cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4925165
title: Enhancing the Performance of Asphalt Mixtures Containing High RAP Content with the Use of Different WMA Technologies

description: The production of Warm-mix asphalt mixtures in conjunction with reclaimed asphalt pavement (RAP) has received considerable interest in recent years for economic and environmental reasons. The primary objective of this project is to enhance the performance of asphalt mixtures containing RAP in Region 6 using different WMA technologies. In this project, the effect of utilizing 0%, 25%, and 35% RAP contents on the performance of different WMA mixtures against rutting, moisture damage, and fatigue cracking are evaluated. The effects of the WMA technologies on the rutting performance of the asphalt mixtures and recovered binders were investigated using loaded wheel tracker (LWT) and multiple stress creep recovery (MSCR), respectively. Further, the influences of these technologies on the cracking performance of the asphalt mixtures and recovered binders were evaluated using Semi-Circular Bending (SCB) and linear amplitude sweep (LAS) test, respectively. Based on the results, WMA mixtures containing no RAP have a lower value of the J<sub>nr</sub> compared to the HMA control mixture. This is an indication of the better performance of the WMA mixtures against the permanent deformation. Moreover, findings from the LWT test completely agree with the results from the MSCR test. On the other hand, the fracture resistance is found to be enhanced with the incorporation of a higher percentage of RAP and WMA technologies. Overall fracture resistance performance of WMA-RAP mixtures is observed to be better compared to HMA-RAP mixtures. Moreover, the results of the LAS test show that the incorporation of RAP materials and WMA technologies is associated with improved fatigue life of the WMA-RAP mixtures.
description: Tran-SET Project: 19BLSU01
DOI: 10.15468/7j3wc2
title: Nineteen new species of Megaselia Rondani (Diptera, Phoridae) from Denmark

description: This dataset contains the digitized treatments in Plazi based on the original journal article Henry, R., Bøggild, Esben (2021): Nineteen new species of Megaselia Rondani (Diptera, Phoridae) from Denmark. Zootaxa 4975 (2): 306-342, DOI: https://doi.org/10.11646/zootaxa.4975.2.4
DOI: 10.25442/hku.12830951
title: Updated dataset used for the study: A functional analysis reveals extremely low redundancy in global mangrove invertebrate fauna

description: Deforestation results in habitat fragmentation, decreasing diversity and functional degradation. For mangroves, no data are available on the impact of deforestation on the diversity and functionality of the specialized invertebrate fauna, critical for their functioning. This is global dataset of mangrove invertebrate fauna comprising 364 species from 16 locations, classified into 64 Functional Entities (FEs). The dataset was utilised to calculate Taxonomic Distinctness, Functional Richness and Functional Redundancy to assess functional integrity of mangroves all over the world.
DOI: 10.25442/hku.12830951.v2
title: Updated dataset used for the study: A functional analysis reveals extremely low redundancy in global mangrove invertebrate fauna

description: Deforestation results in habitat fragmentation, decreasing diversity and functional degradation. For mangroves, no data are available on the impact of deforestation on the diversity and functionality of the specialized invertebrate fauna, critical for their functioning. This is global dataset of mangrove invertebrate fauna comprising 364 species from 16 locations, classified into 64 Functional Entities (FEs). The dataset was utilised to calculate Taxonomic Distinctness, Functional Richness and Functional Redundancy to assess functional integrity of mangroves all over the world.
DOI: 10.25442/hku.12830951.v1
title: Original dataset used for the study: A functional analysis reveals extremely low redundancy in global mangrove invertebrate fauna

description: Deforestation results in habitat fragmentation, decreasing diversity and functional degradation. For mangroves, no data are available on the impact of deforestation on the diversity and functionality of the specialized invertebrate fauna, critical for their functioning. This is global dataset of mangrove invertebrate fauna comprising 364 species from 16 locations, classified into 64 Functional Entities (FEs). The dataset was utilised to calculate Taxonomic Distinctness, Functional Richness and Functional Redundancy to assess functional integrity of mangroves all over the world.
DOI: 10.6084/m9.figshare.14766894
title: environment.yml

