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A digital edition of the 389 Chorale Settings (Choralgesänge) by J.S. Bach
<p>This repository has been made possible by Gertim Alberda, who 'manually' digitalized all the 389 Bach 4-voice chorales, as published by the <a href="https://imslp.org/wiki/Special:ReverseLookup/348824">Breitkopf edition 'nr. 3765'</a>. For this he meticulously transcribed all the notes and lyrics of that edition, by using the music notation editor MuseScore (MS). He checked it against both BGA and NBA (Bach-Gesellschaft Ausgabe and Neue Bach-Ausgabe), if there was any reasonable doubt for it, and even found and improved a couple of mistakes that way (<10). Solely for performance reasons, he also applied many hidden (grey) extras in MS to make the scores and its phrasing sound as realistic as possible; hidden fermatas, tempo changes, breath pauses, phrasing, note-cutbacks, etc. This was all done to 'humanize' the playback and make it sound like a real choir performance (without words), and given the technical limitations at that time (2016-2018). Also see the <a href="https://gertim-alberda.com/chorales/info.html">Info</a> tab on his website.</p> <p>The full corpus resides on Gertim Alberda's <a href="https://gertim-alberda.com/chorales">dedicated website</a>. The scores there have synchronized play back (synthesized and human performances) available and can be downloaded in various formats (mscz/xml/midi/mp3/pdf). <a href="https://gertim-alberda.com/chorales/BachChorales/B288.html">Here is an example</a> of his playback page. His intention is to have only human performances available (YT videos and/or mp3's) for all the scores, but that is still an ongoing process (see the <a href="https://gertim-alberda.com/chorales/changelog_bach_chorales.html">changelog</a> on his website). He has agreed to share his MS files under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons BY-NC-SA 4.0</a> license. The license prohibits the use of the scores for any commercial purpose, including the training of machine learning models for use in commercial products.</p> <h2>Contents</h2> <p>The "mother of all datasets" is available as two versions, each contained in a separate folder:</p> <ul> <li><code>original_complete</code>: Gertim Alberda's original MuseScore files converted to uncompressed MuseScore 3 format (<code>.mscx</code>)</li> <li><code>vocal_parts_only</code>: Alternative version of the dataset containing no instrumental parts.</li> </ul> <h2>Version history</h2> <p>See the <a href="https://github.com/johentsch/389_chorale_settings/releases">GitHub releases</a>.</p> <h2>Questions, Suggestions, Corrections, Bug Reports</h2> <p>Please <a href="https://github.com/johentsch/389_chorale_settings/issues">create an issue</a> and/or feel free to fork and submit pull requests.</p> <h2>License</h2> <p>Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>).</p>
Sets of mutually similar public GitHub repositories (October 2016)
<p>The format is JSON, the list of lists. Each list is the group of very similar repositories (Weighted Jaccard Similarity threshold 0.8~0.9).</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Data set for bionic simulation of double clap-and-fling wing mechanism with SPH FSI method
<p>Data set containing the rigid-body based flapping wing models coupled with smoothed particle hydrodynamics (SPH), by means of <a href="https://github.com/DualSPHysics/DualSPHysics/wiki/9.-New-in-DualSPHysics#new-in-dualsphysics-v50">DualSPHysics version 5.0 </a>and <a href="https://projectchrono.org/download/">Project Chrono</a>.</p><p>These models are part of the paper:</p><blockquote><p>Yanwei Zhang, Zhonglai Wang, Saullo G. P. Castro. Bionic simulation of double clap-and-fling wing mechanism with SPH FSI method. EngXriv Preprint, 2022. <a href="https://doi.org/10.31224/2652">DOI: 10.31224/2652</a></p></blockquote><p>File "simulations.zip" contains the simulation files.</p><p>File "DualSPHysics_v5.0.zip" contains the compiled DualSPHysics software.</p><p>Procedure to run the simulations on a Windows machine:</p><ul><li>Working directory: .\simulations\flappingwing\case01</li><li>Step 1: Obtain rigid bodies by modeling of SOLIDWORKS and Macro command of FreeCAD (e.g. external_wing111.stl)</li><li>Step 2: Run ".bat" file (e.g. flapping01.bat) to start simulation and force acquisition</li><li>Step 3: Revise ".bat" and ".xml" (e.g. flapping01.bat and flapping01_Def.xml)to adapt to the next case. If required, change the model and Macro command of step 1. Common modification items:</li></ul><p><geometry>-<definition> <floatings>- <floating><angularvel> <floatings>- <floating><property> <properties>-<propertyfile> <initials> <execution>-<special>-<chrono> <execution>-<special>-<inout> <parameters> </p><ul><li>Step 4: Run "Filter.m" to handle force data by filters. Step 5: Compare forces of all cases. PS: Other files are revised function files derived from DualSPHysics 5.0.