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3,630 results for “Forming”
Preliminary findings on the bioaccumulation and marine trophic transfer of the antifouling biocide DCOIT in soluble and nanostructured forms (FAIR dataset)
<p>The present dataset includes the raw data regarding the bioaccumulation, trophic transfer, and biomagnification of DCOIT, in the soluble and nanostructured forms, in the marine bivalve <em>Mytillus galloprovincialis</em>, using three different uptake routes. This data supports the main findings of the study that both forms of the biocide can be trophically transferred as well as suggests that encapsulation is a good strategy to minimize biocidal bioaccumulation and thus protect marine ecosystems. </p>
Fig. 8 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
Fig. 8 - Shells index of roundness H/D of the five samples.
Fig. 6 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
Fig. 6 - Shell measurements (drawn by Dael Sassoon).
Forming tests of a uni- and a bidirectional non-crimp fabric for different orientations and geometries (hemisphere, tetrahedron, square box)
<p><strong>Reference:</strong> The data is published as supplementary data for publications and for other researchers to incorporate the data in their work. Details behind the experimental procedure and the methods applied to analyze the tests are published in (please use to reference this data):</p> <p>“B. Schäfer, R. Zheng, J. Colmars, A. Platzer, N. Naouar, P. Boisse, L. Kärger. <em>Experimental analysis of the forming behavior of uni- and bidirectional non-crimp fabrics for different geometries</em>, Composites Part B: Engineering, 287:111765, 2024. <a href="https://doi.org/10.1016/j.compositesb.2024.111765">https://doi.org/10.1016/j.compositesb.2024.111765</a>”</p> <p><strong>Description:</strong> This dataset contains high-resolution images and analyzed numerical data (punch forces, outer contour after forming, local strains measured with DIC) of forming tests on a unidirectional non-crimp fabric (UD-NCF) and a bidirectional non-crimp fabric (Biax-NCF) for different layup configurations and punch shapes (hemisphere, tetrahedron, square box). The tests were conducted for various single- and double-layer configurations with different relative fiber orientations. The experiments were conducted at the “LaMCoS – Laboratoire de Mecanique des Contacts and des Structures” from the “Institut National des Sciences Appliquées de Lyon (INSA Lyon)” in France. The analysis of the data was performed at the “Institute of Vehicle System Technology, Division Lightweight Engineering” from the “Karlsruhe Institute of Technology (KIT)” in Germany. The work was part of the international collaborative research project AMECOMP funded by the ANR in France and the DFG in Germany (DFG: 431354059, ANR: ANR-19-CE06-0031) - <a href="https://doi.org/10.5445/IR/1000172090">https://doi.org/10.5445/IR/1000172090</a>.</p> <p><strong>Test setup and method:</strong> The experimental setup consists of three punch shapes (hemisphere, tetrahedron, square box) with matching planar blank holders and dies. The blank holders apply their own weight to the fabric, and centering pins prevent any in-plane movement. All setups are mounted to a universal testing machine by ZwickRoell. The blank holders and dies are made of transparent methyl methacrylate (PMMA) to allow for the acquisition of digital images by a high-resolution camera. The forming depths are 75 mm, 95 mm, and 85 mm with blank holder weights of 1.7 kg, 6.0 kg, and 1.7 kg for the hemisphere, tetrahedron, and square box punches, respectively. The dimensions and schematics are summarized in the image <em>“Experimental-setup_schematics-dimensions.jpg”</em>. The deformation is measured via Digital Image Correlation (DIC) with a regular grid of white dots of acrylic paint using a stencil with a distance of l<sub>e </sub>= 10 mm.</p> <p><strong>Material:</strong> A unidirectional (UD300) and bidirectional (MD600) non-crimp fabric (NCF) both without binder are used, cf. <em>”Material_schematics.jpg”</em>. The fabrics are manufactured by Zoltek™ and produced from their PX35-50K continuous carbon (CF) fiber heavy tows. Both fabrics are stitched together with a 76 dtex Polyester stitching yarn in a Tricot pattern. A textile layer of UD300 consists of a single fiber layer of aligned CF tows in warp direction with thin glass fibers (GF) in weft direction on the back for improved handleability. A textile layer of MD600 comprises two fiber layers in a 0°/90° orientation. Both fabrics have a similar number of CFs in each fiber layer with about 300 g/m2 in their respective reinforcing directions. </p> <p><strong>Tested configurations for all three punch shapes: </strong></p> <ul> <li> UD-NCF: three types of single-layer ([0°<sub>U</sub>], [45°<sub>U</sub>], [90°<sub>U</sub>]) and two types of double-layer ([0°<sub>U</sub>∕45°<sub>U</sub>], [0°<sub>U</sub>∕90°<sub>U</sub>]) configurations</li> <li> Biax-NCF: two types of single layer ([(0°∕90°)<sub>B</sub>], [(±45°)<sub>B</sub>]) and one type of double-layer [(0°∕90°)<sub>B</sub>∕(±45°)<sub>B</sub>] configurations</li> </ul> <p><strong>Provided data:</strong></p> <ul> <li>High-resolution images of all tested configurations for both materials for the different geometries in the respective .7z archives <a href="https://www.7-zip.org/">(7-zip.org)</a>. [Only one representative image set per configuration due to total size limitations by zenodo.org]</li> <li>Analyzed numerical data (punch forces, outer contours after forming, local strains measured with DIC) for all tests. The data is stored as nested dictionaries in the folder <em>“_analyzed_data”</em> in the respective .json files. A demonstration to access the data via Python and recreate the plots from the original publication is provided in the supplementary Jupyter Notebook (<em>“example_data_analysis.ipynb”)</em></li> </ul>
Fig. 8 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 8. Ecological groups of carabids in terms of humidity (number of specimens).
