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42 results for “Evaluation Metrics”
EOSC Task Force on FAIR Metrics and Data Quality: FAIR Evaluation community survey 2023
<p>The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the <a href="https://ec.europa.eu/eusurvey/">EUsurvey tool</a> between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.</p>
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets
<p>Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong></p> <p><strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019).</p> <p><strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript.</p> <p><strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github</p> <p>The scripts for the dataset generation, processing and visualisation of results are available at <a href="https://github.com/Ebony-Watson/scProximitE">our github for the scProcimitE package</a>, and documentation is available <a href="https://ebony-watson.github.io/scProximitE/">here</a>.</p>
Diverse Topologies for Evaluation of Geometric Similarity Metrics
<p>A collection of 7 datasets with each set containing 3D shapes with varying topological complexity. The datasets can be used to compare different metrics of geometric dissimilarity. Two of the datasets have topologically complex shapes that resemble designs obtained from topology optimization, a widely used design optimization method for engineering structures.</p> <p>We used this dataset for a related journal article with the following abstract: "In the early stages of engineering design, multitudes of feasible designs can be generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Data mining such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representatives for review. Design similarity can be defined not only on a purely functional level but also based on geometric properties, such as size, shape, and topology. While metrics such as chamfer distance measure the geometrical differences intuitively, it is more useful for design exploration to use metrics based on <em>geometric features</em>, which are extracted from high-dimensional 3D geometric data using dimensionality reduction techniques. If the Euclidean distance in the <em>geometric features</em> is meaningful, the features can be combined with performance attributes resulting in an aggregate feature vector that can potentially be useful in design exploration based on both geometry and performance. We propose a novel approach to evaluate such derived metrics by measuring their similarity with the metrics commonly used in 3D object classification. Furthermore, we measure clustering accuracy, which is a state-of-the-art unsupervised approach to evaluate metrics. For this purpose, we use a labeled, synthetic dataset with topologically complex designs. From our results, we conclude that Pointcloud Autoencoder is promising in encoding geometric features and developing a comprehensive design exploration method."</p> <p>For each dataset, shapes/designs are saved as surface mesh files (extension: stl) and point cloud files (extension: ply) in the folders "stls" and "plys" respectively. A brief description of the 7 different datasets is in the following table. For each dataset, the designs are named using numbers starting from 0, e.g., “0.stl, 1.stl, …, 19.stl” in the folder for the surface mesh files. Some of the datasets are labeled, i.e., each design belongs to a class. In a labeled dataset, all classes have the same number of designs, and the designs are named in the order of their class. For example, a labeled dataset with 4 designs and 2 classes contains files whose names start with {0, 1, 2, 3} where the designs {0, 1} belong to class 1, and {2, 3} belong to class 2.</p> <table> <thead> <tr> <th scope="col">Dataset name</th> <th scope="col">Directory name</th> <th scope="col">Number of designs</th> <th scope="col">Number of classes</th> </tr> </thead> <tbody> <tr> <td>Beam-rotation</td> <td>"rotate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-elongation</td> <td>"elongate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-translation</td> <td>"move_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Three cube trusses</td> <td>"three_cube_truss"</td> <td>150</td> <td>6</td> </tr> <tr> <td>Single cube trusses</td> <td>"single_cube_truss"</td> <td>275</td> <td>11</td> </tr> <tr> <td>Random topologies</td> <td>"three_cube_truss_random"</td> <td>1000</td> <td>50</td> </tr> <tr> <td>Topologically optimized designs</td> <td>"cube_opt_shapes"</td> <td>1500</td> <td>None</td> </tr> </tbody> </table>
Galaxy Training Data for "Evaluating and ranking a set of pathways based on multiple metrics"
