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2,359 results for “online”
Search for huntingtin interactors in online databases – 2018/08/08
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale</strong> - To identify different huntingtin interaction partners. </p> <p><strong>Overview</strong> - Different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Data detailing huntingtin interaction partners from 9 different databases was extracted and simplified – worksheets 1-15. The information from each database was collated – worksheet 16. Huntingtin protein interaction partners were ranked according to the number of databases they were found in as well as the number of different experiments detailing the interaction with huntingtin – worksheet 17. </p>
Positive and Negative Affect Schedule (PANAS): psychometric properties for the online version in a clinical sample with emotional disorders
<p>This dataset contains sociodemographic and clinical data about 595 patients with emotional disorders that participated in a study with the objective of examining the psychometric properties of the online version of the Positive and Negative Affect Schedule (PANAS) in Spanish clinical sample.</p> <p> </p> <p> </p>
Go online metrics
<p><strong>IN GAME METRICS: GO ONLINE</strong></p> <p><strong>MINIGAME “SAFE PASSWORD”:</strong></p> <p>USER IDENTIFICATION user _id</p> <p>TIME SPENT IN THIS MINIGAME IN THIS ROUND PLAYING THE MINIGAME minigame_played _time</p> <p>POINTS ACHIEVED IN THIS MINIGAME IN THIS ROUND PLAYING THE MINIGAME minigame_level_points</p> <p>LEVEL IN THE GENERAL GAME AT THIS MOMENT level_in_the_general_game</p> <p>TIME SPENT IN THE GAME GO ONLINE UNTIL THIS MOMENT total_played_time</p> <p>NUMBER OF VERY WEAK PASSWORDS BOUNCED AWAY IN THIS ROUND PLAYING THE MINIGAME number_of_very_weak_passwords_bounced_away</p> <p>NUMBER OF WEAK PASSWORDS BOUNCED AWAY IN THIS ROUND PLAYING THE MINIGAME number_of_weak_passwords_bounced_away</p> <p>NUMBER OF STRONG PASSWORDS BOUNCED AWAY IN THIS ROUND PLAYING THE MINIGAME number_of_strong_passwords_bounced_away</p> <p>NUMBER OF VERY STRONG PASSWORDS BOUNCED AWAY IN THIS ROUND PLAYING THE MINIGAME number_of_very_strong_passwords_bounced_away</p> <p>NUMBER OF VERY WEAK PASSWORDS USED IN THIS ROUND PLAYING THE MINIGAME number_of_very_weak_passwords_used</p> <p>NUMBER OF WEAK PASSWORDS USED IN THIS ROUND PLAYING THE MINIGAME number_of_weak_passwords_used</p> <p>NUMBER OF STRONG PASSWORDS USED IN THIS ROUND PLAYING THE MINIGAME number_of_strongs_passwords_used</p> <p>NUMBER OF VERY STRONG PASSWORDS USED IN THIS ROUND PLAYING THE MINIGAME number_of_very_strong_passwords_used</p> <p> </p> <p><strong>MINIGAME “SAFE FRIENDS”:</strong></p> <p>USER IDENTIFICATION user _id</p> <p>TIME SPENT IN THIS MINIGAME IN THIS ROUND PLAYING THE MINIGAME minigame_played _time</p> <p>POINTS ACHIEVED IN THIS MINIGAME IN THIS ROUND PLAYING THE MINIGAME minigame_level_points</p> <p>LEVEL IN THE GENERAL GAME AT THIS MOMENT level_in_the_general_game</p> <p>TIME SPENT IN THE GAME GO ONLINE UNTIL THIS MOMENT total_played_time</p> <p>NUMBER OF DECLINED STRANGERS IN THIS ROUND PLAYING THE MINIGAME number_of_declined_strangers</p> <p>NUMBER OF ACCEPTED STRANGERS IN THIS ROUND PLAYING THE MINIGAME number_of_accepted_strangers</p> <p>NUMBER OF DECLINED FRIENDS IN THIS ROUND PLAYING THE MINIGAME number_of_declined_friends</p> <p>NUMBER OF ACCEPTED FRIENDS IN THIS ROUND PLAYING THE MINIGAME number_of_accepted_friends</p> <p> </p> <p> </p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
Dataset of FEUTURE Online Paper No. 10 "Knowledge Cohesion in European Regions: Convergence and Cohesion with Turkey" - Data on PhD
