Find research datasets worth reusing

Fig. 1. A in A novel quantitative real-time PCR diagnostic assay for seal heartworm (Acanthocheilonema spirocauda) provides evidence for possible infection in the grey seal (Halichoerus grypus)

Fig. 1. A: graphical representation of cluster 20 (C20). B: representation of the selected contig and the sequence used to design the C20 quantitative real-time PCR assay. The forward primer is in bold, the reverse primer is indicated by a dotted underline, and the double-quenched probe is underlined.

Fig. 2. Standard curve generated using the log10 in A novel quantitative real-time PCR diagnostic assay for seal heartworm (Acanthocheilonema spirocauda) provides evidence for possible infection in the grey seal (Halichoerus grypus)

Fig. 2. Standard curve generated using the log10 of the ng of input A. spirocauda DNA plotted against Ct value. Unknown values are displayed as stars. For unknown samples, the total input DNA was 1 ng, which contains a mixture of seal DNA from the blood and A. spirocauda DNA. R2 = 0.985 for linear fit of standards. Curve is described by the equation y = −5.63x + 17.16, where y is the log (ng) and x is the Ct value. 95% confidence intervals are denoted by the dotted lines.

Fig. 1 in Detecting co-infections of Echinococcus multilocularis and Echinococcus canadensis in coyotes and red foxes in Alberta, Canada using real-time PCR

Fig. 1. Standard curve for qPCR assays to detect E. canadensis and E. multilocularis using Cox143 and Nad234 primers/probes, respectively.

opencc-by-4.0Jul 2024View details →

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"  <a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data  <a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data  <a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data  Figure 2: Figure2.npy Figure 3: Figure 3 abc .npy Figure16:  <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a>  Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a>

opencc-by-4.0Aug 2015View details →

Figure 10. Otsu segmentation of the real (left) and the impossible (inverted) figure (right)

This regularity of cognition is about the geometry of the internal representation of the external world, which somehow most often is approximating the Euclidean space according to L.M. Vekker (1976). In his theory of the Gestalt in perception and thinking, Vekker postulated that sensations, perceptions and imagination are in a constant process of unfolding to match and take the exact places of the physical objects in the external environment. The geometry of the internal representation of the world is abstract and experiential at the same time. The experiential aspect of the process of immediate cognition can be seen by viewing figure 9. Figure 9 right is, at first glance, a quite convincing example of stairs similar to the stairs on the left. Even the context (the cat on the ceiling) is noticed on a second scan. The inverted stairs are more likely to remind us of Escher’s works than, for example, the normally positioned ones. This effect supports the idea of the semantic depth of spatial learning, relating the experience of art to perceiving of Gestalts.

opencc-by-4.0Jan 2018View details →

ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs

ODDS Smart Building Depth Dataset #Introduction: The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box. #Data Collection: We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking. #Annotated Data Description: The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD).  The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories: 1)DepthImages: Contains all the images of training set and validation set.  2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image.  3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split) #UnAnnotated Data Description: The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm. #Citation: If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications: @inproceedings{mithun2018odds, title={ODDS: Real-Time Object Detection using Depth Sensors on  Embedded GPUs}, author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton}, booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)}, year={2018}, }

opencc-by-4.0May 2018View details →

Identifying shape transformations from photographs of real objects

Dataset relative to the following publication: Schmidt, F., & Fleming, R. W. (2018). Identifying shape transformations from photographs of real objects. PLoS ONE, 13(8):e0202115, 1-20. <a href="https://doi.org/10.1371/journal.pone.0202115">https://doi.org/10.1371/journal.pone.0202115</a> Each experiment folder contains the data relative to one experiment and a text file with comments. The stimuli folder contains downsampled image files of the experimental stimuli.

bin3C - Cluster report and CheckM result for bin3C solution of the real human gut microbiome

Supplementary data table S3 from the manuscript bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs) A real human gut microbiome was deconvoluted using bin3C. Subsequently, bin3C produced a report detailing per-cluster statistics for the entire solution. The largest 296 clusters were then analyzed with CheckM and joined to the report.

opencc-by-4.0Oct 2018View details →

Numerical data on real-time brain signals

Real-time brain signals database was collected during an intellectual competition. The Database was acquired via OpenVibe - a software platform dedicated to designing, testing and using brain- computer interfaces. This database shows brains activity during a specific period of time. As a result brain signals were acquired, filtered, processed, classified and visualized. This database can be used in medical (assistance to disabled people, real-time ), multimedia (virtual reality, video games), robotics and other purposes related to brain-computer interfaces and real-time neurosciences.

