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Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</p>
Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM
<p>The datasets are a part of the publication: <strong>Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM</strong></p>
Development of thrips barcode database and multiplex real-time PCR assay for quarantine and agriculture pest species
<p>Thrips (Order Thysanoptera) species are agriculturally important as plant sap sucking pests and vectors of several plant diseases. They are very small insects and commonly associated with imported commodities at New Zealand border in all life stages. Morphological identification of thrips is mainly performed on adults, but the available identification keys for immature stages do not include many species and are inadequate, thus DNA barcode was regularly used for thrips identification, here, we have generated DNA barcode data for over 29 thrips species from over 100 individuals. At New Zealand border,<em> Frankliniella occidentalis </em>is the dominant species intercepted, followed by <em>F. panamensis</em>, <em>Thrips palmi</em> and <em>T. tabaci </em>and several other thrips species. Hence, we have also developed a multiplex real time PCR assay, targeting the four thrips species to facilitate the identification of quarantine interceptions with more accurate and faster diagnostic method for any developmental stages. The DNA barcode database further assists in thrip identification. The assay showed high specificity for all the four target species and could detect 10 copies/ µL of the target DNA. Linear responses and high correlation coefficients between the amount of DNA and <em>C</em><sub>q</sub> values for each species were also achieved. The method was tested on single egg, larva and adult and proved to be applicable for all life stages of the four species. This study has demonstrated the assay is a useful biosecurity tool for rapid and reliable identification of the target thrips species. </p>
Fiware-enabled tool for real-time control of the raw-water conveyance system of Athens
<p>This database includes the data used to produce the results for the following article:</p> <p>Bellos, V., Kossieris, P., Efstratiadis, A., Papakonstantis, I., Papanicolaou, P., Dimas, P., Makropoulos, C. 2022. Fiware-enabled tool for real-time control of the raw-water conveyance system of Athens. Proceedings of the 39th IAHR World Congress, 19-24 June 2022, Granada, Spain (accepted paper for oral presentation, in press). </p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
Real-time observation of alpha nucleation in Ti-6Al-4V
<p>This video was captured during an in-situ heating stage SEM experiment using a secondary electron camera at the University of Manchester, UK. The Ti-6Al-4V sample was heated to 1000°C to the full β-phase field and then slowly cooled (0.3°C/s) through the β transus, and the α-phase nucleation was recorded by the contrast change in the secondary electron camera from topography development on the sample surface due to surface relief. Microscope operated by Dr Alec E Davis and Dr Jack Donoghue, sample preparation by Nick Byres. These results were published in an Acta Materialia paper in 2021:</p> <p>Acta Materialia paper: https://doi.org/10.1016/j.actamat.2021.117315</p> <p>Researchgate (free peer reviewed preprint): https://bit.ly/3EHs7Na</p>
A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus
<p>Raw data associated to the study entitled:</p> <p><em>A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus</em><strong> </strong></p> <p><em>- </em>Metadata file</p> <p>- Computational data</p> <p>- Extraction data</p> <p>- Fluorescence detection</p> <p>- Fluorescence imaging</p> <p>- Labbooks</p> <p>- Surface characterization</p>
Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis
<p>This dataset contains the data used in our manuscript titled "Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis". Data structure is explained in the README.txt file located at the top-level of the dataset.</p> <p>Please contact corresponding author Richard B. Woodward for any questions.</p>
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.
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>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> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs
<p> ODDS Smart Building Depth Dataset</p> <p>#Introduction:</p> <p>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.</p> <p><br> #Data Collection:</p> <p>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.</p> <p><br> #Annotated Data Description:</p> <p>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:</p> <p>1)DepthImages: Contains all the images of training set and validation set. </p> <p>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. </p> <p>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)</p> <p><br> #UnAnnotated Data Description:</p> <p>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.</p> <p><br> #Citation:</p> <p>If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications:<br> @inproceedings{mithun2018odds,<br> title={ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs},<br> author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton},<br> booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)},<br> year={2018},<br> }<br> </p>
Numerical data on real-time brain signals
<p>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.</p> <p>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.</p>
The Effect of QPF on Real-time Deterministic Hydrologic Forecast Uncertainty
<p>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.</p>
Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction (Datasets)
<p>Raw data used for figures in the paper titled "Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction"</p>
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.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.