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73 results for “artificial dataset”
Dataset: Themes Generative Artificial Intelligence ETF (WISE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust Nasdaq Artificial Intelligence and Robotics ETF (ROBT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Datasets underlying the paper 'Electrotaxis of self-propelling artificial swimmers in microchannels'
<p>Datasets underlying the paper 'Electrotaxis of self-propelling artificial swimmers in microchannels', arXiv:2401.14376</p> <p>Source data for all figures in the manuscript. Figures were produced by Python/matplotlib, a corresponding Jupyter notebook is included in the root folder. Compressed videomicrographs of the experiments, and abridged numerical data sets; all raw data are available from the authors on reasonable request.</p>
Study of the Effects of Daylighting and Artificial Lighting at 59° Latitude on Mental States, Behaviour and Perception - Dataset
<p>Dataset relative to manuscript "Study of the Effects of Daylighting and Artificial Lighting at 59° Latitude on Mental States, Behaviour and Perception", submitted to Sustainability journal</p>
Artificial Dataset of Molecular Enthalpies of Formation
<p>This repository contains an artificial dataset constructed for the study of uncertainty characterization and quantification in chemical ML applications. The data are designed to be noise-free and represent group-additivity calculations of enthalpy of formation, rather than calculated or measured enthalpy of formation directly.</p> <p>Where did the targets come from:<br> These data files contain SMILES and targets for a simple group additivity calculation of enthalpy of formation at 298 K. The group additivity coefficients were fitted to the molecules of the qm9 computational chemistry database. Fragments were only considered that appeared in at least 100 molecules. These coefficients were rounded to 3 decimals. Groups only consider a bond radius of 1 from the central atom.</p> <p>Where did the SMILES come from:<br> The group additivity coefficients were applied to the gdb11 computational chemistry dataset. The gdb11 dataset contains 26.4M molecular SMILES, attempting to cover all possible organic molecules up to 11 heavy atoms with the atoms C, H, O, N, F. Molecules that contained groups that were not represented in the group additivity coefficients were excluded, resulting in 7,906,815 SMILES. Though these SMILES contain chiral centers, they are not chirally specified. No SMILES repeats are present.</p> <p>Scripts used for generating data subsets and added-noise datasets are also included.</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
[BIOCOM-PIPE] Artificial dataset from ZymoBIOMICS
<p>This artificial dataset is a microbial community from ZymoBIOMICS.</p> <p>More precisely, it is a mock microbial community consisting of eight bacterial and two fungal strains (see PDF for its specific composition). It includes three easy-to-lyse Gram-negative bacteria (<em>e.g. Escherichia coli</em>), five tough-to-lyse Gram-positive bacteria (<em>e.g. Listeria monocytogenes</em>), and two tough-to-lyse yeasts (<em>e.g. Cryptococcus neoformans</em>). The 16S/18S rRNA sequences (FASTA format) and genomes (FASTA format) of these strains are available at: <a href="https://s3.amazonaws.com/zymo-files/BioPool/ZymoBIOMICS.STD.refseq.v2.zip">https://s3.amazonaws.com/zymo-files/BioPool/ZymoBIOMICS.STD.refseq.v2.zip</a>.</p> <p>A 16S rRNA gene fragment targeting the V3-V4 regions to characterize bacterial diversity was amplified using the primers F479 (5’-CAGCMGCYGCNGTAANAC-3’) and R888 (5’-CCGYCAATTCMTTTRAGT-3’) using the expertise and protocols from the GenoSol platform. The 16S PCR products were then purified and quantified using the QuantiFluor staining kit (Promega, USA). A second PCR of 7 cycles was then duplicated for each sample under similar PCR conditions, with purified PCR products as matrix (7.5ng of DNA were used for a 25μl mix of PCR) and dedicated fusion primers (‘F479/MID’, ‘R888/MID/’) integrating multiplex identifiers at 5’ extremities. All duplicated PCR products were then pooled, purified and quantified using the QuantiFluor staining kit (Promega, USA). Samples were pooled, and then cleaned to remove excess nucleotides, salts and enzymes using the Agencourt AMPure XP system (Beckman Coulter Genomics).</p> <p>The V3-V4 regions of the 16S rRNA genes generated using Illumina MiSeq technology (4 and 3 replicates from two independent runs) were then obtained. Pair-end reads were then trimmed (Q30 for 3' end) with PRINSeq, and assembled with FLASH (a minimum of 10 and a maximum of 100 bases overlapping was required with a minimum of 96% of homology between reads). The seven samples came from two independent RUNs of Illumina MiSeq (RUN01 and RUN02), one encompassing four replicates, and another three replicates, each one from independent PCR reactions.</p>
Artificial datasets for online Declare discovery
