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470 results for “Evaluation dataset”
Dataset from paper "Dynamic Concentration Factor: A Novel Parameter For The Rigorous Evaluation Of Solar Compound Parabolic Collectors"
<ul> <li>Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Diffuse Dynamic Concentration factor for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Experimental validation of the Dynamic Concentration Factor using actinometry results</li> <li>Independence test of Dynamic Concentration results of the number of rays cast in the software tool.</li> <li>Full-precision data of the Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> </ul>
Identification of Performance Changes at Code Level (Jetty Evaluation Dataset)
<p><em>This is the anonymous reviewing version; the source code repository will be added after the review.</em></p> <p>This dataset provides the results of measuring jetty with the 1,000 artificial regressions with Peass and with JMH. The creation of the artificial regressions and the measurement is defined here: https://anonymous.4open.science/r/jetty-evaluation-6F58/ (Repository named jetty-evaluation, GitHub link will be provided after review) An example regression is contained in <a href="https://anonymous.4open.science/r/jetty-experiments-202D">https://anonymous.4open.science/r/jetty-experiments-202D</a> We obtained these data from measurement on Intel Xeon CPU E5-2620 v3 @ 2.40GHz.</p> <p>The dataset contains the following data:</p> <ul> <li>regression-results-peass-0.tar.xz (Results of the measurement with Peass, part 0)</li> <li>regression-results-peass-1.tar.xz (Results of the measurement with Peass, part 1)</li> <li>regression-results-peass-2.tar.xz (Results of the measurement with Peass, part 2)</li> <li>regression-results-peass-3.tar.xz (Results of the measurement with Peass, part 3)</li> <li>regression-results-jmh.tar.xz (Results of the measurement with JMH)</li> <li>tree-results.tar.xz (Metadata of the trees)</li> </ul> <p>To get the data in a usable format, extract the peass data to one folder (the folder will be named <strong>$PEASS_RESULT_FOLDER</strong>):</p> <pre><code class="language-bash">mkdir peass for file in *; do echo $file; tar -xf $file; done for i in {0..3}; do mv $i/* .; done</code></pre> <p>This will yield to a folder containing 1000 folders named regression-$i, where each consists of</p> <ul> <li>deps.tar.xz: The regression test selection results</li> <li>logs.tar.xz: The logs of the test executions</li> <li>results: The traces of the regression test selection and a file named changes_*testcase.json, which contains statistical details of the measured performance change (if present)</li> <li>jetty.project_peass: Detailed measurement data and logs of individual JVM starts</li> </ul> <p>To analyse the Peass results, run</p> <pre><code class="language-bash">cd scripts/peass ./analyzeChangeIdentification.sh $PEASS_RESULTS_FOLDER ./analyzeFrequency.sh $PEASS_RESULTS_FOLDER</code></pre> <p>This will take some time, since partial results need to be unpacked for analysis. The first script will create the following results:</p> <p>and the second will yield the following results:</p> <pre><code>Correct Measurement: 587 Not selected changes: 146 Wrong measurement result: 267 Wrong analysis (should be 0): 0 Overall: 1000 Share of changed method on correct measurements: 0.109571 0.11238 32 Method call count on correct measurement: 15638.4 32853.7 32 Average tree depth on correct measurements: 1.1022 2.6875 32 Share of changed method on wrong measurements: 0.17692 0.180365 968 Method call count on wrong measurement: 711415 180438 968 Average tree depth on wrong measurements: 1.23239 2.42252 968</code></pre> <p>To analyze the JMH data, first extract the metadata (the folder will be named <strong>$TREEFOLDER</strong>):</p> <pre><code class="language-bash">tar -xf tree-results.tar.xz</code></pre> <p>Afterwards extract the JMH results (the folder will be named $<strong>JMH_RESULTS_FOLDER</strong>):</p> <pre><code class="language-bash">tar -xvf regression-results-jmh.tar.xz</code></pre> <p>This will yield a folder containing a measurement for each regression with two files:</p> <ul> <li>basic.json: The performance measurement result of the basic version</li> <li>regression-$i.json: The performance measurement result of the version containing the regression</li> </ul> <p>Afterwards, run the analysis in the jetty-evaluation repository:</p> <pre><code class="language-bash">cd scripts/jmh ./analyzeFrequency.sh $JMH_RESULTS_FOLDER $TREEFOLDER</code></pre> <p>Since the regression are injected in the call tree of the benchmark, there are now unselected changes. The analysis will yield the following results:</p> <pre><code>Share of changed method on correct measurements: 0.184631 0.271968 587 Method call count on correct measurement: 14628.2 4979.6 587 Share of changed method on wrong measurements: 0.180981 0.235614 267 Method call count on wrong measurement: 14902.2 4333.58 267</code></pre> <p> </p>
Datasets for Yue et al. (2022), Atmospheric Measurement Techniques: "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"
<p>Pixel scaled collocated AIRS, Aqua MODIS, SNPP CrIS, and SNPP VIIRS cloud retrieval products. These datasets are used to generate analyses and results presented in Yue et al. (2022, AMT): "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"</p>
HEAR NeurIPS 2021 Datasets (Holistic Evaluation of Audio Representations)
