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opencc-by-4.0Apr 2024View details →

Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Post-Ionization with Matrix-Assisted Laser Desorption/Ionization

Supporting data for the manuscript 'Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Post-Ionization with Matrix-Assisted Laser Desorption/Ionization.'

MFD-2-TM-de: German Moral Foundation Dictionary 2 by Translation Matrix

This dataset was created for the research paper: "Reiter-Haas, M., Kopeinik, S., & Lex, E. (2021, May). Studying moral-based differences in the framing of political tweets. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 1085-1089).". Available at <a href="https://ojs.aaai.org/index.php/ICWSM/article/view/18135/17938">https://ojs.aaai.org/index.php/ICWSM/article/view/18135/17938</a> The translation were created using a translation matrix of positive and negative valence words. The dictionary was used to measure effects in the embedding space for which it is suitable. However, it is to note that the dictionary does not work in a classical sense, as it contains duplicates (e.g., words of the opposite poles). While this was also the case in the original dictionary (e.g., for "wound"), the effect has become much more pronounced in the translated version. Format: <ul> <li>Lines starting with % denote a new section that the following words belong to (e.g., care.vice for harm).</li> <li>Otherwise, one word per line.</li> </ul> For implementation details, refer to the <a href="https://github.com/socialcomplab/icwsm21-framing">GitHub Repo</a>

Data for 'Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual reflection-matrix'

This repository hosts the datasets used in the study 'Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual reflection-matrix.' It includes sets of images of fluorescent samples positioned behind a scattering layer, captured using random illuminations in a microscope setup detailed in the paper. The data correspond to the examples and figures presented in the publication.

Mueller matrix and GPSE during dehydration of bovine skeletal muscle

This video shows the changes in the Mueller matrix and GPSE during dehydration of bovine skeletal muscle

The Effect of Extracellular Matrix Stiffness on Inducing Adipogenic Differentiation of ADSCs

Extracellular matrix (ECM) is a critical biophysical factor in adipose-derived stem cells differentiation. However, the specific regulatory mechanisms and signaling pathways of Yes-associated protein (YAP) are unknown, though it plays a critically important role in ECM-induced differentiation. Here, we embedded ADSCs in collagen hydrogels with varying stiffness, elastic moduli, to be exact, of 61Pa, 217Pa, and 321Pa. The expression of adipogenic-specific genes was highly correlated with stiffness of ECM, in addition, our study has found that the levels of YAP phosphorylation were significantly altered on the 9th day after stimulation with ECM stiffness. Staining of lipid droplets with BODIPY indicates that accumulation of lipid droplets depended on hydrogel stiffness; the most pronounced accumulation was registered in 321Pa hydrogels. By inhibiting the YAP signaling pathway, we found that lipid droplet accumulation increased; thus, inhibition of YAP signaling increased YAP phosphorylation in the cytoplasm, thereby promoting accumulation of lipid droplets. In conclusion, YAP played a crucial role in regulating ECM stiffness-induced adipogenic differentiation. Adipose stem cells are differentiated in a microenvironment that offers new insights into their differentiation process.

Carvalho & Kury (in press) the genus Gyndoides Mello-Leitão, 1927 revisited (Opiliones, Gonyleptidae). TABLE 2. Matrix of morphological characters X terminals.

Open the record for dataset details and reuse information.

HYDROUSA_ Evidence Matrix dataset

The evidence matrix  consists of cases demonstrating water reuse and recovery of other resources in cities, rural settlements, agriculture, coastal areas and industries, using technologies with Technology Readiness Level (TRL) 6 and above to recovery resources from wastewater, including NBS and any others. The aim was to identify cases demonstrated in Europe, and if the course of the search yielded cases demonstrated outside of Europe, but transferable to Europe, these were also taken up in the matrix (7 non-European cases, all located in the Southern Mediterranean). Demonstration cases include Horizon 2020 CIRC, WATER and WASTE and other EU-funded projects (total 96 cases), but also nationally funded projects (5 cases) and private sector projects (6 cases). European and national-level policy and framework initiatives for circular water economy were collected, including signed Innovation Deals (1 Innovation Deal for water reuse identified), public strategies, action plans, laws, Directives, Regulations, financing programmes, platforms, tools and frameworks, all at EU level or national level in EU member states. The collection process involved screening of online databases and reports related to circular economy, with a focus on water and wastewater, as well as reports published by the European Commission, the European Environment Agency, EUROSTAT, Cradle to Cradle, the Ellen MacArthur Foundation and Circle Economy, among others. The cases were categorized with key attributes to provide an overview and allow for filtering and targeted searching by users. Key information was retrieved from project websites, Cordis, reports and online articles.

