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Dataset results
753 results for “metrics”
Example dataset for Tracking Metrics using TrackMate and Oneat
<p>In this dataset we compare the automated tracking results using the standard TrackMate algorithms for frame to frame and segment to segment linking with the ground truth dataset. Furthermore we use Oneat to correct the branches of the lineage trees and using oneat as TrackCorrector we recompute the metrics to show improvements over TrackMate track linking algorithms.</p> <p>Tracking Metrics</p> <p>Simple LAP tracker + Oneat</p> <p>{DET : 0.9964, CT : 0.73531, TRA : 0.9933, TF : 0.97518, BCi : 0.10526}</p> <p>LAP Tracker with track splitting and Quality as additional cost</p> <p>{DET : 0.9900, CT : 0.677033, TRA : 0.986785; TF : 0.95041, BCi : 0.04347}</p> <p>LAP Tracker with track splitting and Quality as additional cost + Oneat</p> <p>{DET : 0.98911, CT : 0.672629, TRA : 0.985774, TF : 0.948692, BCi : 0.05555}</p> <p>LAP Tracker without track splitting and Quality as additional cost + Oneat</p> <p>{DET : 0.990083, CT : 0.6766, TRA : 0.986742, TF : 0.9521, BCi 0.054}</p>
What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes
<p>This is the dataset for the publication "What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes".</p> <p>It contains a random sample of 2533 commits from 54 Java Apache open source projects classified by two researchers into perfective, corrective and other changes (manual_labels.csv). Moreover, we include static source code metrics and static analysis warnings for the 2533 changes in al_changes_gt.csv.gz.</p> <p>In addition, we include the full dataset of 125482 commits in all_changes_sebert.csv.gz with all metrics and automatic labels for every commit that was not manually labeled. The automatic labels were provided by a fine-tuned transformer model (BERT) pre-trained exclusively on software engineering data.</p> <p>We also provide the fine tuned version of the pre-trained model in seBERT_fine_tuned_commit_intent.tar.gz as well as a Snapshot of the SmartSHARK MongoDB database used in gathering the raw data in smartshark_emse.agz.</p> <p>The model can be tested live on the <a href="https://user.informatik.uni-goettingen.de/~trautsch2/emse_2021/commit_intent.html">website</a> accompanying the publication.</p>
Monitoring ML Systems: Challenges, Solutions and Metrics from a Practitioners' Perspective
<p>This is the dataset for the paper "Monitoring ML Systems: Challenges, Solutions and Metrics from a Practitioners' Perspective". The dataset is recorded in an MS Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.</p> <p>(1) <strong>Selected Projects (GitHub)</strong> contain the 15 selected ML projects with the URL of each project.</p> <p>(2) <strong>Raw Data (GitHub)</strong> contains the information about the randomly selected 600 issues, such as issue titles, issue links, issue id.</p> <p>(3) <strong>Raw Data (SO) </strong>contains the information about the randomly selected 2088 SO posts (part of the 2174 SO posts), such as post titles, post link, post id, and open date.</p> <p>(4) <strong>MLOps (SO) </strong>contains the information about the 86 MLOps SO posts (part of the 2174 SO posts), such as post titles, post link, post id, and open date.</p> <p>(5) <strong>Identified Challenges (GitHub) </strong>contain the list of categories, subcategories, and codes of identified challenges from GitHub issues.</p> <p>(6) <strong>Identified Solutions (GitHub) </strong>contain the list of categories, subcategories, and codes of identified solutions from GitHub issues.</p> <p>(7) <strong>Identified Metrics (GitHub) </strong>contain the list of identified metrics from GitHub issues.</p> <p>(8) <strong>Identified Challenges (SO) </strong>contain the list of categories, subcategories, and codes of identified challenges from SO posts.</p> <p>(9) <strong>Identified Solutions (SO) </strong>contain the list of categories, subcategories, and codes of identified solutions from SO posts.</p> <p>(10) <strong>Identified Metrics (SO) </strong>contain the lists of identified metrics from SO posts.</p> <p>(11) <strong>Identified Challenges (Interview)</strong> contain the list of categories, subcategories, and codes of identified challenges from interviews.</p> <p>(12) <strong>Identified Solutions (Interview) </strong>contain the list of categories, subcategories, and codes of identified solutions from interviews.</p> <p>(13) <strong>Identified Metrics (Interview) </strong>contain the list of identified metrics from interviews.</p> <p>(14) <strong>Identified Challenges (Final) </strong>contain the taxonomy of the final challenges identified from GitHub issues, SO posts, and interviews.</p> <p>(15) <strong>Identified Solutions (Final) </strong>contain the<strong> </strong>taxonomy of the final solutions identified from GitHub issues, SO posts, and interviews.</p> <p>(16) <strong>Identified Metrics (Final) </strong>contain the<strong> </strong>final list of metrics identified from GitHub issues, SO posts, and interviews.</p>
Dataset for A catalog of source code metrics – a tertiary study
