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Figure 4. AGLO Name and Description-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we describe an AGLO model example. Figure 4 presents in details all the<br> XML elements containing AGLO metadata.</p>
Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>E-learning is a key area of research with a great influence on the developments of several<br> industries. For example, the nowadays ITC industry is in a continuous growth because of its<br> applications in almost all industrial domains. Companies tend to lack qualified human resources and<br> because of that they reject high economical value projects. In response to this lack of human<br> resource problem, universities started to develop several alternative study programs, many of them<br> are based on e-learning technology and namely on electronic learning materials. Learning objects<br> (LO) are considered to be digital resources that support learning and can be delivered across<br> networks in large or small sizes (Wiley, 2000). In order to increase the reusability and<br> interoperability of LOs, standards were developed by several organizations (IEEE Learning<br> Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)</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>
ENTICE Multi-objective optimization framework synthetic evaluation data-sets
<p>This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories. </p> <p>The data is stored in a Java object and it should be directly loaded. </p> <p> </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
Erlebe objekt-basiertes Audio / Experience object-based audio
<p>The original ORPHEUS version of a radio documentary from BR (Bavarian Public Broadcasting, Munich) in German and English.</p> <p>Produced with object-based audio tools from the ORPHEUS project and pre-renderd to binaural sound. Listen with headphones!</p>
Tomographic X-ray data of a lotus root filled with attenuating objects
<p>This is an open-access dataset of tomographic X-ray data of a slice of a lotus root. The dataset consists of</p> <ul> <li>the X-ray sinogram of a single 2D slice of the lotus root with two different resolutions, and</li> <li>the corresponding measurement matrices modeling the linear operation of the X-ray transform.</li> </ul> <p>Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>Also MATLAB code for reconstructions using filtered back-projection, Landweber iteration, and Tikhonov regularization are provided.</p> <p>Documentation of the dataset is available at <a href="https://arxiv.org/abs/1609.07299">arxiv.org/abs/1609.07299</a>. See also <a href="https://www.fips.fi/dataset.php">www.fips.fi/dataset.php</a>.</p>
Identifying shape transformations from photographs of real objects
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., & Fleming, R. W. (2018). Identifying shape transformations from photographs of real objects. <em>PLoS ONE, 13(8):e0202115</em>, 1-20. <strong><a href="https://doi.org/10.1371/journal.pone.0202115">https://doi.org/10.1371/journal.pone.0202115</a></strong></p> <p>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.</p>
W2Share Case Study: Workflow Research Object (WRO)
<p>Case Study - Molecular Dynamics</p> <p>Our case study is based on a molecular dynamics simulation defined in the following article:</p> <p>Silveira, R.L. and Skaf, M. S. Molecular Dynamics Simulations of Family 7 Cellobiohydrolase Mutants Aimed at Reducing Product Inhibition. J. Phys. Chem. B 119, 9295-9303 (2015). DOI: <a href="https://doi.org/10.1021/jp509911m">https://doi.org/10.1021/jp509911m</a></p>
Observing substructure in circumstellar discs around massive young stellar objects
<p>Synthetic dust continuum and molecular line Atacama Large Millimetre Array (ALMA) observations of massive, self-gravitating disc models surrounding massive young stellar objects are presented here. Semi-analytic models of self-gravitating discs with spiral density waves and clumps/fragments are combined with radiative transfer models, and synthetic observations are produced using CASA software. Models presented here have different disc masses, distances, inclinations, thermal structures, dust distributions, number and orientation of spirals and fragments.<br> <br> Data is in the FITS format, with filenames starting either with 'line' (synthetic molecular line datacube) or 'cont' (synthetic continuum images), and each filename contains the model ID. Tables of model IDs and model parameters are given in files models_table_spiral.dat and models_table_spiral_fragments.dat for models without and with fragments, respectively. Starting from a fiducial disc model, model parameters were varied one by one, with the exception of disc inclination which is separately set in each model.<br> <br> For details about the model parameters, detailed presentation of methods, proposed substructure-enhancing filtering methods, discussion and predictions for the upcoming ALMA observations, see Jankovic et al. 2018 (accepted for publication in MNRAS, arxiv.org/abs/1810.11398).</p> <p> </p>
