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19,799 results for “STEM”
Underlying and Extended Data for Refined and benchmarked homemade media for cost-effective, weekend-free human pluripotent stem cell culture
<p>Extended Data and raw data for the manuscript "Refined home-brew media for cost-effective, weekend-free hiPSC culture and genetic engineering"</p> <p> </p> <p dir="ltr">Extended Data 1.zip - protocol for preparation of the supplement for hE8 and B8+ media</p> <p dir="ltr">Extended Data 2.zip - Gene counts, supporting files, and output results of bulk analyses</p> <p dir="ltr">Extended Data 3.zip - Images of iPS cells adapted to cE8, hE8 and B8+ taken 24, 48 and 72 hours after passage. Contains raw .tiff files for each image and a .pdf with a compiled figure</p> <p dir="ltr">Extended Data 4.zip - Results of miloR analysis on the differences in the distribution of cells adapted to cE8, hE8 and B8+ to assigned monocle clusters </p> <p dir="ltr">Manuscript Data.zip – Raw data underlying the Figures 1, 2, 4, 5, 6 and 7.</p> <p>Bulk_RNA_seq_archive – archived source code used for generating results in Figure 3</p>
Comparison of Leaves and Stem Aqueous Extract of Tridax Procumbens for Antimicrobial Activity
<p>In present study, aq. extracts of leaves and stem part of Tridax Procumbens were compared for antimicrobial activity. Firstly, all organoleptic and physicochemical properties of leaves and stem powder were evaluated and it shows that drug is pure and having required constituents sufficiently. Aq. extract of leaves powder and stem powder was prepared separately using Soxhlet extraction technique. Both the extracts were evaluated for physiochemical screening using standard procedures. Aq. extract of leaves shows presence of tannis, saponins, anthocyanin, coumarins, alkaloids, proteins, amino acids, diterpenes, phytosterol, cardial glycosides, phlobatannins and flavonoids. Aq. extract of stem shows presence of tannins, coumarins, phenol, cardial glycosides and flavonoids. Both extracts were primarily evaluated for antimicrobial activity against E.coli and S. aureus by selecting dose of 500 mg. Finally, antimicrobial assay was performed for both extracts (500mg) against E.coli and S. aureus by well diffusion method using Amoxicillin and Amikacin as standards respectively. Antimicrobial assay shows that aq. extract of stem part is more effective against S. aureus than E.coli; leaves aq. extract also shows antimicrobial activity against S .aureus and E.coli. From present studies we can conclude that aq. extract of stem and leaves of Tridax Procumbens having antimicrobial activity and stem extract is more effective against S. aureus as compared to leaf extract. </p>
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 1
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 1 OF 2 ***</strong></p> <p><strong>Download part 2 here:</strong> <a href="../records/7882857">https://zenodo.org/records/7882857 </a> (now available!) </p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.</p> <p>____________________________</p> <p>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 2
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 2 OF 2 ***</strong></p> <p><strong>Download part 1 here:</strong> <a href="../records/7860223">https://zenodo.org/records/7860223 </a></p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.<br><br>_____________________________<br><br>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
STEM-EELS hyperspectral data: Nanowires, Nanoparticles, Interface
<p>STEM-EELS hyperspectral datasets. These 3D datasets accompany the manuscript titled "Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels" by K. Roccapriore et. al. These data are used to demonstrate correlations between pixels in space, not just energy, using what are known as multichannel rVAE models.</p> <p> </p> <p>See the following url for Jupyter Notebook walkthrough</p> <p>https://github.com/kevinroccapriore/Multichannel-rVAE</p>
MerlinEM quad 4D-STEM dataset
<p>4D-STEM data collected from au-xgrating sample using nano-beam probe on <a href="https://diamondlightsource.atlassian.net/wiki/spaces/EPSICWEB/pages/1511758/ePSIC+Instruments">E02 microscope</a> at ePSIC, Diamond Light Source</p> <p>Grand ARM300F - 300 kV</p> <p>10 um CL aperture</p> <p>~3 msec dwell time. Triggering by AZTEC scan engine with some variation in exposure times.</p> <p>Sample: 500 nm pitch gold cross-grating</p>
Fig. 3 in The first possible remingtonocetid stem whale from North America
Fig. 3. Paleogeographic reconstructions and distributions of Eocene cetaceans. A. Ypresian, reconstructed at 52 Ma. B. Lutetian, reconstructed at 45 Ma. C. Bartonian, reconstructed at 40 Ma. D. Priabonian reconstructed at 36 Ma. Data are derived from occurrences in the Paleobiology Database and includes every published occurrence of archaeocete cetaceans (Uhen 2020).
Fig. 2. A in The first possible remingtonocetid stem whale from North America
Fig. 2. A. Remingtonocetus sp. (IITR-SB 2630) from Lutetian; Kachchh, India; right P4. B.?Remingtonocetidae indet. (USNM 449550) from Lutetian– Bartonian; Martin Marietta Quarry (formerly Superior Stone Quarry), near Castle Hayne, North Carolina, USA. Shown as if it were an upper premolar for comparison, but it may also represent a lower premolar as well. Note the extreme narrowness of the tooth. In lateral (A2, B2), medial (A1, B1), and occlusal (A3, B3) views.
Fig. 1 in The first possible remingtonocetid stem whale from North America
Fig. 1. PCA ordinations of differences in tooth morphology for sampled archaeocetes. This plot includes data from both adult and deciduous premolars. The numbers next to the axis labels indicate the percentage of explained variation in morphology for that axis. The tooth shapes around each point represent the outline generated using the harmonic coefficients produced by elliptic Fourier analysis to achieve 99.9% harmonic power. See SOM: table 1 for a list of included specimens.
