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4,404 results for “digits”
Preliminary metadata for the UHMRL digital tape archive pilot
<p>The dataset (.csv) consists of preliminary metadata for pilot testing digitization and research-driven metadata production of the University of Helsinki Music Research Laboratory and Electronic Music Studio tape archive. PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p>
Supporting codes, data, and outputs to identify a type of lithofacies from rock-core digital photographs and to classify the lithofacies from well-log data
<p>This repository contains the codes and the data used in the study “Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information from Well-log Data and Rock-core Digital Photographs”. The codes are used to identify the lithofacies type from rock-core digital photographs (clustering) and to predict the lithofacies from geophysical well-log data (classification). The lithofacies identification process, including feature extraction and clustering, was encoded by MATLAB, and the classification model was encoded by TensorFlow to construct a neural network-based classification model. </p>
Tables of the paper"A Conceptual Model of Chinese Oral Memory Based on Digital Humanities"
<p>Those are the tables used for the paper "A Conceptual Model of Chinese Oral Memory Based on Digital Humanities".</p>
Figure S1: Tissue detection in StrataQuest software via creation of a digital mask; Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding.
<p><strong>Figure S1: Tissue detection in StrataQuest software via creation of a digital mask</strong><strong>.</strong> A digital ‘mask’ is created to measure the area of tissue for quantification of cell densities. The tissue mask is automatically generated by StrataQuest software via conversion of the scanned image from RGB to grayscale and then application of an intensity threshold. Manual adjustments are made to the tissue mask to remove necrotic areas and/or staining artefacts. Only nuclei present within the tissue mask are quantified. Representative tumour cores with low (A) and high (B) SOX2 densities, respectively, with corresponding overlaid tissue masks are shown (C-D; purple colour). Tissue mask generation is based on haematoxylin staining so is not affected by the level of DAB staining. Scale bars 200µm.</p> <p><strong>Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding. </strong>Inbuilt colour deconvolution algorithms within StrataQuest software separate SOX2 DAB (brown) staining from haematoxylin (blue) staining to produce grayscale images for each channel. Nuclear segmentation was then performed on the resulting grayscale DAB image to detect brown-stained nuclei. This is possible as SOX2 expression is localised to the nucleus. Segmented nuclear masks overlaid onto the DAB (brown) channel (A) and the original colour image (B) allow visualisation of the segmentation algorithm. The software then calculates parameters such as nuclear size, haematoxylin intensity and DAB intensity for each nuclear mask, which are reported on a scattergram, with each dot representing a single nuclear mask. A scattergram displaying DAB intensity vs haematoxylin intensity is used to threshold and accurately detect SOX2+ stained nuclei (C). Gated nuclei from the scattergram (C) can be visualised on an image of segmented nuclear masks overlaid onto the RGB image (D). Red nuclei represent SOX2+ cells (red gate on C) and green nuclei are SOX2- cells (green gate on C). Scale bars 50μm.</p>
Use of digital technologies for the development of algebraic thinking
<p>Data analyzed in this model was obtained from the experiment of a master's degree work concern of use of digital technologies for the development of algebraic thinking. The tests is carried out with two groups of 8th grade in an educational institution program of the Atlántico Department - Colombia.</p>
FIGURE 4. A in A digitally-rendered endocast for Tyrannosaurus rex
FIGURE 4. A, line interpretation of endocast of FMNH PR2081 (Tyrannosaurus rex) in dorsal view, showing full estimated extent of olfactory bulbs; B, transverse CT section (2 mm thick) through skull immediately dorsal to orbits, showing expanded olfactory bulbs anterior to endocranial cavity; C, diagram of skull showing location of section in D. Abbreviations: fm, foramen magnum; I, olfactory tract; olb, olfactory bulb; orb, orbit; se, sphenethmoid.
FIGURE 3 in A digitally-rendered endocast for Tyrannosaurus rex
FIGURE 3. Horizontal slices through the endocranial cavity of FMNH PR2081, Tyrannosaurus rex. Each slice is 0.5 mm thick, synthetically generated from 2 mm thick coronal slices by VoxBlast, ver. 2.2; spacing between slices is 5 mm, with the dorsalmost slice at A and ventralmost at F. Broad separation of the ophthalmic (V1) and maxillarymandibular (V2,3) branches of the trigeminal nerve can be seen. Abbreviations: asc, anterior semicircular canal; ep, epipterygoid;?fov, fenestra ovalis; pf, pituitary fossa; ps, pneumatic sinus; psc, posterior semicircular canal; res, external otic recess; tg, trigeminal ganglion; us, utricular sinus.
