Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Data: Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection

Contains data necessary to reproduce the quantitative results related to a SARS-CoV-2 outbreak in NSW, Australia in the associated paper. icpmr_delta_gisaid.csv Tabular data containing the GISAID accession numbers, dates of collection and submission for all sequences used in the NSW outbreak analysis. The epi set is available on GISAID as EPI_SET_220919ef. The <code>wgs_cluster</code> column contains identifiers of genomic clusters defined at ICPMR, NSW Health Pathology. A value of "Other" means that the sequence either did not belong to a cluster or was part of a cluster that contained fewer than 30 sequences in the study period, and sequences with this value should not be considered to form a single cluster. icpmr_delta_gisaid.dists.tsv.gz Compressed pairwise SNP distance matrix in the format output by snp-dists. The script that creates this file from sequence data is available in the linked code archive.

opencc-by-4.0May 2021View details →

Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021

This dataset is part of the available dataset for Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021, 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). The dataset is licensed under Creative Commons Attribution 4.0 International Public License: Permissions: <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> Conditions: <ul> <li> 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. </li> </ul> Limitations: <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> For more information about the license, check: https://creativecommons.org/licenses/by/4.0/ The following modifications were made: <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>

opencc-by-4.0Jul 2022View details →

DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs

This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".

opencc-zeroJul 2022View details →

MODIS Sea ice leads detections using a U-Net

Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2002 for the season between November through April.

opencc-by-4.0May 2022View details →

PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing

Dataset underlying the paper "PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing." preprint-paper-matches.csv lists all matches found by our algorithm between bioRxiv/medRxiv and PubMed, and preprint_affiliations.csv lists all extracted affiliations from bioRxiv/medRxiv. The Rxivist data dump (https://zenodo.org/record/4738007) was used for all preprint data, and the scrips to download PubMed data are available on our GitHub repository, https://github.com/PeterEckmann1/preprint-match. The full database dump, with all data used in the study, is available on Google Drive at https://drive.google.com/file/d/1ZoafhYUP-DO4Hd_4A_v7mbQLjN3JPzJv/view?usp=sharing. The PostgreSQL database can be restored using the pg_restore command.

TwiBot22: Towards Graph-Based Twitter Bot Detection

Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from limited dataset scale, incomplete graph structure, as well as low annotation quality. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into the TwiBot-22 evaluation framework, where researchers could consistently evaluate new models and datasets. Due to the limitation at Zenodo, you can access the whole dataset and more information on this <a href="https://twibot22.github.io/">website</a>.

opencc-by-4.0May 2022View details →

X-ray polarization detection of Cassiopeia A with IXPE

Data reproduction package for “X-ray polarization detection of Cassiopeia A with IXPE” (Vink, Prokhorov, Ferrazzoli et al. 2022) published by the Astrophysical Journal Abstract of the publication We report on a &sim; 5σ detection of polarized 3–6 keV X-ray emission from the supernova remnant Cassiopeia A with the Imaging X-ray Polarimetry Explorer (IXPE). The overall polarization degree of 1.8±0.3% is detected by summing over a large region, assuming circular symmetry for the polarization vectors. The measurements imply an average polarization degree for the synchrotron component of &sim; 2.5%, and close to 5% for the X-ray synchrotron-domimated forward-shock region. These numbers are based on an assessment of the thermal and non-thermal radiation contributions, for which we used a detailed spatial-spectral model based on Chandra X-ray data. A pixel-by-pixel search for polarization provides a few tentative detections from discrete regions at the &sim; 3σ confidence level. Given the number of pixels, the significance is insufficient to claim a detection for individual pixels, but implies considerable turbulence on scales smaller than the angular resolution. Cas A’s X-ray continuum emission is dominated by synchrotron radiation from regions within 􏰁 1017 cm of the forward- and reverse shocks. We find that i) the measured polarization angle corresponds to a radially-oriented magnetic field, similar to what has been inferred from radio observations; ii) the X-ray polarization degree is lower than in the radio band (&sim; 5%). Since shock compression should impose a tangential magnetic field structure, the IXPE results imply that magnetic-fields are reoriented within &sim; 1017 cm of the shock. If the magnetic-field alignment is due to locally enhanced acceleration near quasi-parallel shocks, the preferred X-ray polarization angle suggests a size of 3 × 1016 cm for cells with radial magnetic fields.

