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121 results for “system identification”
Dynobench - extended Strogatz benchmark for system identification methods
<p>The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from the Strogatz dataset, as well as Lorenz, Coupled phase oscillators and Stuart-Landau models for dynamical systems that often appear in the research community. They also add variety to the benchmark as the Lorenz oscillator model introduces a larger set of state variables (three compared to two), and the coupled phase oscillators model is non-autonomous, which is reflected in the explicit incorporation of time in its equations.</p><p>The repository contains the 'data' folder, where the simulations of ten dynamical systems are stored, simulated under 6 different configurations of data quality. The first dimention modifies the data length and coarseness, where a 'small' dataset includes simulations of 10 seconds with a 0.1 sampling step, and a 'large' dataset includes simulations of 20 seconds with a 0.01 sampling step. The second dimention of data quality modifies the amount of noise in the data, where there are three levels of noise (no noise, moderate levels with 30 dB signal-to-noise ratio and high levels of noise with 13 dB signal-to-noise ratio). The data can be used by itself, without the need to look at the python code.</p><p>The repository also contains the main.py script by which the data can be generated. The 'src' folder contains additional python scripts that are needed to generate the data. The data were created by first randomly setting the initial values for one category, in particular a configuration of 'small', 'noise-free' and 'train' data (using inits_type = "random"). Then, all the other configurations were generated by using the same initial values. Inside the script main.py there is more information about the settings and how to run the script. </p><p>The benchmark was created as a part of the research described in the paper titled <i>Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data, </i>written by Omejc et al.<i> </i>(in submission).</p>
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
Identification of novel genes involved in phosphate accumulation in Lotus japonicus through Genome Wide Association mapping of root system architecture and anion content
<p>130 Lotus japonicus accessions were used. The names and accession numbers are<br> listed in S6 Table. Seeds were scarified with sandpaper and then sterilized 14 minutes in 0.05%<br> sodium hypochlorite. Subsequently, seeds were rinsed and washed 5 times in sterile distilled<br> water. For the germination, seeds were positioned in imbibed filter paper, in sterile Petri dishes,<br> and wrapped in aluminium foil. After 3 days at 21°C, young seedling were transferred to square<br> plates (12 x 12 cm) containing growth medium. Both media used in this<br> study were based on Long-Ashton solution (with two levels of phosphate concentration -20 or<br> 750 μM, LP or HP, respectively) with 0.8% MES buffer (Duchefa Biochemie,<br> Haarlem, The Netherlands), 0.8% agarose (to minimize phosphate contamination), and adjusted<br> to pH 5.7 with 1M KOH. After adding the medium, plates were dried, closed, overnight in a<br> sterile laminar flow hood. Two accessions, with four replicates per each accession, were placed<br> on each plate. Each plate was replicated, with mirrored position of each accession to minimize<br> any positional growth effects. Plates were placed vertically, and plants grown under long-day<br> conditions (21°C, 16 h light/8 h dark cycle) with white light bulbs emitting 50 μmol/m 2 /s and<br> roots were exposed to light. Every day at the same time, the racks were transported to the image<br> acquisition room where images of each plate were acquired with eight Epson V600 CCD flatbed<br> color image scanners (Seiko Epson) and then immediately returned to the growth chamber.</p>
Efficient coding of natural scenes improves neural system identification
<p>Dataset for <a href="https://www.biorxiv.org/content/10.1101/2022.01.10.475663v3">Qiu et al., 2022</a>.</p> <blockquote> <p>This work was supported by the German Research Foundation (DFG; SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, projects 10 and 12, project number 276693517; GRK2381, project number 335549539), the Germany’s Excellence Strategy (EXC 2064/1, project number 390727645), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No 674901); the Max Planck Society (M.FE.A.KYBE0004); the German Ministry of Education and Research (BMBF; FKZ: 01GQ1002), and the Tübingen AI Center (FKZ: 01IS18039A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p> </blockquote>
