cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19775548
title: Descriptive data of pretest and posttest measurements.

description: Socio-environmental conflicts arise due to disputes over access to resources in the territories, which is usually exacerbated by pollution problems and even more so by climate change effects on the environment, such as intense droughts, water shortages or river overflows. The current investigation analyzes precisely the effect that a natural disaster, specifically a flood, had on a population of Sucre, Colombia, where there were constant socio-environmental conflicts due to their attitudes and behaviors, before the Pansegüita river and the way of relating to the communities. other people in the community. The sample consisted of 400 adults who lived on the boundaries of the water resource, with an average age of 45.7 years, to whom the Socio-environmental Conflict Identification Scale (ICS-ENVIRONMENTAL) was applied before and after the flood. The effect of this event is analyzed using the Wilcoxon statistical test, approving the research hypothesis, finding a statistically significant difference p=0.000) before and after flooding, for all the dimensions evaluated: use of water, land and in waste management. Finally, conclusions and practical implications of the current research are expressed.. Socio-environmental conflicts arise due to disputes over access to resources in the territories, which is usually exacerbated by pollution problems and even more so by climate change effects on the environment, such as intense droughts, water shortages or river overflows. The current investigation analyzes precisely the effect that a natural disaster, specifically a flood, had on a population of Sucre, Colombia, where there were constant socio-environmental conflicts due to their attitudes and behaviors, before the Pansegüita river and the way of relating to the communities. other people in the community. The sample consisted of 400 adults who lived on the boundaries of the water resource, with an average age of 45.7 years, to whom the Socio-environmental Conflict Identification Scale (ICS-ENVIRONMENTAL) was applied before and after the flood. The effect of this event is analyzed using the Wilcoxon statistical test, approving the research hypothesis, finding a statistically significant difference p=0.000) before and after flooding, for all the dimensions evaluated: use of water, land and in waste management. Finally, conclusions and practical implications of the current research are expressed..
DOI: 10.5066/p99dbtkt
title: Data release for tempest1d: Recursive Estimation of Vertical Groundwater/Surface-Water Exchange using Heat Tracing

description: This data release provides a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. A heat-transport problem was formulated as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS) algorithms. This data release contains both algorithms, synthetic and field-experimental data, as well as VFLUX model results (Neversink_obs.csv, VFLUX_results.csv) used for validation and comparison. The tempest1d.py file contains the code to run the algorithm, while the compressed notebooks folder contains both ipython notebooks (.ipynb) and html versions of worksflows for algorithm application to provided data. More detailed instructions can be found in the readme.txt file attached.
DOI: 10.6084/m9.figshare.19775551.v1
title: Shower Water Contributes Viable Nontuberculous Mycobacteria to Indoor Air

description: These are sequencing data include 1) 16S rRNA gene sequencing to identify bacterial communities in shower water and indoor air and 2) rpoB gene sequencing to identify composition of nontuberculous mycobacteria.
DOI: 10.5281/zenodo.5987415
title: Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

description: Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022. We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.
DOI: 10.5281/zenodo.5987414
title: Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

description: Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022. We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.
DOI: 10.6084/m9.figshare.16862536.v10
title: Workflow

description: For the Kiefl 2022 paper
DOI: 10.5517/ccdc.csd.cc27x91j
title: CCDC 2082767: Experimental Crystal Structure Determination

description: Related Article: Li Dou, Huabin Shi, Xue Niu, Hong Zhang, Kankan Zhang, Zhibing Wu|2022|New J.Chem.|46|9017|doi:10.1039/D2NJ01709A
DOI: 10.5517/ccdc.csd.cc28rnk8
title: CCDC 2108110: Experimental Crystal Structure Determination

description: Related Article: Yuanye Yin, Aixiang Ding, Longmei Yang, Lin Kong, Jiaxiang Yang|2022|Mater. Chem. Front.|6|1261|doi:10.1039/D2QM00067A
DOI: 10.5517/ccdc.csd.cc28rnrg
title: CCDC 2108116: Experimental Crystal Structure Determination

description: Related Article: Yuanye Yin, Aixiang Ding, Longmei Yang, Lin Kong, Jiaxiang Yang|2022|Mater. Chem. Front.|6|1261|doi:10.1039/D2QM00067A
DOI: 10.5517/ccdc.csd.cc298pbm
title: CCDC 2123510: Experimental Crystal Structure Determination

