lokeryggen, ~100 km apart) located in coastal Dronning Maud Land, Antarctica. Density, water stable isotopes (δ<sup>18</sup>O, δD and d-excess), ions concentrations (Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>+</sup>, Ca<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>), and continuous electrical conductivity measurement (ECM) records, as well as age models and resulting surface mass balance (SMB) are presented here. These ice cores are dated back to the end of the eighteenth century (CE 1793 ± 3 years for FK17and 1780 ± 5 years for TIR18) by manual and automatic multivariable layer counting and volcanic tie points validation. The SMB is calculated after correction for vertical strain rates.</p> <p>A detailed description of the measurement procedures, data treatments and uncertainties can be found in: Wauthy S., Tison, J.-L., Inoue, M., El Amri, S., Sun, S., Claeys, P., and Pattyn, F.: Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica): an open window on spatial and temporal regional variability of environmental proxies, submitted to Earth System Science Data (ESSD).</p> <p>These new records can be compared to those from other coastal ice cores in DML and to regional or global model results, and represent a great opportunity for further studies addressing the mechanisms behind SMB spatial and temporal variability in East Antarctica.</p> <p>The study was funded in the framework of the Mass2Ant project, contract n° BR/165/A2 by the Belgian Science Policy BELSPO.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
Fig. 1. Malawi Hydraenidae, habitus. A in New species and new records of minute moss beetles from East Africa (Coleoptera: Hydraenidae)
Fig. 1. Malawi Hydraenidae, habitus. A – Coelometopon dedzae sp. nov.; B – Hydraena mulanje sp. nov.; C – Ochthebius erinaceus sp. nov.
Fig. 4 in New species and new records of minute moss beetles from East Africa (Coleoptera: Hydraenidae)
Fig. 4. Distribution map for taxa covered in this paper. Red circles indicate localities for Decarthrocerus jeanneli Orchymont, 1948 (bordered circle type locality).
Fig. 3. Decarthrocerus jeanneli Orchymont, 1948. A–C in New species and new records of minute moss beetles from East Africa (Coleoptera: Hydraenidae)
Fig. 3. Decarthrocerus jeanneli Orchymont, 1948. A–C – habitus, D–F – aedeagi, lateral view. A & D – Malawi, Mt Dedza; B & E – Tanzania, Mt Hanang; C & F – Kenya, Mt Elgon, paratype (photo © MNHN, Paris, A. Mantilleri; aedeagus after PERKINS (2009)).
Fig. 2 in New species and new records of minute moss beetles from East Africa (Coleoptera: Hydraenidae)
Fig. 2. Malawi Hydraenidae, aedeagus, lateral and ventral views. A – Coelometopon dedzae sp. nov.; B – Hydraena mulanje sp. nov.; C – Ochthebius erinaceus sp. nov.
Figs 7–11 in A new species of the genus Synanthedon Hübner, 1819 (Lepidoptera: Sesiidae) from the Russian Far East
Figs 7–11. Male genitalia of Synanthedon bastak O. Gorbunov et Koshkin, sp. n., paratype, genitalia preparation No. OG–001-2023. 7 – tegumen-uncus complex; 8 – valva; 9 – saccus; 10 – phallus; 11 – phallus distally. Scale bar: 0.5 mm and 0.2 mm for 11.
Figs 1–6. Synanthedon spp. 1–2 – S. bastak O in A new species of the genus Synanthedon Hübner, 1819 (Lepidoptera: Sesiidae) from the Russian Far East
Figs 1–6. Synanthedon spp. 1–2 – S. bastak O. Gorbunov et Koshkin, sp. n., holotype ♂, Sesiidae picture No. 0005-0006–2023; alar expanse 20.5 mm; 3–4 – S. soffneri Špatenka, 1983, Russia: Moscow Region, Serebryano-Prudsky District, Lishnyagi, 54°23.89ʹ N, 038°32.15ʹ E, 172 m, 08.VI.2021, O. Gorbunov leg., picture No. 0243-0244–2021; alar expanse 19.7 mm; 5–6 – S. spatenkai O. Gorbunov, 1991, holotype ♂, Sesiidae picture No. 0011-0012–2023; alar expanse 19.4 mm. 1, 3, 5 – dorsal view; 2, 4, 6 – ventral view.
