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2,863 results for “Challenge”
Cursive Character Challenge (CCC)
<p>Cursive Character Challenge (C-Cube) is used as benchmark for machine learning and pattern recognition algorithms. The database contains 57293 cursive characters manually extracted from cursive words, including both upper and lower case versions of each letter.</p> <p>If you use this dataset, please cite the following publication:</p> <p>F.Camastra, M.Spinetti and A.Vinciarelli. <em><a href="https://publications.idiap.ch/index.php/publications/show/364">Offline Cursive Character Challenge: a New Benchmark for Machine Learning and Pattern Recognition Algorithms</a>,</em> Proceedings of International Conference on Pattern Recognition (ICPR), 2006.</p>
Benchmark Collection for A Time Leap Challenge for SAT-Solving
<p>Benchmarks used for the paper</p> <p>Johannes K. Fichte, Markus Hecher, Stefan Szeider: A Time Leap Challenge for SAT-Solving, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020).</p> <p>For details, we refer to the original sources of the benchmarks (<a href="https://www.cs.uni-potsdam.de/wv/sets/">https://www.cs.uni-potsdam.de/wv/sets/</a> and https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz), which was authored by Holger H. Hoos, B. Kaufmann, T. Schaub, and M. Schneider in the publication Robust Benchmark Set Selection for Boolean Constraint Solvers, LION 7, DOI: 10.1007/978-3-642-44973-4_16, 2013.</p>
Data from: Herbivore consumers face different challenges along opposite sides of the stoichiometric knife edge
Anthropogenic activities have reshaped the relative supply rates of essential elements to organisms. Recent studies suggested that consumer performance is strongly reduced by food that is either very high or very low in relative phosphorus content. However, the generality of such 'stoichiometric knife-edge' and its underlying mechanisms are poorly understood. We studied the response of a planktonic rotifer to a tenfold food carbon:phosphorus (C:P) gradient and confirmed the existence of the stoichiometric knife-edge. Interestingly, we observed a complete homeostatic breakdown associated with strong growth reductions at high food C:P. In contrast, at low food C:P animals maintained homeostasis despite pronounced performance reductions. Our results suggest that the mechanisms underlying adverse effects of stoichiometric imbalance are determined by both the identity of elements that are limiting and those that are present in excess. Negative effects of excess P reveal an additional way of how eutrophication may negatively affect consumers.
Learn2Reg Challenge: CT Lung Registration - Test Data
<p>For more information about this dataset go to: https://learn2reg.grand-challenge.org/</p>
FGF23-related hypophosphatemia in patients with low bone mineral density and fragility fractures- challenges in diagnosis and management
<p><em><strong>FGF23-related hypophosphatemia in patients with low bone mineral density and fragility fractures: challenges in diagnosis and management</strong></em></p> <p>Abstract</p> <p><strong>Purpose: </strong> Hypophosphatemia (HP) can be observed in patients evaluated for skeletal fragility. We investigated prevalence of HP among outpatients referred for low bone density or fragility fractures, HP-associated clinical and biochemical features and outcomes of recommended diagnostic algorithm in our cohort.</p> <p><strong>Methods: </strong> Chronic HP (phosphate ≤ 2.7 mg/dL over 6 months or longer) was retrospectively investigated among 2319 patients. In renal wasting-related HP, intact FGF23 was assessed; non-suppressed FGF23 prompted the performance of <sup>68</sup>Ga-DOTATOC PET/CT in the suspicion of tumor-induced steomalacia (TIO).</p> <p><strong>Results: </strong> Renal wasting-related HP (median 2.2, range 1.6-2.6 mg/dL) was observed in 19 patients (0.82%). FGF23 levels were suppressed in two patients diagnosed with renal tubular disease, increased in one and within normal range in most patients. X-linked hypophosphatemic rickets was diagnosed in one woman. In the remaining 16 patients, highly prevalent fragility fractures (50%) and severely reduced bone mineral density were detected, though diagnostic criteria for osteomalacia were not fulfilled. <sup>68</sup>Ga-PET was performed in nine patients and was positive in four. While intact FGF23 levels alone failed to differentiate PET's outcomes (positive: FGF23 median 70.5 pg/mL; negative: 52 pg/mL, P = 0.462), the coexistence of multiple biochemical and radiologic alterations performed better in prediction of PET's positivity.</p> <p><strong>Conclusion: </strong> Mild, apparently unexplained HP is observed in 0.82% of patients with low bone density or fragility fractures. In asymptomatic patients with isolated mild hypophosphatemia, the probability of finding an underlying tumor disease is very low, and utility of extensive and expensive diagnostic workup should be carefully considered in this setting</p>
