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zenodo48/100

MUHAI Benchmark : Task 3 (Understanding Complex Concepts)

Meaning and Understanding in Human-Centric AI (MUHAI) Benchmark Task 3 Understanding complex concepts   This dataset helps investigating whether symbolic reasoning can help statistical models tro understand complex concepts. Complex concepts are expressed in the form of Image Schemas (i.e., mental templates that summarise human experiences in the form of patterns of object relations and actions). The submission includes the the ImageSchema dataset with ground truth labels : 1. A question to be asked 2. the type of Image schema (class) 3. the type of phrasing (literal , metaphoric, a distracting sentence) 4. the type of questioning (one referring to the image schema by name, and another describing its content)  5. a question indicating whether it is a yes or no answer 6. the image schema variables identified Each sample in the datasets starts with a question about the presence of the given schema in the following sentence, and follows with a single sentence to be classified as either "yes" or "no" (presence or absence of a schema). This can be used by a system (eg a language model, a symbolic system, a neuro-symbolic approach) to identify image schemas. The file "language-models.csv" includes the results of two language models that were tested (T0pp, GPT-3). Metrics used to evaluate: 1. Accuracy : no. correct predictions  / no. of total sentences (TP + TN / P + N) 2. Precision: no. correct image schema predictions  / total correct image schema predictions (TP / TP + FP) 3. Recall :   : no. correct image schema predictions  / total predicted image schema (TP / TP + FN) 4. F1 : harmonic mean of Precision and Recall Code : https://github.com/kmitd/muhai-EPL

opencc-by-4.0Sep 2022View details →

zenodo48/100

MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.

This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as: <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.

opencc-by-4.0May 2024View details →

zenodo48/100

SenTopX: A Benchmark Twitter Dataset for User Sentiment on Various Topics

This is a longitudinal Twitter dataset of 143K users during the period 2017-2021. The following is the detail of all the files: <ul> <li><a href="11243662" target="_blank" rel="noopener noreferrer">SenTopX_userIDs.txt</a>: contains user IDs of 143K Twitter users.</li> <li><a href="../api/records/11243662/draft/files/userIDs_tweetIDs.zip/content" target="_blank" rel="noopener noreferrer">userIDs_tweetIDs.zip</a>: contains Tweet IDs of users, the name of the file is the user ID and the file contains the list of all the tweet IDs.</li> <li><a href="../api/records/11243662/draft/files/users_16_perspective_toxicity_scores.csv/content" target="_blank" rel="noopener noreferrer">users_16_perspective_toxicity_scores.csv</a> contains user IDs and 16 median Perspective API scores, the vector is shared as mean, median, and Gini Index of scores calculated over all tweets of a user.</li> <li><a href="../api/records/11243662/draft/files/LDAvis_top30_words_for_extracted_topics.csv/content" target="_blank" rel="noopener noreferrer">LDAvis_top30_words_for_extracted_topics.csv</a> contains the top 30 most relevant words extracted from each topic extracted by tweet-level topic modeling using the BERTweet topic model.</li> <li><a href="../api/records/11243662/draft/files/topic_modelling_statistics_per_user.csv/content" target="_blank" rel="noopener noreferrer">topic_modelling_statistics_per_user.csv</a> contains important and relevant statistics related to topic modeling results: <ul> <li> 1. user: This column represents the identifier for the user. Each row in the CSV corresponds to a specific user, and this column helps to track and differentiate between the users. 2. avg_topic_probability: This column contains the average probability of the topics for each user calculated across all of the tweets in order to compare users in a meaningful way. It represents the average likelihood that a particular user discusses various topics over the observed period. 3. maximum_topic_avg: This column holds the value of the highest average probability among all topics for each user. It indicates the topic that the user most frequently discusses, on average. 4. index_max_avg_topic_probability_200: This column specifies the index or identifier of the topic with the highest average probability out of 200 possible topics. It shows which topic (out of 200) the user discusses the most. 5. global_avg: This column includes the global average probability of topics across all users. It provides a baseline or overall average topic probability that can be used for comparative purposes. 6. max_global_avg: This column contains the maximum global average probability across all topics for all users. It identifies the most discussed topic across the entire user base. 7. index_max_global_avg: This column shows the index or identifier of the topic with the highest global average probability. It indicates which topic (out of 200) is the most popular across all users. 8. entropy_200_topic: This column represents the entropy of the topics for each user, calculated over 200 topics. Entropy measures the diversity or unpredictability in the user's discussion of topics, with higher entropy indicating more varied topic discussion. In summary, these columns are used to analyze the topic engagement and preferences of users on a platform, highlighting the most frequently discussed topics, the variability in topic discussions, and how individual user behavior compares to overall trends. </li> </ul> </li> </ul>

