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768 results for “reinforcement”
Damage assessment of a physical beam reinforced with masses - dataset
<p>The dataset beam-signal contains the spectrum vibration signals in the frequency domain measured from a beam reinforced with masses under healthy and faulty conditions. This data is for a commonly used system in various industrial applications. The data can be used for online condition process monitoring to detect and diagnose any anomaly or faulty condition in the system. Hence, the datasets provide the geometric and experimental measurements performed on the beam reinforced with masses for various mass losses considered structural damage. The collected data included the following datasets:</p> <ul> <li>Dataset Mass-position contains 70 sampling positions for the six masses attached to the beam. (<a href="../api/records/8081690/draft/files/Mass%20position.xlsx/content">Mass position</a>)</li> <li>Dataset DI contains 280 damage indexes calculated using the FRAC method. (<a href="../api/records/8081690/draft/files/DI_FRAC_Exp-estimation.xlsx/content">DI_FRAC_Exp-estimation</a>)</li> <li>Dataset beam-signal includes 280 inertances responses magnitudes and respective phases considering 70 samples of healthy and 210 sampled of damaged conditions ( <a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Healthy.zip/content">Dataset Beam-signal_Healthy, </a><a href="../api/records/8081690/draft/files/Dateset%20Beam-signal_Damaged-2.96.zip/content">Dateset Beam-signal_Damaged-2.96, </a></li> </ul> <p><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-5.92.zip/content"> Dataset Beam-signal_Damaged-5.92, </a><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-8.87.zip/content">Dataset Beam-signal_Damaged-8.87) .</a></p> <p>The dataset beam-signal can be used to develop structural health monitoring techniques for detecting damage and anomalies in the structure. The dataset's Mass-position and DIs can impose parametric uncertainty in the experiment. Stochastic and damage identification metrics can be used for further insights on new monitoring and control techniques. Since the tests include paramedic uncertainty, they can also be employed in uncertainty quantification, stochastic modelling and supervised and unsupervised machine learning techniques. </p> <p>Therefore, the datasets are intended to benefit the scientific community investigating the dynamics of structures and readers interested in experimental practices applied to systems and modelling. These datasets can be used for numerical model validation, identification techniques, uncertainty quantification, machine learning, and structural integrity monitoring algorithms based on experimental measurement samples on the beam reinforced with mass.</p> <p>A detailed description of the experiment can be found in </p> <p>[1] Sousa, A.A.S.R., da Silva Coelho, J., Machado, M.R. et al. Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response. J. Vib. Eng. Technol. (2023). https://doi.org/10.1007/s42417-023-01072-7</p> <p>[2] Monitoramento da Integridade Estrutural de Vigas utilizando Técnicas de Aprendizado de Máquina, 2023. Mestrado em Integridade de Materiais da Engenharia - Universidade de Brasília (In Portuguese)</p> <p>[3] Amanda A.S.R. de Sousa, Marcela R. Machado, Experimental vibration dataset collected of a beam reinforced with masses under different health conditions, Data in Brief, 2024, 110043, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110043.</p>
Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements
<p>This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements".</p> <p>Dataset is one excel file divided in various sheets containing:</p> <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>
Data for: Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy
<p><em><strong>Version v2:</strong> <br></em>Added tiff-image stacks</p> <p><em><strong>Version v1:</strong><br></em>The data is supplementary to the publication "Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy", DOI: <a href="https://doi.org/10.1016/j.compscitech.2022.109362">10.1016/j.compscitech.2022.109362</a> as well as to the dissertation: "Morphology and Fracture of Block Copolymer and Core-Shell Rubber Particle Modified Epoxies and their Carbon Fibre Reinforced Composites", urn: <a href="https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-63437">urn:nbn:de:hbz:386-kluedo-63437</a></p> <p>Key words: Polymer-matrix composites (PMCs), Impact behaviour, Low velocity impact, Barely visible impact damage, Damage tolerance, X-ray computed tomography, Fractography, Carbon fibre reinforced composite (CFRP)</p> <p>The data set is a collection of TXRM data of several low energy impact damages in CFRP specimens. The data was acquired via XCT (X-Ray Computed Tomography).