cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10271071
title: Frédéric Chopin - Mazurkas  (A corpus of annotated scores)

description: This corpus of annotated MuseScore files has been created within the DCML corpus initiative and employs the DCML harmony annotation standard. It is one out of nine similar corpora that have been grouped together to An Annotated Corpus of Tonal Piano Music from the Long 19th Century which comes with a data report that is currently under review.


The dataset lives on GitHub (link under "Related identifiers") and is stored on Zenodo purely for conservation and automatic DOI generation for new GitHub releases. For technical reasons, we include only brief, generic instructions on how to use the data. For more detailed documentation, please refer to the dataset's GitHub page.


What is included


The dataset includes annotated MusicScores .mscx files that have been created with MuseScore 3.6.2 and can be opened with any MuseScore 3, or later version. Apart from that, the score information (measures, notes, harmony labels) have been extracted in the form of TSV files which can be found respectively in the folders measures, notes, and harmonies. They have been extracted with the Python library ms3 and its documentation has a column glossary for looking up the meaning of a column.


Getting the data


You can download the dataset as a ZIP file from Zenodo or GitHub. Please note that these automatically generated ZIP files do not include submodules, which would appear as empty folders. If you need ZIP files, you will need to find the submodule repositories (e.g. via GitHub) and download them individually.


Apart from that, there is the possibility to git-clone the GitHub repository to your disk. This has the advantage that it allows to version-control any changes you want to make to the dataset and to ask for your changes to be included ("merged") in a future version.
DOI: 10.5281/zenodo.14939889
title: Frédéric Chopin - Mazurkas  (A corpus of annotated scores)

description: This corpus of annotated MuseScore files has been created within the DCML corpus initiative and employs the DCML harmony annotation standard. It is one out of nine similar corpora that have been grouped together to An Annotated Corpus of Tonal Piano Music from the Long 19th Century which comes with a data report that is currently under review.


The dataset lives on GitHub (link under "Related identifiers") and is stored on Zenodo purely for conservation and automatic DOI generation for new GitHub releases. For technical reasons, we include only brief, generic instructions on how to use the data. For more detailed documentation, please refer to the dataset's GitHub page.


What is included


The dataset includes annotated MusicScores .mscx files that have been created with MuseScore 3.6.2 and can be opened with any MuseScore 3, or later version. Apart from that, the score information (measures, notes, harmony labels) have been extracted in the form of TSV files which can be found respectively in the folders measures, notes, and harmonies. They have been extracted with the Python library ms3 and its documentation has a column glossary for looking up the meaning of a column.


Getting the data


You can download the dataset as a ZIP file from Zenodo or GitHub. Please note that these automatically generated ZIP files do not include submodules, which would appear as empty folders. If you need ZIP files, you will need to find the submodule repositories (e.g. via GitHub) and download them individually.


Apart from that, there is the possibility to git-clone the GitHub repository to your disk. This has the advantage that it allows to version-control any changes you want to make to the dataset and to ask for your changes to be included ("merged") in a future version.
DOI: 10.5281/zenodo.15292700
title: Frédéric Chopin – Mazurkas (A corpus of annotated scores)

description: https://dcmlab.github.io/chopin_mazurkas/
DOI: 10.5281/zenodo.7473566
title: Frédéric Chopin – Mazurkas (A corpus of annotated scores)

description: https://dcmlab.github.io/chopin_mazurkas/
DOI: 10.5281/zenodo.7535622
title: Frédéric Chopin - Mazurkas (A corpus of annotated scores)

description: This corpus of annotated MuseScore files has been created within the DCML corpus initiative and employs the DCML harmony annotation standard. It is one out of nine similar corpora that have been grouped together to An Annotated Corpus of Tonal Piano Music from the Long 19th Century which comes with a data report that is currently under review. <strong>Version 1</strong> has been released for submitting it as part of the data report <code>Hentschel, J., Rammos, Y., Neuwirth, M., Rohrmeier, M. (forthcoming). An Annotated Corpus of Tonal Piano Music from the Long 19th Century</code> that accompanies nine corpora grouped under the DOI 10.5281/zenodo.7483349. <strong>Version 1.1</strong> comes with a complete set of metadata and score headers. Among more accurate composition dates, the metadata now include URIs that identify the compositions in terms of the Virtual International Authority File (VIAF), Wikidata, IMSLP and MusicBrainz. The data has been re-extracted from the scores using ms3 1.1.1.
DOI: 10.5281/zenodo.8329063
title: Frédéric Chopin - Mazurkas (A corpus of annotated scores)