<pre>This dataset provides the inputs needed for the Galaxy Pathway Analysis workflow training tutorial (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). This workflow asseses the performance of predicted pathways by computing 4 criteria (target product flux, thermodynamic feasibility, pathway length, and enzyme availability). A score inform the user about the best candidate pathways to produce a compound of interest. The generated output is a collection of scored and ranked heterologous pathways. The content of the dataset is as follows: - A set of pathways provided in the SBML format (Systems Biology Markup Language) to be ranked, modeling heterologous pathways such as those outputted by the RetroSynthesis workflow (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). - The GEM (Genome-scale metabolic models) which is a formalized representation of the metabolism of the host organism (the model is E. coli iML1515), provided in the SBML format.</pre>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
Sound stimuli: Considerations for the perceptual evaluation of steady-state and time-varying sounds using psychoacoustic metrics
<p>The current dataset provides all the sound stimuli (wav files) to recreate the figures from the study with the title "Considerations for the perceptual evaluation of steady-state and time-varying sounds using psychoacoustic metrics" by the same authors. There are three datasets: <strong>dataset 1</strong> for fly-by aircraft sounds (directory <em>1-Aircraft-fly-by</em>), <strong>dataset 2</strong> for pass-by train sounds (directory <em>2-Train-pass-by</em>), and <strong>dataset 3</strong> for sounds from a resonating tube called hummer (directory <em>3-Hummer-resonances</em>). A brief description of the datasets is included. The reproduction of figures requires the installation of the sound quality analysis toolbox (SQAT) for MATLAB.</p> <p><strong>Use these data</strong>:<br> Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the <strong>SQAT toolbox</strong> (open access at: <a href="https://github.com/ggrecow/SQAT">https://github.com/ggrecow/SQAT</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_SQAT;', without quotation marks in MATLAB), run the script <strong>copy_sounds_for_pub_Osses2023c_to_SQAT.m</strong>. This last step is not required but recommended. Subsequently, you can run the toolbox script pub_Osses2023c_Forum_Acusticum_SQAT.m and follow the instructions on screen.</p>
Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation: extended material
<p>This dataset contains extended material for the paper:</p> <p>Frantz, D., Rufin, P., Janz, A., Ernst, S., Pflugmacher, D., Schug, F., Hostert, P.<strong>: Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation. </strong><em>In revision.</em></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>
Data from: Evaluating predator control using two non-invasive population metrics: a camera trap activity index and density estimation from scat genotyping
<p>Includes datasets from the Wimmera and Mallee, Victoria, Australia:</p> <p>- Fox camera trap data used to model activity</p> <p>- Fox scat SECR capture and trap files used to model density</p>
Evaluation of Outcome Metrics in Alexander Disease
ClinicalTrials.gov study NCT02714764. IPD Sharing: YES. Countries: 1. Publications: 1.
Code from: Metrics for conservation success: using the bird‐friendliness index to evaluate grassland and aridland bird community resilience across the Northern Great Plains ecosystem
Open the record for dataset details and reuse information.
Data from: Evaluating LiDAR-derived structural metrics for predicting bee assemblages in managed forests
Open the record for dataset details and reuse information.
nRCFV: A sequence, taxon and character state-normalised metric for the pre-reconstruction evaluation of compositional heterogeneity
Open the record for dataset details and reuse information.
Stimuli and Results for "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription"
<p>This contains the stimuli and the participants data for the listening tests presented in the paper:</p> <p>Adrien Ycart, Lele Liu, Emmanouil Benetos, Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription". <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1):68-81, 2020 .</p> <p>More precisely, it contains:</p> <ul> <li>MAPS_midi_cut.zip: The MIDI files used to create the stimuli </li> <li>cut_points_seconds.zip: The points in seconds at which the MAPS music pieces were cut to make the stimuli. These correspond to manually-selected 5 to 10 seconds chunks, roughly corresponding to musical phrases.</li> <li>listening_test_results.zip: The data gathered during the listening test: <ul> <li>user_data.csv contains data about participants</li> <li>answers_data.csv contains the answers given by all participants</li> <li>comments.txt contains the comments left by the participants.</li> </ul> </li> </ul> <p>For any enquiries, please contact Adrien Ycart (a.ycart@qmul.ac.uk) or Emmanouil Benetos (emmanouil.benetos@qmul.ac.uk).</p> <p> </p>