</p> <p>Material details:</p> <ul> <li>Carbon fibre reinforced composite</li> <li>Thickness: ~ 1.65mm</li> <li>Matrix polymer: Epoxy-based (DGEBA): Sika CR144 + Anhydride curing agent (Huntsman Aradur917) + 1-Methylimidazole</li> <li>Carfon-fibre fabric: ECC Carbon fabric Style 763, based on Toho Tenax HTA40 E13, 140g/m²</li> <li>Layup: 13 layers, stacking sequence (45/-45/45/-45/90/0/90)s, (15% 0°/23% 90°/62% ± 45°) </li> <li>the average carbon fibre volume content was 52.5 ± 1.8 vol.-%</li> <li>cured ply-thickness: 126.1 μm</li> <li>Impact energies: 1J, 3J, 7J, 9J, 13J</li> <li>manufactured via autoclaving</li> </ul> <p>The reasearch received funding from the German Academic Exchange Service (DAAD) within the funding program “Kurzstipendien fuer Doktoranden” (grant number: 57438025).</p>
Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood – Supplementary material
<p>Supplementary material to the article "Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood"</p>
Monotonic Flexural Testing of Corroded Reinforced Concrete Beams Database
<p>The database presented here provides a collection of 804 corroded reinforced concrete beams from 54 experimental programs available in the literature. All beam specimens were tested under simply-supported monotonic three/four-point bending conditions and failed in flexure-dominated modes. The database includes 45 independent variables, 11 dependent variables, 649 corroded members, and 155 uncorroded control beams, tested across 14 countries. Of the corroded beams, 11 were naturally corroded, 30 were corroded via long-term environmental exposure (typically in the form of salt spray or fog), and 608 were corroded artificially through the impressed-current method. All observations (individual beam tests) are statistically independent, as each data entry represents one independent test. Highlighted cells indicate non-reported variables.</p> <p>This database was compiled as part of the author's Ph.D. research for the purpose of predictive machine learning. Published articles applying the database can be accessed at:<br><br><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dibe.2024.100527" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dibe.2024.100527</a></p> <p>Please see the accompanying User's Manual PDF for a complete description of all nomenclature, abbreviations, assumptions, and calculations used to derive the input and response parameters.</p> <p>Please feel free to reach out to the authors if you have any queries or concerns. </p>
EEG: Reinforcement Learning in Parkinson's
Open the record for dataset details and reuse information.
EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge
Open the record for dataset details and reuse information.
Deep reinforcement learning for the control of microbial co-cultures in bioreactors
<p>Data for the figures in the paper:<br> <a href="https://www.biorxiv.org/content/10.1101/457366v2">https://www.biorxiv.org/content/10.1101/457366v2</a><br> (in press PLoS Comp Biol.)</p> <p>Abstract:<br> Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence – reinforcement learning – for the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.</p>
Dataset: Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
<p>This repository contains supplementary materials for the following conference paper:<br> <br> Pavel Seda, Jan Vykopal, Valdemar Švábenský, Pavel Čeleda.<em><br> Reinforcing Cybersecurity Hands-on Training With Adaptive Learning. </em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637252">https://doi.org/10.1109/FIE49875.2021.9637252</a><br> <br> Preprint available at: <a href="https://arxiv.org/abs/2201.01574">https://arxiv.org/abs/2201.01574</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Seda2021reinforcing, author = {Seda, Pavel and Vykopal, Jan and \v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel}, title = {{Reinforcing Cybersecurity Hands-on Training With Adaptive Learning}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637252}, doi = {10.1109/FIE49875.2021.9637252}, }</code></pre> <p> </p>
Raw and analyzed data to manuscript "Influence of air plasma pretreatments on mechanical properties in metal-reinforced laminated wood"