Corresponding spreadsheet to the Paper 'Comparative analysis of pre-Covid19 child immunization rates across 30 European countries and identification of underlying positive societal and system influences'
<p>This study provides a macro-level societal and health system focused analysis of child vaccination rates in 30 European countries, exploring the effect of context on coverage. The importance of demography and health system attributes on health care delivery are recognized in other fields, but generally overlooked in vaccination. The analysis is based on correlating systematic data built up by the Models of Child Health Appraised (MOCHA) Project with data from international sources, so as to exploit a one-off opportunity to set the analysis within an overall integrated study of primary care services for children, and the learning opportunities of the ‘natural European laboratory’. The descriptive analysis shows an overall persistent variation of coverage across vaccines with no specific vaccination having a low rate in all the EU and EEA countries. However, contrasting with this, variation between total uptake per vaccine across Europe suggests that the challenge of low rates is related to country contexts of either policy, delivery, or public perceptions. Econometric analysis aiming to explore whether some population, policy and/or health system characteristics may influence vaccination uptake provides important results - GDP per capita and the level of the population’s higher education engagement are positively linked with higher vaccination coverage, whereas mandatory vaccination policy is related to lower uptake rates. The health system characteristics that have a significant positive effect are a cohesive management structure; a high nurse/doctor ratio; and use of practical care delivery reinforcements such as the home-based record and the presence of child components of e‑health strategies.</p>
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
ERA5 daily meteorological data (Puget Sound) for weather system identification
<p>This dataset includes the ERA5-based daily meteorological data over the US Puget Sound region. The data includes the following meteorological variables at 850hPa pressure level: temperature (T), relative humidity (RH), horizontal wind vector (U and V), vertical wind (W), and geopotential height (Z).</p> <p>The scripts here are used to establish the weather system classification model as in Chen et al. (submitted). More details, including the complete scripts for analysis/plotting will be updated here after the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, N. Sun, Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. (submitted)</p>
Adaptive System Identification Model
<p>This is a test.</p>
Identification of system-level features in HIV migration within a host
<p><strong>Objective</strong>: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. </p> <p><strong>Method</strong>: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates ve potential features of the viral migration network: (1) bi-directional ow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran's Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. </p> <p><strong>Results</strong>: We observe strong evidence for bi-directional ow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequently than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. </p> <p><strong>Discussion</strong>: Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.</p>
SYSTEMS LEVEL IDENTIFICATION OF A MATRISOME-ASSOCIATED MACROPHAGE POLARIZATION STATE IN MULTI-ORGAN FIBROSIS
<p>This Zenodo repository contains the processed Seurat objects for "SYSTEMS LEVEL IDENTIFICATION OF A MATRISOME-ASSOCIATED MACROPHAGE POLARIZATION STATE IN MULTI-ORGAN FIBROSIS".</p> <p>The Seurat objects include:</p> <ul> <li>Tissue-specific monocytes / macrophages (used in Figure 1,3,4) <ul> <li>endo.rds: Endometrium monocytes / macrophages</li> <li>heart.rds: Heart monocytes / macrophages</li> <li>kidney.rds: Kidney monocytes / macrophages</li> <li>liver.rds: Liver monocytes / macrophages</li> <li>lung.rds: Lung monocytes / macrophages</li> <li>skin.rds: Skin monocytes / macrophages</li> </ul> </li> <li>SPP1 macrophages across all six tissues (used in Figure 2,3,4,5) <ul> <li>SPP1seu.rds</li> </ul> </li> </ul> <p>Code for generating these objects can be found at: https://github.com/the-ouyang-lab/mam-reproducibility</p>
The JustWalk JITAI Study: A System Identification Experiment to Understand Just-in-Time States of Physical Activity
ClinicalTrials.gov study NCT05273437. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Identification of system-level features in HIV migration within a host
Open the record for dataset details and reuse information.
