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179 results for “data matrix”
Non-perturbative phase structure of the bosonic BMN matrix model --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice calculations investigating the phase structure of the bosonic part of the Berenstein--Maldacena--Nastase matrix model. See the README for further information.</p>
Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment
<p><a name="_Hlk158643946"></a><strong>Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment</strong></p> <p><em>Malin Hammerman, Maria Pierantoni, Hanna Isaksson<sup> *</sup>, Pernilla Eliasson <sup>*</sup></em></p> <p><em><sup>* </sup></em><em>joint<sup> </sup>last authors</em></p> <p>This dataset contains microscope images obtained from sections of healing and intact rat Achilles tendons undergoing different in vivo loading protocols and different time points post-transection. The data presented are the full resolution microscope images available in lower resolution in the accompanying manuscript’s Supplementary Figures 4-6.</p> <p>Each zipped folders contain images (tif-files) from all time-points for each respective staining and loading group. </p> <ul> <li>Col1: Sections stained with Collagen 1 antibodies</li> <li>Col3: Sections stained with Collagen 3 antibodies</li> <li>Elastin: Sections stained with Elastin antibodies</li> <li>Full_loading: Free cage activity</li> <li>Reduced_loading: Paralysis of the calf muscle with Botox</li> <li>Minimal_loading: Botox combined with joint fixation using a steel-orthosis</li> <li>Intact_reference: Contralateral uninjured Achilles tendons, used as reference</li> </ul> <p>More description of the datasets inside the zipped files are available below and in the file 'Data Description.pdf'</p> <p> </p> <p><strong>Brief re-cap of methods</strong></p> <p>Histological analysis was performed on healing Achilles tendons from Female Sprague-Dawley rats, specific-pathogen free (11-12 weeks, weight 299 ± 15 g), that had undergone full transection [13] of the right Achilles tendon, and been exposed to different levels of loading. Altered loading was imposed through two mechanisms. Reduced loading involved intramuscular Botox injections in the right calf muscles to induce plantar flexor muscle paralysis [24]. Additionally, the rats in the minimal loading group received a steel-orthosis around their right hindlimb directly after surgery [24].</p> <p>Snap frozen tendons in OCT were sectioned longitudinally (7 μm thickness) and stained with immunofluorescent staining for collagen 1, collagen 3, or elastin. Sections were counterstained with DAPI followed by mounting. The tissue sections were imaged under a microscope (DMi8, Leica Microsystems, Wetzlar, Germany, with a Hamamatsu Orca LT Flash sCMOS camera) where fluorescence was detected at 550 nm (secondary antibody Alexa Fluor 594), 470 nm (secondary antibody Alexa Fluor 488) and 385 nm (DAPI), and exposure time was held constant for each color channel regarding magnification and staining.</p> <p>Mapping images of the entire tendon were obtained for one section per group (n=1 per healing time, loading group and ECM matrix protein). All images were adjusted to the negative control, where the primary antibody was omitted, to correct for unspecific antibody detection.</p> <p><strong>Microscope images and description of file-names </strong></p> <p>All data is presented in the form of .tif files. Please refer to the scale bars in the images. All image-files are named using the following abbreviations, as described below. As an example, the file name “Tendon_col1_FL_1W_col1.tif” refers to a tendon section stained for collagen 1 from a rat exposed to full loading for a period of 1 week after tendon transection, where only the channel for collagen 1 is shown, whereas “Tendon_col1_FL_1W_merged.tif” includes the channels for both staining for collagen 1 and DAPI of the same section.</p> <p>Col1: Sections stained with Collagen 1 antibodies<br>Col3: Sections stained with Collagen 3 antibodies<br>Elastin: Sections stained with Elastin antibodies<br>dapi: Sections stained with 4',6-Diamidino-2-Phenylindole Dihydrochloride.<br>FL: Full loading (free cage activity),<br>RL: Reduced loading (paralysis of the calf muscle with Botox),<br>ML: Minimal loading (Botox combined with joint fixation using a steel-orthosis)<br>IT: Intact contralateral Achilles tendons, used as reference.</p> <p>1W: Healing time point 1 week after transection<br>2W: Healing time point 2 weeks after transection<br>3W: Healing time point 3 weeks after transection<br>20W: Healing time point 20 weeks after transection</p> <p><strong>Settings for brightness and contrast</strong></p> <p><em>Collagen 1</em><br>1w FL 2000-12 000, UL 4000-10 000, ML 4000-12 000<br>2w FL 2500-10 000, UL 4000-10 000, ML 5000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 3000-11 000<br>IT 2000-8 000</p> <p>Collagen 3<br>1w FL 3000-12 000, UL 4000-10 000, ML 4000-13 000<br>2w FL 2000 - 7 000, UL 2500-12 000, ML 2000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 2000-12 000<br>IT 3000-12 000</p> <p>Elastin<br>1w FL 4000-10 000, UL 5000 - 8000, ML 3500-12 000<br>2w FL 3000-12 000, UL 3000-12 000, ML 3000-12 000<br>3w FL 2500-12 000, UL 2000-12 000, ML 2500-12 000,<br>12w FL 3500-12 000<br>20w FL 3500-12 000<br>IT 2000-12 000</p>
Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab
<p><strong>Matrices_SpatialSpan.csv</strong> includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, & Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.).</p> <p><strong>robustCor.R </strong>is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned.</p> <p>Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.</p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
<p>Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.</p>
Data from: Context matters: the landscape matrix determines the population genetic structure of temperate forest herbs across Europe