<p>This file contains two datasets.</p> <p><strong>1. Periodical Sudden Drifts</strong></p> <p>For this case study, we have generated two synthetic logs (<span class="math-tex">\(\mathcal{L}_1\)</span> and <span class="math-tex">\(\mathcal{L}_2\)</span>) by modeling two variants of the insurance claim process described in [1] in CPN Tools and by simulating the models. <span class="math-tex">\(\mathcal{L}_1\)</span> contains 14,840 events and <span class="math-tex">\(\mathcal{L}_2\)</span> contains 16,438 events.</p> <p>We merged the logs (eight alternations of <span class="math-tex">\(\mathcal{L}_1\)</span> and <span class="math-tex">\(\mathcal{L}_2\)</span>) using the <em>Stream Package</em> in ProM (the source code of the package is publicly available at https://svn.win.tue.nl/repos/prom/Packages/Stream/Trunk). The same package has been used to transform the resulting log into an event stream. The event stream contains 250,224 events and has several sudden concept drifts (one for every switch from <span class="math-tex">\(\mathcal{L}_1\)</span> to <span class="math-tex">\(\mathcal{L}_2\)</span>).</p> <p><strong>2. Gradual Drifts</strong></p> <p>We have considered two variants of the insurance claim process described in [1], <span class="math-tex">\(\mathcal{M}_1'\)</span> (with 21 activities) and <span class="math-tex">\(\mathcal{M}_2'\)</span> (with 19 activities). We have also designed 6 additional models <span class="math-tex">\(\mathcal{M}_a,\dots, \mathcal{M}_f\)</span> to represent the intermediate steps to go from <span class="math-tex">\(\mathcal{M}_1'\)</span> to <span class="math-tex">\(\mathcal{M}_2'\)</span>. Therefore, <span class="math-tex">\(\mathcal{M}_1'\)</span> and <span class="math-tex">\(\mathcal{M}_a\)</span> are very similar and the same happens for <span class="math-tex">\(\mathcal{M}_a\)</span> compared to <span class="math-tex">\(\mathcal{M}_b\)</span>, for <span class="math-tex">\(\mathcal{M}_b\)</span> compared to <span class="math-tex">\(\mathcal{M}_c\)</span>, and so on. We have simulated these models generating 8 logs (<span class="math-tex">\(\mathcal{L}_1', \mathcal{L}_a, \dots,\mathcal{L}_f, \mathcal{L}_2'\)</span>). <span class="math-tex">\(\mathcal{L}_1'\)</span> contains 139,938 events, <span class="math-tex">\(\mathcal{L}_2'\)</span> contains 128,696 events and <span class="math-tex">\(\mathcal{L}_a,\dots,\mathcal{L}_f\)</span> contain 77,231 events (altogether).</p> <p>Using the <em>Stream Package</em>, we have generated an event stream containing 345,865 events.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>R. J. C. Bose, “Process Mining in the Large: Preprocessing, Discovery, and Diagnostics,” Ph.D. dissertation, Eindhoven University of Technology, 2012.</li> </ol>
Artificial dataset for "Automatic Determination of Parameters Values for Heuristics Miner++"
<p>This set of processes, built for test purposes [1], is composed of 125 process models. These processes were created using PLG [2, 3]. The generation of the random processes is based on some basic “process patterns”, like the AND-split/join, XORsplit/join, the sequence of two activities, and so on.</p> <p>For each of the 125 process models, two logs were generated: one with 250 traces and one with 500 traces. In these logs, the 75% of the activities are expressed as time intervals (the other ones are instantaneous) and 5% of the traces are noise. In this context “noise” is considered either a swap between two activities or removal of an activity.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>A. Burattin, A. Sperduti. "<em>Automatic Determination of Parameters Values' for Heuristics Miner+</em>+". In <em>Proceedings of IEEE Congress on Evolutionary Computation</em> (IEEE WCCI CEC 2010); 10.1109/CEC.2010.5586208</li> <li>A. Burattin. “<em>PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings</em>”. In <em>CoRR</em> abs/1506.08415, Jun. 2015.</li> <li>A. Burattin and A. Sperduti. “<em>PLG: a Framework for the Generation of Business Process Models and their Execution Logs</em>”. In <em>Proc. of the 6th Int. Workshop on Business Process Intelligence</em> (BPI 2010); 2010.10.1007/978-3-642-20511-8_20.</li> </ol>
Artificial datasets for multi-perspective Declare analysis
<p>This file contains the dataset we used for the evaluation of the multi-perspective Declare analysis.</p> <p><strong>Logs</strong></p> <p>In particular, it contains logs with different sizes and different trace lengths. We generated traces with 10, 20, 30, 40, and 50 events and, for each of these lengths, we generated logs with 25000, 50000, 75000, and 100000 traces. Therefore, in total, there are 20 logs.</p> <p><strong>Declare models</strong></p> <p>In addition, the dataset contains 10 Declare models. In particular, we prepared two models with 10 constraints, one only containing constraints on the control-flow (without conditions on data and time), and another one including real multi-perspective constraints (with conditions on time and data). We followed the same procedure to create models with 20, 30, 40, and 50 constraints.</p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Dataset: An improved rearing method and artificial diet for greyback canegrubs (Dermolepida albohirtum, Coleoptera: Scarabaeidae).
