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47,155 results for “evaluations”
Benchmarks for Evaluation and Comparison of Three-way Model Merging Techniques
<p>The attached files are intended to allow the interaction of researchers in the field of Model Merging Conflict Detection and Resolution. For people who want to add the results of evaluating a new technique and contribute to the creation of the actual body of knowledge, it is necessary to download the raw files and fill them out.</p>
Field campaign data for evaluating green infrastructure in Guildford
<p>Field campaign data collected using reference instruments before and after hedges (Green infrastructure) in Guildford by University of Surrey.</p>
Replication package of A benchmark-based evaluation of search-based crash reproduction
<p>Release of the reproduction package of Soltani, M., Derakhshanfar, P., Devroey, X. and van Deursen, A. (2020). A benchmark-based evaluation of search-based crash reproduction. In Empirical Software Engineering. 25, 1 (Jan. 2020), pp. 96–138.</p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Bayesian evaluation of the mass calibration example from EA 4/02
<p>The example describes how the measurement uncertainty of the calibration of a 10 kg weight can be performed using Bayes’ rule and a measurement model. The purpose of the example is to demonstrate how Bayes’ rule can be implemented in the context of the Guide to the expression of Uncertainty in Measurement and the uncertainty can be propagated. Special attention is paid to the assignment of probability density functions of type A and type B evaluations of standard uncertainty.</p> <p>The files contained in the dataset are:</p> <ul> <li>Readme.txt: instructions on how to install and run the project</li> <li>Mass_example_EA_4_02.pdf: Report "Bayesian evaluation of the mass calibration example from EA 4/02"</li> <li>Compendium.bib: bibliography file</li> <li>Mass_example_EA_4_02_Article.Rproj: RStudio project file</li> <li>Mass_example_EA_4_02.Rnw: source file to generate Mass_example_EA_4_02.tex in RStudio</li> <li>Mass_example_EA_4_02.bbl: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.log: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.synctex.gz: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.tex: LaTeX file to be compiled in order to produce Mass_example_EA_4_02.pdf</li> <li>Mass_example_EA_4_02-concordance.tex: file generated by RStudio when compiling Mass_example_EA_4_02.Rnw.</li> </ul>
DCASE 2020 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <strong>DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" </strong><a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">[task description]</a>. </p> <p>In the task, three datasets have been released: "<a href="http://zenodo.org/record/3678171">development dataset</a>", "<a href="https://zenodo.org/record/3727685">additional training dataset</a>", and "evaluation dataset". This evaluation dataset was the last of the three released. This dataset includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have a condition label (i.e., normal or anomaly).</p> <p>The recording procedure and data format are the same as the <a href="http://zenodo.org/record/3678171">development dataset</a> and <a href="https://zenodo.org/record/3727685">additional training dataset</a>. The Machine IDs in this dataset are the same as those in the <a href="https://zenodo.org/record/3727685">additional training dataset</a>. For more information, please see the pages of the <a href="http://zenodo.org/record/3678171">development dataset</a> and the <a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">task description</a>. </p> <p>After the DCASE 2020 Challenge, we released the <a href="https://zenodo.org/record/3951620">ground truth for this evaluation dataset</a>.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>Once you unzip the downloaded files from Zenodo, you can see the following directory structure. Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as:</p> <ul> </ul> <p>/eval_data</p> <ul> <li>/ToyCar <ul> <li>/test (Normal and anomaly data for all Machine IDs are included, but they do not have a condition label.) <ul> <li>/id_05_00000000.wav</li> <li>...</li> <li>/id_05_00000514.wav</li> <li>/id_06_00000000.wav</li> <li>...