FIGURE 1 in A digitally-rendered endocast for Tyrannosaurus rex
FIGURE 1. Digital endocast for FMNH PR2081, Tyrannosaurus rex. A, stereopair of endocast in ventrolateral view; B, stereopair of endocast in dorsolateral view; C, D, line interpretations of endocast in ventrolateral (C) and dorsolateral (D) view. Cranial nerves indicated with Roman numerals; note separation between ophthalmic (V1) and maxillomandibular (V2,3) branches of the trigeminal nerve. The left facial (VII) and vestibular statoacoustic (VIII.v) are shown. Abbreviations: cblm, cerebellar region (cerebellum itself not visible); cbm, cerebrum; f, forebrain; fm, foramen magnum; fpr, cast of perilymphatic foramen; fr, mold of floccular recess; h, hindbrain; m, midbrain; mf, cast of metotic fissure; olb, olfactory bulb; orb, right orbit; pf, pituitary fossa; ssc, semicircular canals; us, utricular sinus; vcd, dorsal cerebral vein; vcm, middle cerebral vein; vs, vascular structure;? unknown structure ventral to right trigeminal ganglion.
Innovation and Active Aging in digital environment dataset
<p>Dataset corresponding to a 785 cases from Madrid province, a representative sample of people from 65 to 80 years old made by telephone survey during March, 2019.</p> <p>Please, use v2. Fixed typos and errors</p> <p>Research Project funded by UCM-Santander grant (PR26/16-8B)</p> <p> </p>
Figure 8 Digitized comparisons between tyrannosaur maxillae and BMR P2007.4.1 in Feeding traces attributable to juvenile Tyrannosaurus rex offer insight into ontogenetic dietary trends
Figure 8 Digitized comparisons between tyrannosaur maxillae and BMR P2007.4.1. Interactive manipulation of digitized NextEngine 3D scan of a cast of the right maxilla of BHI #3033 and BMR P2007.4.1 caudal vertebra. Full-size DOI: 10.7717/peerj.6573/fig-8
FIGURE2. Digital radiograph of Siphamia guttulata (CSIRO H 6648-02, 25 mm SL). Blue circle highlighting two supraneurals. Scale bar = 0.5 mm. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE2. Digital radiograph of Siphamia guttulata (CSIRO H 6648-02, 25 mm SL). Blue circle highlighting two supraneurals. Scale bar = 0.5 mm.
Noise study data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>This dataset contains noise study data used in the paper: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation. These include raw CT scans and processed digital volume correlation data.</p> <p>This dataset is part of another dataset which covers other aspects of the paper DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3352268</a></p> <p>The structure of the dataset is as follows:</p> <ul> <li>Noise study CT raw volumes are contained in a zip file. There are four raw files corresponding to the four noise study steps. These files are 8-bit unsigned, dimensions are 1800 x 1800 x 1400 pixels. A txt file giving more details to the data is included. <ul> <li><strong>CT_Raw_data_Noise_Scans.zip</strong></li> </ul> </li> <li>Metadata files generated for each scan given details of scan parameters are found in the zip file: <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Tabulated digital volume data for the noise study scans are contained in the zip file. Tabulated data for each subset size is included in subfolders. A .txt file explains the structure of the tab separated .dat files, i.e. what each column of data represents, and what CT scan each of the four .dat files relate to. <ul> <li><strong>DVC_Noise_Study_Data.zip</strong></li> </ul> </li> </ul> <p> </p> <p> </p>
Connecting digital citizen science data quality issue to solution mechanism table
<p>A table explaining how to solve data quality issues in digital citizen science. A total of 35 issues and 64 mechanisms to solve them are proposed</p>
Digital Elevation Models for use with Fogpy
<p>Digital Elevation Models (DEMs) in geotiff format. One for central Europe and one for northeast USA. </p> <p><strong>Data sources</strong></p> <p>Central Europe: <a href="https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1">EU-DEM v1.1 (Copernicus Land Monitoring Service)</a></p> <p>USA: <a href="https://viewer.nationalmap.gov/datasets/">USGS National Map</a></p> <p><strong>Data processing</strong></p> <p>Data were processed with GDAL. After collecting the required source files in geotiff, the final file was produced with two steps:</p> <pre>gdal_merge.py -o merged.tif */*.tif</pre> <pre>gdalwarp -r bilinear -t_srs '+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs +type=crs' -tr 500 500 merged.tif merged-2-500.tif</pre> <p><strong>Context</strong></p> <p>These elevation models are needed by <a href="https://github.com/pytroll/fogpy">Fogpy</a>, a satellite fog retrieval package within the Pytroll satellite processing set of tools.</p>
Sinhala Prakrit. SIDDHAM digital glossary: the letter Ā (version 1.0).