<p>The dataset of the paper on "Flow of Knowledge" consists of the following variables: </p> <p>- Doctorate holders by region of doctoral award 2009</p> <p>- Mobility intentions of doctorate holders by intended region of destination in year 2009</p> <p>- Principal job of employed doctorate holders by occupation and field of science, 2009 </p> <p>- Doctorate holders' satisfaction level on their principal job by selected title, 2009</p> <p>- Recent doctorate recipients' satisfaction level on their principal job by selected title, 2009</p> <p>- Doctorate holders by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Employed doctorate holders : perception regarding their job qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by employment situation and sex, 2009</p> <p>- Doctorate recepients's average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by age group and sex, 2009</p> <p>- Employed doctotare holders by employment situation, type of contract, working time in current employment and sex, 2009</p> <p>- Proportion of recent(**) doctorate recipients by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p> <p>- Employment situation of doctorate holders by sex and year of doctorate award, 2009</p> <p>- Recent doctorate recipients by employment situation and sex, 2009</p> <p>- Doctorate recepints' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders who moved out Turkey for a period of at least 3 months between January 2000 and December 2009(*)</p> <p>- Principal job of employed recent doctorate recipients by occupation, 2009</p> <p>- Reasons of mobility intentions of doctorate holders in the next 12 months</p> <p>- Proportion of doctorate holders by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p>
Dataset of FEUTURE Online Paper No. 10 "Knowledge Cohesion in European Regions: Convergence and Cohesion with Turkey"
<p>The dataset provides network statistics based on the EU Framework Programme data from the first round (FP1, 1984-1987) till the last round (FP8 -H2020, 2013-2020) (Nodes (Vertices), Unique Edges, Edges With Duplicates, Total Edges, Self-Loops, Average Geodesic Distance, Graph Density, Average Degree, Average Betweenness, Centrality Average Closeness, Centrality Average Eigenvector, Centrality Average Clustering Coefficient).</p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Log files data from online store
<p>As the number of online stores as well as buyers are increasing rapidly, researchers are working on understanding and improving the performance of online stores by studying customer behavior, interests, engagement etc., along with the technical aspects of online stores. This however requires access to log files of real-world. With this objective in mind, we have prepared and made publicly available high-frequency data-set containing one month of log files from an actual and popular Polish online store. This data-set can provide insights to user behavior as well as performance of the online store.</p>
Log files data from online store
<p>As the number of online stores as well as buyers are increasing rapidly, researchers are working on understanding and improving the performance of online stores by studying customer behavior, interests, engagement etc., along with the technical aspects of online stores. This however requires access to log files of real-world. With this objective in mind, we have prepared and made publicly available high-frequency data-set containing one month of log files from an actual and popular Polish online store. This data-set can provide insights to user behavior as well as performance of the online store.</p>
Accuracy of online survey assessment of mental disorders and suicidal thoughts and behaviors in Spanish university students. Results of the WHO World Mental Health-International College Student initiative.
<p>This dataset contains clinical data about 287 university students that participated in a clinical reappraisal study with the objective of examining the accuracy of WMH-ICS online screening scales for evaluating four common mental disorders (Major Depressive Episode, Mania/Hypomania, Panic Disorder, Generalized Anxiety Disorder) and suicidal thoughts and behaviors used in a survey of Spanish university students(UNIVERSAL project).</p>
Perception of edible insects and insect-based foods among children in Denmark: educational and tasting interventions in online and in-person classrooms
Open the record for dataset details and reuse information.