opencc-by-4.0May 2024View details →

zenodo48/100

TCM: Benchmark Datasets for Predictive Maintenance in Steel Manufacturing

<h1>Anomaly-TCM</h1> Predictive Maintenance (PdM) is a strategy that uses advanced data analytics to predict equipment failures and maintain industrial machinery in good condition. Its goals are to minimize downtime, reduce operational costs, and ensure product quality. PdM methods are applicable across various industries, including steel manufacturing. In steel production, cold rolling is a critical process that reduces the thickness of hot-rolled steel. Developing PdM methods for tandem cold mills (TCM) can significantly improve production efficiency. However, researchers often rely on real manufacturing data, which is typically unavailable, unlabeled, and noisy, making it difficult to validate and compare methods. To overcome this, we created synthetic datasets for the cold rolling process to identify anomalies based on physical principles. These datasets were generated using a mathematical model of a 5-stand TCM, calculating key process parameters like rolling force, torque, speed, tension, gap, thickness reduction, and motor power. We introduced anomalies related to specific failures in the process. We produced six diverse datasets, each with varying complexity, to enable benchmarking of machine learning-based PdM methods for the cold rolling process. Four different types of anomalies were introduced, which are related to a physics-based deviations in the process: <ol> <li>Anomaly in reduction scheme</li> <li>Anomaly in work roll (increased work roll friction)</li> <li>Anomaly in bearing (increased motor torque)</li> <li>Anomaly in electric motor (decrease efficiency)</li> </ol>  The details of the datasets are provided below. <table> <tbody> <tr> <td>Dataset</td> <td>Observations</td> <td>Anomalies</td> <td>Share of Anomalies</td> <td>Features</td> <td>Anomaly Types</td> <td>Products</td> <td>Data Drift</td> </tr> <tr> <td>tcm5_dataset_1</td> <td>20009</td> <td>1045</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_2</td> <td>20001</td> <td>1035</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_3</td> <td>20003</td> <td>981</td> <td>4.9%</td> <td>51</td> <td>4 (16)</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_4</td> <td>20001</td> <td>925</td> <td>4.6%</td> <td>51</td> <td>4 (16)</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_5</td> <td>20005</td> <td>1031</td> <td>5.2%</td> <td>51</td> <td>4 (16)</td> <td>5</td> <td>TRUE</td> </tr> <tr> <td>tcm5_dataset_6</td> <td>20008</td> <td>954</td> <td>4.8%</td> <td>51</td> <td>4 (16)</td> <td>25</td> <td>TRUE</td> </tr> </tbody> </table>   Each dataset is generated as a data stream, meaning the observations follow a chronological order, represented by increasing work roll mileage (which is reset after a predefined threshold). The table below provides details about the features and labels present in the datasets. Several features are recorded for each rolling stand, totaling 51 features. Apart from the anomaly related to reduction, the other anomalies are specific to individual stands, resulting in 16 anomaly labels in total. <table> <tbody> <tr> <td>Feature</td> <td>Suffixes</td> <td>Unit</td> <td>Description</td> </tr> <tr> <td>thickness_entry</td> <td>-</td> <td>mm</td> <td>steel entry thickness</td> </tr> <tr> <td>thickness_exit</td> <td>-</td> <td>mm</td> <td>steel exit thickness</td> </tr> <tr> <td>width</td> <td>-</td> <td>mm</td> <td>steel width</td> </tr> <tr> <td>ys_entry</td> <td>-</td> <td>MPa</td> <td>steel entry yield strength</td> </tr> <tr> <td>ys_exit</td> <td>-</td> <td>MPa</td> <td>steel exit yield strength</td> </tr> <tr> <td>work_roll_diam</td> <td>1 to 5</td> <td>mm</td> <td>work roll diamaeter (stands 1 to 5)</td> </tr> <tr> <td>work_roll_mileage</td> <td>1 to 5</td> <td>km</td> <td>work roll mileage (stands 1 to 5)</td> </tr> <tr> <td>reduction</td> <td>1 to 5</td> <td>-</td> <td>thickness reduction (stands 1 to 5)</td> </tr> <tr> <td>tension</td> <td>0 to 5</td> <td>N</td> <td>interstand tension (0 is tension before stand 1, 1-5 refer to tension after stands 1-5)</td> </tr> <tr> <td>roll_speed</td> <td>1 to 5</td> <td>NaN</td> <td>linear work roll speed (stands 1 to 5)</td> </tr> <tr> <td>force</td> <td>1 to 5</td> <td>N</td> <td>rolling force (stands 1 to 5)</td> </tr> <tr> <td>torque</td> <td>1 to 5</td> <td>Nm</td> <td>rolling torque (stands 1 to 5)</td> </tr> <tr> <td>gap</td> <td>1 to 5</td> <td>mm</td> <td>stand gap (stands 1 to 5)</td> </tr> <tr> <td>motor_power</td> <td>1 to 5</td> <td>kW</td> <td>electric motor power (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Reduction</td> <td>-</td> <td>-</td> <td>(label) anomaly in reduction scheme</td> </tr> <tr> <td>Anomaly_Electric</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in electric motor (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Bearing</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in stand bearing (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_WorkRoll</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in work roll friction (stands 1 to 5)</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2024View details →