description: Related Article: Frédéric Justaud, Hippolyte Paysant, Louis Bastien Weiswald, Abdelghani Jebahi, Marie Jouanne, Nicolas Elie, Anne Sophie Voisin-Chiret, Thierry Roisnel, Clément Orione, Nicolas Levoin, Laurent Poulain, René Grée|2022|New J.Chem.|46|9119|doi:10.1039/D1NJ05987D
DOI: 10.5517/ccdc.csd.cc29l031
title: CCDC 2132462: Experimental Crystal Structure Determination

description: Related Article: Aurora Rodríguez-Álvarez, Cristina E. González-Espinoza, Carla Gabriela Martínez-De-León, Mauricio Carrillo-Tripp, Minhhuy Hô, Jean-Michel Grévy|2022|New J.Chem.|46|9344|doi:10.1039/D2NJ00924B
DOI: 10.5517/ccdc.csd.cc29lbcm
title: CCDC 2132780: Experimental Crystal Structure Determination

description: Related Article: Yueyue He, Zhuo Chen, Xiaogang Chen, Xian-Ming Zhang, Dongying Fu|2022|Mater. Chem. Front.|6|1292|doi:10.1039/D2QM00058J
DOI: 10.5517/ccdc.csd.cc29lbdn
title: CCDC 2132781: Experimental Crystal Structure Determination

description: Related Article: Yueyue He, Zhuo Chen, Xiaogang Chen, Xian-Ming Zhang, Dongying Fu|2022|Mater. Chem. Front.|6|1292|doi:10.1039/D2QM00058J
DOI: 10.5517/ccdc.csd.cc29lbkt
title: CCDC 2132786: Experimental Crystal Structure Determination

description: Related Article: Yueyue He, Zhuo Chen, Xiaogang Chen, Xian-Ming Zhang, Dongying Fu|2022|Mater. Chem. Front.|6|1292|doi:10.1039/D2QM00058J
DOI: 10.5517/ccdc.csd.cc29lbmw
title: CCDC 2132788: Experimental Crystal Structure Determination

description: Related Article: Yueyue He, Zhuo Chen, Xiaogang Chen, Xian-Ming Zhang, Dongying Fu|2022|Mater. Chem. Front.|6|1292|doi:10.1039/D2QM00058J
DOI: 10.5517/ccdc.csd.cc29kzzv
title: CCDC 2132458: Experimental Crystal Structure Determination

description: Related Article: Aurora Rodríguez-Álvarez, Cristina E. González-Espinoza, Carla Gabriela Martínez-De-León, Mauricio Carrillo-Tripp, Minhhuy Hô, Jean-Michel Grévy|2022|New J.Chem.|46|9344|doi:10.1039/D2NJ00924B
DOI: 10.5517/ccdc.csd.cc2b7t4k
title: CCDC 2152458: Experimental Crystal Structure Determination

description: Related Article: Si-Chao Ji, Shanshan Jiang, Tianxiang Zhao, Lingyi Meng, Xu-Lin Chen, Can-Zhong Lu|2022|New J.Chem.|46|8991|doi:10.1039/D2NJ01072K
DOI: 10.5517/ccdc.csd.cc2b7tfv
title: CCDC 2152467: Experimental Crystal Structure Determination

description: Related Article: Si-Chao Ji, Shanshan Jiang, Tianxiang Zhao, Lingyi Meng, Xu-Lin Chen, Can-Zhong Lu|2022|New J.Chem.|46|8991|doi:10.1039/D2NJ01072K
DOI: 10.5517/ccdc.csd.cc28nkbv
title: CCDC 2105127: Experimental Crystal Structure Determination

description: Related Article: Rumpa Saha, Aparajita Mukherjee, Samaresh Bhattacharya|2022|New J.Chem.|46|9098|doi:10.1039/D1NJ04736A
DOI: 10.5517/ccdc.csd.cc28nk9t
title: CCDC 2105126: Experimental Crystal Structure Determination

description: Related Article: Rumpa Saha, Aparajita Mukherjee, Samaresh Bhattacharya|2022|New J.Chem.|46|9098|doi:10.1039/D1NJ04736A
DOI: 10.5517/ccdc.csd.cc26yw46
title: CCDC 2054498: Experimental Crystal Structure Determination

description: Related Article: Jiabin Liu, Yuxiang Xin, Yiyang Bai, Wei She, Jing Wang, Gaungming Li|2022|New J.Chem.|46|9229|doi:10.1039/D2NJ00308B
DOI: 10.5517/ccdc.csd.cc28nk8s
title: CCDC 2105125: Experimental Crystal Structure Determination

