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138 results for “Toolbox”
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
Dataset to reproduce firgures for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"
<p>Dataset provided to reproduce figures <br> for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"</p> <p>Jupyter notebooks are available here:<br> https://github.com/samuelgarcia/physio_benchmark</p> <p>Human dataset<br> =============</p> <p>Context: A research aimed to decipher the impact of respiration on brain oscillations</p> <p>Data collection methods: ECG and Respiration of 15 healthy adults subjects <br> (age : 30.9 +/- 9.5 yo).All participants gave informed consent to take part to the study, and all experiments<br> were approved by the national french committee (CPP number 4090). They were sitting quietly and instructed just<br> to relax. Recording lasted 5 minutes. Respiration signal was recorded from a nasal sensor<br> (Sensortechnics GmbH, Puchheim , Germany) at a sampling rate of 1000 Hz, amplified by actiCHamp<br> Plus amplifier (Brain Products GmbH, Gilching, Germany). ECG signal was recorded from 3 skin electrodes<br> (right forearm, left forearm, left iliac region), at a sampling rate of 1000 Hz (same amplifier).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respiratorysignal.<br> The sampling rate is 1000Hz</p> <p>Data manipulations: The original dataset has longer durationand and contain channels (EEG).<br> This sub-dataset was extracted from the original using the neo python package from the VHDR brain product format.<br> Signal tarces haven't been preprocessed they correspond to the "raw" signal.</p> <p><br> Data confidentiality and permissions: Experiments were approved by the national french committee (CPP number 4090)</p> <p><br> Animal dataset<br> ==============</p> <p>Context: The dataset was recorded to validate the device telemetric jacket from Etisense.</p> <p>Data collection methods: ECG and Respiration of 1 adult rat were recorded. Recording lasted 30 seconds during<br> freely behaving. Respiration signal and ECG were recorded from a thoraco-abdominal telemetric<br> jacket at which it was habituated before. Recorded were done at a sampling rate of 500 Hz, amplified by<br> Etisense acquisition unit (Etisense, MedTech company, Lyon, France).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respirator signal.<br> The sampling rate is 500Hz</p> <p><br> Data manipulations:<br> The dataset was extracted from the original HDF5 structure.<br> The ECG signal correcpond to the "raw" traces from the HDF5 files.<br> The respiratory signal was originaly sample at 200Hz on the device and resample with linear interpolation<br> to 500Hz to be easy aligned with the ECG signal.</p> <p>Data confidentiality and permissions: Experiments were carried according to the ethical guidelines of the<br> European Communities Council Directive of 24 November 1986 (86/609/EEC), as well as the approval 16979 of the<br> Lyon 1 University CEEA-55 ethical committee and of the Ministry of Higher Education, Research and Innovation.<br> </p>
Life on the edge: A new toolbox for population-level climate change vulnerability assessments
<p>Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.</p> <p>We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity and Genotype-Environment Associations) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD-seq, ddRAD-seq, whole genome sequencing data) representing environmental variation across the species geographic range.</p> <p>We apply our toolbox to a georeferenced genomic dataset for the East African spiny reed frog (<em>Afrixalus fornasini</em>) to predict population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced using data for two European bat species (<em>Myotis escalerai</em>, and <em>M. crypticus</em>).</p> <p>Our framework sets the stage for large scale, multi-species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity, and range shift potential.</p>
Life on the edge: A new toolbox for population-level climate change vulnerability assessments
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Tutorial video for: A toolbox for the retrodeformation and muscle reconstruction of fossil specimens in Blender
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MSSiS - Diversity of MDE Toolboxes and Their Uncommon Properties