zenodo48/100

DockM8_Benchmarking_results

The repository contains the benchmarking data obtained alongside the first version of DockM8. The file structure is explained in DockM8_v1_file_structure_explanation.txt We hope this data is useful for benchmarking scoring functions and machine learning models, as well as being a large repository of pre-docked poses using a variety of algorithms.

opencc-by-4.0Jul 2024View details →

zenodo48/100

A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

Description This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset.  The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods.  The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California.  In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data.  We also include sample codes of baseline models for benchmarking of more elaborated models. Data usage The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper: Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494 Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data. Sample code As part of the data release, we are also including the sample code written in Python 3.  The preprocessed data used in the scripts are also provided.  The code can be used to reproduce the results presented in this work and as a starting point for future studies.  Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models.  All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip.  Units All time stamps are in UTC (YYYY-MM-DD HH:MM:SS). All irradiance and weather data are in SI units. Sky image features are derived from 8-bit RGB (256 color levels) data. Satellite images are derived from 8-bit gray-scale (256 color levels) data. Missing data The string "NAN" indicates missing data File formats All time series data files as in CSV (comma separated values) Images are given in tar.bz2 files Files  <ul> <li>Folsom_irradiance.csv                           Primary       One-minute GHI, DNI, and DHI data.</li> <li>Folsom_weather.csv                              Primary       One-minute weather data.</li> <li>Folsom_sky_images_{YEAR}.tar.bz2    Primary       Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li>Folsom_NAM_lat{LAT}_lon{LON}.csv    Primary       NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li>Folsom_sky_image_features.csv           Secondary    Features derived from the sky images.</li> <li>Folsom_satellite.csv                               Secondary   10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li>Irradiance_features_{horizon}.csv          Secondary   Irradiance features for the different forecasting horizons ({horizon} 1&frasl;4 {intra-hour, intra-day, day-ahead}). </li> <li>Sky_image_features_intra-hour.csv       Secondary   Sky image features for the intra-hour forecasting issuing times. </li> <li>Sat_image_features_intra-day.csv         Secondary   Satellite image features for the intra-day forecasting issuing times. </li> <li>NAM_nearest_node_day-ahead.csv      Secondary   NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li>Target_{horizon}.csv                              Secondary   Target data for the different forecasting horizons.</li> <li>Forecast_{horizon}.py                            Code            Python script used to create the forecasts for the different horizons. </li> <li>Postprocess.py                                      Code             Python script used to compute the error metric for all the forecasts.</li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo48/100

