Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
369
datasets available to search
ShareScore release 0.9.0
Dataset results
369 results for “Datasets Benchmarking”
Benchmark datasets for ITN
<p>Here are two benchmark datasets (pMHC I and peptide-SH3 domain) mentioned in ITN publication.</p>
Game of Life Benchmark Dataset
<p>This dataset includes grids of various sizes and percentages of living cells to benchmark <a href="https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life">Conway's Game of Life</a> implementations.</p><p>An example implementation in Python is available at <a href="https://github.com/girgink/game-of-life">https://github.com/girgink/game-of-life.</a></p><p>Grid files can be converted into <a href="https://conwaylife.com/wiki/Run_Length_Encoded">Run Length Encoded (RLE)</a> format by using the <a href="https://github.com/girgink/game-of-life/blob/main/convert_to_rle.py">convert_to_rle.py</a> tool available on the code repository.</p><p><strong>Grid file format</strong></p><ul><li>Grids are provided as ASCII text files.</li><li>The first line includes] the width and height of the grid as unsigned integer values separated by a space.</li><li>The other lines indicate the initial locations of the living cells:<ul><li>Each line has two unsigned integer values separated by space indicating vertical and horizontal coordinates of the living cells, respectively.</li><li>The top left cell has the coordinates of (0, 0)</li><li>Valid vertical coordinate values range between 0 - (height-1), increasing from top to bottom.</li><li>Valid horizontal coordinate values range between 0 - (width-1), increasing from left to right.</li></ul></li></ul><p><strong>Grids</strong></p><ul><li>1,000 x 1,000 grids:<ul><li>10% living cells: 1000x1000_0.1.txt.zip, 0.76MB uncompressed</li><li>20% living cells: 1000x1000_0.2.txt.zip, 1.48MB uncompressed</li><li>50% living cells: 1000x1000_0.5.txt.zip, 3.70MB uncompressed</li></ul></li><li>10,000 x 10,000 grids<ul><li>10% living cells: 10000x10000_0.1.txt.zip, 93MB uncompressed</li><li>20% living cells: 10000x10000_0.2.txt.zip, 186MB uncompressed</li><li>50% living cells: 10000x10000_0.5.txt.zip, 466MB uncompressed</li></ul></li><li>100,000 x 100,000 grids<ul><li>0.1% living cells: 100000x100000_0.001.txt.zip, 112MB uncompressed</li><li>1.0% living cells: 100000x100000_0.01.txt.zip, 1.09GB uncompressed</li><li>2.5% living cells: 100000x100000_0.025.txt.zip, 2.74GB uncompressed</li></ul></li><li>1,000,000 x 1,000,000 grids<ul><li>0.001% living cells: 1000000x1000000_0.00001.txt.zip, 131MB uncompressed</li><li>0.010% living cells: 1000000x1000000_0.0001.txt.zip, 1.28GB uncompressed</li><li>0.025% living cells: 1000000x1000000_0.00025.txt.zip, 3.20GB uncompressed</li></ul></li></ul>
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods
GEO Series GSE118767. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (Drop-Seq)
GEO Series GSE118706. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell ATAC-Seq (scATAC-Seq) dataset to validate long read RNA-Seq isoforms and benchmark scATAC-Seq analysis methods II
GEO Series GSE224045. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Benchmarking dataset for measurement and removal of index hopping artifacts in multiplexed droplet-based single-cell RNA-seq data
GEO Series GSE149087. Homo sapiens. 4 samples. Type: Other.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods
GEO Series GSE142286. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (Cel_Seq)
GEO Series GSE118704. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples
GEO Series GSE175937. Homo sapiens. 23 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell ATAC-Seq (scATAC-Seq) dataset to validate long read RNA-Seq isoforms and benchmark scATAC-Seq analysis methods
GEO Series GSE142285. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Benchmarking of Computational Demultiplexing Methods for Single-Nucleus RNA Sequencing Data [dataset 2]
GEO Series GSE298266. Homo sapiens; Ovis aries. 10 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (9 cell mixture dataset).
