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5,039 results for “Natural history collections”
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Juan Joaquín Rodríguez y Femenías, <a href="http://www.wikidata.org/entity/Q5950135">http://www.wikidata.org/entity/Q5950135</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Alexander Gilli, <a href="http://www.wikidata.org/entity/Q8194912">http://www.wikidata.org/entity/Q8194912</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Mariano Lagasca, <a href="http://www.wikidata.org/entity/Q891966">http://www.wikidata.org/entity/Q891966</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Zacharia C. Panțu, <a href="http://www.wikidata.org/entity/Q730640">http://www.wikidata.org/entity/Q730640</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Theodor Kotschy, <a href="http://www.wikidata.org/entity/Q113299">http://www.wikidata.org/entity/Q113299</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Michel Charles Durieu de Maisonneuve, <a href="http://www.wikidata.org/entity/Q703990">http://www.wikidata.org/entity/Q703990</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Fig. 1 in An Annotated Catalog of the African Primate Genera Colobus and Procolobus (Cercopithecidae: Colobinae) in the Collections of the American Museum of Natural History
Fig. 1. Collection localities in the Congo Basin. - represent localities where blackandwhite colobus were collected, Z represent localities where red colobus were collected. Localities where both red, and blackandwhite colobus were collected are marked with a l. The localities shown on the map are as follows: Congo (Brazzaville): 1. Makoua, 2. Ouesso. Democratic Republic of the Congo: 3. Abawe, 4. Akenge, 5. Angumu, 6. Avakubi, 7. Bafuka, 8. Bafwabaka, 9. Beni, 10. Bolobo, 11. Faradje, 12. Gamangui, 13. Kananga, 14. Lukolela, 15. Medje, 16. Niapu, 17. Poko, 18. Risimu, 19. Rutshuru, 20. Ukaturaka, 21. Vankerckhovenville, 22. Yakuluku. Uganda: 23. Kibale Forest, Kanyawara, 24. Kibale Forest, near Dubona camp, 25. Malabigambo Forest.
Fig. 4 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 4. – Holotype of Rogeria brasiliensis J. Gay ex DC. (––– Pterodiscus brasiliensis (J. Gay ex DC.) Asch.) at P. [Silva s.n., P00435303; © Muséum national d'Histoire naturelle, Paris]
FIG. 4 in The d'Orbigny Palaeontological Collection of the National Museum of Natural History and Science, Lisbon, Portugal: Historical perspective and revision of Cretaceous Cephalopoda
FIG. 4. — Cretaceous ammonites and belemnites of the d'Orbigny Collection of the National Museum of Natural History and Science (Museu Nacional de História Natural e da Ciência): A-C, Coilopoceras requienianus (d'Orbigny, 1840) in lateral (A) and ventral (B) views, and original label (C): Nº 518/Ammonites Requienianus (d'Orb), Andar 21º [corrected] Turoniense, Terreno Cretaceo, Localidade Uchaux (Vanduse);D-F, Turrilites (Turrilites) costatus Lamarck,1801 in lateral (D) and basal (E) views, and original label (F): Nº 466/Turrilites costatus (Lamarck), Andar 20º Cenomaniense, Terreno Cretaceo,Localidade Rouen (Seine inf.re); G-J, Duvalia dilatata (de Blainville, 1827) in dorsal view (G), section (H), lateral view (I), and original label (J): Nº 351/Belemnites dilatatus (Blainville),Andar 17º Neocomiense,Terreno Cretaceo,Localidade Cheiron perto de [near of] Castellanne (Basses Alpes);K-N, Hibolithes subfusiformis(Raspail,1829) in dorsal view (K), section (L), ventral view (M), and original label (N):Nº 352/Belemnites subfusiformis (Raspail), Andar 17º Neocomiense, Terreno Cretaceo, Localidade Cheiron (Basses Alpes); O-R, Belemnitella mucronata (von Schlotheim, 1813) in dorsal view (O), section (P), ventral view (Q), and original label (R): Nº 556/Belemnitella mucronata (d'Orb), Andar 22º Senoniense, Terreno Cretaceo, Localidade Epernay (Marne). Scale bar: 2 cm.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Sarah Williams, <a href="https://orcid.org/0000-0002-2644-9952">https://orcid.org/0000-0002-2644-9952</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Figure 2 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 2 The Museum collections of which ~6% are digitised and already have high usage in scientific publications. Data for period February 2015 to October 2021.
Figure 1 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 1 Overview of thematic, investment and efficiency savings approach including five key thematic areas: biodiversity conservation, invasive species, medicines discovery, agricultural research and development, and mineral exploitation.
Supplementary material 1 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Query summary of IUCN Red List Data - data deficient species
Figure 4 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 4 Summary of investment and efficiency approaches to valuing digitisation of Museum collection.
Figure 3 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 3 Valuing pathways to impact across five key areas: biodiversity conservation (£0.7bn–£1bn), invasive species (£0.7bn–£1.1bn), medicines discovery (£0.8bn–£2.8bn), agricultural research and development (£20m–£70m), and mineral exploitation (£20m–£80m). All estimates are in NPV terms over 30 years using a 3.5% discount factor.
Figure 7 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 7 Theory of change showing the four components (inputs, activities, outputs and outcomes) with examples that lead to impact.
Figure 6 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 6 Summary of the approach, using a theory of change to guide a literature review and benefits modelling.
FIG 3 Glaucis hirsutus female, MHNG 1723.041 in On the Paraguayan specimens of Nothura darwinii (Aves: Tinamidae) and Glaucis hirsutus (Aves: Trochilidae) in the collection of the Natural History Museum of Geneva (Switzerland), with a review of South Brazilian reports of the latter
FIG 3 Glaucis hirsutus female, MHNG 1723.041.
Figure 2 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 2 In the Central Library of Datasets, natural history collection staff will find correctly identified images of their target organisms and download the data for training of an individually customized classifier (photos: Lepidoptera by Entomological Collection of ETH Zürich; Orthoptera by Naturalis Biodiversity Center; Brassicaceae by United Herbaria Z+ZT, ZT-00164967, ZT-00167494, ZT-00171530, CC BY-SA 4.0). The current figure shows a mock-up.
Figure 3 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 3 Sharing of taxonomic knowledge between institutes. (1) Each algorithm contains two basic components: the feature extractor and the classifier. (2) The Central Library of Datasets allows the user to browse through all available images of collection objects; (3) based on all available images, a regularly updated central feature extractor is created and published; (4) custom made algorithms can relatively easily be created by building a classifier based on a selection of taxa from the central library and combining this with the central feature extractor; (5) newly created algorithms together with their metadata (probability & information on content) are published through a web service in the Central Library of Algorithms (6) and can be used through the Identification web services (API) either for batch processing of images or through a mobile app. Models can be easily extended by other institutions by combining data sources (7).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.