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47,155 results for “evaluations”
Data used in the manuscript - A Hierarchical Approach for Evaluating Athlete Performance with an Application in Elite Basketball
<p>The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data. </p> <p>The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.</p> <p>Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.</p> <p>However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset. </p>
OneNet - AccessNet (Czech Demo - part e-fleet) - dataset + KPI evaluation
<p>File contains all datasets and KPI evaluation related to OneNet - AccessNet project (under Czech Demo - part e-fleet).</p>
Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
<p>This repository contains supplementary data for the journal paper:</p> <blockquote> <p>Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi: 10.3389/fnbot.2023.1179224</p> </blockquote> <p>It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive <strong>EMG_model_configs.zip</strong>.</p> <p><br> The files <strong>IP_tracking_opt_res.json</strong><a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and <strong>IP_tracking_opt_res.pkl</strong> contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored.</p> <p>For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.</p> <p> </p> <p>[1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152</p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
Dataset for paper "A Close-Range Photogrammetric Surface Scanner and its Evaluation"
<p>This dataset is supplementary material to the submission “A low-cost close-range photogrammetric surface scanner”, by Panagiotis Koutlemanis, Xenophon Zabulis, Nikolaos Stivaktakis, Nikolaos Partarakis, Emmanouil Zidianakis, and Ioanna Demeridou. The dataset contains 3D reconstructions that show the results of a close-range photogrammetric approach and its implementation using a low-cost CNC device.</p>
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
<p>This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. </p> <blockquote> <p>Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics.</p> </blockquote> <p>For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>).</p> <p><strong>The data is structured as follows:</strong></p> <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained <em>with the background section as input. </em>These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td><em>Results from Table 2</em></td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td><em>Results from Table 3</em></td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td><em>Results from Table 4</em></td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td><em>Results from Table 5</em></td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics
<p>This deposit contains :</p> <p>- a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples</p> <p>- the html report with codes, results and figures</p> <p>- a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy)</p> <p>- a RData containing associated metadata </p>
FIND: A Function Interpretation Dataset and Benchmark for Evaluating Interpretability Methods
<p><strong>FIND</strong> is an interactive dataset for evaluating AI interpretability methods on black box functions. </p> <p>This dataset contains all function files for the <strong>FIND</strong> benchmark and JSON files with associated metadata. The utilities provided in the associated <strong>FIND</strong> <a href="https://github.com/multimodal-interpretability/FIND">GitHub Repository</a> support running and evaluating interpretation of the functions with user-defined interpreters.</p>
Evaluation of buckwheat genetic resources in Slovenia within the ECOBREED project
<p>Publication and supplementary data:</p> <p>Data related to the publication in Fagopyrum 40 (2):67-76: field evaluation data (seed weight, plant height, protein content) and concentrations of phenolic compounds and antioxidant capacity of 17 buckwheat genetic resources and 6 commercial varieties tested in two years (2020 & 2021) in Slovenia.</p>
TrainTicket microservice testbench extracted information for our work: Evaluating ChatGPT's Proficiency in Understanding and Answering Microservice Architecture Queries Using Source Code Insights
<p>It contains the CSV file output of our tool implemented in the paper: "Evaluating ChatGPT’s Proficiency in Understanding and Answering Microservice Architecture Queries Using Source Code Insights." applied to the TrainTicket microservice testbench. The information in this CSV was used for In-Context-Learning for ChatGPT.</p>
Evaluation dataset for Relation Extraction of relationships between Organisms and Natural-Products
<p>A curated evaluation dataset for end-to-end Relation Extraction of relationships between organisms and natural-products.</p> <p>Details about the manual annotation:</p> <ul> <li>For Chemicals: <ul> <li>The chemical labels are annotated as they appear in the abstract.</li> <li>In abstracts, singular chemicals and classes of chemicals produced by a specific organism were distinguished.</li> <li>The "type" attribute {“chemical”, “class”} is used to indicate the nature of the mentioned name.</li> <li>A "class" attribute for chemical entities has also been included if class information is present in the abstract.</li> <li>A Wikidata and PubChem identifiers were assigned to chemicals and classes when available.</li> </ul> </li> <li>For Organisms: <ul> <li>The organism labels are annotated as they appear in the abstract.</li> <li>If in an abstract, the genus name was mention first, e.g. "Plakinastrella sp." and then the specie name e.g "Plakinastrella clathrata" is precise, then only the specie name is used.</li> <li>A Wikidata identifier was assigned to all organisms.</li> <li>In some abstracts, only the genus name is mentioned.</li> </ul> </li> <li>For Relations: <ul> <li>Only the relations explicitly mentioned in the abstract are reported in the output labels.</li> <li>Relations are reported in their order of appearance in the abstract.</li> </ul> </li> </ul>
Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data
<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>
Drop Project Student Plugin for IntelliJ IDEA - Evaluation Survey
<p>Contains a CSV file with students replies to the survey used to evaluate the Drop Project Student plugin for IntelliJ IDEA.</p> <p>To support international readers, the question names (CSV headers) were translated to English and/or match the numbering that appear in the paper. However, the student's textual replies to the open ended questions were left in their original language, Portuguese.</p>
GENEA Challenge 2023 Human-Likeness subjective evaluation data