GEO Series GSE117450. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods [5 Cell Lines Cel-seq]
GEO Series GSE126908. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (RNAmix_Sort-seq)
GEO Series GSE117618. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Datasets for benchmarking of two-pass alignment
<p>This repository contains the datasets for benchmarking of a two-pass alignment method for long reads using the snakemake pipeline which can be found on github at <a href="https://github.com/bartongroup/MP_two_pass_alignment_pipeline">https://github.com/bartongroup/two_pass_alignment_pipeline</a></p> <p>The datasets contained here are genuine nanopore DRS datasets re-basecalled from Parker et al 2020 and Workman et al. 2019. There are also simulated alignments which are each created using the error profiles of a single genuine replicate .</p>
Benchmark datasets used in "Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks"
<p>To aid the reproducibility of the results in the paper “Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks,” we make our aggregation of the following data available. Code used to load the data and generate the results can be found at <a href="https://github.com/kmzzhang/periodicnetwork">https://github.com/kmzzhang/periodicnetwork</a>. These datasets have been constructed from publicly available data sources. If you use these datasets, please cite the original papers [1, 2, 3], in addition to ours [TBD]. Others might find this data useful for testing time-series inference techniques.</p> <p>[1] Jayasinghe, T. et al. The ASAS-SN catalogue of variable stars I: The Serendipitous Survey. Monthly Notices of the Royal Astronomical Society 477, 3145–3163 (2018). URL <a href="https://academic.oup.com/mnras/article/477/3/3145/4961151">https://academic.oup.com/mnras/article/477/3/3145/4961151</a>.<br> [2] Alcock, C. et al. The MACHO Project LMC Variable Star Inventory.II.LMC RR Lyrae Stars- Pulsational Characteristics and Indications of a Global Youth of the LMC. The Astronomical Journal 111, 1146 (1996). URL <a href="http://adsabs.harvard.edu/abs/">http://adsabs.harvard.edu/abs/</a>1996AJ....111.1146A.<br> [3] Udalski, A. The Optical Gravitational Lensing Experiment. Real Time Data Analysis Systems in the OGLE-III Survey. Acta Astronomica 53, 291–305 (2003). URL http: <a href="https://bloomlab-berkeley.slack.com//adsabs.harvard.edu/abs/2003AcA....53..291U">//adsabs.harvard.edu/abs/2003AcA....53..291U</a>.</p>
cigFacies: a massive-scale benchmark dataset of seismic facies and its application
<p>cigFacies is a dataset create by the <a href="https://cig.ustc.edu.cn/main.htm">Computational Interpretation Group (CIG)</a> for the AI-based automatic seismic facies classification in 3-D seismic data, <a href="https://cig.ustc.edu.cn/hui/list.htm">Hui Gao</a>, <a href="https://cig.ustc.edu.cn/xinming/list.htm">Xinming Wu</a>, <a href="http://cig.ustc.edu.cn/xiaoming/list.htm">Xiaoming Sun</a> and Mingcai Hou are the main contributors to the dataset.</p> <p>This is the benchmark skeletonization datasets of seismic facies, guided by the knowledge graph of seismic facies and constructed from three different stategies (field seismic data, synthetic data and GAN-based generation).</p> <p>Below are some brief desription of the datasets:</p> <p>1) The "The benchmark skeletonization datasets" file consists of 5 classes of seismic facies.</p> <p>2) The "parallel_class", "clinoform_class", "fill_class", "hummocky_class" and "chaotic_class" consist of 2000, 1500, 1500, 1500, 1500 stratigraphic skeletonization data constructed from field seismic data, synthetic data and GAN-based generation, respectively.</p> <div> <div> <div> <p>The source codes for constructing the benchmark dataset of seismic facies and deep learning for seismic facies classification have been uploaded to Github and are freely available at <a href="https://github.com/huigcig/cigFaciesNet">cigFaciesNet</a>.</p> </div> </div> </div> <p> </p> <p> </p>
maDLC Parenting Benchmark Dataset - Training
<p>see https://benchmark.deeplabcut.org/ for more information.</p>
MatSeg: Material State Segmentation Dataset and Benchmark
<p><strong>MatSeg Dataset and benchmark for zero-shot material state segmentation.</strong></p> <p><em>MatSeg Benchmark </em>containing 1220 real-world images and their annotations is available at <strong>MatSeg_Benchmark.zip </strong> the file contains documentation and Python readers.</p> <p><em>MatSeg dataset<strong> </strong></em>containing synthetic images with infused natural images patterns is available at <strong>MatSeg3D_part_*.zip</strong> and <strong>MatSeg3D_part_*.zip<em> </em></strong>(* stand for number).