<p><strong>NOTES:</strong></p> <p><strong>* On Zenodo, please make sure you download datasets version 2021.3, not earlier versions. (2021.3 added the Vocal Imitations dataset at 48KHz. 2021.2 updated the tfds datasets.)</strong></p> <p><strong>* The datasets have different open licenses. Please see LICENSE.txt for each individual dataset's license.</strong></p> <p>These are the evaluation tasks for the HEAR (Holistic Evaluation of Audio Representations) 2021 NeurIPS challenge.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning?<br> <br> HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p> <p>For more information, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a> and upcoming PMLR journal article.</p> <p>Datasets were all normalized to a common human-readable format using <a href="https://github.com/neuralaudio/hear-preprocess">hearpreprocess</a>. Until 2022-04-01, datasets will be mirrored at <a href="https://data.neuralaudio.ai/">data.neuralaudio.ai</a>. This Zenodo mirror has all audio task but only at 48000Hz sampling rate. For other sampling rates (16000, 22050, 32000, 44100), please download files (requester pays) from Google Storage gs://hear2021-archive/tasks/ or AWS s3://hear2021-archive/tasks/</p>
METASHIFT: A DATASET OF DATASETS FOR EVALUATING CONTEXTUAL DISTRIBUTION SHIFTS
<p>Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. In this work, we present MetaShift—a collection of 12,868 sets of natural images across 410 classes—to address this challenge. We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. The key construction idea is to cluster images using its metadata, which provides context for each image (e.g. cats with cars or cats in bathroom) that represent distinct data distributions. MetaShift has two important benefits: first, it contains orders of magnitude more natural data shifts than previously available. Second, it provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets. Importantly, to support evaluating ImageNet trained models on MetaShift, we match MetaShift with ImageNet hierarchy. The matched version covers 867 out of 1,000 classes in ImageNet-1k. Each class in the ImageNet-matched Metashift contains 2301.6 images on average, and 19.3 subsets capturing images in different contexts. We also propose a method to construct tasks on the matched version, giving an example to construct 19,024 binary classification tasks on it.</p>
Finding Reliable Sources: Evaluation Datasets
<p><strong>Finding Reliable Sources</strong> (<strong>FRS</strong>) is an NLP/ML task, in which, given a single-sentence claim, we aim to automatically find reliable sources that could be used to confirm or refute it. This repository includes evaluation datasets that could be used to train and test solution for this problem. Datasets are organised as large collections of individual records, each consisting of (1) a claim expressed a short text fragment and (2) a list of identifiers (DOI, ISBN, arXiv ID or URL) of reliable sources associated with it.</p> <p>Two datasets are included:</p> <ul> <li>Claim-Source Pairing Dataset (CSP) contains 32 million textual contexts paired with 24 million source identifiers mined from English Wikipedia,</li> <li>FEVER-FRS contains 15,798 claims gathered for the <a href="https://fever.ai/">FEVER shared task</a>, supplemented with source identifiers using Wikipedia.</li> </ul> <p>The data were gathered within a study described in the article <em>"Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach"</em>, presented at the 2022 International Joint Conference on Neural Networks (IJCNN 2022). Please refer to the article (in <a href="https://doi.org/10.1109/IJCNN55064.2022.9891941">conference proceedings</a> or <a href="https://home.ipipan.waw.pl/p.przybyla/bib/Learning_to_Cite_3_CR.pdf">authors' version</a>) for more information on the FRS task, data collection procedures and evaluation results. The research was done within the <a href="https://homados.ipipan.waw.pl/">HOMADOS</a> project at the <a href="https://ipipan.waw.pl/">Institute of Computer Science</a>, Polish Academy of Sciences.</p> <p>The datasets described here are based on the <a href="https://doi.org/10.5281/zenodo.6539054">Wikipedia Complete Citation Corpus</a> (WCCC), which also includes the train/test split for CSP.</p> <p>The CSP dataset is uploaded in three variants, using different text fragments as 'context'. In '<strong>s</strong>', the context consists of a single sentence preceding (or containing) a citation. The '<strong>ts</strong>' variant additionally includes a title of the Wikipedia article, while '<strong>ss</strong>' contains two sentences preceding a citation. Each variant contains subsequently numbered TSV files, including lines with the following values:</p> <ul> <li>document ID (see <a href="http://doi.org/10.5281/zenodo.6539054">WCCC</a> for full document metadata),</li> <li>textual context,</li> <li>a list of (at least one, possibly many) source identifiers.</li> </ul> <p>The FEVER-FRS dataset was created based on the <a href="https://fever.ai">FEVER shared task</a> data. In the original dataset, each claim is matched with a set of 'evidence' sentences from Wikipedia that either support or refute it. For the purpose of evaluating FRS solutions, we replaced each evidence sentence with the identifiers to external sources that are cited alongside it. This was not always possible (e.g. for Wikipedia sentences with no sources, see the paper for details), but we managed to find 4414 and 11384 claims that are, respectively, refuted or supported by the provided sources.</p> <p>Each line of the FEVER-FRS TSV files contains the following:</p> <ul> <li>text of a short claim,</li> <li>a list of (at least one, possibly many) source identifiers.</li> </ul> <p>The source code code for generating both datasets (CSP from Wikipedia and FEVER-FRS from FEVER) can be obtained from a <a href="https://github.com/piotrmp/finding_reliable_sources">GitHub repository</a>.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Dataset for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM"
<p>The dataset includes root files used in generating the main figures for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM" by Jana Vasiljević, Alf Göök and Bo Cederwal. The article will be submitted to MDPI Applied Sciences.</p> <p> </p>
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations
<p>The various data used for the paper "A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model: Its parameterization, implementation, and evaluation" submitted to Geoscientific Model Development for publication are shared here.</p>
