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Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
FMN@TACN AuNPs catalyzed Pt(IV) prodrugs photocatalytic activation
<p>Raw NMR data of the photocatalysis experiments in "Toward supramolecular nanozymes for the photocatalytic activation of Pt(IV) anticancerprodrugs", Chem.Commun., 2020,56,10461, DOI 10.1039/d0cc03450a</p>
A derecho climatology over the United States from 2004 to 2021
<p><em>We develop the high-resolution (4 km and hourly) </em><em>observational derecho and derecho-producing mesoscale convective system (MCS) dataset over the United States east of the Rocky Mountains from </em><em>2004 to 2021 by using a </em><em>MCS </em><em>dataset generated by the Python Flexible Object Tracker </em><em>(PyFLEXTRKR) software, bow echoes detected by a semantic segmentation </em><em>convolutional neural network, gust speed</em><em>s from the Integrated Surface Database and the Storm Events Database, and physically based identification criteria.</em></p>
CARMEN immunopeptidomics publication associated dataset
<h1>CARMEN: CAnceR imMunopeptidogENomics</h1> <blockquote> <div>An immunopeptidomic dataset accompanying the publication "<a href="http://dx.doi.org/10.1101/2025.05.08.651510" target="_blank" rel="noopener">Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN</a>" containing peptides determined by mass-spectrometry associated with MHC Class I bindings from 72 publications (2323 samples).</div> </blockquote> <div> </div> <div> <p><strong>Authors:</strong> <a href="mailto:aleksander.palkowski@gmail.com" target="_blank" rel="noopener">Aleksander Palkowski</a>*, <a href="mailto:mwaleron@gmail.com" target="_blank" rel="noopener">Michal Waleron</a>*, <a href="mailto:emilia.daghir@gmail.com" target="_blank" rel="noopener">Emilia Daghir-Wojtkowiak</a>*, <a href="mailto:ashwinkallor@gmail.com" target="_blank" rel="noopener">Ashwin Adrian Kallor</a>*, <a href="mailto:javier.alfaro@proteogenomics.ca" target="_blank" rel="noopener">Javier Antonio Alfaro</a></p> <p>* <em>These authors contributed equally to this work</em></p> </div> <div> </div> <div>The entire dataset consist of four table files in the <a href="https://parquet.apache.org" target="_blank" rel="noopener">Apache Parquet</a> data file format:</div> <ul> <li>main</li> <li>mapped-protein-annotations-pogo</li> <li>mapped-protein-annotations-msfragger</li> <li>hla-sequences</li> </ul> <div> </div> <div><strong>Please refer to the README.md file for details.</strong></div> <div> </div>
Оцінка сталого розвитку регіонів України за 2013-2021 рр. та виявлення факторів регіональної диференціації: набір даних. Супровідні матеріали до наукового дослідження в рамках виконання науково-дослідної роботи Сумського державного університету «Реструктуризація національної економіки в напрямі цифрових трансформацій для сталого розвитку» (№0122U001232)
<p>Набір даних містить показники, використані для обчислення показника сталого розвитку регіону (на основі методичного підходу щодо оцінки субнаціонального індексу людського розвитку, Human Development Index, HDI, доповненого оцінками екологічної складової), а також ідентифікації факторів його регіональної диференціації. </p><p>Для підготовки набору даних використано дані Державної служби статистики України, дані регіональних доповідей про стан навколишнього природного середовища та екологічних паспортів областей України та міст загальнодержавного підпорядкування, інші дані національних регуляторів.</p><p>Містить річні дані відносно 58 характеристик регіонального розвитку за 2013 рік для 24 областей України, Автономної Республіки Крим, м. Київ та м. Севастополь; за 2014-2021 р. для 24 областей України та м. Київ. Запис та попередня обробка даних здійснені з використанням програмного середовища Microsoft Excel ver16.77.1.</p><p>Набір даних підготовлено в рамках виконання науково-дослідної роботи Сумського державного університету «Реструктуризація національної економіки в напрямі цифрових трансформацій для сталого розвитку» (№0122U001232).</p><p>Дата створення: 12 серпня 2023 р.</p>
Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology
<p>This dataset includes the current version of the indicative distribution maps and profiles for <strong>Ecosystem Functional Groups</strong> - Level 3 of IUCN Global Ecosystem Typology (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith et al. (2022).</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group.</p> <p>Maps are indicative of global distribution patterns and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications.</p> <p>We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into<strong> functional biomes</strong> (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.</p>
