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Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
FIGURES 2 a-g -AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>The program was implemented in Matlab and tested with several patterns. The first, dataset1<br> consisted of 2 patterns each comprised of 100 points falling on two concentric circles of radii 10<br> and 20 respectively. The second, dataset2 consisted of 3 patterns each of 100 points falling on three<br> concentric circles of radii 10, 15, and 20 respectively. The model was further tested for its<br> capability to find clusters in patterns of open spatial form using dataset3 and dataset4 consisting of<br> 200 and 300 points falling on 2 and 3 concentric semi circles respectively. As shown in the<br> Figure2.a and Figure2.b, the algorithm is capable of determining spatial association of a data point<br> with other data points belonging to its appropriate circle only. The results successfully demonstrated<br> the capability of our model to automatically detect clean clusters of arbitrary shapes in the input<br> data represented in closed spatial form. The model was found even capable of determining clusters<br> of open spatial forms also, as shown in Figure2.c and Figure2.e. However, the output of the<br> algorithm was found affected by the values of the algorithm parameters k and a. In the present<br> experiment, k =8 and a =10 was sufficient for performing correct cluster associations. On the other<br> hand, correct clustering for the dataset2, could be obtained with 10NN estimation i.e. k =10, with<br> a.=15. Moreover setting k =15, with a.=15 was required for dataset4, as clustering error was<br> observed with k =10, with a.=15, as in Figure2.d. Figure2.f and Figure2.g show the correct<br> clustering even in presence of combination of open and closed form of input patterns. In each<br> figure, the first sub-plot shows the original data and the second sub-plot shows the clusters<br> identified by our program.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure7. Model 3D of women body.
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 7 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 1. Flowchart of anthropometric system
<p> Our purpose is to develop an automatic measurement and modeling system based on 2D images (front and side images). This system used to image processing methods and machine learning algorithms. Our system has 3 main parts; there are human body feature extraction, training and testing processes, and the classification for new data. The novelty of our approach: - Classification of anthropometric features based on machine learning algorithms. - Development a non-contact anthropometric program for the smartphones on operation system Android. - Construction of a 3D-model of the human body based on the results of anthropometric features extraction. Our system can also be used to integrate to different environments, such as online shopping websites to support users fitting their clothes sizes and medical applications. The flowchart of our anthropometric system is described in figure 1. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 8. Model 3D of man body
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 8 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</p>
Data from: Automatic Definition of Robust Microbiome Sub-states in Longitudinal Data
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome datasets already published.</p> <p>Prefixes:</p> <ul> <li>David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)</li> <li>Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)</li> <li>Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)</li> <li>LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)</li> </ul> <p>Suffixes:</p> <ul> <li> <p>_All: all taxa</p> </li> <li> <p>_Dominant: only 1% most abundant taxa</p> </li> <li> <p>_NonDominant: remaining taxa after removing above dominant taxa</p> </li> <li> <p>_GenusAll: taxa aggregated at genus level</p> </li> <li> <p>_GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa</p> </li> <li> <p>_GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> </li> </ul> <p>Each folder contains 3 output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID</p> <p>Abstract of the associated paper:</p> <p>The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome's "state". Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.</p> <p> </p>
Partially automatically annotated corpus to predict gestural cues in Embodied Conversational Agents
