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Dataset results
256 results for “submissions”
anonymous submission
<p>pending</p>
TreeAlign Input and Output datasets (initial submission)
<p>This repository provides the processed data necessary to reproduce the results for the initial TreeAlign paper submission. </p> <p>This includes the following:</p> <ul> <li>Single cell whole genome sequencing (scDNA) <ul> <li>Copy number profiles</li> <li>B allele frequency profiles</li> <li>phylogenetic trees computed with HDBSCAN</li> <li>benchmarking results vs other methods</li> </ul> </li> <li>10X single cell RNA sequencing (scRNA) <ul> <li>count matrices</li> <li>reference and total read counts at heterozygous SNPs</li> <li>meta data</li> </ul> </li> <li>TreeAlign output <ul> <li>Clone assignment for expression profiles</li> <li>p(k) scores</li> <li>p(a) scores</li> </ul> </li> </ul>
Supplementary Information Materials for the G-Cubed submission by Zakharov et al.
<p>The supporting information is provided for the publication <a href="https://doi.org/10.1029/2022GC010741">https://doi.org/10.1029/2022GC010741</a><em>. </em>The upload contains identification of MGL opal-CT as well as the results of the SIMS and EMPA measurements in cherts. This file also features δD values plotted against the triple O-isotope values of cherts. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.</p>
Bibliographic Information on PNAS Direct and Contributed Submissions
<p>This dataset contains observations of 46,391 articles published as "Direct" or "Contributed" submissions in PNAS. Only includes publications in the Biological, Physical and Social Sciences sections.</p> <p>Variables:</p> <ul> <li>extract_id: incremental ID</li> <li>topic: main section</li> <li>sub_label: labelled submission type</li> <li>pub_year: publication year</li> <li>review_time: number of days from the manuscript was received until it was accepted for publication</li> <li>ncs_full: The field normalised citation score, full counting</li> </ul> <p>-</p>
Evaluation set DCASE 2023 task 4 (for submissions)
<p>This repo contains the dataset to download to submit results and be evaluated in task 4 of DCASE 2023. It also contains the ground-truth for the public and synthetic evaluation dataset, together with the mapping file between the anonymized (official eval) file names and the files name as presented in the annotations.</p> <p>Please, check the submission package in order to follow the instruction to have a submission.</p>
Reproduction Package for ASE 2023 Submission `Improving Verification through Compiler Optimizations'
<p><strong>Artifact</strong></p> <p>In order to run this artifact please clone <a href="https://github.com/sosy-lab/sv-benchmarks">sv-benchmarks</a> into this folder. Afterwards you can just run the program using the running instructions down below. The results used in the paper can be found in the folder <code>transformation-for-verification-data</code>.</p> <p><strong>Setup</strong></p> <p>In order to setup this project, first initialize the submodules or clone this repository with the flag <code>--recursive</code>. Afterwards execute <code>python3 src/setup.py</code> in this directory, in order to add the required files to the submodules.</p> <p><strong>Running</strong></p> <p>In order to run this locally inside <code>./src</code></p> <pre><code>./main_bench.py --specification specification/path.prp program/to/verify.c</code></pre> <p>For example:</p> <pre><code>./main_bench.py --specification ../setup-files/test-run/unreach-call.prp ../setup-files/test-run/test_program.c </code></pre> <p>In order to execute with benchexec, execute the following insider <code>./src</code>, after adapting <code>bench.xml</code> to suit your purposes:</p> <pre><code>./benchmark_local.sh</code></pre>
ReLandProject/MosulDrownedLandscapes: Journal Submission Version
<p>Data and code version submitted to the journal. Updated to account for changes in libraries/api.</p>
Replication Package of Submission #1725 for ICSE 2024
<p>Here is the replication package for the paper "Unraveling Software Decoupling: Contexts, Practices, and Patterns" of Submission 1725.</p>
Submissions DCASE 2023 Task4a
