cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15075188.v2
title: The timing of rifting events in the easternmost Mediterranean: U-Pb dating of zircons from volcanic rocks in the Levant margin

description: The Levant basin, at the easternmost Mediterranean, formed during the opening of the Neo-Tethys at Permian to Jurassic times, as recorded by subsidence and magmatism along its SE passive margin. While the subsurface extensional structure was reconstructed in detail, the spatial and temporal distribution of igneous activity is not well known. Here we present new SIMS U-Pb dating of zircons from rifting related volcanic sequences recovered from four deep boreholes in the Levant margin of coastal Israel. The Asher volcanics, a deeply buried 2.5 km thick basaltic sequence, which accumulated within fault-bounded basin, was previously dated by K-Ar and Ar-Ar to early Jurassic times. Zircons (n=46) separated from the bottom and top of the volcanic sequence yielded 206±2 Ma and 205 to 202 Ma, respectively, indicating that Asher volcanics are of Late Triassic (Rhaetian) age. Zircon xenocrysts indicate Permian (289±2 Ma) felsic magmatism. Rifting activity in the Levant margin included several Permian-Triassic pulses of faulting and subsidence, with a prolonged magmatism-bearing rifting event at Late Carnian to Rhaetian times (~230-201 Ma). Pulsed rifting is also indicated in the conjugate, northern, margin of the southern Neo-Tethys, and the timing of subsidence and magmatic pulses well correlate to the Levant margin.
DOI: 10.6084/m9.figshare.15161153.v2
title: The Colombian geochronological database (CGD)

description: Geochronological databases are powerful tools for characterizing the crustal evolution and the age spectra of a region and allow comparison with other areas at a regional scale. In this contribution, we present the Colombian Geochronological Database (CGD), which contains a curated compilation of ca. 67,406 individual published U-Th-Pb, K-Ar, Ar-Ar, Rb-Sr, Sm-Nd, Lu-Hf, Fission-track, U-Th-He, and Re-Os mineral and whole rock ages that are reported in the published literature. Each date includes geographic coordinates, geological setting, petrologic and chemical information extracted from the respective publications. The structure of the database provides a powerful interface for constructing queries and allows searching and extracting information on geographic domains, provinces, stratigraphic units, isotopic systems, date interpretations, references, etc. This information establishes a framework for regional and global geological interpretations with geochronological, stratigraphic, structural and palaeogeographic implications. With the present effort we present to the geoscience community a clear insight, from a regional perspective, to the geology and tectonics of Colombia since Precambrian times. The comparison of all (detrital and magmatic) single zircon U-Pb dates from the Colombian (Gondwana sourced) geochronological database with the Global and North American (Laurentia sourced) databases provides a temporal constraint on the evolution of the South American continent. U-Pb zircon ages in Colombia define 13 peak clusters centred at 1767, 1530, 1325, 1178, 1007, 605,540, 468, 271, 237, 182, 76 and 10 Ma but of those, only few have a good correlation in all three databases: 1007 (Grenvillian/Orinoquian/Putumayo Orogeny), 605 (Braziliano/Pan-African Orogeny), 468 (Famatinian/Taconic Orogeny), and 182 Ma (Break-up of Pangea) zircon peaks. This correlation suggests that some tectonic events in Colombia are global and might represent crustal production and preservation while the other peaks might just represent local arc magmatic events.
DOI: 10.34740/kaggle/dsv/3851418
title: Text-to-Audio Pairs from the Cambridge Dictionary
DOI: 10.5281/zenodo.6554610
title: Research data supporting "Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water"

description: This repository contains the set of data shown in the paper <strong>"</strong><em>Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water</em>", published on the Journal of Chemical Physics (DOI:10.1063/5.0088599).
DOI: 10.5281/zenodo.6554609
title: Research data supporting "Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water"

description: This repository contains the set of data shown in the paper <strong>"</strong><em>Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water</em>", published on the Journal of Chemical Physics (DOI:10.1063/5.0088599).
DOI: 10.7294/20054426
title: Replication Data for Neary et al 2022 - Recognizing post-castration pain in piglets

