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138 results for “Toolbox”
IPMWORKS Resource Toolbox - database extraction March 2024
<p><span>The IPMWORKS IPM Resource Toolbox (Toolbox) has been developed as an interactive, online repository of integrated pest management (IPM) resources. Populated with high priority resources for farmers and their advisors during the project, its structure enables additional resources added over time. The repository is a public interactive website, available to anyone looking to access, understand, and implement IPM. Built on an open-source content management system, the toolbox is designed to require minimal post-production site maintenance and support, while being easily expanded to integrate resources from future initiatives.<br>At the core of the Toolbox lies MongoDB, a powerful NoSQL database management system. The schema-less nature of MongoDB allows for flexible data modeling, crucial for accommodating the diverse array of materials within the IPMWORKS ecosystem. Additionally, the integration of GridFS, a feature of MongoDB, facilitates the storage and retrieval of large files like images, PDFs, and documents. This architectural choice ensures optimal performance and efficiency in handling a wide range of materials. <br>We here make available all content uploaded to the IPMWORKS Resource Toolbox up to 18 March 2024. Materials are available in two formats, first as MongoDB files which require users to open them as a Mongo database file, and second as JSON files. Note that the JSON format does not include access to any pdfs attached to Toolbox content, only the associated metadata. </span></p> <p> </p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Duhumbi Grammar - Sound Files, Toolbox and Transcriber File, PDFs of files
<p>This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the recordings that belong to the following publication:</p> <p>Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. <a href="https://brill.com/view/title/55767">https://brill.com/view/title/55767</a></p> <p>The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.</p> <p>The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.</p> <p>The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.</p> <p>Transcriber version 1.5.1: <a href="http://trans.sourceforge.net/en/presentation.php">http://trans.sourceforge.net/en/presentation.php</a> or <a href="https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/">https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/</a></p> <p>Toolbox version 1.6.1: <a href="https://software.sil.org/toolbox/download/">https://software.sil.org/toolbox/download/</a></p> <p>This data set contains the files belonging to the sound files as mentioned in the pdf file “Duhumbi Grammar All Files Upload 1”. The S/N code corresponds to the code used in the Grammar to identify the text from which an example was taken. The name of the file refers to the name of the .wav, .trs, .txt and .pdf files in this upload. The subject is a short description of the topic of the text. The duration is the duration of the recording.</p> <p>For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collector of the material. By downloading this material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: monpasang (at) gmail (dot) com</p>
Duhumbi Grammar - Sound Files, Toolbox and Transcriber File, PDFs of files (Part 2)
<p>This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the recordings that belong to the following publication:</p> <p>Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. <a href="https://brill.com/view/title/55767">https://brill.com/view/title/55767</a></p> <p>The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.</p> <p>The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.</p> <p>The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.</p> <p>Transcriber version 1.5.1: <a href="http://trans.sourceforge.net/en/presentation.php">http://trans.sourceforge.net/en/presentation.php</a> or <a href="https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/">https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/</a></p> <p>Toolbox version 1.6.1: <a href="https://software.sil.org/toolbox/download/">https://software.sil.org/toolbox/download/</a></p> <p>This data set contains the files belonging to the sound files as mentioned in the pdf file “Duhumbi Grammar All Files Upload 2”. The S/N code corresponds to the code used in the Grammar to identify the text from which an example was taken. The name of the file refers to the name of the .wav, .trs, .txt and .pdf files in this upload. The subject is a short description of the topic of the text. The duration is the duration of the recording.</p> <p>For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collector of the material. By downloading this material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: monpasang (at) gmail (dot) com</p>
RCT NOVICE Surgeon training Lübeck Toolbox
<p>Surgeon residents were rated by GOALS score at their first operations by masked raters. Some had trained with the Lübeck Toolbox until sufficiently proficient in a waiting group design. </p> <p>The <em><strong>study protocol</strong></em> was published here: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.isjp.2020.02.004" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.isjp.2020.02.004</a></p> <p>The article with the <em><strong>results</strong></em> is published here: <a href="http://dx.doi.org/10.1097/JS9.0000000000002304">http://dx.doi.org/10.1097/JS9.0000000000002304</a> </p>
SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Skin Vibrations Across the Upper Limb
