cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for infrared absorption spectroscopy (IR)
 
 IR (ATR): 2980 (w) [-CH3], 1632 (s) [-C=O], 1514 (s) [(-N-O)], 1480 (m), 1455 (m), 1433 (m) 1406 (m), 1377 (m), 1330 (vs), 1205 (w), 1169 (m), 1084 (m), 1071 (m), 1016 (w), 977 (w), 954 (s), 913 (w), 872 (m), 773 (w), 753 (m), 739 (m), 685 (vs), 666 (m).
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000796
title: high-performance liquid chromatography-mass spectrometry (HPLC-MS) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for high-performance liquid chromatography-mass spectrometry (HPLC-MS)
 
 MS (HPLC-MS): m/z 413 (19), 393 (12), 351 (27), 331 (6), 317 (6), 316 (17), 315 (100) [M- Na+], 285 (13), 173 (7)
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000287
title: fluorescence spectroscopy (fluorimetry) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for fluorescence spectroscopy (fluorimetry)
 
 λFL = 417.5 nm.
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000292
title: ultraviolet-visible spectrophotometry (UV-VIS) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for ultraviolet-visible spectrophotometry (UV-VIS)
 
 λmax = 338 nm.
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 1H NMR (300 MHz, CDCl3, ppm) δ = 8.04–7.90 (m, 2H), 7.47 (m, 3H), 4.67 (q, J = 7.1 Hz, 2H), 3.77 (s, 3H), 3.35 (s, 3H), 1.52 (t, J = 7.1 Hz, 3H).
DOI: 10.14272/osaxflpfgdhqqt-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H16N2O3S/c1-4-19-12-11(14(17)16(2)18-3)20-13(15-12)10-8-6-5-7-9-10/h5-9H,4H2,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 13C NMR (76 MHz, CDCl3, ppm) δ = 15.6, 33.6, 62.0, 67.2, 99.2, 126.5, 126.6, 129.5, 131.3 (2C), 133.6 (2C), 163.0, 167.7.
DOI: 10.6084/m9.figshare.24215967.v3
title: <b>Chlorophyll-a, nutrients, sea surface temperature and salinity, off Campeche, Mexico.</b>

description: Data from four oceanographic surveys were carried out from October 2017 to June 2018, to analyze the Sea Surface Temperature (SST), Sea Surface Salinity (SSS), Chlorophyll-a (Chla), and nutrients, such as Dissolved Inorganic Nutrients (DIN [NO<sub>3</sub><sup>-</sup>+ NO<sub>2</sub><sup>-</sup>+NH<sub>4</sub><sup>+</sup>]), Total Nitrogen (TN), Total Phosphorous (TP) and SiO<sub>2</sub>, off Campeche, Mexico.
DOI: 10.5281/zenodo.10365872
title: Dataset for 'Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement'

description: The original measurement data shown in Figures 1 to 3 of the Euspen conference publication: Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement, https://www.euspen.eu/knowledge-base/ICE23170.pdf. 
Acknowledgements 
The authors would like to thank the UKRI Research England Development (RED) Fund for funding this work via the Midlands Centre for Data-Drive Metrology. This work was supported by the European Metrology Programme for Innovation and Research (EMPIR) project (TracOptic, 20IND07) and the European Union (ERC, AI-SURF, 101054454).
DOI: 10.5281/zenodo.10365871
title: Dataset for 'Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement'

description: The original measurement data shown in Figures 1 to 3 of the Euspen conference publication: Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement, https://www.euspen.eu/knowledge-base/ICE23170.pdf. 
Acknowledgements 
The authors would like to thank the UKRI Research England Development (RED) Fund for funding this work via the Midlands Centre for Data-Drive Metrology. This work was supported by the European Metrology Programme for Innovation and Research (EMPIR) project (TracOptic, 20IND07) and the European Union (ERC, AI-SURF, 101054454).
DOI: 10.6084/m9.figshare.15127464.v1
title: adcp_Chenkan(91.11565W,19.3043N)_jul_2018.csv

