Find research datasets worth reusing

Figure 2 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415

Figure 2 A syntype worker of Ponera coarctata var. lucida representing a dark-colored (score 4) P. testacea worker from Azerbaijan. Specimen: CASENT0903905, from www.antweb.org.

Figure 5 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415

Figure 5 Ordination biplot for shape principal component analysis (sPCA) based on species identity. Color codes represent: Ponera coarctata: blue circles, P. testacea: orange asterisks, P. testacea lectotype: orange triangle, P. lucida syntypes: dark brown rectangles.

opencc-zeroFeb 2022View details →

Antler detection from the sky: deer sex ratio monitoring using drone-mounted thermal infrared sensors

Sex differences in large mammals with sexual dimorphism are important ecological and evolutionary issues and key factors for wildlife management. To examine the potential use of drone (unmanned aerial vehicle; UAV) observation using thermal infrared images for sex ratio monitoring of deer, we conducted UAV surveys at night in a sparse forest located on the distribution periphery of sika deer (Cervus nippon) and wild boar (Sus scrofa) local populations during summer and winter. Of the 163 thermal infrared images of large mammals detected, 132 (81.0%) and 16 (9.8%) were identified for deer and wild boar, respectively. In addition, velvet antlers of deer were visually recognized during summer, and 92% of the detected deer were antlered. This biased sex ratio would be a characteristic in the distribution periphery of local deer populations. Therefore, monitoring abundance and sex ratio using thermal infrared sensors on UAVs can improve deer management especially in the distribution periphery of local populations.

opencc-zeroFeb 2022View details →

Periphyton carbon and nitrogen stable isotopes detect agricultural stressors in low-order streams

Shifts in the stable isotope signatures of carbon (C) and nitrogen (N) in ecological materials have the potential to indicate environmental disturbances. This study examined the δ13C‰ and δ15N‰ ratios of stream water and periphyton from low-order streams in a landscape influenced by agricultural activities. Our key purpose was to assess the influence of best management practice (BMP) presence and age on C and N isotope values as a potential water-quality assessment. We collected stream water and periphyton from 19 streams within the Upper Delaware River watershed in New York, USA, in each of 4 management categories: 1) recently applied BMP treatments, 2) long-standing BMPs, 3) streams lacking BMPs, and 4) minimally disturbed reference streams. We sampled and analyzed water and periphyton for δ13C‰ and δ15N‰ in a repeated-measures design (BMP category ´ time) from April to November 2013. There were large seasonal differences in stream water δ13C-dissolved organic C (DIC)‰ and δ15NO3-N‰, with strong differences between reference and agricultural streams. Periphyton δ13C‰ and δ15N‰ values also differed strongly across streams draining land with agricultural activities, with 85% higher periphyton δ15N‰ signals in all agricultural categories vs reference streams. Periphyton diatom and chlorophyte taxonomic proportions showed the strongest relationship with periphyton δ13C‰ values, where diatoms were negatively associated with increasing δ13C‰. These results suggest that aqueous and periphytic stable isotopes were sensitive in detecting persistent effects of agriculture on these streams despite BMP mitigation, where nutrient (orthosphosphate, nitrite, and ammonia) levels were non-indicative. These results also suggest that BMPs may not have fully eliminated the negative impacts of agricultural stressors on water quality in impacted streams.

opencc-zeroFeb 2022View details →

Data from: Efficacy of spotlights and thermal cameras to detect lions, Panthera leo, and spotted hyenas, Crocuta crocuta, depends on species and management regime

Accurate abundance estimates can contribute to effective management of large carnivore populations. Lion, Panthera leo, and spotted hyena, Crocuta crocuta, populations are frequently estimated at night by eliciting their approach using broadcasted vocalizations. Spotlights are typically used to observe these species on approach but can disturb animals and adversely affect counts. We compared the efficacy of spotlight with red filters and forward looking infrared (FLIR) thermal monocular to enumerate lions and spotted hyenas in Serengeti National Park (SNP; non-hunted area) and Maswa Game Reserve (MGR; hunted area), Tanzania, during 2015−2017. We established 119 call-in sites in SNP and 20 in MGR and conducted repeated call-ins at 1–2 week intervals. During call-ins we conducted systematic paired counts using both devices. We assessed the influence of device order, species, hunting regime, and land cover on species counts. We found that FLIR was more efficacious for counting hyenas in MGR and spotlight for counting lions in SNP. We found evidence for temporary artificial light disturbance in MGR, as counts were higher when FLIR was used as the second device. Habitat type within 200 m of call-in sites did not influence device performances. Greater spotlight efficacy in SNP is a likely consequence of lower perceived risk and less anthropogenic disturbance compared to MGR. To improve accuracy of counts and subsequent population estimates for lions and spotted hyenas, we recommend consideration of variation in device efficacy, based on species surveyed and management regime.

Cyclic dinucleotides detection in MG1655

Cyclic dinucleotides detection in MG1655.

Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring

Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74 108 data files and 74 108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.

openMar 2022View details →

PAN22 Authorship Analysis: Style Change Detection

This is the dataset for the <a href="https://pan.webis.de/clef22/pan22-web/style-change-detection.html">Style Change Detection</a> task of PAN 2022. Task The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Hence, a fundamental question is the following: If multiple authors have written a text together, can we find evidence for this fact; i.e., do we have a means to detect variations in the writing style? Answering this question belongs to the most difficult and most interesting challenges in author identification: Style change detection is the only means to detect plagiarism in a document if no comparison texts are given; likewise, style change detection can help to uncover gift authorships, to verify a claimed authorship, or to develop new technology for writing support. Previous editions of the Style Change Detection task aim at e.g., detecting whether a document is single- or multi-authored (<a href="https://pan.webis.de/clef18/pan18-web/style-change-detection.html">2018</a>), the actual number of authors within a document (<a href="https://pan.webis.de/clef19/pan19-web/style-change-detection.html">2019</a>), whether there was a style change between two consecutive paragraphs (<a href="https://pan.webis.de/clef20/pan20-web/style-change-detection.html">2020</a>, <a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>) and where the actual style changes were located (<a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>). Based on the progress made towards this goal in previous years, we again extend the set of challenges to likewise entice novices and experts: Given a document, we ask participants to solve the following three tasks: <ul> <li>[Task1] Style Change Basic: for a text written by two authors that contains a single style change only, find the position of this change (i.e., cut the text into the two authors’ texts on the paragraph-level),</li> <li>[Task2] Style Change Advanced: for a text written by two or more authors, find all positions of writing style change (i.e., assign all paragraphs of the text uniquely to some author out of the number of authors assumed for the multi-author document)</li> <li>[Task3] Style Change Real-World: for a text written by two or more authors, find all positions of writing style change, where style changes now not only occur between paragraphs, but at the sentence level.</li> </ul> All documents are provided in English and may contain an arbitrary number of style changes, resulting from at most five different authors. Data To develop and then test your algorithms, three datasets including ground truth information are provided (dataset1 for task 1, dataset2 for task 2, and dataset3 for task 3). Each dataset is split into three parts: <ol> <li>training set: Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models.</li> <li>validation set: Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li>test set: Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation (see later).</li> </ol> You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license. Input Format The datasets are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem. We provide one folder for train, validation, and test data for each dataset, respectively. For each problem instance <code>X</code> (i.e., each input document), two files are provided: <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n</code> for tasks 1 and 2. For task 3, we provide one sentence per paragraph (again, split by <code>\n</code>).</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format. An example file is listed in the following (note that we list keys for the three tasks here): <pre><code>{ "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "changes": RESULT_ARRAY_TASK1 or RESULT_ARRAY_TASK3, "paragraph-authors": RESULT_ARRAY_TASK2 }</code></pre> The result for task 1 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). For task 2 (key "paragraph-authors"), the result is the order of authors contained in the document (e.g., <code>[1, 2, 1]</code> for a two-author document), where the first author is "1", the second author appearing in the document is referred to as "2", etc. Furthermore, we provide the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic). The result for task 3 (key "changes") is similarly structured as the results array for task 1. However, for task 3, the <code>changes</code> array holds a binary for each pair of consecutive sentences and they may be multiple style changes in the document. An example of a multi-author document with a style change between the third and fourth paragraph (or sentence for task 3) could be described as follows (we only list the relevant key/value pairs here): <pre><code>{ "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] }</code></pre>   </li> </ol> Output Format To evaluate the solutions for the tasks, the results have to be stored in a single file for each of the input documents and each of the datasets. Please note that we require a solution file to be generated for each input problem for each dataset. The data structure during the evaluation phase will be similar to that in the training phase, with the exception that the ground truth files are missing. For each given problem <code>problem-X.txt</code>, your software should output the missing solution file <code>solution-problem-X.json</code>, containing a JSON object holding the solution to the respective task. The solution for tasks 1 and 3 is an array containing a binary value for each pair of consecutive paragraphs (task 1) or sentences (task 3). For task 2, the solution is an array containing the order of authors contained in the document (as in the truth files). An example solution file for tasks 1 and 3 is featured in the following (note again that for task 1, changes are captured on the paragraph level, whereas for task 3, changes are captured on the sentence level): <pre><code>{ "changes": [0,0,1,0,0,...] }</code></pre> For task 2, the solution file looks as follows: <pre><code>{ "paragraph-authors": [1,1,2,2,3,2,...] }</code></pre>

opencc-by-4.0Mar 2022View details →

Data related to the manuscript "Sequence-specific aggregation of magnetic nanoparticles and single-stranded DNA amplification products for detection of antibiotic resistance gene sul1."

Absorbance and AC susceptometry raw and processed excel files used.

opencc-by-4.0Mar 2022View details →

Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network

Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model.

opencc-by-4.0Mar 2022View details →

Trunk and Grape detection using DPU's FPGA and Vitis-AI

A series of videos showing up the detection of trunks and grapes in natural vineyards

opencc-by-4.0Apr 2022View details →

Supplemental R code and csv files for statistical analysis on Doi et al. "Effects of species traits and ecosystem characteristics on species detection by eDNA metabarcoding in lake fish communities"

Supplemental R code and csv files for statistical analysis on Doi et al. "Effects of species traits and ecosystem  characteristics on species detection by eDNA metabarcoding  in lake fish communities"

opencc-by-4.0Jun 2021View details →

Fig. 2 in New Trematode Collyriclum Faba (Plagiochiida, Collyriclidae) Detected In The Birds Of Uzbekistan

Fig. 2. Collyriclum faba (Bremser in Schmalz, 1831), extracted from subcutaneous cysts in an infected individual of Sylvia althaea Hume, 1878.

opencc-by-4.0Apr 2022View details →

Trained YOLOv3 Model Weights for License Plate Detection

Trained YOLOv3 Model Weights for License Plate Detection

opencc-by-4.0Oct 2021View details →

Detecting Code Smells in React-based Web Apps

Detecting Code Smells in React-based Web Apps - Dataset

opencc-by-4.0Aug 2021View details →

Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images

Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.

opencc-by-4.0May 2022View details →

Haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model

Example dataset for DipPAV

opencc-by-4.0May 2022View details →

3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data

Supplementary data of 3D-MSNet

opencc-by-4.0Jun 2022View details →

RNA modification detection using direct RNA sequencing and nanoDoc2_2

RNA modification detection using direct RNA sequencing and nanoDoc2_2