<p>The repository contains the data for the toolbox released as part of the publication “SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb.” The toolbox and installation and usage instructions can be found on GitHub here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>. If you use these data or our toolbox please cite our publication: <a href="https://doi.org/10.1109/HAPTICS59260.2024.10520852">https://doi.org/10.1109/HAPTICS59260.2024.10520852</a>.</p> <p>Full citation: “Tummala, N., Reardon, G., Fani, S., Goetz, D., Bianchi, M., and Visell, Y. (2024) SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb. IEEE Haptics Symposium 2024. DOI: 10.1109/HAPTICS59260.2024.10520852” </p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.</p> <p><strong> </strong></p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises experimental data of 3-axis surface acceleration at 72 locations on the skin in response to unit impulsive forces supplied at 20 different input locations on the palmar hand surface. For details on our experimental procedure, please see our publication. This data is intended to be used as part of the SkinSource toolbox, which can be found here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>.</p> <p><strong> </strong></p> <p><strong>Data Fields</strong></p> <p>The data is provided as a .mat file. This file contains a single variable “dataTable” of variable type “table.” The table contains 80 rows, each corresponding to a unique experimental condition (4 participants x 20 input locations), and contains the following fields:</p> <p><strong>Data </strong>(522x72x3) - 3D array containing the 3-axis skin acceleration at 522 time points (impulse responses) for each of 72 accelerometers. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for the accelerometer locations on the dorsal surface of the upper limb.</p> <p><strong>Model </strong>- The upper limb model number. This number specifies the participant that data was taken on.</p> <p><strong>Location</strong> -<strong> </strong>Number designating which input location on the palmar hand surface the data corresponds to. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for input location number mapping.</p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Data related to Süsser et al. (2021) QTDIAN modelling toolbox
<p>QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes:</p> <ul> <li>Socially feasible scaling of energy technologies</li> <li>Policy preferences & dynamics</li> <li>Barriers to infrastructural development (wind energy, grid development)</li> <li>Citizen energy</li> <li>Private energy demand</li> </ul> <p>Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4:</p> <p><a href="https://www.iass-potsdam.de/de/ergebnisse/publikationen/2021/qtdian-modelling-toolbox-quantification-social-drivers-and">Süsser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox–Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).</a></p> <p><a href="https://www.iass-potsdam.de/de/ergebnisse/publikationen/2021/integration-socio-technological-transition-constraints-energy-demand">Süsser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).</a></p>
MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies.
<p>MetaboScope is purposefully built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The code is open source (DOI: https://www.cheminfo.org/flavor/metabolomics/index.html) and can be readily installed locally. We provide module notes and video tutorials, in addition to clinical spectral datasets for modelling purposes.</p> <p>View data:</p> <p><a title="nmrium.org" href="https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content" target="_blank" rel="noopener">https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content</a></p>
Gut Analysis Toolbox: Data and code associated with JCS manuscript
<p>The data and python code in jupyter notebooks are associated with the manuscript: <strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <ul> <li><strong>FigS1_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. S1D,E.</li> <li><strong>Fig3_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. 3D-N. <ul> <li>The images and analysis files associated with analysis in GAT are also uploaded: CalR_CalB_GAT_analysis.zip</li> <li>The images used in this analysis are from EXP174 in this dataset: <a href="https://zenodo.org/records/7236748">https://zenodo.org/records/7236748</a></li> </ul> </li> </ul>
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
agroBRIDGES EIP-AGRI Practice Abstracts: A practical toolbox for SFSCs
<p>The present dataset is a collection of the practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. agroBRIDGES Practice Abstracts are also published on the EIP-AGRI website and available at this <strong><a href="https://ec.europa.eu/eip/agriculture/en/find-connect/projects/building-bridges-between-consumers-and-producers">link</a>. </strong></p> <p>The current (first) version of the present dataset contains 13 Practice Abstracts generated to to present the agroBRIDGES toolbox that encompasses a set of 12 practical tools for producers, farmers to develop and grow their business based on Short Food Supply Chains as well as other agri-food actors (retailers, wholesalers, distributors, public food procurers) to facilitate collaborations and awareness.</p> <p>The <a href="https://agrobridges-toolbox.eu">agroBRIDGES Toolbox</a> contains 4 types of tools fully translated in 12 local languages:</p> <ul> <li><strong>Communication material</strong> <ul> <li>Know your local food</li> <li>Label me </li> <li>#WeNeedLocalFood</li> <li>Why am I special?</li> <li>Yes, you can!</li> </ul> </li> <li><strong>Digital spaces / tools:</strong> <ul> <li>Decision Support Tool</li> <li>Hear my voice</li> <li>Net</li> <li>Smart Delivery</li> </ul> </li> <li><strong>Training material:</strong> <ul> <li>Support for food procurement</li> </ul> </li> <li><strong>Event organisation material:</strong> <ul> <li>Let's meet!</li> <li>Let' build our SFSC</li> </ul> </li> </ul> <p>A Practice Abstract has been developed for the agroBRIDGES Toolbox and each of the 12 provided tools, using the standard EIP-AGRI Practice Abstract template. All Practice Abstracts are also supported in video format, also available in the <a href="https://the agroBRIDGES Youtube channel">agroBRIDGES Youtube channel</a>. </p>
A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox
<p>Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"</p>
spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.
