cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6546212
title: On-Glacier Air Temperatures for Miage Debris-Covered Glacier, 2014

description: Miage_Glacier_Air_Temperature_Data_2014.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022 Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk Data period: 1st July - 27th September, 2014 <br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across the debris-covered Miage Glacier, Italy (45.8129°N, 6.8458°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields. Off-Glacier air temperatures (measured as above) are provided for comparison with on-glacier air temperatures. For additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Fyffe, C. L., Pellicciotti, F., Rutter, N., & Diotri, F. (2016).<br> Air temperature distribution and energy-balance modelling of a debris-covered glacier. Journal of Glaciology, 62(231), 1–14. <br> https://doi.org/10.1017//jog.2016.31 Please cite the above article for any usage of the dataset. Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/
DOI: 10.6084/m9.figshare.19672695
title: Supplementary Material for: Global prevalence of myotonic dystrophy: an updated systematic review and meta-analysis

description: Introduction: Myotonic dystrophy (DM), the most common muscular dystrophy in adults, is a group of autosomal inherited neuromuscular disorders characterized by progressive muscle weakness, myotonia and cardiac conduction abnormalities. Due to the different gene mutations, DM has been subclassified into myotonic dystrophy type 1 (DM1) and type 2 (DM2). However, the prevalence studies on DM and its subtypes are insufficient. Methods: The PubMed (1966-2022), MEDLINE (1950-2022), Web of Science (1864-2022) and Cochrane Library (2022) databases were searched for original research articles published in English. The quality of the included studies was assessed by a checklist adapted from STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). To derive the pooled epidemiological prevalence estimates, a meta-analysis was performed using the random effects model. Heterogeneity was assessed using the Cochrane Q statistic and the I2 statistic. Results: A total of 17 studies were included in the systematic review and meta-analysis. Of the 17 studies evaluated, 14 studies were considered medium quality, two studies were considered high quality and one study was considered low quality. The global prevalence of DM varied widely from 0.37 to 36.29 cases per 100,000. The pooled estimate of the prevalence of DM was 9.99 cases (95% CI: 5.62-15.53) per 100,000. The pooled estimate of the prevalence of DM1 was 9.27 cases (95% CI: 4.73-15.21) per 100,000, ranging from 0.37-36.29 cases per 100,000. The pooled estimate of the prevalence of DM2 was 2.29 cases (95% CI: 0.17-6.53) per 100,000, ranging from 0.00-24.00 cases per 100,000. Conclusion: Our study provided accurate estimates of the prevalence of myotonic dystrophy. The high heterogeneity and the lack of high-quality studies highlight the need to conduct higher quality studies on orphan diseases.
DOI: 10.5281/zenodo.6546225
title: Cold Region Data Accessibility Portal for Québec (CRDAP-QC)

description: The Cold Region Data Accessibility Portal for Quebec is a collection of public data query platforms to display, analyze, and compare freeze and thaw, as well as climate data, for the province of Quebec. This suite of products was made by the Water Security and Climate Change Lab, located at Concordia University. To visit the portal, please check http://wscc.encs.concordia.ca/home.html.
DOI: 10.5281/zenodo.5837868
title: Supporting data and code for: Thiophanate-methyl and carbendazim resistance in Fusicoccum amygdali, the causal agent of constriction canker of peach and almond

description: This is the first release of the final data and code for the article accepted for publication in Plant Pathology journal. It contains all the necessary scripts to perform the data analysis and to produce the Figures. All the necessary data can be found in the 'data' folder.
DOI: 10.5281/zenodo.3473232
title: Supporting data and code for: Thiophanate-methyl and carbendazim resistance in Fusicoccum amygdali, the causal agent of constriction canker of peach and almond

description: This is the first release of the final data and code for the article accepted for publication in Plant Pathology journal. It contains all the necessary scripts to perform the data analysis and to produce the Figures. All the necessary data can be found in the 'data' folder.
DOI: 10.5281/zenodo.6534429
title: Synth-Salience Choral Set

