cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2640689
title: Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

description: <strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br>
by V. Pandey, N. Hadadi and V. Hatzimanikatis

"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper:  <br>
• Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br>
• Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br>
• Metabolomics: This contains metabolomics data of aforementioned both studies.<br>
• ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models.  Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M).  Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br>
• AlternativeMCS: We generated alternative states for MCS and saved results.<br>
• FVAMM: This is the result flux variability analysis can be found in this folder.<br>
• Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.
description: VP and VH are supported by the RTD grant MalarX, no. 2013/155,  within SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation and by the École Polytechnique fédérale de Lausanne. NH is supported through the RTD grant MicroScapesX, no. 2013/158, within SystemX, the Swiss Initiative for System Biology evaluated by the Swiss National Science Foundation.
DOI: 10.17632/jwdgdhr4zp.1
title: Post-logging silvicultural effects on dung beetles in Malaysian Borneo

description: Species level dung beetle data collected in unlogged forest, logged forest left naturally regenerating and logged forest subsequently treated with vine-cutting and enrichment planting in Malaysian Borneo. The data comprises information collected across 4 different years, with baited traps set as transects within the three forest types. We explored the hypothesis that post-logging silviculture, through removal of important microhabitats, has deleterious consequences on dung beetle effective diversity, functional diversity and species composition. Contrary to this hypothesis, we found that dung beetle communities in intensively treated forest maintain similar effective and functional diversity to unlogged and naturally regenerating logged forest, with species composition in treated forest also similar to that in the naturally regenerating forest.
DOI: 10.17632/nfhnscr36c.1
title: Test dcm

description: Test
DOI: 10.17632/w922jtts2m.2
title: Magnetic Resonance Imaging of Quadriceps tendon harvest site shows regeneration after 2 years in anterior cruciate ligament reconstruction: A study of 9 cases.

description: excel sheet for the data on MRI measurements
DOI: 10.14469/hpc/5480
title: Co(II)-Tetraphenylporphyrin, doublet, S4 symmetry E= -3295.11874942 G=-3294.586931

description: Gaussian Calculation
DOI: 10.14469/hpc/5481
title: Co(II)-Tetraphenylporphyrin, -1 singlet, S4 sym G=-3294.677131

description: Gaussian Calculation
DOI: 10.14469/hpc/5482
title: Co(II)-Tetraphenylporphyrin, -1 singlet, D2d sym G=-3294.672554, 1 -ve FC

description: Gaussian Calculation
DOI: 10.17632/v5466fbpf9.3
title: JSE Equity Market Transaction Data : TRTH and BLP Daily and Intraday data

description: 1) Intraday-daily: The data set consists of the OHLCV entries sampled at 5-minute intervals for 30 of the JSE Top 40 stocks from 02-01-2018 to 29-06-2018. In addition to the intraday data, daily OHLCV data for the specified period is included for the last transaction on any given day. All data sourced from Bloomberg. 2) Daily data: set of OHLCV data values for 42 stocks on the JSE Top 40 over the period 01-01-2005 to 29-04-2016. All data sourced from Thomson Reuters.
DOI: 10.3886/e100167v17-11261
title: National Surveys on Energy and Environment [United States]

description: The National Surveys on Energy and Environment (NSEE), a core activity in CLOSUP's Energy and Environmental Policy Initiative, reflects a formal partnership between the Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan's Gerald R. Ford School of Public Policy. NSEE surveys include twice per year national opinion surveys on issues directly related to climate change and energy policy, as well as other surveys conducted on a range of topics such as hydraulic fracturing ("fracking"), the Great Lakes, and wider issues of energy and environment. NSEE is co-directed by professor Barry Rabe at the University of Michigan, and professor Christopher Borick at Muhlenberg College. For more information on the collaboration between the University of Michigan and Muhlenberg College, please see the recent article from Muhlenberg Magazine. For more information about the NSEE, contact CLOSUP staff at 734-647-4091 or closup@umich.edu. From 2008-2012 the survey was called the â¿¿National Survey of American Public Opinion on Climate Changeâ¿ (NSAPOOC); starting in 2013 the survey was renamed to the â¿¿National Surveys on Energy and Environmentâ¿ (NSEE). <br><br>The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. <br><br>Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets. <br><br><br>
DOI: 10.3886/e100167v17-11262
title: National Surveys on Energy and Environment [United States]