<p>Sinhala Prakrit. SIDDHAM digital glossary: the letter Ā (version 1.0).</p>
Figure 11.7 in Rex, sit: digital modeling of Tyrannosaurus rex at rest
Figure 11.7. In ascending from repose, the M. caudofemoralis longus is in stretch (-115%) and the moment arm is greatly foreshortened compared with its neutral state when standing, thus providing poor mechanical advantage.
Figure 11.3 in Rex, sit: digital modeling of Tyrannosaurus rex at rest
Figure 11.3. (A) Screen image showing the reconstruction of the trunk superimposed on a reference photograph of an assembly of casts of the Stan specimen. Background image courtesy Black Hills Institute of Geologic Research. (B) DinoMorph model shown with addition of digitized pectoral girdles (including furcula), forelimbs, and pelvic girdle, all based on Stan.
Figure 11.8 in Rex, sit: digital modeling of Tyrannosaurus rex at rest
Figure 11.8. When resting on the pubis, the forelimbs are near ground level. They are brought closer to ground level as a consequence of iniatating a standing movement from this resting pose. A modest tipping of the body, by pivoting about the curved anteroventral surface of the prepubis, would have shifted the overall COM anteriorly and returned the point of balance to between the hindfeet. In the process, the forelimbs would have been available to assist in stabilizing, if not actively contributing toward raising, the body, by pushing against the ground.
Anonymised dataset about Finnish teachers' digital technology usage in teaching
<p>Data have been collected in Finland in 2017–2019 from basic education teachers.The dataset is fully anonymised to make it suitable for public opening to support the reported results submitted for publication.</p> <p>Dataset contains the following variables:</p> <ol> <li>Gender (0=female, 1 = male)</li> <li>Municipality (unique numeric identifier)</li> <li>School (unique numeric identifier)</li> <li>Administrative_district (unique numeric identifier)</li> <li>Usage_of_computers_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_tablets_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_smartphones_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_learning_environments_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_online_learning_materials_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_games_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_internet_for_information_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_videa_sharing_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_blogs_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_networking_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_assessment_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_mobileapps_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_email_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_office_suite_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Total_scores_in_ICTskilltest (range 0-30)</li> <li>Age (years in numbers)</li> <li>Digital_self_efficacy (perceived level of competence on a scale of 0-100% in relation to one's own work)</li> <li>In_service_training (perceived adequacy level on a scale of 0 to 100% in relation to one's own work)</li> <li>STEM_teacher (in the case of a teacher of STEM subjects = 1, otherwise = 0)</li> <li>Humanities_social_science_teacher (in the case of a teacher of humanities or social science subjects = 1, otherwise = 0)</li> <li>Arts_skills_teacher (in the case of a teacher of arts and skills subjects = 1, otherwise = 0)</li> <li>Use_of_devices_for_teaching (the sum variable of the use of digital devices, i.e., the maximum use of any type of device)</li> <li>Versatility_of_usage (the sum variable for regular (= at least weekly) use of different applications)</li> <li>Teacher_type (0 = classroom teacher, 1 = subject teacher, missing value = other teaching staff)</li> <li>Classroom_teacher in the case of a classroom teacher = 1, otherwise = 0</li> </ol>
A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia
<p>This repository hosts the raw data and source code for the paper entitled " A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia" [DOI: 10.7717/peerj.10106].</p> <p>Two zip archives are available:</p> <p>Raw data and methods evaluation.zip - Retains empirical data and method evaluation code (R scripts) that relate to data presented in the results and discussion sections of the article, specifically, Figures 3 -11 and Tables 1 and 2.</p> <p>RTmap_source_code_and_data_structure.zip - This repository hosts the source code for <a href="http://austemperature.live/">http://austemperature.live/</a>, as described in the methods section of the article. A README.docx presents the contents and subsequent procedures for deploying the mapping code and web map application.</p> <p>For further information about this files please email:</p> <p>mweb7041@uni.sydney.edu.au</p> <p> </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.