doclevel-MT-benchmark-discoMT2019

This release contains data sets for experiments with document-level machine translation. The data sets have been used in previous studies and provided here for replicability and comparison with other systems. The data sets are taken from the English-German news translation task at WMT 2019 and the English-German bitext in the OpenSubtitles collection v2016 from OPUS. All data sets are sentence aligned with corresponding lines being aligned to each other. Document boundaries are marked with empty lines (on both sides of the parallel corpus). The data set has been used in the following publication: <pre><code>@inproceedings{scherrer-tiedemann-loaiciga-2019, title = "Analysing concatenation approaches to document-level NMT in two different domains", author = {Scherrer, Yves and Tiedemann, J{\"o}rg and Lo{\'a}iciga, Sharid}, booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation", month = nov, year = "2019", address = "Hong-Kong", publisher = "Association for Computational Linguistics", } </code></pre> Please, cite that paper if you use the data set in your own work.

opencc-by-4.0Oct 2019View details →

zenodo48/100

Benchmark for the Evaluation of Lexical Semantic Change Detection for Ancient Greek

This repository contains a benchmark of Ancient Greek lemmas which underwent semantic change. It is meant as a support for the evaluation of methods detecting lexical semantic change in Ancient Greek. It was created at the University of Groningen, The Netherlands. A publication will follow soon.   1. Overview of the repository This benchmark was created by retrieving and selecting from existing scholarship cases of lexemes which underwent semantic change. The evaluation items are 44 Ancient Greek lemmas, accompanied by the following information (see the column headers in the CSV file): <ul> <li>reference: the literature source of information about the change;</li> <li>which_change: an explanation of the change in meaning. NB: the older meaning(s) do not necessarily disappear after the change, but it can happen that the new meaning(s) are added to the existing one(s), increasing the polysemy of the lemma;</li> <li>when_changed: information about the work(s) or time period in which the change was first recorded; this kind of information was not always available or precise;</li> <li>christian_change: whether the change is triggered by social, religious, or cultural changes related to the spread of Christianity, according to the scholarship.</li> </ul>   2. References The literature used to build this benchmark is the following:     BUCK, Carl Darling. A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press, 1949.     FINKELBERG, Aryeh. "On the History of the Greek ΚΟΣΜΟΣ." Harvard Studies in Classical Philology (1998): 103-136.     GINGRICH, F. Wilbur. "The Greek New Testament as a landmark in the course of semantic change." Journal of Biblical Literature (1954): 189-196.     HORKY, Phillip Sidney. "When did Kosmos become the Kosmos." Cosmos in the Ancient World (2019): 22-41.     LURAGHI, Silvia. "The verb aréskein in Ancient Greek: Constructions and semantic change." Acta Linguistica Petropolitana. Труды института лингвистических исследований 18-1 (2022): 226-245.   These dictionaries of Ancient Greek were also used to double-check the instances of change:     LIDDELL, Henry George, and Robert Scott. A Greek-English Lexicon. revised and augmented throughout by. Sir Henry Stuart Jones. with the assistance of. Roderick McKenzie. Oxford. Clarendon Press. 1940.     ROCCI, Lorenzo. Vocabolario greco-italiano. Roma. Società editrice Dante Alighieri. 1939.     SLUITER, Ineke, and Lucien van Beek, and Ton Kessels, and Albert Rijksbaron. Woordenboek Grieks/Nederlands. 2024. <a href="https://woordenboekgrieks.nl/" target="_blank" rel="noopener">https://woordenboekgrieks.nl/</a>   3. Acknowledgements <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.   We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl/">www.anchoringinnovation.nl</a>.</div> <div>   4. How to cite </div> <div>Until there is no publication about this benchmark, please cite the resource as:</div> <div>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim (2024), Benchmark for the Evaluation of Lexical Semantic Change Detection Measures in Ancient Greek, DOI: 10.5281/zenodo.13364555.</div> <div> </div> <div> </div>