description: The National Surveys on Energy and Environment (NSEE), a core activity in CLOSUP's Energy and Environmental Policy Initiative, reflects a formal partnership between the Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan's Gerald R. Ford School of Public Policy. NSEE surveys include twice per year national opinion surveys on issues directly related to climate change and energy policy, as well as other surveys conducted on a range of topics such as hydraulic fracturing ("fracking"), the Great Lakes, and wider issues of energy and environment. NSEE is co-directed by professor Barry Rabe at the University of Michigan, and professor Christopher Borick at Muhlenberg College. For more information on the collaboration between the University of Michigan and Muhlenberg College, please see the recent article from Muhlenberg Magazine. For more information about the NSEE, contact CLOSUP staff at 734-647-4091 or closup@umich.edu. From 2008-2012 the survey was called the â¿¿National Survey of American Public Opinion on Climate Changeâ¿ (NSAPOOC); starting in 2013 the survey was renamed to the â¿¿National Surveys on Energy and Environmentâ¿ (NSEE). <br><br>The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. <br><br>Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets. <br><br><br>
DOI: 10.14469/hpc/5483
title: Co(II)-Tetraphenylporphyrin, -1 singlet, flattened Ph, E=-3295.10849683 G=-3294.569463 DG=64.7

description: Gaussian Calculation
DOI: 10.5061/dryad.88vr0q3/1
title: 2019-04-12 Leivesley et al final data

description: Data tables and dictionaries for data used for the generalized additive mixed-effects models and structural equation models.
DOI: 10.5281/zenodo.2639612
title: Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices

description: The file ManscuriptDataFiles.7z contains RAW spectroscopy data, origin plot file, micromagnetic simulation MIF files at 100 mT used in the manuscript entitiled "Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices" that appeared in Physical Review B, <strong>98</strong>, 174408 (2018).

Manuscript Abstract: We report angle-dependent spin-wave spectroscopy on aperiodic quasicrystalline magnetic lattices, i.e., Ammann, Penrose P2 and P3 lattices made of large arrays of interconnected Ni<sub>80</sub>Fe<sub>20</sub> nanobars. Spin-wave spectra obtained in the nearly saturated state contain distinct sets of resonances with characteristic angular dependencies for applied in-plane magnetic fields. Micromagnetic simulations allow us to attribute detected resonances to mode profiles with specific mirror symmetries. Spectra in the reversal regime show systematic emergence and disappearance of spin-wave modes indicating reprogrammable magnonic characteristics.
description: The research was supported by the Swiss National Science Foundation via Grant No. 163016 and Nanosystems Initiative Munich II funded by the Deutsche Forschungsgemeinschaft.
DOI: 10.5281/zenodo.2639611
title: Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices

description: The file ManscuriptDataFiles.7z contains RAW spectroscopy data, origin plot file, micromagnetic simulation MIF files at 100 mT used in the manuscript entitiled "Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices" that appeared in Physical Review B, <strong>98</strong>, 174408 (2018).