</p> <p><strong>MatSeg3D_part_*.zip: </strong>contain synthethc 3D scenes</p> <p><strong>MatSeg2D_part_*.zip:</strong> contain syntethc 2D scenes</p> <p><em>Readers and documentation<strong> </strong></em>for the synthetic data are available at:<strong> Dataset_Documentation_And_Readers.zip</strong></p> <p><em>Readers and documentation</em> for the real-images benchmark are available at:<strong> MatSeg_Benchmark.zip<br><br>The Code used to generate the MatSeg Dataset is available at: <a href="../records/11401072">https://zenodo.org/records/11401072</a></strong></p> <p><strong>Additional permanent sources for downloading the dataset and metadata:<a href="https://e.pcloud.link/publink/show?code=kZxsXTZIk88l74Jb3YeMeOcjOlJJVIqvHj7"> 1</a>,<a href="https://icedrive.net/s/XxgZSif7NgYRbjvDN5w9aiWZ1fR3"> 2</a></strong></p> <p><strong>Evaluation scripts for the Benchmark are now available at:</strong></p> <p><a href="../records/13402003"><strong>https://zenodo.org/records/13402003</strong></a> and <a href="https://e.pcloud.link/publink/show?code=XZsP8PZbT7AJzG98tV1gnVoEsxKRbBl8awX">https://e.pcloud.link/publink/show?code=XZsP8PZbT7AJzG98tV1gnVoEsxKRbBl8awX</a></p> <div> <h2> </h2> <h2>Description</h2> <p>Materials and their states form a vast array of patterns and textures that define the physical and visual world. Minerals in rocks, sediment in soil, dust on surfaces, infection on leaves, stains on fruits, and foam in liquids are some of these almost infinite numbers of states and patterns. </p> <p>Image segmentation of materials and their states is fundamental to the understanding of the world and is essential for a wide range of tasks, from cooking and cleaning to construction, agriculture, and chemistry laboratory work.</p> <p>The MatSeg dataset focuses on zero-shot segmentation of materials and their states, meaning identifying the region of an image belonging to a specific material type of state, without previous knowledge or training of the material type, states, or environment. </p> <p>The dataset contains a large set of (100k) synthetic images and benchmarks of 1220 real-world images for testing.</p> <h2>Benchmark</h2> <p>The benchmark contains 1220 real-world images with a wide range of material states and settings. For example: food states (cooked/burned..), plants (infected/dry.) to rocks/soil (minerals/sediment), construction/metals (rusted, worn), liquids (foam/sediment), and many other states in without being limited to a set of classes or environment. The goal is to evaluate the segmentation of material materials without knowledge or pretraining on the material or setting. The focus is on materials with complex scattered boundaries, and gradual transition (like the level of wetness of the surface).</p> <p>Evaluation scripts for the Benchmark are now available at: <a href="../records/13402003"><strong>1</strong></a> and <a href="https://e.pcloud.link/publink/show?code=XZsP8PZbT7AJzG98tV1gnVoEsxKRbBl8awX">2.</a></p> <div> <div><a href="https://sites.google.com/view/matseg/home#h.2otka7pobcz1"> <div> </div> </a></div> </div> <h2>Synthetic Dataset</h2> <p>The synthetic dataset is composed of synthetic scenes rendered in 2d and 3d using a blender. The synthetic data is infused with patterns, materials, and textures automatically extracted from real images allowing it to capture the complexity and diversity of the real world while maintaining the precision and scale of synthetic data. 100k images and their annotation are available to download.</p> </div> <p> </p> <p><strong>License</strong></p> <p>This dataset, including all its components, is released under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. To the extent possible under law, the authors have dedicated all copyright and related and neighboring rights to this dataset to the public domain worldwide. This dedication applies to the dataset and all derivative works.</p> <p>The MatSeg 2D and 3D synthetic were generated using the open-images dataset which is licensed under the https://www.apache.org/licenses/LICENSE-2.0. For these components, you must comply with the terms of the Apache License. In addition, the MatSege3D dataset uses Shapenet 3D assets with GNU license.</p> <p> </p> <h3>Example Usage:</h3> <p>An Example of a training and evaluation code for a net trained on the dataset and evaluated on the benchmark is given at these urls: <a href="https://e.pcloud.link/publink/show?code=XZxIXTZG3APQej3f67JXdRjitC7zHl7jfuV">1, </a><a href="https://icedrive.net/s/RTS2xXTv1Xzh8tbxYZ9hRkGuWktj">2</a></p> <p>This include an evaluation script on the MatSeg benchmark.</p> <p>Training script using the MatSeg dataset.</p> <p>And weights of a trained model</p> <p><strong>Paper:</strong></p> <p><strong>More detail on the work ca be found in the paper<a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/6ef4a4b387a5a547ea699f3df7fc1248-Paper-Datasets_and_Benchmarks_Track.pdf"> "Infusing Synthetic Data with Real-World Patterns for</a><br><a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/6ef4a4b387a5a547ea699f3df7fc1248-Paper-Datasets_and_Benchmarks_Track.pdf">Zero-Shot Material State Segmentation"</a></strong></p> <p> </p> <p> </p> <p>Croissant metadata and additional sources for downloading the dataset are available at <a href="https://e.pcloud.link/publink/show?code=kZxsXTZIk88l74Jb3YeMeOcjOlJJVIqvHj7">1</a>,<a href="https://icedrive.net/s/XxgZSif7NgYRbjvDN5w9aiWZ1fR3">2</a></p> <p> </p>
Dataset for the article "Benchmarks and Workflow for Harmonic IR and Raman Spectra"
<p>This dataset contains the data and scripts used in the article "Benchmarks and Workflow for Harmonic IR and Raman Spectra"</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.