<p>#Structure of the corpus</p> <p>This corpus has been built using speeches of Spanish politicians freely available <a href="http://www.congreso.es/portal/page/portal/Congreso/Congreso/Intervenciones">here</a> along with their transcriptions.</p> <p>Each transcription has been analyzed in terms of:</p> <ul> <li>Surface Syntactic Structure*</li> <li>Deep Syntactic Structure*</li> <li>Morphology (Part of Speech)*</li> <li>Communicative Structure</li> </ul> <p>Gestures (beat vs. no gesture tags) have been annotated using the videos.</p> <p>*All those features have been automatically retrieved using the parser freely available in https://github.com/TalnUPF/miis. The other features have been annotated manually.</p> <p>#Concerns about the corpus</p> <p>This corpus has been mostly annotated manually. Annotation agreement has not been computed.</p> <p>Moreover, it is small. In order to extract reliable correlations from it, it should be extended.</p>
Datasets for automatic acoustic identification of individual birds
<p>Bird individual audio recordings (foreground and background) to accompany the work:</p> <p><em><strong>"Automatic acoustic identification of individuals: Improving generalisation across species and recording conditions"</strong></em><br> by Dan Stowell, Tereza Petrusková, Martin Šálek, Pavel Linhart</p> <p><a href="https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940">https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940</a></p> <p><br> This dataset contains labelled recordings of individuals from three different bird species:</p> <ul> <li>Little owl</li> <li>Chiffchaff</li> <li>Tree Pipit</li> </ul> <p>For more information, please see the README.txt file, and the research article.</p> <p>The dataset takes approx 11 GB of disk space after the ZIP files have been uncompressed.</p> <p> </p>
Data from: Robust and Automatic Definition of Microbiome States
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome dataset already published.</p> <p>Prefixes:<br> * David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)<br> * Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)<br> * Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)<br> * LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)<br> * Dam2016_: original microbiome dataset published in [Dam et al.,2016] (https://www.nature.com/articles/npjsba20167)<br> * Caporaso[Lpalm|Rpalm|Tongue]_: original microbiome dataset published in [Caporaso et al.,2011] (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2011-12-5-r50)<br> * Ravel2011_: original microbiome dataset published in [Ravel et al.,2011] (http://www.pnas.org/content/108/Supplement_1/4680)</p> <p>Sufixes:<br> _All: all taxa<br> _Dominant: only 1% most abundant taxa<br> _NonDominant: remaining taxa after removing above dominant taxa<br> _GenusAll: taxa aggregated at genus level<br> _GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa<br> _GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> <p><br> Each folder contains the following output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID<br> - robustClustering_allTogether_formatted.pdf: graph file, with the results of the robust clustering assessment.<br> - pcoa_definitiveClustering_X_kY_colorByCluster.pdf: graph file, with samples represented in Principal COordinate Analysis, with different point color associated to the assigned cluster.<br> - statesSequence_XXX.pdf (if longitudinal data): graph file, a time series diagram representing the sequence of states over time per subject.</p> <p> </p>
Online Appendix - Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code
<p>Online appendix for the paper "Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code".</p> <p>The full description of the content of this appendix can be found in the README file.</p>
A comparison of automatic cell identification methods for single-cell RNA-sequencing data
<p>Benchmark datasets used to evaluate the performance of 22 classifiers for cell type classification for scRNA-seq data</p>
Automatic and feature-specific prediction related neural activity in the human auditory system
<p>Downsampled (to 100Hz) data of the paper "Automatic and feature specific prediction related neural activity in the human auditory system"</p>
APRSuite: A Suite of Components and Use Cases Based on Categorical Decomposition of Automatic Program Repair Techniques and Tools
<p><strong>During the last decade, we are witnessing the advent of a proliferation of techniques and associated tools for automatic program repair (APR). The current techniques and tools provide rich sources of knowledge that should be taken into consideration for future research. An overview of the current APR techniques and tools can serve the research community as a knowledge accumulator. However, APR techniques and tools differ in many aspects making knowledge accumulation challenging. To overcome this challenge, in this paper, we propose to leverage common components that constitute the APR techniques and tools. To achieve this objective, we surveyed current APR techniques and tools to identify the APR Suite of common constituent components, namely as APRSuite. Repair source and defect class are examples of identified components. We grouped these components into several categories such as patch evaluation and target defects. We have also identified