Fig. 6 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 6. Proportions of the species according to the type of preferred habitat.
Fig. 7 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 7. Ecological groups of carabids in terms of humidity (number of species).
Fig. 5 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 5. Proportions of the species according to their habitat preferendum.
Fig. 3 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 3. Proportions of the carabid species by zoogeographical complexes.
Fig. 2 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 2. Proportions of the species among the tribes.
Fig. 1 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (Central Bulgarian Black sea coast). Part I. Taxonomic and zoogeographic structure, life forms, habitat and humidity preferences
Fig. 1. Proportions of the species among the tribes.
Patent Working Requirements and Complex Products: An Empirical Assessment of India's Form 27 Practice and Compliance
<p>Dataset of the first comprehensive and systematic analysis of 4,916 valid Statements of Working (Form 27) corresponding to 3,126 patents pertaining to mobile technology in India. Released under the Creative Commons-Attribution-Share Alike 4.0 (CC-BY-SA 4.0) License as a part of the <strong>Pervasive Technologies</strong> project at the <strong>Centre for Internet and Society, India</strong>.</p> <p>For the detailed methodology used for this study, refer to <a href="http://cis-india.org/a2k/blogs/methodology-statements-of-working-form-27-of-indian-mobile-device-patents">Methodology: Statements of Working (Form 27) of Indian Mobile Device Patents</a>.</p> <p>For information on how to read this dataset, refer to the section "Detailed legend and process of logging the results" in the methodology.</p> <p>A paper titled "<a href="http://jipel.law.nyu.edu/vol-7-no-1-1-contreras/"><strong>Patent Working Requirements and Complex Products</strong></a>" authored by Prof. Jorge Contreras and Rohini Lakshané, first published in July 2017 presents an analysis of the dataset.</p> <p>The patents chosen to be included in this dataset are a subset of the patents found in another study by the same authors, Patents and Mobile Devices in India: An Empirical Survey.</p> <p>Data is provided AS-IS, without warranty as to accuracy or completeness.</p> <p>All uses of the accompanying data and modifications and derivatives thereof must contain the following attribution: "Data provided by Jorge L. Contreras and Rohini Lakshané (2017)"</p>
RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
Single Crystal X-ray Diffraction Data for PLP internal aldimine form of Sphingopyxis sp. MTA144 FumI protein
<p>Single Crystal X-ray Diffraction Data for PLP internal aldimine form of Sphingopyxis sp. MTA144 FumI protein collected at Diamond Light Source, Beamline I03 2018-07-21</p>
Probing result for robotic assisted machining form error paper
<p>This is the probing result during our maching trials for form error prediction for robotic assisted machining.</p>
Figure 5 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 5. Temporal variation of jellyfish medusae in zooplankton samples.
Figure 3 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 3. Temporal variation in size distribution in 3 populations of C. perezi.
Figure 1. Map showing 3 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 1. Map showing 3 sampling sites: Bhanbore, Mirpur Sakro, and Keti Bunder.
Figure 2 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 2. Morphological measurements of the shell in P. imbricata radiata that were used in PCA.
Figure 1 in Diurnal vertical distribution of zooplankton in a newly formed reservoir (Tahtalı Reservoir, Kocaeli): the role of abiotic factors and chlorophyll a
Figure 1. Study area and sampling station.
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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.