<p>Context. Plant populations in agricultural landscapes are mostly fragmented and their functional connectivity often depends on seed and pollen dispersal by animals. However, little is known about how the interactions of seed and pollen dispersers with the agricultural matrix translate into gene flow among plant populations.</p> <p>Objectives. We aimed to identify effects of the landscape structure on the genetic diversity within, and the genetic differentiation among, spatially isolated populations of three temperate forest herbs. We asked, whether different arable crops have different effects, and whether the orientation of linear landscape elements relative to the gene dispersal direction matters.</p> <p>Methods. We analysed the species' population genetic structures in seven agricultural landscapes across temperate Europe using microsatellite markers. These were modelled as a function of landscape composition and configuration, which we quantified in buffer zones around, and in rectangular landscape strips between, plant populations.</p> <p>Results. Landscape effects were diverse and often contrasting between species, reflecting their association with different pollen- or seed dispersal vectors. Differentiating crop types rather than lumping them together yielded higher proportions of explained variation. Some linear landscape elements had both a channelling and hampering effect on gene flow, depending on their orientation.</p> <p>Conclusions. Landscape structure is a more important determinant of the species' population genetic structure than habitat loss and fragmentation <i>per se</i>. Landscape planning with the aim to enhance the functional connectivity among spatially isolated plant populations should consider that even species of the same ecological guild might show distinct responses to the landscape structure.</p>
Data for the "Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1"
<p>Bottom-up mass spectrometry-based proteomic analysis (trypsin) was performed on four biological replicates of HIV-1 virions. These virions were isolated from HEK293T cells transfected with a HIV-1 proviral plasmid derived from the pNL4-3 molecular clone, rendered biosafe due to inactivating point mutations in both the env and vpr reading frames. There are 8 total spectra, 4 are from unlabeled aliquots of sample, and 4 are from aliquots of sample treated with glutathione to label dehydroamino acids (Spectra can be accessed on MassIVE (MSV000088220). All data was analyzed using MetaMorpheus version 0.0.319 (https://github.com/smith-chem-wisc/MetaMorpheus). Provided here are the results of this analysis.</p>
Data for Corre et al., Bacterial matrix metalloproteases and serine proteases contribute to the extra-host inactivation of enterovirus in lake water, ISMEJ 2022
<p>Data for Corre et al., <em>Bacterial matrix metalloproteases and serine proteases contribute to the extra-host inactivation of enterovirus in lake water</em>, ISMEJ 2022</p> <p>The first file contains all data pertaining to experiments with isolates: collection date, isolation temperature, protease activity measured by 4 different approaches, antiviral effect on E11 and CVA9 (three replicates each); this table corresponds to the data shown in Supplementary Table 2.</p> <p>The second file contains the raw data for all lake water experiments (Figures 1 and 6): information on sample type, antiviral effect (measured in triplicate), presence of a protease inhibitor.</p>
Data Matrix Landmarks in Cluttered Indoor Environments
<p>We used the <a href="https://labelbox.com/">LabelBox</a> online toolbox to create this data set.</p> <p>It consists of 6 different cluttered environments:</p> <ol> <li>a laboratory </li> <li>3 different industrial-like environments </li> <li>a corridor</li> <li>hall</li> </ol> <p>We proposed to split the data set into three sets - training, validation, and test sets - as follows: (1) the training set has 156 frames equally distributed by the laboratory and 1 workshop; (2) the validation set is also divided into two environments - the corridor (158 frames) and a different workshop (66 frames); (3) the test set consists of 145 frames collected on a neat hall with overshadowed and over-lightened landmarks in different planes; a classroom laboratory with various electronic equipment arranged in an orderly manner; and a very challenging scenario with multiple pieces of machinery spread out all over the place.</p> <p>One should filter out images with no markers.</p>
Morphological data matrix for extant and fossil Darwin wasps (Ichneumonidae)
<p>This nexus file contains morphological coding for 289 ichneumonid taxa, including 203 extant and 86 fossil taxa. The descriptions of the coded characters and their states can be found in the following list: <a href="https://doi.org/10.5281/zenodo.13912975" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13912975</a></p> <p>The Excel file shows a more detailed taxon list, including the fossil localities and age ranges for the included fossil taxa.</p> <p> </p> <p> </p>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
FIG. 4 in The data matrix
FIG. 4. — An example of a phylo-phenetic approach used in biogeography, similar to Parsimony Analysis of Endemicity (PAE) analysis: A, list of taxa T1-T3 and the areas they occupy (A, B and C); B, matrix in which similarities are grouped; C, the resulting phylophenogram in which areas B and C are grouped together by the component BC derived from T2 and (possibly) T3. Note that taxa T2 and T3 do not occur in the same areas and therefore do not share any direct relationship.
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
Data for Coupling Covariance Matrix Adaptation with Continuum Modeling for Determination of Kinetic Parameters Associated with Electrochemical CO2 Reduction
<p>This data set contains digitized and tagged polarization and partial current density data for 18 datasets of CO<sub>2</sub> reduction to H<sub>2</sub> and CO over Ag catalysts, as well as 8 datasets of CO<sub>2</sub> reduction to HCOO<sup>-</sup>, CO, and H<sub>2</sub> over Sn catalysts. We analyze this data using a coupled continuum modeling and covariance matrix adaptation approach for which the codebase is provided in DOI: 10.5281/zenodo.7866195.</p>
Raw data and analysis code for "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH". </p>
Preprocessed MPI System Matrix Data in HDF5 Files
<p>This data repository contains preprocessed system matrix data from magnetic particle imaging. The example files are hdf5. The data is used in this code example:</p> <p><a href="https://github.com/Ivo-B/3dSMRnet">github.com/Ivo-B/3dSMRnet</a></p>
Data from: Context matters: the landscape matrix determines the population genetic structure of temperate forest herbs across Europe
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Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics
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Data From: Matrix tropism influences endometriotic cell attachment patterns
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ScienceDex guides
Understand access before you commit
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.