<p>This dataset was generated and analysed for a study aimed at developing a rearing method and artificial diet for greyback canegrubs (<em>Dermolepida albohirtum</em>, Coleoptera: Scarabaeidae). See the published study for more details on the methodology.</p> <p><strong>Egg monitoring</strong></p> <p>Excel spreadsheet. Information about <em>Dermolepida albohirtum</em> eggs and hatching rate. Eggs were laid by adult beetles in the laboratory. Eggs were kept in hatching chambers in small batches and stored in an incubator at 26°C, 75% humidity, and in the dark. Eggs hatched over several days (up to five days).</p> <p><strong>Hatchling monitoring</strong></p> <p>Excel spreadsheet. Information about <em>Dermolepida albohirtum </em>hatchlings and the date at which they hatched and developed into second instar in the laboratory. Hatchlings were fed pieces of carrots.</p> <p><strong>Survival grubs on diet</strong></p> <p>Excel spreadsheet. Information about <em>Dermolepida albohirtum </em>second instar larvae fed different diets or not fed in the laboratory for 18 days: survival, date at which they developed into third instar, date at which they died (if applicable).</p> <p><strong>Weight of larvae on diet </strong></p> <p>Excel spreadsheet. Weight of <em>Dermolepida albohirtum </em>larvae fed different diets or not fed in the laboratory for 18 days.</p> <p><strong>Width of larvae on diet</strong></p> <p>Excel spreadsheet. Width of <em>Dermolepida albohirtum </em>larvae fed different diets or not fed in the laboratory for 18 days. Larval width was measured as a straight line across the maximal width situated between the raster and the last spiracle. The measurements were taken from photographs and using ImageJ software (version 1.52a).</p> <p><strong>Activity level of larvae on diet</strong></p> <p>Excel spreadsheet. Activity level of <em>Dermolepida albohirtum </em>larvae fed different diets or not fed in the laboratory for 18 days. Larvae were observed at 30 seconds intervals for 10 minutes every 3 days (i.e., when new diet was added). We recorded when larvae started digging into the sand and when they were successfully buried at 30 seconds intervals for 10 minutes to determine their relative activity level. We also recorded the percentage of their body outside the substrate if they were not completely buried after 10 minutes.</p> <p><strong>Diet uptake</strong></p> <p>Excel spreadsheet. Diet uptake of <em>Dermolepida albohirtum </em>larvae fed different diets in the laboratory for 18 days. We calculated the amount of food source (diet PL1 or carrot) ingested by larvae by subtracting the initial diet weight with the weight of the remaining diet after three days (i.e., when new diet was added). These values were corrected by adding or subtracting the average proportion of diet or carrot weight change measured in controlled containers (i.e., carrots gained 11.2% of their weight on average and diet PL1 lost 1.9% of their weight in controlled conditions).</p>
Tiny AAM: Sample from the Artificial Audio Multitracks Dataset
<p>This is the tiny demo version with 20 samples from the AAM dataset, which originally contains 3,000 artificial music audio tracks. In contrast to the full version, which has lossless audio, we only provide mp3 tracks for demonstration and testing purposes.</p> <p>For the <strong>full version</strong> and <strong>more information</strong> see: <a href="https://doi.org/10.5281/zenodo.5794629">zenodo.5794629</a></p>
Clinical Dataset for Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
<p><span>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected from electronic Medical Record. </span></p> <p><span>The original analysis dataset includes clinical and demographic data of adults admitted to the neurology and orthopedic departments of a rehabilitation hospital in Italy from January 2015 to August 2022. The completion of patients’ ADR-r form</span><span> resulted in the collection of data for 10520 individuals, whose information is distributed across 120 initial features. We anonymized dataset rows. Clinical data, including the primary reason for rehabilitation, any associated medical conditions, impairments, and admission/discharge mBi scores were collected. </span></p> <p><span>A legend file is enclosed to explain variable labels.</span></p>
ProcarySV Artificial Benchmarking Datasets
Open the record for dataset details and reuse information.