</li> <li>/id_07_00000514.wav</li> </ul> </li> </ul> </li> <li>/ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)</li> <li>/fan</li> <li>/pump</li> <li>/slider</li> <li>/valve</li> </ul> <p> </p> <p>The paths of audio files are:</p> <ul> <li>"/eval_data/<Machine_Type>/test/id_<Machine_ID>_[0-9]+.wav"</li> </ul> <p>For example, the Machine Type and Machine ID of "/ToyCar/test/id_05_00000000.wav" are "ToyCar" and "05", respectively. Unlike the <a href="http://zenodo.org/record/3678171">development dataset</a> and <a href="https://zenodo.org/record/3727685">additional training dataset</a>, its condition label is hidden. </p> <p> </p> <p><strong>Baseline system</strong></p> <p>A simple baseline system is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_baseline">[URL]</a>. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was created jointly by <strong>NTT Corporation</strong> and <strong>Hitachi, Ltd.</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> <p>Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. <a href="https://ieeexplore.ieee.org/document/8937164">[pdf]</a></p> <p>Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">[pdf]</a></p> <p>Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring<em>,"</em> in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Koizumi_3.pdf">[pdf]</a></p> <p><br> <strong>Feedback</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Yuma Koizumi, <a href="mailto:koizumi.yuma@ieee.org">koizumi.yuma@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
Experiment Results: Evaluation of different parallelisation strategies with regard to the performance of parallel program execution
<p><br> Modern processors achieve an increase in performance by adding multiple cores. This means that during software development, care must be taken to parallelise the program sequences. To make predictions about the performance of a software design, there is Palladio. This is very accurate for single core processors.<br> In this bachelor thesis the influence of the chosen parallelization strategy on the performance of software is examined. Different hardware requirements are used for this purpose. They are used to generate individual work packages. These are executed by different parallelization strategies. The used parallelization strategies are: Java Threads, Java ParallelStreams, OpenMp and Akka Actor. Runtime and cache behavior are measured during each execution. In addition, the experiments are performed on different servers. The evaluation is done using acceleration curves and the Cache Miss Rate. The results show that the parallelization strategies differ only slightly in the work packages used.</p>
Audio captioning DCASE 2020 evaluation (testing) split
<p>This is the <strong>evaluation split for Task 6, Automated Audio Captioning, in DCASE 2020 Challenge</strong>. </p> <p>This evaluation split is the Clotho testing split, which is thoroughly described in the corresponding paper: </p> <p><em>K. Drossos, S. Lipping and T. Virtanen, "Clotho: an Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736-740, doi: 10.1109/ICASSP40776.2020.9052990.</em></p> <p>available online at: <a href="https://arxiv.org/abs/1910.09387">https://arxiv.org/abs/1910.09387</a> and at: <a href="https://ieeexplore.ieee.org/document/9052990 ">https://ieeexplore.ieee.org/document/9052990 </a></p> <p>This evaluation split is meant to be used for the purposes of the Task 6 at the scientific challenge DCASE 2020. This split it is not meant to be used for developing audio captioning methods. For developing audio captioning methods, you should use the development and evaluation splits of Clotho. </p> <p>If you want the development and evaluation splits of Clotho dataset, you can find them also in Zenodo, at: <a href="https://zenodo.org/record/3490684">https://zenodo.org/record/3490684</a></p> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== License ==</strong></p> <p>The audio files in the archives:</p> <ul> <li>clotho_audio_test.7z </li> </ul> <p>and the associated meta-data in the CSV file:</p> <ul> <li>clotho_metadata_test.csv</li> </ul> <p>are under the corresponding licences (mostly CreativeCommons with attribution) of Freesound [1] platform, mentioned explicitly in the CSV file for each of the audio files. That is, each audio file in the 7z archive is listed in the CSV file with the meta-data. The meta-data for each file are: </p> <ul> <li>File name</li> <li>Start and ending samples for the excerpt that is used in the Clotho dataset</li> <li>Uploader/user in the Freesound platform (manufacturer)</li> <li>Link to the licence of the file</li> </ul> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== References ==</strong><br> [1] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245</p>
