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2,863 results for “Challenge”
R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
<p>This is the first R&D dataset for the LHC Olympics 2020 Anomaly Detection Challenge. It consists of 1M QCD dijet events and 100k W'->XY events, with X->qq and Y->qq. The W', X, and Y masses are 3.5 TeV, 500 GeV and 100 GeV respectively. The events are produced using Pythia8 and Delphes 3.4.1, with no pileup or MPI included. They are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. </p> <p>The events are randomly shuffled together, but for the purposes of testing and development, we provide the user with a signal/background truth bit for each event. Obviously, the truth bit will not be included in the actual challenge.</p> <p>These events are stored as pandas dataframes saved to compressed h5 format. For each event, all Delphes reconstructed particles in the event are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles, with the truth bit appended at the end. The array format is therefore (Nevents=1.1M, 2101).</p> <p>For more information, including an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage.</p> <p><a href="https://lhco2020.github.io/homepage/">https://lhco2020.github.io/homepage/</a></p> <p><strong>UPDATE May 18 2020</strong></p> <p>We have uploaded a second signal dataset for R&D, consisting of 100k W'->XY with X,Y->qqq (i.e. 3-prong substructure). Everything else about this signal dataset (particle masses, trigger, Pythia configuration, detector simulation) is the same as the previous one described above. </p> <p><strong>UPDATE November 23 2020</strong></p> <p>We now include high-level feature files for the background and 2-prong signal (events_anomalydetection_v2.features.h5) and for the 3-prong signal (events_anomalydetection_Z_XY_qqq.features.h5). To produce the features, we have clustered every event into R=1 jets using the anti-kT algorithm. The features (calculated using fastjet plugins) are the 3-momenta, invariant masses, and n-jettiness variables tau1, tau2 and tau3 for the highest pT jet (j1) and the second highest pT jet (j2):</p> <p>'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'</p> <p>The rows (events) in each feature file should be ordered exactly the same as in their corresponding raw event file. For convenience, we have also included the label (1 for signal and 0 for background) as an additional column in the first feature file (events_anomalydetection_v2.features.h5).</p> <p><strong>UPDATE February 11 2021</strong></p> <p>We have included the Delphes detector card and the Pythia8 command files used to produce the R&D datasets.</p> <p><strong>UPDATE April 17 2022</strong></p> <p>It was brought to our attention that somehow the raw events file events_anomalydetection.h5 was never updated to v2, which had a lower generator-level pT threshold (PhaseSpace:pTHatMin = 500) for QCD events to minimize artificial trigger sculpting. This v2 is the version that the features file (events_anomalydetection_v2.features.h5) corresponds to, as well as the Pythia cmnd file (pythia_RnD_qcd.cmnd). Now the raw events file has been brought up to date as well. </p>
DEBS 2022 Grand Challenge Data Set: Trading Data
<p>The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR). </p> <p>Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged.</p> <p>Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation): <br> <br> <em>Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), </em>June 27-June 30<em>, 2022, Copenhagen. ACM, New York, NY, USA.</em></p> <p><strong>All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).</strong></p>
Cross-Modality Domain Adaptation Challenge 2022 (crossMoDA)
<p>Official training and validation sets of crossMoDA 2022.</p> <p><strong>All data will be made available online with a permissive non-commercial copyright-license (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>), allowing for data to be shared, distributed and improved upon</strong>.</p> <p> </p> <p>If you use the data, please cite:</p> <p>1. Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., & Vercauteren, T. (2021). Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm [Data set]. The Cancer Imaging Archive. <a href="https://doi.org/10.7937/TCIA.9YTJ-5Q73">https://doi.org/10.7937/TCIA.9YTJ-5Q73</a> </p> <p>2. Dorent, R. et al (2022). CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation. ArXiv <a href="https://arxiv.org/abs/2201.02831">https://arxiv.org/abs/2201.02831</a></p> <p> </p> <p>Acknowledgments:</p> <p>This challenge is supported by Wellcome Trust (203145Z/16/Z, 203148/Z/16/Z), EPSRC (NS/A000050/1,<br> NS/A000049/1) and ZonMw (project number: 10070012010006) funding. All the organizers will have access to the<br> test set if needed.</p>
Dataset for Cicerale, Blanzieri, Sacco - How does decision-making change during challenging periods?