<p>Model-Driven Engineering (MDE) has reached some maturity. Due to that, a high diversity of technologies and platforms have emerged to support the resolution of a range of problems and contexts in which MDEis adopted as a solution. As a consequence, when some level of reuse of those artifacts (such as model transformations, Domain-Specific Languages(DSLs) and refinement tools), difficulties are faced due to the high diversity of formats in which all those assets are specified. Since we noted this trend, we decided to search for instances in the literature that supports our hypothesis of a high degree of diversity in MDE artifacts in the state of the practice. Thus, we carried out an exploratory literature review. As a result, we summarized key studies used as input to build a search string adopted to structure a future systematic literature review. Our study contributes by classifying nine types of MDE toolboxes with uncommon properties than those usually found in MDE workbenches.</p>
PanGEM Toolbox - Prochlorococcus pangenome
<p>Collection of all currently (1/29/2020) available genomes of Prochlorococcus, for use with the PanGEM Toolbox (https://github.com/jrcasey/PanGEM). Genomes were downloaded from NCBI and from IMG. </p>
Experiments of the Paper "MORTY: A Toolbox for Mode Recognition and Tonic Identification"
<p>This package contains the complete experimental data explained in:</p> <blockquote> <p>Karakurt, A., Şentürk S., & Serra X. (In Press). MORTY: A Toolbox for Mode Recognition and Tonic Identification. 3rd International Digital Libraries for Musicology Workshop. </p> </blockquote> <p>Please cite the paper above, if you are using the data in your work.</p> <p>The zip file includes the folds, features, training and testing data, results and evaluation file. It is part of the experiments hosted in github (https://github.com/sertansenturk/makam_recognition_experiments/tree/dlfm2016) in the folder call ".<strong>/data</strong>". We host the experimental data in Zenodo (http://dx.doi.org/10.5281/zenodo.57999) separately due to the file size limitations in github.</p> <p>The files generated from audio recordings are labeled with 16 character long MusicBrainz IDs (in short "MBID"s) Please check http://musicbrainz.org/ for more information about the unique identifiers. The structure of the data in the zip file is explained below. In the paths given below <em>task</em> is the computational task ("tonic," "mode" or "joint"), <em>training_type</em> is either "single" (-distribution per mode) or "multi" (-distribution per mode), <em>distribution</em> is either "pcd" (pitch class distribution) or "pd" (pitch distribution), <em>bin_size</em> is the bin size of the distribution in cents, <em>kernel_width</em> is the standard deviation of the Gaussian kernel used in smoothing the distribution, <em>distance</em> is either the distance or the dissimilarity metric, <em>num_neighbors</em> is the number or neighbors checked in <em>k</em>-nearest neighbor classification and <em>min_peak</em> is the minimum peak ratio. 0 <em>kernel_width</em> implies no smoothing. <em>min_peak </em>always takes the value 0.15. For a thorough explanation please refer to the companion page (http://compmusic.upf.edu/node/319) and the paper itself.</p> <ul> <li><strong>folds.json: </strong>Divides the test dataset (https://github.com/MTG/otmm_makam_recognition_dataset/releases) into training and testing sets according to stratified 10-fold scheme. The annotations are also distributed to sets accordingly. The file is generated by the Jupyter notebook <em>setup_feature_training.ipynb (4th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb).</li> <li><strong>Features: </strong>The path is <strong>data/features/[distribution--bin_size--kernel_width]/[MBID--(hist </strong><em>or </em><strong>pdf)].json</strong>. "pdf" stands for probability density function, which is used to obtain the multi-distribution models in the training step and "hist" stands for the histogram, which is used to obtain the single-distribution models in the training step. The features are extracted using the Jupyter notebook <em>setup_feature_training.ipynb (5th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li> <li><strong>Training: </strong>The path is <strong>data/training/[training_type--distribution--bin_size--kernel_width]/fold(0:9).json]</strong>. There are 10 folds in each folder, each of which stores the training model (file paths of the <em>distribution</em>s in "multi" <em>training_type</em> or the <em>distribution</em>s itself in "single" <em>training_type</em>) trained for the fold using the parameter set. The training files are generated by the Jupyter notebook <em>setup_feature_training.ipynb (6th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li> <li><strong>Testing: </strong>The path is <strong>data/testing/[task]/[training_type--distribution--bin_size--kernel_width--distance--num_neighbors--min_peak]</strong>. Each path has the folders <strong>fold(0:9)</strong>, which have the evaluation and the results files obtained from each fold. The path also