<p>This repository contains human-likeness user-study response data and associated analyses from the GENEA Challenge 2023. The code was written by Gustav Eje Henter and is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>The archive contains:</strong><br> * This README.txt file.<br> * A LICENSE.txt file.<br> * A JSON file containing all human-likeness user-study responses included in the analyses.<br> * MATLAB .m files for reproducing the above analyses (both tables and figures).<br> * A folder called "output" that contains all files written by the analysis script, including .mat files with all the results computed by each analysis, as well as files created when compiling the final, pretty-looking pdf figures.<br> * A file output.txt that contains all output written to the MATLAB Command Window when running the analyses.</p> <p><strong>Requirements:</strong><br> * MATLAB and MATLAB Image Processing Toolbox<br> * distinguishable_colors.m by Timothy E. Holy (https://www.mathworks.com/matlabcentral/fileexchange/29702-generate-maximally-perceptually-distinct-colors)<br> * fragmaster by Augustin Martin Domingo and Tilman Vogel (https://ctan.org/pkg/fragmaster), a LaTeX utility<br> This was last tested on 2023-10-08 on MATLAB version 9.10.0.1739362 (R2021a) Update 5, running on macOS version 13.2 build 22D49.</p> <p><strong>To re-generate/reproduce</strong> the contents in the "output" folder:<br> 0. Ensure that all requirements are met.<br> 1. Run through the entire analysis script "run_analyses.m".<br> 2. Run the fragmaster command in the "output" folder.</p> <p><strong>Attribution:</strong><br> If you use this material in a scientific publication, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-10-08) this is our ACM ICMI 2023 paper. You can use the following BibTeX code to cite that work:<br> @inproceedings{kucherenko2023genea,<br> author={Kucherenko, Taras and Nagy, Rajmund and Yoon, Youngwoo and Woo, Jieyeon and Nikolov, Teodor and Tsakov, Mihail and Henter, Gustav Eje},<br> title={The {GENEA} {C}hallenge 2023: {A} large-scale evaluation of gesture generation models in monadic and dyadic settings},<br> booktitle = {Proceedings of the ACM International Conference on Multimodal Interaction},<br> publisher = {ACM},<br> series = {ICMI '23},<br> year={2023}<br> }</p> <p><strong>To find more</strong> GENEA Challenge 2023 material on the web, please see:<br> * https://svito-zar.github.io/GENEAchallenge2023/<br> * https://genea-workshop.github.io/2023/challenge/</p> <p><strong>If you have any questions</strong> or comments, please contact:<br> * Gustav Eje Henter <ghe@kth.se><br> * The GENEA Challenge organisers <genea-challenge@googlegroups.com></p>
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
<p>Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)" </p> <p>Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment.<br> To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model. <br> The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.<br> </p>
Experimental evaluation of louver guidance efficiency for green sturgeon (Acipenser medirostris), Primary Datasets, 2016-2017
Throughout the world, louver-bypass systems are a common method for fish protection at water diversion sites. This study used controlled laboratory experiments to quantify louver efficiency for juvenile green sturgeon under a range of conditions. Green sturgeon juveniles used in the study were spawned from the University of California, Davis (hereafter UC Davis) captive broodstock program. Experimental trials were conducted within an indoor flume at the J. Amorocho Hydraulics Laboratory (JAHL) at UC Davis. The flume had a zero degree bed slop, and was equipped with a louver placed at a 15-degree angle to the streamwise flow. Louver slats had 25-mm clear spacing. The louver terminated at a bypass channel 0.3 m wide, and was operated to maintain water velocity in the bypass that was 1.2 times greater than the velocity in the flume. Full factorial experimental treatments were designed to address the influence of sturgeon size, water velocity, diel period, and water temperature on louver performance and behavior. Sturgeon were tested within three predefined size classes (range: 6 – 34 cm TL). At each size class, fish were tested under combinations of water velocity, water temperature, and photophase. During each experimental trial, 60 (+/-2) juvenile sturgeon were released from an acclimation chamber to enter the test area at the bottom of the flume. Fish were allowed to freely navigate throughout the test area until they were transported through the louver slats or bypass channel, at which point they were removed and the time was recorded. Trials ended when all fish were collected at a downstream endpoint, or after 90 minutes of exposure time, and remaining fish were removed from the flume. Trials were completed across two study years within seven months after hatch, following the same protocols with a few minor adjustments in year two. During daytime trials in both study years the louver face was monitored with video recordings to observe contact between fish and the
Data and code for EDI overview paper, data collection characteristics, FAIR evaluation, downloads, and citations
The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.
A multi-scaled approach to evaluating the fish community structure within southern Appalachian streams, 2011
There is considerable uncertainty regarding the relative roles of stream habitat, landscape characteristics, and interspecific interactions in structuring stream fish assemblages. Thus, I evaluated the relative importance of environmental characteristics and species interactions, at local and landscape scales, on fish occupancy within the upper Little Tennessee River basin, Georgia, and North Carolina. Using a quadrat sample design, fishes were collected at 525 channel units among 48 study reaches during two consecutive years. I evaluated the relative support for the influence of local- and landscape-scale factors on fish occupancy using multi-scaled,hierarchical, multi-species occupancy models.
SBC LTER: Land Ocean Reef: Carbon, Nitogen and Hydrogen isotopes for evaluating food sources for subtidal consumers, 2009-2010
Potentially important food sources to consumers on shallow subtidal reefs include phytoplankton-dominated seston, kelp-derived detritus, and for locations adjacent to sources of freshwater runoff, terrestrially-derived material. The SBC LTER is using stable carbon, nitrogen and deuterium isotope ratio analysis to evaulate the relative contribution of these sources to reef food webs. We collect seasonal samples from five core SBCLTER research reefs (Arroyo Hondo, Naples, Arroyo Burro, Goleta bay and Carpinteria), and areas adjacent. We routinely collect several types of samples: from giant kelp (Macrocystis pyrifera), red, brown and green algae, terrestrial material (Oak leaves), stream, sediment and ocean water particulate organic material, and a benthic polychaete worm (Diopatra). This information will be used to evaluate whether these isotopic values differ enough from one another to permit the use of mixing models to estimate the contribution of each source to the reef food web.
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.