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470 results for “Evaluation dataset”
Fig. 3 in Evaluation of traditionally circumscribed species in the lichen-forming genus Usnea, section Usnea (Parmeliaceae, Ascomycota) using a six-locus dataset
Fig. 3 STACEY maximum clade credibility SMC-tree with posterior probabilities (PP) from STACEY (above branches) and *BEAST (below) analyses together with similarity matrix for the section Usnea dataset. The squares represent posterior probabilities (white = 0, black = 1) for pairs of individuals to belong to the same cluster. The lines in the matrix separate major groups (named above matrix), while
Fig. 1 Bayesian 50 in Evaluation of traditionally circumscribed species in the lichen-forming genus Usnea, section Usnea (Parmeliaceae, Ascomycota) using a six-locus dataset
Fig. 1 Bayesian 50 % consensus tree of 18 currently accepted Usnea species based on six concatenated loci, inferred by BEAST. Major groups and secondary chemistry are indicated right of the tree. Branch supports are given in circles: Black circles reflect strong support from both inferences (PP ≥ 95 % for BEAST and BP ≥ 70 % for RAxML), and gray circles strong support from BEAST only. Location and laboratory code are given in brackets. Scale bar shows the number of substitutions per site. Secondary metabolites: BAR barbatic acid, BMY baeomycesic
Fig. 2 in Evaluation of traditionally circumscribed species in the lichen-forming genus Usnea, section Usnea (Parmeliaceae, Ascomycota) using a six-locus dataset
Fig. 2 Box plots of cortex (a, d) and medulla (b, e) thickness measurements and bar charts of medulla density categories (c, f) in clades barbata-chaetophora-dasopoga-diplotypus (bar-das-dip) and barbata-intermedia-lapponica-substerilis (bar-int-lap; first row of charts), and between Usnea barbata specimens from these clades (second row of charts). Box plots show the percentages of the whole width of the measured branch. Center lines are the medians, box limits indicate the 25th and 75th percentiles as determined by R software, whiskers extend
Datasets and code utilized in the development of the following manuscript: Evaluating the accuracy of eight satellite and reanalysis daily precipitation estimates over Angola: The advantages of targeted regional evaluations to support effective water management strategies
<p>Datasets and code utilized in the development of the following manuscript: Evaluating the accuracy of eight satellite and reanalysis daily precipitation estimates over Angola: The advantages of targeted regional evaluations to support effective water management strategies.</p>
Dataset of the Article Evaluating a Framework of Conceptual Modelling Research
<p>Results and replication package of the article: <span>Evaluating a Framework of Conceptual Modelling Research</span></p>
Dataset for Website Fingerprinting Attack Evaluation
<p>Datasets for evaluating website fingerprinting attacks. More details can be found in <a href="https://github.com/Xinhao-Deng/Website-Fingerprinting-Library" target="_blank" rel="noopener">WFlib</a></p> <div> <div><a href="https://github.com/Xinhao-Deng/Website-Fingerprinting-Library" target="_blank" rel="noopener">WFlib</a> is a Pytorch-based open-source library for website fingerprinting attacks, intended for research purposes only.</div> <br> <div>We provide a neat code base to evaluate 12 advanced DL-based WF attacks on multiple datasets. This library is derived from our ACM CCS 2024 paper. If you find this repo useful, please cite our paper.</div> <br> <div>```bibtex</div> <div>@inproceedings{deng2024wflib,</div> <div>title={Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis},</div> <div>author={Deng, Xinhao and Li, Qi and Xu, Ke},</div> <div>booktitle={Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security},</div> <div>year={2024}</div> <div>}</div> <div>```</div> <br> <div>Contributions via pull requests are welcome and appreciated.</div> </div> <p> </p>
Extended evaluation datasets for IIoT TestBed (Release version 1 from November 28th, 2023)