Figure 3 in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 3. Cartoons of selected characters and character states. Numbers signify characters, bracketed numbers represent character states e.g. 1(0), where 1 is the characters and (0) is the character state.
Figure 6. Most parsimonious result from a in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 6. Most parsimonious result from a phylogenetic analysis of discrete (1—64) and discretized continuous characters identified through gap coding (88—100). A, strict consensus of 30 most parsimonious trees with equally weighted characters (tree length 346). B, most parsimonious solution with implied weighted characters (k = 3) (tree length 27.86). Psammosteidae taxa in bold (for which quantitative characters have been treated as inapplicable, i.e. non-homologous).
Figure 2 in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 2. Reconstruction of a hypothetical Pteraspidiformes (adapted from Blieck 1984). A, dorsal and ventral view of Pteraspidiformes headshield with plates labelled. B, D, E, measurements used in phylogenetic analysis. C, dorsal headshield sensory canals. Anatomical abbreviations: SOC, supraorbital canal; OrbC, orbital canal; PinC, pineal canal; LDC, lateral dorsal canal; MDC, medial dorsal canal; TC, transverse commissures; MTC, median transverse commissures. Measurement abbreviations: DSL, dorsal shield length; DSW, dorsal shield width, not including the cornual plate width; DPL, dorsal plate length; DPW, dorsal plate width; RPL, rostral plate length; RPW, rostral plate width; PPL, pineal plate length; PPW, pineal plate width; BPL, brachial plate length; BOL, branchial opening distance from anterior of dorsal plate; CPL, cornual plate length; OrbPL, orbital plate length; OrbPAPL, orbital plate anterior process length; OrbPMPL, orbital plate medial process length; OrbPPPL, orbital plate posterior process length; Orb—Orb, orbital opening to orbital opening length; DSBW, dorsal spine base width; DSBL, dorsal spine base length; DPEB, dorsal plate embayment; DPEL, distance to beginning of embayment from anterior end of dorsal plate; DPEW, dorsal plate embayment narrowest width.
Figure 1. Previous Pteraspidiformes phylogenies. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 1. Previous Pteraspidiformes phylogenies. A, Blieck's (1984) Pteraspidiformes phylogeny for all the then-known taxa. B, Janvier's (1996) phylogeny for the major clades of Pteraspidiformes. C, Ilyes & Elliott's (1994) phylogeny for the Western USA taxa. D, Perǹegre's (2002) phylogeny to determine the position of Doryaspis. E, Perǹegre & Goujet's (2007) phylogeny to determine the position of Gigantaspis. F, Perǹegre & Elliott's (2008) most recent Pteraspidiformes phylogeny with the identification of major families. The Psammosteidae are highlighted when included in an analysis.
Figure 7. Pteraspidiformes phylogeny with genera plotted against their stratigraphical occurrences. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 7. Pteraspidiformes phylogeny with genera plotted against their stratigraphical occurrences. A, discrete and continuous character analysis with implied weighting (k = 3). B, discretized analysis with implied weighting (k = 3). Colours relate to palaeobiogeographical provinces.
Figure 4. Results from the phylogenetic analysis using discrete data only. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 4. Results from the phylogenetic analysis using discrete data only. A, strict consensus of 275 most parsimonious trees with equal character weights; length 276 steps, consistency index (CI) = 0.35, retention index (RI) = 0.59, and rescaled consistency index (RC) = 0.22. B, strict consensus of four most parsimonious trees with implied character weighting (k = 3) (tree length 23.11). Psammosteidae taxa in bold.
Figure 5 in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 5. Phylogenetic results from data sets containing discrete (1—64) and continuous (66, 68, 70, 72, 77, 80, 82, 86) characters. A, most parsimonious tree with equally weighted characters (tree length 319.36). B, most parsimonious tree with implied weighting (k = 3) (tree length 26.53). Psammosteidae taxa in bold (for which quantitative characters have been treated as inapplicable, i.e. nonhomologous).
Nanobeam 4D-STEM raw data of monolayer WS2-WSe2 lateral heterojunctions
<p>The sample under investigation comprises a monolayer WS2-WSe2 lateral heterojunction featuring in-plane epitaxial interfaces. The datasets were acquired using an electron microscope pixel array detector (EMPAD) at Cornell University in 2016, with the following specifications: Magnification: 27.5 kx, Convergence angle: 1.2 mrad, C2 aperture size: 70, Spot size: 9. This dataset is from the same batch of datasets referenced in the paper by Han et al., Nano Letters 18, 3746-3751 (2018). Additionally, data from the same batch are also cited in the publication by Shi et al., npj Computational Materials 8, 114 (2022). Further details regarding the materials synthesis can be found in the paper by Xie et al., Science 359, 1131-1136 (2018). </p>
Single-cell sequencing data of human umbilical cord and placental mesenchymal stem cells
<p>Expression matrix of umbilical cord and placenta single-cell sequencing data from the same donor.Table1 is the umbilical cord and Table2 is the placenta.</p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 4 relative to Figure 7 – ArhGEF11 CRISPR interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7B </strong>and<strong> Figure 7 - Figure Supplement 6</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM3b; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 2 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 3 relative to Figure 7 – ArhGEF11 morpholino splicing interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7A </strong>and<strong> Figure 7 - Figure Supplement 5</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM2a; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 3 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></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)
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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.