description: The <strong>Synth-salience Choral Set</strong> (SSCS) is a publicly available dataset for voice assignment based on pitch salience. The dataset was created to support research on voice assignment based on pitch salience. By definition, an “ideal” pitch salience representation of a music recording is zero everywhere where there is no perceptible pitch, and has a positive value that reflects the pitches’ perceived energy at the frequency bins of the corresponding F0 values. In practice, for a normalized synthetic pitch salience function we assume a value equal to the maximum energy (salience), i. e., 1, in the time-frequency bins that correspond to the notes present in a song, and 0 elsewhere. We obtain such a synthetic pitch salience representation directly by processing the digital (MusicXML, MIDI) score of a music piece, using the desired time and frequency quantization, i. e., a time-frequency grid. To build the SSCS, we collect scores of four-part (SATB) a cappella choral music from the Choral Public Domain Library (CPDL) using their API. We assemble a collection of <strong>5381 scores</strong> in MusicXML format, which we subsequently convert into MIDI files for an easier parsing. <br> Each song in the dataset comprises five CSV files: one with the polyphonic pitch salience representation of the four voices (*_mix.csv) and four additional files with the monophonic pitch salience representation of each voice separately (*_S/A/T/B.csv). In both cases, the asterisk refers to the name of the song, which is shared between all representations from the same song.<br> Besides the pitch salience files, we provide a metadata CSV file (sscs_metadata.csv) which indicates the associated CPDL URL for each song in the dataset. Note that this dataset contains the input/output features used in the cited study, i.e., salience functions, and not audio files nor scores. However, the accompanying metadata file allows researchers to access the associated open access scores for each example in the dataset. When using this dataset for your research, please cite: Helena Cuesta and Emilia Gómez (2022). <strong>Voice Assignment in Vocal Quartets using Deep Learning Models based on Pitch Salience</strong>. Transactions of the International Society for Music Information Retrieval (TISMIR). <em>To appear.</em> Helena Cuesta (2022). <strong>Data-driven Pitch Content Description of Choral Singing Recordings</strong>. PhD thesis. Universitat Pompeu Fabra, Barcelona.
DOI: 10.5281/zenodo.6534428
title: Synth-Salience Choral Set

description: The <strong>Synth-salience Choral Set</strong> (SSCS) is a publicly available dataset for voice assignment based on pitch salience. The dataset was created to support research on voice assignment based on pitch salience. By definition, an “ideal” pitch salience representation of a music recording is zero everywhere where there is no perceptible pitch, and has a positive value that reflects the pitches’ perceived energy at the frequency bins of the corresponding F0 values. In practice, for a normalized synthetic pitch salience function we assume a value equal to the maximum energy (salience), i. e., 1, in the time-frequency bins that correspond to the notes present in a song, and 0 elsewhere. We obtain such a synthetic pitch salience representation directly by processing the digital (MusicXML, MIDI) score of a music piece, using the desired time and frequency quantization, i. e., a time-frequency grid. To build the SSCS, we collect scores of four-part (SATB) a cappella choral music from the Choral Public Domain Library (CPDL) using their API. We assemble a collection of <strong>5381 scores</strong> in MusicXML format, which we subsequently convert into MIDI files for an easier parsing. <br> Each song in the dataset comprises five CSV files: one with the polyphonic pitch salience representation of the four voices (*_mix.csv) and four additional files with the monophonic pitch salience representation of each voice separately (*_S/A/T/B.csv). In both cases, the asterisk refers to the name of the song, which is shared between all representations from the same song.<br> Besides the pitch salience files, we provide a metadata CSV file (sscs_metadata.csv) which indicates the associated CPDL URL for each song in the dataset. Note that this dataset contains the input/output features used in the cited study, i.e., salience functions, and not audio files nor scores. However, the accompanying metadata file allows researchers to access the associated open access scores for each example in the dataset. When using this dataset for your research, please cite: Helena Cuesta and Emilia Gómez (2022). <strong>Voice Assignment in Vocal Quartets using Deep Learning Models based on Pitch Salience</strong>. Transactions of the International Society for Music Information Retrieval (TISMIR). <em>To appear.</em> Helena Cuesta (2022). <strong>Data-driven Pitch Content Description of Choral Singing Recordings</strong>. PhD thesis. Universitat Pompeu Fabra, Barcelona.
DOI: 10.34740/kaggle/dsv/3627518
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6546305
title: On-Glacier Air Temperatures for Tsanteleina Glacier, 2015

description: Tsanteleina_Glacier_Air_Temperature_Data_2015.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022 Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk Data period: 19th June - 15th September, 2015 <br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across Tsanteleina Glacier, Italy (45.4812°N, 7.0618°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields. Some stations fell over at times during the summer season due to differential ablation and tripod stability.<br> Filtering of this data therefore leaves gaps (NaNs) for various stations at different times of the observation period. Off-Glacier air temperatures in the region can be accessed from the platform of the Regione Autonoma Valle d'Aosta:<br> https://presidi2.regione.vda.it/str_dataview_station/3060. Additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). <br> Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. <br> Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65 Please cite the above article for any usage of the dataset. Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/ <br>
DOI: 10.5281/zenodo.6546304
title: On-Glacier Air Temperatures for Tsanteleina Glacier, 2015