opencc-by-4.0Aug 2024View details →

zenodo48/100

SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

opencc-by-4.0Sep 2024View details →

zenodo48/100

PATRICIA Task 5.3 SPHERE benchmark Dataset.zip

This data set archive is related to the study of the SPHERE experiment performed in the frame of the Task 5.3 of the PATRICIA project. The complete study is documented in the corresponding deliverable D5.3 of PATRICIA. When expanding the archive file, the content is structured as follows: ·       “1 - Neutronics computations”: first level directory containing the input and output data related to the neutronic assessment of the SPHERE experiment. ü  “Input”: second level directory containing the input data required by the neutronic calculations. One can find in particular the definition the neutron spectrum in the HFR along the irradiation cycles of the SPHERE experiment – information provided by NRG, the operator of the HFR. ü  “Output”: second level directory containing the output data issued from the neutronic calculations. The data are distributed in two separate sub-directories, respectively related to the output from C4P-TRAIN and SERPENT-2. ·       “2 - FPCs computations”: first level directory containing the input and output data related to the simulation of the SPHERE experiment with the fuel performance codes. ü  “Input”: second level directory containing the input data required by the computations with the FPCs. One can find in particular the benchmark specifications, the nuclear data derived from the neutronic calculations and used on input by the FPCs (cross sections and fission yields), and the adopted definition of the irradiation history. ü  “Output & Comparisons CC CM”: second level directory containing the output data issued from the computations with the FPCs. A compilation of the PIE results in one EXCEL file is provided. The data are then distributed in two separate sub-directories: “Option 1 FAST” and “Option 2 THERMAL”, corresponding to the two complementary assumptions of fast and thermal spectrum retained for the computations. In each sub-directory are provided the results files from the different codes, and a synthesis file showing the code-to-code and calculation-to-measure comparisons. The synthesis files are named “SPHERE_FAST.xlsx” and “SPHERE_THERMAL.xlsx”.

opencc-by-4.0Aug 2024View details →

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PATRICIA Task 5.3 MARINE benchmark Dataset.zip

This data set archive is related to the study of the MARINE experiment performed in the frame of the Task 5.3 of the PATRICIA project. The complete study is documented in the corresponding deliverable D5.3 of PATRICIA. When expanding the archive file, the content is structured as follows: ·        “1 - Neutronics computations”: first level directory containing the input and output data related to the neutronic assessment of the MARINE experiment. ü  “Input”: second level directory containing specific input data required by the neutronic calculations, namely the definition the neutron spectra in the HFR along the irradiation cycles of the MARINE experiment – information provided by NRG, the operator of the HFR. ü  “Output”: second level directory containing the output data issued from the neutronic calculations. The data are distributed in two separate sub-directories, respectively related to the output from C4P-TRAIN (radial power profiles) and SERPENT-2 (cross-sections and fission yields). ·        “2 - FPCs computations”: first level directory containing the input and output data related to the simulation of the MARINE experiment with the fuel performance codes. ü  “Input”: second level directory containing the input data required by the computations with the FPCs. One can find in particular the benchmark specifications, the nuclear data derived from the neutronic calculations and used on input by the FPCs (cross-sections and fission yields), and the adopted definition of the irradiation history. The relative axial heating profile along the fuel stack and the time-evolving radial power profiles in the fuel, issued from the preliminary computation by TRANSURANUS, are also provided. ü  “Output & Comparisons CC CM”: second level directory containing the output data issued from the computations with the FPCs. A compilation of the PIE results in one EXCEL file is provided. The computation results are put in a dedicated sub-directory: “THERMAL spectrum”, in reference to the single assumption retained for the simulation of MARINE. In this sub-directory are provided the results files from the different codes, and a synthesis file showing the code-to-code and calculation-to-measure comparisons. The synthesis files is named “MARINE.xlsx”.

