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1,782 results for “algorithms”
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 8) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset8) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor6 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 7) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset7) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor5 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 19) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset19) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 2 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
Discretized bulk data by the discretization step of rFASTCORMICS used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>Bulk data RNAseq data were downloaded from GEO, GTEX, and other sources (see below) and discretized by the discretization step of rFASTCORMICS (Pacheco et al, 2019) used in the optimization step in scFASTCORMICS:</p> <p>CRC bulk RNAseq data were obtained from Lee et al(2020) <br> CRC control (NM) was downloaded from GSE81861 (GTEX, Healthy colon from)</p> <p>Pancreatic Human islet bulk RNAseq data was downloaded from EBI Expression Atlas (Pancreatic islet cells)</p> <p>Immune cells in pancreatic carcinoma bulk data were obtained from GEO (GSE156278)</p> <p>liver and breast cancer bulk RNAseq data were obtained from the TCGA (GSE62944)</p> <p> </p> <p>rFASTCORMICS and tutorial on rFASTCORMICS can be found: https://github.com/sysbiolux</p> <p> </p> <p> </p> <p> </p> <p><br> </p>
single-cell RNAseq data (data set 5) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset5) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor3 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 15) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset15) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD8 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 4) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset4) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor2 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 3) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset3) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal Pancreas donor1 downloaded from the GEO website (GSE114297)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <pre> </pre>
Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - CaseStudy
<p>The dataset "Case Study" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper:</p> <p>Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. <em>Computers and Electronics in Agriculture</em>.</p> <p>The article is currently accepted. For a better reference format, please refer to the journal's official website.</p> <p>If you have used the material presented in this data set, please cite the previous article.</p> <p>For more information regarding the dataset, please refer to the paper mentioned below.</p>
An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm.
<p>This is a data set associated with the paper <em>An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. </em>by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33<sup>rd</sup> European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece.</p>
Data Extraction table for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data
<p>This table contains the data extraction results for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data. It contains 24 columns and 74 rows.</p>
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies - Reproduction artifacts
<p># Reproducibility instructions</p> <p>This document details the steps to reproduce the results presented in the paper 'Chaining of Numerical Black-box Algorithms: Investigating the Impact of Warm-Starting and the Switching Point'</p> <p>## Algorithm code<br> The code for the individual algorithms and the switching routines is included in the 'dynas.zip' folder. </p> <p>## Data collection<br> To collect the performance data for the static algorithms, the file 'data_collection.py' can be used. This file runs all 5 static algorithm on the function suite used in the paper, using the 'ioh' package. To run it, please modify the 'ioh_dir' variable to a place where the data should be stored. <br> The resulting data is made available under the 'static.zip' file.</p> <p>## Processing code<br> The code which processes the performance data into the list of usecases is part of the 'Processing_R' notebook. <br> This results in a set of rds-files with the data in IOHanalyzer's internal format, and in a csv file ('Split_performances_relative.csv') with the selected usecases. </p> <p>This csv file can then be read in using the 'visualization' notebook, which can generate Figure 1 from the paper. This notebook can then be used to count the specific usecases, which is the data shown in Table 1. The csv-file containing the selected usecases ('usecases.csv') is also generated here.</p> <p>The 'usecases' csv file can be used in the 'Data_collection_switch' notebook to run the dynamic configurations. This results in the 'Dynamic.zip' data, which can again be read using IOHanalyzer in the 'Processing_R' notebook. This then generates several csv-files, for both the static and dynamic case, containing the per-run hitting times ('RT_samples.zip'). These are then used in the 'Visualization' notebook to generate figure 3 and 4, as well as table 2. </p> <p>## Countour plots<br> The code for generating Figure 5 is included in the end of the 'Visualization' notebook.</p> <p>## Performance comparison plot<br> Figure 6 has been generated using IOHanalyzer web-interface (iohanalyzer.liacs.nl), by uploading the files in 'cma_bfgs.zip'. </p> <p>## Switch point impact plot<br> Figure 7 has been generated using the 'split_experiment' notebook</p>
