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285 results for “Computational analysis”
Rhyme analysis of Ukrainian Ballads: Towards a Computational Poetics
<p>This dataset is based on the folklore collection <em>Folk songs of Khmelnytsky region</em> (Iefremova & Dmytrenko, 2014). The text corpus includes 200 ballads in Ukrainian language. Ballads collected in the period between 1918 and 2010. There are 9125 ballad lines in the table; 40543 tokens.</p> <p>To analyze the rhyming and rhythmic elements of the text corpus of Khmelnytsky region ballads, the programming language R along with RStudio was used. Code written for text analysis in Estonian Literary Museum. </p> <p><br>This dataset consist of such files:</p> <ul> <li><strong>ballads_corpus_Khmelnytsky region</strong>: contains text data of Khmelnytsky region ballads in CSV;</li> <li><strong>stanza+syllables</strong>: R script to analyse the stanzas types and to calculate the number of syllables in each line;</li> <li><strong>finals in lines+POS analysis</strong>: R script to analyse the rhyme scheme by determine the final syllable in a line, and to do the PoS tags analysis of the rhyme;</li> <li><strong>rhyme_schemes:</strong> R script to analyze the distribution of rhyme schemes in Khmelnytsky region ballads;</li> <li><strong>ballads_corpus_POS:</strong> CSV file containing the text data of Khmelnytsky region ballads with part-of-speech (PoS) tags for the final word in each line;</li> <li><strong>rhyme_by_PoS:</strong> R script to analyze the distribution of rhymes by part of speech across the ballads;</li> <li><strong>ballads_fin_str</strong>: contains text data of Khmelnytsky region ballads with marked stress position in the last word in each line (in CSV);</li> <li><strong>rhyme_stressed_position</strong>: R script to analyse rhyme by stress position.</li> </ul>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags
<p>This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>:</p> <blockquote> <p>B. Hoffman, M. Cusimano, V. Baglione, D. Canestrari, D. Chevallier, D. DeSantis, L. Jeantet, M. Ladds, T. Maekawa, V. Mata-Silva, V. Moreno-González, A. Pagano, E. Trapote, O. Vainio, A. Vehkaoja, K. Yoda, K. Zacarian, A. Friedlaender, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023.</p> </blockquote> <p>Standardized code to implement, train, and evaluate models can be found at <a href="https://github.com/earthspecies/BEBE/">https://github.com/earthspecies/BEBE/</a>. </p> <p>Please note the licenses in each dataset folder.</p> <p><strong>Zip folders beginning with "formatted":</strong> These are the datasets we used to run the experiments reported in the benchmark paper. </p> <p><strong>Zip folders beginning with "raw": </strong>These are the unprocessed datasets used in BEBE. Code to process these raw datasets into the formatted ones used by BEBE can be found at <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>.</p> <p><strong>Zip folders beginning with "experiments": </strong>Results of the cross-validation experiments reported in the paper, as well as hyperparameter optimization. Confusion matrices for all experiments can also be found here. Note that dt, rf, and svm refer to the feature set from Nathan et al., 2012.</p> <p><em>Results used in Fig. 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (deep neural networks vs. classical models)</em><br>{dataset}_ harnet_nogyr<br>{dataset}_CRNN<br>{dataset}_CNN<br>{dataset}_dt<br>{dataset}_rf<br>{dataset}_svm<br>{dataset}_wavelet_dt<br>{dataset}_wavelet_rf<br>{dataset}_wavelet_svm</p> <p><em>Results used in Fig. 5D of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (full data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN_nogyr<br>{dataset}_rf_nogyr<br><br>Otherwise:<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN<br>{dataset}_rf</p> <p><em>Results used in Fig. 5E of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (reduced data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data_nogyr<br>{dataset}_rf_low_data_nogyr</p> <p>Otherwise:<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data<br>{dataset}_rf_low_data<br><br></p> <p><strong>CSV files</strong>: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. </p> <p><em>experiments_summary.csv - results averaged over individuals and behavior classes<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds<br>f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds<br>prec_mean, prec_std (float): analogous for precision<br>rec_mean, rec_std (float): analogous for recall<em><br><br>experiments_by_fold_individual.csv - results per individual in the test folds<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>individual (int): individuals are numbered zero-indexed, starting from fold 1<br>f1 (float): macro-averaged f1 score for this individual<br>precision (float): macro-averaged precision for this individual<br>recall (float): macro-averaged recall for this individual<em><br></em></p> <p><em>experiments_by_fold_behavior.csv - results per behavior class, for each test fold<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>behavior_class (str): name of behavior class<br>f1 (float): f1 score for this behavior, averaged over individuals in the test fold<br>precision (float): precision for this behavior, averaged over individuals in the test fold<br>recall (float): recall for this behavior, averaged over individuals in the test fold<br>train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set<em><br></em></p>
Computer Applications in Archaeology conference proceedings citation analysis
<p>Comma-separated values (csv) files to accompany the paper: </p> <p>Huggett, J. (2024). 'Changing Theory and Practice? CAA and Archaeology's Digital Turn'. <em>Journal of Computer Applications in Archaeology</em> 7(1), pp. 316–331. DOI: <a href="https://doi.org/10.5334/jcaa.144" target="_blank" rel="noopener">10.5334/jcaa.144</a></p> <p>The data are based on the Scopus citation database (see <a href="https://www.elsevier.com/products/scopus" target="_blank" rel="noopener">https://www.elsevier.com/products/scopus</a>). The copyright for the database itself is held by Elsevier and requires a subscription to access. These files are derived data only, consisting of counts and associated statistics.</p>
A Computational Analysis of Telegram's Narrative Affordances
<p><strong>Overview</strong></p> <p>Anonymized message classification data and actantial analyses of public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances". </p> <p><strong>Message classification data</strong></p> <p>All files are included in the zipped folder 'narrative_affordances_data.zip'</p> <p>Each file contains the message classification data for a single Telegram channel. Numbered files are included for each of the six datasets (1 combined, 5 thematic) discussed in the paper. </p> <p><strong>Actantial analysis</strong></p> <p>Frequency lists of retrieved actants are included in the zipped folder 'overview_of_actants.zip'</p> <p> </p>
MCR LTER: Coral Reef: Computer Vision: Multi-annotator Comparison of Coral Photo Quadrat Analysis
This repository contains the Moorea portion of a larger data package published in conjuncture with: "Towards automated annotation of benthic survey images: variability of human experts and operational modes of automation", Beijbom et al. PLOS One, 2015. The rest of the data package is hosted at the Dryad data repository (doi:10.5061/dryad.m5pr3). The larger data package is an aggregate dataset from four Pacific coral reef monitoring projects in: Moorea (French Polynesia), the northern Line Islands, Nanwan Bay (Taiwan) and Heron Reef (Australia). It contains 5090 coral reef survey images, and 251,988 random-point annotations by coral ecology experts. The point-annotations indicate the dominant benthic substrate at 10 to 200 random point locations per image, using a label-set of 20 categories. In addition, 200 images from each location have been cross-annotated by 6 experts, for a total of 7 sets of annotations for each image. This set of cross-annotations can be used to contextualize the performance of automated annotation methods for coral reef ecology. The full data package can also be used by computer-vision and machine learning researchers to develop object classification, image segmentation, and domain transfer learning methods. These data contain a subset of the raw data from which dataset knb-lter-mcr.4 is derived.
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
<p>We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."</p>
Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles
<p>Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes in the literature as well as notable gaps. We also compared trends in studied conditions to their relative prevalence in the general population using insurance claims (42 million Americans). HDMH publications represent 1% of articles in MEDLINE. Most studies are observational in nature, though randomized trial reporting has increased five-fold in the last twenty years. Half of all HDMH articles concentrate on only three disease groups (cancer, mental health, endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well-represented despite substantial prevalence. To support further investigation, we also present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.</p>
Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.
