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10,006 results for “randomness”
Sentiment Analysis of RUU PDP with Naive Bayes, Support Vector Machine, and Random Forest Classification Algorithm
<p>Dataset from the results of data crawling via Twitter which discusses the Rancangan Undang Undang Pelindungan Data Pribadi to be used in the sentiment analysis process. The dataset is divided into several parts according to the process executed on RapidMiner.</p>
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type.
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
<p>We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R<sup>2</sup> of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.</p>
Data from article: "Wide‑field magnetometry using nitrogen‑vacancy color centers with randomly oriented micro‑diamonds"
<p>This repository contains the dataset obtained from the CW-ODMR magnetic imaging experiment with nitrogen-vacancy (NV) centers using a custom-built wide-field setup.</p> <p><strong>Related publication: </strong></p> <p>Sengottuvel, S., Mrózek, M., Sawczak, M. <em>et al.</em> Wide-field magnetometry using nitrogen-vacancy color centers with randomly oriented micro-diamonds. <em>Sci Rep</em> <strong>12</strong>, 17997 (2022). <a href="https://doi.org/10.1038/s41598-022-22610-5">https://doi.org/10.1038/s41598-022-22610-5</a>.</p> <p><strong>Authors:</strong></p> <ul> <li>Saravanan Sengottuvel, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mariusz Mrózek, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mirosław Sawczak, (Szewalski Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Poland)</li> <li>Maciej J. Głowacki, (Gdańsk University of Technology, Poland)</li> <li>Mateusz Ficek, (Gdańsk University of Technology, Poland)</li> <li>Wojciech Gawlik (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Adam M. Wojciechowski (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> </ul> <p><strong>Abstract: </strong></p> <p>Magnetometry with nitrogen-vacancy (NV) color centers in diamond has gained significant interest among researchers in recent years. Absolute knowledge of the three-dimensional orientation of the magnetic field is necessary for many applications. Conventional magnetometry measurements are usually performed with NV ensembles in a bulk diamond with a thin NV layer or a scanning probe in the form of a diamond tip, which requires a smooth sample surface and proximity of the probing device, often limiting the sensing capabilities. Our approach is to use micro- and nano-diamonds for wide-field detection and mapping of the magnetic field. In this study, we show that NV color centers in randomly oriented submicrometer-sized diamond powder deposited in a thin layer on a planar surface can be used to detect the magnetic field. Our work can be extended to irregular surfaces, which shows a promising path for nanodiamond-based photonic sensors.</p> <p><strong>Funding: </strong></p> <p>The research was carried out within the TEAM NET programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund, project POIR.04.04.00-00-1644/18. This research was funded in part by National Science Centre, Poland grant number 2020/39/I/ST3/02322<strong>. </strong></p> <p><strong>Description of the data: </strong></p> <p>The dataset consists of 24 individual data files labelled chronologically, starting from f0.fits to f24.fits. The data format is Flexible Image Transport System (FITS). Each FITS file consists of a header and 3-dimensional image data. The header contains the experimental parameters set during data acquisition, which may also be helpful for data analysis. The FITS file can be read using any software (e.g., MATLAB, Python) that supports the FITS file format.</p> <p><strong>An example header:</strong></p> <p> {'STARFREQ'} {[ 2700]} {' in MHz '}<br> {'STOPFREQ'} {[ 3000]} {' in MHz '}<br> {'STEPSIZE'} {[ 1.500000000000000]} {' Frequency interval '}<br> {'MWPOWER'} {[ 5]} {' in dBm '}<br> {'NSCANS'} {[ 5]} {' Total number of scan repetitions '}<br> {'EXPOSURE'} {[ 20]} {' in ms '}<br> {'FPS'} {[ 20]} {' no. of frames per second '}<br> {'LEDCURR'} {[ 0.990000000000000]} {' LED current in mA }<br> {'EXPTIME'} {[1.942112698000000e+02]} {' Measurement time in seconds '}<br> {'END' } {0×0 char } {0×0 char }</p> <p><strong>Table:</strong> Data file name and the associated current value set in the wire during the magnetic imaging measurement</p> <table> <thead> <tr> <th>File name</th> <th>Current value (mA)</th> <th>File name</th> <th>Current value (mA)</th> </tr> </thead> <tbody> <tr> <td>f0.fits</td> <td>0</td> <td>f13.fits</td> <td>-300</td> </tr> <tr> <td>f1.fits</td> <td>+50</td> <td>f14.fits</td> <td>-550</td> </tr> <tr> <td>f2.fits</td> <td>+100</td> <td>f15.fits</td> <td>-250</td> </tr> <tr> <td>f3.fits</td> <td>-50</td> <td>f16.fits</td> <td>+550</td> </tr> <tr> <td>f4.fits</td> <td>-100</td> <td>f17.fits</td> <td>+350</td> </tr> <tr> <td>f5.fits</td> <td>-150</td> <td>f18.fits</td> <td>+400</td> </tr> <tr> <td>f6.fits</td> <td>+250</td> <td>f19.fits</td> <td>-400</td> </tr> <tr> <td>f7.fits</td> <td>+450</td> <td>f20.fits</td> <td>-450</td> </tr> <tr> <td>f8.fits</td> <td>+600</td> <td>f21.fits</td> <td>-500</td> </tr> <tr> <td>f9.fits</td> <td>-200</td> <td>f22.fits</td> <td>+150</td> </tr> <tr> <td>f10.fits</td> <td>-350</td> <td>f23.fits</td> <td>+300</td> </tr> <tr> <td>f11.fits</td> <td>+200</td> <td>f24.fits</td> <td>-600</td> </tr> <tr> <td>f12.fits</td> <td>+500</td> <td> </td> <td> </td> </tr> </tbody> </table> <p>For more information on the data analysis methods and results, we recommend you to read the article.</p>