Performance Evaluation of Seven Remote Sensing Datasets for TRB Cropland Area Estimation (Accuracy Metrics and Temporal Trends)
<p>This dataset contains accuracy assessments and trend analyses for cropland area estimation using seven remote sensing datasets in the TRB region. The data is organized into the following structure:<br>1. Accuracy Evaluation ("RMSE+Rt+MPE" directory)</p> <p>Contains individual evaluation files for each of the seven remote sensing products<br>File naming convention: [DatasetName]_Evaluation.csv<br>Each file contains four columns:</p> <p>County: Administrative region<br>Rt: Temporal correlation<br>RMSE: Root Mean Square Error<br>MPE: Mean Percentage Error</p> <p>2. Area Change Trends ("Trend" directory)</p> <p>Contains trend analysis files for seven remote sensing products and reference observations (OBS)<br>File naming convention: [DatasetName/OBS]_trend.csv<br>Each file contains three columns:</p> <p>County: Administrative region<br>Slope: Trend slope coefficient<br>P_value: Statistical significance value</p> <p>3. Spatial Correlation (Rs.csv)</p> <p>Single file containing annual spatial correlation coefficients (Rs) for all seven remote sensing datasets.</p>
User Evaluation and Metrics Analysis of a Prototype Web-based Federated Search Engine for Art and Cultural Heritage
<p>This dataset includes the quantitative data of the usage during the evaluation phase of a prototype web-based federated search engine for art and cultural heritage related content. The metrics which resulted in the dataset were in the form of a timeline of actions taken from a user (evaluator) in the course of a single session of interaction with the platform. A total of 20 different metrics were being monitored regarding the usage of the search engine, including submitting a query, a voice query, preforming a visual search, viewing a result, viewing a visual search result, updating an avatar, editing a user profile or changing user preferences, bookmarking and removing bookmarks of results and visual search results, using text to speech of all the various elements, opening the source view of a result and clicking a concept tag. All metrics included the timestamp of the event taking place and the value of the related event (e.g. the term of a search query).</p>
Assessing Word Similarity Metrics for Traceability Link Recovery - Evaluation Dataset
<p>This dataset includes all data that was used for the evaluation of my bachelor's thesis:</p> <p><em>Assessing Word Similarity Metrics for Traceability Link Recovery</em></p> <p>The following files correspond to the following data sets from the evaluation:</p> <ul> <li>cc-en-300.tar.gz corresponds to fastText's cc.en.300.bin embedding</li> <li>crawl-300d-2M-subword.tar.gz corresponds to fastText's crawl-300d-2M-subword.bin embedding</li> <li>wiki-news-300d-1M-subword.tar.gz corresponds to fastText's wiki-news-300d-1M-subword.bin embedding</li> <li>wordnet.tar.gz corresponds to the WordNet 3.1 semantic network</li> <li>sewordsim.tar.gz corresponds to SEWordSimDB's vector similarity database</li> <li>glove_cc_840B_300d.tar.gz corresponds to GloVe's CC vector embedding</li> <li>glove_wikigiga_300d.tar.gz corresponds to GloVe's 300 dimensional WIGI vector embedding</li> <li>glove_wikigiga_200d.tar.gz corresponds to GloVe's 200 dimensional WIGI vector embedding</li> <li>glove_wikigiga_100d.tar.gz corresponds to GloVe's 100 dimensional WIGI vector embedding</li> <li>glove_wikigiga_50d.tar.gz corresponds to GloVe's 50 dimensional WIGI vector embedding</li> <li>glove_twitter_200d.tar.gz corresponds to GloVe's 200 dimensional TWTR vector embedding</li> <li>glove_twitter_100d.tar.gz corresponds to GloVe's 100 dimensional TWTR vector embedding</li> <li>glove_twitter_50d.tar.gz corresponds to GloVe's 50 dimensional TWTR vector embedding</li> <li>glove_twitter_25d.tar.gz corresponds to GloVe's 25 dimensional TWTR vector embedding</li> <li>eval_results.tar.gz contains the detailed evaluation results for each configuration of all measures</li> </ul> <p>The licenses of all data sets are included in their respective files.</p> <p>Some of these data sets are .sql files. To use these files to reproduce the evaluation, they need to be imported into a sqlite3 database. The version of ArDoCo used for the evaluation is only able to work with sqlite3 databases and not with sql files.</p>
Postural stability metrics associated to the publication: Additive manufacturing of spinal braces: evaluation of production process and postural stability in patients with scoliosis
<p>Data was collected for each condition (3D-printed brace, conventional brace, unbraced) for 60 seconds with patients in a standing posture, open eyes and both feet together.</p>
Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing 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.