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41 results for “collective dynamics”
Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics
<p>Full-resolution representative data sufficient to repeat analyses for the work of: AE Wolf, MA Heinrich, IB Breinyn, TJ Zajdel, and DJ Cohen in "Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics".</p> <p>Please see the _README2.0.0.txt file for explanations on contents in this Zenodo repository.</p> <p>Relevant codes used in our analyses are available on Github (github.com/CohenLabPrinceton/ElectrotaxisSupracellularMemory).</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest
<p>Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest".</p> <p>The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic).</p> <p>Abstract of the paper:</p> <p>A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels.</p> <p>The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare.</p> <p>We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.</p>
Data of publication: "Collective atom-cavity coupling and nonlinear dynamics with atoms with multilevel ground states"
<p>The uploaded files contain the raw data of the measurements and simulations presented in <a href="https://doi.org/10.1103/PhysRevA.107.023714">https://doi.org/10.1103/PhysRevA.107.023714</a></p>
Rainforest phenology: flower, fruit and seed production from biweekly collections of 200 traps in the Yasuní Forest Dynamics Plot, Ecuador, 2000-2018
We provide data on flowering and fruiting phenology from an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuni National Park. This is the first long-term study (18 years) of phenology in a diverse equatorial neotropical forest. Although the site is ever-wet, there is some seasonal variation in rainfall and irradiance. One major question was to determine whether the seasonal variation in climate was sufficient to drive seasonality in reproduction in this hyper-diverse forest. The study began in 2000 with various funding, and became an LTREB-funded project in 2006. We used twice monthly censuses of 200 traps to document phenology. Parts of >1000 species were identified in the traps in the 18 year period (ending early in 2018), including trees, shrubs, lianas and epiphytes. Parts identified included buds, flowers, mature fruits and mature seeds, and aborted, damaged and immature fruits and seeds. The project is on-going, and additional data will be added as it is processed.
Research Data - Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures
<p>Source data from ferromagnetic resonance experiments and micromagnetic simulations in <em>tetmag</em> software (<a href="https://github.com/R-Hertel/tetmag">https://github.com/R-Hertel/tetmag</a>), used in the paper "Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures"<em> </em>in <em>ACS Applied Materials & Interfaces </em>(<a href="https://doi.org/10.1021/acsami.4c02366">https://doi.org/10.1021/acsami.4c02366</a>).</p>
Dynamics of Collective Modes in an unconventional Charge Density Wave system BaNi2As2 - Raw Data
<p>This repository includes two datasets included in the study "Dynamics of Collective Modes in an unconventional<br> Charge Density Wave system BaNi2As2". Two datasets are included:</p> <p>Temperature_dependent_reflectivity_changes.dat</p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat</p> <p>Temperature_dependent_reflectivity_changes.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 for sample temperatures between 13 K and 149K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective temperatures (constant fluence of 0.4 mJ cm<sup>−2</sup>).</p> <p> </p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 at 10 K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective fluences. Each signal has been normalized to the respective fluence.</p> <p> </p>
Рис. 5. Общая схема Δинамики эпизоотии в приамурской попуΛяции коΛьчатого шеΛкопряΔа. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата сбора гусениц на территории УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 5. General scheme of the Lackey moth epizootic dynamics for the Cisamurian population. Vertical: number of the deaths, (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. — death from the NPV; — death from the bacteriosis in Lackey Moth (Malacosoma Neustria L., Lasiocampidae, Lepidoptera) Population During The Eruptive Phase
Рис. 5. Общая схема Δинамики эпизоотии в приамурской попуΛяции коΛьчатого шеΛкопряΔа. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата сбора гусениц на территории УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 5. General scheme of the Lackey moth epizootic dynamics for the Cisamurian population. Vertical: number of the deaths, (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. — death from the NPV; — death from the bacteriosis