The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures
<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>
Random dataset of HTR-ed police ordinances Bern - to test Annif
<p>This dataset consists of 4450 (out of the original 4932) police ordinances that were identified by <span>Claudia Schott-Volm, <em>Repertorium der Policeyordnungen der Frühen Neuzeit (#7). Orte der Schweizer Eidgenossenschaft: Bern und Zürich</em> (Frankfurt am Main, 2006) for the Swiss City-State of Bern (1528-1798). </span></p> <p><span>They have been randomly placed in a test (500), training (3550) and validation set (500) to test the tool Annif. The HTR-recognition was done in 2020 with the - now terminated HTR+-model 'German-Kurrent_XVI-XVIII++French' in the then commonly used Expert Version of Transkribus (READ-COOP SCE).</span></p>
Randomized controlled oncology trials with tumor stage inclusion criteria
<p><em>Background:</em></p> <p>Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question "Did this trial enroll patients with localized disease, metastatic disease, or both?" could be used to narrow down the number of potentially relevant trials when conducting a search.</p> <p><em>Dataset collection:</em></p> <p>600 randomized controlled trials from high-impact medical journals were classified depending on whether they allowed for the inclusion of patients with localized and/or metastatic disease. The dataset was randomly split into a training/validation and a test set of 500 and 100 trials respectively. However, the sets could be merged to allow for different splits.</p> <p><em>Data properties:</em></p> <p>Each trial is a row in the csv file. For each trial there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).</p>
Code from: Enhancing data-limited assessments with random effects: A case study on Korea chub mackerel (Scomber japonicus)
<div> <div> <div> <p>In a state-space framework, temporal variations in fishery-dependent processes can be modeled as random effects. This modeling flexibility makes state-space models (SSMs) powerful tools for data-limited assessments. Though SSMs enable the model-based inference of the unobserved processes, their flexibility can lead to overfitting and non-identifiability issues. To address these challenges, we developed a suite of state-space length-based age-structured models and applied them to the Korean chub mackerel (<em>Scomber japonicus</em>) stock. Our research demonstrated that incorporating temporal variations in fishery-dependent processes can rectify model mis-specification but may compromise robustness, which can be diagnosed through a series of model checking processes. To tackle non-identifiability, we used a non- degenerate estimator, implementing a gamma distribution as a penalty for the standard deviation parameters of observation errors. This penalty function enabled the simultaneous estimation of both process and observation error variances with minimal bias, a notably challenging task in SSMs. These results highlight the importance of model checking and the effectiveness of the penalized approach in estimating SSMs. Additionally, we discussed novel assessment outcomes for the mackerel stock.</p> </div> </div> </div>
Effects of personalized music listening on post-stroke cognitive impairment: A randomized controlled trial
<p><strong><span>Background and purpose:</span></strong><span> Previous studies have suggested that music listening has the potential to positively affect mood and cognitive functions in individuals with <a name="_Hlk140153246"></a>post-stroke cognitive impairment (PSCI), with a preference for self-selected music likely to yield better outcomes. However, there is insufficient clinical evidence to suggest the use of music listening in routine rehabilitation care to treat PSCI. This randomized control trial (RCT) aims to investigate the effects of personalized music listening on mood improvement, <a name="_Hlk140153263"></a>activities of daily living (ADLs), and cognitive functions in individuals with PSCI.