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Fig. 1 in First Molecular Detection of Giardia duodenalis Assemblage B in a Free-Living European Wildcat (Felis s. silvestris) from Luxembourg

Fig. 1. Neighbor-joining distance analysis of the β-giardin nucleotide sequences. FS1 – sequence of partial β-giardin gene of the isolate from wildcat (KX685669). Reference human isolates: WB and KC8, (X85958 and AY072723); LD18, Nij5, VAN/90/UBC/44, GH- 202 (AY072727, AY072725, KP687755, AB618785); A101 and – cat isolate, (AY647264 and EU769206); P15 – reference cow isolate, (AY072729); A29 and A27 – reference dog isolates (AY545646 and AY545648). G. muris (AY258618) represents an outgroup.

PoqueiraVehicleLPR: A Dataset of Vehicle Detection Sensors in the region of Barranco de Poqueira in the Alpujarra Granadina.

This dataset is linked to the analysis of different aspects related to the conservation of the Sierra Nevada National Park through advanced digital systems. The devices have been deployed along the road that passes through the municipalities of Pampaneira, Bubión and Capileira, in the Alpujarra region of Granada. Data is collected by 4 devices equipped with vehicle detection sensors. These devices are Hikvision LPR IP cameras with Automatic number-plate recognition (ANPR) based on Deep Learning. The devices have a 2MP resolution, 2.8-12 mm varifocal optics, and IR LEDs with a range of 50 m. To cover the entrances and exits of each village in the target area, we strategically positioned the cameras. The locations are entrance to Pampaneira from the western part of the Alpujarra (PAM1), entrance to Pampaneira from the eastern part of the Alpujarra (PAM2), entrance to Bubión via a single road (BUB), and entrance to Capileira via a single road (CAP).  The attached data in the CSV files DATA_VEHICLES_2022 and DATA_VEHICLES_2023 contain information about vehicle passages in the years 2022 (from February to December) and 2023 (from January to August) for each installed camera. It includes anonymized license plate information based on an identifier to facilitate cross-referencing and in-depth analysis. The collected variables include: <ul> <li> camera_ID: License Plate Recognition (LPR) camera identifier. </li> <li> date: Timestamp indicating the date and time of the vehicle passage through the camera. </li> <li> num_plate_id: Anonymized license plate identifier. A value of -1 indicates that the vehicle was not correctly identified by the camera. </li> <li> direction: Binary value (IN/OUT) indicating whether the vehicle is entering or exiting the municipality referenced by the camera. </li> </ul>

Capturing Pertinent Symbolic Features for Enhanced Content Based Misinformation Detection

Datasets for the paper Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection. The compressed folder contains annotated data carried out using the symbolic models presented in the paper.

opencc-zeroSep 2023View details →

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Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect

Grassland restoration is an important conservation intervention supporting declining insect pollinators, particularly in threatened calcareous grassland landscapes. While restoration is often assessed using simple diversity or the similarity to a target community metrics, this can fail to represent key aspects of community reconstruction. Here, we compare a new method, species-habitat networks, with techniques previously relied upon to understand the process of pollinator community restoration. The species-habitat network approach reveals details relevant to insect conservation that are not visible using standard measures of species richness, abundance, community similarity or network metrics. For instance, a shared set of butterflies and bumblebees found in ancient extensively managed grassland, the target community for restoration, were more likely to inhabit previously disturbed grassland than recently disturbed or reverting grasslands. We propose that species-habitat networks should be part of the standard analytical toolkit assessing the effectiveness of restoration, particularly for mobile species such as insects.

Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier Abstract: Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate Plasmodium species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for P. falciparum, P. malariae and P. ovale/vivax, respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking. Data collection: The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), Trypanosoma brucei (n=2,773), and red blood cells infected by P. falciparum (n=43,545), P. ovale (n=4,651), P. vivax (n=4,115), P. malariae (n=2,849) and Babesia divergens (n=5,142). The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients. Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).

opencc-by-4.0May 2023View details →

Out-of-the-box calving front detection method using deep learning

The calving front predictions of the <a href="https://www.nature.com/articles/s41592-020-01008-z">nnU-Net</a> on a benchmark <a href="https://doi.pangaea.de/10.1594/PANGAEA.940950">dataset</a>. GitHub: https://github.com/ho11laqe/nnUNet_calvingfront_detection Publication: https://tc.copernicus.org/preprints/tc-2023-34/

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Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes

DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.

opencc-zeroOct 2023View details →

openmit-licenseApr 2023View details →

ÖWF-OD - ÖWF Object Detection Dataset

This dataset contains object detection annotations and quality metadata of historic film with scientific and educational content. The content is from the collection <a href="https://www.mediathek.at/wissenschaft-als-film/die-sammlung-des-oewf/">"Österreichische Bundesinstitut für den Wissenschaftlichen Film (ÖWF)"</a> of the <a href="https://www.mediathek.at">Austrian Mediathek</a>. This is a snapshot of the dataset's repository at https://github.com/TailoredMediaProject/OEWF_ObjectDetection

