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NeuroVox: Bilingual Brain-to-Speech Translation and Neural Activity Detection

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Detecting the effect of intensive agriculture on Odonata diversity using citizen science data

These datasets are used in the following article : Detecting the effect of intensive agriculture on Odonata diversity using citizen science data By Renaud Baeta1, Justine Léauté1,2, Éric Sansault1 and Sylvain Pincebourde2* In Ecological Applications (Research article) Abstract Agricultural areas represent one of the major ecosystems of the world. Intensification of agricultural practices produced openfields characterized by low biological diversity. Nevertheless, the distance up to which intensive agricultural fields alter surrounding natural systems is rarely quantified. We determined the spatial scale at which agricultural landscapes alter the diversity of Odonates, a key taxon in wetland ponds, and we tested to what extent citizen-science data can be used reliably for this purpose. We compiled 7,731 observations made in a portion of the region Centre-Val-de-Loire (France) over 10 years by naturalists on 729 water bodies to analyze the effect of agricultural landscapes (mainly wheat, rapeseed, sunflower) on the species richness of both damselflies and dragonflies in lentic systems. Sixty species were reported over the 10-years period. For dragonflies, intensive agricultural landscapes best explained their richness at the scales of 800m and 1,600m for overall and autochthonous species, respectively, when using the full dataset. The spatial scale was smaller for damselflies, at 200m for both overall and autochthonous species. These distances were not severely impacted when constraining the data to consider several biases. Multi-model averaging showed that the proportion of intensive agriculture decreased species richness, despite the potential biases inherent to an imperfect database acquired by citizens. This imperfect citizen dataset allows to infer the lowest effect size of agriculture on species richness. Quantitatively, this effect was more important for autochthonous species. Interestingly, both relatively rare taxa and common or generalist species can be under threat in intensive agricultural landscapes, calling for more ecotoxicological studies. The influence of agricultural practices from a distance implies that conservation and management plans of wetland ponds should consider the landscape ecological characteristics and not only the pond features. Conservation efforts focusing too locally on a site may be undermined because intensive agriculture from a distance limits the potential for the site to recover highly diverse communities. These distant effects should be integrated by policy-makers when deciding which wetland pond should benefit from a conservation plan or which conservation action may be planned, implementing for instance buffer zones and/or ecological corridors composed of natural vegetation.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments. Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section. Hyperspectral data are submitted separately due to their size; see the Related works.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a

Benchmarking Illumina RNA-seq fusion transcript detection methods - cancer cell lines RNA-seq

Cancer cell line RNA-seq data (reads or names of reads from CCLE data) used for benchmarking Illumina-based fusion detection methods as used in: Haas, B.J., Dobin, A., Li, B. et al. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol 20, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9   For CCLE data, direct sharing of fastq files was not possible.  CCLE data must be obtained from:      https://portals.broadinstitute.org/ccle/home Instead, the identifiers for the reads leveraged as part of our study are made available, and these reads can be extracted from the CCLE fastq files directly once obtained from the primary source. For the non-CCLE data, the exact reads leveraged by our study are made directly available here in fastq format.

opencc-by-4.0Jul 2024View details →

Datasets from "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks". JGR Biogeosciences.

This repository contains the datasets linked to "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks" (Journal of Geophysical Research: Biogeosciences, <a href="https://doi.org/10.1029/2024JG008047">https://doi.org/10.1029/2024JG008047</a>). This includes: <ul> <li>The images and annotation* text files (in YOLO format) used for the detection of siliceous microfossils. </li> <li>The models and training results for the trainings presented in the main text and supporting information.</li> </ul> *Note that while annotations were attributed to 14 general categories, only twelve of these were used during training (i.e. Pennate, Centric, Silicoflagellate, Centric_debris, Spore, Other_Biomin, Cocco, Silicoflagellate_debris, Undetermined_silica, Chateoceros_Bacteriastrum, Calcispheres, Foraminifer), and all were pooled into a single "Microfossil" category for the purpose of the training.

