cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2619499
title: MAST rhythm re-annotated subset

description: In the work described in the paper <em>A dataset of rhythmic pattern reproductions and baseline automatic assessment system</em><em> (ISMIR, 2019)</em>, Falcão et al. applied efforts on annotating a subset of the full MAST rhythm dataset with grades for a subset of performances. Out of a total of 1040 student submissions comprised by the original dataset, 80 performances (equally distributed between 20 references) were graded by annotators using a custom annotation tool. During this process annotators could play each <em>(reference, performance)</em> pair as many times as desired before deciding on one of the available assessments: <em>4-Perfect, 3-Minor errors, 2-Major errors, 1-Completely off.</em> <strong>Naming convention</strong> This dataset is a collection of audio files, onset features and user-annotated data. Audio files are stored into the <em>Only Performances</em> and <em>Only References </em>folders, together with some extra files containing their original onsets, quantized onsets and scaling parameters (for more information regarding this auxiliary data, please check the paper). An indexed list of files (<em>listreferences </em>and<em> listperformances</em>) is also stored in each of these folders in order to allow for mapping between audio files and auxiliary data. Example: The 10th line in <em>Only Performances/MAST Onsets [Performances] </em>contains the pure onsets for the 10th audio file listed in the <em>Only Performances/listperformances </em>file ('55_rhy2_per138459_pass.wav') Also, in order to check the reference which relates to a specific performance, one must also look for audio file names in the list of files: the <em>i-eth</em> audio file listed in <em>Only Performances/listperformances </em>is a specific performance whose reference is defined by the<em> i-eth</em> line of <em>Only References/listreferences. </em>Example: The 10th audio file listed in <em>Only Performances/listperformances </em>file ('55_rhy2_per138459_pass.wav') is a performance whose reference is stored in the 10th line of <em>Only References/listreferences </em>(55_rhy2_ref162559.wav) Such convention is also applied to the folder that contains the annotations (<em>Performances Annotations</em>). Each one of the <em>annotator_<strong>X</strong>.txt </em>file lists the grades assigned by the <em><strong>X</strong>-ith</em> annotator, and the mapping between the assessments and their referred performances is indexed in <em>listfiles.txt</em>
DOI: 10.5281/zenodo.2619498
title: MAST rhythm re-annotated subset

description: In the work described in the paper <em>A dataset of rhythmic pattern reproductions and baseline automatic assessment system</em><em> (ISMIR, 2019)</em>, Falcão et al. applied efforts on annotating a subset of the full MAST rhythm dataset with grades for a subset of performances. Out of a total of 1040 student submissions comprised by the original dataset, 80 performances (equally distributed between 20 references) were graded by annotators using a custom annotation tool. During this process annotators could play each <em>(reference, performance)</em> pair as many times as desired before deciding on one of the available assessments: <em>4-Perfect, 3-Minor errors, 2-Major errors, 1-Completely off.</em> <strong>Naming convention</strong> This dataset is a collection of audio files, onset features and user-annotated data. Audio files are stored into the <em>Only Performances</em> and <em>Only References </em>folders, together with some extra files containing their original onsets, quantized onsets and scaling parameters (for more information regarding this auxiliary data, please check the paper). An indexed list of files (<em>listreferences </em>and<em> listperformances</em>) is also stored in each of these folders in order to allow for mapping between audio files and auxiliary data. Example: The 10th line in <em>Only Performances/MAST Onsets [Performances] </em>contains the pure onsets for the 10th audio file listed in the <em>Only Performances/listperformances </em>file ('55_rhy2_per138459_pass.wav') Also, in order to check the reference which relates to a specific performance, one must also look for audio file names in the list of files: the <em>i-eth</em> audio file listed in <em>Only Performances/listperformances </em>is a specific performance whose reference is defined by the<em> i-eth</em> line of <em>Only References/listreferences. </em>Example: The 10th audio file listed in <em>Only Performances/listperformances </em>file ('55_rhy2_per138459_pass.wav') is a performance whose reference is stored in the 10th line of <em>Only References/listreferences </em>(55_rhy2_ref162559.wav) Such convention is also applied to the folder that contains the annotations (<em>Performances Annotations</em>). Each one of the <em>annotator_<strong>X</strong>.txt </em>file lists the grades assigned by the <em><strong>X</strong>-ith</em> annotator, and the mapping between the assessments and their referred performances is indexed in <em>listfiles.txt</em>
DOI: 10.17632/ffr9v7yyzv.1
title: Data for: Relicts of mud diapirism of the emerged wedge-top as an indicator of gas hydrates destabilization of the Manila accretionary prism in the southern Taiwan (Hengchun Peninsula).

description: 40 m DEM stable isotope results seismic profiles
DOI: 10.17632/mk6y7cxhsf.1
title: Data for: Relicts of mud diapirism of the emerged wedge-top as an indicator of gas hydrates destabilization of the Manila accretionary prism in the southern Taiwan (Hengchun Peninsula).

description: 40 m DEM stable isotope results
DOI: 10.17632/t45kpxd879.1
title: Data for: Relicts of mud diapirism of the emerged wedge-top as an indicator of gas hydrates destabilization of the Manila accretionary prism in the southern Taiwan (Hengchun Peninsula).

description: 40 m DEM stable isotope results
DOI: 10.5281/zenodo.2620419
title: Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

description: <strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1 and S2. Legends are included under each figure.

<strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.
description: {"references": ["Budczies J, Brockmoller SF, et al. (2013). Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism. J Proteomics. 2013;94:279-88.", "Peixoto TP (2014). The graph-tool python library. 10.6084/m9.figshare.1164194"]}
DOI: 10.5281/zenodo.2620636
title: MedleyDB - Melody Subset

description: <strong>Subset of MedleyDB</strong>: Mix audio files and Melody Annotations for the 108 files in the MedleyDB multitrack dataset containing melody.

There are 3 types of melody annotations released:


	melody1: "The f0 curve of the predominant melodic line drawn from a single source"
	melody2: "The f0 curve of the predominant melodic line drawn from multiple sources"
	melody3: "The f0 curves of all melodic lines drawn from multiple sources"


For further details, refer to the MedleyDB website.

<strong>Further Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: </strong><em>Metadata</em> can be found here, <em>Annotations</em> can be found here.

For detailed information about the dataset, please visit MedleyDB's website.

 

If you make use of MedleyDB for academic purposes, please cite the following publication:<br>
<br>
<em>R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.</em>
description: {"references": ["R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, \"MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research\", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014."]}
DOI: 10.14469/hpc/5390
title: test

description: test
DOI: 10.14469/hpc/5391
title: test

description: test
DOI: 10.14469/hpc/5392
title: test

description: test
DOI: 10.17632/7mp25kyb4p.3
title: varRhoTurbVOF

description: The volume of fluid (VOF) method is a popular approach for multiphase flow modeling. The open-source computational fluid dynamics (CFD) software, OpenFOAM, has implemented a variety of VOF-based solvers and provides users a wide range of turbulence models. Since flows under the VOF framework belong to the variable-density incompressible flow category, the isothermal VOF-based solvers in OpenFOAM fail to use the correct turbulence models. varRhoTurbVOF is designed to solve this issue and with the hope to replace all the corresponding existing solvers in the future. With the object-oriented paradigm, varRhoTurbVOF guarantees the usability, reusability and maintainability of the codes. Aside from turbulence modeling, all other features in the original solvers are preserved in varRhoTurbVOF.
DOI: 10.17632/77jp7b5853.1
title: Data for: Differential crustal deformation across the Cona-Oiga rift, southern Tibetan Plateau

description: Online Supplement for: “Differential crustal deformation across the Cona-Oiga rift, southern Tibetan Plateau”
DOI: 10.17632/tdws77xns2.1
title: Data for: Coupling plankton-sediment trap-surface sediment coccolithophore regime in the North Aegean Sea (NE Mediterranean)

description: Plankton, sediment trap, surface sediment coccolithophore assemblage in M2 site (North Aegean Sea, Athos basin)
DOI: 10.5281/zenodo.1413513
title: "Marsh" database: characterization of the state of the necton in two depolderized zones of the Gironde estuary

description: In the Gironde estuary, two accidentally de-polderized marshes have been the subject of biological monitoring (necton) since 2008: the Marais de Mortagne-sur-Gironde and the northern part of Île Nouvelle. The Mortagne-sur-Gironde marsh is located in the mesohalin sector of the Gironde estuary. With an area of 191 hectares, the marsh was dammed in 1966 and cultivated for more than 30 years. Its protective dike gave way during the December 1999 storm. Ile Nouvelle is located in the Oligaline Sector of the Gironde Estuary. The northern part of the island - an area of 141 hectares - has been depolderized during the passage of storm Xynthia in 2010, following the opening of a breach.<br>
Biological monitoring (necton) was carried out in the Gironde estuary over the period 2008-2016 to test the hypothesis that the depoldisation of the estuarine shorelines makes it possible to recreate nursery and feeding areas for fish and fish. estuarine and coastal macrocrustaceans. This work was carried out by the National Institute for Research in Science and Technology for the Environment and Agriculture (Irstea), in partnership with the Conservatoire du Littoral, the Gironde Departmental Council and the Regional Council for Natural Areas ( CREN) of Poitou-Charentes.<br>
The "Marsh" database includes all the surveys carried out throughout the study, between 2008 and 2016, and in particular samplings carried out within the necton.
DOI: 10.5281/zenodo.1413514
title: "Marsh" database: characterization of the state of the necton in two depolderized zones of the Gironde estuary