description: Raw ADCP observations from an Awac 1Mhz located at 15 m depth.<br>If you use these data set please cite the following reference:Kurczyn, J.A., Duran, R., Beier, E, Souza, A. (2021). On the advection of upwelled water on the western Yucatan Shelf. Frontiers in Marine Science (in progress).
DOI: 10.17632/rss68zwhd6.1
title: DATA-Risk Prediction

description: Gynecologic laparoscopic surgery
DOI: 10.17632/38tkkyz7wf.1
title: NCT05757986_DATA_Prediction

description: Risk predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery
DOI: 10.17632/38tkkyz7wf.2
title: DATA_Prediction

description: Risk predictive
DOI: 10.17632/mmbxz3k9g9.1
title: Future Aviation Scenarios: Projected GHG Emissions and Technology use

description: We have assembled a sample of 67 recent aviation decarbonization scenarios from 15 studies publicly issued between March 2021 and June 2023. 

Each of the 67 simulations under scrutiny reports: 
(i) the annual volume of projected GHG emissions by 2050 (in million tCO2/year),
(ii) the volume of Sustainable Aviation Fuels (SAFs) consumed by aviation that year (in ExaJoules)
(iii) the projected size of the aviation market in 2050 (in Revenue Passenger Kilometer equivalent) 
(iv) the posited residual consumption of energy in the form of either hydrogen or electricity (in ExaJoules), 
and (v) the projected share of e-fuel in SAFs (a real number between 0 and 1).

That dataset also include a group of seven dummy (aka binary) variables conveying further information on the assumptions retained in these scenarios. These binary indicators aim to correct the substantial differences prevailing in the assumptions and treatments conducted in the studies under scrutiny. The list includes a binary indicator signaling whether freight transportation is included or not; a variable indicating whether non-CO2 effects are accounted for in the projected GHG emissions; and a variable indicating a scenario with zero posited consumption of hydrogen or electricity (i.e., a zero value for the variable H2_Elec). In addition, we generate four specific dummy variables that indicate whether each scenario has been authored by a source from academia, industry, institution, or think-tank/NGO.
DOI: 10.17632/ggzcy64h5d.1
title: Review of Saudi journals

description: Review of statistical requirements in two Saudi journals
DOI: 10.17632/zdzbxrfcd8.1
title: Infinitely Fast Heterogeneous Catalysis Model for Premixed Hydrogen Flame-Wall Interaction

description: 1D profile of H2/Air laminar premixed flames impinging on a cold wall (Head-On Quenching) at quenching instant for various grid resolutions. Operating conditions are rate are: T_u = T_wall = 300K, P = 1atm, equivalence_ratio = 1.0.
DOI: 10.17632/p2s94pwhgs.1
title: Level Up Your Speaking Skills: Unleashing the Power of Duolingo's Mobile Game-Based Language Learning

description: Level Up Your Speaking Skills: Unleashing the Power of Duolingo's Mobile Game-Based Language Learning Data Set
DOI: 10.17632/zddy95648h.1
title: 3D velocity model

description: 1. 3D P and S velocity model 
2. Traveltime Data (P and S phases)
DOI: 10.17632/37td5g2psx.1
title: Nutrition facts label

description: The dataset includes a comprehensive collection of nutritional information displayed on packaged foods available in shops across Ghana, encompassing both locally produced and imported products.
DOI: 10.17632/37td5g2psx.2
title: Nutrition facts label

description: The dataset includes a comprehensive collection of nutritional information displayed on packaged foods available in shops across Ghana, encompassing both locally produced and imported products.
DOI: 10.17632/wmw9f3dpg7.1
title: Health and Extractive Resources: A Research Note based on Evidence from Indonesian Districts

description: Replication data and code: Pramudianti, Dina, Papyrakis, Elissaios & Rieger, Matthias (2023). Health and Extractive Resources: A Research Note based on Evidence from Indonesian Districts, Bulletin of Indonesian Economic Studies, forthcoming.
DOI: 10.5281/zenodo.10358205
title: MP2 potential energy surface of HeH2p

