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376 results for “scalable”
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
Dataset for "Electrical and optical control of single spins integrated in scalable semiconductor devices"
<p>Accompanying data for the main text and supplemental materials of <em>Electrical and optical control of single spins integrated in scalable semiconductor devices</em></p>
Geographica 2 Scalability Dataset
<p>The <strong>Scalability dataset</strong> is part of the Scalability Workload of the <a href="https://geographica2.di.uoa.gr/" target="_blank" rel="noopener">Geographica 2</a> GeoSPARQL benchmark which aims at discovering the limits of spatial RDF stores as the number of triples in the dataset increase.</p> <p>It data is sourced from two real-world datasets that are synthesized in a specific manner to achieve the desired goals. The <a href="https://www.openstreetmap.org/" target="_blank" rel="noopener">OSM</a> data concern the following list of countries: Wales, Scotland, Greece, Northern Ireland, England and Germany. The feature classes selected are: buildings, landuse, natural, places, points of interest, railways, roads, traffic, transport, water and waterways. The <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012">CLC-2012</a> is the 2012 version of the CLC dataset and its data covers the 33 European Environment Agency member countries and six cooperating countries. The logic and steps taken are described in the original paper <a href="https://geographica2.di.uoa.gr/files/Geographica2-long.pdf">"Evaluating Geospatial RDF stores Using the Benchmark Geographica 2"</a>. </p> <p>The actual distribution format of the dataset is the form of a compressed reference dataset file (500M triples, 48M features of which 24M polygons) and an accompanying script which allows for the dynamic generation of 6 increacingly larger (10K, 100K, 1M, 10M, 100M, 500M) proper subsets of the reference dataset.</p>
Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth </p> <p>by Jasna Mannayil, Olli Pitkänen, Minna Mannerkorpi, and Krisztian Kordas</p>
LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package
<p>LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models</p> <p>This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models".</p> <p><strong>Replication Package Contents:</strong></p> <p>This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Fault Detection Rate (FDR), Total Minimization Time (MT), Time Saving Rate (TSR) , statistical tests for all the minimization budgets (i.e., 25%, 50%, and 75%), results for the preliminary study, results for UniXcoder/Cosine with preprocessed code on 16 projects.</p> <p><strong>Data:</strong></p> <p>We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J.</p> <p><strong>Code:</strong></p> <p>We provide in the<em> <strong>Code</strong> </em>directory the code (Python) and bash files required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong></p> <p>We provide in the<em> <strong>Results </strong></em>directory the detailed results for our approach (called LTM). We also provide the summarized results of LTM and a baseline (ATM) for comparison purposes. Additional technical details about ATM can be found at https://zenodo.org/record/7455766.</p> <p><strong>_________________________________</strong></p> <p><strong>LTM's Similarity Measurement:</strong></p> <p>The source code of this step is in the <strong><em>Code/LTM/Similarity</em></strong> directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <em><strong>Code/LTM/Similarity/requirements.txt</strong></em> file should be installed, as follows:</p> <p>cd Code/LTM/Similarity</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <ul> <li>Data/LTM/TestMethods</li> </ul> <p><strong>Output:</strong></p> <ul> <li>Data/LTM/similarity_measurements</li> </ul> <p><strong>Running the experiment:</strong></p> <p>To measure the similarity between all pairs of test cases, the following bash script should be executed:</p> <p>bash measure_similarity.sh</p> <p>The source code of test methods of each project in the <strong><em>Data/LTM/TestMethods</em></strong> is parsed to generate pairs of test cases. This steps includes test methods tokenization, test methods embeddings extraction and similarity calculation. Then, all similarity scores are stored in <em><strong>Data/LTM/similarity_measurements</strong></em> folder. Due to the large size of the calculated similarity scores (60 GB), they were not uploaded on Zenodo, but they can be available upon request.</p> <p><strong>LTM's Test Suite Minimization:</strong></p> <p>The source code of this step is in the Code/LTM/Search directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <strong><em>Code/LTM/Search/requirements.txt</em></strong> file should be installed, as follows:</p> <p>cd Code/LTM/Search</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <p>Data/LTM/similarity_measurements</p> <p><strong>Output:</strong></p> <p>Results/LTM/minimization_results</p> <p><strong>Running the experiments:</strong></p> <p>To minimize the test suite for each project version, the following bash script should be executed:</p> <p>bash minimize.sh</p> <p>The similarity scores of all test case pairs per project version are parsed by the search algorithm (Genetic Algorithm). Each experiment runs ten times using three minimization budgets (25%, 50%, and 75%). The results are stored in the <em><strong>Results/LTM/minimization_results</strong></em> directory.</p> <p><strong>LTM's Evaluation:</strong></p> <p>To evaluate the minimization results for each version and each project, the following bash script should be executed:</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_version.sh</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_project.sh</p> <p>This will evaluate the FDR, MT and TSR results for each version and each project for each minimization budget. These results are stored in the <em><strong>Results/LTM</strong></em> directory.</p> <p>Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.</p>