SWDD: Sonar Wall Detection Dataset

This repository contains three side scan sonar datasets: <ul> <li>SWDD: Sonar Wall Detection Dataset, which is part of the paper <a href="https://arxiv.org/abs/2403.09313">Knowledge Distillation in YOLOX-ViT for Side Scan Sonar Object Detection</a>. </li> <li>SWDD-Validation is an extension version and part of the paper <a href="https://arxiv.org/abs/2410.10554">ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</a>.</li> <li>SWDD-Adversarial is another extended version and part of the paper <a href="https://arxiv.org/abs/2410.10554">ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</a>.</li> </ul> SWDD Dataset SWDD has been recorded with a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan-MST and carrying a Klein 3500 side scan sonar (SSS). The AUV has been deployed in the Porto de Leixões harbor following the harbor's walls while collecting SSS raw data. The SSS operated at a high frequency of 900kHz with a range of 75m for a total range of 150m from port to starboard. This setup produced a resolution of 4.168 pixels per line. The data were processed using Neptus software, transforming the raw data into waterfall images. The 216 images have been manually annotated with two different classes, wall and noWall. Data augmentation such as noise, flips, and combined noise-flip transformations have been applied, increasing the data to 864 images. Across the 864 images, there are a total of 2,616 labeled samples. To ensure robust training, with respect to the data quality, the original dataset has been mixed with the augmented images. The dataset is divided into 70% for training, 15% for validation, and 15% for testing. The authors chose to generate images with 500 lines, meaning the images have a resolution of 4.168x500. Finally, the images have been resized to 640 × 640 to use this data in specific computer vision algorithms. The dataset is annotated following the COCO annotation format. YOLOX and YOLOX-ViT have been trained and compared using the SWDD dataset. A 6-minute 57-second video from another survey was used for model comparison. From this video, 6243 frames have been extracted with its manually annotated ground truth.   Thus, this dataset repository offers an SSS dataset, a 6-minute 57-second SSS video, and 6243 extracted frames from this video with its manually annotated ground truth.  The Knowledge Distillation in YOLOX-ViT code using the SWDD dataset is publicly available at <a href="https://github.com/remaro-network/KD-YOLOX-ViT">KD-YOLOX-ViT.</a> For (re-)using/publishing SWDD-Validation, please include the following copyright text: SWDD is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the H2020 <a href="https://remaro.eu/">REMARO</a> project.   ⚠️The two following datasets (SWDD-Validation and SWDD-Adversarial) are parts of the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection, submitted to the  2025 IEEE Symposium on Maritime Informatics & Robotics (MARIS 2025). The paper is still under review. ⚠️   SWDD-Validation The SWDD dataset has been recorded using the LAUV with the Klein 3500 side scan sonar deployed in the Porto de Leixões harbor following the harbor's walls. The SWDD-Validation comports three field-collected detected: SWDD-Clean, SWDD-Surface, and SWDD-Noisy. The SWDD-Clean dataset, which includes data from the same mission as the original SWDD dataset; the SWDD-Surface dataset, captured while the LAUV was on the surface during windy weather, featuring a non-straight wall and wave-induced variations; and the SWDD-Noisy dataset, collected under stormy conditions, where the SSS transducer intermittently exited the water, resulting in data loss represented by black lines in the images. The SWDD-Validation dataset provides three datasets collected under different weather and sonar setups aiming to study object detection models' robustness variation under different sonar and noise conditions. The metadata of the SWDD-Validation dataset is depicted in the following table. <div> <div>   <table> <tbody> <tr> <td>Dataset</td> <td># Image</td> <td># Bbox</td> <td>Freq. (kHz)</td> <td>Range (m)</td> <td>Resolution</td> </tr> <tr> <td>SWDD-Clean</td> <td>148</td> <td>248</td> <td>900</td> <td>50</td> <td>4168 x 500</td> </tr> <tr> <td>SWDD-Surface</td> <td>98</td> <td>153</td> <td>900</td> <td>75</td> <td>6552 x 500</td> </tr> <tr> <td>SWDD-Noisy</td> <td>551</td> <td>800</td> <td>455</td> <td>100</td> <td>4168 x 500</td> </tr> </tbody> </table> </div> <div> </div> <div> For (re-)using/publishing SWDD-Validation, please include the following copyright text: SWDD-Validation is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the H2020 <a href="https://remaro.eu/">REMARO</a> project. </div> <div> </div> <div> </div> <div>SWDD-Adversarial</div> <div> </div> <div> The SWDD-Adversarial dataset has been generated by the ROSAR framework proposed in the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection, which, by leveraging adversarial PGD and Patch attack on the SWDD dataset, generates the three following datasets: P1-SWDD, P2-SWDD, and Patch-SWDD. The ROSAR framework is publicly available in our GitHub [<a href="https://github.com/remaro-network/ROSAR-framework">repository</a>]. P1 and P2-SWDD datasets result from adversarial PGD attacks, each resulting from different safety properties (P1 and P2), resulting in 1017 and 1462 images for P1 and P2.  Patch-SWDD dataset results from the adversarial patch attack, resulting in 151 images provided by the following repository: <a href="https://github.com/SamSamhuns/yolov5_adversarial" target="_blank" rel="noopener">Patch Attack.</a> </div> <div> </div> <div>For (re-)using/publishing SWDD-Adversarial, please include the following copyright text:</div> <div> SWDD-Adversarial is a public dataset based on the original SWDD dataset, which was collected using a Light Autonomous Underwater Vehicle by Oceanscan-MST and is within the scope of the H2020 <a href="https://remaro.eu/">REMARO</a> project. </div> </div>