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>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Catalog of 1600+ New Variables around V1821 Cyg in NGC 6871
<p>Utilizing TESS data, the author has screened a small sky region of a one-degree radius centered on the known $\delta$ Scuti star V1821 Cyg within the young open cluster NGC 6871. The selected samples had TESS magnitude values ranging from 6 to 16 mag, matching the TESS premium targets exhibiting good quality photometry and effective temperature between 10000 and 6000 K, roughly corresponding to main sequence stars of spectral types A to F. Among the 3600 candidate stars surveyed, 1530 were discovered to be new variables of types $\delta$ Scuti, $\gamma$ Doradus, Maia, eclipsing binary, rotational, and solar-like oscillators.</p>
Isotopic analysis of extracted water from a larch (Larix decidua) stand in a high mountain watershed (Vallon de Nant - Switzerland)
<p>A total of 185 samples of soil and trees were taken from a stand of larch (Larix decidua) spanning from 1500 to 1600 m.a.s.l. in the Vallon de Nant in the Swiss canton of Vaud. Twenty individual trees and soil were sampled along two transects perpendicular to the main river channel of the Avançon de Nant at approximately midday on seven days in the foliage season between July 2017 and June 2018.</p><p>The data consists of two data (csv) and one document (pdf) files. The sample data file includes the date and estimated time of sampling, the type of sample (vegetation or soil), the transect and tree ID for look-up in the tree metadata file, the soil depth in centimeters, and the determined mean and standard deviation of deuterium, oxygen-18, and oxygen-17. The tree metadata file includes the transect (north or south), the tree ID number, the latitude, longitude, elevation (meters above sea level), and tree height and diameter at breast height in centimeters. Finally, a document describing the methods in more detail is included. </p>
Instructor Perspectives on APA Style in Nursing Education: Implicit and Explicit Value
<p><strong>OBJECTIVE:</strong> Disparities in how nursing faculty teach APA Style can confuse students and the librarians who help them. To clarify faculty expectations of and approaches to APA </p><p>Style, this study seeks to answer the following questions: (1) What do nursing faculty perceive as the impact or value of APA Style? (2) How do nursing faculty teach and grade APA Style? By revealing the unspoken assumptions about APA Style and the value it adds to nursing education, it is hoped that health sciences librarians can more intentionally and effectively support this aspect of the nursing curriculum.</p><p><strong>METHODS: </strong>A mixed-methods study was designed to investigate potential gaps between nursing instructors' expectations for APA and their teaching/grading practices. The study incorporated an online survey of 75 nursing faculty at 14 Carnegie institutions with nursing programs, as well as qualitative interviews with 12 faculty at those institutions. A grounded theory approach was used to uncover salient themes.</p><p><strong>RESULTS:</strong> Nursing instructors consistently emphasized the importance of APA for referencing/in-text citations in both the survey and the interviews. When discussing the value of APA Style in nursing education, interviewees stressed APA Style as essential in developing professional nursing communication skills and evidence-based practice. However, faculty expectations for students' APA skills were not in line with their teaching and grading practices.</p><p><strong>CONCLUSION:</strong> Given the wide range of reporting teaching and grading practices for APA Style, nursing programs should work to clarify expectations for APA Style, and standardize how it is taught.</p>
Anyskop Blowout Prehistoric Dataset, Western Cape, South Africa
<p>These Stone Age archaeological datasets were collected in 2001 and 2002 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. Many South African researchers collaborated on this project, with Pippa Haarhoff, John Compton, Dave Roberts, and Stephan Woodborne deserving special mention.</p> <p>The field work took place at the Anyskop Blowout (ANY1) located within the West Coast Fossil Park near Langebaanweg, Western Cape, South Africa. The field work was conducted with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some German as well) and include field data in the MAIN table. Further analytical data for many classes of artifacts include: LITHICS, FAUNA, POTTERY, MODERN, BUCKETS, REFITS.</p> <p>All collected materials are curated by the Iziko South African Museums in Cape Town under accession numbers SAM-AA-8903 (finds collected by other teams before 2001) and SAM-AA-9007 (finds from this study, 2001-2002). Some of the finds are exhibited in the museum at the West Coast Fossil Park.</p> <p>Funding for this research project came mainly from the German Research Foundation (DFG - CO 226/5-1, 5-2, 5-5 and 5-6) and the University of Tübingen. Significant support was provided by the Iziko South African Museums, the West Coast Fossil Park, and the University of Cape Town.</p>