Cytosolic crowding drives the dynamics of both genome and cytosol in Escherichia coli challenged with sublethal antibiotic treatments
<p>Datasets used in the publication:<br> "Cytosolic crowding drives the dynamics of both genome and cytosol in Escherichia coli challenged with sublethal antibiotic treatments". <br> The archives are named according to the treatment administered and contain microscopy images of the chromosomal locus ORI2.<br> 21 fields of view were imaged for 45s at a 9.6 frame rate per second (producing 442 images) every 20 minutes for up to two hours. <br> The number after "Scan" indicates the 20 minutes intervals (1 to 6 meaning 20 to 120 minutes). The number after "Position" indicates the field of view (1 to 21). Each of these folders contains 442 images. <br> <br> Every archive also contains phase contrast images, which are in the folders containing PhC (for Phase Contrast) in the name. Each of these folders contains 21 folders (one for each position) which in turn contain 6 images, taken at the beginning of the aforementioned 45s intervals.</p> <p>Due to space limitations, we could only upload one experiment (out of 3 replicates) from only one of the 3 markers (Ori, Ter and cytoplasmic). The remaining data is available upon reasonable request from the corresponding author of the publication.<br> <br> The Excel files contain data points values and raw measurements for the plots given in Fig. 2, 6A, 6B, 6D and SI11B&C and SI12.</p> <p>The codes used to analyse the data are given at: <a href="https://github.com/ver228/bacteria-loci-tracker">https://github.com/ver228/bacteria-loci-tracker</a> </p>
GENEA Challenge 2020 user-study video stimuli
<p>This Zenodo repository contains all video stimuli used in the two crowdsourced user studies (appropriateness and human-likeness) of the GENEA Challenge 2020.</p>
GENEA Challenge 2020 3D coordinates of submitted motion
<p>This Zenodo repository contains all 3D coordinates for the all the motions submitted by the participants of the GENEA Challenge 2020.</p>
Data from: Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring
Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer bio- diversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allow information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β-diversity) might add crucial information related to relative abundance of dif- ferent species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure β-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β-diversity from remotely sensed imagery and potentially relating them to species diversity in the field.
Allergic Non-Asthmatic Adults have Regional Pulmonary Responses to Segmental Allergen Challenge
<p>Data reported in the manuscript, 'Allergic Non-Asthmatic Adults have Regional Pulmonary Responses to Segmental Allergen Challenge' published in PLOS One. November 2015</p>
Results Dataset for Addressing the Sustainable Aviation Fuel Grand Challenge: Economic and Environmental Insights from Feedstock Optimization
<p>This dataset contains the results from the reserach article titled "Addressing the Sustainable Aviation Fuel Grand Challenge: Economic and Environmental Insights from Feedstock Optimization"</p>
The ULS23 Challenge Public Training Dataset Part 5
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for part of the weakly annotated DeepLesion data. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </p>
The ULS23 Challenge Public Training Dataset Part 2
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for previously released data. It consists of 333 kidney lesions from the KiTS21 dataset, 2.246 lung lesion from LIDC-IDRI and 888 liver lesions from the LiTS challenge. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </p>
CHALLENGES IN COUNTERING AND PREVENTING SOCIAL MEDIA CALLS FOR MASS PUBLIC SAFETY VIOLATIONS
Open the record for dataset details and reuse information.
Figure 2 from: Salama AH (2024) The promise and challenges of ChatGPT in community pharmacy: A comparative analysis of response accuracy. Pharmacia 71: 1-5. https://doi.org/10.3897/pharmacia.71.e116927
Figure 2 Templet about alternative therapy questions.
Figure 1 from: Salama AH (2024) The promise and challenges of ChatGPT in community pharmacy: A comparative analysis of response accuracy. Pharmacia 71: 1-5. https://doi.org/10.3897/pharmacia.71.e116927
Figure 1 Templet of questions posed.
Figure 3 from: Salama AH (2024) The promise and challenges of ChatGPT in community pharmacy: A comparative analysis of response accuracy. Pharmacia 71: 1-5. https://doi.org/10.3897/pharmacia.71.e116927
Figure 3 Percentage of correct answers.