opencc-by-4.0Jun 2024View details →

Detecting Code Smells using ChatGPT: Initial Insights

<h2>Replication Package: Detecting Code Smells using ChatGPT: Initial Insights</h2> <h3> Replication Package</h3> This repository contains data files to replicate our study presented in the paper Detecting Code Smells using ChatGPT: Initial Insights. <h3>Dataset Structure</h3> We provide raw and preprocessed data in different formats:  <ul> <li><code>csv - the database in CSV format with comma separated columns.</code></li> <li><code>csv-semi-comma - the database in CSV format with semi-comma separated columns.</code></li> <li><code>html - the database in HTML format to ease the visualization of the dataset. </code></li> <li><code>sql - the original SQL (schema and data) of the database used in this study.</code></li> </ul> Each compressed directory contains a README file explaining which information each column includes.  <h3>Dataset Overview</h3> A. <code>sql</code> directory consists of two tables:  1. The table <code>tb_unique_bad_smell</code> contains information we collect from ChatGPT.   <code>CREATE TABLE public.tb_unique_bad_smell (</code> <code>    id_unique_bad_smell integer NOT NULL,</code> <code>    id_bad_smell bigint,</code> <code>    id_source_code bigint,</code> <code>    chat_gpt_response text,</code> <code>    question text,</code> <code>    badsmell_base text,</code> <code>    bad_smell_gpt text,</code> <code>    found_any boolean,</code> <code>    valid_bad_smell boolean,</code> <code>    bad_smell_in_base boolean,</code> <code>    bad_smell_not_in_the_base text,</code> <code>    bad_smell_not_found text,</code> <code>    index integer,</code> <code>    index_base integer,</code> <code>    url_github text,</code> <code>    id_base bigint,</code> <code>    dt_insertion timestamp without time zone,</code> <code>    nr_question smallint</code> <code>);</code> <code>id_source_code</code> - integer identifier when we imported the original dataset to our database. <code>chat_gpt_response</code> - contains ChatGPT's response to our prompts.  <code>question</code> - This column contains all the prompts we submitted to ChatGPT. Each question includes the prompt under evaluation and the source code we were interested in evaluating. <code>badsmell_base</code> - code smells assigned in the original dataset.  <code>bad_smell_gpt</code> - code smells identified by the ChatGPT. We extracted these smells from `chat_gpt_response`. <code>found_any</code> - a boolean field indicating if any of the smells found by ChatGPT are in the dataset. In other words, if ChatGPT answered yes, finding any smell (even if it is not in the original dataset). <code>valid_bad_smell</code> - text field containing the smells in the original dataset that the ChatGPT identified. <code>bad_smell_in_base</code> - a boolean field indicating if the smells found by ChatGPT are in the original dataset. <code>bad_smell_not_in_the_base</code> - text field containing the smells that ChatGPT found and they are not in the dataset.  <code>bad_smell_not_found</code> - text field containing the smells in the dataset that ChatGPT did not detect. <code>index</code> and <code>index_base</code> - indexes imported from the original dataset. GitHub provided them. <code>url_github</code> - the GitHub URL of the source code extracted from the original dataset.  <code>id_base</code> - id field in the original dataset. <code>nr_question</code> - integer field containing 1 or 2, identifying the prompt we submitted to ChatGPT.  2. The table `tb_unique_source_code` contains information we imported from the original dataset we used to submit to ChatGPT and information to evaluate ChatGPT's performance. The primary data we relied on are: <code>smell</code> - presents the smell assigned to the code. <code>severity</code> - the severity of the smells, Major, Critical, Minor.  <code>type</code> - function or class. <code>code_name</code> - the full path for the smell, e.g., <code>nm_package.nm_class.nm_method</code> <code>start_line</code> - the code where the smell starts. <code>end_line</code> - line in the code where the smell ends. <code>link</code> - the GitHub URL of the source code evaluated.

Silva, Harder et al. Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history Data

Dataset used in the "Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history" paper by Silva, Harder et al. This dataset includes Fasta files for Simulated dataset and VCF files for both Empirical and Simulated datasets.