description: This corpus of annotated MuseScore files has been created within the DCML corpus initiative and employs the DCML harmony annotation standard. It is one out of nine similar corpora that have been grouped together to An Annotated Corpus of Tonal Piano Music from the Long 19th Century which comes with a data report that is currently under review. The dataset lives on GitHub (link under "Related identifiers") and is stored on Zenodo purely for conservation and automatic DOI generation for new GitHub releases. For technical reasons, we include only brief, generic instructions on how to use the data. For more detailed documentation, please refer to the dataset's GitHub page. <strong>What is included</strong> The dataset includes annotated MusicScores <strong>.mscx</strong> files that have been created with MuseScore 3.6.2 and can be opened with any MuseScore 3, or later version. Apart from that, the score information (measures, notes, harmony labels) have been extracted in the form of TSV files which can be found respectively in the folders <code>measures</code>, <code>notes</code>, and <code>harmonies</code>. They have been extracted with the Python library ms3 and its documentation has a column glossary for looking up the meaning of a column. <strong>Getting the data</strong> You can download the dataset as a ZIP file from Zenodo or GitHub. Please note that these automatically generated ZIP files do not include submodules, which would appear as empty folders. If you need ZIP files, you will need to find the submodule repositories (e.g. via GitHub) and download them individually. Apart from that, there is the possibility to git-clone the GitHub repository to your disk. This has the advantage that it allows to version-control any changes you want to make to the dataset and to ask for your changes to be included ("merged") in a future version.
DOI: 10.57760/sciencedb.20948
title: RNA seq
title: 瘤胃微生物测序

description: Rumen Microbial RNA Sequencing Data
description: Rumen Microbial RNA Sequencing Data
DOI: 10.6084/m9.figshare.28877909
title: Congenital Cataract

description: Long term congenital cataract surgery results
DOI: 10.15468/dl.kj8uc4
title: Occurrence Download

description: A dataset containing 95236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "Geometry POLYGON((-105.41811 24.31122,-106.01252 20.00176,-103.23862 18.26807,-96.60106 18.11947,-97.59174 22.72613,-102.79281 25.50004,-105.41811 24.31122))",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 95236 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001219-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.20950
title: 16S 核糖体 RNA （16S rRNA） 测序数据集：适用于 MAFLD 小鼠
title: 16S 核糖体 RNA （16S rRNA） 测序数据集：适用于 MAFLD 小鼠

description: 该数据集包含使用饲喂高脂肪饮食的 MAFLD 小鼠收集的肠道菌群数据。
description: 该数据集包含使用饲喂高脂肪饮食的 MAFLD 小鼠收集的肠道菌群数据。
DOI: 10.5281/zenodo.14996952
title: Clara Schumann – Lieder (A corpus of annotated scores)

description: No description provided.
DOI: 10.5281/zenodo.15292701
title: Clara Schumann – Lieder (A corpus of annotated scores)

description: No description provided.
DOI: 10.57760/sciencedb.20952
title: 56例产科抗磷脂综合征（OAPS）及其亚型患者的病历资料
title: 56例产科抗磷脂综合征（OAPS）及其亚型患者的病历资料

description: 2023年4月至2024年9月期间在河北医科大学第二医院免疫风湿科就诊的56例产科抗磷脂综合征（OAPS）及其亚型患者的病历资料(包括一般资料、凝血功能指标、免疫指标和辅助生殖结局等）
description: 2023年4月至2024年9月期间在河北医科大学第二医院免疫风湿科就诊的56例产科抗磷脂综合征（OAPS）及其亚型患者的病历资料(包括一般资料、凝血功能指标、免疫指标和辅助生殖结局等）
DOI: 10.57760/sciencedb.20953
title: Parameter-aware Reservoir Computing
title: Parameter-aware Reservoir Computing