[LS2N_IPI_DIBR_Videos] 3D-TV System with Depth-Image-Based Rendering Architectures, Techniques and Challenges
<h3>Presentation & Goal</h3> <p>The <strong>IRCCyN IVC DIBR Videos</strong> quality database contains <strong>102 video sequences</strong> of 6 seconds in 1024x768 resolution with between 15 and 30 frames per second. Individual votes and Mean Opinion Scores (MOS) obtained by an Absolute Category Rating with Hidden Reference (ACR-HR) experiment are provided.</p> <p>Three different multiview plus depth (MVD) sequences are considered in this database. The sequences are Book Arrival (1024x768, 16 cameras with 6.5cm spacing), Lovebird1 (1024x768, 12 cameras with 3.5 cm spacing) and Newspaper (1024x768, 9 cameras with 5 cm spacing). Seven DIBR algorithms processed the three sequences to generate, for each sequence, four new viewpoints.</p> <h4>Initial goal</h4> <p>This database was done to consider the reliability of usual assessment methods when evaluating virtual synthesized views in the multiview video context. Virtual views are generated from Depth Image Based Rendering (DIBR) algorithms. Because DIBR algorithms involve geometric transformations, new types of artifacts come up. The question regards the ability of commonly used methods to deal with such artifacts.</p> <h4>Possible usage</h4> <p>This video quality database can be used to test a video quality metric precision in a context of DIBR.</p> <p> </p> <p> </p> <h3>Dataset description</h3> <div> <p>We used Absolute Category Rating - Hidden Reference (<strong>ACR-HR</strong>) and as test methodology.</p> <p>We provide a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Scores.xls">spreadsheet</a> with the individual scores, the Mean Opinion Score (MOS) and the Differential Mean Opinion Score (DMOS) for each of the 102 Processed Video Sequences (PVS).</p> <p>The <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/Videos">PVS (avi videos)</a> are available on our FTP server and are in the AVI format.</p> <p>The information about the source contents, video tested generation, subjective experiment environment and available data are provided in a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Description_v1.0.pdf">description file</a>.</p> <h4>Source contents (SRC)</h4> <table> <tbody> <tr> <th> <h4>Source number</h4> </th> <td> <h4>Source name</h4> </td> <td> </td> <td> <h4>Description</h4> </td> </tr> <tr> <th>01</th> <td><strong>Book Arrival</strong></td> <td> </td> <td><u>Description</u> : A man in an office standing up to welcome another man. <br><u>Author</u> : Heinrich-Hertz-Institut. <br><u>Video frame rate</u> : 15Hz <br><u>Number of frames</u> : 100</td> </tr> <tr> <th>02</th> <td><strong>Lovebird1</strong></td> <td> </td> <td><u>Description</u> : A couple walking in front of a temple. <br><u>Author</u> : Electronics and Telecommunications Research Institute (ETRI). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 150</td> </tr> <tr> <th>03</th> <td><strong>Newspaper</strong></td> <td> </td> <td><u>Description</u> : A man and a girl reading the newspaper around a table when a man coming behind them. <br><u>Author</u> : Gwangju Institute of Science and Technology (GIST). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 200</td> </tr> </tbody> </table> <p> </p> <p> </p> <h3>Hypothetical Reference Circuits (HRC)</h3> <div> <p>The Processed Video Sequences (PVS) were created using the following Hypothetical Reference Circuits (HRC).</p> <table> <tbody> <tr> <th>HRC name</th> <th>HRC description</th> </tr> </tbody> <tbody> <tr> <td>cam_xx</td> <td>[No impairment - Reference sequence view 1]</td> </tr> <tr> <td>cam_yy</td> <td>[No impairment - Reference sequence view 2]</td> </tr> <tr> <td>cam_zz</td> <td>[No impairment - Reference sequence view 3]</td> </tr> <tr> <td>A1</td> <td>Fehn cropped : where the depth map is pre-processed by a low-pass filter. Borders are cropped, and then an interpolation is processed to reach the original size.</td> </tr> <tr> <td>A2</td> <td>Fehn interpolated : Borders are inpainted by the method proposed by Telea</td> </tr> <tr> <td>A3</td> <td>Tanimoto et al., it is the recently adopted reference software for the experiments in the 3D Video group of MPEG.</td> </tr> <tr> <td>A4</td> <td>Müller et al., proposed a hole filling method aided by depth information.</td> </tr> <tr> <td>A5</td> <td>Ndjiki-Nya et al., the hole filling method is a patch-based texture synthesis.</td> </tr> <tr> <td>A6</td> <td>Köppel et al., uses depth temporal information to improve the synthesis in the disoccluded areas.</td> </tr> <tr> <td>A7</td> <td>corresponds to the unfilled sequences (i.e. with holes).</td> </tr> <tr> <td>cam_xx_qp26</td> <td>Reference sequence view 1 coded by H.264 with a QP = 26</td> </tr> <tr> <td>cam_xx_qp34</td> <td>Reference sequence view 1 coded by H.264 with a QP = 34</td> </tr> <tr> <td>cam_xx_qp44</td> <td>Reference sequence view 1 coded by H.264 with a QP = 44</td> </tr> </tbody> </table> <p>There are several references (3 first lines of the table) for each content, because there are the 3 views used for each content. So there are 3 HRC with no impairment.