<p>The dataset is divided into two excel files. </p> <p>The excel file "Characteristics quality assessment search results Of Secondary Studies" contains the meta data related to the included secondary studies, quality assessment score, and quality criteria used.</p> <p>The excel file "sourceCodeMetrics" contains the unique source code metrics.</p> <p>-Within this file, "Catalog of Metrics" contains the final list of metrics that are reported in the 52 secondary studies.</p> <p>- "CodeUnit Definition" provides the description of the code units used by the source code metrics. </p> <p>- "Scope Definition" provides the description of the scope or hierarchy level at which these source code metrics report their values.</p> <p>- "Attribute definition" describes the internal quality attributes.</p> <p> </p>
NANCY SNS-JU PROJECT "ITALTEL ITALIAN IN-LAB TESTBED - LATENCY METRICS"
<p>In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity/CoMP Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted with different resolutions, 480p, 720p, 1080p, 2160p; both in uplink (UL) and in downlink (DL); the type of metrics monitored is RTT (Round Trip Time).</p>
Metrics of Collaboration Across Programming Paradigms and Languages: Comprehensive Dataset
<h3>[New version] : </h3> <p>Following a recent revision of our taxonomy of programming paradigms, we have observed slight changes in the data of our dataset. Consequently, to ensure accuracy and relevance, the dataset has been updated to reflect these changes. We encourage you to download the latest version to benefit from the updated data.</p> <h3>Description :</h3> <p>This dataset provides a comprehensive analysis of collaboration metrics across various programming paradigms and languages, aimed at researchers and practitioners interested in the interoperability and integration challenges within multi-language development environments. The data encapsulates both raw and cleaned metrics detailing potential collaboration avenues and interoperability between different programming paradigms, as well as between distinct programming languages, with and without redundancy.</p> <h3>Contents:</h3> <p>CollaborationMetric_Paradigms_Raw.csv - Contains the raw metrics of collaboration potential between programming paradigms based on a novel collaboration metric developed in our research. This dataset encompasses various paradigms' compatibility and integration potential, offering a wide-angle view on the landscape of programming paradigm interoperability.<br>CollaborationMetric_Paradigms_Cleaned.csv - A cleaned and refined version of the raw paradigm collaboration metrics, with redundant data removed for clarity and ease of analysis. This file is optimized for researchers seeking to delve directly into the significant findings of paradigm compatibility without the need for further data cleaning.<br>CollaborationMetric_Languages_Raw.csv - Offers raw collaboration metrics between different programming languages, providing insights into how languages may synergize or face integration challenges within multi-language projects. The data reflects an extensive analysis of language interoperability, grounded in the theoretical framework outlined in our associated research paper.<br>CollaborationMetric_Languages_Cleaned.csv - A streamlined version of the language collaboration metrics, free from redundancies, and focused on the core insights into language interoperability. This dataset is intended for immediate application in research and practical scenarios, facilitating quick identification of potential language synergies or conflicts.</p> <h3><br>Methodology:</h3> <p>The datasets were generated through a rigorous quantitative analysis, employing a collaboration metric that considers structural characteristics, kinship relationships, and fundamental computational and behavioural properties of programming paradigms and languages. Detailed methodology and theoretical underpinnings are described in our associated research paper, "[Towards a quantitative evaluation of paradigmatic collaboration: insights from Prolog and beyond]," intended for submission to the International Conference on Logic Programming (ICLP).</p>
Supplementary Material for A Metrics Suite for Quantifying Legal Compliance of Smart Contracts
<p>This repository contains the supplementary material for the paper titled "A Metrics Suite for Quantifying Legal Compliance of Smart Contracts". It includes natural-language legal contracts, their smart contract implementations, Petri net models of said contracts, and their reachability graphs. The Petri net models are presented as graphics, as well as .cpn files that can be opened with either <a href="https://cpntools.org/" target="_blank" rel="noopener">CPN Tools</a> or <a href="https://cpnide.org/" target="_blank" rel="noopener">CPN IDE</a>.</p>
HydroPenIndia: A catalogue of streamflow metrics, meteorological time series and catchment attributes of Peninsular India
