Op02n06b #264
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Op02n06b #264
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testing
Possible misprint - m. 12, no sharp sign on F but sharp present in figured bass. should be F#?
Two main issues I see, labeled in last two commits. I've copied exactly for now, hopefully someone has some input!
BeckerHanne
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Feb 23, 2025
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Hi @vzhyu the flat symbol figure above the D in m.14 means the third above the bass should be minor 3, in this case F. That is a signal to the continuo player beyond any doubt that they should play a d minor chord, not D major, as was the case a few bars before (m12).
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@BeckerHanne Thanks for that - in that case I should leave the note as is, nothing needs changing. |
…notator" role, swapping first and last names as well as keys
This is a README file for a data repository originating from the [DCML corpus initiative](https://github.com/DCMLab/dcml_corpora) and serves as welcome page for both * the GitHub repo [https://github.com/DCMLab/corelli](https://github.com/DCMLab/corelli) and the corresponding * documentation page [https://dcmlab.github.io/corelli](https://dcmlab.github.io/corelli) For information on how to obtain and use the dataset, please refer to [this documentation page](https://dcmlab.github.io/corelli/introduction). When you use (parts of) this dataset in your work, please read and cite the following article: _Hentschel, J., Moss, F. C., Neuwirth, M., & Rohrmeier, M. A. (2021). A semi-automated workflow paradigm for the distributed creation and curation of expert annotations. Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR, 262–269. https://doi.org/10.5281/ZENODO.5624417_ This corpus forms part of the larger [Distant Listening Corpus](https://github.com/DCMLab/distant_listening_corpus) which constitutes a data infrastructure the data report of which has implications for the present corpus, too: _Hentschel, J., Rammos, Y., Neuwirth, M., & Rohrmeier, M. (2025). A corpus and a modular infrastructure for the empirical study of (an)notated music. Scientific Data, 12(1), 685. https://doi.org/10.1038/s41597-025-04976-z_ # Arcangelo Corelli – Trio Sonatas (A corpus of annotated scores) This corpus of annotated [MuseScore](https://musescore.org) files has been created within the [DCML corpus initiative](https://github.com/DCMLab/dcml_corpora) and employs the [DCML harmony annotation standard](https://github.com/DCMLab/standards). It was relased together with and as part of the "workflow paper" (for the reference [see below](#cite-as) or the file `CITATION.cff`). The corpus comprises 36 `Sonate a tre`, divided into 149 separate movements. Together they make up for three of the four famous cycles of 12 trio sonatas each: | Opus | Cycle | Publication | Included | |------|---------------------|-------------|----------| | 1 | 12 sonate da chiesa | Rome 1681 | Yes | | 2 | 12 sonate da camera | Rome 1685 | No | | 3 | 12 sonate da chiesa | Rome 1689 | Yes | | 4 | 12 sonate da camera | Rome 1694 | Yes | ## Getting the data * download repository as a [ZIP file](https://github.com/DCMLab/corelli/archive/main.zip) * download a [Frictionless Datapackage](https://specs.frictionlessdata.io/data-package/) that includes concatenations of the TSV files in the four folders (`measures`, `notes`, `chords`, and `harmonies`) and a JSON descriptor: * [corelli.zip](https://github.com/DCMLab/corelli/releases/latest/download/corelli.zip) * [corelli.datapackage.json](https://github.com/DCMLab/corelli/releases/latest/download/corelli.datapackage.json) * clone the repo: `git clone https://github.com/DCMLab/corelli.git` ## Data Formats Each piece in this corpus is represented by five files with identical name prefixes, each in its own folder. For example, the first movement of the first trio sonata has the following files: * `MS3/op01n01a.mscx`: Uncompressed MuseScore 3.6.2 file including the music and annotation labels. * `notes/op01n01a.notes.tsv`: A table of all note heads contained in the score and their relevant features (not each of them represents an onset, some are tied together) * `measures/op01n01a.measures.tsv`: A table with relevant information about the measures in the score. * `chords/op01n01a.chords.tsv`: A table containing layer-wise unique onset positions with the musical markup (such as dynamics, articulation, lyrics, figured bass, etc.). * `harmonies/op01n01a.harmonies.tsv`: A table of the included harmony labels (including cadences and phrases) with their positions in the score. Each TSV file comes with its own JSON descriptor that describes the meanings and datatypes of the columns ("fields") it contains, follows the [Frictionless specification](https://specs.frictionlessdata.io/tabular-data-resource/), and can be used to validate and correctly load the described file. ### Opening Scores After navigating to your local copy, you can open the scores in the folder `MS3` with the free and open source score editor [MuseScore](https://musescore.org). Please note that the scores have been edited, annotated and tested with [MuseScore 3.6.2](https://github.com/musescore/MuseScore/releases/tag/v3.6.2). MuseScore 4 has since been released which renders them correctly but cannot store them back in the same format. ### Opening TSV files in a spreadsheet Tab-separated value (TSV) files are like Comma-separated value (CSV) files and can be opened with most modern text editors. However, for correctly displaying the columns, you might want to use a spreadsheet or an addon for your favourite text editor. When you use a spreadsheet such as Excel, it might annoy you by interpreting fractions as dates. This can be circumvented by using `Data --> From Text/CSV` or the free alternative [LibreOffice Calc](https://www.libreoffice.org/download/download/). Other than that, TSV data can be loaded with every modern programming language. ### Loading TSV files in Python Since the TSV files contain null values, lists, fractions, and numbers that are to be treated as strings, you may want to use this code to load any TSV files related to this repository (provided you're doing it in Python). After a quick `pip install -U ms3` (requires Python 3.10 or later) you'll be able to load any TSV like this: ```python import ms3 labels = ms3.load_tsv("harmonies/op01n01a.harmonies.tsv") notes = ms3.load_tsv("notes/op01n01a.notes.tsv") ``` ## Version history See the [GitHub releases](https://github.com/DCMLab/corelli/releases). ## Questions, Suggestions, Corrections, Bug Reports Please [create an issue](https://github.com/DCMLab/corelli/issues) and/or feel free to fork and submit pull requests. ## Cite as > Hentschel, J., Moss, F. C., Neuwirth, M., & Rohrmeier, M. A. (2021). A semi-automated workflow paradigm for the distributed creation and curation of expert annotations. Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR, 262–269. https://doi.org/10.5281/ZENODO.5624417 ## License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)).
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One duration fix and one typo fix. |
johentsch
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May 20, 2025
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johentsch
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johentsch
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Thanks @vzhyu and thanks for checking, @BeckerHanne!
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mvt 2 ready for review. See two issues at mm. 12 and 14, details in commits.