</p> <p> </p> <p> </p> <h3>Experiment information</h3> <div> <table> <tbody> <tr> <td> <h4>Features</h4> </td> <td> <h4>Value</h4> </td> </tr> <tr> <td><strong>Highlights of this database</strong></td> <td>Depth Images Based Rendering</td> </tr> <tr> <td>Free-viewpoint</td> </tr> <tr> <td>video quality</td> </tr> <tr> <td><strong>Video</strong></td> </tr> <tr> <td><strong>Frame frequency</strong></td> <td>30 or 15Hz</td> </tr> <tr> <td><strong>Video format</strong></td> <td>1024x768</td> </tr> <tr> <td><strong>Video length</strong></td> <td>100, 150 or 200 frames</td> </tr> <tr> <td><strong>Video duration</strong></td> <td>5 to 7 seconds</td> </tr> <tr> <td><strong>Number of videos</strong></td> <td>102</td> </tr> <tr> <td><strong>Subjective test</strong></td> </tr> <tr> <td><strong>Observation distance</strong></td> <td>4H</td> </tr> <tr> <td><strong>Environment</strong></td> <td>ITU-R BT.500-11</td> </tr> <tr> <td><strong>Background luminance</strong></td> <td>25cd/m²</td> </tr> <tr> <td><strong>Methodology</strong></td> <td>ACR</td> </tr> <tr> <td><strong>Duration</strong></td> <td>30 minutes</td> </tr> <tr> <td><strong>Pre screening</strong></td> <td>Snellen, Ishihara</td> </tr> <tr> <td><strong>Post screening (Rejection method)</strong></td> <td>VQEG Multimedia</td> </tr> <tr> <td><strong>Observers</strong></td> </tr> <tr> <td><strong>Number of observers</strong></td> <td>32 naives</td> </tr> <tr> <td><strong>Display</strong></td> </tr> <tr> <td><strong>Display</strong></td> <td>TVLogic LMV401</td> </tr> <tr> <td><strong>Display resolution</strong></td> <td>Full HD (1920x1080 pixels)</td> </tr> <tr> <td><strong>Display frequency</strong></td> <td>60Hz</td> </tr> <tr> <td><strong>Display mode</strong></td> <td>progressive</td> </tr> <tr> <td><strong>Display luminance max</strong></td> <td>180cd/m²</td> </tr> </tbody> </table> </div> </div> </div>
DCASE 2024 Challenge Task 10 Development Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>engine-sounds.zip</strong><br>|----- car [<em>car engine sounds</em>]<br>|----- cv [<em>commercial vehicles engine sounds</em>]</p> <p><strong>locX.zip</strong><br>|----- meta.json [<em>contains meta information of traffic condition and sensor setup corresponding to the location</em>]<br>|----- train [<em>train flac files inside</em>]<br>|----- train.csv<br>|----- val [<em>val flac files inside</em>]<br>|----- val.csv</p> <p><strong>simulation.zip</strong><br>|----- locX<br> |----- car<br> | |_____ left [<em>flac files and label csv inside</em>]<br> | |_____ right [<em>flac files and label csv inside</em>]<br> |----- cv<br> |_____ left [<em>flac files and label csv inside</em>]<br> |_____ right [<em>flac files and label csv inside</em>]</p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc1.zip --out unsplit-loc1.zip</code></p> <p><code>unzip unsplit-loc1.zip</code></p> <p>in your terminal once you have downloaded <code>loc1.z01</code>, <code>loc1.z02</code>, <code>loc1.z03</code> and <code>loc1.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p> <p> </p>
Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake
<p>This repository contains the datasets from the supporting information for the manuscript:</p> <p>Rutland, C., Bie, L., Johnson, J.H., Ou, Q. & Mildon, Z.K. (2025). "Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake."</p> <p>Data Set S1. V E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S2. σV E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S3. ε̇ shear derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S4. V E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S5. σV E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S6. ε̇ shear derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S7. V E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S8. σV E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S9. ε̇ shear derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S10. V E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S11. σV E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S12. ε̇ shear derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S13. V E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S14. σV E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S15. ε̇ shear derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S16. V E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S17. σV E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S18. ε̇ shear derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S19. V E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S20. σV E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S21. ε̇ shear derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S22. V E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S23. σV E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S24. ε̇ shear derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S25. V E derived from InSAR data 2017/05/29–2020/05/25.<br>5Data Set S26. σV E derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S27. ε̇ shear derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S28. V E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S29. σV E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S30. ε̇ shear derived from InSAR data 2018/05/24–2021/05/20.</p> <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.