cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19827373.v1
title: Distribution analysis, updated checklist, and DNA barcodes of the endemic vascular flora of the Altai mountains, a Siberian biodiversity hotspot

description: The Altai Mountains of central Asia are biologically rich and comprise a wide diversity of ecosystems and lineages, including numerous endemic vascular plant species. Here we provide an updated checklist of the endemic vascular flora of the Altai Mountains with more taxa and higher geographic resolution than previously reported, as well as first molecular data and specimen images for many of these species. This flora is now known to contain 321 endemic species distributed in 34 families, many of which are narrowly restricted to one subregion of the Altai. The Fabaceae has given rise to the most endemic species in the Altai (74 spp.), and most of this diversity is found in the large and ecologically important genera <i>Astragalus</i> and <i>Oxytropis</i>. Approximately 60% of the endemic flora was imaged and successfully barcoded with at least one of three common DNA barcoding loci, and a phylogenetic tree based upon these loci is also presented to display the evolutionary breadth of endemism in the Altai. The distribution of each endemic species is presented in terms of a standard geographic subdivision of the Altai region, with general conservation priorities discussed based on areas currently afforded protected status.
DOI: 10.6084/m9.figshare.19146521.v1
title: First-principles insight into the structural and optoelectronic properties of Sn- and Pb-based hybrid halide perovskites for photovoltaic applications

description: In this work, we have theoretically investigated structural and optoelectronic properties of Sn and Pb halide perovskites incorporating trimethylammonium (TMA+) organic cation by employing density functional theory formalism. Here, our focus is to devise less toxic, efficient and stable perovskite materials for application in photovoltaics. From the calculation of formation energy (ΔE) and tolerance factor, it is found that both TMAPbX3 and TMASnX3 exhibit structural and thermal stability. From the observed band gap values and Bader charge analysis, it can be inferred that halide ions play an important role in tuning the electronic properties of the designed Organic inorganic hybrid halide perovskite (OIHP) materials. Our results also reveal that compounds with I− ion possess lower value of fundamental band gap and higher thermal stability. Reorganisation energies indicate that our designed compounds will predominantly act as hole transporting material. The calculated theoretical power conversion efficiency (<i>η</i>) indicates that TMASnI3 may act as an alternative material for Pb-based perovskite materials. Moreover, TMASnX3 shows strong absorption in the visible range of electromagnetic spectrum therefore, it is imperative to replace Pb by Sn to fabricate less toxic OIHP material.
DOI: 10.6084/m9.figshare.19146521
title: First-principles insight into the structural and optoelectronic properties of Sn- and Pb-based hybrid halide perovskites for photovoltaic applications

description: In this work, we have theoretically investigated structural and optoelectronic properties of Sn and Pb halide perovskites incorporating trimethylammonium (TMA+) organic cation by employing density functional theory formalism. Here, our focus is to devise less toxic, efficient and stable perovskite materials for application in photovoltaics. From the calculation of formation energy (ΔE) and tolerance factor, it is found that both TMAPbX3 and TMASnX3 exhibit structural and thermal stability. From the observed band gap values and Bader charge analysis, it can be inferred that halide ions play an important role in tuning the electronic properties of the designed Organic inorganic hybrid halide perovskite (OIHP) materials. Our results also reveal that compounds with I− ion possess lower value of fundamental band gap and higher thermal stability. Reorganisation energies indicate that our designed compounds will predominantly act as hole transporting material. The calculated theoretical power conversion efficiency (<i>η</i>) indicates that TMASnI3 may act as an alternative material for Pb-based perovskite materials. Moreover, TMASnX3 shows strong absorption in the visible range of electromagnetic spectrum therefore, it is imperative to replace Pb by Sn to fabricate less toxic OIHP material.
DOI: 10.17632/k84dbvvv4m.1
title: Data of: Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