description: Tsanteleina_Glacier_Air_Temperature_Data_2015.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022 Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk Data period: 19th June - 15th September, 2015 <br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across Tsanteleina Glacier, Italy (45.4812°N, 7.0618°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields. Some stations fell over at times during the summer season due to differential ablation and tripod stability.<br> Filtering of this data therefore leaves gaps (NaNs) for various stations at different times of the observation period. Off-Glacier air temperatures in the region can be accessed from the platform of the Regione Autonoma Valle d'Aosta:<br> https://presidi2.regione.vda.it/str_dataview_station/3060. Additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). <br> Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. <br> Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65 Please cite the above article for any usage of the dataset. Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/ <br>
DOI: 10.25345/c5tm7249s
title: MassIVE MSV000089465 - Multiple Layers of Complexity in O-Glycosylation Illustrated with the Urinary Glycoproteome
title: Sample: Human Urine (from 10 male donors) Sample preparation: FASP Digestion: Trypsin Enrichment: WGA (wheat germ agglutinin) affinity chromatography LC-MS/MS analysis: HCD and HCD-pd-EThcD Peak list files were collected into folders by patient and MS/MS activation type.
DOI: 10.24417/bmdc.be:dataset:561
title: CRA: Nutrients in the Southern Bight of the North Sea, by VUB, ULB and MUMM (1976-1982)

description: Missing
DOI: 10.24417/bmdc.be:dataset:2011
title: Abundance across trophic guilds dataset in support of the Marine Strategy Framework Directive (MSFD) Descriptor 4 (D04C02, 2018 Reporting)
title: Dataset i.v.m. de dichtheid van trofische gilden voor de Kaderrichtlijn Mariene Strategie (KRMS) Descriptor 4 (D04C02, Rapportage 2018)
title: Jeu de données sur les abondances des différentes guildes trophiques en support de la Directive-cadre stratégie pour le milieu marin (DCSMM) Descripteur 4 (D04C02, Rapport 2018)
title: Abundance across trophic guilds dataset in support of the Marine Strategy Framework Directive (MSFD) Descriptor 4 (D04C02, 2018 Reporting)
title: Datensatz über die Dichten trophischen Gilden zur Unterstützung der Meeresstrategie-Rahmenrichtlinie Descriptor 4 (D04C02, Bericht 2018)

description: This dataset is part of the 2018 Belgian submission for the Marine Strategy Framework Directive (MSFD) linked to descriptor 4, criterion 2. Three distinct datasets are published for this criterion.'Occurrence of breeding seabirds' describes the observation of eight seabird species (Common tern, Black-headed Gull, Herring Gull, Common Gull, Small Black-backed Gull, Yellow-legged gull, Big Stern and Little Stern) on the Belgian coast between 1992 and 2015. 'Density of seabirds' describes the density of seabirds (N birds/km) observed at large in the Belgian part of the North Sea between 1987 and 2016 (three surveys a year). Thirteen species are monitored and the evolution of each species density is shown as diagrams.'Occurrence of important benthic species' describes the results of Van Veen sampling campaigns performed between 2009 and 2014 in the Belgian part of the North Sea. Benthic species are counted and identified (e.g. Annelida, Echinodermata,..).
DOI: 10.5445/ir/1000146001
title: Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models - Supplementary Material
DOI: 10.5517/ccdc.csd.cc268j67
title: CCDC 2033978: Experimental Crystal Structure Determination

description: Related Article: Xiao Xiao, Stephan K. Pedersen, Daniel Aranda, Jingjing Yang, Ren A. Wiscons, Michael Pittelkow, Michael L. Steigerwald, Fabrizio Santoro, Nathaniel J. Schuster, Colin Nuckolls|2020|J.Am.Chem.Soc.|143|983|doi:10.1021/jacs.0c11260
DOI: 10.5281/zenodo.6546367
title: Rate file for diffuse and translucent cloud chemistry

description: Rate file for astrochemical models shown in Section 5.7.2 of the book "Astrochemistry: Chemistry in Interstellar and Circumstellar Space" by DA Williams and C Cecchi-Pestellini
DOI: 10.5281/zenodo.6546366
title: Rate file for diffuse and translucent cloud chemistry

description: Rate file for astrochemical models shown in Section 5.7.2 of the book "Astrochemistry: Chemistry in Interstellar and Circumstellar Space" by DA Williams and C Cecchi-Pestellini
DOI: 10.5281/zenodo.6546188
title: Learning to quantify: LeQua 2022 datasets