<p><strong>Abstract</strong><br> The use of wood-based materials in building and construction is constantly increasing as environmental aspects and sustainability gain importance. For structural applications, however, there are many examples where hybrid material systems are needed to fulfil the specific mechanical requirements of the individual application. In particular, metal reinforcements are a common solution to enhance the mechanical properties of a wooden structural element. Metal-reinforced wood components further help to reduce cross-sectional sizes of load-bearing structures, improve the attachment of masonry or other materials, enhance the seismic safety and tremor dissipation capacity, as well as the durability of the structural elements in highly humid environments and under high permanent mechanical load. A critical factor to achieve these benefits, however, is the mechanical joint between the different material classes, namely the wood and metal parts. Currently, this joint is formed using epoxy or polyurethane (PU) adhesives, the former yielding highest mechanical strengths, whereas the latter presents a compromise between mechanical and economical constraints. Regarding sustainability and economic viability, the utilization of different adhesive systems would be preferable, whereas mechanical stabilities yielded for metal-wood joints do not permit for the use of other common adhesive systems in such structural applications.<br> This study extends previous research on the use of non-thermal air plasma pretreatments for the formation of wood-metal joints. The plasma treatments of Norway spruce (Picea abies (L.) Karst.) wood and anodized (E6/EV1) aluminum AlMgSi0.5 (6060) F22 were optimized, using water contact angle measurements to determine the effect and homogeneity of plasma treatments. The adhesive bond strengths of plasma-pretreated and untreated specimens were tested with commercial 2-component epoxy, PU, melamine-urea formaldehyde (MUF), polyvinyl acetate (PVAc), and construction adhesive glue systems. The influence of plasma treatments on the mechanical performance of the compounds was evaluated for one selected glue system via bending strength tests. The impact of the hybrid interface between metal and wood was isolated for the tests by using five-layer laminates from three wood lamellae enclosing two aluminum plates, thereby excluding the influence of congeneric wood-wood bonds. The effect of the plasma treatments is discussed based on the chemical and physical modifications of the substrates and the respective interaction mechanisms with the glue systems. </p>
Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset
<p>One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement</p> <p> </p> <p>This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.</p>
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
<p>This Zenodo repository contains the runs data for the paper <strong>RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models </strong>[<a href="https://arxiv.org/abs/2408.16118" target="_blank" rel="noopener">https://arxiv.org/abs/2408.16118</a>] presented at the NeurIPS 2024 workshop on Tackling Climate Change with Machine Learning, as well as the Master of Research (MRes) report <strong>Towards improving weather and climate models using reinforcement learning</strong> at the University of Cambridge<strong>.</strong> For questions, please contact Pritthijit Nath, <a href="mailto:pn341@cam.ac.uk" target="_blank" rel="noopener">pn341@cam.ac.uk</a>. Full documentation is available in the README.md file of the associated <a href="https://github.com/nathzi1505/climate-rl" target="_blank" rel="noopener">GitHub repo</a>.</p>
GEOLAB-PEBSTER-Deltares: Small-scale experiments on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<p><strong>DOI: 10.5281/zenodo.12627309</strong></p> <h1><strong>Small-scale experiments on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB-PEBSTER-Deltares project on Zenodo! This repository contains the measurement data from four small-scale experiments on Piled Embankments with Basal Steel Reinforcement (PEBSTER). Pile-supported (PS) embankments with basal geosynthetic reinforcement (GR) are commonly built in soft soil areas (van Eekelen and Han, 2020). Steel mesh reinforcement (SR) is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from four small-scale tests on steel-reinforced piled embankments, conducted in the Deltares laboratory (van Eekelen et al., 2024a,b). These tests are part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes a large-scale test (Schneider et al., 2024a,b). The small-scale tests were conducted at Deltares, Delft, Netherlands, while the large-scale test was conducted in Darmstadt, Germany, at the Institute of Geotechnics, Technical University of Darmstadt.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>Dataset of four small-scale experiments</h2> <p>Here, we share the measurement data from the small-scale experiments that were part of one of the GEOLAB TA projects: PEBSTER. Four piles (diameter 0.1 m, centre to centre 0.55m) passed through a steel plate, that supported a foam cushion, that was sealed and soaked. A tap allowed for drainage of the foam cushion, simulating the consolidation of the subsoil between the piles. The 0.55 m high embankment consisted of medium coarse sand, and was reinforced at its base with a steel mesh reinforcement. A surcharge load up to 100 kPa was applied with a water cushion.