opencc-by-4.0Sep 2024View details →

zenodo48/100

ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-Based Type Inference

<ul> <li>The dataset is gathered on Sep. 17th 2020 from GitHub.</li> <li>It has clean and complete versions (from v0.7): <ul> <li>The clean version has 5.1K type-checked Python repositories and 1.2M type annotations.</li> <li>The complete version has 5.2K Python repositories and 3.3M type annotations.</li> </ul> </li> <li>The dataset's source files are type-checked using <a href="https://mypy.readthedocs.io/">mypy</a> (clean version).</li> <li>The dataset is also de-duplicated using the <a href="https://github.com/saltudelft/CD4Py">CD4Py</a> tool.</li> <li>Check out the README.MD file for the description of the dataset.</li> <li>Notable changes to each version of the dataset are documented in CHANGELOG.md.</li> <li>The dataset's scripts and utilities are available on <a href="https://github.com/saltudelft/many-types-4-py-dataset">its GitHub repository</a>.</li> </ul>

opencc-by-4.0Sep 2020View details →

zenodo48/100

Floating Car Data Collection for Processing and Benchmarking

The dataset is outcome of a paper "Floating Car Data Map-matching Utilizing the Dijkstra Algorithm" accepted for 3rd International Conference on Data Management, Analytics & Innovation held in Kuala Lumpur, Malaysia in 2019. The floating car data (FCD representing movement of cars with their position in time) is produced by the traffic simulator software (further referred to as Simulator) published in [1] and can be used as an input for data processing and benchmarking. The dataset contains FCD of various quality levels based on the routing graph of the Czech Republic derived from Open Street Map <a href="https://www.openstreetmap.org">openstreetmap.org</a>. Should the dataset be exploited in scientific or other way, any acknowledgement or references to our paper [1] and dataset are welcomed and highly appreciated. Archive contents The archive contains following folders. city_oneway and city_roadtrip - FCD from the city of Brno, Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip intercity_oneway and intercity_roadtrip - FCD from cities of Brno, Ostrava, Olomouc and Zlin, all Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip Content explanation All four of mentioned folders contain raw FCD as they come from our Simulator, post-processed FCD enriching Simulator FCD, and obfuscated raw FCD (of both low and high obfuscation level). In the both obfuscated data sets, each measured point was moved in a random direction a number of meters given by drawing a number from a Gaussian distribution. We utilized two Gaussian distributions, one for the roads outside the city (N(0,10) for the lower and N(0,20) for the higher obfuscation level) and one for the roads inside the city (N(0,15) and N(0,30) respectively). Then some predefined number of randomly chosen points were removed (3% in our case). This approach should roughly represent real conditions encountered by FCD data as described by El Abbous and Samanta [2]. In case of post-processed road trip data, there is one extra dataset with "cache" suffix representing the very same dataset limited to a 5-minute session memoization. This folder also contains a picture of processed FCD represented on a map. Data format Standard UTF-8 encoded CSV files, separated by a semicolon with the following columns: RAW Header session_id;timestamp;lat;lon;speed;bearing;segment_id Data session_id: (Type: unsigned INT) - session (car) identifier timestamp: (Type: datetime) - timestamp in UTC lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps speed: (Type: unsigned INT) - actual speed in kmh bearing: (Type: unsigned INT) - actual bearing in angles 0-360 segment_id: (Type: unsigned