Experimental online quantum dots charge autotuning using neural networks - Output data
<p>Outputs of the model training and the online autotuning experiments presented in the paper: "Experimental online quantum dots charge autotuning using neural networks".</p> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</p>
Online Appendix of "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance"
<div> <div><strong>Abstract</strong></div> <div>This is the dataset of the paper entitled "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance", presented at SCAM 2024. It contains information related to the target commits and their attributes, as well as simulation results pertaining to the research questions in the paper.</div> <div> </div> <div>The target projects and commits in the dataset are based on a prior study: Nugroho, et al.: "How different are different diff algorithms in Git?: Use --histogram for code changes", Empirical Software Engineering, 2020, https://doi.org/10.1007/s10664-019-09772-z</div> <div> </div> </div> <div> <div><strong>Survey Overview</strong></div> <div> <div><em>1. Filtration</em></div> <div> <div>We collected attributes related to the changes for filtering and RQ purposes.</div> <ul> <li>Number of lines</li> <li>Number of changed lines</li> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> <div><em>2. RQ1</em></div> <div> <div>We investigate the minimum number of feedback actions needed to correct the initial diffs to the target diffs. Regarding the simulation, two types of heuristic functions (non-admissible and admissible functions) have been used to reduce costs. When the search with the non-admissible heuristic function of the initial state does not match the ideal optimal result, i.e., when there is room for improvement in the number of feedback actions, we applied another A* search with the admissible heuristic function.</div> <div> </div> <div> <div>We obtained the following results through search:</div> <ul> <li>Number of feedback actions (A* search with non-admissible heuristic)</li> <li>Number of feedback actions (A* search with admissible heuristic)</li> </ul> <div> </div> <div><em>3. RQ2</em></div> <div> <div> <div>We investigated the various effects that feedbacks have on the diffs by examining the diffs at depth 1 of the search tree. The dataset records the maximum, minimum, median, mean, and standard deviation for each search problem.</div> <br> <div>The study yielded the following results:</div> <ul> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> </div> </div> </div> </div> </div> </div> <div> <div> </div> <div> <div><strong>Dataset Columns</strong></div> <div>The following are the contents represented by the columns in the CSV file and their descriptions.</div> <div> </div> <div> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>project_name</td> <td>Name of the project associated with the data.</td> </tr> <tr> <td>filename</td> <td>Name of the file being analyzed.</td> </tr> <tr> <td>filepath</td> <td>Path to the file within the project.</td> </tr> <tr> <td>commit_id</td> <td>Commit hash representing the new version of the file.</td> </tr> <tr> <td>parent_commit</td> <td>Commit hash representing the old version of the file.</td> </tr> <tr> <td>error_commit</td> <td>An error occurred when retrieving the commit from the repository.</td> </tr> <tr> <td>error_setup</td> <td>Any error when generating the new and old versions of the file.</td> </tr> <tr> <td>error_analyze</td> <td>An error when collecting information for filtering.</td> </tr> <tr> <td>new_loc</td> <td>Lines of the new version of the source code.</td> </tr> <tr> <td>old_loc</td> <td>Lines of the old version of the source code.</td> </tr> <tr> <td>histogram_len</td> <td>Path length of the diff when using the Histogram algorithm.</td> </tr> <tr> <td>myers_len</td> <td>Path length of the diff when using the Myers algorithm.</td> </tr> <tr> <td>histogram-myers#edge</td> <td>Number of difference edges between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#edge</td> <td>Number of difference edges between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#edge</td> <td>Number of difference edges between Myers and initial diff.</td> </tr> <tr> <td>histogram-myers#area</td> <td>Number of mismatch diff areas between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#area</td> <td>Number of mismatch diff areas between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#area</td> <td>Number of mismatch diff areas between Myers and initial diff.</td> </tr> <tr> <td>dp#candidate</td> <td>Number of feedback candidates of initial diff (similarity distance).</td> </tr> <tr> <td>#insert</td> <td>Number of lines added in the change.</td> </tr> <tr> <td>#delete</td> <td>Number of lines deleted in the change.</td> </tr> <tr> <td>#change</td> <td>#insert + #delete.</td> </tr> <tr> <td>error_Asearch</td> <td>An error occurring during A* search with a non-admissible heuristic.