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26 results for “challenge set”
A challenging data set for evaluating part-of-speech taggers
<p>This data set contains 2,227 sentences, with a part-of-speech (POS) tag specified for a single word in the sentence. The data file is a tab-separated text file where each row (after the header row) is formatted as follows:</p><p><i>sentence <TAB> POS tag <TAB> (optional) motivation</i></p><p>Note that, in the sentence (= a string of space-separated characters), the POS-tagged word is indicated by the POS tag in brackets, placed just after the word to which it refers. Example:</p><p><i>The road bends [VERB] to the right . VERB</i></p><p>In this example, the optional motivation is not included, as the tagged word can easily be identified as being a verb.</p>
Neuromotor dynamics of human locomotion in challenging settings
<p><strong>Background</strong></p> <p>Is the control of movement less stable when we walk or run in challenging settings? Intuitively, one might answer that it is, given that challenging locomotion externally (e.g. rough terrain) or internally (e.g. age-related impairments) makes our movements more unstable. Here, we investigated how young and old humans synergistically activate muscles during locomotion when different perturbation levels are introduced. Of these control signals, called muscle synergies, we analyzed the stability over time and the complexity (or irregularity). Surprisingly, we found that perturbations force the central nervous system to produce muscle activation patterns that are less unstable and less complex. These outcomes show that robust locomotion in challenging settings is achieved by producing less complex control signals which are more stable over time, whereas easier tasks allow for more unstable and irregular control.</p> <p><strong>How to use the data set</strong></p> <p>This supplementary data set contains: a) the metadata with anonymized participant information, b) the raw electromyographic (EMG) data acquired during locomotion, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via non-negative matrix factorization and f) the code written in R (R Found. for Stat. Comp.) to process the data, including the scripts to calculate the short-term Maximum Lyapunov Exponents (sMLE) and Higuchi's fractal dimension (HFD) of motor primitives. In total, 476 trials from 86 participants are included in the supplementary data set.</p> <p>The file “participant_data.dat” is available in ASCII and RData (R Found. for Stat. Comp.) format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Experiment: the experimental setup in which the participant was involved (E1 = walking and running, overground and treadmill; E2 = walking and running, even- and uneven-surface; E3 = unperturbed and perturbed walking, young and old)</li> <li>Group: the group to which the participant was assigned (see methods for the details)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (two values separated by a comma mean that recordings were done at two different speeds, i.e. walking and running)</li> <li>Age: the participant’s age in years (participants were considered old if older than 65 years, but younger than 80)</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 consist of 22, 27 and 23 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0053_OW_02”, where the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data are named, following the same rules, like “FILT_EMG_P0053_OG_02”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData (R Found. for Stat. Comp.) format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times and the characters “P0020” indicate the participant number (in this example the 20th). Please note that the overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 only contain 22, 27 and 23 cycles, respectively.</p> <p>The files containing the raw, filtered and the normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining thirteen columns contain the raw EMG data, named with muscle abbreviations that follow those reported in the methods section. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0053_OW_02”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data is named, following the same rules, like “FILT_EMG_P0053_OG_02”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorization and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “Time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed above to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P0012_PW_02”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P0012” indicate the participant number (in this example the 12th), ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported in the methods section, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P0082_PW_02”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P0082” indicate the participant number (in this example the 82nd) ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>The files containing the sMLE calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “sMLE.RData”. sMLE results are presented in a list of lists containing, for each trial, 1) the divergences, 2) the sMLE, and 3) the value of the R<sup>2</sup> between the divergence curve and its linear interpolation made using the specified amount of points. The divergences are presented as a one-dimensional vector. sMLE are one number like the R<sup>2</sup> value. Trials are named like “MLE_P0081_EW_01”, where the characters “sMLE” indicate that the trial containss sMLE data, the characters “P0081” indicate the participant number (in this example the 81st) ), the characters “EW” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st).</p> <p>The files containing the HFD calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time k used for the calculations. HFDs are presented as one number, as are the interval times k. Trials are named like “HFD_P0048_TR_01”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P0048” indicate the participant number (in this example the 48th), the characters “TR” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st).</p> <p>All the code used for the preprocessing of EMG data, the extraction of muscle synergies, the calculation of sMLE and HFD is available in R (R Found. for Stat. Comp.) format. Explanatory comments are profusely present throughout the scripts (“SYNS.R”, which is the script to extract synergies, “fun_NMF.R”, which contains the NMF function, “sMLE.R”, which is the script to calculate the sMLE of motor primitives, “HFD.R”, which is the script to calculate the HFD of motor primitives, “fun_sMLE.R”, which contains the sMLE function and “fun_HFD.R”, which contains the HFD function).</p>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</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>
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>
Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis
<p>The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in<br> simulations of electrocatalysis".</p> <p>Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.</p>
LoDoPaB-CT Challenge Set
