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57 results for “history of computation”
Phlorest phylogeny derived from Chacon & List 2015 'Improved computational models of sound change shed light on the history of the Tukanoan languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chacon TC, List J-M (2015) Improved computational models of sound change shed light on the history of the Tukanoan languages. Journal of Language Relationship, 3:177–203.</p> </blockquote>
Fig. 20. A in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 20. A. Part of a pinned Omorgus gigas (Harold, 1872) beetle a few hundred slices away from the pinned area. B. The normal morphology of the beetle is no longer visible due to the metal artefact appearing in the pinned area. Image by the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 15 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 15. Polychaete specimen (Lumbrineris latreillii Audouin & Milne Edwards, 1834) in a composite rendering showing the location of organs of interest within the animal. Soft tissues are volume-rendered, jaws were segmented individually and surface-rendered in different colours. The coloured arrows at the upper left corner indicate the orientation of the scanned specimen in three views (x, y and z axes). Image by HCMR micro-CT lab, CC-BY Sarah Faulwetter.
Fig. 1 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 1. Schematic overview of the image acquisition process. Image by the Hellenic Centre for Marine Research (HCMR) micro-CT lab.
Fig. 19 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 19. Scan of a marine worm (polychaete) with motion artefacts. The structures are not clearly defined due to specimen movement during the scanning procedure. Image by HCMR micro-CT lab.
Fig. 4 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 4. Example of the projection images resulting from the scanning process. Image by HCMR micro- CT lab.
Fig. 22. A. Monkey vertebra without metal support. B. A in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 22. A. Monkey vertebra without metal support. B. A metal artefact (yellow arrow) is created due to the metal rod used to support a series of vertebrae on a mounted skeleton. Photo courtesy of the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 3 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 3. The spectrum generated by an X-ray generator at 100kV with and without filtering. Image generated by the simulation environment https://www.oem-xray-components.siemens.com/x-ray-spectra-simulation.
Fig. 21. A. 3D in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 21. A. 3D model of the Omorgus gigas (Harold, 1872) beetle after a quick segmentation, including the metal artefact. B. 3D model of the same specimen after manual removal of the pin in the Dragonfly software (http://www.theobjects.com/dragonfly/). Clicking on the image opens the 3D model. Photo courtesy of the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 10. Measuring the maximum width W in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 10. Measuring the maximum width W (in pixels) of the projected specimen (as the distance from the rotation axis - dotted line - to the farthest end of the sample) to calculate the number of radiographs needed. This measurement is done for the angular position of the rotating platform where the projected specimen is the widest. For a complete rotation, the projected specimen would stay within the limits of the rectangle. Photo by MNHN.
Fig. 17 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 17. Scan of a marine worm (polychaete) without (A) and with (B) ring artefacts correction during the reconstruction procedure. The red square indicates the presence of ring artefacts which are reduced in (B) following the ring artefacts correction. Images by HCMR micro-CT lab.
Fig. 9 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 9. External morphology of the polychaete Lumbrineris latreilli Audouin & Milne Edwards, 1834. Specimen had been preserved in ethanol and was subsequently blotted dry on a tissue and scanned in air. The remaining ethanol (shown by arrows) can be observed clinging to the anterior end of the polychaete. Image by HCMR micro-CT lab, CC-BY Sarah Faulwetter.
Fig. 12 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 12. Cross-section image without (A) and with (B) a selection of a region of interest (red square) for the reconstruction of polychaete jaws. Image by HCMR micro-CT lab.
Fig. 7 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 7. Setup of scanning containers. Images by HCMR micro-CT lab.
Fig. 2 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 2. Schematic overview of the X-Ray generator.
Computational phylogenetics reveal the history of sign languages
Open the record for dataset details and reuse information.
Data from: Reconstructing the demographic history of orang-utans using approximate Bayesian computation
Investigating how different evolutionary forces have shaped patterns of DNA variation within and among species requires detailed knowledge of their demographic history. Orang-utans, whose distribution is currently restricted to the Southeast Asian islands of Borneo (Pongo pygmaeus) and Sumatra (Pongo abelii), have likely experienced a complex demographic history, influenced by recurrent changes in climate and sea levels, volcanic activities and anthropogenic pressures. Using the most extensive sample set of wild orang-utans to date, we employed an approximate Bayesian computation (ABC) approach to test the fit of 12 different demographic scenarios to the observed patterns of variation in autosomal, X-chromosomal, mitochondrial and Y-chromosomal markers. In the best-fitting model, Sumatran orang-utans exhibit a deep split of populations north and south of Lake Toba, probably caused by multiple eruptions of the Toba volcano. In addition, we found signals for a strong decline in all Sumatran populations ~24 ka, probably associated with hunting by human colonizers. In contrast, Bornean orang-utans experienced a severe bottleneck ~135 ka, followed by a population expansion and substructuring starting ~82 ka, which we link to an expansion from a glacial refugium. Therefore, we showed that orang-utans went through drastic changes in population size and connectedness, caused by the recurrent contraction and expansion of rainforest habitat during Pleistocene glaciations, and probably also by the impact of hunting by early humans. Our findings also emphasize the fact that important aspects of the evolutionary past of species with complex demographic histories might remain obscured when applying overly simplified models.
Data from Wilson et al.: Applying computer vision to digitised natural history collections for climate change research: temperature-size responses in British butterflies
<p>This dataset supports the publication: Wilson et al. "Applying computer vision to digitised natural history collections for climate change research: temperature-size responses in British butterflies". These are the data used for the data figures (Fig 3-6, SI Figs 1-2) and the supplementary information tables.</p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
Computational History of Philosophy of Science (Comp HOPOS) Dataset
<p>The Computational History of Philosophy of Science (Comp HOPOS) aims to be a comprehensive set of article and (when available) book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the dataset is available at <a href="https://github.com/dhicks/comp-HOPOS">https://github.com/dhicks/comp-HOPOS</a>. See the file data_dictionary.txt for data dictionary and additional information.</p>
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Allen Brain Atlas
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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.