<p>Dataset for experiment 1 (Bataclan terror attacks) and 2 (COVID pandemic - 1st and 2nd wave), results described in Cicerale, Blanzieri, Sacco - How does decision-making change during challenging periods?, submitted to PLOS ONE.</p> <p>Version 2: Added dataset in CSV format, column keys and the original questions in Italian</p>
Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data additional data provided for the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p> </p> <p>The main dataset, documentation and scripts of the <strong>SPATIAL SCALING CHALLENGE</strong> must be downloaded from<strong>: <a href="https://doi.org/10.5281/zenodo.6451335">https://doi.org/10.5281/zenodo.6451335</a></strong></p> <p>This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m<sup>-2</sup> µm<sup>-1</sup>) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants. </p> <p> </p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p> </p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p> </p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p> <p> </p>
MItosis DOmain Generalization Challenge 2022 (MICCAI MIDOG 2022), Training data set (PNG version)
<p>This is the training dataset of the MItosis DOmain Generalization (MIDOG) challenge 2022, held in conjunction with MICCAI 2022. Please find the structured challenge description at 10.5281/zenodo.6362337.</p> <p>The training set consists of 405 tumor cases in total across six tumor types:</p> <ul> <li>Canine Lung Cancer (44 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human Breast Cancer (150 cases, scanned using three scanners, part of MIDOG2021 dataset)</li> <li>Canine Lymphoma (55 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human neuroendocrine tumor (55 cases, scanned with Hamamatsu NanoZoomer XR)</li> <li>Canine Cutaneous Mast Cell Tumor (50 cases, scanned with Aperio ScanScope CS2)</li> <li>Human melanoma (51 cases, scanned with Hamamatsu NanoZoomer XR) (no labels provided)</li> </ul> <p>From each WSI, a trained pathologist selected an area of 2mm² corresponding to approximately 10 high power fields, according to the grading scheme of Elston and Ellis. We cropped this area and provide it as PNG files in this data set due to restrictions in data set size on zenodo. Each file includes the resolution (in dots per inch, DPI) of the original scanned images.</p> <p>The training set contains 9501 mitotic figures (MF) and 11051 hard examples (non-mitotic figures). All annotations are provided in MS COCO JSON format and as SQLITE database (SlideRunner format).</p>
Challenges of the New Accreditation in Digital Competences for Citizens (ACTIC): Update, Validation and Results
<p>The aim of this study is to explain the process used to update and validate the Catalan Accreditation of Information and Communications Technology Skills (ACTIC), conducted by the Open University of Catalonia and the Blanquerna Foundation of Ramon Llull University, at the initiative of the Catalan Department for Digital Policy and Public Administration, Government of Catalonia.</p> <p>Using a mixed method, the study is divided into 3 phases and presents the definitions of the 5 competence areas, 19 competences, 3 achievement levels and a total of 233 indicators. The definitions were validated by a group of experts. As a result of the study, three key dimensions have emerged that improve digital awareness among citizens while empowering them and providing them with a way of having their skills accredited and acknowledged in the workplace: (i) identity, centred around participatory, social, ethical and civic issues; (ii) skills learning among citizens; and (iii) technological development and digital autonomy of citizens.</p>
The challenge of being slow: Effects of tempo, laterality, and experience on dance movement consistency
<p>Data set for the study published in Journal of Motor Behavior.</p>
The PI-CAI Challenge: Public Training and Development Dataset
<p>This dataset represents the <strong><a href="https://pi-cai.grand-challenge.org/">PI-CAI</a>: Public Training and Development Dataset</strong>. It contains 1500 anonymized prostate biparametric MRI scans from 1476 patients, acquired between 2012-2021, at three centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuis Groep Twente) based in The Netherlands.</p> <p>The PI-CAI challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at clinically significant prostate cancer (csPCa) detection/diagnosis in MRI, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting. The study hypothesizes that state-of-the-art AI algorithms, trained using thousands of patient exams, are non-inferior to radiologists reading bpMRI.</p> <p>Key aspects of the PI-CAI study design have been established in conjunction with an international scientific advisory board of 16 experts in prostate AI, radiology and urology —to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p>
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
<p>We propose the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidences and predicting a truthfulness label (i.e., support, refute and not enough information), and to generate a rationalization statement to explain the reasoning and ruling process. To support this research, we construct MOCHEG, a large-scale dataset consisting of 21,184 claims where each claim is annotated with a truthfulness label and ruling statement, with 43,148 text evidences and 15,373 image evidences. </p>
