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1,474 results for “Reliability”
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Dataset for paper entitled "Developing Reliable Foam Sensors with Novel Electrodes"
<p>This dataset includes all the experimental results presented in the IEEE Sensors 2019 paper "Developing Reliable Foam Sensors with Novel Electrodes" (DOI: 10.1109/SENSORS43011.2019.8956750).<br> URL of IEEE Xplore:<br> https://ieeexplore.ieee.org/document/8956750</p> <p>List of data in this dataset:<br> Fig-1-Stress-Strain curves of PU foam and Coated foam.xlsx<br> Fig-3-Conductive foam without electrodes.xlsx<br> Fig-4-Foam sensor with Ag electrodes.xlsx<br> Fig-5-Foam sensor-stability-100cycles.xlsx</p> <p>All the data included in this dataset were collected by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
Supplementary input data for accounting for component condition and preventive retirement in power system reliability of supply analyses
<div> <div>This data set contains supplementary data used for case studies on accounting for transformer condition in reliability of supply analyses in the following manuscripts: <br>1) H. Toftaker, J. Foros, I. B. Sperstad, "Accounting for component condition and preventive retirement in power system reliability of supply analyses", IET Generation, Transmission & Distribution, vol. 5, no. 1, 2023, DOI: 10.1049/gtd2.12761. <br>2) I. Bjerkebæk, I. B. Sperstad, H. Toftaker, G. Kjølle, "Simulating the Long Term Effect of Asset Management Strategies on Reliability of Supply", pre-print submitted for peer review, 2024. DOI: 10.36227/techrxiv.172107759.95745501/v1.</div> <div> See README.md for details.</div> </div>
Data and script for Van Berkel et al: Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?
<p>Supporting materials for:</p> <p><strong>Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?</strong></p> <p>Menno van Berkel<sup>a</sup>, Melissa Bateson<sup>a</sup>, Daniel Nettle<sup>a</sup> and Jonathon Dunn<sup>a</sup>*</p> <p><sup>a</sup>Centre for Behaviour and Evolution & Institute of Neuroscience, Newcastle University, Newcastle, UK</p> <p>*Author for correspondence (email: jonathon.dunn@newcastle.ac.uk; telephone: (+44)7730015855; postal address: Institute of Neuroscience, Henry Wellcome Building, The Medical School, Framlington Place, Newcastle University, Newcastle upon Tyne, UK, NE2 4HH).</p> <p>R script and 3 .csv files.</p>
LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div> </div> <div> </div>
LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <p>This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 10m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div>
LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 10m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> </div> <div> </div>
Data of "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment"
<p>These are the data used for generating the figures in the ACP article "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment" by Sperber and Gierens.</p> <p>The data sets labeled "Box" have been generated by the stochastic box model, "adj" refers to the parameterisation using saturation adjustment and data labeled "par" originate from the newly developed parameterisation.</p> <p>The label "const" followed by a number refers to simulations with a constant updraught of the speed specified by the number in cm/s. The label "cos" refers to the simulations in which the updraught velocity follows a cosine function in time.</p> <p>"a10" labels simulations with less initial clear sky humidity fluctuations of plus/minus 10% instead of plus/minus 25%. "al0028" labels simulations with a higher deposition rate of 0.0028 1/s instead of 0.0003 1/s. "step10" labels simulations with a longer time step of 10 minutes instead of 1 minute.</p> <p>"Box_const2_rh1.txt" contains data from a simulation similar to "Box_const2.txt" but with an initial mean relative humidity of 100% instead of 110%. "Box_het.txt" contains data from a simulation including heterogeneous nucleation. "Box_slow_nuc.txt" contains data from a simulation where the deposition rate increases over time from zero after nucleation in every air parcel. "Box_upvar.txt" contains data from a simulation, where the updraught velocity in every air parcel varies randomly between 1 cm/s and 3 cm/s and the deposition rate inside the air parcel depends on the updraught velocity at the time of nucleation.</p> <p> </p> <p>The columns in the "Box" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity across all air parcels</p> <p>4. Mean specific humidity across all air parcels</p> <p>5. Mean specific ice content across all air parcels</p> <p>6. Mean relative humidity across all cloudy air parcels</p> <p>7. Mean relative humidity across all clear air parcels</p> <p>8. Mean equilibrium supersaturation</p> <p>9. Mean threshold relative humidity for homogeneous nucleation</p> <p>10. Mean deposition rate across all cloudy air parcels</p> <p>11. Mean updraught velocity</p> <p> </p> <p>The columns in the "adj" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p> </p> <p>The columns in the "par" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p>8. Obsolete</p> <p>9. Equilibrium supersaturation</p>