description: # Learning to Quantify The aim of LeQua 2022 (the 1st edition of the CLEF “Learning to Quantify” lab) is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) will be required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set. ## Links https://lequa2022.github.io/ https://github.com/HLT-ISTI/LeQua2022_scripts ## Tasks T1A: This task is concerned with evaluating binary quantifiers, i.e., quantifiers that must only predict the relative frequencies of a class and its complement. Participants in this task will be provided with documents already converted into vector form; the task is thus suitable for participants who do not wish to engage in generating representations for the textual documents, but want instead to concentrate on optimizing the methods for learning to quantify. T1B: This task is concerned with evaluating single-label multi-class quantifiers, i.e., quantifiers that operate on documents that each belong to exactly one among a set of n&gt;2 classes. Like in Task T1A, participants will be provided with documents already converted in vector form. T2A: Like Task T1A, this task is concerned with evaluating binary quantifiers. Unlike in Task T1A, participants will be provided with the raw text of the documents; the task is thus suitable for participants who also wish to engage in generating suitable representations for the textual documents, or to train end-to-end systems. T2B: Like Task T1B, this task is concerned with evaluating single-label multi-class quantifiers; like in Task T2A, participants will be provided with the raw text of the documents.
DOI: 10.5281/zenodo.5734464
title: Learning to quantify: LeQua 2022 datasets

description: # Learning to Quantify The aim of LeQua 2022 (the 1st edition of the CLEF “Learning to Quantify” lab) is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) will be required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set. ## Links https://lequa2022.github.io/ https://github.com/HLT-ISTI/LeQua2022_scripts ## Tasks T1A: This task is concerned with evaluating binary quantifiers, i.e., quantifiers that must only predict the relative frequencies of a class and its complement. Participants in this task will be provided with documents already converted into vector form; the task is thus suitable for participants who do not wish to engage in generating representations for the textual documents, but want instead to concentrate on optimizing the methods for learning to quantify. T1B: This task is concerned with evaluating single-label multi-class quantifiers, i.e., quantifiers that operate on documents that each belong to exactly one among a set of n&gt;2 classes. Like in Task T1A, participants will be provided with documents already converted in vector form. T2A: Like Task T1A, this task is concerned with evaluating binary quantifiers. Unlike in Task T1A, participants will be provided with the raw text of the documents; the task is thus suitable for participants who also wish to engage in generating suitable representations for the textual documents, or to train end-to-end systems. T2B: Like Task T1B, this task is concerned with evaluating single-label multi-class quantifiers; like in Task T2A, participants will be provided with the raw text of the documents.
DOI: 10.48479/q20r-hb79
title: The Curious Case of the 'Heartworm' Nebula

description: The data release associated with this paper, which presents MeerKAT observations of the curious Galactic features near G357.2-0.2, includes Stokes I (L band, UHF+L band, UHF enhanced surface brightness sensitivity), spectral index (UHF+L band), and Stokes Q and U L-band cubes.
DOI: 10.6073/pasta/447a8ed4d4128dd2221a9ad4c383ef0a
title: Morphometric measures from birds in the Serra do Cipó National Park (1999-2009), Minas Gerais, Brazil

description: Morphometric data of 111 bird species, and 1732 individuals, captured in mist nets over a period of 10 years, from February 1999 to October 2009, were presented. The captures were made in the locality of 'Lagoa das Capivaras', inserted in the Serra do Cipó National Park (-19.346023, -43.609918). The capture with misty nets consisted of the use of bird capture nets of 12, nine, and six meters in length by three meters in height. The captured birds were identified at the species level, with the aid of guides and identification keys, and marked with numbered metal rings provided by CEMAVE - Centro de Pesquisas para Conservação das Aves Silvestres. Capture sessions were carried out by placing two to 20 nets in different locations throughout the area, remaining open throughout the morning, for several days and months over the years mentioned. All individuals were listed following the nomenclature and classification suggested by the Brazilian Committee for Ornithological Records. Biometric data of each individual were measured regarding body mass, wing length tarsus, the beak (height, length, and width), occiput (occipital region to nasofrontal suture), and total length. For this, calipers, rulers, and dynamometers with a precision of 0.1 g were used for small birds (such as those of the family Trochilidae), 1 g for medium birds, and 10 g for birds over 100 g. In addition to the age classification: juvenile (J), adult (A), and indeterminate (I); and sex: male (M), female (F), and indeterminate (I). Bird's morphometric is important data to support a variety of studies, especially those of taxonomy, physiology, ecology, and evolutionary implications of a particular species.
DOI: 10.17637/rh.19738333
title: Annual reports - Da Silva doctoral research

description: This dataset contains the annual reports for 18 companies researched as part of Joseph Da Silva's doctoral research into the purpose of Chief Information Security Officers in commercial organisations.
DOI: 10.17637/rh.19738333.v1
title: Annual reports - Da Silva doctoral research

description: This dataset contains the annual reports for 18 companies researched as part of Joseph Da Silva's doctoral research into the purpose of Chief Information Security Officers in commercial organisations.
DOI: 10.34740/kaggle/dsv/3627706
title: YouTube Trending Video Dataset (updated daily)