Light-driven multidirectional bending in artificial muscles dataset
<h1>General Description</h1> <p>This dataset accompanies our research on the development of programmable artificial muscles. By integrating photothermal components into shape memory polymer actuators, we have created a system that exhibits on-command multidirectional bending, controlled by illumination intensity and the chirality of the actuators. The dataset showcases the rapid response of these artificial muscles, with reaction times significantly faster than natural heliotropic systems.</p> <p>The Python code that processes shared data can be found at <a href="https://github.com/p3d2/LTCAM">https://github.com/p3d2/LTCAM</a>.</p> <h1>About Dataset</h1> <p>The folder "data" (inside data.zip) contains thermal videos and recordings of yarns for photothermal actuation measurements. Zahra Madani produced and extruded the filaments, Maija Vaara and Laura Koskelo twisted and winded the filaments, Susobhan Das, and Camilo Arias made the photoactivation recordings and Pedro Silva made the measurements. Samples names can be found in the table below and measurements were performed not sequentially. The "Materials Characterization.zip" contains DMA, DSC, FTIR, Rheology, Tensile, and TGA measurements. The following information in this readme considers the documents inside the data folder only. </p> <h3>Folders, Files & File formats</h3> <p>If the data includes images or audio, you can mention the file format eg.(.svg, .png, .mpeg)</p> <ul> <li>LTCAM:<br>- 14 npy pixel data with temperatures (mat files)<br>- 14 npy time data (time files)<br>- Supplementary images of samples and experimental setup</li> <li>Butterfly: <br>- 2 npz pixel with temperatures and time data<br>- 2 mp4 recordings of the activation of wings</li> <li>Rotating:<br>- 6 npz pixel with temperatures and time data<br>- 6 mp4 recordings of activation of samples in a rotating platform</li> </ul> <h3>Optical parameters setup for 'LTCAM' experiments</h3> <ul> <li><strong>Laser specification</strong>: Ultrafast laser with pulse width 230 fs and repetition rate 2kHz. (Spectra-Physics, TOPAS)</li> <li>Incident wavelength: 800nm (Horizontal polarization)</li> <li>Beam Diameter: 5 mm</li> <li>Average Power: tunable based on experimental need</li> <li>Beam Diameter is larger than the width of the sample. Therefore, to calculate the effective incident power, it has to be the fraction of the total power based on the area of the sample exposed.</li> <li><strong>Lens System</strong>: A part of experiment is done under the focused light condition with a lens of 100mm focal length.</li> <li><strong>Power Meter</strong>: Thorlab Power meter (Model No. S401C) is used to measure the incident power on the sample.(Wavelength range: 190nm to 20000nm). <br>- Setup photo:<br><br><br></li> </ul> <h3>Optical parameters setup for 'Butterfly' and 'Rotating' experiments</h3> <ul> <li>Laser: Cobolt 06-MLD | 808 nm | max power = 100 mW</li> <li>Setup photo:</li> </ul> <h1>LTCAM Samples details</h1> <p>Pictures of samples:</p> <p>| Name in the article | Sample No. | Mandrel Dia (mm) | Original length (cm) | Twisting (rounds) | Twists/cm | Twisting Direction | coiling direction |<br>| ---------- | ---------- | ----------- | ---------------- | ------------- | -------- | ------------- | ------------ |<br>| ZSØ1A | M733 | 1 | 50 | 260 | 5.20 | z | s |<br>| ZSØ2A | M734 | 2 | 50 | 301 | 6.02 | z | s |<br>| ZSØ1B | M781 | 1 | 50 | 258 | 5.16 | z | s |<br>| ZSØ1C | M782 | 1 | 50 | 262 | 5.24 | z | s |<br>| ZSØ2B | M783 | 2 | 48 | 270 | 5.63 | z | s |<br>| ZSØ2C | M784 | 2 | 50 | 227 | 4.54 | z | s |<br>| ZZØ2A | M785 | 2 | 50 | 339 | 6.78 | s | s |<br>| ZZØ2B | M786 | 2 | 50 | 226 | 4.52 | s | s |</p> <h3>Experiments 'LTCAM'</h3> <p>| Filename | Sample name | Laser power (mW) | Time ON (s) | Time OFF (s) | Cycles |<br>|-------------------|----------------|---------------------|---------------|---------------|--------|<br>| 230414_112622 | ZSØ2A | 50 | 60 | 30 | 1 |<br>| 230414_112851 | ZSØ2A | 100 | 60 | 30 | 1 |<br>| 230414_113149 | ZSØ2A | 150 | 60 | 30 | 1 |<br>| 230414_113420 | ZSØ2A | 