<p>Predictions and technical reports of the systems submitted to <a href="https://dcase.community/challenge2023/task-sound-event-detection-with-weak-labels-and-synthetic-soundscapes">DCASE 2023 Task4a</a> including file name mapping and ground truth for public youtube evaluation set. Challenge results can be found on <a href="http://dcase.community/challenge2023/task-sound-event-detection-with-weak-labels-and-synthetic-soundscapes-results">results page</a> and additional post-processing independent evaluations can be found in [1].</p> <p>[1] J. Ebbers, R. Haeb-Umbach, and R. Serizel, "Post-Processing Independent Evaluation of Sound Event Detection Systems", Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2023, arXiv: <a href="https://arxiv.org/abs/2306.15440">https://arxiv.org/abs/2306.15440</a></p>
Original MAB Object Set for CASTAway M5 Submission Exploration
<p>The zip file containts the ephemeris of the MAB objects used in the published works of:</p> <p>J.P. Sánchez, A. Gibbings, C. Snodgrass, N. Bowles, S. Green, Asteroid Belt Multiple Flyby Options for M-Class Missions, in: 67th International Astronautical Congress International Astronautical Federation, Guadalajara, Mexico, 26-30 September 2016.</p> <p>M. Di Carlo, M. Vasile, J. Dunlop, Low-thrust tour of the main belt asteroids, Advances in Space Research, 62 (2018) 2026-2045.</p> <p>Currently, a new manuscrip is being considered for publication, which also uses the same ephemeris set to comput asteroid tours in the Main Asteroid Belt. </p> <p>A. Bellome, J.P. Sánchez, J.C. García-Mateas, L. Felicetti and S. Kemble, Modified Dynamic programming for Asteroid Belt exploration, Acta Astronautica, Under Review. </p> <p>The files containt the ephemeris and and some matlab functions to retrieve position and velocity of the asteroids at a given epoch or time. </p> <p> </p>
GLH-NDRS Centralised Data Submission Survey Results
<p>Full responses from the 2023 UK CSG Diagnostic Laboratory Survey component focused on centralised data submission to PHE/NHSD and LIMS system organisation. To preserve anonymity, free-text comments are not provided.</p>
Anonymous submission
<p>## Representations</p> <p>llmcomp_data_{humaneval,winogrande}.zip files contain the representations of the LLMs used in our work. Unzipped they will take approximately 73 GB of storage.</p> <p>## Similarity Scores</p> <p>The pre-computed similarity scores are contained in .parquet files.</p>
The influence of the global COVID-19 pandemic on manuscript submissions and editor and reviewer performance at six ecology journals
Open the record for dataset details and reuse information.
Higher ultraviolet skin reflectance signals submissiveness in the anemonefish, Amphiprion akindynos
Open the record for dataset details and reuse information.
Evaluation set DCASE 2020 task 4 (for submissions)
<p>This repo contains the dataset to download to submit results and be evaluated in task 4 of DCASE 2020.</p> <p> </p> <p>Please, check the submission package in order to follow the instruction to have a submission.</p> <p>*Note: some files are 5 mins long, so if your system is not suitable for this, make sure you aggegate the results.*</p>
ATTA-Ar calibration data for GRL submission
<p>ATTA-Ar calibration data for GRL submission</p>
lpeyruchat/JHD-paper-zenodo: Zenodo version for submission
<p><strong># Transconductance quantization in a topological Josephson tunnel junction circuit</strong></p> <p>By Léo Peyruchat, Joël Griesmar, Jean-Damien Pillet, Çağlar Girit</p> <p> </p> <p>Python source code to generate data from https://arxiv.org/abs/2009.03291</p>
[2019 QSM Reconstruction Challenge] Metrics and Submission Information
<p>This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available <a href="http://dx.doi.org/10.5281/zenodo.3687342">here</a> and <a href="http://dx.doi.org/10.5281/zenodo.3688703">here</a>.</p> <p>The package contains seven Comma-Separated Values (CSV) files and two PDF files:</p> <ul> <li><em>master_stage1_anonymized.csv</em>: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr1_anonymized.csv</em>: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr2_anonymized.csv</em>: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>submission_form_stage1.pdf</em>: PDF export of the online form used in stage 1;</li> <li><em>submission_form_stage2.pdf</em>: PDF export of the online form used in stage 2.</li> </ul> <p>For the manuscript, we analyzed these CSV files with scripts reported <a href="https://doi.org/10.5281/zenodo.4559540">here</a>.