Find research datasets worth reusing

Dataset of Brazilian Federal Senate Session Transcriptions From 2023 with Relevant Topics and Stance Detection Annotations.

[pt-BR] Conjunto de dados de transcrições de sessões do Senado Federal brasileiro de 2023 com anotações de tópicos relevantes e de deteção de posicionamento. Dataset description This set contains transcript data from 203 Federal Senate sessions from the year 2023, with annotations of relevant topics and positioning detection. The file <a href="../api/records/11106904/draft/files/meetings.csv/content" target="_blank" rel="noopener">meetings.csv</a> has the following structure: session_id: Unique event identifier speaker_name: Name of the person who gave the speech party: Political party speech: Speech given The folder <a href="../api/records/11106904/draft/files/ground_truth.zip/content" target="_blank" rel="noopener noreferrer">ground_truth.zip</a> contains 6 JSON annotation files related to the detection of relevant topics and positions, and its main keys are the following: id_session: Unique event identifier response: Model response      list_latent_topics: List of latent topics considered by the model      stances: The stances of each person, according to the model, on a given topic model_response_evaluation: Evaluation of the model's response according to a human annotation     mapping: Mapping between the topic named by the model and the topic named in the annotation     list_latent_topics: It contains four keys which are the lists of topics considered true positives, false positives, true negatives and false negatives. Code used All the code used to process the data can be found at: <a href="https://github.com/helenbc/tcc-notas-taquigraficas">https://github.com/helenbc/tcc-notas-taquigraficas</a> [pt-BR] Descrição do conjunto de dados Este conjunto contém dados de transcrição de 203 sessões do Senado Federal do ano de 2023, com anotações de tópicos relevantes e detecção de posicionamento. O arquivo meetings.csv possui a seguinte estrutura: session_id: Identificador único do evento  speaker_name: Nome da pessoa que fez o discurso  party: Partido político speech: Discurso proferido A pasta ground_truth.zip contém 6 arquivos de anotação JSON relacionados à detecção de tópicos relevantes e posições, e suas principais chaves são as seguintes: id_session: Identificador único do evento response: Resposta do modelo     list_latent_topics: Lista de tópicos latentes considerados pelo modelo     stances: As posições de cada pessoa, de acordo com o modelo, sobre um determinado tópico model_response_evaluation: Avaliação da resposta do modelo de acordo com uma anotação humana     mapping: Mapeamento entre o tópico nomeado pelo modelo e o tópico nomeado na anotação     list_latent_topics: Contém quatro chaves que são as listas de tópicos considerados verdadeiros positivos, falsos positivos, verdadeiros negativos e falsos negativos. Código utilizado Todo o código utilizado para processar os dados pode ser encontrado em: <a href="https://github.com/helenbc/tcc-notas-taquigraficas" target="_new" rel="noreferrer">https://github.com/helenbc/tcc-notas-taquigraficas</a>

opencc-by-4.0Nov 2023View details →

Dataset for Non-resonant Anomaly Detection with Background Extrapolation

These are the datasets used in the journal version of the Non-resonant Anomaly Detection with Background Extrapolation paper. The datasets are simulated using MadGraph5 aMC@NLO, Pythia 8.310, and Delphes. There are 0.2M signal events of semi-visible jets in five sets of parameters (invisible-ratio, Z' mass) = { (1/3, 4 TeV), (1/3, 2 TeV), (1/3, 3 TeV), (0, 4 TeV), (2/3, 4 TeV) }, 18.6M background events of SM QCD jets (including background, ideal AD background, and simulated background) for training, and 21.4M background events for testing. The detailed breakdown of number of events after selections in different regions is listed in Table1 of the paper. The input parameter cards used for generating background and signal events are also included.