description: This data was collected using an online Qualtrics survey (SAP, Provo, UT, USA) distributed (August-September 2020) with the aim to receive responses from experienced swine industry respondents (“industry”) and respondents from the general public without swine industry work experience (“public”) at a 1:1 ratio. Industry respondents were recruited via Facebook and by direct email to industry stakeholders and university faculty within the authors’ network. Facebook posts were not sponsored and were not distributed in any Facebook groups. Facebook users, swine industry contacts and university faculty were invited to disseminate the survey to others with a relevant background, including farm owners, operators, technicians and veterinarians. Simultaneously, public respondents were recruited through Amazon Mechanical Turk (Amazon Web Services, Seattle WA, USA) and received a monetary compensation for their time through the website. Industry respondents did not receive compensation. Inclusion criteria required respondents to be over the age of 18 and living in the U.S. Responses were entered anonymously. We received 129 complete survey responses. Five were omitted because respondents did not live in the U.S., and 5 were omitted because respondents failed the attention check question. Survey respondents were categorized as either ‘public respondents’ which was defined as having no professional swine industry experience, or as ‘industry respondents’ which was defined as having any professional, paid swine industry experience. We included 119 completed surveys in the data analysis, 66 from the public respondents (55%) and 53 from industry respondents (45%). Respondents completed a short training module on classifying piglet grimace levels (indicators of pain) based on three facial features using the Piglet Grimace Scale (Viscardi & Turner, 2018). They were then asked to score 12 images on the scale and complete a series of questions about their demographic background, industry experience, and knowledge of swine agricultural practices. The primary anonymized data is found in pgs_mturk_working_data.csv. with additional data from coding of the images by 4 experts in gold_standard_csv.csv. Together, these form the basis for the 2022 publication "Recognizing post-castration pain in piglets: a survey of swine industry stakeholders and the general public" in Frontiers in Veterinary Medicine. The instrument and summary demographic statistics are in Survey_Instrument_and_Demographics_Table.docx. R Code used to produce the analyses reported in the paper is in pgs_data_prep_and_analysis_anonymized.R. Additional output data used for further analysis can be found in the remaining 2 csv files, as well as labelled_tables.xlsx.
DOI: 10.7294/20054426.v2
title: Replication Data for Neary et al 2022 - Recognizing post-castration pain in piglets

description: This data was collected using an online Qualtrics survey (SAP, Provo, UT, USA) distributed (August-September 2020) with the aim to receive responses from experienced swine industry respondents (“industry”) and respondents from the general public without swine industry work experience (“public”) at a 1:1 ratio. Industry respondents were recruited via Facebook and by direct email to industry stakeholders and university faculty within the authors’ network. Facebook posts were not sponsored and were not distributed in any Facebook groups. Facebook users, swine industry contacts and university faculty were invited to disseminate the survey to others with a relevant background, including farm owners, operators, technicians and veterinarians. Simultaneously, public respondents were recruited through Amazon Mechanical Turk (Amazon Web Services, Seattle WA, USA) and received a monetary compensation for their time through the website. Industry respondents did not receive compensation. Inclusion criteria required respondents to be over the age of 18 and living in the U.S. Responses were entered anonymously. We received 129 complete survey responses. Five were omitted because respondents did not live in the U.S., and 5 were omitted because respondents failed the attention check question. Survey respondents were categorized as either ‘public respondents’ which was defined as having no professional swine industry experience, or as ‘industry respondents’ which was defined as having any professional, paid swine industry experience. We included 119 completed surveys in the data analysis, 66 from the public respondents (55%) and 53 from industry respondents (45%). Respondents completed a short training module on classifying piglet grimace levels (indicators of pain) based on three facial features using the Piglet Grimace Scale (Viscardi & Turner, 2018). They were then asked to score 12 images on the scale and complete a series of questions about their demographic background, industry experience, and knowledge of swine agricultural practices. The primary anonymized data is found in pgs_mturk_working_data.csv. with additional data from coding of the images by 4 experts in gold_standard_csv.csv. Together, these form the basis for the 2022 publication "Recognizing post-castration pain in piglets: a survey of swine industry stakeholders and the general public" in Frontiers in Veterinary Medicine. The instrument and summary demographic statistics are in Survey_Instrument_and_Demographics_Table.docx. R Code used to produce the analyses reported in the paper is in pgs_data_prep_and_analysis_anonymized.R. Additional output data used for further analysis can be found in the remaining 2 csv files, as well as labelled_tables.xlsx.
DOI: 10.7294/20054426.v1
title: Replication Data for Neary et al 2022 - Recognizing post-castration pain in piglets