<h1><a name="_Toc169648661"></a><span>Overview</span></h1> <p><strong><span> </span></strong></p> <p><span>The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “<em>WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students</em>”. <span> </span>Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.</span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Background and Related Work</span></em></strong></p> <p><em><span>As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].<span> </span></span></em></p> <p><em><span>“Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.<span> </span>The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces. </span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>2</span></em></strong><em><span><span> </span><strong>Goals of the Working Group</strong></span></em></p> <p><em><span>By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):</span></em></p> <p><em><span> </span></em></p> <p><em><span>RQ1: How does AI influence work engagement for IT professionals?</span></em></p> <p><em><span>RQ2: How does AI affect the socio-technical work dynamics for IT professionals?</span></em></p> <p><em><span>RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?</span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>3</span></em></strong><em><span><span> </span><strong>Methodology</strong></span></em></p> <p><em><span>This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.<span> </span>Further implications for computing curricula design and assessment will be developed from this analysis. </span></em></p> <p><span>REFERENCES</span></p> <p><span>[1]<span> </span>ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York.</span></p> <p><span>[2]<span> </span>Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? <em>Qualitative research in psychology</em>, <em>18</em> (3). 328-352.</span></p> <p><span>[3]<span> </span>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science<span> </span>Competency Statements: A Process and Curriculum Model for the 21st Century in <em>Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education</em>, ACM, New York.</span></p> <p><span>[4]<span> </span>Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. <em>A Computing Curricula Series Report </em>ACM, New York.</span></p> <p><span>[5]<span> </span>Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in <em>2011 international symposium on empirical software engineering and measurement</em>, IEEE, 2011, 275-284.</span></p> <p><span>[6]<span> </span>Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in <em>2020 IEEE Frontiers in Education Conference (FIE)</em>, IEEE, Uppsala, Sweden. </span></p> <p><span>[7]<span> </span>Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. <em>Information and Software Technology</em>, <em>106.</em> 101-121</span></p> <p><span>[8]<span> </span>Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. <em>ACM Inroads</em>, <em>14</em> (2). 40-46.</span></p> <p><span>[9]<span> </span>Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. <em>arXiv preprint arXiv:2308.06921</em>.</span></p> <p><span>[10]<span> </span>Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. <em>arXiv preprint arXiv:2310.00658</em>.</span></p> <p><span>[11]<span> </span>Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in <em>Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5</em>, Springer, 158-172.</span></p> <p><span>[12]<span> </span>SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London.</span></p> <p><span>[13]<span> </span>Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. <em>Information and software technology</em>, <em>51</em> (1). 219-233.</span></p> <p><em><span> </span></em></p>
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>
Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury
<p>This is the dataset accompanying the manuscript:</p> <p><strong>"Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury"</strong></p> <p>Panagiotis Kratimenos<sup>1,2,5 </sup>*, Abhya Vij<sup>5</sup>, Robinson Vidva<sup>6</sup>, Ioannis Koutroulis<sup>3,4,5</sup>, Maria Delivoria-Papadopoulos<sup>7</sup>**, Vittorio Gallo<sup>1,5</sup>, and Aaron Sathyanesan<sup>1,5</sup>*</p> <p><em><sup>1</sup></em><em>Center for Neuroscience Research, Children’s National Research Institute, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>2</sup></em><em>Department of Pediatrics, Division of Neonatology, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>3</sup></em><em>Department of Pediatrics, Division of Emergency Medicine, Children’s National Hospital, Washington, DC, USA</em></p> <p><em><sup>4</sup></em><em>Center for Genetic Medicine Research, Children’s National Research Institute and Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA</em></p> <p><em><sup>5</sup></em><em>George Washington University School of Medicine and Health Sciences, Washington DC, USA</em></p> <p><em><sup>6</sup></em><em>Digirobi Solutions, Bengaluru, Karnataka, India</em></p> <p><em><sup>7</sup></em><em>Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, USA</em></p> <p>*Corresponding Authors:</p> <p>Panagiotis Kratimenos, MD, PhD: <a href="mailto:panagiotis.kratimenos@childrensnational.org">panagiotis.kratimenos@childrensnational.org</a></p> <p>Aaron Sathyanesan, PhD: <a href="mailto:asathyanesan@childrensnational.org">asathyanesan@childrensnational.org</a></p> <p>111 Michigan Avenue, Washington, DC, 20010, USA</p>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