description: We use the parameter-aware RC method to predict the time series, return map, and bifurcation diagram of the Logistic map. We also use the same method to predict the time series, phase diagram, and bifurcation diagram of the Chua circuit. In addition, we apply the RC method to predict the bifurcation diagram of the Lorenz 96 system, a higher-dimensional chaotic system. We demonstrate that for Reservoirs of the same size with the same system, after training with the same method, the prediction accuracy is affected by the number and position of the bifurcation parameters in the training set.
description: We use the parameter-aware RC method to predict the time series, return map, and bifurcation diagram of the Logistic map. We also use the same method to predict the time series, phase diagram, and bifurcation diagram of the Chua circuit. In addition, we apply the RC method to predict the bifurcation diagram of the Lorenz 96 system, a higher-dimensional chaotic system. We demonstrate that for Reservoirs of the same size with the same system, after training with the same method, the prediction accuracy is affected by the number and position of the bifurcation parameters in the training set.
DOI: 10.57760/sciencedb.j00186.00591
title: Retina algorithm for heavy-ion tracking in single-event effects localization
title: Retina algorithm for heavy-ion tracking in single-event effects localization

description: The data illustrates the distribution of single-track slope, intercept, and position resolution achieved by the Retina algorithm under various granularity settings. Additionally, it presents the distribution of vertical intercepts and position deviations for 5,500 single-track samples at a granularity of 72×100. Furthermore, the reconstruction results of multi-track slope, intercept, and position resolution by the IR Retina algorithm across different granularity settings are displayed, along with the algorithm's efficiency under these varying granularities.
description: The data illustrates the distribution of single-track slope, intercept, and position resolution achieved by the Retina algorithm under various granularity settings. Additionally, it presents the distribution of vertical intercepts and position deviations for 5,500 single-track samples at a granularity of 72×100. Furthermore, the reconstruction results of multi-track slope, intercept, and position resolution by the IR Retina algorithm across different granularity settings are displayed, along with the algorithm's efficiency under these varying granularities.
DOI: 10.57760/sciencedb.20968
title: Chinese Natural Speech Complex Emotion Dataset
title: Chinese Natural Speech Complex Emotion Dataset

description: Although Chinese speech affective computing has received increasing attention, existing datasets still have defects such as lack of naturalness, single pronunciation style, and unreliable annotation, which seriously hinder the research in this field. To address these issues, this paper introduces the first Chinese Natural Speech Complex Emotion Dataset (CNSCED) to provide natural data resources for Chinese speech affective computing. CNSCED was collected from publicly broadcasted civil dispute and interview television programs in China, reflecting the authentic emotional characteristics of Chinese people in daily life. The dataset includes 14 hours of speech data from 454 speakers of various ages, totaling 15777 samples. Based on the inherent complexity and ambiguity of natural emotions, this paper proposes an emotion vector annotation method. This method utilizes a vector composed of six meta-emotional dimensions (angry, sad, aroused, happy, surprise, and fear) of different intensities to describe any single or complex emotion. The CNSCED released two subtasks: complex emotion classification and complex emotion intensity regression. In the experimental section, we evaluated the CNSCED dataset using deep neural network models and provided a baseline result. To the best of our knowledge, CNSCED is the first public Chinese natural speech complex emotion dataset, which can be used for scientific research free of charge.
description: Although Chinese speech affective computing has received increasing attention, existing datasets still have defects such as lack of naturalness, single pronunciation style, and unreliable annotation, which seriously hinder the research in this field. To address these issues, this paper introduces the first Chinese Natural Speech Complex Emotion Dataset (CNSCED) to provide natural data resources for Chinese speech affective computing. CNSCED was collected from publicly broadcasted civil dispute and interview television programs in China, reflecting the authentic emotional characteristics of Chinese people in daily life. The dataset includes 14 hours of speech data from 454 speakers of various ages, totaling 15777 samples. Based on the inherent complexity and ambiguity of natural emotions, this paper proposes an emotion vector annotation method. This method utilizes a vector composed of six meta-emotional dimensions (angry, sad, aroused, happy, surprise, and fear) of different intensities to describe any single or complex emotion. The CNSCED released two subtasks: complex emotion classification and complex emotion intensity regression. In the experimental section, we evaluated the CNSCED dataset using deep neural network models and provided a baseline result. To the best of our knowledge, CNSCED is the first public Chinese natural speech complex emotion dataset, which can be used for scientific research free of charge.
DOI: 10.57760/sciencedb.20967
title: Daily 0.1° high-resolution XCO2 dataset, China 2015-2020
title: Daily 0.1° high-resolution XCO2 dataset, China 2015-2020

