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2,863 results for “Challenge”
The IBIS Challenge 2024 Complete Data Package
<p><strong>The IBIS Challenge 2024</strong></p> <p><em><strong>Complete Data Package, 17 Nov 2024</strong></em></p> <p><a href="https://ibis.autosome.org">https://ibis.autosome.org</a></p> <p>This repository provides the complete data of the Codebook/GRECO-BIT open challenge in Inferring Binding Specificities of human transcription factors from multiple experimental data types. Both the Leaderboard stage and the Final stage data are included. This repository is accompanied by the IBIS Benchmarking repo (doi:10.5281/zenodo.14176443), which includes the benchmarking suite and the challenge 'answers', i.e. the labeled test data.</p> <p><em>* Please refer to the supplied README for more details.</em></p> <p><em>** Revision 2: fixed the final/leaderboard attribution errors in the list of Codebook datasets used in IBIS.</em></p>
Challenges of sampling and how phylogenetic comparative methods help: Supplementary data
<p>Supplementary data and results files for the paper:</p> <p>Macklin-Cordes, Jayden L. & Erich R. Round (2022). Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast. <em>Linguistic Typology</em> (advance online publication). <a href="https://doi.org/10.1515/lingty-2021-0025">https://doi.org/10.1515/lingty-2021-0025</a></p>
Model weights for a Weather4cast 2021 Challenge IEEE Big Data Cup Stage solution
<p>This repository contains the pre-trained model weights for the TensorFlow/Keras models used in the <a href="https://www.iarai.ac.at/weather4cast/2021-competition/challenge/">Weather4cast 2021 Challenge IEEE Big Data Cup Stage</a> by the team "antfugue". The model code can be found in <a href="https://github.com/jleinonen/weather4cast-bigdata">https://github.com/jleinonen/weather4cast-bigdata</a> along with instructions on where to extract the weights.</p>
Additional Data to "A Challenge for Contrastive L1/L2 Corpus Studies"
<p>Additional plots and data to the paper "A Challenge for Contrastive L1/L2 Corpus Studies: Large Inter- and Intra-Individual Variation Across Morphological, but Not Global Syntactic Categories in Task-Based Corpus Data of a Homogeneous L1 German Group", published in Frontiers of Psychology on Nov 25th, 2021.<br> <br> This repository contains the Falko data and additional plots of the individual distributions of morphological noun classes, frequent dependency types, and parts of speech in the Falko data. The Kobalt data discussed in the paper can be found in Shadrova (2021).</p>
Transcriptomic profiling of clobetasol propionate-induced immunosuppression in challenged zebrafish embryos
<p>We have conducted an immune challenge experiment on 48h zebrafish embryos, which were previously treated with the immunosuppressive drug clobetasol propionate (CP). The embryos' immune system was challenged by injection of a mix of different pathogen associated molecular patterns (PAMPs). RNA sequencing was performed in order to detect altered molcular expression levels induced by CP, PAMPs and a combination of both. The data was published in <a href="https://doi.org/10.1016/j.ecoenv.2022.113346" target="_blank" rel="noopener">Essfeld <em>et al.</em> 2022</a>.</p> <p>The uploaded data archive (<a href="https://www.7-zip.org/">7-zip</a> compressed) consists of three major data types:<br>1. MultiQC reports from raw RNA-Seq read processing and QC (50bp SR)<br>2. Result tables from differential gene expression analysis (DGEA) with DESEq2<br>3. Result tables from Overrespresentation Analysis (ORA) with clusterProfiler</p> <p>Gene count normalization and DGEA was conducted with DESeq2 (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">Love et al., 2014</a>, DOI 10.1186/s13059-014-0550-8) . Three biological replicates per condition, exposure treatments were compared with respect to the control group in a pairwise fashion, applying Wald’s t-test. P values were corrected for multiple testing with independent hypothesis weighting (IHW) (<a href="https://www.nature.com/articles/nmeth.3885">Ignatiadis et al., 2016</a>, DOI 10.1038/nmeth.3885 ) after Benjamini-Hochberg (BH). To improve the signal to statistical noise ratio, the obtained log<sub>2</sub>-fold change (lfc) values were shrunk with the apeglm method described by Zhu and colleagues (<a href="https://academic.oup.com/bioinformatics/article/35/12/2084/5159452?login=true">2019</a>, DOI 10.1093/bioinformatics/bty895 ) before DGEA result tables were subjected to ORA via clusterProfiler (<a href="https://www.liebertpub.com/doi/10.1089/omi.2011.0118">Yu et al., 2012</a>, DOI 10.1089/omi.2011.0118).</p> <p>The ArrayExpress accession number E-MTAB-11092, provides access to the raw and DESeq2 normalized gene count matrices upon which these analysis were performed. Genes were annotated through the biomaRt package (<a href="https://www.nature.com/articles/nprot.2009.97.pdf?origin=ppub">Durinck et al., 2009</a>, DOI 10.1038/nprot.2009.97 ) in R (<a href="https://www.r-project.org/">R Core Team 2021</a>).</p>
Restoring multi-employer bargaining in Europe: prospects and challenges.