Dataset for "Towards Better Evaluation for Dynamic Link Prediction"
<p>These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em></p> <p>For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as <network>.csv.</p> <p>The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges.</p> <p>The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * <ml_network>.csv: this file contains the timestamp edge-list.<br> * <ml_network>.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * <ml_network_node>.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.</p>
Dataset for the paper "Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts"
<p>This dataset was used in the context of the article <em>Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts</em>, at the Computational Humanities Research 2022 conference.</p> <p>Predictions.zip contains the XML ALTO for the HTR and Segmentation prediction of around 10 pages of 1900 manuscripts from the Bibliothèque nationale de France.</p> <p>Varying ground truth contains the original training material for training a classifier for CER classification (see the article).</p> <p>The ManuscriptsIIIF.csv contains metadata about the manuscripts.</p> <p>This work was funded by the DIM MAP under the CREMMALab funding.</p>
Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI Dataset
Open the record for dataset details and reuse information.
Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets
<p>Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.<strong> </strong></p> <p>Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate are available for ProstateX collection, whole prostate gland and PZ for QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection.</p> <p>We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12]. </p> <p>This dataset is organized in three parts: </p> <p>AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern:</p> <p>PatientID/<br> └───Modality-%StudyInstanceUID/<br> └───%SeriesInstanceUID-%SeriesDescription.dcm.</p> <p>AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects.</p> <p>Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here:<br><a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Additional Notes</h2> <p><strong> </strong>This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.<br>https://portal.imaging.datacommons.cancer.gov/</p> <p>nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> <br>Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a><br>Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> <br>Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> <br>DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> <br>Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Related information</h2> <p>ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a><br><br>QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a><br><br>PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a></p> <h2>References </h2> <p>[1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.</p> <p>[2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11.</p> <p>[3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.</p> <p>[4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817.</p> <p>[5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[6] Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND</p> <p>[7] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris</p> <p>[9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57.</p> <p>[10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9.</p> <p>[11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90.</p> <p>[12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.</p> <p> </p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Dataset for publication "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation"
<p>This is a dataset for a paper "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation". All details about the data are included in the readme file.</p>
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, vol. 271, p. 108171, 2022.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
simulated datasets for evaluating polygenic detection methods
<p>This dataset contains simulation files corresponding to a combination of each demographic model (1/2/3), environment (linear/quadratic), selection duration (200/400/600/800/1000), and simulation replicate(1-20). This resulted in 600 simulation files with 600 unique combinations of demographic models, environments, selection durations, and simulation replicates. For each individual in the genotype data file, we have the files containing the values of selective pressure(linear and quadratic environment) in the metadata folder. </p> <p>The variant position are 1-based which is default SLiM output. To compare the results with the causal loci user must make the positions 0-based (i.e. POS-1). The details are provided in a github tutorial.</p> <p>Please refer to the documentation for a detailed description of the files and folder structure.</p> <p>The article describing the simulated data and its application is accepted for publication in Nucleic Acids Research (https://doi.org/10.1093/nar/gkae1027).</p>
Write-only File System for Privacy-aware Wireless Sensor Networks Evaluation Dataset
<p>Evaluation dataset for the paper <strong>"WoFS: A Write-only File System for Privacy-aware Wireless Sensor Networks"</strong> published at the <em>49th IEEE Conference on Local Computer Networks (2024)</em></p>
EAHE-D: a Dataset for Modeling and Performance Evaluation of Earth to Air Heat Exchangers
<p><strong>The dataset is available in its original form, allowing users to perform data preprocessing according to their specific needs. Additionally, we offer a processed version of the dataset, which can be used to further investigate the effectiveness of EAHE deployment in buildings and its integration with other passive and active systems, aiming to enhance energy efficiency while maintaining occupant comfort.</strong></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.