description: This data was collected using an online Qualtrics survey (SAP, Provo, UT, USA) distributed (August-September 2020) with the aim to receive responses from experienced swine industry respondents (“industry”) and respondents from the general public without swine industry work experience (“public”) at a 1:1 ratio. Industry respondents were recruited via Facebook and by direct email to industry stakeholders and university faculty within the authors’ network. Facebook posts were not sponsored and were not distributed in any Facebook groups. Facebook users, swine industry contacts and university faculty were invited to disseminate the survey to others with a relevant background, including farm owners, operators, technicians and veterinarians. Simultaneously, public respondents were recruited through Amazon Mechanical Turk (Amazon Web Services, Seattle WA, USA) and received a monetary compensation for their time through the website. Industry respondents did not receive compensation. Inclusion criteria required respondents to be over the age of 18 and living in the U.S. Responses were entered anonymously. We received 129 complete survey responses. Five were omitted because respondents did not live in the U.S., and 5 were omitted because respondents failed the attention check question. Survey respondents were categorized as either ‘public respondents’ which was defined as having no professional swine industry experience, or as ‘industry respondents’ which was defined as having any professional, paid swine industry experience. We included 119 completed surveys in the data analysis, 66 from the public respondents (55%) and 53 from industry respondents (45%). Respondents completed a short training module on classifying piglet grimace levels (indicators of pain) based on three facial features using the Piglet Grimace Scale (Viscardi & Turner, 2018). They were then asked to score 12 images on the scale and complete a series of questions about their demographic background, industry experience, and knowledge of swine agricultural practices. The primary anonymized data is found in pgs_mturk_working_data.csv. with additional data from coding of the images by 4 experts in gold_standard_csv.csv. Together, these form the basis for the 2022 publication "Recognizing post-castration pain in piglets: a survey of swine industry stakeholders and the general public" in Frontiers in Veterinary Medicine. The instrument and summary demographic statistics are in Survey_Instrument_and_Demographics_Table.docx. R Code used to produce the analyses reported in the paper is in pgs_data_prep_and_analysis_anonymized.R. Additional output data used for further analysis can be found in the remaining 2 csv files, as well as labelled_tables.xlsx.
DOI: 10.25403/upresearchdata.20103191
title: Wheat rhizosphere microbiome

description: The dataset shows distributions of bacterial and fungal operational taxonomic units present in all wheat rhizosphere and field soil samples and their classifications according to phyla, class etc. The datasets also indicate Bacillus subtilis concentration on the roots of different wheat cultivars at harvest. Biocontrol activity of Bacillus subtilis against Fusarium pseudograminearum dataset is attached.
DOI: 10.25403/upresearchdata.20103191.v1
title: Wheat rhizosphere microbiome

description: The dataset shows distributions of bacterial and fungal operational taxonomic units present in all wheat rhizosphere and field soil samples and their classifications according to phyla, class etc. The datasets also indicate Bacillus subtilis concentration on the roots of different wheat cultivars at harvest. Biocontrol activity of Bacillus subtilis against Fusarium pseudograminearum dataset is attached.
DOI: 10.6084/m9.figshare.20132849
title: Juniperus drupacea chorology