some of the possible use cases per component as well as different lessons learned in studies for each component and for each use case. In addition, we developed a principled way for application of the components. The <em>APRSuite</em> and the <em>principled way</em> to apply it comprise a <em>framework</em> for knowledge accumulation, evaluation, and comparison of APR techniques and tools. The novelty of our work lies in its original viewpoint to the process of literature review in the APR research field. To demonstrate the applicability of the framework, we mapped out several concrete APR techniques, as a first instantiation of the framework. We observed that the framework brings discipline into the evaluation and/or comparison of APR techniques and tools. The framework offers these benefits objectively and systematically. We concluded that knowledge accumulation and characterization through literature reviews can be therefore facilitated through the identified suite of components while at the same time the existing component suite can be modified, augmented, or improved.</strong></p>
Automatically fixing dependency breaking changes
<h1>Replication Data for Automated Dependency Update Experiments</h1> <h2>Dataset Description</h2> <p>This repository contains a comprehensive collection of data from experiments on automated dependency updates using Large Language Models (LLMs). The dataset consists of two main components:</p> <ol> <li><strong>Execution Traces</strong>: Detailed logs of LLM interactions and repair attempts, captured using OpenTelemetry and OpenInference (<a href="https://github.com/Arize-ai/openinference">https://github.com/Arize-ai/openinference</a>). These traces provide insights into the behavior and performance of various LLMs in addressing breaking changes caused by dependency updates in Java projects.</li> <li><strong>Docker Images</strong>: Pre-built Docker images containing the state of Java projects after successful repair attempts. These images allow for direct inspection and verification of the changes made by our automated repair system.</li> </ol> <p>This dataset enables analysis and replication of our study on automated dependency updates, offering both low-level interaction data and high-level repair outcomes.</p> <h2>Data Components</h2> <h3>1. Execution Traces</h3> <ul> <li><strong>Format</strong>: JSON Lines, persisted via a custom OpenTelemetry exporter</li> <li><strong>Content</strong>: Spans capturing various aspects of LLM interactions, including: <ul> <li>Input processing</li> <li>LLM query generation</li> <li>LLM response parsing</li> <li>Code modification attempts</li> <li>Compilation and test execution results</li> </ul> </li> <li><strong>Purpose</strong>: Enables detailed analysis of LLM decision-making processes and performance metrics</li> </ul> <h3>2. Docker Images</h3> <ul> <li><strong>Format</strong>: Compressed Docker image files (.tar.gz) in a .zip</li> <li><strong>Content</strong>: Maven project states after successful repair attempts with patch(es) applied and seperated in the second layer of each image.</li> <li><strong>Purpose</strong>: Allows direct inspection and verification of code changes made during repairs and the successful maven outcome</li> </ul> <h2>Research Context and Data Usage</h2> <p>This dataset corresponds to experiments described in our study investigating the effectiveness of zero-shot prompting and agentic approaches in automating dependency updates. It provides valuable insights into:</p> <ul> <li>Performance variations across different LLMs</li> <li>Influence of various factors on repair success rates</li> <li>Specific code changes made during successful repairs</li> </ul> <p>This dataset can be used for:</p> <ol> <li>Replicating the experimental results presented in the associated study</li> <li>Conducting further analysis on LLM behavior in software engineering tasks</li> <li>Developing and benchmarking new approaches for automated dependency updates</li> <li>Investigating the decision-making processes of LLMs in code modification tasks</li> <li>Verifying and inspecting successful repair attempts through Docker images</li> </ol> <p> </p> <div> <h2>Repair Replication with Docker</h2> To replicate specific repairs using our pre-built Docker images:<br> <div>1. Ensure you have Docker installed on your system.</div> <br>2. Use the included docker-images.zip, which first has to be unzipped <div> <pre><code>unzip docker-images.zip</code></pre> </div> <div>after which the images can be loaded as follows:</div> <div> <pre><code>docker load < image_name.tar.gz</code></pre> </div> <br> <div>3. Run the Docker container:</div> <pre><code>docker run -it [image-name]</code></pre> <br> <div>These Docker images contain the state of projects after repair attempts, allowing for easy inspection and verification of the changes made by our automated repair system.</div> </div>
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.