has the <strong>overall_eval.json</strong> file, which stores the overall evaluation of the experiment. The optimal value of <em>min_peak </em>is selected in the 4th code block, testing is carried in the 6th code clock and the evaluation is done in the 7th code block in the Jupyter notebook <em>testing_evaluation.ipynb</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/testing_evaluation.ipynb). <br> <strong>data/testing/ </strong>folder also contains a summary of all the experiments in the files <strong>data/testing/evaluation_overall.json </strong>and <strong>data/testing/evaluation_perfold.json</strong>. These files are created in MATLAB while running the statistical significance scripts. <strong>data/testing/evaluation_perfold.mat </strong>is the same with the json file of the same filename, stored for fast reading.</li> </ul> <p>For additional information please contact the authors.</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p>
DEMO_MRS4Brain_toolbox
<p>Live demos of MRS4Brain toolbox used for 1H-FID- MRSI data processing. </p> <p>The <em>MRS4Brain Toolbox</em> is developed by <a href="https://www.epfl.ch/labs/mrs4brain/">MRS4Brain research group</a> @ CIBM MRI EPFL AIT and was designed to offer advanced functionalities for Bruker preclinical MRSI data, encompassing preprocessing, fitting, quantification, semi-automatic quality control, co-registration and segmentation of metabolic maps using anatomical images, all conveniently integrated within a single open-source graphical user interface (GUI). The development of this user-friendly toolbox aims to streamline the processing workflow and enhance the accessibility of MRSI for researchers in the preclinical field. </p> <p>MRS4Brain toolbox is available for download here: <a href="https://github.com/MRS4Brain/MRS4Brain-toolbox">MRS4Brain/MRS4Brain-toolbox (github.com)</a> and <span><a href="https://github.com/AlvBrayan/MRS4Brain-toolbox/">https://github.com/AlvBrayan/MRS4Brain-toolbox/</a></span></p> <p>Please cite the following article if you are using MRS4Brain toolbox to process your data</p> <p><em>“Fast high-resolution metabolite mapping in the rat brain using 1 H-FID-MRSI at 14.1T”</em></p> <p><em>Dunja Simicic, Brayan Alves, Jessie Mosso, Guillaume Briand, Thanh Phong Lê, Ruud B. van Heeswijk, Jana Starčuková, Bernard Lanz, Antoine Klauser, Bernhard Strasser, Wolfgang Bogner, Cristina Cudalbu</em></p> <p><em>under review in NMR in Biomed</em></p>
Image datasets associated with Gut Analysis Toolbox
<div>The images are sample image datasets associated with the software: <a href="https://gut-analysis-toolbox.gitbook.io/docs/">Gut Analysis Toolbox (GAT)</a>.</div> <div>The dataset contains immunofluorescence images of enteric neurons and glia labeled with different markers. The data is mostly from mouse and human colon or small intestine. </div> <div>Channels corresponding to Hu labelling can be used for segmenting enteric neurons in GAT. </div> <div>Channels corresponding to GFAP (enteric glia) or neurons with markers labelling the cell body and processes (ChAT, Calbindin, Calretinin) can be used as a ganglia marker for segmenting the ganglia</div> <div>The data is two-dimensional (2D) with some images having multiple channels. The data is mostly in tif format, except for one dataset that is czi (Fiji using bioformats or aicspylibczi in Python). Calcium imaging data is 2D+Time.</div> <div> </div> <div>Data curated by: <a href="https://www.linkedin.com/in/rajapradeep/">Pradeep Rajasekhar, Walter and Eliza Hall Institute of Medical Research</a>, Australia</div> <h2><strong>Data from<a href="https://www.monash.edu/mips/themes/drug-discovery-biology/labs/inm"> INM lab, Monash University</a> (mouse images)</strong></h2> <p><strong>Immunofluorescence images</strong></p> <ul> <li> 181107_ms_distal_colon_GFAP_Hu_40X.tif <ul> <li>Channel 1: GFAP</li> <li>Channel 2: Hu</li> </ul> </li> </ul> <div> <ul> <li>181107_ms_distal_colon_nNOS_GFAP_Hu_40X.tif (Same as above, but got an extra channel)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: nNOS</li> <li>Channel 2: GFAP</li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div>In both images above, GFAP can be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li>ms_distal_colon_Hu_20X.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>ms_distal_colon_Hu_40X_1.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>Tilescan_GAT_ms_distal_colon_MP_hu.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <h3><strong>Calcium imaging data (video)</strong></h3> <div>• calcium_imaging_mouse_distal_colon_25X.tif</div> <div>Tissue was incubated with calcium dye Fluo8-AM calcium dye (Myenteric wholemount from distal colon of mouse)</div> <div>142 frames in total acquired at 1.162 frames per second. At frame 52, 100 uM of ATP is added which causes enteric glia, neurons and blood vessels to respond. This causes slow drifting in the field of view.