description: MP2 potential energy surface of HeH2pAuthors: L.I. Vazquez-Salazar and M. MeuwlyThis repository contains the data to construct the potential energy surface at MP2 level with the basis set aug-cc-pVTZ for the HeH_{2}^{+} system obtained with the MOLPRO software. Inside the folder, detailed instructions are available.
ContactFor any questions, please contact  Markus Meuwly (m.meuwly@unibas.ch) or Luis Vazquez-Salazar (luisitza.vazquezsalazar@unibas.ch) ReferenceHorn, K. P., Vazquez-Salazar, L. I., Koch, C. P., & Meuwly, M. (2023). Improving Potential Energy Surfaces Using Experimental Feshbach Resonance Tomography. arXiv preprint arXiv:2309.16491.
DOI: 10.5281/zenodo.10358206
title: MP2 potential energy surface of HeH2p

description: MP2 potential energy surface of HeH2pAuthors: L.I. Vazquez-Salazar and M. MeuwlyThis repository contains the data to construct the potential energy surface at MP2 level with the basis set aug-cc-pVTZ for the HeH_{2}^{+} system obtained with the MOLPRO software. Inside the folder, detailed instructions are available.
ContactFor any questions, please contact  Markus Meuwly (m.meuwly@unibas.ch) or Luis Vazquez-Salazar (luisitza.vazquezsalazar@unibas.ch) ReferenceHorn, K. P., Vazquez-Salazar, L. I., Koch, C. P., & Meuwly, M. (2023). Improving Potential Energy Surfaces Using Experimental Feshbach Resonance Tomography. arXiv preprint arXiv:2309.16491.
DOI: 10.17632/w9jf53792g.1
title: Inmmunophynotype for Erythroid progenitors and new MPP4_2

description: Inmmunophynotype for Erythroid progenitors and new MPP4_2
DOI: 10.7303/syn53144941.1
title: Effects of exercise and olfactory training on verbal fluency and depression dataset.xls
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n.1
title: C16H19NO2S
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3, and canonical SMILES descriptor[cheminf_000007]: CCCCC(=O)c1sc(nc1OCC)c1ccccc1, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-36660

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 83.6 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000796 | high-performance liquid chromatography-mass spectrometry (HPLC-MS)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0001150 | 1H--1H correlation spectroscopy (1H-1H COSY)
 
 CHMO:0000292 | ultraviolet-visible spectrophotometry (UV-VIS)
 
 CHMO:0000287 | fluorescence spectroscopy (fluorimetry)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 1H NMR (300 MHz, CDCl3, ppm) δ = 8.04–7.87 (m, 2H), 7.45 (m, 3H), 4.65 (q, J = 7.1 Hz, 2H), 2.97–2.82 (m, 2H), 1.76–1.66 (m, 2H), 1.45 (dt, J = 7.1 Hz, 5H), 0.96 (t, J = 7.3 Hz, 3H).
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000796
title: high-performance liquid chromatography-mass spectrometry (HPLC-MS) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for high-performance liquid chromatography-mass spectrometry (HPLC-MS)
 
 MS (HPLC-MS): m/z 429 (29), 413 (20), 393 (14), 313 (17), 312 (100) [M- Na+].
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 13C NMR (75 MHz, Chloroform-d [77.2 ppm], ppm) δ = 193.3, 169.1, 163.0, 132.9 (2C), 131.0 (2C), 128.7, 126.1, 114.5, 66.7, 40.5, 26.5, 22.3, 14.8, 13.6.
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0001150
title: 1H--1H correlation spectroscopy (1H-1H COSY) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for 1H--1H correlation spectroscopy (1H-1H COSY)
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000292
title: ultraviolet-visible spectrophotometry (UV-VIS) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for ultraviolet-visible spectrophotometry (UV-VIS)
 
 λmax = 346 nm
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for infrared absorption spectroscopy (IR)
 
