cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21593310.v2
title: StMary-PPGRR.zip

description: StMary Introduction The StMary dataset is Photoplethysmography(PPG) and Respiratory Rate(RR) dataset acquired from surgical intensive care unit(SICU) of Catholic University Seoul St. Mary's Hospital, South Korea. Data Collection The StMary dataset was acquired from a SICU at the hospital using our in-house web application, PPG Signal Collector. Using laptop, we connected a network between the web application and a patient monitor. Through pulse oximeter which connected to the patient monitor, we collected photoplethysmogram of patients and healthy volunteers. Subject's demographic information must be entered before collecting. After that, PPG signal continuously recorded during the time limit we had set and Respiraotry Rate also recorded manually by medical staff pressing spacebar at the exhalation time. The time limit was set up to 2 minutes and 10 seconds. When the time ends, the data is recorded as history and has stored in the database. The collection procedure is as follows: Move into local monitoring page through the browser installed in the laptop. (Set up the measuring time additionally) Register subject’s demographic – gender, age, diagnosis – to start recording. If a window pop-up, click the start button to record PPG. Press spacebar when the subject exhales. When the time outs, the window closed, and the data stored automatically. Data Files The StMary dataset consists of 50 patients and 50 healthy volunteers both aged 18 and older. The dataset has a demographic information, PPG signal and exhalation timestamp for each de-identified subject. 2-minute PPG was collected for each subject twice, and it was sampled into 125Hz. A unique ID is generated for each collected 2-minute PPG. While this ID serves as an identifier for individual data, serving as a de-identifier for the subject at the same time. Patient.csv Diagnosis: Indicates diagnosis of the subject. For volunteers, mark '0'. For patients, name the diagnosis. Gender: Indicates gender of the subject. ("M"/"F") Age: Indicates age of the subject. startMeasureTime: Indicates time when the data starts to move into DB server. endMeasureTime: Measurement ending time. Pleth.csv Timestamp: Indicates time when the PPG at that time collected. Pleth: Indicates PPG which acquired from pulse oximeter.The StMary dataset is Photoplethysmography(PPG) and Respiratory Rate(RR) dataset acquired from surgical intensive care unit(SICU) of Catholic University Seoul St. Mary's Hospital, South Korea. respirationTimeline.csv Timestamp: Indicates time when subject exhales. <br> All protocol followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. All data were anonymized, deidentified and aggregated. This data was approved by the Institutional Review Board of the Ethics Committee of Seoul St. Mary’s Hospital (IRB No. KC21ONSI0839) and the need for consent was waived by IRB.
DOI: 10.6084/m9.figshare.17105456
title: Positive effects of warming do not compensate growth reduction due to increased aridity in Mediterranean mixed forests

description: Basal area increment and climate data over the period 1951-2015 for three pine species (<i>Pinus pinaster</i>, <i>P. nigra</i> and <i>P. sylvestris</i>) in the Iberian Northern Plateau
DOI: 10.6084/m9.figshare.17105456.v1
title: Positive effects of warming do not compensate growth reduction due to increased aridity in Mediterranean mixed forests

description: Basal area increment and climate data over the period 1951-2015 for three pine species (<i>Pinus pinaster</i>, <i>P. nigra</i> and <i>P. sylvestris</i>) in the Iberian Northern Plateau
DOI: 10.5281/zenodo.7052545
title: Aerodynamic characterisation of porous fairings : pressure drop and Laser Doppler Velocimetry measurements

description: Aviation has become a mass transportation industry, and all prospective studies foresee growth in this sector. Among the challenges, noise in the vicinity of airports has gone from a marginal annoyance to a real public health concern. To address this problem, as well as others such as fuel consumption, aircraft manufacturers are considering radically new aircraft architectures that could enter service quickly. In the meantime, however, the noise of traditional aircraft must be reduced significantly. Aircraft noise, during takeoff and landing, results primarily from a combination of (i) engine noise, which is generated by the fan and jet, and (ii) airframe noise, primarily due to the landing gear (LG) and high lift devices (HLD), the latter including slats and trailing edge flaps, which are deployed at low speeds to increase lift. During takeoff, engine noise remains dominant, while on approach and landing, engines operate at low speeds (typically 50% of N1), and airframe noise becomes a significant contributor, especially for newer aircraft equipped with latest generation turbofans. Its mitigation is therefore of primary interest.<br> However, due to the strong integration constraints imposed by other disciplines than acoustics on components such as LGs and HLDs, the development of noise reduction technologies (NRT) on these airframe components has been limited. This lack of breakthroughs is also due to the complexity of flow physics, and thus our still limited knowledge of airframe noise generation mechanisms. The noise of the landing gear, slats and flaps has been studied on a real and reduced scale, mainly on the basis of experimental means. The maturity of numerical simulations now allows to study the mechanisms of the noise sources on various complex configurations. Moreover, numerical simulation methods can be sufficiently accurate to predict the noise generated by such configurations. In order to take the next step in the maturity of numerical prediction, these NIRs must be accurately evaluated and modeled. Experimental data based on academic configurations are therefore needed to validate the new tools and numerical models. One promising NRT is the use of a fairing in front of the landing gear to reduce the noise of this system. The present study aims at collecting an experimental database (pressure drop and turbulence characteristics) of several fairing solutions in order to have validation test cases for CFD simulation and thus develop new models for such complex geometries. The fairing samples are thus tested on the "Acoustic and Aerothermal Bench" (B2A), by measuring the pressure drop of each sample and the flow field by Laser Doppler Velocimetry (LDV). The experimental methodology will be presented first. The database will then be described. Some technical validations will also be proposed on the basis of a comparison with the literature.
DOI: 10.5281/zenodo.7052546
title: Aerodynamic characterisation of porous fairings : pressure drop and Laser Doppler Velocimetry measurements