</div> <div> </div> <div>• mouse_GCamp_calcium_movement.tif</div> <div> </div> <div>Wnt1-GCaMP3 mouse where GCamP3 is a genetically encoded calcium sensor expressed by enteric neurons and enteric glia</div> <div>Imaging performed at Monash University. 745 frames in total acquired at 1.162 frames per second with drifting over time</div> <div>Tissue source: <a href="https://biomedicalsciences.unimelb.edu.au/sbs-research-groups/anatomy-and-physiology-research/neuroscience/development-of-the-enteric-nervous-system">Stamp & Hao laboratory, University of Melbourne.</a></div> <div> </div> <h2><strong>Images from McQuade Lab, University of Melbourne</strong></h2> <ul> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse colon)</li> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse ileum) <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div> <ul> <li>ms_distal_colon_nNOS_Hu_10X.czi (This is a .czi file which can be opened in Fiji using bioformats or aicspylibczi in Python)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <h2><strong>Images from public repository (SPARC)</strong>:</h2> <ul> <li>DYM_22_7_Pr_Chat_BYFP_DIN_GFP-g_nNOS-m_VIP-r_Hu-b.tif is a crop from File 100 05-07-2019 DYM 22 7 Pr Chat%3BYFP DIN GFP-g nNOS-m VIP-r Hu-b (Mouse Proximal Colon)</li> <li>DYM_22_7_Pr_Hu_crop.tif is a crop from above. <ul> <li>Channel 1: Choline acetyltransferase</li> <li>Channel 2: nNOS</li> <li>Channel 3: Calretinin</li> <li>Channel 4: Hu (pan-neuronal marker)</li> </ul> </li> </ul> <div> Channel 1 and 3 can be used as ganglia segmentation channels in GAT using DeepImageJ.</div> <div> </div> <div> <ul> <li>146_02_14_20DYM8_6_mouse_Mid_Chat-g CalB-r CalR-b_max.tif is from File 146 02-14-20 DYM 8 6 Mid Chat-g CalB-r CalR-b (Mouse mid colon)</li> </ul> </div> <div> No Hu staining, any channel could be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li> <ul> <li>Channel 1: ChAT</li> <li>Channel 2: Calbindin</li> <li>Channel 3: Calretinin</li> </ul> </li> </ul> </div> <h3><strong>Reference</strong>:</h3> <div>Thanks goes to Marthe Howard for depositing the data in the SPARC repository.</div> <div>Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium.<a href="https://doi.org/10.26275/9FFG-482D"> https://doi.org/10.26275/9FFG-482D</a></div> <div>**************</div> <h2><strong>Multiplex data (Flinders University)</strong></h2> <div><strong>Multiplexing_H2202Desc_Layer 1_Ganglia1_Hu.zip </strong>is from:</div> <div><a href="https://pubmed.ncbi.nlm.nih.gov/37355216/">Chen, B. N., Humenick, A., Yew, W. P., Peterson, R. A., Wiklendt, L., Dinning, P. G., Spencer, N. J., Wattchow, D. A., Costa, M., & Brookes, S. J. H. (2023). Types of Neurons in the Human Colonic Myenteric Plexus Identified by Multilayer Immunohistochemical Coding. Cellular and molecular gastroenterology and hepatology, 16(4), 573–605.</a></div> <div>This data is a myenteric wholemount from the descending colon of a Human. It has 14 different markers, 6 different rounds of staining. Every round has pan-neuronal marker Hu as a reference marker.There are 19 images. The filenames follow the convention:</div> <div> </div> <div>H2202Desc_<em>layer num</em>_<em>ganglia num</em>_<em>markername</em>.tif </div> <div><em>H2202 </em>is the sample name, <em>Desc </em>means descending colon</div> <div>Here <em>layer num</em> corresponds to the round of staining, so Layer3, means its the 3rd round of staining.</div> <div><em>ganglia num</em> is specified as multiple ganglia can be imaged from same tissue. </div> <div><em>markername</em> corresponds to the marker used. </div> <div> </div> <div>Markers used are: Hu, 5HT, ChAT, NOS, CGRP, Enk, SP, Somat, VACht, NPY, Calbindin, Calretinin, NF, VIP</div> <div> </div> <div><strong>Abbreviations:</strong></div> <div> </div> <ul> <li>Hu: Pan-neuronal marker</li> <li>5HT: Serotonin (5-Hydroxytryptamine)</li> <li>ChAT: Choline acetyltransferase</li> <li>nNOS: neuronal Nitric Oxide Synthase (NOS in these images are actually nNOS)</li> <li>CGRP: Calcitonin Gene-Related Peptide</li> <li>Enk: Enkephalin</li> <li>SP: Substance P</li> <li>Somat: Somatostatin</li> <li>VACht: Vasoactive Intestinal Peptide (VIP) </li> <li>NPY: Neuropeptide Y</li> <li>NF: neurofilament 200 </li> </ul>
CamoEvo: an open access toolbox for artificial camouflage evolution experiments