Delta GUI change detection using inferred models replication package

TESTAR is a scriptless automated open-source tool developed by the Universitat Politècnica de València and the Open University of the Netherlands. TESTAR Change detection .NET is an open-source tool that simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. This tool is also developed by the Universitat Politècnica de València and the Open University of the Netherlands. This replication package contains: <ul> <li>The Excel file that was used to perform and store the systematic mapping of the literature.</li> <li>The TESTAR Change detection .NET version that was used to compare the state models inferred from the OBS, Calibre, and MyExpenses applications. </li> <li>The state models that were inferred from the OBS, Calibre, and MyExpenses applications. These are stored in the OrientDB graph database. </li> <li>Three documents (OBS, Calibre, MyExpenses) that detail with images the GUI changes results detected using the TESTAR Change detection .NET tool.</li> <li>A video demo that shows how to use the TESTAR Change detection .NET tool with the inferred models from the Calibre web system.</li> </ul>

TUD-GV Dataset for Floating Litter Detection (object detection task)

This dataset contains the data used for the publication: Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research, 266, 122405. This dataset is a subset of the large-scale "TU Delft - Green Village" (TUD-GV), which includes 9,473 RGB images. More details on the TUD-GV dataset can be found at: <a href="https://doi.org/10.5281/zenodo.7636124" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7636124</a>. This subset used in this publication consists of 1,501 images, selected from the full TUD-GV dataset. All floating litter items in this subset have been annotated with bounding boxes. This subset is specifically for detecting floating litter in object detection tasks. The 1,501 images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the class of the annotation (i.e., litter) is stored in the classes.txt file. If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>

Amsterdam Dataset for Floating Litter Detection

This dataset contains the data used for the publication: Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research, 266, 122405. The Amsterdam dataset is for detecting floating litter with computer vision. We created the Amsterdam dataset from one experiment conducted on 1st March 2023, in canals and ponds at Amsterdam, the Netherlands. We recorded images using an action camera (GoPro Hero 10). All images used in this publication are captured by the device positioned at a distance of maximum 2 m from the water surface. The image resolution is 5568*4176. This dataset consists of 9 RGB images. We manually labeled the litter items in these images with bounding boxes. The 9 images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the class of the annotation (i.e., litter) is stored in the classes.txt file.   If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>

Oostpoort Dataset for Floating Litter Detection

This dataset contains the data used for the publication: Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research, 266, 122405. The Oostpoort dataset is for detecting floating litter with computer vision. We generated this dataset from experiments conducted during 26 days from February to March 2022, in a canal at Oostpoort, Delft, the Netherlands. We collected data employing action cameras (GoPro MAX 360 and GoCam3) mounted outside the windows of a tower at Oostpoort with a viewing angle of 0 degree. We recorded video sequences with a time-lapse recording (1 image/30 sec) and a FPS (frame per second) of 17.98. We generated the Oostpoort dataset by saving images from these videos. The resolution of images are 3840*2160 and 1920*1440. This dataset consists of 562 RGB images. We manually labeled the litter items in these images with bounding boxes. The 562 images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the class of the annotation (i.e., litter) is stored in the classes.txt file. The Oostpoort Dataset.xlsx file contains the detailed information of images, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, the number of images, and the number of annotated litter items.   If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>

Groningen Dataset for Floating Litter Detection

This dataset contains the data used for the publication: Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research, 266, 122405. The Groningen dataset is for detecting floating litter with computer vision. We conducted several experiments in a canal in Groningen, the Netherlands, in 2023. We captured data employing a security cameras (Obscape HQ time-lapse), mounted on a bridge at a height of 4m. We recorded images with a time-lapse recording (1 image/6 sec). The image resolution is 2592*1944. This dataset consists of 63 RGB images. We manually labeled the litter items in these images with bounding boxes. The 63 images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the class of the annotation (i.e., litter) is stored in the classes.txt file.    If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>

Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection

Relevant datasets for scientific manuscript:  Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection Maximiliano Jesus Jara Fornerod a, Alberto Alvarez-Fernandez a, Mate Furedi a, Anandapadmanabhan A Rajendran b, Beatriz Prieto-Simon c,d, Nicolas H. Voelcker e,f*, Stefan Guldin a,g*   a Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK. b Department of Electronic Engineering, Universitat Rovira i Virgili, 43007, Tarragona, Spain c Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Av. Països Catalans, 16, 43007, Tarragona, Spain d ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain e Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia f Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, Clayton, Victoria, 3168, Australia g Technical University of Munich, Department of Life Science Engineering, Gregor-Mendel-Straße 4, 85354 Freising, Germany *corresponding authors. E-mail addresses: nicolas.voelcker@monash.edu, s.guldin@ucl.ac.uk, guldin@tum.de

PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing

Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing

Dataset for "Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)"

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