Benchmark for the evaluation of named entity recognition over ancient documents
<p>The dataset consists of a multilingual noisy corpora for named entity recognition (NER).<br> The noisy versions are simulated from the CoNLL-02 (Spanish and Dutch) and CoNLL-03 (English) NER corpora.<br> The original collections are re-OCRed and four types of noises at two different levels are added in order to simulate various OCR output.</p> <p>More precisely, we first extracted raw texts and converted them into images. These images have been contaminated by adding some common noises when using a scanner. We further extract OCRed data using tesseract open source<br> OCR engine v-3.04.01. Consequently to the image noise insertions, OCRed data contains degradations. Original and noisy texts are finally aligned.</p> <p>This archive contains three folders (one per language). The folders contain the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data.</p> <p>These are the supplementary materials for the TPDL 2020 paper <a href="https://zenodo.org/record/4734376#.YJKAcKE6-Uk">Assessing and minimizing the impact of OCR quality on named entity recognition</a>. If you end up using whole or parts of this resource,<br> please cite this paper:</p> <pre><code>@InProceedings{10.1007/978-3-030-54956-5_7, author="Hamdi, Ahmed and Jean-Caurant, Axel and Sid{\`e}re, Nicolas and Coustaty, Micka{\"e}l and Doucet, Antoine", editor="Hall, Mark and Mer{\v{c}}un, Tanja and Risse, Thomas and Duchateau, Fabien", title="Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition", booktitle="Digital Libraries for Open Knowledge", year="2020", publisher="Springer International Publishing", address="Cham", pages="87--101", isbn="978-3-030-54956-5" }</code></pre> <p><strong>Acknowledgments</strong><br> This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 [NewsEye](https://www.newseye.eu/).</p>
Cluster randomized controlled trial evaluating the impact on children's linear growth of an unconditional cash transfer program implemented in rural areas of North Togo
<p>A parallel cluster randomized controlled trial was implemented in North Togo. 162 rural landlocked villages were randomized into either an intervention arm (cash transfers + package of community activities, <em>n</em>=82) or a control arm (package of community activities only, <em>n</em>=80). </p> <p>Two different representative samples of children aged 6-29 months and their mothers were surveyed, one before the intervention (2014, <em>n</em>=2,658), the other two years afterwards (2016, <em>n</em>=2,031).</p> <p>You will found here:</p> <ul> <li>Two datasets, fully anonymized (French, CSV files): one for observations on households and the other for observations on mother-child pairs</li> <li>Two dictionaries of variables (French, Excel files): one for the household database and the other for the mother-child pairs database</li> <li>The study protocol as submitted to the Ministry of Health of Togo (French, PDF)</li> <li>The technical and financial proposal (to evaluate the CT program) as submitted to funders (English, PDF)</li> </ul> <p> </p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez GeoTIFF
<p>Multiscale elevation models centered on Valdez, Alaska, USA</p> <p>Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 2.0.0 replaced the previous erroneous elevation model of another geographic area.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Fig. 8 in Type specimens of Andrena wollastoni C , 1922 (Hymenoptera, Anthophila): deposition, evaluation and designation of a lectotype
Fig. 8: Paratype of Andrena dourada KRATOCHWIL & SCHEUCHL, 2003, formerly type of A. wollastoni COCKERELL, 1922, collected by W. Porter Cockerell; (a) habitus lateral; (b) head frontal; (c) metasoma dorsal; (d) head dorsal; (e) paratype label (USNM) with type number; (f) barcode label; (g) locality label written by Cockerell (pencil); (h) label written by Wilmatte Porter Cockerell (pencil); (i) cotype label written by Cockerell (ink); photos by K. Darrow (USNM).
Figure 1 in Evaluation of the taxonomy of Helix cincta (Muller, 1774) and Helix nucula (Mousson, 1854); insights using mitochondrial DNA sequence data
Figure 1. Map showing the localities of samples used in the present study representing the morphologically defined species and the distribution of Helix cincta (dash line, light grey) and Helix nucula (continuous line, dark grey).