Genome wide variations of rice detected by PED

Next generation sequence data of '<a href="https://www.gene.affrc.go.jp/databases-core_collections_wr_en.php" target="_blank" rel="noopener">World Rice Core Collection</a>' and '<a href="https://www.gene.affrc.go.jp/databases-core_collections_jr_en.php" target="_blank" rel="noopener">Rice Core Collection of Japanese Landraces</a>' distributing NARO genebank have been analyzed by the software 'Polymorphic Edge Detection'. This data is an additional supplementary data of <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2955-6" target="_blank" rel="noopener">the PED paper</a>. Polymorphisms between Japonica rice Nipponbare (wrc01) and other cultivars were detected by the PED software. More than 2 million polymorphic loci were detected.  <ul> <li>Lists are separated by 12 rice chromosomes.</li> <li>Column order is chromosome number, position, cultivar name, ref, alt, genotype, allele frequency, number of reads, mutation type, gene name, RAP ID, position of nucleotide sequence of gene, position of amino acid sequence of gene, left primer, right primer, amplified size.</li> <li>Accession Numbers of NGS data are listed in Japanese page 'World Rice Core Collection' and in NCBI SRA page 'Rice Core Collection of Japanese Landraces'.</li> <li>The PED software is available at: <a href="https://github.com/akiomiyao/ped" target="_blank" rel="noopener">https://github.com/akiomiyao/ped</a></li> <li>Miyao, A., Kiyomiya, J.S., Iida, K. et al. Polymorphic edge detection (PED): two efficient methods of polymorphism detection from next-generation sequencing data. BMC Bioinformatics 20, 362 (2019). <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2955-6" target="_blank" rel="noopener">https://doi.org/10.1186/s12859-019-2955-6</a></li> </ul>

Atmospheric River Database & Detection Code

This database contains the global atmospheric river catalogs detected by the PanLu algorithm within ERA5 and CMIP6 models. The PanLu detection code is also included now.

Reproduction Package for "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization"

This repository stores all data used in the evaluation of the master's thesis "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization". It ensures the continuous reproducibility of the results of the thesis. Content: <ul> <li>JPlag v5.1.0 including the Java CPG frontend <ul> <li>Code base</li> <li>Runnable JAR</li> </ul> </li> <li>Data sets used for evaluation</li> <li>Evaluation results</li> <li>R script used to process the results</li> <li>Graphics and tables generated from the results</li> </ul> The data sets were generated by Nils Niehues and Moritz Brödel and were originally published here: <ul> <li><a href="../records/10430322">Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization" (zenodo.org)</a></li> <li><a href="../records/10149536">Reproduction package for: Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors (zenodo.org)</a></li> </ul> The data sets are based partly on PROGPedia, available here: <ul> <li><a href="../records/7449056">PROGpedia (zenodo.org)</a></li> </ul> Visit <a title="State-of-the-Art Software Plagiarism & Collusion Detection" href="jplag.github.io/JPlag/" target="_blank" rel="noopener">JPlag</a> on GitHub for the current version. See the thesis document for more information. Read about similar publications about Plagiarism Detection <a title="JPlag" href="https://jplag.github.io/MinimalLandingPage/" target="_blank" rel="noopener">here</a>.

The qPCR results of Asian arowana eDNA detection in Muda Lake, Kedah, Malaysia

We present a qPCR-based eDNA method to detect the presence of the endangered Asian arowana (Teleostei: Osteoglossidae: Scleropages formosus) in Muda Lake, where resides one of its last wild populations on the west side of Peninsular Malaysia. Flanked by a pair of newly developed species-specific primer, a 163-base pair fragment of the cytochrome oxidase subunit I (COI) gene was selected as the eDNA marker. We applied our eDNA method at five sampling sites throughout Muda Lake and at five different periods. The study observed a marked seasonal variation in detection rates, with significantly higher rates during the wet months of August, January, and December compared to the dry season spanning May and June. The Asian arowana is known to breed during the rainy season and we therefore hypothesize its reproductive biology influences the concentration of eDNA in the lake. Our results also highlight the possible impact of abiotic factors in tropical freshwater ecosystems (such as high-water temperature) on eDNA persistence. In conclusion, future applications of qPCR-based eDNA methods in tropical environments would benefit from locally evaluating the effects of both biotic and abiotic factors on species detection.

ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 1

@article{schieber2024asdf,   title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},   author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},   journal={arXiv preprint arXiv:2403.16400},   year={2024} }

Replication package of "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation"

Replication package for the paper "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation" accepted at the Symposium on Search-based Software Engineering (SSBSE) 2024.