</li></ul><p> </p><p>Abstract: Three-dimensional numerical simulations of flexible flapping wings based<br>on the fluid-structure interaction in biological and bioinspired flow have become a<br>vibrant and challenging research topic. The present paper focuses on a parametric<br>study of the aerodynamic performance of a bionic flexible clapping wing. The proposed<br>model deforms the wing in spanwise and chordwise directions based on the six rigid<br>bodies connected along the wing veins using ball links and springs. Unsteady effects of<br>flapping wing micro air vehicles with a double clap-fling configuration are investigated<br>using an air-solid interaction model based on smoothed particle hydrodynamics and<br>rigid multi-body dynamics. A validation experiment determined the convergence<br>conditions and computational model accuracy. The proposed numerical model is<br>evaluated in terms of flexible variation law and aerodynamic performance. The results<br>indicate that the flapping frequency, angle of attack, and wind velocity significantly<br>influence the lift. Furthermore, increasing the frequency will monotonically expand<br>the maximum and time-averaged lift curve values. When the angle of attack is less<br>than 30 ◦, the influence on the time-averaged and maximum lift is proportional to the<br>angle of attack. When the angle of attack is larger than 45 ◦, a stall-like condition<br>is detected. To broaden the applicability of the present findings, a dimensionless<br>parameter, reduced frequency, is defined, and its influence on the maximum and time-<br>averaged lift is investigated. This parametric study shows that as the reduced frequency<br>increases, the maximum and time-averaged lift increases and then decreases. The<br>present study could reach a modeling framework that better explains the clapping<br>wing aerodynamics.<br> </p>
Cryptocurrency Fraud and Code Sharing Data Set and Analysis Code
<p>This release covers the state of the data and associated analysis code for determining code sharing between cryptocurrency codebases funded through the end of the original NSF CRII award. This material is based on work supported by the National Science Foundation under Grant CNS-1849729.</p>
Data set for design for bird strike crashworthiness using a building block approach applied to the Flying-V aircraft
<p>Data set for the manuscript:</p> <p>Chen SY, van de Waerdt W, Castro SGP (2022). Design for bird strike crashworthiness using a building block approach applied to the Flying-V aircraft. Preprint. DOI: <a href="https://doi.org/10.31224/2559">https://doi.org/10.31224/2559</a></p>
Psychological adjustment in Third Culture Kids living in Switzerland: Data Set
<p>demographic variables:</p> <p>parent's assignment length, child's agreeableness with the relocation, child's age (7–12 years and 13–17 years), school type (international or other), and history of psychological treatment </p> <p>Labels:</p> <p>parent variables (P1) parent filled child well being and mental health (CC1), child filled variables (C1)</p> <p>variables:</p> <p>Well-being using the 10-item KIDSCREEN-10 Index (The KIDSCREEN Group Europe, 2006). </p> <p>Mental health problems using the 25-item SDQ (R. Goodman, 1997). </p> <p>Emotion regulation strategies using the Emotion Regulation Questionnaire for Children and Adults (Gullone & Taffe, 2012), a 10-item self-report scale that measures expressive EES and CCS. </p> <p>Perceived stress using the 13-item Perceived Stress Scale for Children (White, 2014). </p> <p>Resilience using the 12-item Child and Youth Resilience Measure (CYRM-12; Liebenberg et al., 2013). </p> <p>Negative social cognitions using the 10-item Social Threat Subscale of the Children's Automatic Thoughts Scale (Schniering & Lyneham, 2007; Schniering & Rapee, 2002),</p> <p> </p>
Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model
<p>Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model</p>
Image data set based on the age of giant pandas
<p>The conservation of the giant panda (<em>Ailuropoda melanoleuca</em>), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information which is mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.</p>
Data set for sperm storage in female squid, Todarodes pacificus
<p><span>Female eumetazoans often develop sperm storage organs (SSOs). Although the processes of sperm storage may influence post-copulatory sexual selection in polyandrous species, the significance of multiple SSOs is not understood. In contrast to coastal squids (which develop no more than two SSOs), the female <em>Todarodes pacificus</em>, a more oceanic species, develops more than 20 SSOs, which take the form of specialized pockets, called seminal receptacles (SRs), near the mouth. We investigated the sperm storage pattern of SRs by paternity analysis of hatchlings obtained after artificial insemination using sperm retrieved from 6 arbitrarily selected SRs. The results showed that </span><span>females</span><span> were capable of storing sperm contributed by 9 to 23 males, indicating that females are broadly </span><span>promiscuous</span><span>. In the pattern of sperm storage, the number of males and proportion of their sperm present in the SRs varied among SRs, and sperm storage was biased towards particular males at the individual SR level. However, when calculated as a proportion of all the SRs within a female, the number of sires increased and the paternity bias towards any particular male weakened. These results suggest that one function of having multiple SRs in <em>T. pacificus</em> may be to ensure genetic diversity of the offspring.</span></p>
Carrefour's Product Set
<p><strong>El dataset representa els productes de carrefour.es que fan referència a menjar. Consta de 10746 productes dels quals se’n representen 31 atributs. El camp id fa referència a l’identificador únic de cada producte.</strong></p> <p><strong>Les dades han estat obtingudes de la web carrefour.es entre els dies 03/11/2022 a 08/11/2022.</strong></p>
Saskatchewan seismic data set 2
<p>Seismic data from Saskatchewan glacier. Includes the waveform data, the log files, and the instrument response file.</p>
Displacement measurement via self mixing interferometry and neural network training set
