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Fig. 3 in New Eocene South American native ungulates from the Quebrada de los Colorados Formation at Los Cardones National Park, Argentina
Fig. 3. New fossil specimens of Typotheria and Toxodontia from the Quebrada de Los Colorados Formation exposed at Quebrada Grande locality, Los Cardones National Park (Salta Province), Casamayoran SALMA. A, B. Colbertiasp. A. IBIGEO-P 58a, left maxillary fragment with M2–M3 in occlusal view. B. IBIGEO-P 58b, left maxillary fragment with P4–M3 in occlusal view. C–E. Pampahippus secundus Deraco and García-López, 2016. C. IBIGEO-P 62, right trigonid and incomplete talonid of a lower right molar in occlusal view. D. IBIGEO-P 63, fragmented left p3 or p4 in occlusal view. E. IBIGEO-P 64, left m1? in occlusal (E1), labial (E2), and lingual (E3) views. Photographs (A1–E1, E3, E4), explanatory drawings (A2–E2). Dashed area indicates broken or missing dental areas.
Fig. 1 in New Eocene South American native ungulates from the Quebrada de los Colorados Formation at Los Cardones National Park, Argentina
Fig. 1. New fossil materials of Litopterna, Astrapotheria, and Notostylopidae from the Quebrada de Los Colorados Formation exposed at Quebrada Grande locality, Los Cardones National Park (Salta Province), Casamayoran SALMA, Eocene. A. cf. Ernestokokenia sp. (IBIGEO-P 65), fragmented left upper cheek tooth in occlusal view. B. Astrapotheria indet. (IBIGEO-P 66), left maxillary fragment with three broken molariforms in lingual (B1) and occlusal (B3) views, and detail showing Hunter-Schreger bands (B4); dashed area in B2 indicates broken or missing dental areas. C. Homalostylops sp. (IBIGEO-P 57b), right m2? in occlusal (C1), lingual (C3), and labial (C4) views. D.?Homalostylops sp. (IBIGEO-P 57a), left m1? in occlusal view. Photographs (A1, B1, B3, B4, C1, C3, C4, D1), explanatory drawings (A2, B2, C2, D2).
MERICS China Podcast: National People's Congress 2024: The state implements the party's decisions, with Nis Grünberg and Jacob Gunter
<p>The National People's Congress (NPC), China's top legislature, has held its yearly session in Beijing from the 5th to the 11th of March. This episode of the MERICS China Podcast looks at what we have learned from this year's NPC and its repercussions for China and Europe. <strong>Johannes Heller-John</strong> is joined by MERICS Lead Analysts <strong>Nis Grünberg</strong> and <strong>Jacob Gunter</strong>. They argue that science and technology as well as security took precedence over socio-economic issues. </p>
National Archives of India. Plan. Fort of Dhar, 1860.
<p>National Archives of India. Plan. Fort of Dhar, 1860 (registration number 1841-60-74).<strong> © National Archives of India. Not to be reproduced without written permission.</strong></p>
A checklist of the spiders (Arachnida, Araneae) of the Garden Route National Park
<p>An annotated species list of spiders from the Garden Route National Park (GRNP) is provided. The checklist was compiled from data collected from the South African National Survey of Arachnida (SANSA) database. A total of 245 species from 52 families and 166 genera are presently protected in the park. The most species-rich families are the Thomisidae (34 spp.), Salticidae (28 spp.) and Araneidae (22 spp.), while 15 families are represented by singletons. The global distribution, endemicity and conservation status for each species is provided. A large percentage (83.2%) of the species have a wide distribution range and are of Least Concern while 17 species are Data Deficient and 10 species are of special concern. Seven species are known only from the Garden Route National</p>
Figure 2 in Archival sea turtles in National Zoological Collections of Zoological Survey of India
Figure 2. Representatives of the archival Sea turtle specimens (the Green Sea Turtle, Chelonia mydas) preserved in Zoological Survey of India, Kolkata. A. Hatchling of C. mydas, Reg. No. ZSI 14544 (dorsal view, wet collection). B. Hatchling of C. mydas, Reg. No. ZSI 14543 (ventral view, wet collection). C. Eggs of C. mydas (wet collection). D. Skull of C. mydas, Reg. No. ZSI 389 (1404) (lateral view, dry collection). E. Adult individuals of C. mydas, Reg. No. ZSI 22489 (lateral view, wet collection).
Figure 1 in Archival sea turtles in National Zoological Collections of Zoological Survey of India
Figure 1. Representatives of the archival Sea turtle specimens (the Hawksbill Sea Turtle, Eretmochelys imbricata) preserved in Amphibia and Reptilia gallery of Indian museum, Kolkata and morphometric measurements. HD= Head diameter, FL= Forelimb length, HL= Hindlimb length, CCL=Curved carapace length, CPL= Curved plastron length, CCW= Curved carapace width, CPW= Curved plastron width, TL= Total length.