Nanopore sequencing assay to detect and diagnose tuberculous meningitis via cerebrospinal fluid

This study aimed to evaluate the efficiency of nanopore sequencing for the early diagnosis of tuberculous meningitis (TBM) using cerebrospinal fluid and compared it with acid-fast bacilli (AFB) smear, mycobacterial growth indicator tube (MGIT) culture, and Xpert MTB/Rifampicin (RIF). We enrolled 64 adult patients with presumptive TBM admitted to our hospital from August 2021 to August 2023. We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of AFB smear, culture, Xpert MTB/RIF, and nanopore sequencing to evaluate their diagnostic efficacy compared with a composite reference standard for TBM. Among these 64 patients, all tested negative for TBM by AFB smear. The sensitivity, specificity, PPV, and NPV were 11.11%, 100%, 100%, and 32.2% for culture, 13.33%, 100%, 100%, and 2.76% for Xpert MTB/RIF, and 77.78%, 100%, 100% and 65.52% for nanopore sequencing, respectively. The diagnostic accuracy of the nanopore sequencing test was significantly higher than that of conventional testing methods used to detect TBM.

Supplemental Figures - Detection of SARS-CoV-2-Specific Secretory IgA and Neutralizing Antibodies in the Nasal Secretions of Exposed Seronegative Individuals

Figure S1: Flow diagram of exposed seronegative cohort. Figure S2: SARS-CoV-2-specific neutralization activity at Days 1 and 8 relative to enrollment in exposed seronegative nasal SIgA positive and infected participants. NPS SARS-CoV-2-specific neutralization activity is shown for exposed seronegative and infected participants with normalized OD490 at Day 1 and Day 8, respectively. Sample sizes (N) are indicated in parentheses. Wilcoxon signed-rank tests were used to determine if the median SARS-CoV-2 nasal SIgA neutralization activity differed significantly. A two-tailed p < 0.05 was considered significant.

Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy

Mice are the most commonly used model animals for itch research and for the development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best-trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and the chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening. This dataset includes all videos for the study to establish a novel deep learning-based system for automatic mouse scratching behavior quantification.

Phase picker models and training data for paper "Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic"

This ZIP file includes tensorflow models for seismic phase detection. Please see how to use these models here: https://github.com/NorwegianSeismicArray/tphasenet The HDF5 files includes waveforms and labels which are part of the training data set (only NORSAR event catalogue and station ARA0).

Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga

Early detection of nuisance species is crucial for the conservation and management of threatened ecosystems, reducing the risk of widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, oftentimes with minimal cost and effort. However, eDNA analyses have inherent errors that can complicate the integration of molecular survey methods into existing management frameworks. Therefore, it is crucial for eDNA studies to consider imperfect detections and estimate error rates accordingly. Detecting nuisance species in low abundance with minimal uncertainty is vital to increase the chance of containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world's largest marine reserves. Chondria tumulosa, a cryptogenic red alga with invasive characteristics, has been documented forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantification polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of C. tumulosa that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting C. tumulosa at occupied sites was at least 92% when multiple qPCR replicates were positive. Modeled false-positive inferences were 3% or less and false-negative errors were 11% or less. The developed assay is suitable for routine monitoring at shallow sites (less than 10 m), even when C. tumulosa abundance was less than 1%. Successful implementation of eDNA tools in conservation decision-making relies on balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay's sensitivity to C. tumulosa, and we outline the necessary steps to infer ecological presence-absence from molecular detection data. By providing a reliable, cost-effective tool for detecting low-abundance species, eDNA analyses have the potential to enhance the surveillance of nuisance species and inform timely management interventions.