Figs 12–16 in A new species of the genus Synanthedon Hübner, 1819 (Lepidoptera: Sesiidae) from the Russian Far East
Figs 12–16. Male genitalia of Synanthedon soffneri Špatenka, 1983, Russia; Moscow Region, Serebryano-Prudsky District, Lishnyagi, 54°23.89ʹ N, 038°32.15ʹ E, 172 m, 08.VI.2021, O. Gorbunov leg., genitalia preparation No. OG–001-2023. 12 – tegumen-uncus complex; 13 – valva; 14 – saccus; 15 – phallus; 16 – phallus distally. Scale bar: 0.5 mm, and 0.2 mm for 16.
Figure 1 in The Chironomus species studied by Letha Karunakaran in Singapore, with a review of the status of selected South-East Asian Chironomus
Figure 1. Male hypopygium of Chironomus flaviplumus-Type B. Note that the anal point is not black and not sharply turned-down, and that the SVo is beaked and not gently curved.
Landsat-derived rangeland condition indicators in East Africa from 2000 to 2022
<p>Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R<sup>2</sup> = 0.801, <em>p</em> < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km<sup>2</sup>, dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 and Google Earth Engine (GEE; details in data availability section).</p>
Fig. 90 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 90. Aphaniosoma sp., ♀ (NMWC), undescribed species from Syria. A. Habitus, lateral view. B. Head, frontal view. C. Head and thorax, dorsal view.
Fig. 87 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 87. Aphaniosoma yaromi sp. nov., ♂, paratype, (SMNHTAU). A. Hypopygium, lateral view. B. Pregenital sternites, lateral view.C. Hypopygium and pregenital sternites, ventral view. Abbreviations: See Material and methods.
Fig. 89. Aphaniosoma spp.A in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 89. Aphaniosoma spp.A. Aphaniosoma yaromi sp. nov., ♂ (SMNHTAU), non-type specimen infected with Stigmatomyces aphaniosomae Rossi & Weir, 2011 (Laboulbeniales Lindau). B. Aphaniosoma curvistylus sp. nov., ♂ (SMNHTAU), non-type specimen with a phoretic mite on abdomen.
Fig. 85 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 85. Aphaniosoma yaromi sp. nov., ♂, holotype (SMNHTAU 405830). A. Habitus, lateral view. B. Head and thorax, dorsal view.
Fig. 86 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 86. Aphaniosoma yaromi sp. nov., ♂, paratype (SMNHTAU), hypopygium in situ. A. Lateral view. B. Posterior view.
Fig. 84. Aphaniosoma verecundum Ebejer, 1998 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 84. Aphaniosoma verecundum Ebejer, 1998, ♂ (MJE). A. Hind trochanter and femur. B–C. Hypopygium in situ. B. Postero-ventral view. C. Postero-lateral view.
Fig. 83. Aphaniosoma verecundum Ebejer, 1998 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 83. Aphaniosoma verecundum Ebejer, 1998, ♂, (MJE). A. Habitus, lateral view. B. Head and thorax dorsal view.
Fig. 81 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 81. Aphaniosoma sp. aff. sodalis Collin, 1949, ♂, paralectotype (NHMUK, B.M. 1935–354, bar code 013435848). A. Habitus, lateral view. B. Hypopygium in situ, lateral view.
Fig. 82. Aphaniosoma trisetum Ebejer, 1998 in The genus Aphaniosoma Becker, 1903 (Diptera: Chyromyidae) in the Eastern Mediterranean and Middle East, with descriptions of new species
Fig. 82. Aphaniosoma trisetum Ebejer, 1998, ♂ (SMNHTAU). A. Head, lateral view. B. Hypopygium, posterior view. C. Pregenital sternites, ventral view. Abbreviations: See Material and methods.
ScienceDex guides
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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.