description: Related Article: Rumpa Saha, Aparajita Mukherjee, Samaresh Bhattacharya|2022|New J.Chem.|46|9098|doi:10.1039/D1NJ04736A
DOI: 10.6084/m9.figshare.3761562.v2220
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25346/s6/fs9tdl/q9g8a7
title: lfactivehiss_polycoeff.dat

description: The active polynomial coefficients.
DOI: 10.25346/s6/fs9tdl/tys0b1
title: lfnonactivehiss_polycoeff.dat

description: The non-active polynomial coefficients
DOI: 10.25346/s6/fs9tdl
title: Polynomial Coefficients for Active and Non-Active Hiss

description: This contains two .dat files for the active and non-active hiss waves. These files contain the polynomial coefficients derived from active (24) and non-active (3) hiss wave spectra. They are 7th ordered polynomials. See readme for more details.
DOI: 10.6084/m9.figshare.16862536.v11
title: Workflow

description: For the Kiefl 2022 paper
DOI: 10.6084/m9.figshare.16862536.v12
title: Workflow

description: For the Kiefl 2022 paper
DOI: 10.5281/zenodo.6555001
title: UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)

description: Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.
DOI: 10.5281/zenodo.6555000
title: UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)

description: Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.
DOI: 10.25345/c5cf9jb0x
title: MassIVE MSV000089484 - pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level enabling the functional exploration of site-specific glycosylation
title: Interpreting large-scale glycoproteomic data for intact glycopeptide identification has been tremendously advanced by software tools. However, software tools for quantitative analysis of intact glycopeptides remain lagging behind, which greatly hinders exploring the differential expression and functions of site-specific glycosylation in organisms. Here, we report pGlycoQuant, a generic software tool for quantitative intact glycopeptide analysis, supporting both primary and tandem mass spectrometry quantitation for multiple quantitative strategies. pGlycoQuant advances in glycopeptide evidence matching through applying a deep learning model that reduces missing values for glycopeptide quantification by over 60% compared with Byologic, MSFragger-Glyco and Skyline, as well as an optional function of Match-In-Run (MIR) algorithm for more quantitative coverage of glycopeptides, thus greatly expanding the quantitative function of several powerful search engines, currently including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. The pGlycoQuant-based site-specific N-glycoproteomic study conducted here quantifies 6435 intact N-glycopeptides in three hepatocellular carcinoma cell lines with different metastatic potentials and, together with in vitro molecular biology experiments, illustrates core fucosylation at site 979 of the L1 cell adhesion molecule (L1CAM) as a potential regulator of HCC metastasis. pGlycoQuant is freely available at https://github.com/Power-Quant/pGlycoQuant/releases. We have demonstrated pGlycoQuant to be a powerful tool for the quantitative analysis of site-specific glycosylation and the exploration of potential glycosylation-related biomarker candidates, and we expect further applications in glycoproteomic studies.
DOI: 10.25346/s6/fexjou/98z5pt
title: DC_scaling_updated.plt
DOI: 10.25346/s6/fexjou/vzmsls
title: DC_Hiss_Lpp3-4.plt
DOI: 10.25346/s6/fexjou/6xpar3
title: DC_Hiss_Lpp4-5.plt
DOI: 10.25346/s6/fexjou/gzke0r
title: DC_Hiss_Lpp5-6.plt
DOI: 10.25346/s6/fexjou/pxabbi
title: DC_Hiss_Lpp2-3.plt
DOI: 10.25346/s6/fexjou
title: Malaspina_et_al_Diffusion_Coefficients_and_updated_scaling