<br><br><span>The load distribution is measured by pressure cells and load transducers. </span>Soil strains and displacements were monitored at five elevations within the fill, utilizing distributed fibre optic sensing (DFOS) technology from the Nerve-Sensors family, as depicted in Figure 2. Additionally, the steel mesh reinforcement was extensively instrumented with optic fibres.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes Deltares, Netherlands, the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Keller (Germany, France, Poland), SHM System, Poland, and FOLAB, Germany.</p> <p> </p>
FRIBAS database: collection of the main characteristics of 237 reinforced concrete and 71 masonry buildings
<p>FRIBAS-DB is a database that comprises information about 312 buildings (237 reinforced concrete moment resisting frame, 71 unreinforced masonry, 4 mixed type). For each building 37 parameters related to the main building characteristics (age, height, structural typology and main vibrational period), and foundation soil characteristics (e.g. resonance frequency, outcropping geology, seismic soil class, topographic class) are reported. The 312 buildings are located in Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively) and in different geological and built-environment settings. The FRIBAS-DB allows studying the influence of these parameters for the building dynamic response.</p> <p>In the following the details of each field of the FRIBAS-DB are given.</p> <p>ID_GIS: unique identifier for each building;</p> <p>ID: building identifier containing a number and the province acronym (MT=Matera; PZ=Potenza; VdA=Villa d’Agri; FVG=Friuli Venezia Giulia);</p> <p>Municipality: name of the municipality;</p> <p>COD_COM: municipality code according to the national institute of statistics (ISTAT);</p> <p>LAT, LONG: coordinates of the building in the WGS84-UTM - zone 33N (EPSG:32633);</p> <p>Construction material: RC for Reinforced Concrete Moment Resisting Frame; Masonry for unreinforced masonry buildings; Mixed refers to buildings with both reinforced concrete and load-bearing masonry elements;</p> <p>Soft storey: presence of a soft storey, i.e. a floor that can activate a weak-floor failure mechanism;</p> <p>Building use: Residential, Public, Industrial, Turistic;</p> <p>Age of Construction: < 1919; <1988*; 1919-1945; 1946-1961; 1962-1971; 1972-1975; 1976-1981; 1982-1991; 1992-1996; 1992-2001; 1997-2001; 2002-2008; >2008; (*a more accurate class attribution has not been possible);</p> <p># Floors: the last floor was included when its estimated volume was comparable with the those of other floors in the building;</p> <p>Presence of basement: all the floors that are partially or totally below ground are considered as basement;</p> <p>Building height from the ground to the top of the roof (m): if the building is located on a slope, the ground floor is considered to be the one at the higher side of the slope;</p> <p>Building height from the basement to the top of the roof (m): total height, including also the basement and the structures present at the top of the building; </p> <p>Building width B (m): the shorter dimension of a circumscribing polygon;</p> <p>Building length L (m): the longer dimension of a circumscribing polygon;</p> <p>B/L: ratio between building width and building length as a measure for regularity in plan;</p> <p>B/H: ratio between building width and building height (from the ground level to the top of the building);</p> <p>Floor area (m<sup>2</sup>): the building area calculated based on building footprints (e.g. from openstreetmaps or available national/regional digital maps);</p> <p>Polygon area (m<sup>2</sup>): the area of the circumscribing polygon;</p> <p>Area ratio: ratio between floor and polygon area</p> <p>Building shape: geometric shape of the building, R (rectangle), S (square), T (T-shape), L (L-shape), C (C-shape), H (H-shape), Tr (trapezoid);</p> <p>Seismic provisions (masonry): Description of any seismic provisions (e.g. additional pillars, ring beam, walls reinforcement, tie-rods) if present;</p> <p>Masonry openings (%): percentage of openings with respect to the building lateral surface;</p> <p>Masonry type: type of load-bearing masonry, including material (e.g. stone, bricks, concrete blocks), layout (regular, irregular) and quality;</p> <p>Slab: rigid or flexible, rigid floors often consist of reinforced concrete and hollow tiles;</p> <p>Roof type: wood (with or without hollow tiles), reinforced concrete (with or without hollow tiles);</p> <p>Additional floors: added afterwards to the building, but not included in the original project;</p> <p>Foundation type: shallow or