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Dataset results
753 results for “metrics”
Data Usage Metrics at Repositories: A Survey
<p>Results of a survey undertaken by the Research Data Alliance Data (RDA) Usage Metrics Working Group during February and March 2019 and presented at the 13th RDA Plenary Meeting in Philadelphia on 3 April 2019.</p>
Joint distribution between rankings (leader and metric based) and metric values
<p>These images represent the joint distribution between productivity metric values and rankings based on these productivity metrics and the development team leader's information.</p>
Dataset and Code Accompanying "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy"
<p>Dataset and code related to the work outlined in "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy".</p> <p>File Description:</p> <ul> <li>seis_seg.h5 - trained model weights.</li> <li>topseis_temp.npy - seismic survey.</li> <li>train_demo.ipynb - training demo including synthetic modelling pipeline.</li> <li>inference_and_plot - inference and reproducing main results.</li> </ul> <p>Pre-calculated phase volume and encoded features for reproducing figures:</p> <ul> <li>unwrapped_phase_volume_full.npy.</li> <li>features_full_ds222_ps32_s222.npy.</li> <li>indices_full_ds222_ps32_s222.npy.</li> </ul>
RESSPECT metric
<p>Data whose results are described on Malz, Dai et al., <a href="https://arxiv.org/pdf/2305.14421" target="_blank" rel="noopener">arXiv:astro-ph/2305.14421</a>.</p>
TOPOLOGY OF MINKOWSKI METRIC
<p>"Topology of Minkowski Metric" investigates the mathematical structure and features of the Minkowski space, which is essential to the study of special relativity and theoretical physics.</p>
What if Smart Contracts Could Whisper their Weaknesses: From Software Metrics to Vulnerability Classification
<p>This repository contains the code and data associated with our submission to FSE 2025, entitled 'What if Smart Contracts Could Whisper their Weaknesses: From Software Metrics to Vulnerability Classification'. If the paper is accepted for publication, the authors' information and affiliations will be made publicly available in accordance with the conference proceedings.</p>
A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package
<div> <p><strong>Title:</strong> A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the replication package artefact for the paper entitled "A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures".</p> <p><strong>Paper Abstract:</strong> In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.</p> </div>
Towards Understanding the Impact of Code Modifications on Software Quality Metrics
<p>The provided dataset contains the data used by "Towards Understanding the Impact of Code Modifications on Software Quality Metrics", in order to examine the impact of code changes in software quality metrics and identify types of code changes with similar impact and the results obtained.</p>
Data for Publication "Comparing individuals buried in flexed and extended positions at the Greek colony of Chersonesos (Crimea) using cranial metric, dental metric, and dental nonmetric traits"
<p>Data for Publication: H. Rathmann, R. Stoyanov, and R. Posamentir, Comparing individuals buried in flexed and extended positions at the Greek colony of Chersonesos (Crimea) using cranial metric, dental metric, and dental nonmetric traits.</p>
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and 'nearest neighbour distance' measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: 1) via the Zooniverse citizen science project Penguin Watch and 2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.
Fig. 2 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 2. Schematic view of the Baguari Dam with the positioning of the fish ladder entrance.
Fig. 1 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 1. Location of Baguari Dam in the middle Doce River Basin, Minas Gerais State, Brazil.
Repository Analytics and Metrics Portal (RAMP) 2018 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.</p>
Repository Analytics and Metrics Portal (RAMP) 2017 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p>
All metrics from the regridding benchmark with SCRIP, XIOS,ESMF and YAC
<p>All metrics from the regridding benchmark with SCRIP, XIOS,ESMF and YAC</p>
Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
<p>The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.</p>
Repository Analytics and Metrics Portal (RAMP) Production Snapshot Dataset, 2018-11-01
<p>The data are publicly available via Globus: <a href="https://app.globus.org/file-manager?origin_id=a40be90c-f8c2-11e8-9340-0e3d676669f4&origin_path=%2F">https://app.globus.org/file-manager?origin_id=a40be90c-f8c2-11e8-9340-0e3d676669f4&origin_path=%2F</a></p> <p>The data consist of a snapshot of the production RAMP Elasticsearch instance [http://ramp.montana.edu/](http://ramp.montana.edu/). The snapshot was taken on November 1, 2018, and consists of 51 indices (one index each for 50 participating institutional repositories (IR) plus one master index or alias that provides computational access to all indices at once). In addition to the snapshot itself, the published dataset includes documentation describing data collection and processing, separate documentation of the requirements and steps to restore the snapshot to a working instance of Elasticsearch, a CSV file listing participating IR and their corresponding Elasticsearch index names, and a Jupyter Notebook with sample Python code for accessing Elasticsearch.</p> <p>The snapshot ID needed to restore the snapshot to a working index is '2018-11-01.' Please see the included file, 'restore_RAMP_snapshots.pdf' for more info.</p> <p>Because of the large file size, download via high speed network is recommended.</p> <p>RAMP development was funded by the Institute of Museum and Library Services (IMLS) as part of the "Measuring Up" project: IMLS: LG-06-14-0090<br> </p>
Vibration analysis metrics of a ball bearing during different operational states