description: Distribution map of <em>Juniperus drupacea</em>. <br> These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature. The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn"). Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com). <br> Please cite as: Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007 <br> Additional information and used references are on 'supplementary materials' document: https://doi.org/10.6084/m9.figshare.5091901 <br> Chorological maps are part of the "European Atlas of Forest Tree Species" project: https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.6084/m9.figshare.20132849.v1
title: Juniperus drupacea chorology

description: Distribution map of <em>Juniperus drupacea</em>. <br> These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature. The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn"). Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com). <br> Please cite as: Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007 <br> Additional information and used references are on 'supplementary materials' document: https://doi.org/10.6084/m9.figshare.5091901 <br> Chorological maps are part of the "European Atlas of Forest Tree Species" project: https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.14272/xsdmqmvidcrhrr-uhfffaoysa-k/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2918 (w), 1706 (w), 1609 (vs), 1594 (vs), 1545 (vs), 1496 (s), 1486 (s), 1449 (s), 1400 (vs), 1298 (m), 1261 (m), 1220 (vs), 1159 (m), 1145 (m), 1094 (s), 1028 (s), 958 (m), 863 (vs), 809 (s), 759 (vs), 734 (s), 696 (s), 681 (m), 657 (vs), 642 (s), 571 (m), 547 (s), 523 (m), 499 (m), 450 (m), 418 (m), 411 (m), 391 (m), 380 (m) cm–1.
DOI: 10.14272/xsdmqmvidcrhrr-uhfffaoysa-k.1
title: C49H36EuF3N4O6
title: InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3

description: This dataset contains experimental data from a sample (InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) 19F nuclear magnetic resonance spectroscopy (19F NMR)
DOI: 10.14272/xsdmqmvidcrhrr-uhfffaoysa-k/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6 [2.50 ppm], ppm) δ = 8.33 (d, J = 8.2 Hz, 4H), 7.86 (s, 4H), 7.61 (d, J = 8.2 Hz, 4H), 7.18 (bs, 4H), 6.74 (bs, 8H), 2.78 (s, 12H).
DOI: 10.14272/xsdmqmvidcrhrr-uhfffaoysa-k/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, DMSO-d6 [39.5 ppm], ppm) δ = 160.6 (d, J = 254 Hz, 3C), 158.3 (4C), 144.7 (4C), 136.4 (4C), 132.4 (d, J = 6.9 Hz), 131.6 (4C), 126.6 (4C), 125.5 (4C), 123.4 (4C), 123.1 (d, J = 2.3 Hz, 3C), 114.6 (d, J = 20.8 Hz, 3C), 25.0 (4C). Missing signals due to line broadening (7 C).
DOI: 10.14272/xsdmqmvidcrhrr-uhfffaoysa-k/chmo0000597
title: 19F nuclear magnetic resonance spectroscopy (19F NMR) ()
title: InChI=1S/2C14H12N2.3C7H5FO2.Eu/c2*1-9-3-5-11-7-8-12-6-4-10(2)16-14(12)13(11)15-9;3*8-6-4-2-1-3-5(6)7(9)10;/h2*3-8H,1-2H3;3*1-4H,(H,9,10);/q;;;;;+3/p-3

description: dataset for 19F nuclear magnetic resonance spectroscopy (19F NMR) 19F NMR (376 MHz, ppm) δ = –112.08.
DOI: 10.21227/tzgg-fw33
title: DeepRTSNet: Deep Robust Two-Stage Network for Practical Wearable ECG Recorders Denoising under Severe Noise Corruption and Various Physiologies

description: In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation. The various heart physiologies and noise levels in different amplitude and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database(APNEA-ECG) and the different noises consisting of muscle artifacts (MA), baseline wander (BW),and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation(FFT) of the simulated noisy signal, and the practical noisy signal has a maximum cross-correlation. Based on the results, Deep-RTS-Net outperforms prior learning-based methods and non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD).
DOI: 10.5281/zenodo.6687407
title: The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data

description: <pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>