200 | 60 | 30 | 1 |<br>| 230414_113656 | ZSØ2A | 250 | 60 | 30 | 1 |<br>| 230414_113955 | ZSØ2A | 300 | 60 | 30 | 1 |<br>| 230414_114838 | ZSØ1B | 300 | 30 | 30 | 3 |<br>| 230414_115518 | ZSØ1C | 300 | 30 | 30 | 3 |<br>| 230414_120115 | ZSØ2B | 300 | 30 | 30 | 3 |<br>| 230414_120714 | ZSØ2C | 300 | 30 | 30 | 3 |<br>| 230414_121714 | ZZØ2B | 300 | 30 | 30 | 3 |<br>| 230414_122255 | ZZØ2A | 300 | 30 | 30 | 3 |<br>| 230414_122808 | ZSØ2A | 300 | 30 | 30 | 3 |<br>| 230414_123244 | ZSØ1A | 300 | 30 | 30 | 3 |</p> <h3>Experiments 'Butterfly'</h3> <p>| Filename (Recording) | Filename (IR video) | Laser power (mW) | Time ON (s) | Time OFF (s) | Cycles | Obs. |<br>|---------------------------|----------------------|---------------------|---------------|---------------|--------|------------------------|<br>| 20231002_134454162 | 231002_164924 | 100 | 1 | 1 | 100 | Left wing activation |<br>| 20231002_135842563 | 231002_170312 | 100 | 1 | 1 | 100 | Right wing activation |</p> <h3>Experiments 'Rotating'</h3> <p>| Filename (Recording) | Filename (IR video) | Laser power (mW) | Time ON (s) | Rotation deg/s (measured) |<br>|---------------------------|-----------------------|---------------------|-------------|--------------------------------|<br>| 20231003_073512244 | 231003_103940 | 100 | 120 | -28.0 |<br>| 20231003_073755982 | 231003_104223 | 100 | 120 | -6.6 |<br>| 20231003_074535766 | 231003_105003 | 100 | 120 | -15.3 |<br>| 20231003_085141041 | 231003_115608 | 100 | 120 | 5.2 |<br>| 20231003_085417756 | 231003_115845 | 100 | 120 | 27.3 |<br>| 20231003_085716949 | 231003_120144 | 100 | 120 | 12.8 |</p> <p> </p>
Artificially-generated Lecture Video Fragmentation Dataset and Ground Truth
<p>We provide a large-scale lecture video dataset consisting of artificially-generated lectures, and the corresponding ground-truth fragmentation, for the purpose of evaluating lecture video fragmentation techniques.</p> <p>For creating this dataset, 1498 speech transcript files (generated automatically by ASR software) were used from the world's biggest academic online video repository, the VideoLectures.NET. These transcripts correspond to lectures from various fields of science, such as Computer science, Mathematics, Medicine, Politics etc. In order to create the synthetic video lectures, all transcripts were randomly split in fragments, the duration of which ranges between 4 and 8 minutes. Each synthetic lecture was then assembled by combining (stitching) exactly 20 randomly selected fragments. 300 such artificially-generated lectures are included in the released dataset. Each such lecture file has a mean duration of about 120 minutes, thus the dataset contains altogether about 600 hours of artificially-generated lectures. Every pair of consecutive fragments in these lectures originally comes from different videos, consequently the point in time where such two fragments are joined is a known ground-truth fragment boundary. All these boundaries form the dataset's ground truth. We should stress that we do not generate the corresponding video files for the artificially-generated lectures (only the transcripts), and one should not try to reverse-engineer the dataset creation process so as to use in some way the visual modality for detecting the fragments in this dataset.</p> <p><strong>File format</strong></p> <p>After you download the provided .zip and unpack it, the extracted folder will contain two sub-folders:</p> <pre><code>1. ALV_srt 2. ALV_srt_GT </code></pre> <p>Each of them contains 300 files.</p> <p>The <strong>ALV_srt</strong> folder contains the transcripts of every artificially-generated lecture, in the standard SRT format:</p> <pre><code>1. A numeric counter identifying each sequential subtitle 2. The time that the subtitle should appear on the screen, followed by --> and the time it should disappear 3. Subtitle's text itself on one or more lines 4. A blank line containing no text </code></pre> <p>The <strong>ALV_srt_GT</strong> folder contains the ground truth (GT) fragments corresponding to the lectures (transcripts) of the <strong>ALV_srt</strong> folder. Each GT file consists of 3 tab-separated columns and 20 rows, in the following format:</p> <pre><code><Fragment_ID_1> <StartTime_1> <EndTime_1> <Fragment_ID_2> <StartTime_2> <EndTime_2> <Fragment_ID_3> <StartTime_3> <EndTime_3> . . . <Fragment_ID_20> <StartTime_20> <EndTime_20> </code></pre> <p>Each row indicates a fragment. The first column indicates the ID of a fragment while the second and the third column indicate the start and the end time of the fragment respectively.