 IR (ATR, ṽ) = 3048 (vw) [-CH3], 2983 (w) [-CH3], 2958 (m) [-CH2], 2931 (w) [-CH2], 2864 (w) [-CH2], 1631 (vs) [-C=O], 1524 (vs) [-C=C], 1380 (vs), 1366 (vs), 1353 (vs), 1333 (vs), 1314 (m), 1270 (s), 1204 (s), 1110 (vs), 1097 (vs), 1070 (s), 1029 (m), 1016 (s), 933 (m), 857 (m), 785 (m), 767 (vs), 737 (vs), 713 (m), 687 (vs), 671 (vs) cm–1.
DOI: 10.14272/absytdgsqmuqge-uhfffaoysa-n/chmo0000287
title: fluorescence spectroscopy (fluorimetry) ()
title: InChI=1S/C16H19NO2S/c1-3-5-11-13(18)14-15(19-4-2)17-16(20-14)12-9-7-6-8-10-12/h6-10H,3-5,11H2,1-2H3

description: dataset for fluorescence spectroscopy (fluorimetry)
 
 λFL = 427 nm.
DOI: 10.17632/z4vrrwzsr7.1
title: Antiangiogenic treatment facilitates the abscopal effect of radiotherapy combined with Anti-PD-1 in hepatocellular carcinoma

description: Chemokine chip
DOI: 10.17632/3j5w87djyh.1
title: Multi-site Airborne Hyperspectral Datasets for Precision Crop Mapping Applications

description: Technology infusion in agriculture has been progressing steadily, touching up on various spheres of agriculture such as crop identification, soil classification, yield prediction, disease detection, and weed-crop discrimination. On-demand detection of crop type, often realized as crop mapping, is a primary requirement in agriculture. Hyperspectral remote sensing has emerged as the most versatile technique for the mapping and prediction of various parameters of interest in agriculture. Thanks to the readily availability of a host of machine leaning models and frameworks, the prospect of automatic identification of crop is perceived a realistic goal achievable in the near-future. The ongoing developments in the methods and algorithms of remote sensing data analyses for crop mapping requires the availability of curated, multi-site hyperspectral datasets, under various cases of variations by type and number of crops, geographic site and verifiable ground truth data. The availability of high-resolution airborne hyperspectral datasets across multiple sites with compositions of different types of crops is scare in remote sensing literature. Aimed at enabling the development of knowledge-transfer and potentially automatic approaches for multi-crop mapping using hyperspectral remote sensing, we present a comprehensive high-resolution airborne hyperspectral datasets acquired using AVIRIS-NG for five different sites encompassing different types of crops and agro-climatic regions with associated growth truth data. Though the acquired over sites in India, the diversity in terms of types and number of crops and geographic and agro-climatic diversity maintained by the hyperspectral imaging settings enable the study, implementation and validation of a host of methods and algorithms for agriculture studies using hyperspectral imaging datasets. This is the first of its kind datasets acquisition in which the sensor-imaging and acquisition geometry are maintained consistently similar across different sites and the composting and diversity of crop types maintained for undertaking multi-pronged studies on precision crop mapping suing remote sensing.
DOI: 10.17632/ft62gkx5tt.1
title: Sample of BRÉHAT traffic data

description: A sample of one week of train traffic on the French National Rail Network (NRN), from the BRÉHAT database of SNCF Réseau. The data ranges from 2018-01-08 to 2018-01-14, and all files are compressed using zstandard. The brehat files contains raw observations in json format, as a list of events. The filteredData files contains only trains filtered by our data preparation step, each key of the array is a train number and the associated value is a list of all events for that train on that day. Finally, the trainingData are formatted for network training: they are snapshots every 12 minutes of a list of trains, in a format described in the associated publication "Transformers à Grande Vitesse".
DOI: 10.17632/ft62gkx5tt.2
title: Sample of BRÉHAT traffic data

description: A sample of one week of train traffic on the French National Rail Network (NRN), from the BRÉHAT database of SNCF Réseau.

The data ranges from 2018-01-08 to 2018-01-14, and all files are compressed using zstandard.