Manuscript Abstract: We report angle-dependent spin-wave spectroscopy on aperiodic quasicrystalline magnetic lattices, i.e., Ammann, Penrose P2 and P3 lattices made of large arrays of interconnected Ni<sub>80</sub>Fe<sub>20</sub> nanobars. Spin-wave spectra obtained in the nearly saturated state contain distinct sets of resonances with characteristic angular dependencies for applied in-plane magnetic fields. Micromagnetic simulations allow us to attribute detected resonances to mode profiles with specific mirror symmetries. Spectra in the reversal regime show systematic emergence and disappearance of spin-wave modes indicating reprogrammable magnonic characteristics.
description: The research was supported by the Swiss National Science Foundation via Grant No. 163016 and Nanosystems Initiative Munich II funded by the Deutsche Forschungsgemeinschaft.
DOI: 10.5061/dryad.1h1f63h/1
title: S1-Database_of_the_meta-analysis-2019
DOI: 10.15151/esrf-es-186759119
title: High-resolution structure of the homotetramer tyrosin hydroxylase (TH)
DOI: 10.14469/hpc/5488
title: test of license metadata

description: sdfsdf
DOI: 10.5285/86532abd-d894-2c4c-e053-6c86abc0db01
title: CTD profiles (chlorophyll, density, dissolved oxygen concentration, conductivity, optical backscatter, turbidity, salinity, temperature, transmittance, pressure, down-welling PAR) collected during UK Shelf Sea Biogeochemistry cruise RRS James Cook JC105.

description: This dataset contains in-situ sensor data from 31 CTD casts (downcast profiles and at upcast bottle firings) collected in the Celtic Sea. The measurements were collected during RRS James Cook cruise JC105 in June 2014, as part of the UK Shelf Sea Biogeochemistry research programme (SSB), in order to provide information on the water mass characteristics and water column structure across the Celtic Sea shelf. Measurements were carried out using a CTD unit that comprised a Sea-Bird Electronics (SBE) 9 plus underwater unit, an SBE 11 plus deck unit, a 24-way SBE 32 carousel and 24 10 L TMF Water Samplers; all of which were mounted on a stainless steel 24-way CTD frame. Attached to the CTD were two SBE 3P temperature sensors, two SBE 4C conductivity sensors, one Paroscientific Digiquartz pressure sensor, one SBE 43 dissolved oxygen sensor, two Chelsea Technology Group (CTG) 2-pi PAR sensors, one WETLabs BBRTD light scattering sensor, one Benthos 916T altimeter one CTG Aquatracka MKIII fluorometer and one CTG Alphatracka MKII transmissometer. This was the instrument set up for the CTD during the entire cruise with the exception of casts 027 and 028, during which the two PAR sensors were not attached to the CTD. Raw data were initially extracted and processed using Seabird Data Processing Software. Modules DatCnv, WildEdit, Filter, AlignCTD, CellTM, Derive and BottleSum were all run. Custom Matlab routines were then used to remove out of water and surface soak values, to filter anomalous data points and to average the profiles onto a 1 db pressure grid. Data were calibrated by comparison with independent samples (where available). Measurements at upcast bottle firings were also calibrated (when possible). The data were collected as part of the SSB research programme in the CaNDyFloSS: Carbon and Nutrient Dynamics and Fluxes over Shelf Systems (NE/K00185X/1) research programme component. SSB was co-funded by the Natural Environment Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles. The British Oceanographic Data Centre (BODC) created the metadata entry and is responsible for holding master copies of the data.
DOI: 10.5517/ccdc.csd.cc1z8zt1
title: CCDC 1825894: Experimental Crystal Structure Determination

description: Related Article: Javier López-Cabrelles, Jorge Romero, Gonzalo Abellán, Mónica Giménez-Marqués, Miguel Palomino, Susana Valencia, Fernando Rey, Guillermo Minguez Espallargas|2019|J.Am.Chem.Soc.|141|7173|doi:10.1021/jacs.9b02686
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21rw5w
title: CCDC 1899778: Experimental Crystal Structure Determination

description: Related Article: Javier López-Cabrelles, Jorge Romero, Gonzalo Abellán, Mónica Giménez-Marqués, Miguel Palomino, Susana Valencia, Fernando Rey, Guillermo Minguez Espallargas|2019|J.Am.Chem.Soc.|141|7173|doi:10.1021/jacs.9b02686
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21rw6x
title: CCDC 1899779: Experimental Crystal Structure Determination