</span></p> <p><strong><span>Materials and methods:</span></strong><span> A total of 34 patients with PSCI were randomly assigned to either the music group or the control group. Patients in the music group underwent a three-month personalized music-listening intervention. The intervention involved listening to a personalized playlist tailored to each individual's cultural, ethnic, and social background, life experiences, and personal music preferences. In contrast, the control group patients listened to white noise as a placebo. Cognitive function, neurological function, mood, and ADLs were assessed. </span></p> <p><strong><span>Results:</span></strong><strong><span> </span></strong><span>After three months of treatment, the music group showed significantly higher <a name="_Hlk140153278"></a>Montreal Cognitive Assessment (MoCA) scores compared to the control group (<em>p=</em>0.027), particularly in the domains of delayed memory (<em>p=</em>0.019) and orientation (<em>p=</em>0.023). Moreover, the music group demonstrated significantly better scores in <a name="_Hlk140153297"></a>National Institute of Health Stroke Scale (NIHSS) (<em>p=</em>0.008), <a name="_Hlk140153304"></a>Barthel Index (BI) (<em>p=</em>0.019), and <a name="_Hlk140153315"></a>Zarit Caregiver Burden Interview (ZBI) (<em>p=</em>0.008) compared to the control group. No effects were found on mood as measured by the Hamilton Rating Scale for Anxiety (HAMA) and the Hamilton depression scale (HAMD).</span></p>
Accompanying data for the paper "Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles"
<h2>Contributions</h2> <ul> <li><strong>Iaquinta Sarah</strong> did contribute to the first draft edition, the development of the theoretical background of the algorithms and their implementation</li> <li><strong>Khazaie Sharam</strong> did contribute to the revision and edition of the draft, and to the development of the theoretical background of the algorithms</li> <li><strong>Jacquemin Frédéric</strong> did contribute to the project management and to the revision of the article.</li> <li><strong>Fréour Sylvain</strong> did contribute to the project management and to the revision of the article.</li> </ul> <h2>Funding sources</h2> <p>i-Site NExT : Grant/Award Number: ANR-16-IDEX-0007, Région Pays de la Loire and CNRS (French National Centre for Scientific Research).</p> <h2>Data structure and information</h2> <ul> <li>code - <code>np_uptake source and data directory</code> <ul> <li>workflow - <code>scripts to reproduce figures</code></li> <li>np_uptake - <code>source code producing results and figures</code> <ul> <li>figures - <code>utility module to produce figures</code></li> <li>model - <code>see detailed description below</code></li> <li>metamodel_implementation - <code>see detailed description below</code></li> <li>sensitivity_analysis - <code>see detailed description below</code></li> </ul> </li> </ul> </li> </ul> <h3>Detailed description</h3> <h4>Abstract</h4> <p>In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion.</p> <h4>Code</h4> <p>This repository is divided into 4 folders:</p> <ul> <li> <p><em>model</em>: contains the code used to compute the total variation of energy of the interface between a circular NP and a membrane by accounting for the mechanical accommodation of the latter. This folder also contains the routine to determine the final wrapping phase of the system.</p> </li> <li> <p><em>metamodel_implementation</em>: contains a script to check for the representativeness of the dataset used to create a metamodel, a script to create Kriging and PCE metamodels using the Openturns opensource library, and a routine to validate the metamodel that has just been created;</p> </li> <li> <p><em>sensitivity_analysis</em>: contains a script that allows to create samples based on the Kriging metamodels that have been created and exported as .pkl files in the metamodel folder. These samples are then used to the apply sensitivity algorithms. The user can choose among the various sensitivity algorithms provided by Openturns. For PCE metamodels, a routine is implemented to directly get the Sobol indices from the coefficients of the PCE metamodel. The indices can be plotted through plot routines;</p> </li> <li> <p><em>figures</em>: contains a utils script to display the graphs and save them as PNG files with consistency.</p> </li> </ul>
Figure 9. Experimental Page Rank dependency on Markov Chain length with balanced distribution-Study of a Random Navigation on the Web Using Software Simulation
<p>This paper explored different implementation choices for analyzing the most important<br> parameters about a web. Many researchers explored the use of new search engines for studying the<br> evolution of the web (Ntoulas, Cho and Olston, 2004). Another important research is realized about<br> the Link Structure Graph (LSG). The LSG captures a complete hyperlink structure from the web<br> and models link associations reflected in the page layout (Rodrigues, Milic-Frayling and Fortuna,<br> 2007). For further works ideas like extrapolation methods for accelerating page rank calculation can<br> be developed (Kamvar et al., 2003).</p>
Figure 7. Experimental Page Rank dependency on Markov Chain length-Study of a Random Navigation on the Web Using Software Simulation
<p>The next diagram proves that the values for Experimental Page Rank depend on the length<br> of the Markov Chain, while Algorithmic Page Rank remains constant.</p>
Figure 6. Outlinks degree distribution for all web sites-Study of a Random Navigation on the Web Using Software Simulation