description: Aviation has become a mass transportation industry, and all prospective studies foresee growth in this sector. Among the challenges, noise in the vicinity of airports has gone from a marginal annoyance to a real public health concern. To address this problem, as well as others such as fuel consumption, aircraft manufacturers are considering radically new aircraft architectures that could enter service quickly. In the meantime, however, the noise of traditional aircraft must be reduced significantly. Aircraft noise, during takeoff and landing, results primarily from a combination of (i) engine noise, which is generated by the fan and jet, and (ii) airframe noise, primarily due to the landing gear (LG) and high lift devices (HLD), the latter including slats and trailing edge flaps, which are deployed at low speeds to increase lift. During takeoff, engine noise remains dominant, while on approach and landing, engines operate at low speeds (typically 50% of N1), and airframe noise becomes a significant contributor, especially for newer aircraft equipped with latest generation turbofans. Its mitigation is therefore of primary interest.<br> However, due to the strong integration constraints imposed by other disciplines than acoustics on components such as LGs and HLDs, the development of noise reduction technologies (NRT) on these airframe components has been limited. This lack of breakthroughs is also due to the complexity of flow physics, and thus our still limited knowledge of airframe noise generation mechanisms. The noise of the landing gear, slats and flaps has been studied on a real and reduced scale, mainly on the basis of experimental means. The maturity of numerical simulations now allows to study the mechanisms of the noise sources on various complex configurations. Moreover, numerical simulation methods can be sufficiently accurate to predict the noise generated by such configurations. In order to take the next step in the maturity of numerical prediction, these NIRs must be accurately evaluated and modeled. Experimental data based on academic configurations are therefore needed to validate the new tools and numerical models. One promising NRT is the use of a fairing in front of the landing gear to reduce the noise of this system. The present study aims at collecting an experimental database (pressure drop and turbulence characteristics) of several fairing solutions in order to have validation test cases for CFD simulation and thus develop new models for such complex geometries. The fairing samples are thus tested on the "Acoustic and Aerothermal Bench" (B2A), by measuring the pressure drop of each sample and the flow field by Laser Doppler Velocimetry (LDV). The experimental methodology will be presented first. The database will then be described. Some technical validations will also be proposed on the basis of a comparison with the literature.
DOI: 10.25403/upresearchdata.21528750.v1
title: Properties of South African selected medicinal plants for combating multi drug-resistant bacteria

description: This is data collected for a PhD experimental research study titled 'Antipathogenic properties of selected South African medicinal plants to combat multidrug-resistant bacteria'. The data provides a determination of phytochemicals and plant extracts for their antiquorum sensing against <em>Chromobacterium violaceum; biofilm </em>inhibitory effects of plant extracts and phytochemicals against multidrug-resistant bacteria.
DOI: 10.25403/upresearchdata.21528750
title: Properties of South African selected medicinal plants for combating multi drug-resistant bacteria

description: This is data collected for a PhD experimental research study titled 'Antipathogenic properties of selected South African medicinal plants to combat multidrug-resistant bacteria'. The data provides a determination of phytochemicals and plant extracts for their antiquorum sensing against <em>Chromobacterium violaceum; biofilm </em>inhibitory effects of plant extracts and phytochemicals against multidrug-resistant bacteria.
DOI: 10.6084/m9.figshare.21610674
title: Chromosome Sequences

description: Chromosome sequences of the genomes used to infer the K1-<em>cps </em>synteny
DOI: 10.21227/w67n-0q72
title: Peach Tree Disease Detection Dataset

description: This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two experts (agronomists) annotated the dataset by identifying whether the trees are healthy or stressed, where the identified stress is either Anarsia lineatella or Grapholita molesta.
DOI: 10.5281/zenodo.7351665
title: Dataset of human manipulation actions