opencc-by-4.0Oct 2022View details →

DongTing: A Large-scale Dataset for Anomaly Detection of the Linux Kernel

DongTing is the first large-scale dataset dedicated to Linux kernel anomaly detection. The dataset covers Linux kernels released in the last five years and includes a total of 18,966 well-labeled normal and attack sequences. The entire dataset is 85 GB in size (after decompression). The attack data covers 26 major kernel releases and contains a total of 12,116 system call sequences collected from running 17,855 bug-triggering programs. The normal data comes from 6,850 normal programs in four kernel regression test suites. We maintain the dataset and source code in Zenodo and Github, respectively, and back up the dataset and code in Baidu netdisk. <h3>Dataset</h3> The dataset is stored at <a href="http://doi.org/10.5281/zenodo.6627050">http://doi.org/10.5281/zenodo.6627050</a> <ul> <li>The data includes `abnormal_data`, `normal_data`, `models`, `npz` and baseline data, with a total volume of nearly 87 GB (including 85 GB for abnormal data and normal data, it's after decompression files size).</li> <li>The `Abnormal_data` directory contains 12,116 files containing system call sequence for 26 kernel releases, and the `Normal_data` directory contains 6,850 files containing system call sequences collected from four regression test suites. All of which are raw sequences.</li> <li>CNN/RNN, LSTM, and Wavenet (three sets of hyperparameters per model) machine learning models are selected, the ECOD model (without hyperparameters) was also chosen for the evaluation of DT. DT_abnormal, DT_normal, ADFA-LD, and PLAID are used for training respectively. The results of DT training models are stored in the directory `Models-DongTing`, and the results of ADFA-LD and PLAID training models are stored in the  directory `Models-Comparison`.</li> <li>The directory `npz `stores the encoded dataset of DongTing, ADFA-LD, and PLAID (sequence length varies from  8 to 4495), according to syscall_64.tbl in Linux kernel 5.17, including the training set, validation set, and test set.</li> <li>The file `Baseline.xlsx` contains all the information about DongTing dataset, which can be used in training machine learning models. For example, the whole dataset is  randomly divided into three sets with the ratio of 80%:10%:10% (training: validation: test). The implementation of dataset division can be found in the source code.</li> </ul> <h3>Source Code</h3> The source code for dataset development is stored at <a href="https://github.com/HNUSystemsLab/DongTing">https://github.com/HNUSystemsLab/DongTing</a> and the following is a brief introduction. <ul> <li>The source code contains three folders, i.e., `Source Code Files`, `Documents` and `DB`, where `Documents `stores the detailed  documents related to development, `DB` stores samples data, and `Source Code Files` stores the source code related to the development of our dataset.</li> <li>The detailed description about the source code can be found in `Documents/Documentation.pdf`. The document consists of four parts: environment requirements, database, program structure and working steps, model training and evaluation (including training and evaluation). It details the preparation of the environment, data import method, functional description of each file in the source code directory, how model training and evaluation work and other related contents.</li> </ul> We additionally maintain the dataset and source code on Baidu.com <a href="https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds">https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds</a> to facilitate the access from China.   <h3>Tips: </h3> If you find DongTing useful for your research, please cite the article as "DongTing: A large-scale dataset for anomaly detection of the Linux kernel". <blockquote> @article{DUAN2023111745, title = {DongTing: A large-scale dataset for anomaly detection of the Linux kernel}, journal = {Journal of Systems and Software}, volume = {203}, pages = {111745}, year = {2023}, issn = {0164-1212}, doi = {https://doi.org/10.1016/j.jss.2023.111745}, url = {https://www.sciencedirect.com/science/article/pii/S0164121223001401}, author = {Guoyun Duan and Yuanzhi Fu and Minjie Cai and Hao Chen and Jianhua Sun} } </blockquote>

opencc-zeroAug 2022View details →

Recordings from: Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy, and exclusion zone monitoring