Dataset from TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]
<p>TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]</p> <p>Pre-publicatoin upload for VLDB review.</p> <p>Code is at https://github.com/RelationalAI/annotated-tables/. The Github repository name is from a previous draft version and cannot be changed. It is code for TableLabler.</p>
Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
<p>Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"</p> <p>ArraySeq_Method.zip contains the following folder and contents:</p> <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> <p>ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes. </p>
Exploring Data at Scale with Arkouda: A Practical Introduction to Scalable Data Science
<p><span>Data scientists can be thought of as modern-day explorers, venturing into the vast unknown of information. However, this exciting journey is not without its hurdles. One of the biggest challenges they face is the sheer immensity of data they encounter. Modern datasets cannot fit in laptop memory, containing terabytes or even petabytes of information. Working with such massive data requires specialized tools and techniques to extract meaningful insights. As data sets are growing ever larger, data science demands interactivity, where scientists can learn while working with the data. At the same time, data science demands scalability, where scientists are able to work with data sets in their entirety. Data scientists have naturally been drawn to Python as it provides interactivity through its read, evaluate, print loop and performance through its utilization of libraries written in other languages, like C and Fortran. These libraries typically are not designed for HPC and run into problems when attempting to scale. The gap that Arkouda fills in the data science landscape is a library that is both interactive, providing a familiar Python API, and scalable, leveraging a scalable Chapel server in the backend. Arkouda is a framework for scalable Python packages for interactive data science and has applications ranging from oceanography to net flow analysis.</span></p>
Improving the Scalability of DBRepo - Runtime Measurement Data
<p>Contains the results for the runtime evaluation of the changes to DBRepo's architecture proposed in the bachelor's thesis "Enabling the Scalability of DBRepo" by Tobias Grantner reported in the unit of nanoseconds.</p> <p>The files are named according to the scheme <code><operation>_<change>_<state>.csv</code>, where <code><operation></code> specifies the operation that is evaluated, <code><change></code> corresponds to the name of the proposed change to the architecture and <code><state></code> describes whether the system was evaluated before or after the change was implemented.</p>
Improving the Scalability of DBRepo - Code Duplication Data
<p>Contains the results for the code duplication evaluation of the changes to DBRepo's architecture proposed in the bachelor's thesis "Enabling the Scalability of DBRepo" by Tobias Grantner.</p> <p>The file <code>overall-duplication.csv</code> contains information about the code duplication in the whole code base of DBRepo.</p> <p>The files starting with <code>service-duplication-*.csv</code> contain information about the code duplication per service of DBRepo and are named according to the scheme <code>service-duplication_<change>_<state>.csv</code>, where <code><change></code> corresponds to the name of the proposed change to the architecture and <code><state></code> describes whether the system was evaluated before or after the change was implemented.</p>
Improving the Scalability of DBRepo - Memory Usage Data
<p>Contains the results for the memory usage evaluation of the changes to DBRepo's architecture proposed in the bachelor's thesis "Enabling the Scalability of DBRepo" by Tobias Grantner reported in the unit of bytes.</p> <p>The files are named according to the scheme <code><change>-<state>.csv</code>, where <code><change></code> corresponds to the name of the proposed change to the architecture and <code><state></code> describes whether the system was evaluated before or after the change was implemented.</p>
Data from: APPLES: Scalable distance-based phylogenetic placement with or without alignments
<p>Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze datasets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome-skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.</p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
Data and analysis result for "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics"
<p>Data set and analysis results from our paper "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics" (pre-print). This file contains GWAS summary statistics of 2,483 traits and 51,399 SNP variants from European individuals, originally downloaded and processed from Pan-UK Biobank (https://pan.ukbb.broadinstitute.org/). Additionally, we include results of 100 pleiotropic factors inferred by our method and tSVD as comparison. Please see README for detailed breakdown.</p>
Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)
<p><strong>THE PUBLICATION</strong></p> <p>The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "<strong>Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings</strong>": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice.</p> <p><strong>THE DATA</strong></p> <p>This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below.</p> <p><strong>*** PLEASE NOTE ***</strong></p> <p><strong>This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com. </strong></p> <table> <tbody> <tr> <td><em>Citation</em></td> <td><em>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>First Author</em></td> <td><em>Surname of the main author, for reference purposes only.</em></td> <td><em>E.g. Al-Atrash</em></td> </tr> <tr> <td><em>Publication</em></td> <td><em>Publication year</em></td> <td><em>E.g. 2020</em></td> </tr> <tr> <td><em>Dependent A</em></td> <td><em>List of variables used to measure thermal perception</em></td> <td><em>E.g. Neutral temperature/ Thermal sensation</em></td> </tr> <tr> <td><em>Dependent B</em></td> <td><em>Scale used to measure each dependent variable</em></td> <td> </td> </tr> <tr> <td><em>Interaction A</em></td> <td><em>List of interaction effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Thermal and age/ Thermal and acoustical and personality</em></td> </tr> <tr> <td><em>Interaction B</em></td> <td><em>Is/are the effect(s) statistically significant?</em></td> <td><em>E.g. yes/ no/ (unknown)</em></td> </tr> <tr> <td><em>Crossed A</em></td> <td><em>List of crossed effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Acoustical/ Personality/ Age</em></td> </tr> <tr> <td><em>Crossed B</em></td> <td> </td> <td><em>*Note: Temperature is a main effect and is not included in the list</em></td> </tr> <tr> <td><em>Explanatory A</em></td> <td><em>Type of explanatory model</em></td> <td><em>E.g. Observation/ Statistical/ N/A</em></td> </tr> <tr> <td><em>Explanatory B</em></td> <td><em>Description of the explanatory model</em></td> <td><em>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</em></td> </tr> <tr> <td><em>Predictive A</em></td> <td><em>Does the article include a predictive model?</em></td> <td><em>E.g. yes/ no</em></td> </tr> <tr> <td><em>Predictive B</em></td> <td><em>Type of predictive algorithm</em></td> <td><em>E.g. Logistic regression/ N/A</em></td> </tr> <tr> <td><em>Predictive C</em></td> <td><em>Description or formulation of the predictive model</em></td> <td><em>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</em></td> </tr> <tr> <td><em>Performance</em></td> <td><em>Reported predictive performance</em></td> <td><em>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</em></td> </tr> <tr> <td><em>Location</em></td> <td><em>City in which the measurements take place</em></td> <td><em>E.g. Paris</em></td> </tr> <tr> <td><em>Period</em></td> <td><em>Period over which the measurements take place</em></td> <td><em>E.g. Jan-Feb 2020</em></td> </tr> <tr> <td><em>Start time</em></td> <td><em>Time of day at which the measurements begin</em></td> <td><em>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</em></td> </tr> <tr> <td><em>Study type</em></td> <td><em>Type of building and whether the experimental conditions are controlled by the experiment leader</em></td> <td><em>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</em></td> </tr> <tr> <td><em>Building layout</em></td> <td><em>Building layout</em></td> <td><em>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</em></td> </tr> <tr> <td><em>Exposure</em></td> <td><em>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</em></td> <td><em>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</em></td> </tr> <tr> <td><em>Number of buildings/chambers</em></td> <td><em>Number of different locations used for conducting measurements</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>Number of participants</em></td> <td><em>Number of individuals who take part in each experiment</em></td> <td><em>*Note: Outliers who are subsequently excluded from the modelling phase are not included.</em></td> </tr> <tr> <td><em>Survey type</em></td> <td><em>Description of the type of survey used for subjective measurements</em></td> <td><em>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</em></td> </tr> <tr> <td><em>Survey content</em></td> <td><em>Are the contents of the survey provided in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey source</em></td> <td><em>Is/are the source(s) of the survey items mentioned in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey reliability</em></td> <td><em>Is the reliability of the survey items reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey duration</em></td> <td><em>Is the survey duration reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Context A</em></td> <td><em>Overview of the contextual information provided by the authors</em></td> <td><em>E.g. Room layout/ Room dimennsions</em></td> </tr> <tr> <td><em>Context B</em></td> <td><em>Qualitative/quantitative contextual information</em></td> <td><em>E.g. Figure containing room layout/ 3m x 3m x 5m</em></td> </tr> <tr> <td><em>Contextual variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Contextual variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. figure</em></td> </tr> <tr> <td><em>Social variables A</em></td> <td><em>List of social variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Social variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [1,2,3,4,5]</em></td> </tr> <tr> <td><em>Personal variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Personal variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [red, blue]</em></td> </tr> <tr> <td><em>Physical variables A</em></td> <td><em>List of physical variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Physical variables B</em></td> <td><em>Range of values included in the experiment and their respective units</em></td> <td><em>E.g. dB(A)</em></td> </tr> <tr> <td><em>Full-factorial</em></td> <td><em>Is/are the experiment(s) full-factorial?