Reliability Assessment of rock slopes by evidence theory
<p>These are the data sets on orientation collected at El Pedregal Mine by:</p> <p>1. Compass in 1997, 2011 and 2016</p> <p>2. ShapeMetrix in 2017 (station #N)</p> <p>Besides, shear strength parameters are included.</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Raw frequency data: Thoughts on "Reliable" Learner's Vocabularies for Classical and Literary Chinese
<p>This dataset includes the raw frequency counts (classical_chinese_learners_vocabularies_raw_frequencies.zip) used in the article Thoughts on “Reliable” Learner’s Vocabularies for Classical and Literary Chinese. </p> <p>Corpus I – Micheal Loewe (1993)’s <em>Early Chinese Texts</em><br> Corpus II – Official Histories (zhengshi 正史)<br> Corpus III Six Novels (xiaoshuo 小說), as defined in Hsia 1968</p> <p>The download includes one folder per corpus, structured as follows:</p> <ul> <li>xx_corpus.csv > list of texts and sources / used versions, token and type counts</li> <li>xx_freq_1-1.csv > unigram / character frequencies and counts</li> <li>xx_freq_1-4.csv > 1 to 4 character word frequencies and counts, "words" according to Hanyu da cidian 漢語大詞典 (Luo 1986–1994))</li> <li>xx_freq_2-4.csv > 2 to 4 character words</li> </ul> <p>Additionally, pca_zhengshi_vs_loewe_vs_xiaoshuo.html is an interactive version of the Principal Component Analysis (PCA) presented in the article, texts from the three corpora are represented using the 1.000 most frequent 1–4 character combinations from the dataset.</p>
Webinar Nuberu: Reliable RAN Virtualization in Shared Platforms @ IMDEA Networks
<p><strong>VIEW IN OUR YOUTUBE CHANNEL:</strong> <a href="https://youtu.be/iAoyuzHuZP0">https://youtu.be/iAoyuzHuZP0</a></p> <p>RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station¿s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings.</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
Chapter 3: Scale Separation Reliability: What Does it Mean in the Context of Comparative Judgement?
<p>This is the supplementary material for Chapter 3 of the dissertation "Beyond a Mere Rank Order: The Method, the Reliability and the Efficiency of Comparative Judgment" and the article "Scale separation reliability: What does it mean in the context of comparative judgment?" published in "Applied Psychological Measurement".</p>
The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance
<p>We present the open design of the PIRATE, an anthropometric earPlug with exchangable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance. Its outer shape is available in 5 sizes and provides a deep, tight and reproducible fit in virtually all human ears. The design includes a recess to accommodate a MEMS microphone. Thus, the same microphone can be conveniently used in different earplugs without losing accuracy, and the microphone can be removed for calibration. The PIRATE or previous versions of it have been utilized in several studies with more than 200 subjects</p> <p>From the provided model, the earplugs can be 3D printed, and only minor working steps are necessary before use. These steps are described in the documentation.</p> <p> </p> <p>Reference:</p> <p>Denk F., Brinkmann F., Stirnemann S., Kollmeier B. (2019) "The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance," Fortschritte der Akustik - DAGA, Rostock, Germany</p>
Data and Code for: 'Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907', Huber et al. 2024.
<p>Data and code to reproduce plots for the paper '<em>Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907'</em>, Huber et al. 2024.</p> <p>Descriptions of the enclosed data files are as follows:</p> <div> <ul> <li>Freqs_best_fit.dat: Best-fitting GARSTEC model frequencies (Figure 3, right)</li> <li>KIC10006158_spec.txt: Reduced and normalized HDS spectrum of KIC10006158 (Figure 1)</li> <li>KIC8144907_ps.txt: Power spectrum of the Kepler light curve of KIC8144907 (Figure 3, left)</li> <li>KIC8144907_spec.txt: Reduced and normalized HDS spectrum of KIC8144907 (Figure 1)</li> <li>apokasc2.tsv: APOKASC sample from Pinsonneault+ 2014 (Figure 2)</li> <li>dwarfs.csv: Asteroseismic sample from Serenelli+ 2017 (Figure 2) </li> <li>freqs.csv: Data in Table 1</li> <li>hosts.tsv: Asteroseismic ages from Silva Aguirre+ 2015 (Figure 4) </li> <li>legacy-t1.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>legacy-t2.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>li-2020.csv: Asteroseismic ages from Li+ 2020 (Figure 4) </li> <li>matsuno.txt: Asteroseismic sample from Matsuno+ 2021 (Figure 2) </li> </ul> </div>