description: Related Article: Javier López-Cabrelles, Jorge Romero, Gonzalo Abellán, Mónica Giménez-Marqués, Miguel Palomino, Susana Valencia, Fernando Rey, Guillermo Minguez Espallargas|2019|J.Am.Chem.Soc.|141|7173|doi:10.1021/jacs.9b02686
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc223kyq
title: CCDC 1910063: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Murray V. Baker, Peter J. Barnard|2019|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc223m0v
title: CCDC 1910096: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Richard Kemp|2019|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc223mvp
title: CCDC 1910122: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2019|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.2640585
title: Use Case 1 Data set

description: Effect of nanofiller on the stiffness of a Carbon Fiber Reinforced Thermoplastic (CFRP).<br>
The effect of different volume fractions (0%, 2%, 5% and 12%) of Multiwall Carbon Nanotubes (MWCNT) are  computed for PEEK reinforced Carbon Fiber UD. Two different Carbon Fiber(CF) volume fractions are considered (40% and 60%).
DOI: 10.5281/zenodo.2640586
title: Use Case 1 Data set

description: Effect of nanofiller on the stiffness of a Carbon Fiber Reinforced Thermoplastic (CFRP).<br>
The effect of different volume fractions (0%, 2%, 5% and 12%) of Multiwall Carbon Nanotubes (MWCNT) are  computed for PEEK reinforced Carbon Fiber UD. Two different Carbon Fiber(CF) volume fractions are considered (40% and 60%).
DOI: 10.5519/qd.vgd1u54c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (112 records)
DOI: 10.21227/bm4a-fw77
title: Supplementary Material for "An Euler-Lagrange Equation Oriented Solution for Write Energy Minimization of STT-MRAM"

description: This material is devoted to details of the toy model used in the main text, including the approximations made and the detailed computations made.
DOI: 10.21227/x6sk-bn33
title: Supplementary Material for "An Euler-Lagrange Equation Oriented Solution for Write Energy Minimization of STT-MRAM"

description: This material is devoted to details of the toy model used in the main text, including the approximations made and the detailed computations made.
DOI: 10.5519/qd.6i0lfos0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.17632/8gdpz5845x.3
title: Ungulate-vehicle collision clusters and hotspots in Lithuania 2002-2017

description: The ungulate-vehicle collision (UVC) number is increasing every year, while the number of other road traffic accidents is decreasing. In 2002–2017, UVC accounted for about 56% of all animal-vehicle collisions (AVC) in Lithuania. Identification of UVC hotspots is important for the effective application of mitigation measures. In order to identify UVC clusters (short, dangerous and significant road sections), kernel density estimation (KDE+) clustering was applied to the entire road network of the country. After identifying UVC clusters for every year from 2002 to 2017, we identified so-called UVC hotspots on the roads where UVC clusters reoccurred at least once, also noting where UVC clusters occurred within fenced road sections. One-third of UVC hotspots were created from recurrence in consecutive years, while the rest over 2–13 years.
DOI: 10.17632/8gdpz5845x.4
title: Ungulate-vehicle collision clusters and hotspots in Lithuania 2002-2017

description: The ungulate-vehicle collision (UVC) number is increasing every year, while the number of other road traffic accidents is decreasing. In 2002–2017, UVC accounted for about 56% of all animal-vehicle collisions (AVC) in Lithuania. Identification of UVC hotspots is important for the effective application of mitigation measures. In order to identify UVC clusters (short, dangerous and significant road sections), kernel density estimation (KDE+) clustering was applied to the entire road network of the country. After identifying UVC clusters for every year from 2002 to 2017, we identified so-called UVC hotspots on the roads where UVC clusters reoccurred at least once, also noting where UVC clusters occurred within fenced road sections. One-third of UVC hotspots were created from recurrence in consecutive years, while the rest over 2–13 years.