<p>Observation data for the <a href="https://lodopab.grand-challenge.org/">LoDoPaB-CT challenge</a>, which asks to reconstruct CT images of the human lung from (simulated) low photon count measurements.</p> <p>The setting is identical to the one of the <a href="https://zenodo.org/record/3384092">LoDoPaB-CT dataset</a> (documented in this <a href="https://www.nature.com/articles/s41597-021-00893-z">Data Descriptor article</a>), which is supposed to be employed for training learned methods. This challenge set contains observations for a separate set of patients.</p> <p>Python utilities for accessing this challenge set and creating the submission file are available at <a href="https://github.com/jleuschn/lodopab_challenge">github.com/jleuschn/lodopab_challenge</a>. The LoDoPaB-CT dataset for training can be accessed using the <a href="https://zenodo.org/record/3970517">DIVal</a> python library (<a href="https://github.com/jleuschn/dival">github.com/jleuschn/dival</a>).</p> <p>Like for the LoDoPaB-CT dataset, reconstructions from the <a href="http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX">LIDC/IDRI dataset</a> are used as a basis for this challenge set.</p> <p> </p> <p>The ZIP file contains multiple <a href="https://www.hdfgroup.org/HDF5/">HDF5</a> files. Each HDF5 file contains one HDF5 dataset named <code>"data"</code>, that provides a number of samples (128 except for the last file). For example, the <code>n</code>-th observation sample is stored in the file <code>"observation_challenge_%03d.hdf5"</code> where <code>"%03d"</code> is <code>floor(n / 128)</code>, at row <code>(n mod 128)</code> of <code>"data"</code>.</p> <p>For this challenge set no patient IDs are provided (in contrast to the fully public parts of the LoDoPaB-CT dataset), since the reconstruction algorithm should not rely on this information.</p> <p><em>Acknowledgements</em></p> <p>Johannes Leuschner, Maximilian Schmidt and Daniel Otero Baguer acknowledge the support by the Deutsche<br> Forschungsgemeinschaft (DFG) within the framework of GRK 2224/1 “π3: Parameter Identification – Analysis,<br> Algorithms, Applications”. We thank Simon Arridge, Ozan Öktem, Carola-Bibiane Schönlieb and Christian<br> Etmann for the fruitful discussion about the procedure, and Felix Lucka and Jonas Adler for their ideas and<br> helpful feedback on the simulation setup. The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.</p>
Data set Procedural Challenges
<p>For the article "Procedural Challenges: A case study in sharing PRC electronic resources" (submitted to the International Journal of Digital Humanities", special issue "Digital Humanities in East Asian Studies", editors Hilde De Weerdt and Aliz Horvath) we conduct a literature review of research into Republican Chinese newspapers as it appears in popular Chinese academic journal databases and their accompanying data publications. In essence, we repeatedly run identical keyword searches using different interfaces and compare results. We first gather materials in the form of saved html files and screenshots which are then prepared for further analysis.</p> <p>The data set contains the source files that support the findings of our study, along with descriptive metadata and additional sources not used in the final article version. All data is collected on Windows 10 Enterprise Edition, using Firefox Browser 88.0.1, and cross-checked with both Google Chrome 90.0.4430.212 and Microsoft Edge 90.0.818.62.</p>
Data from: Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance
<p>Background: It is not known how the number of deaths due to COVID-19 compare to the number of deaths due to "unsafe water, sanitation, and handwashing" during the COVID-19 global health emergency.</p> <p>Methods: A dataset of deaths due to COVID-19 was downloaded from the World Health Organization. A dataset summarizing deaths due to unsafe water, sanitation, and handwashing was obtained from the Institute for Health Metrics and Evaluation (IHME). </p> <p>Results indicate that COVID-19 deaths in Africa and South East Asia regions exceeded those due to unsafe water, sanitation, and hygiene. </p>
Pulmonary lymphangitis pose a major challenge for radiologists in an oncological setting during the COVID-19 pandemic
<p>I uploaded the images of the manuscript "Pulmonary lymphangitis pose a major challenge for radioLogists in an oncological setting during the COVID-19 pandemic".</p>
Trojan Detection Challenge Datasets (New Detection Track Validation Set)
<p>This upload contains the new version of the detection track validation set for the Trojan Detection Challenge NeurIPS 2022 competition. This is meant for participants in the detection who do not want to redownload the full dataset. For the full dataset including data for other tracks, see <a href="https://zenodo.org/record/6894041">https://zenodo.org/record/6894041</a>.</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Test Set
<p>This contains the test set for the trigger synthesis track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full trigger synthesis track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Trojan Detection Challenge - Evasive Trojans Track Test Set
<p>This contains the test set for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full evasive trojans track Zenodo record, which can be found <a href="https://zenodo.org/record/7213753">here</a>.</p>
Trojan Detection Challenge - Detection Track Test Set
<p>This contains the test set for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full detection track Zenodo record, which can be found <a href="https://zenodo.org/record/7213750">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Test Set
<p>This contains the test set for the target label prediction track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full target label prediction track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge (CtrSVDD Track, Test Set)
<p>For more information about SVDD Challenge 2024, please refer to https://challenge.singfake.org/.<br><br>We have released the test set here.</p> <p>The training and development set is at https://zenodo.org/records/10467648.</p> <p>The Interspeech paper that describes the dataset details and baseline analysis is https://arxiv.org/abs/2406.02438.</p>
Assemblies of the CAMI 2 challenge data sets
<p>Assembly submissions, including participants' submissions, of the CAMI 2 challenge data sets.</p> <p>The gold standards can be found either on the CAMI 2 challenge website: <a href="https://data.cami-challenge.org/participate">https://data.cami-challenge.org/participate</a> or in publisso alongside the data sets: https://repository.publisso.de/resource/frl:6425521</p> <p>Also see <a href="https://www.microbiome-cosi.org/cami">https://www.microbiome-cosi.org/cami</a> </p>
Direct Oral Challenges in Private Practice Setting to Delabeling Children With Beta-lactam Allergy
ClinicalTrials.gov study NCT07002814. IPD Sharing: NO. Countries: 1. Publications: 11.
Data from: Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance
Open the record for dataset details and reuse information.
ScienceDex guides
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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.