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>Uncharged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13164944">https://doi.org/10.5281/zenodo.13164944</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165450">https://doi.org/10.5281/zenodo.13165450</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165725">https://doi.org/10.5281/zenodo.13165725</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13166024">https://doi.org/10.5281/zenodo.13166024</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> </ul> </li> <li>Charged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13166152">https://doi.org/10.5281/zenodo.13166152</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167128">https://doi.org/10.5281/zenodo.13167128</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167338">https://doi.org/10.5281/zenodo.13167338</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167551">https://doi.org/10.5281/zenodo.13167551</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167614">https://doi.org/10.5281/zenodo.13167614</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes With Various Surface Charges</li> </ul> </li> <li>Plots: <ul> <li><a href="https://doi.org/10.5281/zenodo.13168242">https://doi.org/10.5281/zenodo.13168242</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations Confined Between Charged Electrodes With Various Surface Charges: Plots</li> </ul> </li> </ul>
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes in the Bulk and Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes in the bulk and confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>In the Bulk: <ul> <li><a href="https://doi.org/10.5281/zenodo.13144737">https://doi.org/10.5281/zenodo.13144737</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations in the Bulk</li> </ul> </li> <li>Confined Between Electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13169120">https://doi.org/10.5281/zenodo.13169120</a>:<br>Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes</li> </ul> </li> </ul>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Spatial Data collection for study spatial transition dynamic of Rohingya settlement in Bangladesh: 1st version
<p>Full open access article can be found in: <a href="https://doi.org/10.1016/j.landusepol.2023.106874">https://doi.org/10.1016/j.landusepol.2023.106874</a></p> <p> </p> <p><strong>Full Changelog</strong>: <a href="https://github.com/ssujit/SpatialTransitionDynamic/commits/version">https://github.com/ssujit/SpatialTransitionDynamic/commits/version</a></p>
The Dynamics of Collective Action Corpus
<p>This respository includes two datasets, a Document-Term Matrix and associated metadata, for 17,493 <em>New York Times</em> articles covering protest events, both saved as single R objects.</p> <p>These datasets are based on the original <a href="https://web.stanford.edu/group/collectiveaction/cgi-bin/drupal/">Dynamics of Collective Action (DoCA) dataset</a> (Wang and Soule 2012; Earl, Soule, and McCarthy). The original DoCA datset contains variables for protest events referenced in roughly 19,676 <em>New York Times</em> articles reporting on collective action events occurring in the US between 1960 and 1995. Data were collected as part of the Dynamics of Collective Action Project at Stanford University. Research assistants read every page of all daily issues of the <em>New York Times</em> to find descriptions of 23,624 distinct protest events. The text for the news articles were not included in the original DoCA data.</p> <p>We attempted to recollect the raw text in a semi-supervised fashion by matching article titles to create the <strong>Dynamics of Collective Action Corpus</strong>. In addition to hand-checking random samples and hand-collecting some articles (specifically, in the case of false positives), we also used some automated matching processes to ensure the recollected article titles matched their respective titles in the DoCA dataset. The final number of recollected and matched articles is 17,493.</p> <p>We then subset the original DoCA dataset to include only rows that match a recollected article. The "20231006_dca_metadata_subset.Rdata" contains all of the metadata variables from the original DoCA dataset (see <a href="https://web.stanford.edu/group/collectiveaction/cgi-bin/drupal/node/17">Codebook</a>), with the addition of "pdf_file" (used to link to original article pdfs) and "pub_title" (which is the title of the recollected article and may differ from the "title" variable in the original dataset), for a total of 106 variables and 21,126 rows (noting that a row is a distinct protest events and one article may cover more than one protest event).