Dataset for the manuscript "Are remote sensing evapotranspiration models reliable 2 across South American ecoregions?" published in WRR
<p><strong>Metadata of ‘<em>Are remote sensing evapotranspiration models reliable across South American ecoregions?</em>’ </strong></p> <p>This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model.</p> <p> </p> <p><strong>File format and time stamps</strong></p> <p>Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used:</p> <ul> <li>GLEAM: Year (YYYY); Day of Year (DDD)</li> <li>PT-JPL: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-MOD: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-VI: Date (MM/DD/YYYY)</li> </ul> <p> </p> <p><strong>Missing data</strong></p> <p>Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported. </p> <p> </p> <p><strong>Data format</strong></p> <p>The column headers Name, Description and Units are adopted used in the data files to describe the following variables::</p> <ul> <li>ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day<sup>-1</sup>);</li> <li>ETobs, Observed evapotranspiration (mm day<sup>-1</sup>);</li> <li>Rn, Surface Net Radiation (w m<sup>-2</sup>);</li> <li>Rg, Daylight shortwave Incoming Radiation (w m<sup>-2</sup>);</li> <li>Rgs_out, Shortwave Radiation - outgoing (w m<sup>-2</sup>);</li> <li>G, Soil heat flux (w m<sup>-2</sup>);</li> <li>P, Rainfall (mm day<sup>-1</sup>);</li> <li>T, Surface Air Temperature (ºC);</li> <li>Tmax, Maximum Temperature (ºC);</li> <li>Tmin, Minimum Temperature (ºC);</li> <li>Tday, Daytime Temperature (ºC);</li> <li>TminDay, Daytime Minimum Temperature (ºC);</li> <li>TminNight, Nighttime Minimum Temperature (ºC);</li> <li>Patm, Atmospheric Air Pressure (Pa);</li> <li>ea, Actual Vapor Pressure (kPa);</li> <li>es, Saturation Vapor Pressure (kPa);</li> <li>VPD, Vapor Pressure Deficit (kPa);</li> <li>eaDay, Daytime Actual Vapor Pressure (kPa);</li> <li>eaNight, Nighttime Actual Vapor Pressure (kPa);</li> <li>RH, Air Relative Humidity;</li> <li>RHDayTime, Daytime Air Relative Humidity;</li> <li>RHNightTime, Nighttime Air Relative Humidity;</li> <li>LAI, Leaf Area Index (m² m<sup>-</sup>²);</li> <li>SWC, Soil Water Content (mm m<sup>-1</sup>).</li> </ul> <p> </p> <p><strong>Forcing data per model</strong></p> <p>Each model requires a different set of forcing data, as follows:</p> <ul> <li>GLEAM: Rn, P, T, Rgs_out;</li> <li>PT-JPL: Tmax, Rn, RH (or e<sub>a</sub>);</li> <li>PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight;</li> <li>PM-VI: ETo.</li> </ul> <p> </p> <p><strong>Tower sites (IDs) and co-authors/PIs:</strong></p> <ul> <li>SDF: J. P. Quezada and M. Galleguillos;</li> <li>TF1 and TF2: L. Kutzbach and D. Holl;</li> <li>GRO and SLU: G. Posse;</li> <li>BAL and MCC: M. Gassman and C. Perez;</li> <li>PDG, EUC and USR: O. Cabral;</li> <li>FM and SIN: J.S. Nogueira and T. Range;</li> <li>CAA: M. Moura;</li> <li>CST: A. C. D. Antonino;</li> <li>SJO: E. S. Souza and J. R. S. Lima;</li> <li>ESEC: B. Bezerra.</li> </ul>
Concentration-, Temperature- and Solvent-Dependent Self-Assembly: Merocyanine Dimerization as a Showcase Example for Obtaining Reliable Thermodynamic Data
<p><strong>Abstract:</strong> Mathematical models for the concentration-, temperature- and solvent-dependent analysis of self-assembly equilibria are derived for the most simple case of dimer formation, to highlight the assumptions these models and the thus determined thermodynamic parameters are based on. The three models were applied to UV/Vis absorption data for the dimerization of a highly dipolar merocyanine dye in 1,4-dioxane. Isothermal titration calorimetry (ITC) dilution experiments were performed as an independent reference technique. While the concentration-dependent analysis is according to our studies the most reliable method, also the less time-consuming temperature-dependent evaluation can give accurate results in the present example, despite small thermochromic effects. In contrast, the strong negative solvatochromism of the merocyanine tampers with the results from the solvent-dependent evaluation. Even though the studies presented in this work are limited to the monomer-dimer equilibrium of a dipolar dye, the basic principles can be transferred to other chromophores and different self-assembly models, including those for supramolecular polymerization.</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.