description: In the Gironde estuary, two accidentally de-polderized marshes have been the subject of biological monitoring (necton) since 2008: the Marais de Mortagne-sur-Gironde and the northern part of Île Nouvelle. The Mortagne-sur-Gironde marsh is located in the mesohalin sector of the Gironde estuary. With an area of 191 hectares, the marsh was dammed in 1966 and cultivated for more than 30 years. Its protective dike gave way during the December 1999 storm. Ile Nouvelle is located in the Oligaline Sector of the Gironde Estuary. The northern part of the island - an area of 141 hectares - has been depolderized during the passage of storm Xynthia in 2010, following the opening of a breach.<br>
Biological monitoring (necton) was carried out in the Gironde estuary over the period 2008-2016 to test the hypothesis that the depoldisation of the estuarine shorelines makes it possible to recreate nursery and feeding areas for fish and fish. estuarine and coastal macrocrustaceans. This work was carried out by the National Institute for Research in Science and Technology for the Environment and Agriculture (Irstea), in partnership with the Conservatoire du Littoral, the Gironde Departmental Council and the Regional Council for Natural Areas ( CREN) of Poitou-Charentes.<br>
The "Marsh" database includes all the surveys carried out throughout the study, between 2008 and 2016, and in particular samplings carried out within the necton.
DOI: 10.17632/9mbdcpx39w.1
title: Data for: Marinitoga lauensis sp. nov., a novel deep-sea hydrothermal vent thermophilic anaerobic heterotroph with a prophage.

description: Marinitoga lauensis sp. nov., a novel deep-sea hydrothermal vent thermophilic anaerobic heterotroph with a prophage. Stéphane L’Haridona*, Léna Gouhiera, Emily St. Johnb and Anna-Louise Reysenbachb Univ Brest, CNRS, IFREMER, Laboratoire de Microbiologie des Environnements Extrêmes, F-29280, Plouzané, Francea; Portland State University, Department of Biology and Center for Life in Extreme Environments, P.O. Box 751, Portland, OR 97207 USAb.
DOI: 10.5061/dryad.bc7tv17/1
title: Dryad Item 10.5061/DRYAD.BC7TV17/1

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/2
title: Dryad Item 10.5061/DRYAD.BC7TV17/2

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/3
title: Dryad Item 10.5061/DRYAD.BC7TV17/3

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/4
title: Dryad Item 10.5061/DRYAD.BC7TV17/4

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/10
title: Dryad Item 10.5061/DRYAD.BC7TV17/10

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/11
title: Dryad Item 10.5061/DRYAD.BC7TV17/11

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/12
title: Dryad Item 10.5061/DRYAD.BC7TV17/12

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/13
title: Dryad Item 10.5061/DRYAD.BC7TV17/13

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/5
title: Dryad Item 10.5061/DRYAD.BC7TV17/5

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/6
title: Dryad Item 10.5061/DRYAD.BC7TV17/6

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/7
title: Dryad Item 10.5061/DRYAD.BC7TV17/7

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/8
title: Dryad Item 10.5061/DRYAD.BC7TV17/8

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/9
title: Dryad Item 10.5061/DRYAD.BC7TV17/9

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/14
title: Dryad Item 10.5061/DRYAD.BC7TV17/14

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/15
title: Dryad Item 10.5061/DRYAD.BC7TV17/15

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/16
title: Dryad Item 10.5061/DRYAD.BC7TV17/16

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/17
title: Dryad Item 10.5061/DRYAD.BC7TV17/17

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/18
title: Dryad Item 10.5061/DRYAD.BC7TV17/18

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/19
title: Dryad Item 10.5061/DRYAD.BC7TV17/19

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/20
title: Dryad Item 10.5061/DRYAD.BC7TV17/20

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/21
title: Dryad Item 10.5061/DRYAD.BC7TV17/21

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/22
title: Dryad Item 10.5061/DRYAD.BC7TV17/22

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/23
title: Dryad Item 10.5061/DRYAD.BC7TV17/23

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/24
title: Dryad Item 10.5061/DRYAD.BC7TV17/24

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/25
title: Dryad Item 10.5061/DRYAD.BC7TV17/25

description: (:tba)
DOI: 10.5061/dryad.bc7tv17/26
title: Dryad Item 10.5061/DRYAD.BC7TV17/26

description: (:tba)
DOI: 10.5281/zenodo.2620292
title: CODEBRIM: COncrete DEfect BRidge IMage Dataset

description: <strong>CODEBRIM: COncrete DEfect BRidge IMage Dataset</strong> for multi-target multi-class concrete defect classification in computer vision and machine learning.