</em></td> <td><em>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</em></td> </tr> <tr> <td><em>(Participant) Control</em></td> <td><em>Do participants have control over one or more experimental conditions?</em></td> <td><em>E.g. Yes/ No</em></td> </tr> <tr> <td><em>With/between subjects</em></td> <td><em>Are the experimental conditions shared between or within the participants?</em></td> <td><em>E.g. w/ b</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>List of variables reported as constant during the measurements</em></td> <td><em>E.g. Relative humidity/ Metabolic rate</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>(Range of) values and their respective units.</em></td> <td><em>E.g. 30-40%/ 1.2 met</em></td> </tr> <tr> <td><em>Summary</em></td> <td><em>Description of the research outome (outcome of the explanatory and/or predictive modelling)</em></td> <td><em>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</em></td> </tr> <tr> <td><em>Evaluation</em></td> <td><em>Are the participants invited to evaluate their experience once the experiment has been completed? </em></td> <td><em>E.g. Yes/ no</em></td> </tr> </tbody> </table> <p>Note: The data in v1.1.0 has not yet been optimised for analytics.</p>
Scalable Bayesian divergence time estimation with ratio transformations
<div class="page"> <div class="layoutArea"> <div class="column"> <p><span>Divergence time estimation is crucial to provide temporal signals for dating bio</span><span>logically important events, from species divergence to viral transmissions in space and </span><span>time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic </span><span>studies have analyzed hundreds to thousands of sequences. Such large-scale analyses</span><span> </span><span>challenge divergence time reconstruction by requiring inference on highly-correlated</span><span> </span><span>internal node heights that often become computationally infeasible. To overcome this</span><span> </span><span>limitation, we explore a ratio transformation that maps the original </span><span>N - </span><span>1 internal</span><span> </span><span>node heights into a space of one height parameter and </span><span>N - </span><span>2 ratio parameters. To</span><span> </span><span>make the analyses scalable, we develop a collection of linear-time algorithms to com</span><span>pute the gradient and Jacobian-associated terms of the log-likelihood with respect to </span><span>these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio trans</span><span>form in a Bayesian framework to learn the divergence times in four pathogenic viruses</span><span> </span><span>(West Nile virus, rabies virus, Lassa virus and Ebola virus) and the coralline red algae.</span><span> </span><span>Our method both resolves a mixing issue in the West Nile virus example and improves</span><span> </span><span>inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well</span><span> </span><span>as for the algae example. Our method now also makes it computationally feasible to</span><span> </span><span>incorporate mixed-effects molecular clock models for the Ebola virus example, confirms</span><span> </span><span>the findings from the original study and reveals clearer multimodal distributions of the</span><span> </span><span>divergence times of some clades of interest.</span></p> </div> </div> </div>
ICPP23 Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication Input Graphs
<p>Input graphs and checkpoints used for generating results in the ICPP 23 paper "Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication". </p>
Datasets for "Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents"
<p>Datasets used in the manuscript titled, "<strong>Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents</strong>" are uploaded here. </p> <p><strong>Brightfield and fluorescent stained images of brain tissue used for Fig. 5(a):</strong></p> <p>Malt3-20190624_Region 009_DAPI.png</p> <p>Malt3-20190624_Region 009_qCy5.png</p> <p>Malt3-20190624_Region 009_qFITC.png</p> <p>Malt3-20190624_Region 009_qTexasRed.png</p> <p>Malt3_BF20190628_Region 001.png</p> <p><strong>Fluorescent image of brain with embedded CFETs:</strong></p> <p>MALT2_Rat_100um_MOR1_x500_TSA_AF488.jpg</p> <p>Rat_100um_MOR1_x500_TSA.czi</p> <p><strong>In vivo dopamine recording data for Fig. 3:</strong></p> <p>ratarrays822_163.mat</p> <p>ratarrays822_57.mat</p>
metaFlye: scalable long-read metagenome assembly using repeat graphs
<p>Supplementary files for the manuscript titled "metaFlye: scalable long-read metagenome assembly using repeat graphs".</p> <p>The archive includes generated assemblies and the corresponding metaQUAST evaluations.</p>
Testing Scalable, IVR-supported Cancer Prevention Interventions in the Rural Alabama Black Belt
ClinicalTrials.gov study NCT03903874. IPD Sharing: NO. Countries: 1. Publications: 1.
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.