Sim2real flower detection towards automated Calendula harvesting

This dataset serves as supplementary material for the research paper titled 'Sim2real flower detection towards automated Calendula harvesting', which was published in the October 2023 issue of Biosystems Engineering. Within this upload, you will find a collection of both authentic and computer-generated images featuring Calendula (Calendula officinalis L.) flowers. Additionally, we have included the resources and original data utilized in generating the synthetic images. This dataset proves instrumental in demonstrating the successful transference of a deep neural network from simulation to real-world applications. The contents of this upload includes: <ol> <li>Original RGB and depth images of Calendula flowers captured in a natural flower field, complete with bounding box annotations.</li> <li>The test data employed in our experiments.</li> <li>Unedited RGB images that were used in the photogrammetry pipeline.</li> <li>Three-dimensional models representing Calendula flowers.</li> <li>The resulting dataset of synthetic images.</li> </ol> The synthetic dataset follows the Synthetic Optimized Labeled Objects (SOLO) Dataset Schema, <a href="https://github.com/Unity-Technologies/com.unity.perception/blob/main/com.unity.perception/Documentation~/Schema/SoloSchema.md">as defined in the Unity Perception package</a>. For completeness, the data scheme is also included in the upload. For more comprehensive details regarding this dataset and its associated metadata, we invite you to consult the published article in Biosystems Engineering, available at the following link: <a href="https://doi.org/10.1016/j.biosystemseng.2023.08.016">https://doi.org/10.1016/j.biosystemseng.2023.08.016</a>.

openapache2.0Oct 2023View details →

Turfgrass Divot Dataset (Synthetic ) for divot detection object detection system

The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage. Used for paper:  Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection https://github.com/stevefoy/Turfgrass-Divot-Object-Detection <pre>@inproceedings{IMVIP2024, author = {Stephen Foy and Simon McLoughlin}, title = {Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection}, booktitle = {Irish Machine Vision and Image Processing Conference (IMVIP)}, year = {2024} }</pre> Contents of the Zip File: <ul> <li> synthDivot_416x416 Folder: <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Corresponding masks for each image</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> <li> synthDivot_608x608 Folder: <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> </ul>

opencc-by-4.0Jul 2023View details →

A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli

Flexible electronics have gained popularity because of their capability to combine softness and functionality. Soft resistive sensors are susceptible to mechanical stimuli and detecting temperature selectively remains a challenge. In this study, soft flexible thermistors are developed for detecting temperature changes based on the positive temperature coefficient (PTC) effect, selectively. By thermomechanical analysis and differential scanning calorimetry, it is observed that thermoplastic elastomers with higher thermal expansion and semicrystalline morphology result in a sensitive thermistor response. To achieve a high sensitivity in temperature and low sensitivity in the detection of mechanical stimulus, a low-carbon filler content is required. The opposite trend is seen for the piezoresistive sensors for mechanical strain detection. Both sensory material types are compatible with thermoplastic material extrusion-based additive manufacturing. The method is used for the fabrication of the sensing elements and an open-source prosthetic hand. The strain sensor detects the bending of the fingers and the temperature sensor detects the temperature when in contact with a heated surface, successfully. In addition, the temperature sensor is used as a tactile sensor to detect contact with a non-heated surface. Combining selective multisensory capabilities will significantly affect the future development of sensorized prosthetic devices and wearable electronics.

Figure 4 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 4 Spotted towhee predictors from the AICc top model for PC1approach (a), PC2fly (b), and PC3song (c). (a) Towhees approach more slowly and maintain a greater distance from the speaker as sound level increases. Shading represents 95% CI and rug plot denotes sound level of individual trials. California spotted towhees fly more frequently on control trials (b) and display a weaker song response during treatment-off trials (c) than on all other trials, within and among populations (see [a] for color legend). (b–c) Violins denote kernel density probabilities differentiated by trial type (x axis) and population (color), with means (±1 SE) connected by dotted line; boxplots denote median and quartiles, and whiskers show 1.5 times the interquartile range; points represent individual trials; and asterisks denote significant contrasts corresponding to 95% CIs (see Supplementary Appendix Table A3 for values). Data displayed as untransformed components.

Figure 2 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 2 Site design schematic of Idaho and California study areas. Tripods represent loudspeaker setups. aAny trial with chorusing cicadas was counted as cicada treatment, regardless of the trial type (treatment-on, treatment-off, control). bControl sites had the same layout as treatment sites, but with mock loudspeaker setups. cWe excluded cicada trials from analysis for spotted towhees due to low sample size.

Figure 3 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 3 Lazuli bunting predictors from the AICc top model for PC1fly (a) and PC3approach (b) response variables. (a) Buntings fly more as sound level increases in the presence of cicada noise (dashed line) and fly less as sound level increases in the absence of cicada noise (solid line). Rug plot indicates sound level for individual trials differentiated by presence/absence of cicadas (see [b] for color legend). (b) As sound level increases, buntings approach the conspecific speaker more slowly and maintain a greater distance from it. (a & b) Shaded bands denote 95% CIs. Data displayed as untransformed components.

Figure 1 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 1 Spectrograms (a) of spotted towhee song (left) and lazuli bunting song (right) in quiet control conditions. (b) Power spectra of the three treatments are overlayed with bunting and towhee song power spectra. Power spectra are normalized to a relative peak amplitude of 70 dB (re 1 dimensionless sample units). Treatment noise and song spectra overlap substantially, suggesting high masking potential.

Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.

Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].

Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.

opencc-by-4.0Oct 2023View details →

Data pertaining to the published article "Quantitative T1 mapping detects blood-brain barrier breakdown in apparently non-enhancing multiple sclerosis lesions" by Donatelli et al., 2023.

Data pertaining to the published article "Quantitative T1 mapping detects blood-brain barrier breakdown in apparently non-enhancing multiple sclerosis lesions" by Donatelli et al., NeuroImage: Clinical (2023). PMID: 37717382; PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/pmc10514220/">PMC10514220</a>; DOI: <a href="https://doi.org/10.1016/j.nicl.2023.103509">10.1016/j.nicl.2023.103509</a>