</p> <p>Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names:</p> <ul> <li><em>normalized rmse</em>: Whole-brain root-mean-squared error relative to ground truth;</li> <li><em>rmse_detrend_tissue</em>: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask;</li> <li><em>rmse_detrend_blood</em>: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask;</li> <li><em>rmse_detrend_DGM</em>: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate);</li> <li><em>DeviationFromLinearSlope</em>: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0;</li> <li><em>CalcStreak</em>: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth;</li> <li><em>DeviationFromCalcMoment</em>: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model);</li> <li><em>Submission Identifier</em>: Self-chosen unique identifier of the submission;</li> <li><em>Submission Identifier of the corresponding Stage 1 submission</em>: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1;</li> <li><em>Changes with respect to Stage 1 submission</em>: Self-reported information about modifications made to the algorithm for Stage 2;</li> <li><em>Number of submissions in Stage 2</em>: The number of solutions that were submitted to Stage 2 with a similar algorithm;</li> <li><em>Sim1/Sim2</em>: Filename of the submitted solutions for Stage 1;</li> <li><em>File name of the zip-file you are going to upload</em>: Filename of the file uploaded to Stage 2;</li> <li><em>Full name of the algorithm</em>: Self-reported full name of the algorithm used;</li> <li><em>Preferred Acronym</em>: Self-reported acronym of the algorithm used;</li> <li><em>Algorithm-type</em>: Self-reported type of algorithm used;</li> <li><em>Does your algorithm incorporate information derived from magnitude images?</em>: Self-reported Yes/No;</li> <li><em>Regularization terms</em>: Self-reported types of regularization terms involved;</li> <li><em>Did your algorithm use the provided frequency map or the four individual echo phase images?</em>: Self-reported information about involved magnitude information;</li> <li><em>Publication-ready description of the reconstruction technique</em>: Self-reported description of the algorithm;</li> <li><em>Publications that describe the algorithm</em>: Self-reported literature reference;</li> <li><em>Algorithm publicly available?</em>: Self-reported public availability of the algorithm;</li> <li><em>If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?</em>: Self-reported willingness to share the algorithm code with the public;</li> <li><em>Specific information about this solution</em>: Self-reported detailed information about the solution;</li> <li><em>Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge</em>: Self reported agreement with publication of submitted solution;</li> <li><em>Ground truth was not explicitly or implicitly incorporated into your algorithm or solution</em>: Self-reported confirmation that the ground truth was not incorporated in the solution.</li> </ul>
Perceived and actual fighting ability: determinants of success via decision, knockout or submission in human combat sports
Animal contest theory assumes individuals to possess accurate information about their own fighting ability or resource holding potential (RHP) and, under some models, that of their opponent. However, due to the difficulty of disentangling perceived and actual RHP in animals, how accurately individuals are able to assess RHP remains relatively unknown. Furthermore, it is not just individuals within a fight that evaluate RHP. Third party observers evaluate the fight performance of conspecifics in order to make behavioural decisions. In human combat sports, when fights remain unresolved at the end of the allotted time, bystanders take a more active role, with judges assigning victory based on their assessment of each fighter's performance. Here, we use fight data from mixed martial arts in order to investigate whether perceived fighting performance (judges' decisions) and actual fighting success (fights ending in knock out or submission) are based on the same performance traits, specifically striking skill and vigour. Our results indicate that both performance traits are important for victory, but that vigour is more important for fights that resolve naturally. These results suggest that while similar traits are important for fighting success across the board, vigour is undervalued in judges' perceptions of RHP.
mkrnc/set-trie-datasets: PlosONE submission
<p>The minimal data sets necessary to replicate experiments described in [doi]</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.