<p>The database is the result of an inventory of the management - trade union relationships of the five largest companies in each of 232 EU countries in five industries (575 companies).<br> Metal and electronics, Retail, Wholesale, ICT, Transport and telecommunication<br> Datacollection covers the period 2012-2014<br> Reference to the dataset<br> Van Klaveren, M., & Gregory, D. (2019). Restoring multi-employer bargaining in Europe: prospects and challenges. ETUI, Brussels, ISBN 978-2-87452-529-2<br> https://wageindicator.org/documents/publicationslist/wageindicator-org-publications-2019/wibar-book-web.pdf <br> The transnational ‘WageIndicator Support for BARgaining’ (WIBAR-3) project received funding from the European Commission as part of the Industrial Relations and Social Dialogue programme (VS/2014/0533).<br> </p>
Data for 'Formalizing Artisanal and Small-scale Gold Mining: a Grand Challenge of the Minamata Convention'
<p>Signatories to the Minamata Convention on Mercury with ‘more than insignificant’ artisanal and small-scale gold mining (ASGM) sectors are required to develop and implement National Action Plans (NAPs) to reform their ASGM sectors in line with Annexe C of the Convention. We compiled the budgets of available NAPs for reducing mercury emissions from ASGM sectors. As of 2021-12-31, these were available for 16 countries from: www.mercuryconvention.org/en/parties/national-action-plans. We used these data to estimate the approximate costs of expanding such approaches globally.</p>
Plankton DSG Challenge
<p>This repository contains ResNet50 model weights and sample images required to demonstrate the Rapid Identification of Plankton using Machine Learning Project undertaken by Cefas, The Alan Turing Institute and Plankton Analytics Ltd. GitHub https://github.com/alan-turing-institute/plankton-dsg-challenge</p>
Open access data from the International Design Engineering Annual (IDEA) Challenge 2021
<p>Open access dataset from the IDEA challenge 2021. </p> <p>The generation of this dataset has been undertaken as part of the ProtoTwin project (Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping). The work was conducted at the University of Bristol in the Design and Manufacturing Futures Lab (<a href="http://www.dmf-lab.co.uk/">http://www.dmf-lab.co.uk</a>) and is funded by the Engineering and Physical Sciences Research Council (EPSRC), Grant reference <a href="https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R032696/1">EP/R032696/1</a>. The dataset was generated in collaboration with the Norwegian Technical University (NTNU), University of Zagreb and University of Twente.</p> <p>For more information please contact Mark (mark.goudswaard @ bristol.ac.uk) or James ( james.gopsill @ bristol.ac.uk)</p>
Mars Express Power Challenge dataset
<p>The Mars Express Power Challenge dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "Mars Express Power Challenge"</strong>. It consists of a training set of 3 Martian Year composed of context and electric current measurements data of ESA's Mars Express spacecraft.</p> <p>Goal of the competition was to predict the average electric current per hour. A test set of a fourth Martian year is provided for this purpose.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/mars-express-power-challenge/home/">https://kelvins.esa.int/mars-express-power-challenge/home/</a>.</p>
DCASE 2022 Challenge Task 2 Development Dataset
<p><strong>Description</strong></p> <p>This dataset is the "development dataset" for the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring"><strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>"</a>.</p> <p>The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task:</p> <ul> <li>Fan</li> <li>Gearbox</li> <li>Bearing</li> <li>Slide rail</li> <li>ToyCar</li> <li>ToyTrain</li> <li>Valve</li> </ul> <p> </p> <p><strong>Overview of the task</strong></p> <p><strong>Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. </strong>Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial intelligence (AI)-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines. </p> <p>This task is the follow-up to DCASE 2020 Task 2 and DCASE 2021 Task 2. The task this year is to detect anomalous sounds under three main conditions:</p> <p>1. Only normal sound clips are provided as training data (i.e., unsupervised learning scenario). In real-world factories, anomalies rarely occur and are highly diverse. Therefore, exhaustive patterns of anomalous sounds are impossible to create or collect and unknown anomalous sounds that were not observed in the given training data must be detected. This condition is the same as in DCASE 2020 Task 2 and DCASE 2021 Task 2.</p> <p>2. Factors other than anomalies change the acoustic characteristics between training and test data (i.e., domain shift). In real-world cases, operational conditions of machines or environmental noise often differ between the training and testing phases. For example, the operation speed of a conveyor can change due to seasonal demand, or environmental noise can fluctuate depending on the states of surrounding machines. This condition is the same as in DCASE 2021 Task 2.