<p>This contribution serves as an extension of the existing evaluation data set and thus also for the evaluation of planning strategies for the IIoT test bed environment, which was presented here: https://zenodo.org/records/10212298.</p> <p>This evaluation data set presented here comprises a total of four different files, each containing 1000 entries and thus describing 1000 different initial situations. </p> <p> </p> <p>The objective of this dataset is to assess and compare the policies achieved with different algorithms.</p> <p>The first data set (#1) is identical to the data set already published in https://zenodo.org/records/10212319. The same environmental conditions apply here as in the training, however the situations for the agents are unseen. </p> <p>In the second evaluation data set (#2), the number of carriers used at the same time was increased by 25%, i.e. from 16 to 20.</p> <p>In evaluation data set three (#3), the number of products to be completed varies between 50 and 500. </p> <p>In evaluation data set four (#4), the lot size was limited to 1, which means that each order only includes one product. This increases the diservity (product types and families) that are manufactured at the same time, which can lead to higher set-up efforts. <br><br>In evaluation data set five (#5), the number of carriers used at the same time was increased by 50%, i.e. from 16 to 24.</p> <p><br>In evaluation data set six (#6), the previously used work plans are no longer used, instead 3 new, previously unknown product variants are presented:<br>variant number 7: A -> C -> E -> G -> I<br>variant number 8: A -> B -> D -> F -> H -> I<br>variant number 8: A -> B -> C -> G -> H -> I</p> <p>In evaluation data set seven (#7), The probability of occurrence of the errors as well as the error duration were doubled.<br><br></p> <p>In evaluation data set eight (#8), various environmental changes are combined together. <br>- The probability of occurrence of errors and the error duration have been increased by a factor of 1.2<br>- The number of products to be produced has been set to 128 instead of 64 per episode<br>- The batch size can vary between 1 and 10 instead of 1 and 3<br>- The number of carriers has been set to 20 instead of 16<br>- The previous 6 routings have been extended by 3 new, previously unknown work plans (analogous to dataset #6), so that an order can now be composed of 9 different product variants.</p> <p> </p>
Test Dataset to evaluate embargo
<p>This is a description of what the test record is about.</p>
LoRa on Ice: dataset to evaluate the LoRa radio technology for sea ice research in Antarctica
<p>We present the design and implementation of a wireless sensor network (WSN) tailored for sea ice research. Our custom-built data acquisition unit employs Long Range (LoRa) radio technology and the Long Range Wide Area Network (LoRaWAN) protocol. We describe the deployment of a scientific measurement system in the vicinity of the Neumayer III research station in Antarctica, utilizing IoT technologies.<br>This dataset contains different measurements to evaluate the performance of the LoRa radio link and the LoRaWAN protocol. In this work we use a data acquisition unit sending data via LoRa using the LoRaWAN protocol. The data acquisition unit is a custom made electronics designed to sample data from connected sensors used for sea ice research in Antarctica. Three datasets are available to evaluate the performance of the LoRa radio technology and the LoRaWAN protocol: one range test on a motor boat in Germany and two range tests performed in Antarctica near the Neumayer III station. </p>
SeaCast Evaluation Dataset
<p>Evaluation data for the SeaCast model. This includes 15-day forecasts initialized daily during the timespan 24.07.2024 to 04.08.2024, the relevant atmospheric forcing, ground truth analysis data, in-situ observations, and satellite SST for the Mediterranean Sea. The filenames in the test directory are formatted as:</p> <p><strong>Med-PHY</strong></p> <ul> <li>Analysis: ana_data_*.npy</li> <li>Forecasts: for_data_*.npy</li> </ul> <p><strong>Atmospheric Forcings</strong></p> <ul> <li>ENS: ens_forcing_*.npy</li> <li>AIFS: aifs_forcing_*.npy</li> </ul> <p><strong>SeaCast Forecasts</strong></p> <ul> <li>Analysis Init + AIFS forcing: aifs_ana_data_*.npy</li> <li>Simulation Init + AIFS Forcing: aifs_for_data_*.npy</li> <li>Analysis Init + ENS Forcing: ens_ana_data_*.npy</li> <li>Simulation Init + ENS Forcing: ens_for_data_*.npy</li> </ul> <p><strong>Observations</strong></p> <ul> <li>In-situ: cmems_obs*.csv</li> <li>Satellite: sst.nc</li> </ul>
The official dataset of the paper "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient"
<p>This is the official repository of the paper "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient" (<a href="https://doi.org/10.3390/polym16192779">https://doi.org/10.3390/polym16192779</a>).</p> <p>DISCLAIMER</p> <p>The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication, "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient" (<a href="https://doi.org/10.3390/polym16192779">https://doi.org/10.3390/polym16192779</a>).<strong> </strong>See the README.txt file for more details.</p>
Evaluation Datasets for SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series
Open the record for dataset details and reuse information.
