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18 results for “inductive coding”
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
Data and analysis code for a toxin induction study on two species of Dinophysis dinoflagellates
<h3>General description</h3> <p>This repository contains the datasets, analysis code and output generated and used in the manuscript "Effects of copepod chemical cues on intra- and extracellular toxins in two species of <em>Dinophysis</em>", which has been published as a research article in Harmful Algae (https://doi.org/10.1016/j.hal.2024.102793).</p> <h3>Files</h3> <p><strong>HRMS_data_Dinophysis_induction_experiment.zip </strong>contains the source high-resolution mass-spectroscopy endometabolomics data in open file formats.</p> <p><strong>Put_annot_sign_affect_feat_metabol_data.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 1</strong> in the main manuscript) contains putative annotations of significantly affected features from the metabolomics data, for each <em>Dinophysis </em>species (<em>D.</em> <em>sacculus </em>and <em>D. acuminata</em>) and each mode of ionization. Notably, results obtained from GNPS (Global Natural Products Social Molecular Networking, <a href="https://gnps.ucsd.edu/" target="_blank" rel="noopener noreferrer">https://gnps.ucsd.edu/</a>), and SIRIUS (<a href="https://bio.informatik.uni-jena.de/sirius/" target="_blank" rel="noopener noreferrer">https://bio.informatik.uni-jena.de/sirius/</a>) were mentionned. When available, MS/MS spectra were also provided. </p> <p><strong>Tabl_sign_affect_feat.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 3</strong> in the main manuscript) contains tables of significantly affected features (ANOVA, Tukey’s post hoc test, adjusted p-value cut-offs of 0.001 or 0.01) from the metabolomics data, for each <em>Dinophysis </em>species (<em>sacculus </em>and <em>acuminata</em>) and each mode of ionization. A visual representation (heatmaps) of these significant fetures are available as Figs S3-S6 in the supplementary information of the main mauscript. </p> <p><strong>Toxin_analysis_code_output.Rmd</strong> is the R-markdown file that produces the interactive analysis output output (<strong>Toxin_analysis_code_output.html</strong>, corresponds to <strong>Supplementary code & output </strong>in the main manuscript) of the toxin analysis, and uses the datasets <strong>Toxin_analysis_data.csv</strong>,<strong> pca_score_sacculus.csv</strong>,<strong> </strong>and<strong> pca_score_acuminata.csv</strong> source datasets to perform the statistical analyses and generate figures (details for each dataset below).</p> <p><strong>Toxin_analysis_data.csv</strong> (corresponds to <strong>Supplementary spreadsheet 2</strong> in the main manuscript) contains the main data used to statistically analyse the toxin and growth dynamics of both <em>Dinophysis</em> species in response to different grazer treatments, and to produce the majority of the figures in the main manuscript (Figs. 2-6) and supplementary information (Figs. S1-S2). </p> <p><strong>pca_score_sacculus.csv</strong> & <strong>pca_score_acuminata.csv</strong> contain the scores of the first two principal components of the PCA performed on LC-HRMS derived metabolomic profiles of <em>D. sacculus </em>and <em>D. acuminata</em> respectively, in both positive and negative ion mode. These are used to produce PCA score plots (Fig. 6 in the main manuscript and Fig. S2 in the supplementary information).</p> <p><strong>custom.css</strong> is a custom html style sheet file that formats the <strong>Toxin_analysis_code_output.html</strong> to display scrollable tables correctly. It is used by <strong>Toxin_analysis_code_output.Rmd </strong>and is necessary for true reproduction of the output (<strong>.html</strong>) file.</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Data and code for figures: Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications
<p>This directory contains the datasets, code (if applicable) for measurement libraries, data processing and figure generation for the research article "Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications", Beilstein J. Nanotechnol. 2024, 15, 242-255.</p>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Epigenetic induction may speed up or slow down speciation with gene flow: code and data
<p>Speciation is less likely to occur when there is gene flow between nascent species. Natural selection can oppose gene flow and promote speciation if there is variation in ecological conditions among the nascent species' locations. Previous theory on ecological speciation with gene flow has focused primarily on the role of genetic variation in ecological traits, largely neglecting the role of non-genetic inheritance or transgenerational plasticity. <span>Here we present the simulation code and data from</span> models incorporating both genetic and epigenetic inheritance, the latter representing a form of non-genetic inheritance. We investigate the rate of speciation for a population that inhabits two patches, connected by migration, and find that adaptively-biased epigenetic induction can speed up or slow down speciation, depending on the form of the map from genotype and epigenotype to phenotype. While adaptively-relevant epigenetic variation can speed up speciation by reducing the fitness of migrants and hybrids, it can also slow down speciation. This latter effect occurs when the epialleles are able to achieve adaptation faster than the genetic alleles, thereby weakening selection on the latter.</p>
Epigenetic induction may speed up or slow down speciation with gene flow: code and data
Open the record for dataset details and reuse information.
Table-Matrix, containing all the codes generated from the inductive analysis process of the 78 articles that made up the final sample. (Not only Opportunity, but also Uncertainty: A systematic review of how entrepreneurship literature appropriates both constructs.)
Open the record for dataset details and reuse information.
Long non-coding RNA TILR constitutively represses TP53 and apoptosis induction in lung cancer II
GEO Series GSE180944. Homo sapiens. 6 samples. Type: Expression profiling by array.
Mechanism of Induction of Mouse Breast Cancer by Non-coding Heterochromatic RNAs
GEO Series GSE88961. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Long non-coding RNA TILR constitutively represses TP53 and apoptosis induction in lung cancer
GEO Series GSE180940. Homo sapiens. 8 samples. Type: Expression profiling by array.
Dual genome-wide coding and lncRNA screens in neural induction of induced pluripotent stem cells
GEO Series GSE150062. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.
Long non-coding RNA ELDR promotes cell cycle progression in normal oral keratinocytes through induction of a CTCF-FOXM1-AURKA signaling axis
GEO Series GSE198818. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>.</p> <p>Code originally written for R version 3.6.2.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p> <p> </p>
A shade-induced long non-coding RNA promotes shade response through repressing the transcriptional induction of PHYTOCHROME A
GEO Series GSE210807. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional signature of islet neogenesis-associated protein peptide-treated rat pancreatic islets reveals induction of novel long non-coding RNAs
GEO Series GSE208002. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
UNRAVELING A NOVEL TRANSCRIPTION FACTOR CODE INDUCTIVE FOR THE HUMAN ARTERIAL-SPECIFIC ENDOTHELIAL CELL SIGNATURE
GEO Series GSE43475. Homo sapiens. 38 samples. Type: Expression profiling by array.
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.