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Figure 2 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 2. Variation of multiple factors (temperature, salinity, oxygen (mg.L-1), and pH) in each collection station over the sampled months of 2010 and 2011.
Figure 1 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 1. Map of the coast of the state of Rio de Janeiro pointing out the 8 sampling stations of the Araruama lagoon.
Figure 4 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 4. Temporal correlations between larvae of Cirripedia and Acartia tonsa (A) and between Acartia tonsa and temperature (B).
Figure 3 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 3. Relationship between temperature, salinity, and pH and their effect on the abundance of Cirripedia larvae over the months.
Figure 5 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 5. Variation of zooplankton density in each collection station, variations in the index of Shannon-Weaver which measures the Diversity (H) and the Pielou's uniformity which measures the Equitability (J) over the sampled months.
MHONGOOSE single-track moment maps
<h2>MHONGOOSE single-track moment maps</h2> <p>This repository contains all MHONGOOSE single-track moment maps as described in de Blok et al. (2024).</p> <p>For each galaxy, two resolutions are provided, derived with robust parameter 0.5 and 1.5, respectively. The<br>former highlights small-scale structures at the cost of a reduced column density sensitivity, the<br>latter emphasises low column density structures.</p> <p>For each galaxy and resolution we provide:</p> <ul> <li>zeroth moment map (mom0) - integrated HI intensity map</li> <li>primary-beam-corrected zeroth moment map (mom0_pb)</li> <li>first moment map - intensity-weighted mean velocity field</li> <li>second moment map (mom2) - velocity spread map</li> <li>number of channels (nchan) contributing to each pixel</li> </ul> <p>Average properties of the observations are given below. Beam sizes for the individual galaxies areprovided in the headers.</p> <table> <tbody> <tr> <td>robust value</td> <td>pixel size (arcsec)</td> <td>beam (arcsec)</td> <td>noise (mJy/beam)</td> <td>log(NHI) (cm-2) [1sigma; 1 chan]</td> <td>log(NHI) (cm-2) [3sigma; 16 km/s]</td> </tr> <tr> <td>1.5</td> <td>7</td> <td>30</td> <td>0.49</td> <td>17.94</td> <td>18.94</td> </tr> <tr> <td>0.5</td> <td>3</td> <td>12</td> <td>0.54</td> <td>18.79</td> <td>19.79</td> </tr> </tbody> </table> <p>For a full description of the derivation of the moment maps see de Blok et al (2024).</p>
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
Example binding lifetime analysis: Kymographs and tracks
<p>Kymograph recorded on the LUMICKS C-Trap. LacI is labeled in green.</p> <p>The binding events on the kymograph were tracked using Pylake. The corresponding tracks are included in this dataset as tracks1.csv and tracks.csv.</p>
AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
<p><span>This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for </span><span>folder structure with descriptions of each folder and file.</span></p>
Apache Jira Issue Tracking Dataset
<p>This dataset contains the Jira Issue Tracking data of the Apache Software Foundation, enriched with topic modeling information using the BERTopic technique.</p> <p>You can use the dataset with the following steps:</p> <ol> <li>Set up a MongoDB instance.</li> <li>Download the data.</li> <li>Navigate to the download folder and use the mongorestore command (<a href="https://docs.mongodb.com/database-tools/mongorestore/" target="_blank" rel="noopener">https://docs.mongodb.com/database-tools/mongorestore/</a>) with the --gzip flag.</li> </ol> <p>Detailed instructions for setting up/reproducing/updating the dataset are also provided in the website <a href="https://authecesofteng.github.io/semantics-jira-dataset/" target="_blank" rel="noopener">https://authecesofteng.github.io/semantics-jira-dataset/</a> (relevant repos <a href="https://github.com/AuthEceSoftEng/jira-apache-downloader" target="_blank" rel="noopener">https://github.com/AuthEceSoftEng/jira-apache-downloader</a> and <a href="https://github.com/AuthEceSoftEng/jira-topic-extractor" target="_blank" rel="noopener">https://github.com/AuthEceSoftEng/jira-topic-extractor</a>)</p> <p><em>Note: you do not need to download the models .rar files if you do not use them, as the issue-topic distribution (along with probabilities) is already computed and stored in the topics Mongo collection.</em></p>
Web browser useragent and activity tracking data
<p>600 000 000 web traffic records normalized into MySQL tables using TokuDB storage, complete with original web server response codes. Suitable for browser data and trend analysis as well as AI training of exploit and bot detection algorithms. The data had been collected from multiple Apache 2.x web servers across 8000+ domain names with special care for GDPR compliance.</p> <p> </p>
A phenopushing platform to identify compounds that alleviate acute hypoxic stress by fast-tracking cellular adaptation
<p><span>Severe acute hypoxic stress is a major contributor to the pathology of human diseases, including ischemic disorders. Current treatments focus on managing consequences of hypoxia, with few addressing cellular adaptation to low-oxygen environments. Here, we investigate whether accelerating hypoxia adaptation could provide a strategy to alleviate acute hypoxic stress. We develop a high-content phenotypic screening platform to identify compounds that fast-track adaptation to hypoxic stress. </span><span>Our platform</span><span> captures a high-dimensional phenotypic hypoxia response trajectory consisting of normoxic, acutely stressed, and chronically adapted cell states. Leveraging this trajectory, we identify compounds that phenotypically shift cells from the acutely stressed towards the adapted state, revealing mTOR/PI3K or BET inhibition as strategies to induce this phenotypic shift. Importantly, </span><span>our</span><span> compound hits promote the survival of liver cells exposed to ischemia-like stress, and rescue cardiomyocytes from hypoxic stress. Our “phenopushing” platform offers a general, target-agnostic approach to identify compounds and targets that accelerate cellular adaptation, applicable across various stress conditions.</span></p>
Supporting information for: An assessment of monazite fission-track thermochronology as a proxy for low-magnitude cooling, Catalina-Rincon Metamorphic Core Complex, AZ, U.S.A.