Canada's national artificial intelligence governance system: Dataset from interviews with 20 government leaders & subject matter experts
<p><strong>Summary</strong></p> <p>Anonymized aggregate data from interviews with 20 government leaders and subject matter experts. The data was collected as part of a study of Canada's national system of artificial intelligence governance. The data was collected from February 2023 to July 2023. The dataset contains 610 topics that emerged from thematic analysis of interview transcripts from July 2023 to October 2023. The contexts, actors, resources, networks, evaluations, logics, functional bounds, rules, ecosystem-level dynamics, opportunities for improvement, and other topics contained in the dataset collectively represent the most significant components of Canada's national AI governance system that emerged over the course of the interviews with the 20 participants.</p> <p> </p> <p><strong>Notes for interpreting this dataset</strong></p> <p>Topics in analytical dimensions 1, 3, and 6-11 contain counts of the frequency with which aggregate topics emerged across each of the interviews with the 20 participants. Topics in analytical dimensions 2, 4, and 5 contain categories instead of frequency counts: the topics in these dimensions represent every unique actor, resource, and network that emerged over the course of the interviews instead of aggregate topics. </p> <p>Column titles contain the following abbreviations:<br>LEAD: Interviews with leaders of public sector AI governance initiatives.<br>SME-PS: Interviews with subject matter experts employed in the private sector.<br>SME-CS: Interviews with subject matter experts employed in the academic or civil sectors.</p> <p> </p> <p><strong>Full report</strong></p> <p>A report containing more information about this dataset and about the findings of our study can be found on SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525</a></p> <p> </p>
Figures 1–4 in Two new species of eriophyoid mites (Acari: Eriophyoidea) from Qilian Mountain National Park, China
Figures 1–4. Epitrimerus fruticus sp. nov., female. 1. Lateral view. 2. Dorsal view. 3. Coxal-genital region. 4. Empodium (enlarged). Scale bars = 10 μm.
Figure 3 in Diversity and community structure of oribatid mites (Acari: Oribatida) in the dominant habitats of Machakhela National Park (Georgia, Caucasus)
Figure 3. The results of the cluster analysis of the different forest habitats in Machakhela National Park.
Figure 1 in Diversity and community structure of oribatid mites (Acari: Oribatida) in the dominant habitats of Machakhela National Park (Georgia, Caucasus)
Figure 1. Distribution of the sites of the sample collection of the oribatid mite diversity in the Machakhela National Park.
Plate 2. Figures 11-20. Chimarra spp. Male genitalia.11-15. Chimarra nepalensis. 11. Lateral view, 12 in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Plate 2. Figures 11-20. Chimarra spp. Male genitalia.11-15. Chimarra nepalensis. 11. Lateral view, 12. Dorsal view,
Plate 5. Figures 34-44. Chimarra spp. Adult.34-35. C. biungulata. 34. Dorsal view, 35 in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Plate 5. Figures 34-44. Chimarra spp. Adult.34-35. C. biungulata. 34. Dorsal view, 35. Ventral view.
Plate 1. Figures 1-10. Chimarra spp. Male genitalia. 1-5. Chimarra sikkimensis. 1. Lateral view, 2. Dorsal view, 3. Inferior appendage Ventral view, 4. Phallic apparatus lateral view, 5. Phallic apparatus ventral view. 6-10. C in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Plate 1. Figures 1-10. Chimarra spp. Male genitalia. 1-5. Chimarra sikkimensis. 1. Lateral view, 2. Dorsal view, 3. Inferior appendage Ventral view, 4. Phallic apparatus lateral view, 5. Phallic apparatus ventral view. 6-10. C. rifati.
Plate 4. Figures 29-33. Chimarra spp. Male genitalia. 29-33. Chimarra biungulata. 29. Lateral view, 30. Inferior appendage Ventral view, 31. Dorsal view, 32. Phallic apparatus ventral view, 33 in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Plate 4. Figures 29-33. Chimarra spp. Male genitalia. 29-33. Chimarra biungulata. 29. Lateral view, 30. Inferior appendage Ventral view, 31. Dorsal view, 32. Phallic apparatus ventral view, 33. Phallic apparatus lateral view.
Plate 3. Figures 21-28. Chimarra spp. Male genitalia. 21-25. Chimarra biungulata. 21. Lateral view, 22 in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Plate 3. Figures 21-28. Chimarra spp. Male genitalia. 21-25. Chimarra biungulata. 21. Lateral view, 22. Dorsal view,
Forest gap dynamics with repeat lidar in Berchtesgaden National Park - Data and analysis scripts
<p>This repository holds data and code for the paper: Krüger, K., Senf, C., Jucker, T., Pflugmacher, D., Seidl, R. (2024). Gap expansion is the dominant driver of canopy openings in a temperate mountain forest landscape. Journal of Ecology. <span><a href="http://doi.org/10.1111/1365-2745.14320">http://doi.org/10.1111/1365-2745.14320</a> </span></p> <p><strong>NOTE:</strong> This is a static repository, but the project might evolve. See the connected GitHub repository for latest updates!</p> <p>All data to reproduce the results are available, all other layers can be generated with the code provided in this repository. The Canopy Height Models underlying the analysis and respective processing scripts for the lidar data, are available from the corresponding author upon reasonable request. Gap layers derived from the Canopy Height Models are available in this repository.</p> <p>Empty folders are set up to follow the directory structure of the scripts. </p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>hydroelectric</span> <span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1011</span></span><span><span> datapoints from </span></span><span><span>11</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span> Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>battery energy storag</span><span>e </span><span>systems</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Battery energy storage is the fastest growing form of power system </span><span>flexibility, and</span><span> will be critical to integrating large shares of variable renewable energy.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>671</span></span><span><span> datapoints from </span></span><span><span>18</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the </span><span>literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>wind</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1506</span></span><span><span> datapoints from </span></span><span><span>28</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.</p>
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.