QMC Raw Data for Stable Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble
<p><strong>Data Summary</strong></p> <p>Raw data of 'A Stable, Recursive Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble: Applications to Thermometry and the Hubbard Model'.</p> <p>Random seeds are generated using the default Julia RNG, with the seed number being 1234+the file ID.</p> <p>For more details, please check README.md on the GitHub repository.</p> <p> </p> <p><strong>Fidelity_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for fidelity measurements that correspond to plot_fidelity.ipynb</li> <li>The random seeds for numerator measurements ('Fidelity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>Purity_(CE|GCE)_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for purity measurements that correspond to plot_purity.ipynb</li> <li>The random seeds for numerator measurements ('Purity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>StructFactGCE_Lx6_Ly6_U2.0.zip</strong></p> <ul> <li>Raw data for the momentum distribution and structural factor measurements that correspond to plot_nk.ipynb and plot_Cq.ipynb</li> </ul>
Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction
<p>Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction</p> <p>This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. </p> <p>The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA).</p>
Folding-unfolding asymmetry and a RetroFold computational algorithm
<p>We treat protein folding as the molecular self-assembly, while unfolding is viewed as disassembly. Self-assembly and disassembly (fracture) are two opposite non-equilibrium dynamic processes; however, they cannot be converted to each other by a simple time variable reversal. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture may be a constant rate process as the driving force is opposed by damping. Typically, protein folding takes two orders of magnitude longer time than unfolding, and it consumes a lot of computational resources to model folding. Based on energy dissipation rates, we suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modeling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns while unfolding (denaturation) takes only about 5.0 ns, and therefore, fewer computational resources are needed for its simulation. This "RetroFold" approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.</p>
Grey Level Co-Occurrence Matrix and Learning Algorithms to Quantify and Classify Use-Wear on Experimental Flint Tools
<p>These are the images and the grey levels co-occurence matrix analysis results from my experimental dataset</p>
Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS. Terra MODIS data are gap filled to better represent on the ground snow. The data was used in the a publication for The Cyrosphere titled Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, doi.org/10.5194/tc-2022-159. Geotiffs and PNG files for Landsat 8 are self describing. The .mat files for Terra MODIS contain three variables:</p> <p>snow_fraction: the gap filled snow fraction stored as uint8 with 255 as the NoData value and valid values between and including 0 to 100.</p> <p>mstruct: projection structure describing the standard MODIS tile projection structure. The data represent data from tile h08v05 and h09v05</p> <p>RefMatrix: affine spatial referencing matrix for the snow_fraction grid with the projection described by mstruct</p>
ICSE'23: How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms (Replication Materials)
<p>Formal methods are used successfully in high-assurance software, but they require rigorous mathematical and logical training that practitioners often lack. As such, integrating formal methods into software has been associated with numerous challenges. While educators have placed emphasis on formalisms in undergraduate theory courses, such courses often struggle with poor student outcomes and satisfaction. In this paper, we present a controlled eye-tracking human study (n=34) investigating the problem-solving strategies employed by students with different levels of incoming preparation (as assessed by theory coursework taken and pre-screening performance on a proof comprehension task), and how educators can better prepare low-outcome students for the rigorous logical reasoning that is a core part of formal methods in software engineering. We find that incoming preparation is not a good predictor of student outcomes for formalism comprehension tasks, and that student self-reports are not accurate at identifying factors associated with high outcomes for such tasks. Instead, and importantly, we find that differences in outcomes can be attributed to performance for proofs by induction and recursive algorithms, and that better-performing students exhibit significantly more attention switching behaviors, a result that has several implications for pedagogy in terms of the design of teaching materials. Our results suggest the need for a substantial pedagogical intervention in core theory courses to better align student outcomes with the objectives of mastery and retaining the material, and thus bettering preparing students for high-assurance software engineering.</p> <p>This artifact makes publicly available the de-identified eye-tracking and facial behavior analysis data that we collected in our controlled study of cognition of proofs about algorithms. We also include our Python scripts (as several Jupyter notebooks) used for the statistical analyses of the collected data. </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.