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures - MALDI-ToF Mass Spectra
<p>Datasets in support of the <em>bioRxiv </em>submitted paper Doellinger et al. (<strong>2019</strong>) "<em>Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures"</em> - MALDI-ToF Mass Spectra.</p> <p>The experiment and sample description and spectra numbering is consistent with the publication. Mass spectral data files are provided as unprocessed raw data in the manufacturer's original data format (Bruker Daltonics). Data is compressed using the freely available 7zip software.</p> <p><strong>Content:</strong></p> <p><em><strong>Figure 1.zip</strong></em>: Cereulide detection in <em>B. cereus</em> samples cultivated using different cultivation media and different sample preparation, or cereulide extraction methods.</p> <p><em><strong>Figure 2.zip</strong></em>: Effectivity of cereulide extraction by different solvents from <em>B. cereus</em> F4810/72 colony material.</p> <p><em><strong>Figure 3.zip</strong></em>: MALDI LIFT-ToF /ToF MS spectrum of cereulide.</p> <p><em><strong>Figure 4.zip</strong></em>: Determination of the limit of detection (LOD) of cereulide by MALDI- and LDI-ToF</p> <p><em><strong>Table 1.zip</strong></em>: Analysis of cereulide in <em>B. cereus</em> strains by MALDI-ToF MS.</p> <p><em><strong>Fig.SI.01.zip: </strong></em> Ultraperformance Liquid Chromatography – Mass Spectrometry (UPLC-MS/MS) analysis of ethanolic washing solutions of <em>B. cereus</em> F4810/72.</p> <p><em><strong>Fig.SI.02.zip:</strong></em> A selection of MALDI-ToF and LDI-ToF technical replicate mass spectra obtained from a commercial cereulide standard.</p> <p><em><strong>Fig.SI.03.zip:</strong></em> Limit of detection (LOD) of cereulide determined by MALDI- and LDI-ToF MS of ethanol wash solutions from <em>B. cereus</em> ATCC 10987 spiked by a cereulide standard. </p> <p><em><strong>Fig.SI.04.zip:</strong></em> Direct cereulide detection by means of MALDI- (panels <strong>A</strong>-<strong>F</strong>) and LDI-ToF MS (panels <strong>G</strong>-<strong>M</strong>) in linear and reflectron measurement mode.</p>
Superposition with Lambdas - Evaluation Data
<p>This package contains supplementary material for the Evaluation section of the article Superposition with Lambdas.</p> <p>Archive <a href="https://zenodo.org/api/files/16d43d1e-2b21-4892-82f8-7239a4a94237/lamsup_article_problems.zip">lamsup_article_problems.zip</a> contains problems used for the evaluation.</p> <p>Archive <a href="https://zenodo.org/api/files/16d43d1e-2b21-4892-82f8-7239a4a94237/lamsup_article_problems.zip">lamsup_article_results.zip</a> contains raw results presented in the section as well as scripts for running Zipperposition to obtain these results.</p>
RTPTorrent: An Open-source Dataset for Evaluating Regression Test Prioritization
<p>This dataset is designed to be used in evaluation studies of regression test prioritization techniques. It includes 20 open-source Java projects from GitHub and over 100,000 logs of real-world build logs from TravisCI. The projects span a wide range with regard to size, number of contributors, and maturity of open-source Java projects available on GitHub.</p> <p>Futher, the dataset includes the results of baseline approaches to ease the comparison of new techniques applied to the dataset.</p> <p>A readme file with a more detailed description of the structure of the dataset is included. For even more information see the corresponding MSR 2020 publication.</p> <p>Versions:</p> <ul> <li> 2020-09-23 (version 1.1) <ul> <li>Updated archived `deeplearning4j` repository with a fork that contains all of the original commits. Repository at the original GitHub location had been replaced. Defect identified by Daniel Elsner (Technische Universität München).</li> <li>Renamed root folder from MSR2 to rtp-torrent</li> </ul> </li> <li>2020-05-25 (version 1.0) <ul> <li>Initial release</li> </ul> </li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.