description: none
DOI: 10.6084/m9.figshare.14766894.v1
title: environment.yml

description: none
DOI: 10.15468/6rqftk
title: Erebonyx catacumbae, n. gen. et sp.: a blind, troglobitic cricket from Brazil (Orthoptera, Grylloidea, Phalangopsidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article De Mello, Francisco De A. G., Ferreira, Rodrigo Lopes (2021): Erebonyx catacumbae, n. gen. et sp.: a blind, troglobitic cricket from Brazil (Orthoptera, Grylloidea, Phalangopsidae). Zootaxa 4975 (2): 343-356, DOI: https://doi.org/10.11646/zootaxa.4975.2.5
DOI: 10.6084/m9.figshare.14766870
title: Dataset_Models_FUTURIST_thermal_impact_prediction

description: Dataset on the selected dams for training and validating the FUTURIST framework and on the planned dams used for predicting their thermal impacts. The spreadsheet contains all the necessary inputs for simulating the FUTURIST framework along with the ancillary information on their location, periods of analysis, and other logistics.
DOI: 10.6084/m9.figshare.14766870.v1
title: Dataset_FUTURIST_development_dams_info.xlsx

description: Dataset on the selected dams for training and validating the FUTURIST framework and on the planned dams used for predicting their thermal impacts. The spreadsheet contains all the necessary inputs for simulating the FUTURIST framework along with the ancillary information on their location, periods of analysis, and other logistics.
DOI: 10.15468/kr5rnn
title: A new planthopper species in the genus Omolicna (Hemiptera: Auchenorrhyncha Derbidae) from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zumbado Echavarria, Marco A., Barrantes Barrantes, Edwin A., Bartlett, Charles R., Helmick, Ericka E., Bahder, Brian W. (2021): A new planthopper species in the genus Omolicna (Hemiptera: Auchenorrhyncha Derbidae) from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica. Zootaxa 4975 (2): 357-368, DOI: https://doi.org/10.11646/zootaxa.4975.2.6
DOI: 10.15468/mfv8bk
title: Oxynoemacheilus isauricus, a new nemacheilid loach from Central Anatolia (Teleostei: Nemacheilidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Yoğurtçuoğlu, Baran, Kaya, Cüneyt, Özuluğ, Müfit, Freyhof, Jörg (2021): Oxynoemacheilus isauricus, a new nemacheilid loach from Central Anatolia (Teleostei: Nemacheilidae). Zootaxa 4975 (2): 369-378, DOI: https://doi.org/10.11646/zootaxa.4975.2.7
DOI: 10.6084/m9.figshare.14766924.v1
title: Database_SPSS_NLRMaterials.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766918.v1
title: Database_SPSS_LinearMaterials.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766915.v1
title: Database_SPSS_NLRAllDataPoints.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766867.v1
title: Clamped-clamped beam model from Detecting internal resonances during model reduction

description: Finite Element model of beam used in our study.
DOI: 10.6084/m9.figshare.14766924
title: Database_SPSS_NLRMaterials.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766918
title: Database_SPSS_LinearMaterials.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766915
title: Database_SPSS_NLRAllDataPoints.CSV from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid. They have been widely investigated to boost heat transfer since they were proposed in the 1990s. We present a statistical correlation analysis of experimentally measured thermal conductivity of water-based nanofluids available in the literature. The influences of particle concentration, particle size, temperature and surfactants are investigated. For specific particle materials (alumina, titania, copper oxide, copper, silica and silicon carbide), separate analyses are performed. The conductivity increases with the concentration in qualitative agreement with Maxwell’s theory of homogeneous media. The conductivity also increases with the temperature (in addition to the improvement due to the increased conductivity of water). Surprisingly, only silica nanofluids exhibit a statistically significant effect of the particle size, whereby smaller particles lead to faster heat transfer. Overall, the large scatter in the experimental data prevents a compelling, unambiguous assessment of these effects. Taken together, the results of our analysis suggest that more comprehensive experimental characterizations of nanofluids are necessary to estimate their practical potential.
DOI: 10.6084/m9.figshare.14766912
title: Database from Statistical analysis of thermal conductivity experimentally measured in water-based nanofluids

description: Data used for analyses.
DOI: 10.6084/m9.figshare.14766867
title: Clamped-clamped beam model from Detecting internal resonances during model reduction

description: Finite Element model of beam used in our study.
DOI: 10.5281/zenodo.4925376
title: GloVe 6B Vectors