long) - unique edge identifier POST-PROCESSED Header gid;car_id;point_time;lat;lon;segment_id;speed_kmh;speed_avg_kmh;distance_delta_m;distance_total_m;speedup_ratio;duration;segment_changed;duration_segment;moved;duration_move;good;duration_good;bearing;interpolated Data gid: (Type: unsigned long) - global identifier of a record car_id: (Type: unsigned INT) - session (car) identifier point_time: (Type: datetime) - timestamp with timezone lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps segment_id: (Type: unsigned long) - unique edge identifier speed: (Type: unsigned INT) - actual speed in kmh speed_avg_kmh: (Type: unsigned long) - actual average speed of a car in kmh distance_delta_m: (Type: unsigned long) - actual distance delta in metres distance_total_m: (Type: unsigned long) - actual total distance of a car in metres speedup_ratio: (Type: unsigned long) - actual speed-up ratio of a car duration: (Type: time) - actual duration of a car segment_changed: (Type: boolean) - signals if actual segment of a car differs from the previous one duration_segment: (Type: time) - actual duration on a segment of a car moved: (Type: boolean) - signals if actual position of a car differs from the previous one duration_move:(Type: time) - actual duration of a car since moving good: signals if actual record values satisfies all data constraints (all true as derived from Simulator) duration_good: actual duration of a car since when all constraints conditions satisfied bearing: (Type: unsigned INT) - actual bearing in angles 0-360 interpolated: (Type: boolean) - signals if actual segment identifier is calculated (all false as derived from Simulator) References [1] V. Pto&scaron;ek, J. &Scaron;evčík, J. Martinovič, K. Slaninová, L. Rapant, and R. Cmar, Real-time traffic simulator for self-adaptive navigation system validation, Proceedings of EMSS-HMS: Modeling & Simulation in Logistics, Traffic & Transportation, 2018. [2] A. El Abbous and N. Samanta. A modeling of GPS error distri-butions, In proceedings of 2017 European Navigation Conference (ENC), 2017.

opencc-by-4.0Dec 2018View details →

zenodo48/100

Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus

This repository contains associated datasets and accession numbers for a study entitled 'Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus'. The scripts for this project can be found on the GitHub project<a href="https://github.com/ahfitzpa/Benchmarking-bioinformatics-norovirus-amplicons"> page</a>.  Expected composition tsv files are the OTU tables for each simulation performed (001-010). OTU IDs in this case are the expected taxonomy with the associated accession numbers. Samples are numbered 1-40, including the simulation number. Expected sequences fasta files contain the sequences used as input for each simulation, without primers or Illumina adapter sequences. Amplicons were generated using the following primers: GI Primers  GISKF: CTG CCC GAA TTY GTA AAT GA 4 GISKR: CCA ACC CAR CCA TTR TAC A 5 GII Primers  G2SKF: CNT GGG AGG GCG ATC GCAA 8 G2SKR: CCR CCN GCA TRH CCR TTR TAC AT In this study, three databases and multiple classifiers were compared. Here we include the taxonomy and fasta files for each database; noronet =NoroNet RIVM, calicinet= HuCat CDC and custom, randomly generated database. Fasta files for the classifiers include the GI/GII primers listed above in a 5-3 orientation.  The tags.txt file contains the Illumina adapters used for the simulation component of the study.

opencc-by-4.0Oct 2022View details →

zenodo48/100

International benchmark for ALS individual tree segmentation

This upload aims to provide an international benchmark dataset for airborne LiDAR-based individual tree segmentation algorithm comparison and development.