<p>Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of colour patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimisation by using tailored genetic algorithms, animal and egg maculation theory and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (~1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimised to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and genetic algorithm as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer based phenotype optimisation experiments.</p>
A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast
<p>Computational models and figure data for the publication "A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast" (preprint on <a href="https://doi.org/10.1101/2022.05.04.490403"><em>bioRxiv</em></a>). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p> </p> <p><em>Abstract</em></p> <p>The fission yeast <em>Schizosaccharomyces pombe</em> is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, <em>S. pombe</em> also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast <em>Saccharomyces cerevisiae</em>, a major workhorse in these areas, cell physiology and metabolism of <em>S. pombe</em> remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of <em>S. cerevisiae</em> to generate a manually-curated high-quality reconstruction of <em>S. pombe’s</em> metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in <em>S. pombe</em>. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of <em>S. pombe</em> with <em>S. cerevisiae</em>, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.</p>
soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes
<p>1. Soundscapes contain rich acoustic information associated with animal behaviors, environmental characteristics, and human activities, providing opportunities for predicting biodiversity changes and associated drivers. However, assessing the diversity of animal vocalizations remains challenging due to the interference of environmental and anthropogenic noise. A tool for separating sound sources and delineating changes in acoustic signals is crucial for an effective assessment of acoustic diversity.</p> <p>2. We present soundscape_IR, an open-source Python toolbox dedicated to soundscape information retrieval in which non-negative matrix factorization is applied. This toolbox provides algorithms for supervised and unsupervised source separation (SS). It also enables the use of a snapshot recording for model training and subsequently applying adaptive and semi-supervised SS when target species produce sounds with varying features and when unseen sound sources are encountered.</p> <p>3. Our results demonstrated that SS could enhance the vocalizations of target species, characterize the complexity of vocal repertoires, and investigate the spatio-temporal divergence of soundscapes. In tropical forest soundscapes, the application of SS effectively detected the rutting vocalizations of sika deer and revealed a graded structure in their acoustic characteristics. In subtropical estuarine soundscapes, SS automated the process of identifying distinct biotic and abiotic sounds, and the result uncovered divergent sound compositions between inshore and offshore waters.</p> <p>4. Implementation of SS in soundscape analysis offers a promising method for streamlining the assessment of acoustic diversity in diverse environments. Future application of SS will open new directions to acoustically quantify ecological interactions across individual, species, and ecosystem levels.</p>
Ocean Carbon States Database and Toolbox
<p>The "Ocean Carbon States Database and Toolbox" includes observational and climate model datasets and matlab scripts to compute regimes of the ocean carbon cycle. </p>
Development of an imaging toolbox to assess the therapeutic potential and biodistribution of macrophages in a mouse model of multiple organ dysfunction
<p>Data set to accompany manuscript entitled "Development of an imaging toolbox to assess the therapeutic potential and biodistribution of regenerative therapies in a mouse model of multiple organ dysfunction " which can be found on BioRxiv.</p>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Digital Forestry Toolbox - Sample data
<p>This repository contains airborne laser scanning data samples acquired by the states of Geneva, Solothurn and Zurich (in Switzerland). They are used in the tutorials of the <a href="https://github.com/mparkan/Digital-Forestry-Toolbox">Digital Forestry Toolbox for Matlab/Octave</a>.</p> <p>The full datasets (covering the complete state extents) are available from here: </p> <ul> <li><a href="https://maps.zh.ch/">https://maps.zh.ch/</a></li> <li><a href="https://geoweb.so.ch/map/lidar">https://geoweb.so.ch/map/lidar</a></li> <li><a href="https://ge.ch/sitg/donnees">https://ge.ch/sitg/donnees</a></li> </ul> <p><strong>Sources</strong> <strong>and usage conditions</strong>:</p> <ul> <li>Canton de Genève, Département de l'aménagement, du logement et de l'énergie (DALE), Système d'information du territoire à Genève (SITG). Dataset extracted on December 6, 2018. <a href="https://ge.ch/sitg/media/sitg/files/documents/conditions_generales_dutilisation_des_donnees_et_produits_du_sitg_en_libre_acces.pdf">See usage conditions</a>.