</p> <p><strong>License and Citation</strong></p> <p>This dataset is provided for academic, non-commercial use only. If you find this dataset useful in your work, please cite the following publication where the dataset is introduced:</p> <p><em>D. Galanopoulos, V. Mezaris, “Temporal Lecture Video Fragmentation using Word Embeddings”, Proc. 25th Int. Conf. on Multimedia Modeling (MMM2019), Thessaloniki, Greece, Jan. 2019.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreement No 693092 MOVING. We are grateful to JSI/VideoLectures.NET for providing the lectures’ transcripts.</p>
Synthetic 4D STEM dataset based on a SrTiO3 supercell with two additional artificial spatial frequencies
<p>This dataset allows to investigate phase contrast methods for 4D scanning transmission electron microscopy, such as ptychography.</p> <p>A synthetic dataset has been simulated, based on an SrTiO<sub>3</sub> unit cell as a starting point. Then, a five by five super cell was created by repetition. Two artificial spatial frequencies were added to the phase grating, one with a wavelength of a single unit cell and one with a wavelength of the super cell. To eliminate dynamical scattering, a 4D-STEM simulation with 20 × 20 scan points per unit cell was performed using only one slice with a thickness of one unit cell along electron beam direction [001].</p> <p><strong>Files</strong></p> <ul> <li><em>conf_01.mat</em>: HDF5 file with the phase grating.</li> <li><em>Data extraction and plot of the phase grating.ipynb</em>: Jupyter notebook showing how to access the phase grating file and plot the data.</li> <li><em>slice_00001_thick_1.9525_nm_blocksz100.raw</em>: Simulated 4D STEM dataset as a raw binary file. Shape 100 x 100 x 596 x 596, dtype float32.</li> <li><em>ssb-example.ipynb</em>: Jupyter notebook showing first moment analysis and ptychography with the dataset.</li> </ul> <p><strong>Simulation parameters</strong></p> <ul> <li>Scan points: 100x100</li> <li>Field of view: 1.9525nm</li> <li>Convergence angle: 23mrad, 136 px</li> <li>Acceleration voltage: 300 kV</li> <li>Center: (297, 297)</li> <li>Rotation angle: 0°</li> </ul>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Plant virus SNP prediction artificial dataset Performance Study
<p>Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research on viral pathogens, especially for virus discovery and monitoring. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS technologies have greatly facilitated the generation of new genomes of the detected viruses and their comparison. Nevertheless, the bioinformatics analyses allowing the reconstruction of genomes and the detection of Single Nucleotide Polymorphisms (SNPs) can potentially create bias, although it has not been widely addressed so far. </p> <p>Therefore, more knowledge is required on the limitation and possibility of predicting SNPs based on HTS-generated sequence datasets. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) through large-scale Performance Testing (PT) using three artificially designed datasets. The bioinformatics analyses were divided into three key steps: reads pre-processing (quality trimming, merging …), virus identification (assembly, alignment, mapping …) and variant calling. Each step was evaluated independently through an original, step-by-step PT design with iteration between participants. </p> <p>Overall, this work underlines key parameters in SNP detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the prediction were the most impactful analysis step for the success or failure of the predictions. Strategies to improve SNPs prediction are also discussed. </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.