Data of Rosenbrock method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"

This deposit contains the datasets generated by the Rosenbrock method for the paper: <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> Download the individual files and store them inside the directory <code>data/rosenbrock/</code>. The file names are structured as follows. <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution at the previous time step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=200</code>: maximum number of iterations</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>nsteps=45|150</code>: number of Rosenbrock steps</li> <li><code>tspan=(4500.0, 0.0)</code>: global time span of DRE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries: <ul> <li><code>file["rosenbrock_metrics"]</code>: data frame containing execution metrics of Rosenbrock iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Rosenbrock iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed (always <code>false</code>) </li> </ul> All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>. Generating this dataset took ~22h and consumed ~2.72kWh of electricity.

Data of Newton method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"

This deposit contains the datasets generated by the Newton method for the paper: <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> Download the individual files and store them inside the directory <code>data/newton-adi/</code>. The file names are structured as follows. <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution of the previous Newton step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=1000</code>: maximum number of iterations</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>newton_kwargs=...</code>: keyword arguments passed to Newton method <ul> <li><code>inexact=true|false</code>: whether to use inexact Newton method</li> <li><code>inexact_hybrid=true|false</code>: whether to switch back to classical Newton method in later iterations (only present if <code>inexact=true</code>)</li> <li><code>linesearch=true|false</code>: whether to employ line search</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> </ul> </li> <li><code>β=1000</code>: scaling of the quadratic term in the ARE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries: <ul> <li><code>file["newton_metrics"]</code>: data frame containing execution metrics of Newton iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Newton iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed</li> </ul> All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>. Generating this dataset took ~3h and consumed ~0.25kWh of electricity.

Implementation of K-Nearest Neighbor Algorithm and Gray Level Co-Occurance Matrix Method in Mushroom Type Classification

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

Spectral and computational dataset for "Hydrogen-atom-assisted processes on thioacetamide in para-H2 matrix – Formation of thiol tautomers"

TA-pH2_exp.zip: IR spectral data (both blank and 'real' experiments) including difference spectra, lab note files TA-pH2_calc.zip: Gaussian job and output files

Ill conditioned matrix for Reduced Order Model

Matrix for Random-SVD ROM

Data matrix for generating co-mentioning circle

data used in  Nielsen, T., Pettersson, M., Toft, L., Lindemann, D. M., & Nielsen, E. H. (2024). Development and Initial Validation of the Practice Self-Efficacy Questionnaire (PSEQ) for Student Teachers. Educational Methods & Psychometrics.

Experimental Data for 'Beyond memory-effect matrix-based imaging in scattering media by acousto-optic gating'

This repository contains the data used in Figures 3 and 4 of our study on noninvasive imaging beyond the optical memory-effect utilizing acousto-optic gating. The data files are organized into two folders: 'Figure 3' and 'Figure 4'. All data files can be loaded using PyTorch. Folder Structure and Content: <ol> <li> Figure 3: <ul> <li>This folder contains the measurements that have been digitally propagated to the conjugated plane.</li> <li>The folder also includes a ground truth file named 'object_direct_imaging.trc'.</li> </ul> </li> <li> Figure 4: <ul> <li>This folder contains two data files: <ul> <li>One file includes measurements with acoustic modulation.</li> <li>The other file includes measurements without acoustic modulation.</li> </ul> </li> <li>The folder also contains a ground truth file named 'digits_direct_imaging.trc'.</li> </ul> </li> </ol>

opencc-by-4.0Jun 2018View details →

The origin and role of organic matrix in coral calcification: insights from comparing coral skeleton and abiogenic aragonite

This file contains all the data and code for "The origin and role of organic matrix in coral calcification: insights from comparing coral skeleton and abiogenic aragonite" by DeCarlo et al. in Frontiers in Marine Science. Run the file, "run.R" in R to reproduce the analysis and create all the figures. Please see the published paper for methods and details: https://www.frontiersin.org/articles/10.3389/fmars.2018.00170/full

opencc-by-4.0Sep 2018View details →

The dissimilarity matrix of valid 21 subjects

The dissimilarity matrix of 21 subjects that can be used for CLASCAL model.