description: This dataset consists of nine object manipulation actions performed by 12 humans: pick and place, put on top/take down, put inside/take out, hide/uncover, and push/pull with a total of 7,652 movement examples. See the README.md file for more information.
DOI: 10.5281/zenodo.7351664
title: Dataset of human manipulation actions

description: This dataset consists of nine object manipulation actions performed by 12 humans: pick and place, put on top/take down, put inside/take out, hide/uncover, and push/pull with a total of 7,652 movement examples. See the README.md file for more information.
DOI: 10.5281/zenodo.7350483
title: Walk 3a: Difoinarti, 4 March 2017: Images & text (belongs to: Ille & Salah 2022, Walking on fire)

description: Text and images from a walking interview conducted in frame of research on date palm fires in Sudan's Northern State. Translated from original Sudanese Arabic by author. Georeference: N19°57.026940' E30°29.081760'.
DOI: 10.5281/zenodo.7314098
title: Spanish CBOW Word Embeddings in Floret

description: <strong>Spanish CBOW Word Embeddings in Floret</strong> The embeddings have been trained with the corpus from the National Library of Spain (Biblioteca Nacional de España or BNE) using floret with the following hyperparameters: mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128, Detailed information about the corpus can be found here The processing took place on an HPC node equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads. <strong>How to use</strong> First initialize the spacy vectors from the floret table (.floret file): <pre><code>spacy init vectors es floret_embeddings_bne_es.floret floret_embeddings_bne_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bne_es") # Get the embeddings of some words playa = floret_embeddings.vocab["playa"] frío = floret_embeddings.vocab["frío"] invierno = floret_embeddings.vocab["invierno"] verano = floret_embeddings.vocab["verano"] # Get some similarities print(frío.similarity(invierno)) print(frío.similarity(verano)) # frío should be more similar to invierno than verano. print(playa.similarity(invierno)) print(playa.similarity(verano)) # playa should be more similar to verano than invierno.</code></pre> <strong>Intended Uses and Limitations</strong> At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated. <strong>Authors</strong> The Text Mining Unit from Barcelona Supercomputing Center. <strong>Contact Information</strong> For further information, send an email to plantl-gob-es@bsc.es <strong>Funding</strong> This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. <strong>Copyright </strong> Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL).
DOI: 10.5281/zenodo.7314097
title: Spanish CBOW Word Embeddings in Floret

description: <strong>Spanish CBOW Word Embeddings in Floret</strong> The embeddings have been trained with the corpus from the National Library of Spain (Biblioteca Nacional de España or BNE) using floret with the following hyperparameters: mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128, Detailed information about the corpus can be found here The processing took place on an HPC node equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads. <strong>How to use</strong> First initialize the spacy vectors from the floret table (.floret file): <pre><code>spacy init vectors es floret_embeddings_bne_es.floret floret_embeddings_bne_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bne_es") # Get the embeddings of some words playa = floret_embeddings.vocab["playa"] frío = floret_embeddings.vocab["frío"] invierno = floret_embeddings.vocab["invierno"] verano = floret_embeddings.vocab["verano"] # Get some similarities print(frío.similarity(invierno)) print(frío.similarity(verano)) # frío should be more similar to invierno than verano. print(playa.similarity(invierno)) print(playa.similarity(verano)) # playa should be more similar to verano than invierno.</code></pre> <strong>Intended Uses and Limitations</strong> At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated. <strong>Authors</strong> The Text Mining Unit from Barcelona Supercomputing Center. <strong>Contact Information</strong> For further information, send an email to plantl-gob-es@bsc.es <strong>Funding</strong> This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. <strong>Copyright </strong> Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL).
DOI: 10.15129/414d46ee-fe46-4ec0-9cbb-67f29c5efdf6
title: Data for: "Multicomponent chiral quantification with UV circular dichroism spectroscopy: ternary and quaternary phase diagrams of Levetiracetam"

description: The following folder contains raw experimental data files and data treatment files used to produce the article "Multicomponent chiral quantification with UV circular dichroism spectroscopy: ternary and quaternary phase diagrams of Levetiracetam"
DOI: 10.5281/zenodo.5791008
title: LoGov Italy Interview Transcript n°11

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.5791007
title: LoGov Italy Interview Transcript n°11

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.5791015
title: LoGov Italy Interview Transcript n°13

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.5791016
title: LoGov Italy Interview Transcript n°13

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.5777826
title: LoGov Italy Interview Report n°6

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To access the full transcription of this interview, the other interview reports on Italy, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.5777825
title: LoGov Italy Interview Report n°6

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy. To access the full transcription of this interview, the other interview reports on Italy, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5517/ccdc.csd.cc1lrr36
title: CCDC 1482578: Experimental Crystal Structure Determination

description: Related Article: Güneş Süheyla Kürkçüoğlu, Okan Zafer Yeşilel, Seray Kekeç, Onur Şahin|2022|J.Mol.Struct.|1274|134540|doi:10.1016/j.molstruc.2022.134540
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.