description: GloVe 6B word embeddings from https://nlp.stanford.edu/projects/glove/ (Wikipedia 2014 + Gigaword 5: 6B tokens, 400K vocab, uncased, 50d, 100d, 200d, & 300d vectors), split into single files, converted to gensim's binary word2vec format and zip-compressed (LZMA). Splitting this data into single files allows for faster downloads and inclusion in memory-restricted environments such as Binder. To load these vectors, use gensim.models.KeyedVectors.load_word2vec_format(path, binary=True). To uncompress, use Python's zipfile.ZipFile. This data is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/.
DOI: 10.5281/zenodo.4925375
title: GloVe 6B Vectors

description: GloVe 6B word embeddings from https://nlp.stanford.edu/projects/glove/ (Wikipedia 2014 + Gigaword 5: 6B tokens, 400K vocab, uncased, 50d, 100d, 200d, & 300d vectors), split into single files, converted to gensim's binary word2vec format and zip-compressed (LZMA). Splitting this data into single files allows for faster downloads and inclusion in memory-restricted environments such as Binder. To load these vectors, use gensim.models.KeyedVectors.load_word2vec_format(path, binary=True). To uncompress, use Python's zipfile.ZipFile. This data is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/.
DOI: 10.15468/csk9jr
title: Two new species of Microterys Thomson, 1876 (Hymenoptera: Encyrtidae) from Tibet, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Li, Hong-Liang, Zhang, Yan-Zhou (2021): Two new species of Microterys Thomson, 1876 (Hymenoptera: Encyrtidae) from Tibet, China. Zootaxa 4975 (2): 379-388, DOI: https://doi.org/10.11646/zootaxa.4975.2.9
DOI: 10.15468/bp7mxh
title: Sardinella ventura n. sp. (Actinopterygii: Clupeiformes: Clupeidae), a new sardine from Mauritius

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hata, Harutaka, Motomura, Hiroyuki (2021): Sardinella ventura n. sp. (Actinopterygii: Clupeiformes: Clupeidae), a new sardine from Mauritius. Zootaxa 4975 (2): 389-396, DOI: https://doi.org/10.11646/zootaxa.4975.2.10
DOI: 10.18141/1784272
title: Supplementary data for Technical Report: Towards A Geo-Data Science Method for Assessing Rare-Earth Element Occurrences in Coal and Other Sedimentary Systems

description: Supplementary data includes two appendices: -Appendix A: Bibliography of references that helped inform development of the REE-SED assessment method. -Appendix B: Sample data template.
DOI: 10.25345/c5jn7p
title: MassIVE MSV000087610 - Pathogenic BRCA1 variants disrupt PLK1-regulation of mitotic spindle orientation and luminal differentiation
title: Proteomics files for MCF10A cells with BRCA1 knockdown.
DOI: 10.25345/c5pg1x
title: MassIVE MSV000087609 - Leaf Metabolomics LC/MS Data Set - Sara Jackrel
title: Plant metabolomic sample data. Contains .RAW and .mzXML formatted files. Data was acquired using data-dependent acquisition on a Thermo Q Exactive mass spectrometer.
DOI: 10.25345/c5dz65
title: MassIVE MSV000087611 - GNPS Germ Free Samples (3NPH-neg)
title: 3NPH derivatized germ-free and specific pathogen free mice cecal samples
DOI: 10.25345/c5952s
title: MassIVE MSV000087612 - Stein Schizophrenia and aging study
title: Schizophrenia and aging study Tanya T. Nguyen, Ph.D. Assistant Professor of Psychiatry Stein Institute for Research on Aging University of California San Diego
DOI: 10.15121/1787503
title: Utah FORGE Phase Native State FALCON Model Files

description: The submission includes FALCON input file and mesh for the an initial pressure-temperature simulation, and a second set for pressure-temperature-displacement simulation. All simulations are steady state. Data and input for the FORGE Phase 2 native state model were compiled from historic and FORGE specific characterization efforts. The simulations look to find the in-situ pressure, temperature, and stress distributions in the vicinity of the Utah FORGE site. The purpose of the native state model was to establish a baseline for all other modeling analyses—so that the whole modeling team can start from a consistent set of conditions which makes for comparison of results more straightforward and meaningful. Requires the FALCON simulator which can be found in the "FALCON Simulator" link in the submission. Instructions for compiling and running FALCON can be found on the FORGE branch of the FALCON git repository in the README.
DOI: 10.15121/1787545
title: Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

description: Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.
DOI: 10.15121/1787546
title: Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

description: The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.