Resultados totales (Incluyendo duplicados): 34416
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CORA.Repositori de Dades de Recerca
doi:10.34810/data43
Dataset. 2021

SEQUENCE OF MODELS OBTAINED BY BACKWARDS SELECTION FOR UREA OF CALVES FED A MILK REPLACER SUPPLEMENTED WITH DIFFERENT AA COMBINATION

  • Terré, Marta
  • Ortuzar-Fernández, Iban
  • Graffelman, Jan
  • Bassols, Anna
  • Vidal Amigo, Maria
  • Bach, Alex
The effects on growth performance of supplementation of four different AA combinations in a milk replacer (MR, 25.4% CP and 20.3% fat) based on skimmed milk powder and whey protein concentrate were evaluated in 76 Holstein male calves (3 ± 1.7 d old). The 4 MR were: CTRL with no AA supplementation; PG supplying additional 0.3% Pro and 0.1% Gly; FY supplying additional 0.2% Phe and 0.2% Tyr; KMT providing additional 0.62% Lys, 0.22% Met, and 0.61% Thr. All calves were fed the same milk allowance program and were weaned at 56 d of study. Concentrate intake was limited to minimize interference of potential differences in solid feed intake among treatments. Animals were weighed weekly, intakes recorded daily, and blood samples obtained at 2, 5, and 7 wk of study to determine serum urea and plasma AA concentrations. The file presents data of the backwards selection process to assess the model that fitted better ADG with the study variables (performance, intake, plasma AA balances). Data was collected at Torre Marimon IRTA calf facilities (Caldes de Montbui, Spain) during 2017.

Proyecto: //
DOI: https://doi.org/10.34810/data43
CORA.Repositori de Dades de Recerca
doi:10.34810/data43
HANDLE: https://doi.org/10.34810/data43
CORA.Repositori de Dades de Recerca
doi:10.34810/data43
PMID: https://doi.org/10.34810/data43
CORA.Repositori de Dades de Recerca
doi:10.34810/data43
Ver en: https://doi.org/10.34810/data43
CORA.Repositori de Dades de Recerca
doi:10.34810/data43

CORA.Repositori de Dades de Recerca
doi:10.34810/data436
Dataset. 2022

TCGI PRESCRIPTION

  • Tamborero Noguera, David
  • Rubio Pérez, Carlota
  • Déu Pons, Jordi
  • Schroeder, Michael Philipp, 1986-
  • Vivancos Prellezo, Ana
  • Rovira Guerín, Ana
  • Tusquets, Ignasi
  • Albanell Mestres, Joan
  • Rodon, Jordi
  • Tabernero Cartula, Josep
  • Dienstman, Rodrigo
  • González-Pérez, Abel
  • López Bigas, Núria
CGI drug prescription assigns targeted drugs to a tumor, based on its genomic alterations, according different levels of evidence (from pre-clinical assays to clinical guidelines).

Proyecto: //
DOI: https://doi.org/10.34810/data436
CORA.Repositori de Dades de Recerca
doi:10.34810/data436
HANDLE: https://doi.org/10.34810/data436
CORA.Repositori de Dades de Recerca
doi:10.34810/data436
PMID: https://doi.org/10.34810/data436
CORA.Repositori de Dades de Recerca
doi:10.34810/data436
Ver en: https://doi.org/10.34810/data436
CORA.Repositori de Dades de Recerca
doi:10.34810/data436

CORA.Repositori de Dades de Recerca
doi:10.34810/data437
Dataset. 2023

MARD: MULTIMODAL ALBUM REVIEWS DATASET

  • Oramas, Sergio
-

Proyecto: //
DOI: https://doi.org/10.34810/data437
CORA.Repositori de Dades de Recerca
doi:10.34810/data437
HANDLE: https://doi.org/10.34810/data437
CORA.Repositori de Dades de Recerca
doi:10.34810/data437
PMID: https://doi.org/10.34810/data437
CORA.Repositori de Dades de Recerca
doi:10.34810/data437
Ver en: https://doi.org/10.34810/data437
CORA.Repositori de Dades de Recerca
doi:10.34810/data437

CORA.Repositori de Dades de Recerca
doi:10.34810/data438
Dataset. 2023

AUTOMATIC LABELING OF VASCULAR STRUCTURES WITH TOPOLOGICAL CONSTRAINTS VIA HMM [RESEARCH DATA]

  • Wang, Xingce
  • Liu, Yue
  • Wu, Zhongke
  • Mou, Xiao
  • Zhou, Mingquan
  • González Ballester, Miguel Ángel, 1973-
The project contains the implementation of the method described in: Wang et al., "Automatic labeling of vascular structures with topological constraints via HMM", MICCAI 2017. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment.

Proyecto: //
DOI: https://doi.org/10.34810/data438
CORA.Repositori de Dades de Recerca
doi:10.34810/data438
HANDLE: https://doi.org/10.34810/data438
CORA.Repositori de Dades de Recerca
doi:10.34810/data438
PMID: https://doi.org/10.34810/data438
CORA.Repositori de Dades de Recerca
doi:10.34810/data438
Ver en: https://doi.org/10.34810/data438
CORA.Repositori de Dades de Recerca
doi:10.34810/data438

CORA.Repositori de Dades de Recerca
doi:10.34810/data439
Dataset. 2016

TURKISH-OTTOMAN MAKAM (M)USIC ANALYSIS TOOLBOX (TOMATO)

  • Sentürk, Sertan
Research data from the thesis "Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music".