Evaluating an instrument of the research software related to software quality - Dimension 2 - Dataset of Focus Groups
<p>Artifacts used for data collection and analysis of the focus group sessions during the evaluation of an instrument for research software related to software quality - dimension 2.</p>
Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks: Dataset 2
<p>Coastal communities rely on levees and seawalls as critical protection against sea-level rise; in the U.S. alone, $300 billion in shoreline armoring costs are forecast by 2100. But despite the local flood risk reduction benefits, these structures can exacerbate flooding and associated damages along other parts of the shoreline—particularly in coastal bays and estuaries, where nearly 500 million people globally are at risk from sea-level rise. The magnitude and spatial distribution of the economic impact of this dynamic, however, are poorly understood. Here we combine hydrodynamic and economic models to assess the extent of both local and regional flooding and damages expected from a range of shoreline protection and sea-level rise scenarios in San Francisco Bay, California. We find that protection of individual shoreline segments (5-75 km) can increase flooding in other areas by as much as 36 million cubic meters and damages by $723 million for a single flood event, and in some cases can even cause regional flood damages that exceed the local damages prevented from protection. We also demonstrate that strategic flooding of certain shoreline segments, such as those with gradually sloping baylands and space for water storage, can help alleviate flooding and damages along other stretches of the coastline. By matching the scale of the economic assessment to the scale of the threat, we reveal the previously uncounted costs associated with uncoordinated adaptation actions and demonstrate that a regional planning perspective is essential for reducing shared risk and wisely spending adaptation resources in coastal bays.</p> <p>This dataset is associated with a github repository https://github.com/rmgriffin/OLU-flood-externalities</p> <p>This dataset is part of a two record data repository associated with the paper "Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks." This is part two.</p>
Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks: Dataset 1
<p>Coastal communities rely on levees and seawalls as critical protection against sea-level rise; in the U.S. alone, $300 billion in shoreline armoring costs are forecast by 2100. But despite the local flood risk reduction benefits, these structures can exacerbate flooding and associated damages along other parts of the shoreline—particularly in coastal bays and estuaries, where nearly 500 million people globally are at risk from sea-level rise. The magnitude and spatial distribution of the economic impact of this dynamic, however, are poorly understood. Here we combine hydrodynamic and economic models to assess the extent of both local and regional flooding and damages expected from a range of shoreline protection and sea-level rise scenarios in San Francisco Bay, California. We find that protection of individual shoreline segments (5-75 km) can increase flooding in other areas by as much as 36 million cubic meters and damages by $723 million for a single flood event, and in some cases can even cause regional flood damages that exceed the local damages prevented from protection. We also demonstrate that strategic flooding of certain shoreline segments, such as those with gradually sloping baylands and space for water storage, can help alleviate flooding and damages along other stretches of the coastline. By matching the scale of the economic assessment to the scale of the threat, we reveal the previously uncounted costs associated with uncoordinated adaptation actions and demonstrate that a regional planning perspective is essential for reducing shared risk and wisely spending adaptation resources in coastal bays.</p> <p>This dataset is associated with a github repository https://github.com/rmgriffin/OLU-flood-externalities</p> <p>This dataset is part of a two record data repository associated with the paper "Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks." This is part one.</p>
Dataset supporting the manuscript "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, Kasal-Hoc et al, Veterinary Sciences, 2021)
<p>These are the data supporting reported results in the publication "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, A, Kasal-Hoc N. et al, Veterinary Sciences, 2021). DOI not yet available.</p>
Ground Truth for DCASE 2021 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This data is the ground truth for the "<a href="https://zenodo.org/record/4884786">evaluation dataset</a>" for the <a href="http://dcase.community/challenge2021/task-unsupervised-detection-of-anomalous-sounds"><strong>DCASE 2021 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions"</strong></a>. </p> <p>In the task, three datasets have been released: "<a href="http://zenodo.org/record/4562016">development dataset</a>", "<a href="https://zenodo.org/record/4660992">additional training dataset</a>", and "<a href="https://zenodo.org/record/4884786">evaluation dataset</a>". The evaluation dataset was the last of the three released and includes around 200 samples for each machine type, section index, and domain, none of which have a condition label (i.e., normal or anomaly). This ground truth dataset contains the condition labels.</p> <p> </p> <p><strong>Data format</strong></p> <p>The CSV file for each machine type, section index, and domain includes the ground truth data like the following:</p> <p>---------------------------------</p> <p>section_03_source_test_0000.wav,1<br> section_03_source_test_0001.wav,1</p> <p>...