Stimuli examples for Object-based attention in complex, naturalistic auditory streams
<p>This is an example collection of stimuli used to study Object-based attention in complex, naturalistic auditory streams. The stimuli consist of single auditory streams of speech signals and environmental sounds signals that can contain a repetition of 750ms (starting name "target")or not (starting name "clean"). Two sounds stimuli, one with repetition and one without were randomly overlapped to form an auditory scene of two speech streams (starting name "sceneSS") or one speech and one environmental stream (starting name "sceneSE"). </p>
Data for Laminography in the Lab: Imaging planar objects using a conventional x-ray CT scanner
<p>This data accompanies the publication</p> <p> </p> <p><strong>Laminography in the Lab: Imaging planar objects using a conventional x-ray CT scanner</strong></p> <p>by Sarah L Fisher, Danny J Holmes, Jakob S Jørgensen, Parmesh Gajjar, Julia Behnsen, William R B Lionheart and Philip J Withers</p> <p>Measurement Science and Technology, Vol. 30, No. 3, 2019</p> <p><a href="https://doi.org/10.1088/1361-6501/aafcae">https://doi.org/10.1088/1361-6501/aafcae</a></p> <p> </p> <p> </p> <p>The computed laminography (CL) and limited angle CT (LACT) data sets for the lego sample of the paper are provided, including both raw projection data and the final reconstructions, a total of 4 files for each of the CL and LACT cases. The files are (for CL)</p> <p>CLProjectionData.zip: zip file with the raw projection images and meta data as produced by the Nikon instrument.</p> <p>CLShadingCorrection.zip: zip file containing dark and flat field images.</p> <p>CLreconstruction.mat: MATLAB mat-file with final CL reconstruction, size 798x798x200 voxels.</p> <p>CLreconstruction.vol: Same CL reconstruction but in a generic binary file that for example simplify the loading of data into Avizo.</p> <p> </p> <p>The same set of files is available for the CT data. The CT data reconstruction has size 798x798x798.</p> <p> </p> <p>Accompanying MATLAB reconstruction code is available from</p> <p><a href="https://github.com/sarahfisher1/Laminography">https://github.com/sarahfisher1/Laminography</a></p>
Getting "Fumpered": Classifying objects by what has been done to them
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., & Fleming, R. W. (in press). Getting "Fumpered": Classifying objects by what has been done to them. <em>Journal of Vision, 19(4):15</em>, 1-12<em>. </em>https://doi.org/10.1167/19.4.15</p> <p>Each folder contains either the data or the experimental stimuli relative to the experiments and a text file with comments.</p>
R data objects for the HumanDEU package
<p>This submission contains several R data objects that are part of the R package HumanDEU available through Github (https://github.com/areyesq89/HumanTissuesDEU). The objects correspond to processed data needed to reproduce the statistics, tables and figures presented in the manuscript:<br> <br> A Reyes and W Huber. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic Acids Research, 2017. doi: https://www.doi.org/10.1093/nar/gkx1165<br> <br> For more details, please visit the Github repository.</p>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
An evaluation of compression algorithms applied to moving object trajectories
<p>This file contains the dataset, source code and results presented in the paper entitled "An evaluation of compression algorithms applied to moving object trajectories" published in the International Journal of Geographical Information Science in 2019.</p> <p>Abstract: The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.</p>
Dataset to demonstrate the use of NicheNet on a Seurat object
<p>Single-cell RNAseq dataset to demonstrate the use of NicheNet directly on a Seurat object. The data came from "Medaglia et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq, Science 2017". This data was generated via the NICHE-seq method to characterize immune cell composition in the T cell area of inguinal lymph nodes, both in steady-state and 72 hours after lymphocytic choriomeningitis virus (LCMV) infection. The Seurat objects contain the aggregated data after applying the Seurat alignment pipeline.</p> <p><a href="https://zenodo.org/api/files/316332c2-9718-4869-864f-ab3bcd69fe7a/seuratObj.rds">seuratObj.rds</a>: full dataset</p> <p><a href="https://zenodo.org/api/files/316332c2-9718-4869-864f-ab3bcd69fe7a/seuratObj_test.rds">seuratObj_test.rds</a>: dataset with reduced size (only highly variable genes and CD8 T cells and monocytes) </p> <p> </p>
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.