description: 此数据集提供每日高分辨率（0.1° 空间分辨率）XCO22015 年至 2020 年中国的数据。这些数据是使用基于深度学习的时空融合模型 （DSTM） 生成的，该模型集成了多个数据源，包括来自 OCO-2 和 CarbonTracker 的卫星观测，以及大气传输、碳排放和碳汇数据。
description: 此数据集提供每日高分辨率（0.1° 空间分辨率）XCO22015 年至 2020 年中国的数据。这些数据是使用基于深度学习的时空融合模型 （DSTM） 生成的，该模型集成了多个数据源，包括来自 OCO-2 和 CarbonTracker 的卫星观测，以及大气传输、碳排放和碳汇数据。
DOI: 10.57760/sciencedb.20976
title: Non-targeted metabolomics data of longissimus dorsi muscle in Taoyuan black and Duroc pigs at different ages
title: 不同日龄桃源黑猪和杜洛克猪背最长肌非靶向代谢组学数据

description: Non-targeted metabolomics data of longissimus dorsi muscle in Taoyuan black and Duroc pigs at 60, 120, 180, and 210 days. T represents Taoyuan Black pig, D represents Duroc pig.
description: Non-targeted metabolomics data of longissimus dorsi muscle in Taoyuan black and Duroc pigs at 60, 120, 180, and 210 days. T represents Taoyuan Black pig, D represents Duroc pig.
DOI: 10.57760/sciencedb.20980
title: The method description of support vector machine
title: The method description of support vector machine

description: The method description of support vector machine
description: The method description of support vector machine
DOI: 10.15468/dl.d4afaa
title: Occurrence Download

description: A dataset containing 68624 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "Geometry POLYGON((-105.83978 22.77567,-105.39397 19.7541,-104.30422 18.86249,-96.92366 20.19991,-97.86481 22.92427,-101.28265 24.4103,-105.83978 22.77567))",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 68624 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001220-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e243962
title: NNDSS - Table II. Tetanus to Varicella

description: NNDSS - Table II. Tetanus to Varicella - 2017. In this Table, provisional cases of selected notifiable diseases (≥1,000 cases reported during the preceding year), and selected low frequency diseases are displayed. The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories.

Note:
These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting.

Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available.

Footnotes:
C.N.M.I.: Commonwealth of Northern Mariana Islands.

U: Unavailable. —: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum.

*Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions.

† Case counts for reporting year 2017 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for tuberculosis are displayed in Table IV, which appears quarterly.
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description: (Downloaded 1/12/2026)
DOI: 10.3886/e243962v1
title: NNDSS - Table II. Tetanus to Varicella

description: NNDSS - Table II. Tetanus to Varicella - 2017. In this Table, provisional cases of selected notifiable diseases (≥1,000 cases reported during the preceding year), and selected low frequency diseases are displayed. The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories.

Note:
These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting.

Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available.

Footnotes:
C.N.M.I.: Commonwealth of Northern Mariana Islands.

U: Unavailable. —: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum.

*Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions.

† Case counts for reporting year 2017 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for tuberculosis are displayed in Table IV, which appears quarterly.
Read less
description: (Downloaded 1/12/2026)
DOI: 10.15468/dl.q7xjmw
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aphelasterias"
 ]
}

 The dataset includes 177 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007430-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j9ju4g
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerithium scabridum",
 "Country is Egypt",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084322-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwkw2m
title: Occurrence Download

description: A dataset containing 161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerithium scabridum",
 "Country is Israel",
 "HasCoordinate is true"
 ]
}

 The dataset includes 161 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084323-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avx7yg
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerithium scabridum",
 "Country is Cyprus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084321-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc110808134
title: Mesha Inscription, KAI 181, Mesha' Inscription, Mesha'-Inschrift, Mesha Stela, Mesha' Stela

description: [Text Description:] Commemoration of military actions and building projects of Mesha, King of Moab ❧ [Text or Publication Number Note:] KAI 181 ❧ [Excavation Description:] Discovered in 1868 by a German missionary in Diban (Dhiban) ❧ [Medium:] Stone ❧ [Script Note:] Moabite ❧ [Physical Object Description:] Basalt Stone, 1.1 meters high, 60-68 cm wide (or 1.15 m x 60 cm), rounded at the top ❧ [Physical Object Note:] Present stele has been restored from fragments, and missing text reconstructed based on paper squeezes