description: There are two sets of instances with routing and maintenance parameters. The first one, denoted by Re, is generated as described in Dahite et al. (2020). The second set is derived from the Solomon benchmark. These benchmark problems are available on Solomon’s web page http://web.cba.neu.edu/~msolomon/problems.htm. The maintenance parameters were generated as described in López-Santana et al. (2016).
DOI: 10.17632/v5nvysr84k.1
title: The impact of metabolic syndrome and obesity on the evolution of diastolic dysfunction in apparently healthy patients suffering from post-COVID-19 syndrome

description: Background: It was observed that the coronavirus disease (COVID-19) has a worse prognosis in individuals with obesity and metabolic syndrome (MS) who also develop commonly cardiovascular complications which last frequently during the recovery from the acute illness; (2) Methods: we followed-up the evolution of diastolic dysfunction (DD) assessed by transthoracic echocardiography (TTE) in 203 individuals with and without obesity and/or MS, diagnosed with post-COVID-19 syndrome; (3) Results: DD was diagnosed in 62.71% patients with MS and obesity, respectively in 56.6% of those with MS without obesity, in comparison to 21.97% of subjects without MS (p˂0.001). Almost half of the obese patients with MS had more severe DD (type 2 and 3). As for the evolution, the prevalence and severity of DD, particularly of type 1 and 2, decreased gradually after 6 months, in parallel with the improvement of symptoms, progress more evident in subjects without MS. DD of type 3 didn't show a significant reduction (p=0.47) suggesting irre-versible myocardial damages such as interstitial fibrosis. Multivariate regression analysis indicated that the number of MS factors, the severity of the initial pulmonary injury and protein-C levels could explain DD's evolution; (4) Conclusions: DD was commonly diagnosed in individuals with post-COVID-19 syndrome, particularly in those with MS and obesity. After 6 months, DD's evolution, excepting of type 3, showed a significant improvement, mostly in patients without MS.
This study was done by the researchers from the University of Medicine and Pharmacy "Victor Babes" Timisoara.
DOI: 10.48544/92778884-babf-47d4-8e1f-e95b7a6401c5
title: Whatsappen, pizza eten en op safari
DOI: 10.17632/vyzh9hzfrk.1
title: LINF_280005800

description: oxygen-sensing adenylate cyclase | HemAC
Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.3688540
title: WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement

description: This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER Kiselev2018. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). If you use this dataset please cite: <strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong> === Each sample is a 16-channel wav file in which the order of the channel follows the following logic: 0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4 Refer to the floor plan for a visual illustration of the microphone arrangement. The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation. In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4) In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB) Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav). == A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on this beamforming library and this tool to evaluate the quality of the separation. == Refer to the README.md in the dataset for more information. For any question please contact enea.ceolini@gmail.com
DOI: 10.5281/zenodo.3725911
title: 3D models of the forearm's Interosseous membrane

description: <strong>DATA CONTENT</strong> This dataset corresponds to the 3D models generated through Micro-CT and standard CT of 5 cadaveric forearms. Each Folder contains the following subfolders: Bones: containing radius and ulna 3D STL models Insertion_Points : containing radial and ulnar attachment of the individual ligaments of the IOM IOM: contains the STL of the entire interosseous membrane We kindly ask to cite our work in case of usage of any of the presented models. You may contact the corresponding author if you wish more information about the data or if you consider some details are missing. <br> © The Balgrist 2019. <br> Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following: • you may use the data as a part of a research project, providing you cite our institution and publication<br> • you may print or download to a local hard disk extracts for your personal and non-commercial use only<br> • you may copy the content to individual third parties for their personal use, but only if you acknowledge the authors as the source of the material You may not, except with our express written permission, distribute or commercially exploit the content. Nor may you transmit it or store it in any other website or other form of electronic retrieval system. <br> Fabio Carrillo<br> ETH Zürich<br> University Hospital Balgrist<br> fabio.carrillo@balgrist.ch
DOI: 10.5281/zenodo.3725910
title: 3D models of the forearm's Interosseous membrane