<p><span>The following supporting information contains: The detailed location and age data for the geochronological, isotopic, and geochemical data used in this study, and their associated publications. Detailed thermochronometric data and associated thermal history modelling information for all thermochronology and modelling presented in the study.</span></p>
Data from: An Easily Compatible Eye Tracking System for Free-moving Small Animals
<p>These datasets are associated with human labled eye tracking datasets in DLC formate and pixel formate from the paper Huang et. al., <em>An Easily Compatible Eye Tracking System for Free-moving Small Animals, </em>2021.<em> </em></p>
RTX 3060 and RTX3060 ti price tracking dataset
<p><strong>El dataset contiene los siguientes campos:</strong></p> <ol> <li> <p><strong>Identificador “id”: dato cualitativo nominal que identifica a un único producto en la web de amazon. Podría utilizarse en una fase de procesado de datos para agrupar los productos y calcular tendencias de precio.</strong></p> </li> <li> <p><strong>Nombre “name”: dato cualitativo nominal que indica el nombre del producto. En los nombres de las tarjetas gráficas se suelen especificar datos de esta como el montador, el tipo y la cantidad de memoria y las interfaces. En una fase de procesado de los datos se podría extraer esta información buscando patrones en los nombres. En el ejemplo del apartado de representación gráfica podemos ver que la tarjeta es montada por Zotac Gaming, tiene 12Gb de memoria GDDR6 y tiene una interfaz HDMI 2.1 y 3 interfaces DisplayPort 1.4a.</strong></p> </li> <li> <p><strong>Enlace “link”: dato cualitativo nominal. Dirección web de donde se han extraído los datos.</strong></p> </li> <li> <p><strong>Modelo “model”: dato categorico. Indica el modelo de la tarjeta gráfica. Con las búsquedas que estamos haciendo este campo vale “rtx 3060” o “rtx 3060 ti”</strong></p> </li> <li> <p><strong>Valoración “valoration”: dato cuantitativo. Valoración del producto.</strong></p> </li> <li> <p><strong>Valoraciones “valorations”: dato cuantitativo. Cantidad de valoraciones que se han hecho del producto. Sirve sobre todo para tomar en consideración la valoración media del producto.</strong></p> </li> <li> <p><strong>Precio “price”: dato cuantitativo. Precio de la oferta.</strong></p> </li> <li> <p><strong>Coste de envío “shippingCosts”: dato cuantitativo. Coste de envío de la oferta.</strong></p> </li> <li> <p><strong>Vendedor “seller”: dato cualitativo nominal. Vendedor de la oferta</strong></p> </li> <li> <p><strong>Fecha de obtención del registro “date”. Tiempo en que los datos son capturados.</strong></p> </li> </ol>
SANER 2022 - Industrial Track - Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset related to the paper Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange. </p> <p> </p> <p>Saner 2022 Industrial Track</p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Sound examples of an Impulse Pattern Formulation model synchronizing to different click tracks
<p>In the publication, supplemented by these sound examples, the Impulse Pattern Formulation is used to model the synchronization of musicians to a collective tempo. Several click tracks are numerically created, representing eighth notes played by a musician or a metronome for different tempo changes.<br> By replacing every beat with a sound sample, audio files are created for a more musical evaluation of the results. The IPF is represented by a cowbell and the underlying click track with claves. Those sound files are in stereo, whereby the click track is at the left channel, and the IPF's signal is at the right channel. Thus, e.g., the balance potentiometer of a stereo system can be used to blend both sounds freely. The practical examples are:</p> <p><strong>Fig.2:</strong><br> IPF reacts to four different step changes in tempo.</p> <p><strong>Fig.5:</strong><br> The IPF reacting to the same changes in tempo as shown in Figure 2, when changing the tempo linear during 24 beats instead of step changes.</p> <p><strong>Fig.8:</strong><br> IPF adapting to a noisy click track: the upper line (a) and b)) shows white noise, and the lower line (c) and d)) Brownian noise. On the left (a) and c)), the fluctuation is <span class="math-tex">\(\pm1~\%\)</span>, and on the right (b) and d)) <span class="math-tex">\(\pm 5~\%\)</span>.</p> <p><strong>Fig.9:</strong><br> IPF adapting to a sinusoidally modulated click track: the upper line (a) and b)) shows a modulation period of 32 eighth notes, and the lower line (c) and d)) shows a modulation period of 8 eighth notes. On the left (a) and c)), the amplitude is 36 bpm, and on the right (b) and d)) 6 bpm, both centered around 113 bpm.</p> <p><strong>Fig.12:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF which considers phase differences: a) step change from 120 to 100 bpm, b) linear change from 120 to 130 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 120 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p><strong>Fig.13:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF optimized for polyrhythms: a) step change from 120 to 140 bpm, b) linear change from 100 to 120 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 90 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p>In all Figures, blue lines refer to the tempo of the click track, and red lines correspond to the tempo of the IPF. The single crosses represent single beats.</p> <p>A more in-depth description of how these sounds were synthesized can be found in the publication supplemented by these examples:</p> <p>Linke, S., Bader, R., & Mores, R. (2021). Modeling synchronization in human musical rhythms using Impulse Pattern Formulation (IPF). http://arxiv.org/pdf/2112.03218v1</p>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.