<p>This labeled dataset is provided in the form of a CSV file and contains vibration analysis metrics (v-RMS, a-RMS, a-Peak, Temperature, Crest Factor) of a 6204 2RS ball bearing by the use of an IFM VVB001 vibration sensor and measurements were captured every two minutes by the sensor device.<br> An experimental assembly was set up comprised by a 0.75kW - 1450rpm Bonfiglioli BN80B4 FD motor with an i=80 reduction gear, a coupler and an axle with the ball bearing mounted on the latter for the purpose of conducting experiments based on three operational states of the bearing in regard to the level of grease applied.</p> <p>Attribute description as per the sensor’s manual and operation instructions:</p> <ul> <li>The <strong>v-RMS</strong> (effective value of the vibration velocity) measures the total load of a rotating machine. The most frequent types of overload (unbalance, alignment errors, etc.) are reflected in the v-RMS. An increased load can damage the machine in the long term (fatigue, fatigue strength) or, in extreme cases, destroy it within a short time. It is expressed in m/s.</li> <li>The <strong>a-RMS </strong>(effective value of the acceleration) detects mechanical contact of machine components. This contact typically occurs in case of wear (faulty bearing, worn out toothed wheels, etc.) or problems with lubricants (contaminated grease, water in oil, etc.). It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>a-Peak</strong> monitors the maximum value of the acceleration. Shocks in the acceleration can occur once or periodically, as in a crash, for example in the event of bearing damage. a-Peak is a measure for the forces occurring on the machine. It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>Crest </strong>(or crest factor) is a described characteristic value of the signal analysis. It is defined as the ratio of the maximum value to the effective value (peak/RMS). In condition monitoring the characteristic value is used for the evaluation of the bearing condition. The high-frequency signals with a short pulse duration of a bearing damage generate higher peak values in relation to the effective value. This relation can be read from the crest factor.</li> <li>The <strong>Temperature </strong>attribute is self explanatory and is expressed in degrees of Celsius.</li> <li>Labels of the <strong>Bearing State</strong> regarding the recorded measurements:<br> 1 - sealed ball bearing with recommended amount of industrial grease<br> 2 - unsealed ball bearing with no amount of grease<br> 3 - unsealed ball bearing with excess amount of grease</li> </ul> <p>This dataset was produced for the purpose of training an Artificial Neural Network model for the task of multi-class classification in the frames of a Predictive Maintenance strategy.</p>
nRCFV: A sequence, taxon and character state-normalised metric for the pre-reconstruction evaluation of compositional heterogeneity
<p><strong><span>Motivation</span></strong></p> <p><span>Compositional heterogeneity – when the proportions of nucleotides and amino acids are not broadly similar across the dataset – is a cause of a great number of phylogenetic artefacts. Whilst a variety of methods can identify it post-hoc, few metrics exist to quantify compositional heterogeneity prior to the computationally intensive task of phylogenetic tree reconstruction. Here we assess the efficacy of one such existing, widely used, metric: Relative Composition Frequency Variability (RCFV), using both real and simulated data.</span></p> <p><strong><span>Results</span></strong></p> <p><span>Our results show that RCFV can be biased by sequence length, the number of taxa, and the number of possible character states within the dataset. However, we also find that missing data does not appear to have an appreciable value on RCFV. We discuss the theory behind this and the consequences of this for the future of the usage of the RCFV value and propose a new metric, nRCFV, which accounts for these biases. Alongside this, we present a new software that easily calculates both RCFV and nRCFV, called nRCFV_Reader.</span></p> <p><strong><span>Availability and Implementation</span></strong></p> <p><span>nRCFV has been implemented in RCFV_Reader, available at: </span><a href="https://github.com/JFFleming/RCFV_Reader"><span>https://github.com/JFFleming/RCFV_Reader</span></a><span>. Both our simulation and real data are available in this dataset.</span></p>
Proof of Concept database with inputs and outputs of the Master thesis: Analyzing Software Delivery Performance behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics
<p>The software has become one of the main assets to deliver services today. Thus, software delivery has been dealing with a competitive and dynamic environment where the demand for faster and more assertive deliverables, called here Releases, only increases. Agile development methods emerged helping to accelerate software delivery, embracing industry and open source community. Since then, the software delivery frequency has expanded and improved bringing more adopters of rapid release cycles to reduce their time-to-market. However, using only rapid releases can not be enough as measuring software delivery can answer essential questions, like how software delivery is happening and how it should be. Some approaches for measuring software delivery appeared such as Software Delivery Performance (SDP) where software delivery is measured as a consequence of capabilities evolution. Popularity in Open Source Software Projects (OSSP) means that a project is mature enough in the community to fit the software demand and, therefore, is likely to be ready to be measured through a software delivery approach like SDP. In light of it, this work offers means to analyze SDP behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics. The results demonstrated that popularity is efficient filtering, as it improves the OSSP delivery, supporting the work's reliability and accuracy. The source code and methodology are published as a replication package to encourage reproducibility and future research.</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.