deep;</p> <p>Position of the building: single block is for buildings that consist of a single unit; in case of multiple blocks (e.g. in the case of attached buildings), we distinguish between internal buildings (attached to two or more buildings) and buildings located at the edge (far end blocks attached only to one building). The presence of seismic joints or staircases is specified in the text field;</p> <p>F1_building (Hz): the experimental fundamental frequencies in two directions of the buildings (longitudinal and transversal) were considered, defined as F1_building (lower value) and as F2_buildings (higher value). The fundamental vibrational frequencies for all buildings have been estimated from single station ambient noise measurements analysed through the Horizontal-to-Vertical Spectral Ratio technique. The noise was recorded at the top of each building, aligning the horizontal axes of the sensor parallel to the two main building axes. Measurements were carried out using two different instruments (Tromino and Lunitek Sentinel GEO). The recording time varies between 10 and 30 minutes. The HVSRs have been estimated by the following procedure: each component was divided into non-overlapping windows of 20 s; each window was detrended, tapered (set to 0.5), padded, Fast Fourier Transformed and smoothed with triangular windows with a width equal to 5% of the central frequency. For each of the 20 s windows, the arithmetic mean of the two horizontal component’s spectra was used to combine E-W and N-S components in the single horizontal (H) spectrum; then the HVSR is computed. Finally, the average HVSR spectrum is obtained, providing also the relative ± 2 standard deviations. </p> <p>F2_building (Hz): see above</p> <p>F0_ Foundation Soil (Hz): the main resonance frequency obtained from HVSR analysis of single station ambient noise measurements. Measurements were carried out using two different instruments, Tromino or Reftek datalogger equipped with Lennartz 3D-Lite. The recording time varies between 10 and 30 minutes. For data analysis see “F1_building (Hz)” field. For some cases, the HVSR from microzonation studies were used;</p> <p>Geology: the geological classification was inferred from field surveys or the detailed geological maps of microzonation studies at the scale of 1:5000 or 1:10.000, if available. Otherwise the geological map at the scale 1:50.000 was considered. The outcropping geology classes present are: Gravina Calcarenite (coarse-grained carbonate sandstone); Calcari M.te Viggiano (Limestones and carbonate sandstones); Marsicovetere Breccia (massive calcareous breccias); Subappennine clay; Conglomeratic deposits; Sands and sandstones; Clean Gravels; Silts and Clays; Sand; Silty Gravels; Gravels and Sands, with silt and clay; Alluvial deposits; Colluvial deposits; Eluvial and colluvial deposits; Anthropic deposits;</p> <p>Soft soil/rigid soil: soils with Vs > 360 m/s have been considered as rigid soil. This class is composed mainly by outcropping bedrock (limestones, sandstones and breccias of the South-Appennine Units) and by clean coarse gravels of the Upper Friulian Plain. Soils with Vs < 360 m/s have been considered as soft soils. These are loose sediments (silts, clays, sands, gravels and their mixture) of different origin (alluvial, colluvial, eluvial or antropic).</p> <p>Seismic soil class: this soil classification refers to the national building code (NTC2018, § 3.2.2) based on Vs30. Vs profiles have been measured nearby the studied buildings. If deduced by microzonation studies, they are marked by star (*).</p> <p>Topographic class: The topographic class refers to the national building code classification (NTC2018, § 3.2.2).</p>
PsPM-PCF2: PSR, SCR, ECG, respiration and startle-eyeblink EMG measurements in a delay fear conditioning task with 4 CS and different reinforcement rates
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration, electromyogram (EMG), and auditory startle output (snd, as delivered by sound card) measurements. Also included are CS and US information, keypress responses and keypress response times for 19 healthy unmedicated participants (5 males and 14 females aged 24.68 +/- 3.65 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were 4 coloured rectangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. In the last learning block, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
lilGym: Natural Language Visual Reasoning with Reinforcement Learning, model files
<p>Baselines models for the paper <a href="https://lil.nlp.cornell.edu/lilgym"><em>lil</em>Gym: Natural Language Visual Reasoning with Reinforcement Learning</a>.</p>
Enhanced Westermo dataset - Transformed and Modified for Test case Selection and Priorotization in the context of Continuous Integration and Reinforcement Learning.