description: <strong>DATA CONTENT</strong> This dataset corresponds to the 3D models generated through Micro-CT and standard CT of 5 cadaveric forearms. Each Folder contains the following subfolders: Bones: containing radius and ulna 3D STL models Insertion_Points : containing radial and ulnar attachment of the individual ligaments of the IOM IOM: contains the STL of the entire interosseous membrane We kindly ask to cite our work in case of usage of any of the presented models. You may contact the corresponding author if you wish more information about the data or if you consider some details are missing. <br> © The Balgrist 2019. <br> Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following: • you may use the data as a part of a research project, providing you cite our institution and publication<br> • you may print or download to a local hard disk extracts for your personal and non-commercial use only<br> • you may copy the content to individual third parties for their personal use, but only if you acknowledge the authors as the source of the material You may not, except with our express written permission, distribute or commercially exploit the content. Nor may you transmit it or store it in any other website or other form of electronic retrieval system. <br> Fabio Carrillo<br> ETH Zürich<br> University Hospital Balgrist<br> fabio.carrillo@balgrist.ch
DOI: 10.5281/zenodo.3565491
title: WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement

description: This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER Kiselev2018. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). If you use this dataset please cite: <strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong> === Each sample is a 16-channel wav file in which the order of the channel follows the following logic: 0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4 Refer to the floor plan for a visual illustration of the microphone arrangement. The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation. In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4) In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB) Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav). == A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on this beamforming library and this tool to evaluate the quality of the separation. == Refer to the README.md in the dataset for more information. For any question please contact enea.ceolini@gmail.com
DOI: 10.5281/zenodo.3978982
title: LMD Inconel 718 single tracks

description: Description of dataset doi:10.5281/zenodo.3978982 Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal powers = 600, 300, 500, 400, 700, 200 (W)<br> - Nominal velocities = 300, 450, 600, 750, 900, 1050 (mm/min)<br> - Powder flux = 0.032 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient The dataset is constituted by:<br> - Melt pool images, in file Images.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trackData_20191008.csv containing:<br> - t: timestamp in ms. Synchronized with imageData_20191008.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imageData_20191008.csv containing:<br> - t: timestamp in ms. Synchronized with trackData_20190212.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)
DOI: 10.5281/zenodo.3980733
title: LMD Inconel 718 V-tracks 2020-07-31

description: Description of dataset 10.5281/zenodo.3980733 Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocities = 350, 600, 900 (mm/min)<br> - Angles = 20°, 45°, 90°<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient The dataset is constituted by:<br> - Melt pool images, in file Experiment_2020_7_31__16_19_31.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)
DOI: 10.17632/hhrnpr5hxf.1
title: Study on the relationship between restoration benefit and visual effect satisfaction of "LONG-PLAN" indoor vertical greenery to create a restorative environment

description: This data is the questionnaire result of the study.
DOI: 10.5281/zenodo.3978981
title: LMD Inconel 718 single tracks

description: Description of dataset doi:10.5281/zenodo.3978982 Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal powers = 600, 300, 500, 400, 700, 200 (W)<br> - Nominal velocities = 300, 450, 600, 750, 900, 1050 (mm/min)<br> - Powder flux = 0.032 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient The dataset is constituted by:<br> - Melt pool images, in file Images.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trackData_20191008.csv containing:<br> - t: timestamp in ms. Synchronized with imageData_20191008.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imageData_20191008.csv containing:<br> - t: timestamp in ms. Synchronized with trackData_20190212.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)
DOI: 10.5281/zenodo.3980732
title: LMD Inconel 718 V-tracks 2020-07-31

description: Description of dataset 10.5281/zenodo.3980733 Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocities = 350, 600, 900 (mm/min)<br> - Angles = 20°, 45°, 90°<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient The dataset is constituted by:<br> - Melt pool images, in file Experiment_2020_7_31__16_19_31.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)