<p>spectrapepper is a Python package that makes advanced analysis of spectroscopic data easy and accessible through straightforward, simple, and intuitive code. This library contains functions for every stage of spectroscopic methodologies, including data acquisition, pre-processing, processing, and analysis. In particular, advanced and high statistic methods are intended to facilitate, namely combinatorial analysis and machine learning, allowing also fast and automated traditional methods. The following is a short list of some main procedures that spectrapepper package enables: i) Baseline removal functions, ii) Normalization methods, iii) Noise filters, trimming tools, and despiking methods, iv) Chemometric algorithms to find peaks, fit curves, and deconvolution of spectra, v) Combinatorial analysis tools, such as Spearman, Pearson, and n-dimensional correlation coefficients, vi) Tools for Machine Learning applications, such as data merging, randomization, and decision boundaries, and vii) Sample data and examples</p>
Tutorial video for: A toolbox for the retrodeformation and muscle reconstruction of fossil specimens in Blender
<p>Accurate muscle reconstructions can offer new information on the anatomy of fossil organisms and are also important for biomechanical analysis (multibody dynamics and finite element analysis). For the sake of simplicity, muscles are often modeled as point-to-point strands or frustra (cut off cones) in biomechanical models. However, there are cases in which it is useful to model the muscle morphology in 3D, to better examine the effects of muscle shape and size. This is especially important for fossil analyses, where muscle force is estimated from the reconstructed muscle morphology (rather than based on data collected in vivo). The two main aims of this paper are as follows. First, we created a new interactive tool in the free open access software Blender to enable interactive 3D modeling of muscles. This approach can be applied to both palaeontological and human biomechanics research to generate muscle force magnitudes and lines of action for finite element analysis. Second, we provide a guide on how to use existing Blender tools to reconstruct distorted or incomplete specimens. This guide is aimed at palaeontologists but can also be used by anatomists working with damaged specimens or to test functional implication of hypothetical morphologies.</p>
Demo datasets for: ArtiaX: An Electron Tomography Toolbox for the Interactive Handling of Sub-Tomograms in UCSF ChimeraX
<p>ArtiaX is an open-source extension of the molecular visualisation program ChimeraX and is primarily intended for visualization and processing of cryo-electron tomography data. It allows easy import and export of particle lists in various formats and performant interaction with the data on screen and in virtual reality.</p> <p>This dataset contains cryo-electron tomograms, particle lists, demo command scripts and scripts for performance measurements used for the creation of figures and measurements in the paper "ArtiaX: An Electron Tomography Toolbox for the Interactive Handling of Sub-Tomograms in UCSF ChimeraX"</p> <p><strong>Contents</strong></p> <ul> <li>empiar_10304.zip -- used for Figure 2 and Supplementary Video 1 of the companion paper <ul> <li>Tomogram 10 of the <a href="https://www.ebi.ac.uk/empiar/EMPIAR-10304/">EMPIAR-10304</a> dataset<sup>1</sup> reconstructed using super-sampling SART</li> <li>A particle list containing particle positions and poses determined using template matching</li> <li>A lowpass-filtered map of <a href="https://www.ebi.ac.uk/emdb/EMD-10211">EMD-10211</a><sup>1</sup> used for surface display<br> </li> </ul> </li> <li>mycoplasma_genitalium.zip -- used for Figures 1, 4, 5 and 6, as well as Supplementary Videos 2, 3 and 4 of the companion paper <ul> <li>A tomogram of a Mycoplasma genitalium cell<sup>2</sup> reconstructed using super-sampling SART</li> <li>A demo dataset comprising particle lists, segmentation maps and a demo script related to the above tomogram</li> <li>Python scripts for measuring ChimeraX rendering performance of the demo dataset scene.