opencc-by-4.0Dec 2022View details →

zenodo48/100

Simulation dataset to benchmark 3D force inference methods

Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions. The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy). This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>. Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>. Credits, contact, citations If you use this dataset, please cite the published version of the following preprint:  Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. <a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a> We hope that this dataset may be useful to benchmark future 3D force inference methods. If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>. License Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.

opencc-by-4.0Apr 2023View details →

zenodo48/100

hybrid-graph-benchmark

This is a benchmark dataset for evaluating hybrid-graph (hypergraph and hierarchical graph) learning algorithms. It contains: <ul> <li>21 real-world higer-order graphs from the domains of biology, social media, and wikipedia</li> </ul> For accessing additional functionalities, please access through <a href="https://github.com/Zehui127/hybrid-graph-benchmark">Project Page</a> <ul> <li>Built-in functionalities for preprocessing hybrid-graphs</li> <li>A framework to easily train and evaluate Graph Neural Networks</li> </ul>

opengpl-3.0-or-laterMay 2023View details →

zenodo48/100

Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery

Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the claims.csv file, the AirportCode implies AirportName, as each code should be unique for a given airport. The file ground_truth.csv is a comma separated file containing approximate functional dependencies. table describes the relation we refer to, lhs and rhs reference two columns of those relations where semantically we found that lhs implies rhs. The file excluded_candidates.csv and included_candidates.csv list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the g3_prime value was too small. Dataset References <ul> <li>adult.csv: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li>claims.csv: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li>dblp10k.csv: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li>hospital.csv: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li>t_biocase_... files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li>tax.csv: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo48/100

A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices

Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research. We provide here a benchmark dataset for time series reconstruction method based on the <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview. Specifically, following the <a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as: \(NDVI = {NIR - Red \over NIR + Red}\) only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif where TILE_NAME is one between the above specified ones, YYYY is the corresponding year (spanning from 2015 to 2022) and DDD is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of 184 images (23 images for 8 years) that can be easily manipulated, for example using the <a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a> in Python. Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both. The data including the artificial gaps are stored with the naming structure HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations.  This dataset was used to compare the performance of some gap-filling methods and we provide a <a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619).  If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you! References: <ol> <li> Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a>  </li> <li> Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a>  </li> <li> Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a>  </li> <li> Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a>  </li> <li> Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a> </li> <li> Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a> </li> </ol>

opencc-by-4.0Dec 2022View details →

zenodo48/100

Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function

Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function    The folder ‘’simulation’’ includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip’’ is deprecated.) ........................................................................................... The ultrasound data were simulated using the k-Wave toolbox (version 1.3.)  [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources. The folder ‘’simulation’’  must be added to the path: ''…r-Wave/data/simulation/…'' For running the Matlab example scripts in the project in the github, the user has two choices:  <ol> <li>Simulate the k-Wave ultrasound data by setting data_sim=true; in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting  data_sim=false; in the examples in the project.</li> </ol> Please read the description in the example scripts! ………………………………………………………………………………… The folder simulation includes 2 subfolders, ‘’phantom’’ and ‘’data_ust_kWave_transmission’’. 1) The subfolder ‘’simulation/phantom’’  includes ‘’OA-BREAST’’.  In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/, the user must upload the folder ‘’Neg_47_Left’’ , and add it as  ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/’’. ....................................................................................................................................................................... 2) The subfolder ‘’simulation/data_ust_kWave_transmission’’  includes 2 subfolders, ‘’2D’’  and ‘’3D’’ . The subfolder ‘’2D’’  includes: data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section ‘’6.1. data simulation’’ in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’  are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach. data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’  are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations. data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water  as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section ‘’6.2. Numerical validation of the ray approximation to the Green’s function’’ in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’  are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers. data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat  This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20. …………………………………………………………………………………………………………………. The subfolder ‘’3D’’  includes: data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation.  The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3]. References 1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> . 2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021.  <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a> 3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010.  <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a>   4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a> 5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a>

opencc-by-4.0Mar 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record