Dataset as presented and detailed in our CVPR 2019 publication: http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html or https://arxiv.org/abs/1904.08486 . If you make use of the dataset <strong>please cite it as follows</strong>:

<strong>"Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. <em>Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset</em>. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019"</strong>

We offer a supplementary GitHub repository with code to reproduce the paper and data loaders: https://github.com/ccc-frankfurt/meta-learning-CODEBRIM

For ease of use we provide the dataset in multiple different versions.

Files contained:<br>
* CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations<br>
* CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes <br>
* CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning<br>
* CODEBRIM_classification_balanced_dataset: similar to "CODEBRIM_classification_dataset" but with the exact replication of training images to balance the dataset in order to reproduce results obtained in the paper.
DOI: 10.17632/942p238n4r.1
title: Data for: Antecedent precipitation influences the bacterial processing of terrestrial dissolved organic matter in a North Carolina estuary

description: Site identification, hydrographic properties, DOC concentrations and qualitative indicators, stable isotope values, bacterial production, and phenanthrene mineralization rates.
DOI: 10.21227/ahk8-tr21
title: Situational Awareness Assisted MmWave Vehicular Beam Training

description: Data and codes for journal paper"MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning"submitted to IEEE Access.The code assumes Python 3.The datasetWe simulate and collect the data in Ray tracing simulation in an urban canyon. An example is given in Fig. 2 and Fig. 3 in the paper. The dataset includes the features (defined situational awareness vector of a certain receiver) and the beam RSRP for different beam pairs used. DFT beam codebook is deployed at the transmitter and receiver.Noisy situational awareness featuresIn the paper, we consider several different sources of noise for situational awareness. We include the dataset with localization error, different connecting rates, and different location reporting frequencies.Channel statistics with UPAs of different sizesThe dataset includes the channel information with 4x4, 4x8, 16X16 UPA deployed.To limit the size of data which includes 16^4 = 65536 beam RSRP of over 100K samples, we only include the information of the top 100 beams (including the beam power and the beam pair index) in the data. The beam index file can be found in "beam_index_nx_XXX_ny_XXX. csv" and the corresponding beam RSRP is in "channel_power_nx_XXX_ny_XXX.csv".Due to the size limit of uploaded data in github, the data can be retrieved fromGoogle Drivethrough the following link.https://drive.google.com/drive/u/0/folders/1v8TmiMa2ATYmFxn2nB9sGDS5e3UcCk_B
DOI: 10.21227/mcpe-pq39
title: Situational Awareness Assisted MmWave Vehicular Beam Training

description: Data and codes for journal paper"MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning"submitted to IEEE Access.The code assumes Python 3.The datasetWe simulate and collect the data in Ray tracing simulation in an urban canyon. An example is given in Fig. 2 and Fig. 3 in the paper. The dataset includes the features (defined situational awareness vector of a certain receiver) and the beam RSRP for different beam pairs used. DFT beam codebook is deployed at the transmitter and receiver.Noisy situational awareness featuresIn the paper, we consider several different sources of noise for situational awareness. We include the dataset with localization error, different connecting rates, and different location reporting frequencies.Channel statistics with UPAs of different sizesThe dataset includes the channel information with 4x4, 4x8, 16X16 UPA deployed.To limit the size of data which includes 16^4 = 65536 beam RSRP of over 100K samples, we only include the information of the top 100 beams (including the beam power and the beam pair index) in the data. The beam index file can be found in "beam_index_nx_XXX_ny_XXX. csv" and the corresponding beam RSRP is in "channel_power_nx_XXX_ny_XXX.csv".Due to the size limit of uploaded data in github, the data can be retrieved fromGoogle Drivethrough the following link.https://drive.google.com/drive/u/0/folders/1v8TmiMa2ATYmFxn2nB9sGDS5e3UcCk_B
DOI: 10.3886/e100366v1-11081
title: Social-Economic Change and its Impact on Violence: Homicide History of Qing China

description: In
Social-Economic
Change and its Impact on Violence: Homicide History of Qing China, Zhiwu Chen, Kaixiang Peng and Lijun Zhu offer the first estimate of interpersonal homicide rates of China for the period 1661–1898.
The researchers find that the annual homicide rate ranged between 0.35 and
1.47 per 100,000 inhabitants
during that period, a low level unmatched by
Western Europe until the late 19th century. China’s
homicide rate rose steadily from 1661 to 1821 but declined gradually thereafter until the turn of
the century.
DOI: 10.5281/zenodo.2611108
title: Discovery of tandem and interspersed segmental duplications using high throughput sequencing

description: We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the  simulation experiments, using a 30x coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5\% for the top 50 predictions).
description: Here we deposit current versions of TARDIS (1.0.2) and CNVSim, and all predictions, truth sets, and the CRAM files for the simulation data.