</li> </ul> </li> </ul> <p><strong>References</strong></p> <p><strong>1.</strong> Eisenstein F, Danev R, Pilhofer M (2019) <a href="https://doi.org/10.1016/j.jsb.2019.08.006">Improved applicability and robustness of fast cryo-electron tomography data acquisition</a>. Journal of Structural Biology 208:107–114.</p> <p><strong>2.</strong> Seybert A, Gonzalez-Gonzalez L, Scheffer MP, Lluch-Senar M, Mariscal AM, Querol E, Matthaeus F, Piñol J, Frangakis AS (2018) <a href="https://doi.org/10.1111/mmi.13938">Cryo-electron tomography analyses of terminal organelle mutants suggest the motility mechanism of Mycoplasma genitalium.</a> Molecular Microbiology 108:319–329.</p>
Automated temporal front tracking toolbox in Matlab
<p>This toolbox provides the Matlab scripts that achieve temporal tracking of coherently evolving density fronts in numerical modes. It consists of three components: (1) scripts to detect density fronts based on the Canny edge detection algorithm at each time step in the model outputs; (2) scripts to automatically track coherently evolving front in time; and (3) scripts to do front pruning and remove the incoherent frontal segment that shows inconsistent frontal propagation direction. A dataset ('G_time_rho.mat') containing modeled density at successive time steps is provided for demonstration. More details of this method can be found in our work that is expected to be published soon (Wu, X., F. Feddersen and S. N. Giddings, 2021, Automated temporal tracking of coherently evolving density fronts in numerical models, Journal of Atmospheric and Oceanic Technology, in revision). Contact Xiaodong Wu (x1wu@ucsd.edu) for any questions. </p>
FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking
<p>This is the software described in our article 'FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking' and the used datasets for image analysis. It is intended as a easy to use software for tracking of cytoskeletal fibers offering both source code and GUI-based executable. Datasets are sorted according to figures in the article with folders containing raw images, analysis results and resulting figure files. The source folder also includes a tutorial and installation notes.</p> <p>For a system running Ubuntu 21.04 there is a slightly modified command line needed: java --module-path /usr/share/openjfx/lib –add-modules=javafx.base,javafx.controls,javafx.fxml,javafx.graphics,javafx.media,javafx.swing,javafx.web -jar GUIFocalAdhesionOnly.jar</p>
Immerse Downstream User Toolbox - example data for FerryBox validation
<p>Example data for FerryBox Validation use case within </p> <p><a href="https://github.com/immerse-project/Downstream-Users-Toolbox">immerse-project/Downstream-Users-Toolbox: Analysis and assessment tools from IMMERSE WP8 (github.com)</a></p> <p>containing exemplary temperature and salinity data from a ship attached FerryBox (<a href="https://www.ferrybox.org/">https://www.ferrybox.org/</a>) and according surface fields of the the CMEMS Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast model <a href="https://resources.marine.copernicus.eu/product-detail/NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013/INFORMATION">NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013</a>, the high resolution NNEMO.v4.2_RC Southern North Sea configuration developed at <a href="https://www.hereon.de/">Helmholtz-Zentrum Hereon</a> in the context of IMMERSE, and the German Bight configuration operated at Hereon, using the <a href="http://ccrm.vims.edu/schismweb/">SCHISM unstructured grid moddeling framework</a> for an assesment of the model performance on those variables within the south eastern German Bight on 2018-11-05.</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.