Dataset: An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned
<p>This dataset contains the related data for the paper titled: "An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned". </p>
Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021
<p>This dataset is part of the available dataset for <em>Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021</em>, available at https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22).</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International Public License:</p> <p>Permissions:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Conditions:</p> <ul> <li> <p>Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> </li> </ul> <p>Limitations:</p> <ul> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> <p>For more information about the license, check: https://creativecommons.org/licenses/by/4.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea, *.dat and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>
GENEA Challenge 2022 subjective evaluation data
<p>This zip archive contains user-study response data and associated analyses and analysis results from the GENEA Challenge 2022 (<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a>). The code was written by Gustav Eje Henter and is released under a Creative Commons Attribution 4.0 International license.</p> <p>If you use this material, please cite the following paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM</p>
GENEA Challenge 2022 submitted BVH files
<p>This Zenodo repository contains 3D motion in the Biovision Hierarchy (BVH) format for all test-set motion submitted by teams participating in the GENEA Challenge 2022.</p> <p> </p> <p>Contents:</p> <p>The "full-body" zip file corresponds to the full-body tier of the challenge and the "upper-body" file to the upper-body tier.</p> <p>The repository also contains the time stamps for cutting out the evaluation segments we used (matched and mismatched) in the file "segment_selection_info.csv".</p> <p> </p> <p>Attribution:</p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-08) this is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p><a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p> </p> <p>Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>*<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/"> https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>*<a href="https://genea-workshop.github.io/2022/challenge/"> https://genea-workshop.github.io/2022/challenge/</a></p> <p> </p> <p>If you have any questions or comments, please contact:</p> <p>* The GENEA Challenge & Workshop organisers <genea-contact@googlegroups.com</p>
GENEA Challenge 2022 objective evaluation data
<p>This Zenodo repository contains objective evaluation results for all test-set motion submitted by teams participating in the GENEA Challenge 2022.</p> <p> </p> <p>We caution the user that objective metrics are known to have poor correlation with actual, perceived motion quality. For definitions and explanations of the "Average jerk magnitude", "Average acceleration magnitude", and "Average Hellinger distance", please see the paper "Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation" by Kucherenko et al., published in the International Journal of Human–Computer Interaction in 2021. For a definition and explanation of the "Canonical correlation analysis (CCA) coefficient", see the paper "Speech-driven animation with meaningful behaviors" by <a href="https://www.sciencedirect.com/science/article/abs/pii/S0167639318300013?via%3Dihub#!">Sadoughi and Busso</a>, published in <a href="https://www.sciencedirect.com/journal/speech-communication">Speech Communication</a> in 2019. Code for computing these objective metrics is available through the challenge webpage.</p> <p> </p> <p>Attribution:</p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-10) this is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p><a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p> </p> <p>The material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>*<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/"> https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>*<a href="https://genea-workshop.github.io/2022/challenge/"> https://genea-workshop.github.io/2022/challenge/</a></p> <p> </p> <p>If you have any questions or comments, please contact:</p> <p>* The GENEA Challenge & Workshop organisers <genea-contact@googlegroups.com</p>
Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material