Systemic inflammation: methodological approaches to identification of the common pathological process
<p>Files are dataset for submitted manuscript <strong>Systemic inflammation: methodological approaches to identification of the common pathological process. </strong>All primary data both of septic and nonifectious pathologies are included.</p>
Identification of local thresholds of TWL for triggering the European coastal flood awareness system, Deliverable 4.3 – Report on the identification of local thresholds of TWL for triggering coastal flooding - ECFAS project (GA 101004211). www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p><p><i><strong>Description of the product</strong></i></p><p>The ECFAS Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding aims to describe the methodology developed to identify local thresholds that will trigger the coastal flood mapping activity. To this end, it was necessary to identify both a total water level triggering threshold, used as a local reference to trigger the system in case of forecasted TWL exceedence, and a duration threshold, used to set the storm duration. In order to compute both thresholds, an Extreme Value Analysis (EVA) and a Duration Analysis (DA) were performed on the ECFAS combined hindcast. As the local TWL thresholds (triggering and duration) were identified using the ECFAS combined hindcast, and the system will instead be operative with the input of CMEMS forecast, a methodology was developed to establish a correction to be applied before integrating the thresholds into the warning system. The document also describes some limitations and possible future improvements of the employed methodology.</p><p>The Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding is accompanied by an accessory data file. This file, named "ThresholdsFile.csv", contains the values of the triggering and duration thresholds for all the ECFAS combined hindcast of TWL points and their coordinates.</p><p>This <strong>ECFAS Thresholds Dataset</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the ECFAS Thresholds Dataset are licensed under the Database Contents License: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p><p>This <strong>Report</strong> on the identification of thresholds is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p><p> </p>
Dataset of WiFi-based Environment-independent In-baggage Object Identification System
<p><strong>Description:</strong></p> <p>The dataset of environment-independent in-baggage object identification system leveraging low-cost WiFi. The dataset contains the extracted CSI features from 14 representative in-baggage objects of 4 different materials. The experiments are conducted in 3 different office environments with different sizes. We hope this dataset will help researchers to reproduce the former work of in-baggage object identification through WiFi sensing. </p> <p> </p> <p><strong>Dataset Format: </strong></p> <p>.mat files</p> <p> </p> <p><strong>Section 1: Device Configuration: </strong></p> <ul> <li> <p><strong>Transmitter: </strong>Aaronia HyperLOG 7060 direction antenna with a Dell Inspiron 3910 desktop for control. </p> </li> <li> <p><strong>Receiver: </strong>Hawking HD9DP orthogonal antennas with a Dell Inspiron 3910 desktop for control</p> </li> <li> <p><strong>NIC:</strong> Atheros QCA9590. The configuration and installation guide of CSI tool can be found at <a href="https://wands.sg/research/wifi/AtherosCSI/">https://wands.sg/research/wifi/AtherosCSI/</a></p> </li> <li> <p><strong>WiFi Packet Rate: </strong>1000 pkts/s </p> </li> </ul> <p> </p> <p><strong>Section 2: Data Format</strong></p> <p>We provide the CSI features through .mat files. The details are shown in the following:</p> <ul> <li> <p>14 different objects made of 4 different materials are included in 3 different environments and 3 different days.</p> </li> <li> <p>Each object is tested for 60 seconds and repeated for 3 times. </p> </li> <li> <p>The dataset file name is presented as "Object_Number". The detailed information are:</p> <ul> <li> <p>Object: The object we involved in the experiment (e.g., book, laptop)</p> </li> <li> <p>Number: The number of repeats. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are 3 different office experiment setups for our data collection. The detailed setups are shown in the paper. For the objects, we involve 14 types of objects made of 4 different materials. </p> <ul> <li> <p><strong>Environments: </strong></p> <ul> <li> <p>3 different environments are involved, including 3 office environments with the size of 15 ft × 13 ft, 16 ft × 12 ft, 28 ft × 23 ft, respectively. </p> </li> <li> <p>For each room environment, data is collected on different days and with different furniture settings (i.e., 2 desks and 2 chairs are moved at least 3 ft. )</p> </li> </ul> </li> <li> <p><strong>Representative objects: </strong></p> <ul> <li> <p>Data is collected using 14 representative objects of 4 different materials including fiber: book, magazine, newspaper; metal: thermal cup, laptop; cotton/polyester: cotton T-shirts (×2), cotton T-shirts (×4), hoodie, polyester T-shirts, polyester pants; water: 1L bottle with 1L water, 1L bottle with 500ml water, 500ml bottle with 500ml water. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>For our data organization, we separate the data files into different folders based on different days and different environments. Under these folders, data are further distributed in terms of different objects and repeat times. All the files are .mat files, which can be directly read for further applications. </p> <ul> <li> <p><strong>Features of CSI amplitude: </strong>We calculate 7 different types of statistical features, including mean, variance, median, skewness, kurtosis, interquartile range and range, and polarization feature from CSI amplitude. Particularly, we calculate the features for all 56 subcarriers with different operating frequencies and responses to the target object. </p> </li> <li> <p><strong>Features of CSI phase: </strong>For the features of CSI phase, the same features with CSI amplitude are extracted and stored in the dataset. </p> </li> </ul> <p> </p> <p><strong>Section 6: Citations</strong></p> <p>If your work is related to our work, please cite our papers as follows. </p> <p><a href="https://ieeexplore.ieee.org/document/9637801">https://ieeexplore.ieee.org/document/9637801</a></p> <p>Shi, Cong, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, and Yingying Chen. "Environment-independent in-baggage object identification using wifi signals." In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 71-79. IEEE, 2021.</p> <p> </p>
System identification on a nonlinear cantilever-type beam
<p>The zip file includes the algorithm and data used for system identification of a nonlinear cantilever-type beam system. The Abaqus .inp file is also included. The data and the algorithm is written in MATLAB and there are two sets of data one is the full FE model data and the other is a simulated experimental (SE) data. The mode shape matrix 'Phi' and squared natural frequencies 'Om2' matrix are derived from a prior linear modal analysis and included as '.mat' files.</p>
A Taxonomic Information System for Nereididae (Annelida): morphological datasets supporting description, interactive identification and phylogenetic analysis of the family
<p>Nereididae (Polychaeta)–A DELTA database of genera, and Australian species</p><p>Robin S. Wilson1,2 , Christopher J. Glasby3,4 , Torkild Bakken5</p><p> </p><p>1 Sciences Department, Museums Victoria Research Institute, Museums Victoria, GPO Box 666 Melbourne, Victoria 3001, Australia</p><p>2 The University of Melbourne, Melbourne, Victoria 3010, Australia</p><p>3 Museum and Art Gallery Northern Territory, PO Box 4646, Darwin NT 0801, Australia</p><p>4 Australian Museum Research Institute, Australian Museum, 1 William Street, Sydney, NSW 2010, Australia</p><p>5 Norwegian University of Science and Technology, NTNU University Museum, NO-7491 Trondheim, Norway</p><p> </p><p>Corresponding author: Robin S. Wilson (<a href="mailto:rwilson@museum.vic.gov.au">rwilson@museum.vic.gov.au</a>)</p><p>Scope</p><p>This Nereididae (Annelida) Delta database is the work of Robin Wilson, Torkild Bakken & Chris Glasby and was used to generate sections of Wilson et al. (2023a). Status of genera and nominal subfamily placements follow WoRMS <a href="https://www.marinespecies.org/polychaeta/">https://www.marinespecies.org/polychaeta/</a>.</p><p>The version distributed here includes only Nereididae genera and is part of the Wilson et al. (2023b) Zenodo repository which also includes other outputs: Nexus files as .nex and natural language output of taxon descriptions and character lists as .rtf files. </p><p>Updates including fixes to any errors found, and including new taxa and new taxonomic revisions, will be uploaded to Zenodo as new versions (the doi above will resolve to the most recent version). We intend that future versions will include all Nereididae species known from Australia; and all Nereididae species known from bathyal-abyssal depths (~2,000 m and deeper).</p><p>This repository contains an interactive key using the Delta Intkey software version by the Atlas of Living Australia (2014) <strong>but not yet that of Dallwitz (2020)</strong>.</p><p>References</p><p>Atlas of Living Australia (2014) Open-delta. A Java port of the Delta - DEscription Language for TAxonomy suite of applications into Java. Available from: https://github.com/AtlasOfLivingAustralia/open-delta (July 12, 2023).</p><p>Dallwitz MJ (2020) Installing and running the programs of the DELTA System. Reports, Division of Entomology CSIRO Australia. Available from: https://www.delta-intkey.com/www/programs.htm (January 31, 2023).</p><p>Wilson RS, Glasby CJ, Bakken T (2023a) The Nereididae (Annelida) – diagnoses, descriptions, and a key to the genera. ZooKeys 1182: 35–134. <a href="https://doi.org/10.3897/zookeys.1182.104258">https://doi.org/10.3897/zookeys.1182.104258</a></p><p>Wilson RS, Bakken T, Glasby CJ (2023b) A Taxonomic Information System for Nereididae (Annelida): morphological datasets supporting description, interactive identification and phylogenetic analysis of the family. <a href="https://doi.org/10.5281/zenodo.7776745">https://doi.org/10.5281/zenodo.7776745</a></p>
An Ultrasound Guided Automated Spinal Landmark Identification System
ClinicalTrials.gov study NCT03687411. IPD Sharing: NO. Countries: 1. Publications: 1.
Real-Time Identification System of Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence
ClinicalTrials.gov study NCT04203264. IPD Sharing: NO. Countries: 1. Publications: 4.
Early Identification of Mental Disorders: Application of a Multi-modal & Domains System
ClinicalTrials.gov study NCT05939154. IPD Sharing: YES. Countries: 1. Publications: 8.
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.