</li> <li>Kanton Zürich, <a href="https://are.zh.ch/internet/baudirektion/are/de/aktuell.html">Baudirektion, Amt für Raumentwicklung</a>. Dataset extracted on December 6, 2018. <a href="https://are.zh.ch/internet/baudirektion/are/de/geoinformation/geodaten_uebersicht/Open_Data_Kanton_Zuerich.html#datenbezug">See usage conditions</a>.</li> <li>Kanton Solothurn, <a href="https://www.so.ch/verwaltung/bau-und-justizdepartement/">Bau- und Justizdepartement, Amt für Geoinformation</a>. Dataset extracted on December 6, 2018. <a href="https://geoweb.so.ch/geodaten/index.php?action=nutzung&UID=&USR=&user_id=&lang=de&menue=&aktuell=&sogis_zip_ie=">See usage conditions</a>.</li> </ul>
MODELING.VIS: Video Tutorial and Instructions to use the Graphical User Interface Toolbox
<p>INTRODUCTION: MODeLING.Vis was designed as an attempt to perform interactive data analyses. Given the software's effectiveness in extracting valuable information from the experimental data presented in this study, the applied methods and principles have been presented together with the analysis of results, and the code has been shared. Note, however, that MODeLING.Vis is not commercial, which constrains efforts behind scientific investigations.</p> <p>HYPOTHESIS: For a better understanding of the Graphical User Interface Toolbox, a demo and user manual of the toolbox should be provided for the convenience of users. The electrophoretic dataset should be published together with the tutorial for ease of access. </p> <p>METHODOLOGY: Creation of a practical video tutorial demonstrating how to download, install, run and operate MODeLING.Vis. Direct access to the electrophoretic dataset (protLabled.xls) is provided. </p>
Data from: How can we tackle interruptions to human-wildlife feeding management? Adding media campaigns to the wildlife manager's toolbox
<p>In recent years, wildlife managers have been seeking ways to reduce the occurrence of independent, recreational human-wildlife feeding interactions, which continue to gain global popularity and may have negative effects on the humans and wildlife involved. Current popular methods, such as signage and posters, have yielded mixed results and their application is often interrupted, though the effects of these interruptions on feeding levels are currently unknown. This has led to calls to both identify a management option that can be applied successfully from a distance and to determine whether this action may assist in recovering long-term programmes from the potential effects of interruptions. Marketing and media tools have been shown to successfully change human behaviours in conservation campaigns, flagging them as a potential tool that could be applied to human-wildlife feeding management. </p> <p>Here, we performed a 4-year study using a wild fallow deer population in a popular urban green space as our model system. We tracked changes in human feeding behaviours across four different management stages. These included pre-management (stage 1), during traditional management (i.e. "don't feed the deer" signage, stage 2), mid-interruption (i.e. COVID-19 pandemic, stage 3), and during the application of a structured media campaign (stage 4). </p> <p>We found that feeding by visitors decreased during traditional management (stage 2), but rapidly returned to pre-management levels during the interruption (stage 3) despite traditional controls still being in place. However, we discovered that feeding dropped significantly after the release of a media campaign (stage 4), despite the audience and conditions being unchanged. We also identified which imagery and educational messages resonated with viewers; information that can be applied to future campaigns in other locations.</p> <p>We, therefore, recommend that wildlife managers both investigate and be prepared for the negative effects that interruptions of any type (e.g. the recent COVID-19 pandemic, other interruptions to funding) may have on ongoing management campaigns of this ilk. We recommend that media campaigns be explored as a potential tool to reduce the occurrence of the unregulated feeding of wildlife by humans in these sites, thereby promoting better human-wildlife coexistence. </p>
Video FlyingLess Toolbox_ENG
<p>This video gives a brief insight into the FlyingLess toolbox and helps to navigate through the toolbox.</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)
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.