description: This dataset contains the Diffusion Coefficients derived for the Malaspina et al (2020) hiss waves parametrized by the plasmapause (as .plt files). There are 4 files (for plasmapause location (2-3, 3-4, 4-5, 5-6). There is also a scaling file "DC_scaling_updated.plt" which scales with respect to distance from the plasmapause (dLpp), plasmapause (Lpp), and Kp activity level (1-6). The files are formatted by L (1 - highest allowed per file), in 0.2 L intervals), Energy (10 keV- 10 MeV), Pitch Angle (1-89 degrees), Daa, Dpp, Dap. The naming convention falls this format: "DC_Hiss_Lpp(INSERT-PLASMAPAUSE-RANGEHERE).plt"
DOI: 10.25346/s6/3g3rl2/qdnzmx
title: plot_PSD_Q1=0.8_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/8tisvl
title: plot_PSD_Q1=3.3_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/88numi
title: plot_PSD_Q1=2.3_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/pvfa1f
title: plot_PSD_Q1=3.0_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/xrzdl7
title: plot_PSD_Q1=3.3_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/wkx9j8
title: plot_PSD_Q1=2.0_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/ynclde
title: plot_PSD_Q1=0.3_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/bfk2cz
title: plot_PSD_Q1=3.0_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/kvhble
title: plot_PSD_Q1=2.0_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/z12y4r
title: plot_PSD_Q1=2.7_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/9sj9b6
title: plot_PSD_Q1=1.5_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/0fehn3
title: plot_PSD_Q1=3.3_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/qewyat
title: plot_PSD_Q1=1.0_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/c6dqct
title: plot_PSD_Q1=1.0_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/xnjbd6
title: plot_PSD_Q1=1.75_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/xdskeu
title: plot_PSD_Q1=0.5_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/shfvpr
title: plot_PSD_Q1=2.0_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/yn3dpp
title: plot_PSD_Q1=1.75_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/vzfv9c
title: plot_PSD_Q1=4.2_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/5krwbx
title: plot_PSD_Q1=3.0_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/qaww6s
title: plot_PSD_Q1=2.5_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/8mcrni
title: plot_PSD_Q1=3.5_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/hin0uq
title: plot_PSD_Q1=1.3_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/uevywd
title: plot_PSD_Q1=3.0_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/gut3tc
title: plot_PSD_Q1=0.7_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/dkbuid
title: plot_PSD_Q1=2.5_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/1adkjm
title: plot_PSD_Q1=0.5_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/jb9o4g
title: plot_PSD_Q1=4.2_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/ljwzs2
title: plot_PSD_Q1=1.5_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/2m3qds
title: plot_PSD_Q1=3.5_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/6b9bxi
title: plot_PSD_Q1=1.7_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/umhzpf
title: plot_PSD_Q1=0.9_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/mcjfru
title: plot_PSD_Q1=1.5_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/lkwkk4
title: plot_PSD_Q1=2.5_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/y5v76y
title: plot_PSD_Q1=4.2_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/hsuhzh
title: plot_PSD_Q1=0.5_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/ksiqzy
title: plot_PSD_Q1=1.75_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/kfrtor
title: plot_PSD_Q1=2.5_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/npzn7u
title: plot_PSD_Q1=1.7_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/hxifhz
title: plot_PSD_Q1=2.5_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/zqrp1f
title: plot_PSD_Q1=0.9_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/3bzjs9
title: plot_PSD_Q1=1.0_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/9ry6na
title: plot_PSD_Q1=0.9_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/uffq5z
title: plot_PSD_Q1=2.3_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/eurvuh
title: plot_PSD_Q1=0.3_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/wgdw0z
title: plot_PSD_Q1=1.7_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/0dgxwl
title: plot_PSD_Q1=0.8_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/4cylvx
title: plot_PSD_Q1=1.0_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/4cn5me
title: plot_PSD_Q1=0.5_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/6eovbl
title: plot_PSD_Q1=0.7_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/qzkvh3