<p><strong>Overview</strong></p> <p>This repository contains a modified version of the existing, recently published dataset, Westermo. The initial dataset was gathered at Westermo Network Technologies AB, located in Västerås, Sweden. It encompasses over <strong>1 Million verdicts</strong> obtained from testing embedded systems, collected over a span of more than <strong>500 consecutive days</strong> of nightly testing. The dataset has been transformed and tailored specifically to cater to the research community, particularly for addressing challenges such as regression test selection, identification of flaky tests, and visualization of test results. The original dataset can be accessed through the reference provided in <strong>[1]</strong>.</p> <p>The Westermo dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different Test case Selection and Prioritization (TSP) techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <p>This dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different test case prioritization and selection techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <table align="left"> <caption><strong>Table 1: Dataset Overview</strong></caption> <tbody> <tr> <td>Test Cases</td> <td>1855</td> </tr> <tr> <td>CI Cycles</td> <td>15,197</td> </tr> <tr> <td>Verdict</td> <td>1,036,818</td> </tr> <tr> <td>Failed</td> <td>5.03%</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>However, the diversity and multitude of the features in the dataset can be irrelevant to some TSP approaches. This led us to perform a dataset conversion, where we customized Westermo to have the same features from Paint Control and IOF/ROL, two widely used datasets in Reinforcement Learning based TSP approaches.</p> <p>This conversion required the combination of multiple variables and generating the target ones. When it comes to generating the “LastResults” and “Cycle” values, further analysis was required and the data handling needed an in-depth understanding of how the nightly testing was conducted. This led us to investigate what a CI cycle is in their context, and we followed their definition of a session, stating that “a session is when we run a suite of tests on one test system with a certain software version and testware version”. When splitting the data according to the 9 different systems used, we were able to generate 9 different sub-sets that fit the CI context.</p> <p> </p> <p><strong>File Format</strong></p> <p>The compressed .zip file contains 9 files, each one corresponding to each of the 9 systems. The datasets are available in CSV format, with the semicolon (;) serving as the delimiter. The columns included are represented in the table below along with their descriptions.</p> <table> <caption><strong>Table 2: Parameters of the dataset</strong></caption> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Content</th> </tr> </thead> <tbody> <tr> <td>Id</td> <td>Unique numeric identifier of the test execution </td> </tr> <tr> <td>Name</td> <td>Unique numeric identifier of the test case</td> </tr> <tr> <td>Duration</td> <td>Approximated runtime of the test case</td> </tr> <tr> <td>CalcPrio</td> <td>Priority of the test case, calculated by the prioritization algorithm (output column, initially 0)</td> </tr> <tr> <td>LastRun</td> <td>Previous last execution of the test case as date-time-string (Format: <em>YYYY-MM-DD HH:ii </em>)</td> </tr> <tr> <td>LastResults</td> <td>List of previous test results (Failed: 1, Passed: 0), ordered by ascending age. Lists are delimited by [ ].</td> </tr> <tr> <td>Verdict</td> <td> <p>Test verdict of this test execution (Failed: 1, Passed: 0)</p> </td> </tr> <tr> <td>Cycle</td> <td>The number of the CI cycle this test execution belongs to.