The brehat files contains raw observations in json format, as a list of events.
The filteredData files contains only trains filtered by our data preparation step, each key of the array is a train number and the associated value is a list of all events for that train on that day.
Finally, the trainingData are formatted for network training: they are snapshots every 12 minutes of a list of trains, in a format described in the associated publication "Transformers à Grande Vitesse".
DOI: 10.7303/syn53144948
title: Effects of physical exercise and olfactory training with pleasant and unpleasant odours on verbal fluency and depression
DOI: 10.48420/24763299
title: Data and code from Lewis et al 2024: Java sparrow song conforms to Mezerath’s Law but not Zipf’s Law of Abbreviation

description: This code analyses data from Lewis at al. 2023 Animal Behaviour (https://doi.org/10.1016/j.anbehav.2023.09.012) to assess whether song in the Java sparrow adheres to two laws that govern most human languages: Menzerath's law and Zipf's law of abbreviation
DOI: 10.48420/24763299.v1
title: Data and code from Lewis et al 2024: Java sparrow song conforms to Mezerath’s Law but not Zipf’s Law of Abbreviation

description: This code analyses data from Lewis at al. 2023 Animal Behaviour (https://doi.org/10.1016/j.anbehav.2023.09.012) to assess whether song in the Java sparrow adheres to two laws that govern most human languages: Menzerath's law and Zipf's law of abbreviation
DOI: 10.25318/9810059401-fra
title: Situation d’activité selon le sous-groupe des professions, les secteurs d'industries, l’âge et le genre : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Données sur la situation d’activité selon le sous-groupe des professions (code à 4 chiffres) de la Classification nationale des professions (CNP) de 2021, les secteurs d'industries (code à 2 chiffres) du Système de classification des industries de l’Amérique du Nord (SCIAN) de 2017, l’âge et le genre pour la population active âgée de 15 ans et plus dans les ménages privés au Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties.
DOI: 10.25318/9810059401-eng
title: Labour force status by occupation minor group, industry sectors, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on labour force status by occupation minor group (4-digit code) from the National Occupational Classification (NOC) 2021, industry sectors (2-digit code) from the North American Industry Classification System (NAICS) 2017, age and gender, for the labour force aged 15 years and over in private households in Canada, provinces and territories, census metropolitan areas and census agglomerations with parts.
DOI: 10.17632/vj38nxxrkd.1
title: Dataset for "Shyness and Mobile Phone Addiction among Young Adults..." by Wang & Liu (2023)

description: Raw data and analytical results through SPSS.
DOI: 10.17632/bbt7sxkmrf.1
title: Novel stress sensitivity of pore-size distribution based porosity-permeability models in unconventional rocks

description: Figure 2. Stress-dependent porosity and permeability of the sandstone and shale samples. (a) Porosity of SD-1 and SD-2. (b) Permeability of SD-1 and SD-2. (c) Porosity of SH-1 and SH-2. (d) Permeability of SH-1 and SH-2.
Figure 3. T2 maps of four samples under different effective confining pressures (a) SD-1 shows continuous trimodal bimodal characteristics. (b) SD-2 show continuous bimodal characteristics. (c) SH-1 shows independent bimodal characteristics. (d) SH-2 shows weak bimodal characteristics.
Figure 4. Comparison of the predicted porosity (a) and permeability (b) by the improved models, McKee's models and the measured results.
DOI: 10.17632/nkcw5r93bp.1
title: Zhu et al., Provenance of Cretaceous-Miocene sediments in Borneo: Implications for paleogeographic patterns and tectonic evolution

description: Geology
DOI: 10.17632/x3p2mbf5tx.1
title: Comparative analysis Machine learning(ML) based path loss dataset for university campus environment

description: Path loss is an important key factor affecting performance and efficiency in wireless communication system. Here primary measured data was collected from Covenant University, Ota, Ogun State, Nigeria & supplementary materials article found at the following address (https://doi.org/10.1016/j.dib.2018.02.026). In order to generalize for every university environment, four key features (Elevation, Altitude, Clutter height, Distance) were used to take as input, and a total of 3616 measured samples used in our cases to build the ML-based model and predict this dataset. Hence 28 different types of ML-based machine learning have been implemented to generate this predicted ML-based data. Each type is under categorized (ML) techniques such as Linear Regression, Tree, SVM (Support vector machine), Efficient Linear method, Ensemble method, GPR (Gaussian Process Regression), Neural Network and Kernel method.