Dataset: Environmental benchmarks for European Cement Industry
<p>This dataset contains the information relative to the article "Environemntal benchmarks for European cement industry".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.spc.2024.01.020" target="_blank" rel="noopener">Reference paper</a></p> <p><a href="https://www.researchgate.net/publication/377796848_Environmental_benchmarks_for_the_European_cement_industry" target="_blank" rel="noopener">ResearchGate link</a></p>
Survey data on attitudes towards salmon aquaculture industry in Norway, Iceland, and Tasmania (AU)
<p>The following data is from an online survey conducted in Norway, Tasmania (Australia), and Iceland. Respondents were recruited by survey companies that distributed e-mail invitations to their panels. A minimum respondent quotas was established for each region, with individuals under the age of 18 being exluded from participating in the survey. The dataset consists of a total of 2085 respondents, comprising 1183 participants from Norway, 406 from Tasmania, and 496 from Iceland. Questions were presented in their respective native language, namely Norwegian, English, and Icelandic.</p> <p>This survey data encompasses various aspects of perceptions of salmon aquaculture industry. Data was generated by the SoLic (Social License to operate for aquaculture) project (2019 - 2022), and funded by The Research Council of Norway (no. 295114). The survey was designed by the SoLic project group. </p> <p>The data and supplementary material is divided in 3 files:</p> <p>The raw survey data in .csv file format (Dataset Solic_2085 respondents.csv). The data file contains 71 variables and data from each of the 2085 respondents. Blank entries in the dataset indicate either a lack of response from the respondents or that specific questions were not applicable to certain respondents (questions exclusively posed to respondents in one country).</p> <p>Overview of survey questions and answer options (Survey.doc). The survey encompassed 28 questions related to the aquaculture industry, along with demographics, respondents’ knowledge of industry, trust in governance system, and environmental concerns. Some demographic variables were sourced from the existing panel data, while others were provided to respondents for their input.</p> <p>The codebook (Codebook.doc). The codebook provides explanations and details regarding all variables included in the survey data file. It includes coding information for each survey question, response options provided in the raw data, and further clarifies the purpose and origin of variables computed by the research group (e.g., variable on aquaculture municipality) or the survey company (e.g., weight variables for data from Norway and Iceland). When used in conjunction with the raw data, this codebook serves as a valuable guide for navigating the dataset. </p>
Soupis terénních výzkumů Státního archeologického ústavu na Slovensku v letech 1919–1938
<p>Soupis terénních výzkumů Státního archeologického ústavu na Slovensku vznikl jako výstup projektu <strong>Státní archeologický ústav na Slovensku (1919–1939) – prvorepubliková archeologie profesionální i pod vlajkou čechoslovakismu</strong>, který byl v rámci tématu Střední Evropa jako fenomén moderních dějin podpořen výzkumným programem Strategie AV21.</p> <p>Soupis se skládá z textové části (<em>soupis_StAU_2023-12-18.pdf</em>) a dat ve formátu CSV (<em>data_StAU_2023-12-18.csv</em>).</p>
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
PsPM-FER02: PSR, SCR, ECG and respiration measurements from a 3 conditions x 3 experimental sessions repeated-measures design to assess the return of fear
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses, keypress response times, key correctness and shock expectancy ratings at the end of the experiment for 74 healthy unmedicated participants (33 males and 41 females aged 24.2+/-3.9 years) participating in a 3 conditions x 3 experimental sessions repeated-measures design to assess the return of fear. CS were colored triangles (yellow/red/blue). US consisted of a 500 ms train of 250 square pulses with individual pulse width of 0.2 ms. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. The ITI was randomly determined as discrete values between 7-11 seconds (mean 9 seconds).</p>
Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.