</td> </tr> </tbody> </table> <p> </p> <p>The implications of this conversion are important as it can help the previous works to re-assess their approaches and have more data for training and testing, as well as opening a broader data spectrum for future researchers in this field to find ready-to-use, rich datasets, on which they could evaluate their approaches and contribute to the TSP community. This also addresses the limitations in the field discussed in the systematic literature review <strong>[2]</strong>, stating that future research on TSP techniques should focus on collecting data from more recent subjects in a CI context with varying failure rates and larger execution times, as reproducible studies with appropriate datasets are needed to develop a usable body of knowledge regarding TSP over time. We believe that this conversion of the Westermo dataset is our contribution to alleviating the gap for the RL-based approaches.</p> <p>The original dataset can be found <a href="https://sites.mdu.se/aidoart/results/open-source/test-results-dataset-westermo">here.</a></p>
Interlocked thin-ply reinforcements for improved fracture toughness and compression after impact - 0 degree tabs
<p>This dataset contains data showing the effect of thin-ply reinforcement units, interlocked with a tab-and-slit geometry, on the fracture toughness of CFRP laminates. This dataset forms an addition to the dataset previously published here: https://doi.org/10.5281/zenodo.1476886. The previous dataset contains toughness data for reinforcements with tabs at 45 degrees. This dataset contains toughness data for reinforcements with tabs at 0 degrees.</p> <p>The reinforcement concept was first presented at the 18th European Conference on Composite Materials (2018, Athens, Greece). More information on the concept can be found at: http://wwwf.imperial.ac.uk/aeronautics/research/pinholab/interlock-tabs/. Links to publications connected to this data will be added to the Zenoto meta-data as they are published.</p> <p>For the reinforcement concept, both mode I and mode II fracture toughnesses were measured. The mode I data was acquired using DCB specimens, and the mode II data was obtained from 4-point end notch flexure (4ENF) tests. Force, displacement, crack length, and strain energy release rate during the test are provided for all specimens.</p>
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity
<p>This dataset contains evaluation data of the paper "Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity".</p> <p><strong>Motivation</strong></p> <p>We address the question whether the assumptions of signal-dependent and constant motor noise in a full skeletal model of the human upper body, together with the objective of movement time minimization, can predict reaching movements.</p> <p>For evaluation of the learned policy, two tasks are defined in the paper: a Fitts' Law type task and an elliptic via-point task.</p> <p><strong>General description of the dataset</strong></p> <ul> <li><strong>Fitts' Law Type Task</strong> <ul> <li>This dataset incorporates detailed information of all 6500 synthesized movements generated in the Fitts' Law type task (following the ISO 9241-9 standard).</li> <li> <p>For each of the 10 task conditions differing in distance between targets ("dist<xxx>" in filename)<br> and ID ('ID<xxx>' in filename), there are two .csv-files:<br> - one with detailed trajectory information on a sample-to-sample basis ("ISO_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis ("ISO_METRICS" in filename).</p> </li> <li> <p>In addition, for each task condition and each of the 13 movement directions in the Fitts' Law type task,<br> we include 6 figures: Position, Velocity, and Acceleration Profiles, as well as 3D movement path, Phasespace, and Hooke plots.<br> Apart from the 3D plots, all figures use centroid projections of the respective trajectory onto the vector between initial and target position.<br> The first integer in the file name denotes the movement direction number, starting with "0" for movements between the targets 1 and 2, "1" for movements between the targets 2 and 3 etc.