</p> <p>Once collected, we prepared these texts using typical preprocessing procedures (and some less typical procedures, which were necessary given that these were OCRed texts). We followed these steps in this order: We removed headers and footers that were consistent across all digitized stories and any web links or HTML; added a single space before an uppercase letter when it was flush against a lowercase letter to its right (e.g., turning "JohnKennedy'' into "John Kennedy''); removed excess whitespace; converted all characters to the broadest range of Latin characters and then transliterated to "Basic Latin'' ASCII characters; replaced curly quotes with their ASCII counterparts; replaced contractions (e.g., turned "it's'' into "it is''); removed punctuation; removed capitalization; removed numbers; fixed word kerning; applied a final extra round of whitespace removal.</p> <p>We then tokenized them by following the rule that each word is a character string surrounded by a single space. At this step, each document is then a list of tokens. We count each unique token to create a document-term matrix (DTM), where each row is an article, each column is a unique token (occurring at least once in the corpus as a whole), and each cell is the number of times each token occurred in each article. Finally, we removed words (i.e., columns in the DTM) that occurred less than four times in the corpus as a whole or were only a single character in length (likely orphaned characters from the OCRing process). The final DTM has 66,552 unique words, 10,134,304 total tokens and 17,493 documents. The "20231006_dca_dtm.Rdata" is a sparse matrix class object from the <a href="https://cran.r-project.org/web/packages/Matrix/index.html">Matrix R package</a>.</p> <p>In R, use the load() function to load the objects `dca_dtm` and `dca_meta`. To associate the `dca_meta` to the `dca_dtm` , match the "pdf_file" variable in`dca_meta` to the rownames of `dca_dtm`.</p> <p> </p>
Dynamic self-organization in fire ant rafts underpins collective longevity and threat responsiveness
Open the record for dataset details and reuse information.
Forest Community Dynamics in Hemlock Overlook, Virginia: A Ten-Year Student-Collected Forest Plot Dataset
During a ten-year observation period three 0.1 ha plots in Hemlock Overlook Regional Park, Fairfax County, VA, were regularly sampled. Student observers recorded and identified to species wherever possible, all trees with a DBH of 3cm or greater in these plots. In total there were 94 sampling events across the ten years with a total of 4,536 trees counted and measured. Of the recorded trees, the most frequently sampled trees were beech (Fagus grandifolia), with 1,681 records and the least frequently recorded trees were maple (Acer spp.) with 410 records. This dataset adds to the literature a longitudinal consistent sampling dataset that can be used for forestry and forest ecology research.
Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"
<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>
Frequency multiplication by collective nanoscale spin wave dynamics
<p>This dataset contains all primary data used in the manuscript.</p>
BioExcel Use Case 1: collection of output data from molecular dynamics simulation
<p>The Use Case aims to address all the challenges related to antibody design through an integrative approach combining the core BioExcel software comprising of GROMACS, HADDOCK and PMX.</p> <p>The folder contains the GROMACS output files (xtc and pdb file). Molecular Dynamics simulations have been performed with GROMACS version 2020 and CHARMM36 force field. The input files and scripts of the final protocol are publicly available on BioExcel GitHub https://github.com/bioexcel/BioExcel-UseCase1.</p> <p>The Use Case 1 protocol was presented at the BioExcel Summer School on Biomolecular Simulation in 2021 (see <a href="https://doi.org/10.5281/zenodo.7009238">https://doi.org/10.5281/zenodo.7009238</a> or <a href="https://youtu.be/_TDKfKX4kwM">https://youtu.be/_TDKfKX4kwM</a>)</p> <p> </p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Data for: "Dynamics of collective motion across time and species"
<p>This repository contains the data accompanying the paper:</p> <p><strong>Papadopoulou M., Fürtbauer I., O’Bryan L., Garnier S., Georgopoulou D., Bracken A., Christensen C., and King A.J. (2022) "Dynamics of collective motion across time and species". Phil. Trans. R. Soc. B 20220068 <a href="https://doi.org/10.1098/rstb.2022.0068">https://doi.org/10.1098/rstb.2022.0068</a></strong></p> <p>This work is supported by an Office for Naval Research (ONR) Global Grant (N629092112030) awarded to AJK.</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.