Data for the analysis of aquifer-system deformation in the Doñana Natural Space (Spain) using unsupervised cloud-computed InSAR data and wavelet analysis
<p>This are the data necessary to correlate InSAR and hydrogeological information through wavelet analysis, by WaSAR Python script (Jiménez-González & Guardiola-Albert, 2022, http://doi.org/10.5281/zenodo.6334996). The structure and information about the data is the following:</p> <p>PSBAS: Processed Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency (ESA) Sentinel-1 satellites to estimate line-of-sight (LOS) ground motion in the period 2014-2020 in the Doñana area (SW Spain). These images have been processed using the P-SBAS approach (Parallel Small BAseline Subset), which is the parallel computing solution for the SBAS processing chain at the ESA Geohazards Exploitation Platform (GEP) by CNR-IREA.</p> <p>Aggregates deformation: Former InSAR information aggregated in polygons</p> <p>Climate: rainfall and ET information in the Doñana area for the 2014-2020 period. Daily records of evapotranspiration and precipitation have been obtained from the agroclimatic stations belonging to the Junta de Andalucía (https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/).</p> <p>Piezometry: piezometry information in Doñana area for the 2014-2020 period. Groundwater level information was provided by the piezometric networks of the Guadalquivir Hydrographic Confederation and the Geological and Mining Institute of Spain.</p> <p>Pump rates: estimated pumping rate time series in the Matalascañas touristic resort</p>
Datasets on approved and ongoing standards for Cloud, Edge and IoT computing in the continuum and analysis and assessment of relevance
<p>Two datasets:</p> <ol> <li><span>the database of standards relevant to the Cloud-Edge-IoT continuum and to the ACES-EDGE Research and Innovation Action funded under the grant agreement No. 101093126 call HORIZON-CL4-2022-DATA-01-02.</span></li> <li><span>the Excel workbook with the analysis of the assessment of the standards in relation to the needs of the ACES-EDGE implementation.</span></li> </ol> <p><span>Both datasets will be used for a more deep assessment of the standardisation requirements of the ongoing technolgical developments.</span></p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Computational Artifacts for the Paper "Are Noise-resilient Logical Timers useful for Performance Analysis?"
<p>This repository contains computational artifacts for the paper "Are Noise-resilient Logical Timers useful for Performance Analysis?" to be submitted to <a href="https://sc-protools-workshop.github.io/protools24/">ProTools@SC24.</a></p> <p>See also the <a href="https://sc24.supercomputing.org/program/papers/reproducibility-initiative/">SC24 reproducibility initiative.</a></p> <p> </p> <p>Contains</p> <ul> <li>Source code of <a href="https://doi.org/10.5281/zenodo.10822140">Score-P </a>, including implementation of the logical clock algorithm from the paper</li> <li>Software to post-process the Cube files generated by measurements</li> <li>Benchmarks <ul> <li>Source code</li> <li>Configuration skripts</li> <li>Measurement results, including output logs, Cube files</li> <li>Post-processing skripts and results</li> </ul> </li> </ul> <p> </p>
Carbon-latitude analysis of the ocean carbon cycle computed from NEMO-Medusa
<p>NetCDF files containing carbon-latitude stream functions computed from the coupled physical-biogeochemical model NEMO-Medusa. The stream functions have been computed for the last years of a historical run and the first years of the RCP 8.5 scenario run (2000-2009). The dataset includes a climatology of the most important tracers, the computed meridional carbon transports in the ocean, and volume distribution in the carbon-latitude space.</p>
Design and in vitro realization of carbon-conserving photorespiration - Computational Analysis
<p>Our aim is to develop a framework for modeling C3 photosynthesis in mesophyll cells that allows us to compare native photorespiration with engineered photosynthetic shunts. In particular, we want to model conditions that are most relevant to agricultural crops, i.e. a range of light intensities and both ambient and low CO2 intercellular airspace concentrations. Here we presented our computational analysis based on pathSeekR, the stoichiometric-kinetic model, kinetic models of photorespiration shunts and pathSeekR pathway architectures. </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.