<p>This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation". </p> <p>Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to study strategies, benefits and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts, and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and<br> evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges, and suggests possible benefits to foster app store-inspired elicitation among practitioners.</p> <p>The package contains the following files:</p> <p>1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution.</p> <p>2. Codebooks:<br> 2.a. Codebook Strategies: codebook of the strategies to select apps<br> 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2)<br> 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2)<br> 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE</p> <p>3. Labelled Data <br> 3.a Strategies - labelled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for explanation of the themes).<br> 3.b Benefits IBE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.c Challenges IBE - labelled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for explanation of the themes).<br> 3.d Benefits ASE - labelled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.e Challenges ASE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for explanation of the themes).</p> <p>4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections).</p> <p>5. SLR data: data related to the lightweight systematic literature review <br> 5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks.<br> 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers. </p> <p>6. Readme.txt: summary file. </p> <p>Note that some of the row in Raw data.xls (and in the corresponding "Labelled Data" files) are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.</p>
Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022
<p>Data is in HDF5 format (with LZF compression). For specifics and details, please see <a href="http://github.com/GilesStrong/mode_diffprog_22_challenge">https://github.com/GilesStrong/mode_diffprog_22_challenge</a></p> <p>The training file contains two datasets:</p> <ul> <li>`'x0'`: a set of voxelwise X0 predictions (float32)</li> <li>`'targs'`: a set of voxelwise classes (int):</li> <li>0 = soil</li> <li>1 = wall</li> </ul> <p> </p> <p>The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position).</p> <p>All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels.</p> <p>The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer.</p> <p>It can be read using e.g. the code below:</p> <p> </p> <p><em>with h5py.File('train.h5', 'r') as f:</em></p> <p><em> inputs = h5['x0'][()]</em></p> <p><em> targets = h5['targs'][()]</em></p> <p>The test file only contains the X0 inputs:</p> <p><em>with h5py.File('test.h5', 'r') as h5:</em></p> <p><em> inputs = h5['x0'][()]</em></p> <p>The private testing sample also contains targets. The private and public splits can be recovered using:</p> <p><em>from sklearn.model_selection import train_test_split</em></p> <p><em>pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)</em></p> <p> </p>
Transforming the UK's diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)
<p>This video is the second talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Transforming the UK’s diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)</p> <p>Bio: Dimitris Grammatopoulos, PhD, FRCPath, is Professor of Molecular Medicine at Warwick Medical School and Consultant in Clinical Biochemistry and Molecular Diagnostics at the University Hospitals of Coventry and Warwickshire, NHS Trust, United Kingdom. He also leads the Novel Biomarkers theme of the Institute of Precision Diagnostics and Translational Medicine, Pathology-UHCW NHS Trust. where he combines clinical expertise in diagnostic laboratory medicine with a research track-record in application of cutting edge multidiscipline methodologies in routine clinical diagnostics. He received academic and clinical training in Newcastle, Bristol, Johns Hopkins-Baltimore and Warwick. He has expertise in biochemical/molecular diagnosis of many endocrine and metabolic disorders. His translational research interest is focused on stress hormones and homeostatic adaptations of fetal development to maternal disease as well as development of novel -omics based biomarker approaches suitable for precision medicine and better characterisation of patient phenotype. He has experience around use of AI and ML for development and refinement of clinical and diagnostic pathways for complex chronic conditions that are considered as national priorities. Dimitris is the Lead in Diagnostics, Global Health Priorities in Health, University of Warwick.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/HiOlRzJPR7Q</p>
Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)
<p>This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ANZKGxj87E0</p>
Data from: Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage
<p>We investigated the effect of storage duration and conditions on the assessment of the soil biota with eDNA metabarcoding. We extracted eDNA from freshly collected soil samples and again from the same samples after storage under contrasting temperature conditions and contrasting exposure (open/closed tubes). We used four different primer sets targeting bacteria, fungi, protists (cercozoans), and general eukaryotes. <span>W</span>e quantified differences in richness, evenness, and community composition. Subsequently, we tested whether we could correctly infer habitat type and original sample identity after storage using a large reference dataset.</p> <p>This repository contains the un-demultiplexed fastq sequences.</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.