Proyecto: //
DOI: https://doi.org/10.34810/data439
CORA.Repositori de Dades de Recerca
doi:10.34810/data439
HANDLE: https://doi.org/10.34810/data439
CORA.Repositori de Dades de Recerca
doi:10.34810/data439
PMID: https://doi.org/10.34810/data439
CORA.Repositori de Dades de Recerca
doi:10.34810/data439
Ver en: https://doi.org/10.34810/data439
CORA.Repositori de Dades de Recerca
doi:10.34810/data439

CORA.Repositori de Dades de Recerca
doi:10.34810/data440
Dataset. 2023

COMPARATIVE EVALUATION AND COMBINATION OF AUTOMATIC RHYTHM DESCRIPTION SYSTEMS [RESEARCH DATA]

  • Zapata González, José Ricardo
The dataset DatasetVocal contains 75 excerpts with highly predominant vocals in WAV format and has been used for beat tracking evaluation.

Proyecto: //
DOI: https://doi.org/10.34810/data440
CORA.Repositori de Dades de Recerca
doi:10.34810/data440
HANDLE: https://doi.org/10.34810/data440
CORA.Repositori de Dades de Recerca
doi:10.34810/data440
PMID: https://doi.org/10.34810/data440
CORA.Repositori de Dades de Recerca
doi:10.34810/data440
Ver en: https://doi.org/10.34810/data440
CORA.Repositori de Dades de Recerca
doi:10.34810/data440

CORA.Repositori de Dades de Recerca
doi:10.34810/data441
Dataset. 2023

BEIJING OPERA PERCUSSION PATTERN DATASET

  • CompMusic
-Dataset-/n/n The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns/n/n-Audio Content-/n/nThe audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. /n/n-Annotations-/n/nEach of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using the reduced set of five syllables described in Table 1 of [1] and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). /n/n-Dataset organization-/n/nThe dataset has wav files and label files. The files are named as /n./nThe pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., NpID}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab/n/n-Availability of the Dataset-/n/n The annotations are publicly shared and available to all. The audio is from commercially available releases. It cannot be publicly shared but can be made available on request for non-commercial research purposes. In the future, the dataset would be available for viewing and download through an interface in Dunya (http://dunya.compmusic.upf.edu). Beijing Opera Percussion Pattern (BOPP) dataset is a collection of 133 audio percussion patterns covering five pattern classes. The dataset includes the audio and syllable level transcriptions for the patterns (non-time aligned). It is useful for percussion transcription and classification tasks. The patterns have been extracted from audio recordings of arias and labeled by a musicologist.

Proyecto: //
DOI: https://doi.org/10.34810/data441
CORA.Repositori de Dades de Recerca
doi:10.34810/data441
HANDLE: https://doi.org/10.34810/data441
CORA.Repositori de Dades de Recerca
doi:10.34810/data441
PMID: https://doi.org/10.34810/data441
CORA.Repositori de Dades de Recerca
doi:10.34810/data441
Ver en: https://doi.org/10.34810/data441
CORA.Repositori de Dades de Recerca
doi:10.34810/data441

CORA.Repositori de Dades de Recerca
doi:10.34810/data444
Dataset. 2023

SOUND AND MUSIC RECOMMENDATION WITH KNOWLEDGE GRAPHS [DATASET]

  • Oramas, Sergio
  • Ostuni, Vito Claudio
  • Vigliensoni, Gabriel
Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n. Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).

Proyecto: //
DOI: https://doi.org/10.34810/data444
CORA.Repositori de Dades de Recerca
doi:10.34810/data444
HANDLE: https://doi.org/10.34810/data444
CORA.Repositori de Dades de Recerca
doi:10.34810/data444
PMID: https://doi.org/10.34810/data444
CORA.Repositori de Dades de Recerca
doi:10.34810/data444
Ver en: https://doi.org/10.34810/data444
CORA.Repositori de Dades de Recerca
doi:10.34810/data444

CORA.Repositori de Dades de Recerca
doi:10.34810/data445
Dataset. 2014

BEIJING OPERA PERCUSSION INSTRUMENT DATASET

  • CompMusic
Beijing Opera percussion dataset is a collection of 236 examples of isolated strokes spanning the four percussion instrument classes used in Beijing Opera. It can be used to build stroke models for each percussion instrument. /nAll the sounds in this pack were played by Ying Wan of the London Jing Kun Opera Association. Recorded by Mi Tian at the Centre for Digital Music, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone under studio conditions.

Proyecto: //
DOI: https://doi.org/10.34810/data445
CORA.Repositori de Dades de Recerca
doi:10.34810/data445
HANDLE: https://doi.org/10.34810/data445
CORA.Repositori de Dades de Recerca
doi:10.34810/data445
PMID: https://doi.org/10.34810/data445
CORA.Repositori de Dades de Recerca
doi:10.34810/data445
Ver en: https://doi.org/10.34810/data445
CORA.Repositori de Dades de Recerca
doi:10.34810/data445

CORA.Repositori de Dades de Recerca
doi:10.34810/data446
Dataset. 2023

TURKISH MAKAM SYMBOLIC PHRASE DATASET

  • CompMusic
This study presents a large machine-readable dataset of Turkish makam music scores segmented into phrases by experts of this music. The segmentation facilitates computational research on melodic similarity between phrases, and relation between melodic phrasing and meter, rarely studied topics due to unavailability of data resources.

Proyecto: //
DOI: https://doi.org/10.34810/data446
CORA.Repositori de Dades de Recerca
doi:10.34810/data446
HANDLE: https://doi.org/10.34810/data446
CORA.Repositori de Dades de Recerca
doi:10.34810/data446
PMID: https://doi.org/10.34810/data446
CORA.Repositori de Dades de Recerca
doi:10.34810/data446
Ver en: https://doi.org/10.34810/data446
CORA.Repositori de Dades de Recerca
doi:10.34810/data446

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