<p><em>HydroPenIndia, </em>a catalogue of streamflow metrics, hydro-meteorological time series and landscape attributes of 204 catchments of Peninsular India is introduced. This catalogue consists of daily hydro-meteorological time series (rainfall, soil moisture, potential evapotranspiration, actual evapotranspiration, maximum temperature, minimum temperature, longwave radiation, shortwave radiation, wind speed and humidity) for a period of 36 years from 1980-2015. The time series of 26 streamflow metrics, 13 topographic metrics, 12 climate indices, 15 hydrologic signatures, 8 land cover descriptors, 6 geologic characteristics, 6 soil characteristics (see table 8) and 13 human intervention indices are also included in this dataset. <em>HydroPenIndia</em> is an initiative to encourage hydrologists to advance knowledge of hydrological processes by contributing to fundamental research questions on Indian catchments. Free availability of the dataset will provide access to global users to represent India in large-sample hydrology studies. <em>HydroPenIndia</em> is derived from multiple databases to help researchers start their research without wasting time on collecting and processing datasets. This catalogue will motivate researchers to solve pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unravelling regional scale hydrologic functioning and climate change impact assessment over Peninsular India.</p>
Quantifying Progress: Metrics and Indicators for Measuring Digital Transformation Maturity in Organizations
<p><span>As organizations increasingly embark on digital transformation journeys, the need for effective metrics and indicators to measure progress and maturity becomes paramount. This paper investigates the development and application of metrics for quantifying digital transformation maturity in organizations. Through an extensive review of literature and examination of case studies, the paper identifies key dimensions and stages of digital maturity. It proposes a framework encompassing both quantitative and qualitative metrics that can be used to assess an organization's digital transformation journey. The paper explores challenges associated with defining meaningful metrics and offers insights into adapting measurement frameworks to diverse organizational contexts. By addressing this critical gap in the literature, the paper aims to provide practitioners, researchers, and decision-makers with a valuable resource for evaluating and benchmarking digital transformation progress, fostering a more nuanced understanding of the multifaceted nature of organizational digital maturity.</span></p>
Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments
<p>This repo contains a complete replication package, including raw data and scripts for the statistical analysis, for the paper "Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments" submitted to the industry track of the <a href="https://conf.researchr.org/home/icsme-2024" rel="nofollow">40th International Conference on Software Maintenance and Evolution (ICSME)</a>, Flagstaff, AZ, USA, Oct 6-11, 2024.</p> <div> <h2>Authors</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#authors"></a>Markus Borg, Marwa Ezzouhri, and Adam Tornhill</div> <div> <h2>Abstract</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#abstract"></a>As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.</div> <div> <h2>Repository Content</h2> Two Jupyter Notebooks.</div> <ul> <li> <ul> <li>uc1_maintainability_prediction.ipynb: A Notebook for Use Case 1 - Maintainability Prediction.</li> <li>uc2_liability_prediction.ipynb: A Notebook for Use Case 2 - Liability Prediction.</li> </ul> </li> <li>maintainability_data.csv: The dataset covering the 404 open-source files from the Maintainability Dataset (Bertrand <em>et al.</em>, 2020) <ul> <li>Majority vote ground truth labels from Schnappinger <em>et al.</em> (2020)</li> <li>Low-level code metrics from Bertrand <em>et al.</em> (2023)</li> <li>Code Health</li> <li>SonarQube output, i.e., TD Ratio and TD Time</li> <li>Microsoft Maintainability index provided by <a href="https://plugins.jetbrains.com/plugin/13959-metricstree" rel="nofollow">MetricsTree</a></li> </ul> </li> </ul> <div> <h2>References</h2> </div> <ul> <li>Schnappinger <em>et al.</em>, <a href="https://figshare.com/articles/dataset/A_Software_Maintainability_Dataset/12801215" rel="nofollow">A Software Maintainability Dataset</a>, 10.6084/m9.figshare.12801215, 2020.</li> <li>Schnappinger <em>et al.</em>, Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability, in <em>Proc. of the 36th International Conference on Software Maintenance and Evolution</em>, pp. 278–289, 2020.</li> <li>S. Bertrand <em>et al.</em>, Replication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics, in <em>Proc. of the 27th International Conference on Evaluation and Assessment in Software Engineering</em>, pp. 241–246, 2023.</li> </ul>
Linear vs. non-linear metrics of Autonomic Nervous System: study on healthy volunteers during controlled breathing