</p> <p>3. In test data, samples unaffected by domain shifts (source domain data) and those affected by domain shifts (target domain data) are mixed, and the source/target domain of each sample is not specified. Therefore, the model must detect anomalies regardless of the domain (i.e., domain generalization).</p> <p> </p> <p><strong>Definition</strong></p> <p>We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".</p> <ul> <li>"Machine type" indicates the kind of machine, which in this task is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics. Each section is dedicated to a specific type of domain shift. </li> <li>The source domain is the domain under which most of the training data and part of the test data were recorded, and the target domain is a different set of domains under which a few of the training data and part of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, SNR, etc.</li> <li>Attributes are parameters that define states of machines or types of noise. </li> </ul> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset consists of three sections for each machine type (Sections 00, 01, and 02), and each section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.</p> <p> </p> <p><strong>File names and attribute csv files</strong></p> <p>File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:</p> <p> [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...</p> <p> </p> <p><strong>Recording procedure</strong></p> <p>Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>- /dev_data <br> - /fan<br> - /train (only normal clips) <br> - /section_00_source_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_source_train_normal_0989_<attribute>.wav <br> - /section_00_target_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_target_train_normal_0009_<attribute>.wav <br> - /section_01_source_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_02_target_train_normal_0009_<attribute>.wav <br> - /test <br> - /section_00_source_test_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_source_test_normal_0049_<attribute>.wav <br> - /section_00_source_test_anomaly_0000_<attribute>.wav <br> - ... <br> - /section_00_source_test_anomaly_0049_<attribute>.wav <br> - /section_00_target_test_normal_0000_<attribute>.wav<br> - ... <br> - /section_00_target_test_normal_0049_<attribute>.wav <br> - /section_00_target_test_anomaly_0000_<attribute>.wav <br> - ... <br> - /section_00_target_test_anomaly_0049_<attribute>.wav <br> - /section_01_source_test_normal_0000_<attribute>.wav<br> - ... <br> - /section_02_target_test_anomaly_0049_<attribute>.wav<br> - attributes_00.csv (attribute csv for section 00)<br> - attributes_01.csv (attribute csv for section 01)<br> - attributes_02.csv (attribute csv for section 02) <br> - /gearbox (The other machine types have the same directory structure as fan.) <br> - /bearing<br> - /slider (`slider` means "slide rail")<br> - /ToyCar <br> - /ToyTrain <br> - /valve </p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two baseline systems are available on the Github repository <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">baseline_ae</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">baseline_mobile_net_v2</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following three papers. </p> <ul> <li>Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [<a href="https://arxiv.org/abs/2206.05876">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
Fast Calorimeter Simulation Challenge 2022 - Dataset 2
<p>This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels.</p> <p>dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 3
<p>This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the <a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04 Geant4 example</a>.</p> <p>dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Data Supplement for ApJ Paper: Stumbling over planetary building blocks: AU Microscopii as an exampleof the challenge of retrieving debris-disk dust properties
<p>Computational data set underlying a manuscript analyzing visible to near infrared wavelength Hubble-STIS spectrographic coronagraph observations of the AU Microscopii debis disk. To interpret this multi-wavelength data set, we calculated light scattering properties of three different debris disk dust grain shape models: spheres, porous spheres, and more realistic irregular agglomerated debris particles. Calculations of scattering efficiency and phase function were done over a range of dust grain sizes and material refractive indices.</p> <p>This bundle contains three .hdf table files. Each table file contains a size parameter array (1D), complex refractive index array (1D), scattering efficiency array (1D), and scattering phase function array (2D). The 1st dimension of each array is the same size. The 2nd dimension of the scattering phase function is 60, the number of scattering angles calculated at an angular resolution of 3 degrees.</p>
Small-scale Concentrated Solar Power (CSP): Challenges & Opportunities