Multilingual Fake News Detection Dataset: Gujarati, Hindi, Marathi, and Telugu

This dataset is designed to support research in fake news detection across four major Indian languages: Gujarati, Hindi, Marathi, and Telugu. The dataset includes a diverse set of news articles collected from various sources, each labeled as either 'fake' or 'real'. The primary goal is to provide a resource that helps in the development and evaluation of natural language processing (NLP) models capable of detecting fake news in these regional languages.

opencc-zeroJun 2024View details →

Neighbor-detection causes shifts in allocation across multiple organs to prepare plants for light competition

To maximize their fitness, plants have to adjust their allocation strategy according to their abiotic and biotic environments. Plants can use the ratio of red to far-red light (R:FR) to sense neighbors, allowing them to modify their growth in response to aboveground competition. In this study, we used supplemental FR light to artificially lower the R:FR of the lower leaves of common sunflowers (Helianthus annuus) to examine how plants change their growth in response to the threat of neighbors. We combined this treatment with a nitrogen fertilization treatment to investigate how responses to neighbor-detection interact with nitrogen limitation. Plants grown in low R:FR increased in height at the expense of root growth, resulting in nitrogen limitation that restricted leaf growth. However, we found that plants reduced their nitrogen investment into leaves in low R:FR. By weakening the nitrogen sink strength of these lower leaves before they experienced low photosynthetically active radiation, plants were able to preemptively allocate nitrogen to leaves higher in the canopy. Plants responded to the perception of neighbors by simultaneously diverting resources from root growth to stem elongation and from leaves threatened by neighbors to leaves that would pose a threat to neighbors. This whole-plant response to neighbor-detection enables plants to change their allocation in a way that simultaneously manages their limited nitrogen and prepares them for future light competition.

opencc-by-4.0Jun 2024View details →

Dataset for the Julia code titled "Image Similarity Detection using ORB & Julia : Leveraging Hamming Distance & Keypoint Optimization"

The above Dataset contains 1) Real PCB Pics 2) Real Car Fusebox pics 3) Real & Filtered Human face pics 4) Readme file The above Dataset is designed to be used for the Julia Code titled "Image Similarity Detection using ORB & Julia : Leveraging Hamming Distance & Keypoint Optimization". The Code should be run on either similar PCB pics or Car Fusebox pics or Human Face Pics(Vikrant or Mat or Paris). Proper Choice of Hamming Distance & the number of Keypoints is necessary to achieve the desired number of similar images.

opencc-by-4.0Jul 2021View details →

Fig. 2 in Molecular detection of Wolbachia endosymbiont in reptiles and their ectoparasites

Fig. 2 Boa constrictor constrictor infested with Ophionyssus natricis (red arrows)

opencc-by-4.0Jul 2021View details →

Fig. 1 in Molecular detection of Wolbachia endosymbiont in reptiles and their ectoparasites

Fig. 1 Lacerta bilineata infested with Ixodes ricinus

openmit-licenseJun 2024View details →

Landmine Detection Using Electromagnetic Time Reversal-Based Methods. Part 1: Classical TR, Iterative TR, DORT and TR-MUSIC

In this repository, you can find the simulation files used to reproduce the results presented in the paper titled "Landmine Detection Using Electromagnetic Time Reversal-Based Methods." If you find these files useful, please cite the paper as it helps acknowledge the work and support future research. This repository is organized into three main folders, each serving a distinct purpose and containing specific types of files: CST-MWS Files: This folder contains the necessary files to simulate the examples discussed in the paper using CST Microwave Studio (CST-MWS). These files are essential for setting up and running the simulations that replicate the experimental results.  TR-MUSIC: In this folder, you will find the MATLAB m-file required to implement the TR-MUSIC (Time Reversal-MUltiple SIgnal Classification) method proposed in the paper. This MATLAB script is designed to help users apply the TR-MUSIC algorithm to their data, enabling them to detect landmines effectively.  2D-GFB: The 2D-GFB folder contains the MATLAB m-file necessary to run the 2D TR-MUSIC and DORT (Decomposition of the Time Reversal Operator) methods. These advanced techniques are crucial for analyzing two-dimensional data and enhancing the accuracy of landmine detection.  By organizing the repository in this manner, we aim to make it easier for researchers and practitioners to access and utilize the simulation files effectively. Whether you are replicating the study's results or applying these methods to new datasets, the files and instructions provided should serve as a valuable resource. If you encounter any issues or have questions regarding the use of these files, please do not hesitate to reach out for support. We hope this repository aids your research and contributes to advancements in landmine detection technologies.