<br> The file "6_distance0.35_ID2_policy2100000_phasespace.png", e.g., shows velocity plotted againt position for all 50 movements between the targets 7 and 8 in the task condition with ID 2 and 35cm diameter of the target circle (see Fig 2 in Paper).</p> </li> </ul> </li> <li> <p><strong>Elliptic Task</strong></p> <ul> <li> <p>This dataset also contains two CSV-files with data of the trajectory generated by the final policy in the elliptic task:<br> - one with detailed trajectory information on a sample-to-sample basis ("ELLIPSE_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis, where a new episode starts<br> whenever the target on the ellipse given to the policy switches ("ELLIPSE_METRICS" in filename).</p> </li> </ul> </li> </ul> <p><strong>Description of the .csv-files</strong></p> <ul> <li><em>SAMPLES </em>Files <ul> <li>"time": time after reaching the initial target (target 1 in Fig 2) for the first time (in seconds)</li> <li>"elv_angle_pos" - "flexion_pos": angle of respective independent DOF (in radians) *</li> <li>"elv_angle_vel" - "flexion_vel": angular velocity of respective independent DOF (in radians/s) *</li> <li>"end-effector_xpos_x" - "end-effector_xpos_z": 3D position of end-effector in global coordinates (in meters) *</li> <li>"target_xpos_x" - "target_xpos_z": 3D position of target sphere in global coordinates (in meters) *</li> <li>"end-effector_xvelp_x" - "end-effector_xvelp_z": positional velocities of end-effector (in meters/s) *</li> <li>"target_xvelp_x" - "target_xvelp_z": positional velocities of target sphere (in meters/s) *</li> <li>"accsensor_end-effector_x" - "accsensor_end-effector_z": positional acceleration of end-effector (in meters/s^2) *</li> <li>"E_elv_angle" - "E_flexion": activation of respective independent DOF *</li> <li>"E_elv_angle_derivative" - "E_flexion_derivative": derivative of activation of respective independent DOF *</li> <li>"difference_vec_x" - "difference_vec_z": vector between the end-effector attached to the index finger and the target, pointing towards the target (in meters) *</li> <li>"centroid_vel_projection": projection of end-effector velocity towards target (in meters/s) *</li> <li>"target_width": radius (!) of the target sphere (in meters) *</li> <li>"A_elv_angle" - "A_flexion": action vector</li> <li>"thorax_tx_frc" - "wrist_hand_r3_frc": net external force at respective DOF (including dependent and fixed DOFs such as "thorax_tx" (thorax translation))</li> <li>"reward": reward obtained in this step</li> <li>"step_type": 0=initial step of episode, 1=intermediate step of episode, 2=terminal step of episode</li> <li>"target_switch": whether the target switched in this step</li> <li>"discount": internal value of tf-agents (does not correspond to the discount factor gamma, which is additionally applied!)</li> <li>"thorax_tx_pos" - "wrist_hand_r3_pos": angle of respective dependent DOF (in radians)</li> <li>"thorax_tx_vel" - "wrist_hand_r3_vel": angular velocity of respective dependent DOF (in radians)</li> </ul> </li> <li><em>METRICS </em>Files <ul> <li>Index: episode ID</li> <li>"Init_X" - "Init_Z": initial position in global coordinates (in meters)</li> <li>"Init_X" - "Init_Z": target position in global coordinates (in meters)</li> <li>"Init_Distance": distance between last target (i.e., desired initial position) and current target (in meters)</li> <li>"Target_Width_Diameter": target width diameter (in meters)</li> <li>"Movement_ID": Index of Difficulty of current movement (using the Shannon Formulation) (in bits)</li> <li>"target_accuracy": 1 - (<remaining distance to target center at the end of the episode>/<target radius>) if end-effector is inside target, 0 else</li> <li>"movement_time": duration of the episode (in seconds)</li> <li>"episode_successful": whether episode terminated successfully within the permitted 1.5 seconds</li> <li>"dist2target": remaining distance to target center at the end of the episode (in meters)</li> </ul> </li> </ul> <p>-------------------------------------<br> * included in state space</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.