</td> </tr> <tr> <td>#feedback_A</td> <td>Number of feedback actions for A* search with a non-admissible heuristic.</td> </tr> <tr> <td>time_A</td> <td>Time taken for A* search with a non-admissible heuristic (ms).</td> </tr> <tr> <td>RQ1_error_iteration</td> <td>An error when iterations exceed the limit (10,000,000) during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_error_timeout</td> <td>An error when the search time exceeds the limit (1,800 seconds).</td> </tr> <tr> <td>RQ1_error_other</td> <td>Other errors encountered during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#feedback</td> <td>Number of feedback actions for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#iter</td> <td>Number of iterations for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_time</td> <td>Time taken for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ2_error_exceed</td> <td>An error due to exceeding time or iteration limits in RQ2.</td> </tr> <tr> <td>RQ2_error_other</td> <td>Other errors encountered during RQ2.</td> </tr> <tr> <td>RQ2#children</td> <td>Number of children nodes of the initial state (= similarity distance).</td> </tr> <tr> <td>RQ2#candidate_min</td> <td>Minimum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_max</td> <td>Maximum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_ave</td> <td>Average of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_median</td> <td>Median of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_sd</td> <td>Standard deviation of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#area_min</td> <td>Minimum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_max</td> <td>Maximum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_ave</td> <td>Average number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_median</td> <td>Median number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_sd</td> <td>Standard deviation of number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>is_used</td> <td>Indicates whether this data is used in the results of RQ1 and RQ2.</td> </tr> </tbody> </table> </div> </div> </div> </div>
Educational data collected from students - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The student questionnaire was designed with 20 questions. It was completed by 1,088 respondents and focuses on students' experiences related to online education. The collected data provides a broad perspective on various aspects of this, including access to technology, experiences with different platforms, perceptions of the advantages and disadvantages of this form of education, as well as direct feedback from students regarding their experiences. The full questionnaire can be accessed at: <a href="https://forms.gle/fhgzCUx1SDnxbCfZ6" target="_new" rel="noopener"><strong>https://forms.gle/fhgzCUx1SDnxbCfZ6</strong></a></p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Educational data collected from teachers - for the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The dataset comes from a questionnaire structured into 24 questions, which can be accessed at <a href="https://forms.gle/bUgYMfoNHh7r6ebs6" target="_new" rel="noopener">https://forms.gle/bUgYMfoNHh7r6ebs6</a>. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.<br>Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Replication package and Online Appendix for: Why Do Some New Products Fail? Evidence from the Entry and Exit of Vanilla Coke
<p>This repository contains the replication package of data and codes to generate the main results reported in "Why Do Some New Products Fail? Evidence from the Entry and Exit of Vanilla Coke" by Robert Clark and Yiran Gong, to be published in <em>International Journal of Industrial Organization</em>, and the online appendix of the paper.</p>
MangroveDB: A comprehensive online database for mangroves based on multi-omics data
<p><span>Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to </span><span>facilitate their exploration</span><span> and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure <em>etc</em>. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.</span></p>
Transgender Stigma - Evaluation of Online Course
<p><span>The course was offered free of charge on Coursera and contained 19 teaching videos (3-7 minutes each), intermittent practice quizzes, and discussion prompts. Employing <span>real voice recordings of transgender children and their caregivers, the videos were designed to elicit empathy and transmit knowledge. </span>Most videos were animated and used a narrative format. 447 participants distributed around the globe completed a survey both prior to and after finishing the course. The survey contained five questions that captured subjects’ levels of transgender stigma. Results of the pre- and post-surveys were then compared.</span></p> <p><span>Location and identification number are removed.</span></p>
Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey 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.