<p>☀️Interested in Concentrated Solar Power technologies?</p> <p>💻Watch the replay of this 1-hour webinar that will give you key information on the challenges and opportunities offered by CSP and the application at small-scale.</p> <p>ℹ️For more information click here <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0NOeFFBczA0Qm52SGhKM2ptWU5laEVtQ3k0UXxBQ3Jtc0tsUGpYT3MtTmZrdFdQb0JjMjVKQ09zTldpQThmVHQtVjU3cjJ6S0JhNlA4TW1pM3AxNGJPLXBZQVJramZ4Uk9XQXJGWWZLZ0RiRkxvN1dSNVFjQWx6cHFJOS1fVzN0QTRrY19VTVZoY3lYbFVLN0hlaw&q=https%3A%2F%2Fwww.polyphem-project.eu%2F2021%2F11%2F15%2Fwebinar-small-scale-csp-challenges-opportunities%2F">https://www.polyphem-project.eu/2021/...</a></p> <p>🔎Join our community!<br> Official website <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbU9JdlNfeTZ4WHktZnhuMERaenROTVNfT3o5UXxBQ3Jtc0tuMXIzTFMtS0pDZGlEZnA0NEd3RlJWNGRWVVBnZlJNQzlYdmh2MGRBZXFBVW5od1o2MkFOWXI3NHRVZksxNWtxUndxUmdzMTVidVNRdl9JODAtbjVRZ0k0RTkzMFdaQXRWR0hjaWd3MkZhcDJZWnV6MA&q=https%3A%2F%2Fwww.polyphem-project.eu%2F">https://www.polyphem-project.eu/</a><br> Linkedin <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqazJuNHVob3RuaUU4ZmFTWlJjcG52dmFqRjEzd3xBQ3Jtc0tsU2lJaHVaQXlPLVhBWEp4WEVxeWxCc3l1WUFBVVFZMk5vQllkdXNleTdodWdkU2kxV0pDTlNlT0pJSnN5d0wzbnFVU3JlcXNSWnZEekZ6Q2VZRjJSeVNnTHI1aFpZZ0lOUFJIako4SHBkYkdPajNlMA&q=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpolyphem%2F">https://www.linkedin.com/company/poly...</a><br> Twitter <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05nanVGWDJ5ZDBqVnB2SVgtSEltMy1iNXF6UXxBQ3Jtc0trb3p5WTRWcVpFTTNYeGlkdkY5MjgxZGRUNTZNOUdLbDk1T2Q2a0NMRDFHaEwzWktseHpYSGxPdjJMTG9FdEJXWjYwcjhWYWJMc0Y2cW8zOGpPd1R1eVUzRWFUYXYzblh4S25GZVZ0WGNuQWZ0cDk3cw&q=https%3A%2F%2Ftwitter.com%2FH2020CSP">https://twitter.com/H2020CSP</a><br> <br> 🇪🇺 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 764048.</p>
Extreme hydrometeorological events, a challenge for gravimetric and seismology networks
<p>“Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022.</p> <p>The data contains:</p> <p>1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00:</p> <p>-Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects),</p> <p>To convert it into mm water: use factor -1/0.39 [mm/nm/s²]</p> <p>-Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer.</p> <p>Calibration factor: 838.86 [count/µm/s]</p> <p>-Rain [mm water]: data inferred from the weather radar</p> <p>-Flow [m³/s]: water entering the Eupen reservoir</p> <p>2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996.</p> <p>3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley</p> <p>4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.</p>
Spatial Scaling Challenge. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data, documentation, and scripts that compose 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>The <strong>SPATIAL SCALING CHALLENGE</strong> is an open exercise where we challenge the remote sensing community to retrieve relevant vegetation biophysical and physiological variables such as leaf chlorophyll content (<em>C</em><sub>ab</sub>), leaf area index (<em>LAI</em>), maximal carboxylation rate (V<sub>c</sub><sub>max,25</sub>), and non-photochemical quenching (<em>NPQ</em>) from simulated (hyperspectral reflectance (<em>HDRF</em>), sun-induced chlorophyll fluorescence (<em>F</em>) and land surface temperature (<em>LST)</em>) imagery.</p> <p>The dataset contains the simulated remote sensing and field data, their description, and scripts in Matlab, Python, and R languages to facilitate importing and handling the data and producing the standardized outputs necessary to participate.</p> <p><strong>IMPORTANT:</strong> <strong>Additional data</strong> that can be used at the discretion of the participants have been released in <strong><a href="https://doi.org/10.5281/zenodo.6530187">https://doi.org/10.5281/zenodo.6530187</a></strong></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><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></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>
Replication Package for Maintainability Challenges in ML: A Systematic Literature Reveiw
<p>In order to ensure transparency and reproducibility, we have made all study artefacts publicly here. Replication package for <strong>Maintainability Challenges in ML : A Systematic Literature Review</strong>. This package contains the data used for this Systematic Literature Review process and additional data synthesised from this study.</p> <ul> <li>Coding Summary Report Generated from Nvivo project. (Coding Summary By Code Report.pdf)</li> <li>Detailed List of Selected papers for Literature Review papers(Literature Review Papers.xlsx)</li> <li>Papers relating to implications for developers and Researchers(Implication for Developer and Researchers.pdf)</li> <li>Search query from Different Database.pdf</li> <li>No of papers used in to answer RQ using the Literature review paper by categories (No of Literature review papers.pdf)</li> <li>Maintainability challenges in ML workflow from SLR.pdf</li> </ul>
DCASE 2022 Challenge Task 2 Additional Training Dataset
<p><strong>Description</strong></p> <p>This dataset is the "additional training dataset" for the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring"><strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>"</a>.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following three papers. </p> <ul> <li>Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [<a href="https://arxiv.org/abs/2206.05876">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
IO500 10 node challenge
<p>10 node challenge data</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.