opencc-by-4.0Feb 2019View details →

Real measurement of Coulomb diamonds

Data used in the paper "Efficiently measuring a quantum device using machine learning". https://arxiv.org/abs/1810.10042 It can be loaded directly from Numpy: import numpy as np data = np.load('data_real.npy')

opencc-by-4.0Jun 2019View details →

The Effect of QPF on Real-time Deterministic Hydrologic Forecast Uncertainty

The use of Quantitative Precipitation Forecast (QPF) in hydrologic forecasting is commonplace, but QPF is subject to considerable error. When QPF is included as a model forcing in the hydrological forecast process, significant error is passed to subsequent hydrologic predictions. Two questions arise: (1) are the resulting observed hydrologic forecast errors sufficiently large to suggest the use of zero QPF in the forecast process; if the use of QPF is indicated, (2) how many periods (hours) of QPF (1-, 6-, 12-,..., 72-h...) should be used? Also, do forecast conditions exist under which the use of QPF should be different? This study presents results from two real-time hydrologic forecast experiments, focused on the NOAA/NWS Ohio River Forecast Center (OHRFC). The experiments rely on forecasts from subbasins at 38 forecast point locations, ranging in drainage area, geographic location within the Ohio River Valley, and watershed response time. Results from an experiment, spanning all flow ranges, for the August 10, 2007 - August 31, 2009 period, show that non-zero QPF produces smaller hydrologic forecast error than zero QPF. A second experiment, January 23, 2009 through September 15, 2010, suggests that QPF should be limited to 6- to 12-h duration for flood forecasts. Beyond 12-h, hydrologic forecast error increases substantially across all forecast ranges, but errors are much larger for flood forecasts.  Increased durations of QPF produce smaller forecast error than shorter QPF durations only for non-flood forecasts. Experimental results are shown to be consistent with NWS, April 2001 to October 2016, forecast verification statistics for the OHRFC.

opencc-zeroJul 2019View details →

Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction (Datasets)

Raw data used for figures in the paper titled "Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction"

Fig. 3 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 3. Phylogenetic tree of seven Trypanosome species and subsequent genotypes constructed with sequences of the amplicons generated by the HRMqPCR primers.

Fig. 4 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 4. Amplification plots, melt curves and standard curves of T. copemani, T. vegrandis G7 and T. noyesi G8 prepared from a plasmid containing trypanosome species.

Fig. 5. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 5. A-D: Derivative melt curves showing mock mixed infections generated from plasmid clones containing the following DNA: (A) T. noyesi G8 and T. copemani; (B) T. vegrandis G7 and T. copemani; (C) T. vegrandis G7 and T. noyesi G8; (D) T. vegrandis G7, T. noyesi G8 and T. copemani.

Fig. 1 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 1. Multiple sequence alignment of a portion of the 18S rDNA of seven Trypanosome species and subsequent genotypes used to design the HRM-qPCR assays.

Fig. 3 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection

Fig. 3. Predicted lines of fit for the multivariable logistic regression model plotted as probability of Toxoplasma gondii qPCR positivity in feral cats on Phillip Island (Victoria) versus body weight for each season. Dashed lines show 95% confidence intervals.

Fig. 1 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection

Fig. 1. Location and Toxoplasma gondii infection status, as detected by real-time PCR (qPCR), of feral cats trapped on Phillip Island (Victoria) from July 2016 to December 2017. Map shows the distribution of different location types (Park, Agricultural, Residential) used in multivariable regression analysis. A circular spread of points around a location marked with 'x' indicates multiple animals were sampled at the same site (i.e. same GPS coordinates). Red = T. gondii qPCR positive, white = T. gondii qPCR negative. Map created using Quantum GIS, version 3.8. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus

Fig. 2. Standard curve based on sensitivity data. A 95% confidence interval for the linear regression model is shaded in grey.

Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus

Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A. Number of sequencing reads for P. decorus compared to outgroup species reads for each repeat family (1–104) on a log scale. Arrows indicate repeat families with no reads from the outgroup species. Plot was made using Tableau Software, 2019. B. Within a cluster, reads with similar sequences are closer together. Edges connect a read with its closest match (creating a pair) and the length of this edge represents the amount of overlap between the reads. The mean edge width provides context for the lengths in the cluster, so in a cluster with a larger mean edge width the edges are actually longer than edges in a cluster with a smaller mean edge width. Reads therefore may be distant because of sequence divergence, or in the case of a long repeat (more than 150 base pairs), because of a lack of overlap between reads. However, because there will likely be continuous reads covering different regions of the repeat, these longer repeats should still appear as a tight, though possibly larger, cluster. Read dots that stray from the central cluster most likely represent sequence divergence. Higher density therefore indicates lower sequence divergence.