</p> <p>section_03_source_test_0198.wav,0<br> section_03_source_test_0199.wav,1</p> <p>---------------------------------</p> <p>The first column shows the name of a wave file. The second column shows the condition label (i.e., 0: normal or 1: anomaly).</p> <p> </p> <p><strong>How to use</strong></p> <p>A script for calculating the AUC, pAUC, precision, recall, and F1 scores for the "evaluation dataset" is available on the Github repository <a href="https://github.com/y-kawagu/dcase2021_task2_evaluator">[URL]</a>. The ground truth data are used by this system. For more information, please see the Github repository.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd.</strong> and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Publication</strong></p> <p>If you use this dataset, please cite <strong>all the following three papers</strong>:</p> <ul> <li>Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo, "Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions," in arXiv e-prints: 2106.04492, 2021. [<a href="https://arxiv.org/abs/2106.04492">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito, "ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions," in arXiv e-prints: 2106.02369, 2021. [<a href="https://arxiv.org/abs/2106.02369">URL</a>]</li> <li>Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," in arXiv e-prints: 2105.02702, 2021. [<a href="https://arxiv.org/abs/2105.02702">URL</a>]</li> </ul> <p><br> <strong>Feedback</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul> <p> </p>
Can Proposed Service Interface Metrics Effectively Evaluate the Quality of RESTful APIs? Dataset and Filter Criteria
<p>The csv datasets contains repositories with API metrics and software quality metrics.</p> <p>We use linear regression to predict a software quality metric with the API metrics.</p> <p> </p> <p>The .txt files contain the lists for the filter criteria to filter out unsuitable repositories.</p>
Trend Dataset: Evaluation Datasets for Trend Forecasting Studies
<p><strong><a href="https://ojs.aaai.org/index.php/ICWSM/article/view/22212">A conference paper</a> has published in AAAI-ICWSM 2023 details the method to create the data.</strong></p> <p><strong>Overview</strong></p> <p>This dataset is created for the purpose of evaluating trend forecasting methods. The dataset contains 400 entities in 21 domains consisting of 5 categories (geography, organization, person, product, and content). Each entity is annotated with three types of trend attributes: trending status (trending or non-trending), degree of trending (how well it is recognized), and trend period (when the trend started and ended).</p> <p>There are three main features of this dataset. First, a questionnaire-based recognition rate is used as the gold standard for annotating trend attributes. Second, a collection of entities from a wide range of domains while covering both trending and non-trending, without significant imbalance. Third, trend period annotation on a weekly resolution through interpolation using Internet search volume data.</p> <p>See our paper "Construction of Evaluation Datasets for Trend Forecasting Studies" *1 (hereafter known as “paper”) for details.</p> <p>*1 This paper is currently under review in single blind.</p> <p><strong>Conditions under this dataset construction</strong></p> <p><strong>Target Trending Phenomenon</strong></p> <table> <thead> <tr> <th>attribute</th> <th>value</th> </tr> </thead> <tbody> <tr> <td>Target Country</td> <td>Japan</td> </tr> <tr> <td>Survey Period</td> <td>from 2015 to 2019</td> </tr> <tr> <td># of entities</td> <td>400</td> </tr> <tr> <td>Target Domains</td> <td>21 domains of 5 categories (See Target Domain Section)</td> </tr> </tbody> </table> <p><strong>Target Domain</strong></p> <table> <thead> <tr> <th>Category</th> <th>Domain</th> </tr> </thead> <tbody> <tr> <td>Location/Geography</td> <td>City/region/landmark</td> </tr> <tr> <td>Organization</td> <td>Restaurant/facility, company/brand</td> </tr> <tr> <td>Person/Group</td> <td>Politician/political party, researcher, athlete, actor/actress, celebrity/entertainer/comedian, music band/music group</td> </tr> <tr> <td>Products</td> <td>Cosmetics, daily necessities, clothing, beverage, foodstuff, others</td> </tr> <tr> <td>Art/Content</td> <td>Game, publication, comic/animation, movie, broadcast program, music</td> </tr> </tbody> </table> <p><strong>Dataset Files</strong></p> <p>The dataset contains two TSV files and a single Markdown file. The description and metadata about the dataset are provided in a Markdown file. The list of files is shown in the table below.</p> <table> <thead> <tr> <th>data label</th> <th>file name</th> <th>file format</th> <th>explanation</th> </tr> </thead> <tbody> <tr> <td>Metadata</td> <td>README.md</td> <td>Markdown</td> <td>Overview of this dataset and the schema of each file in English and Japanese</td> </tr> <tr> <td>Trend Dataset</td> <td>trend_dataset.tsv</td> <td>TSV</td> <td>Main body of this dataset</td> </tr> <tr> <td>Master Entity list</td> <td>master_entity_list.tsv</td> <td>TSV</td> <td>Information of entities included in Trend Dataset</td> </tr> </tbody> </table> <p><strong>TSV File format</strong></p> <table> <thead> <tr> <th>attribute</th> <th>value</th> </tr> </thead> <tbody> <tr> <td>header row</td> <td>exists (first row)</td> </tr> <tr> <td>index column</td> <td>“ID” column (first column)</td> </tr> <tr> <td>encoding</td> <td>UTF-8</td> </tr> <tr> <td>delimiter</td> <td>\t (tab)</td> </tr> <tr> <td>quoting</td> <td>None *2</td> </tr> <tr> <td>escape character</td> <td>\ (back slash)</td> </tr> <tr> <td>line terminator</td> <td>\n</td> </tr> </tbody> </table> <p>*2: <a href="https://docs.python.org/3/library/csv.html#csv.QUOTE_NONE">csv.QUOTE_NONE</a> in Python</p>
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
ScienceDex guides
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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.