<h1>Please cite this article as reference article:</h1> <p>Uryga A, Najda M, Berent I, Mataczyński C, Urbański P, Kasprowicz M, Buchner T. The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability. Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778. </p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (dataset 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (dataset 1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes 49 healthy volunteers (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes 21 healthy volunteers (14 females, 7 males, median age: 22 years, range: 18-31 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in all subjects in dataset 1; CNAP, CNSystems Medizintechnik GmbH, Graz, Austria and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li>Protocol: after a resting epoch lasted at least 5 minutes, a controlled breathing session was initiated with 5-minute recordings at each of the respiratory rate: 6, 10 or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <h1>Data description</h1> <ul> <li>Type of database (database 1/database 2)</li> <li>Type of device used to ABP measurement</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Autonomic Nervous System parameters including:</li> </ul> <p>- <strong>Phase-Rectified Signal Averaging</strong> - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart; more details could be found here: <em>Campana L M, Owens R L, Clifford G D, Pittman S D and Malhotra A 2010 Phase-rectified signal averaging as a sensitive index of autonomic changes with aging J Appl Physiol 108 1668–73</em></p> <p>- <strong>Entropy</strong>: multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</p> <ul> <li> <strong>Heart rate variability (HRV) metrics</strong>: In the frequency domain, the Lomb–Scargle periodogram was used to determine the power spectral density of the interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz). Additionally, the total power of the HRV signal (TP, 0.04–0.40 Hz) and the ratio between low and high-frequency components (LF/HF) were calculated. In the time domain, the following metrics were determined: the standard deviation of the R-R intervals (SDNN) and the square root of the mean of the squared successive differences between adjacent R-R intervals (RMSSD), mean of the R-R intervals (meanNN), and the proportion of R-R intervals greater than 20 ms or 50 ms, out of the total number of R-R intervals (pNN20 and pNN50, respectively); appropriate functions were implemented in NeuroKit2</li> </ul> <p> </p> <p>Update ------version 2</p> <p>After the revision process, SDNNref was added, defined according to formula presented in paper of Monfredi et al. (Monfredi O, Lyashkov AE, Johnsen AB, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014 Dec;64(6):1334-43)</p>
Metrics EOD optimization
<p>Excel file with the results of the optimization of the Earth Observation Data (EOD) Processing and Distribution pilot case using the ENTICE middleware. The EOD pilot consists of four virtual machines: process4EO, monitor4EO, archive4EO, user4EO. The metrics used to measure the optimization of each virtual machine are the sizes, creation times, delivery times, deployment time. Finally, the percentaje reductions were calculated.</p>
Service testing metrics dataset
<p>The dataset includes data collected during the iterative testing, validation and fine-tuning of the INNO-4-AGRIFOOD services and smart tools, aimed at managing ambiguity during the various iterations as well as measuring the impact of improvements after each iteration. In particular, it contains both qualitative and quantitative data on (i) the satisfaction of SMEs that received INNO-4-AGRIFOOD services, (ii) the satisfaction of SMEs and innovation consultants that have used the INNO-4-AGRIFOOD smart tools, (iii) the impact of the INNO-4-AGRIFOOD services on the business of the SMEs that received them, (iv) the activities performed in the framework of each INNO-4-AGRIFOOD service provided in the context of the project, and (v) different aspects of the services and smart tools that can be further streamlined according to users’ needs and expectations.</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>
The reference index files used for RNA-seq workflow benchmark in CWL-metrics manuscript
<p>The reference files used in the RNA-Seq workflow benchmark in the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
The output and the log files from RNA-Seq workflow benchmark for CWL-metrics manuscript
<p>The output files and log files generated by the workflow executions for RNA-Seq workflow benchmark by CWL-metrics, from the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
Heatwave metrics data supporting the paper "Strong influence of aerosol reductions on future heatwaves "
<p>This folder contains the heatwave metrics calculated from the CESM-LENS project. The raw temperature data are archived in the climate data gateway at NCAR. Also included are the heatwave metrics calculated using the NCEP/NCAR reanalysis and the Met Office Hadley Centre gridded daily temperatures. All data are in NetCDF4 format.</p>
Exploring Fluctuations in Internal Quality Metrics
<p>Datasets and analysis script for the article "Exploring Fluctuations in Internal Quality Metric" submitted to ESEM'19 under double-blind review and provided as-is.</p>
Data and Analysis Artifacts for Service-Based Evolvability Patterns (Experiment and Metrics)
<p>Two functionally equivalent service-based web-shop systems (one version with selected service-based patterns, one without) were analyzed with a controlled experiment as well as with structural maintainability metrics. This repo contains all analysis artifacts.</p>
Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech
<p>An interactive user interface and the raw data from the manuscript titled: "Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech". </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.