1. There is strong socio-political support for offshore wind development in US territorial waters, and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely requires exclusion zones as large as 10 km. 2. We have developed a three-hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real-time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021 we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid-Atlantic waters and we played more than >3,500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modeled results of the detection function error were then used to compare the effectiveness of a bearing-based system to a single sensor that can only detect a signal but not ascertain directivity. 3. Field trials indicated maximum detection ranges from 4–7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provides a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement, and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1–3%). 4. We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.

Supplementary Material for the paper "Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells"

Supplementary material for the paper "Daniel Oliveira; Wesley K. G. Assunção; Alessandro Garcia; Baldoino Fonseca; and Márcio Ribeiro. Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells. In: Empirical Software Engineering. 2022. Springer."

Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"

These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres

opencc-zeroSep 2022View details →

VIIRS Sea ice leads detections using a U-Net

Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2011 for the season between November through April.

opencc-by-4.0May 2022View details →

Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"

This is a basic reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole neutron star mergers".

Ship tracks detected using machine learning algorithm

The filtered, vector ship tracks detected using the linked machine learning algorithm and derived from the linked segmentation masks. Each dataset contains the date and other related data for each shiptrack polygon. The `_geo` dataset contains the polygons on a lat/lon coordinate system while the other provides the polygons on the MODIS swath (pixel) indices.

Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.

opencc-zeroSep 2022View details →

Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs

The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections). The data can be used and shared freely.

A copro-antigen ELISA for the detection of ascarid infections in chickens

Dataset for the infection experiments to evaluate the performance of copro-antigen ELISA to assess nematode infections. Chickens were divided into three groups. One group was kept as uninfected control, and the birds of the other two groups were either experimentally infected with 100 or 1,000 embryonated/infective eggs of either Ascaridia galli or Heterakis gallinarum

Dataset for "Quantification of Muscle Fiber Malformations Using Edge Detection to Investigate Chronic Wound Healing"

Primary images, spreadsheets and files for "Quantification of Muscle Fiber Malformations Using Edge Detection to Investigate Chronic Wound Healing"

TIMIT-TTS: a Text-to-Speech Dataset for Synthetic Speech Detection

With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images. The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time. This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms. In this paper we focus on the generation of an audio-visual deepfake dataset. First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions. This highlights the need for multimodal forensic detectors and more multimodal deepfake data. <ul> <li>For the initial version of TIMIT-TTS v1.0 <ul> <li>Arxiv: https://arxiv.org/abs/2209.08000</li> <li>TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159</li> </ul> </li> </ul>

Ethylene (C2H4) point-sources detected by the IASI infrared satellite instrument (2008-2020).

This dataset includes the super-sampled IASI 0.01° × 0.01° C2H4 HRI dataset in GeoTIFF format and the catalogue of the identified and categorized C2H4 point-sources in kml format (C2H4_HRI_pointsources.zip). It also includes the super-sampled IASI C2H4 total columns used to calculate the emission fluxes from the analyzed point-sources (C2H4_column_pointsources.zip) and the source data needed to reproduce the figures (C2H4_SourceData.zip). The code to calculate the C2H4 HRI from IASI spectra and to retrieve the C2H4 total columns is provided (C2H4_codes.zip), along with the artificial neural network used for the retrievals, instructions and example data. The codes of the oversampling, wind rotation and supersampling are available in the paper of Clarisse et al. (2019) at <a href="https://doi.org/10.5194/amt-12-5457-2019">https://doi.org/10.5194/amt-12-5457-2019</a>.