title: plot_PSD_Q1=3.5_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/uqezvo
title: plot_PSD_Q1=3.5_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/otnocj
title: plot_PSD_Q1=3.3_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/5hctr1
title: plot_PSD_Q1=3.0_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/8wbkoa
title: plot_PSD_Q1=3.5_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/acs2az
title: plot_PSD_Q1=0.8_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/18pspv
title: plot_PSD_Q1=4.2_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/pihnmy
title: plot_PSD_Q1=0.7_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/hvahes
title: plot_PSD_Q1=1.75_Q2=1.3090_interp.mat
DOI: 10.25346/s6/au1iz2/402xhj
title: plot_PSD_Q1=1.7_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/vbtohk
title: plot_PSD_Q1=2.7_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/pfaie7
title: plot_PSD_Q1=1.3_Q2=0.2618_interp.mat
DOI: 10.25346/s6/au1iz2/jjmoxt
title: plot_PSD_Q1=0.7_Q2=0.2618_interp.mat
DOI: 10.25346/s6/au1iz2/uslwdg
title: plot_PSD_Q1=3.5_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/rokifc
title: plot_PSD_Q1=0.7_Q2=0.5236_interp.mat
DOI: 10.25346/s6/au1iz2/9v6nle
title: plot_PSD_Q1=1.5_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/tllua3
title: plot_PSD_Q1=2.3_Q2=0.2618_interp.mat
DOI: 10.25346/s6/au1iz2/te9kx9
title: plot_PSD_Q1=3.0_Q2=0.5236_interp.mat
DOI: 10.25346/s6/au1iz2/kcknbp
title: plot_PSD_Q1=4.2_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/0yha5o
title: plot_PSD_Q1=3.5_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/fznx1a
title: plot_PSD_Q1=1.7_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/pz5ahp
title: plot_PSD_Q1=0.7_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/ozcrro
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DOI: 10.25346/s6/au1iz2/gbneqi
title: plot_PSD_Q1=2.5_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/trfwg4
title: plot_PSD_Q1=0.5_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/e92bfh
title: plot_PSD_Q1=1.5_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/pj7tv2
title: plot_PSD_Q1=1.7_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/ksppiu
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DOI: 10.25346/s6/3g3rl2/yu9nq9
title: plot_PSD_Q1=4.2_Q2=1.3090_interp.mat
DOI: 10.25346/s6/au1iz2/zizw6j
title: plot_PSD_Q1=1.75_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/ivqvdq
title: plot_PSD_Q1=1.5_Q2=1.4835_interp.mat
DOI: 10.25346/s6/au1iz2/k9aotn
title: plot_PSD_Q1=2.3_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/umt5zg
title: plot_PSD_Q1=1.75_Q2=0.5236_interp.mat
DOI: 10.25346/s6/au1iz2/awp9kx
title: plot_PSD_Q1=3.0_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/yekzdr
title: plot_PSD_Q1=1.3_Q2=0.5236_interp.mat
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title: plot_PSD_Q1=1.3_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/t2zknp
title: plot_PSD_Q1=0.7_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/qlsmee
title: plot_PSD_Q1=2.7_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/brrchc
title: plot_PSD_Q1=0.3_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/its8jd
title: plot_PSD_Q1=1.5_Q2=1.0472_interp.mat
DOI: 10.25346/s6/au1iz2/ced0jq
title: plot_PSD_Q1=3.5_Q2=1.3090_interp.mat
DOI: 10.25346/s6/au1iz2/srz1kd
title: plot_PSD_Q1=0.3_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/0feyrm
title: plot_PSD_Q1=2.5_Q2=1.3090_interp.mat
DOI: 10.25346/s6/au1iz2/4ut3na
title: plot_PSD_Q1=2.3_Q2=0.5236_interp.mat
DOI: 10.25346/s6/3g3rl2/xh8kj3
title: plot_PSD_Q1=3.3_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/icqk0s
title: plot_PSD_Q1=2.7_Q2=0.5236_interp.mat
DOI: 10.25346/s6/au1iz2/krqc89
title: plot_PSD_Q1=0.8_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/s6jfuk
title: plot_PSD_Q1=0.3_Q2=1.0472_interp.mat
DOI: 10.25346/s6/au1iz2/kvybmt
title: plot_PSD_Q1=2.3_Q2=1.0472_interp.mat
DOI: 10.25346/s6/3g3rl2/epf1eg
title: plot_PSD_Q1=2.3_Q2=1.3090_interp.mat
DOI: 10.25346/s6/3g3rl2/pco22c
title: plot_PSD_Q1=2.0_Q2=0.5236_interp.mat
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title: plot_PSD_Q1=0.8_Q2=0.5236_interp.mat
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DOI: 10.25346/s6/au1iz2/jgrqm0
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DOI: 10.25346/s6/3g3rl2/1obuhs
title: plot_PSD_Q1=1.0_Q2=0.2618_interp.mat
DOI: 10.25346/s6/3g3rl2/nkpe4l
title: plot_PSD_Q1=2.0_Q2=0.7854_interp.mat
DOI: 10.25346/s6/au1iz2/tleluq
title: plot_PSD_Q1=3.3_Q2=0.7854_interp.mat
DOI: 10.25346/s6/3g3rl2/tz3ujs
title: plot_PSD_Q1=0.3_Q2=1.3090_interp.mat
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DOI: 10.25346/s6/au1iz2/iipd0o
title: plot_PSD_Q1=2.0_Q2=1.4835_interp.mat
DOI: 10.25346/s6/3g3rl2/oem0nu
title: plot_PSD_Q1=0.9_Q2=0.7854_interp.mat
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title: plot_PSD_Q1=2.7_Q2=0.2618_interp.mat