FIGURES 22–24. Figures 22–23. Plectrocnemia g. geniculata McLachlan 1871, 5 in The larva of Plectrocnemia renetta Malicky 1975 (Trichoptera, Polycentropodidae), including a discriminatory matrix to the larvae of Plectrocnemia Stephens 1836 species of Greece

FIGURES 22–24. Figures 22–23. Plectrocnemia g. geniculata McLachlan 1871, 5th instar larva: 22, head, dorsal (dotted lines = half-trapezoidal pattern of spots on posterior frontoclypeus, about 100°); 23, labrum, dorsal. Figure 24. P. c. conspersa (Curtis 1834), 5th instar larva, labrum, dorsal (arrow = posterior mark). Scale bars: 0.5 mm.

FIGURES 7–11. Plectrocnemia renetta Malicky 1975, 5 in The larva of Plectrocnemia renetta Malicky 1975 (Trichoptera, Polycentropodidae), including a discriminatory matrix to the larvae of Plectrocnemia Stephens 1836 species of Greece

FIGURES 7–11. Plectrocnemia renetta Malicky 1975, 5th instar larva. 7, metathorax, right lateral (ad = anterodorsal seta (sa1); mp = metapleuron; pd = posterodorsal seta (sa2); pl = pleural seta (sa3)); 8, left foreleg, posterior (m = epimeron; s = episternum; t = foretrochantin; double arrow = projected half length of tibia); 9, right midleg, anterior; 10, right hind leg, anterior (mp = metapleuron; arrows = apical spurs on tibia); 11, abdominal segment V, right lateral (ad = anterodorsal seta (sa1); pd = posterodorsal seta (sa2); pl = pleural seta (sa3); v = ventral seta; dotted oval = lateral fringe). Scale bars: 0.5 mm.

FIGURES 12–17. Figures 12–14. Plectrocnemia renetta Malicky 1975, 5 in The larva of Plectrocnemia renetta Malicky 1975 (Trichoptera, Polycentropodidae), including a discriminatory matrix to the larvae of Plectrocnemia Stephens 1836 species of Greece

FIGURES 12–17. Figures 12–14. Plectrocnemia renetta Malicky 1975, 5th instar larva: 12, abdominal segment IX, ventral (ip = innermost primary seta; op = outermost primary seta; p = intermediate primary setae); 13, abdominal segment IX, dorsal, posterolateral setal trio (is = inner secondary seta; os = outer secondary seta; p = primary seta); 14, left anal proleg, left lateral (sc = distal sclerotized section; so = proximal unsclerotized section; dotted lines = right angle superimposed on obtuse-angled anal claw). Figure 15. P. kingi McLachlan 1881, 5th instar larva, left foreleg, posterior (double arrow = projected half length of tibia). Figure 16. P. g. geniculata McLachlan 1871, 5th instar larva, abdominal segment IX, ventral (ip = innermost primary seta; op = outermost primary seta; p = intermediate primary setae). Figure 17. P. c. conspersa (Curtis 1834), 5th instar larva, abdominal segment IX, ventral (ip = innermost primary seta; op = outermost primary seta; p = intermediate primary setae). Scale bars: 0.5 mm.

FIGURES 1–6. Plectrocnemia renetta Malicky 1975, 5 in The larva of Plectrocnemia renetta Malicky 1975 (Trichoptera, Polycentropodidae), including a discriminatory matrix to the larvae of Plectrocnemia Stephens 1836 species of Greece

FIGURES 1–6. Plectrocnemia renetta Malicky 1975, 5th instar larva. 1, head and prothorax, dorsal (dotted lines = halftrapezoidal pattern of spots on posterior frontoclypeus, about 100°; small numerals refer to setal positions); 2, head and prosternum, ventral (a = anterior ventral apotome; ls – labial spinneret; ml = maxillary lobe; mp = maxillary palp; m = ventral process of epimeron; ss = submental sclerite; arrows = ventral processes of pronotum); 3, head and prothorax, right lateral (m = epimeron; s = episternum; t = foretrochantin); 4, labrum, dorsal (arrow = central pigmented mark); 5, mandibles, dorsal (a = apical tooth; arrows = subapical teeth); 6, mandibles, ventral (a = apical tooth; arrows = subapical teeth). Scale bars: 0.5 mm.