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844]. This data set was used to train the network in that work (after data augmentation). It consists of a python dictionary with two keys: `truth` and `signal`. The `truth` part is a 195011-elements long numpy array corresponding to the displacement of the target in units of wavelength per 1.024 ms. The `signal` part is the interferometric signal corresponding to the displacement. It is arranged in a (195011,256,1) numpy array. Each segment of length 256 corresponds to the interferometric signal acquired during a 1.024 ms time window. For instance, the displacement value in `truth[618]` corresponds to the interferometric signal segment `signal[618,:,0]`.</p>
Data set - Stress, Mental Health and Sociocultural Adjustment in Third Culture Kids: The Mediating Roles of Resilience and Family Functioning
<p>this data set contains data derived from a cross-sectional study which explores the contributions of proximal and contextual factors in the adjustment process of a sample of internationally mobile children and adolescents having relocated to Switzerland. </p> <p>scales include child perceived stress (PSS-C; White, 2014), acculturative stress (ASIC; Suarez-Morales et al., 2007), resilience (CYRM-12; Liebenberg et al., 2013), mental health difficulties SDQ (R. Goodman, 1997), socio cultural adjustmen (SCAS-Child; Ward & Kennedy, 1999) and family functioning (McMaster Family Assessment Device (Epstein et al., 1983)). </p> <p>child age, arrival in Switzerland and cemographic information on country of origin are included</p>
Data set for: Genetic dissection of marker trait associations for grain micro-nutrients and thousand grain weight under heat and moisture deficit stress conditions in wheat
<p>The study material in the GWAS panel with 193 bread wheat genotypes from Indian and exotic collections was selected to map the genomic regions responsible for grain iron and Zinc content under drought and heat stress treatments.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m) under Irrigated (IR), Restricted Irrigated (RI) and Late sown (LS) treatment conditions over 2 years i.e. 2020 and 2021 with augmented RCBD design. Data was collected on Grain Iron and Grain zinc content along with thousand-grain weight. Around 20 g of grain sample from each of 282 genotypes from the GWAS panel under all three conditions were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >10%, and heterozygote frequency >50% were removed from the analysis. The remaining set of 13,947 high-quality SNPs was used in GWAS analysis.</p>
Data Set Literature Review Digital Forensic and Forensic Sciences
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>Digital Forensic</em>" dan "<em>Forensic Sciences</em>"</p>
Data Set for Operando Identification of a Side-On Nickel Superoxide Intermediate and the Mechanism of Oxygen Evolution on Nickel Oxyhydroxide
<p>Dataset of the paper entitled "Operando Identification of a Side-On Nickel Superoxide Intermediate and the Mechanism of Oxygen Evolution on Nickel Oxyhydroxide"</p>
Data Set "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"
<p>This data set accompanies the publication "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- Results data from the harmonic and anharmonic vibrational calculations.</p> <p>- Data and code for calculating 2D-IR spectra.</p>
Simulated data set for SAXS tensor tomography, sample "M"
<p>This is a simulated data set intended for testing SAXS tensor tomographic reconstructions, including the original field from which the data was generated.</p>
Data set for "Landscape-variability of the carbon balance across managed boreal forests"
<p>This data set is a compilation of biometric- and chamber-based annual CO<sub>2</sub> flux estimates obtained during the period 2016–2018. Data were collected in 50 main forest stands within the Krycklan Catchment Study (KCS, https://www.slu.se/Krycklan), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden. Seven additional old forest stands were also included with the aim to reconcile our CO<sub>2</sub> flux estimates for the old age class.</p> <p>Selected forest stands encompassed different landscape attributes: 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age class (i.e., initiation, young, middle-aged, mature, and old stands). Annual CO<sub>2</sub> fluxes included are the following: net ecosystem production (NEP), net primary production (NPP), net primary production of trees (NPP<sub>t</sub>), net primary production of understory (NPP<sub>u</sub>) as well as total heterotrophic respiration (RH) and its soil and dead wood components (RH<sub>s</sub> and RH<sub>dw</sub>, respectively). All component CO<sub>2</sub> fluxes are presented as positive terms, whereas positive and negative NEP refers to net C sink and source, respectively.</p> <p>It also includes a set of potential abiotic and biotic drivers controlling NEP and its component fluxes at the landscape-scale.</p> <p>This data set is composed of three Microsoft Excel worksheets: readme, metadata, and data.</p> <p>More details can be found in Peichl et al. (2022) Landscape-variability of the carbon balance across managed boreal forests. Global Change Biology, 00, 1–14. https://doi.org/10.1111/gcb.16534.</p> <p>Contact information:</p> <p>Professor Matthias Peichl (<a href="mailto:matthias.peichl@slu.se">Matthias.Peichl@slu.se</a>)</p> <p>Ph.D. Eduardo Martínez García (<a href="mailto:eduardo.martinez@slu.se">Eduardo.Martinez@slu.se</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</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.