<p>Some of the most important aspects of the analysis is obtaining the parameters which can<br> give the main information about a web. In the simulation implementation information as: page,<br> number of inlinks, number of outlinks, value for Algorithmic Page Rank and Experimental Page<br> Rank will be processed for obtaining the results of the analysis. For the first part it was necessary to<br> use experimental values as: inlinks, outlinks and in and out frequencies.</p>
Figure 1. Markov Chain Model&Figure 2. Transition matrix-Study of a Random Navigation on the Web Using Software Simulation
<p>For a good simulation it is very important to find methods for<br> navigating through the web (Levene and Wheeldon, 2004). John Kemeny and Laurie Snell have<br> proposed the use of Markov models for web simulations (Kemeny and Snell, 1960). Cadez et al. (2000)<br> used Markov models for classifying the sessions into different categories for browsers. Some other<br> proposed techniques choose to combine different order Markov models for obtaining low state<br> complexity and improving accuracy, as Deshpande and Karypis (2004). Dongshan and Junyi (2002)<br> used for predicting the access providing good scalability and high coverage a hybrid-order tree-like<br> Markov model. As an alternative to the Markov model Pitkow proposed a longest subsequence model<br> (Pitkow and Pirolli, 1999), also for predicting the next page accessed by the user Sarukkai chose<br> Markov models (Sarukkai, 2000).<br> Transitions are simulated using the Markov Chain nodes, Google matrix and an arbitrary initial<br> probability distribution. Examples can be seen in Figure 1 and Figure 2.</p>
Figure 8. Prototype Front-End-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>We modeled classes for each AGLO section: scenario, theory, questions, and<br> feedbacks. We modeled domain specific classes for the learned concepts like trees and graphs<br> having generative methods controllable through parameters.<br> Figure 8 depicts our prototype front-end.</p>
Figure 7. Generated Tree Example-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we give an example of a model instantiation. We describe the instantiation<br> process for all the symbols in the AGLO model. The nNoOfElements symbol was instantiated with<br> the generated value of 10. The tabKeys symbol was instantiated with the [A,B,C,D,E,F,G,H,I]<br> generated array. The tree symbol was instantiated with a ”Tree” object built with arguments equal<br> with previously instantiated symbols. Class ”Tree” is part of the auxiliary JavaScript library.The treeSVG symbol was assigned to the tree SVG string representation obtained by a method call and<br> is depicted in figure 7.</p>
Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>analyzed AGLO that is displayed to the learner for localization and selection purposes.<br> The second XML element is the scenario element containing a text description of the AGLO<br> and a set of symbols. The description is expressed in natural language and we can notice that it<br> contains four main steps:<br> i) random tree generation;<br> ii) index computation for presentation;<br> iii) parent index computation for answer validation;<br> iv) access to the first two keys for particular feedback generation.<br> In the scenario section depicted in figure 5 several symbols are defined with the following<br> semantics.</p>
Figure 6. AGLO Presentation Sections-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The seventh and eighth symbols named node1 and node2, both of type character, are<br> initialized with the first two values from the keys array. These symbols will be used later in the<br> feedback section. Their initialization is based on accessing the keys using the array indexing<br> operator.<br> Next, we discuss about the presentation sections of the AGLO depicted in figure 6.</p>
Fig. 8. Algorithmic Page Rank-Study of a Random Navigation on the Web Using Software Simulation
<p>Another diagram shows that for each site there is only one constant value independent of the<br> length of Markov Chain. Algorithmic Page Rank only depends on the number of inlinks and<br> outlinks.Table 4 contains the values for Experimental Page Rank with balanced distribution. In the<br> following figure it is shown the diagram for the values obtained for Experimental Page Rank.<br> The Experimental Page Rank values oscillate between the same limits independently of the<br> change of Markov Chain length (N).</p>
Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>Formally, the theory section (see line 11) contains only static data. Here the educator places<br> theoretical information about the learned concept in HTML format.<br> The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix<br> of static data and dynamic values referring the previously computed symbol values. The semantic of<br> these sections is to create a dynamic content to be presented to the learner. Formalisms for specific<br> details are also present, like:<br> i) correctness - because we need to know which is the correct answer if they are multiple;<br> ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and<br> showed to the learner only in certain conditions.</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.