@data{10.22002/D1.20298 title = {InteractiveResource: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20298}, abstract = {Extended figure 7: Alignment of the RecA-like domains 2 of Utp25 and of eIF4A. ChimeraX file of the aligned domains 2 of the AlphaFold yeast Utp25 (P40498) and yeast eIF4A (1FUU; (Caruthers et al. 2000)) crystal structure.}, year = {2022}, } @data{10.22002/D1.20297 title = {InteractiveResource: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20297}, abstract = {Extended figure 6: Alignment of the RecA-like domains 1 of Utp25 and of eIF4A. ChimeraX file of the aligned domains 1 of the AlphaFold yeast Utp25 (P40498) and yeast eIF4A (1FUU; (Caruthers et al. 2000)) crystal structure.}, year = {2022}, } @data{10.22002/D1.20296 title = {InteractiveResource: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20296}, abstract = {Extended figure 5: Alignment of the full-length Utp25 and eIF4A structures. ChimeraX file of the aligned full-length (containing RecA domains 1 and 2) AlphaFold yeast Utp25 (P40498) and yeast eIF4A (1FUU; (Caruthers et al. 2000)) crystal structure. Note the structural similarity of domains 1 of Utp25 and of eIF4A and similarly of domains 2 of both proteins. Structural overlap is only possible for one or the other of the two RecA-like domains but not for both domains simultaneously.}, year = {2022}, } @data{10.22002/D1.20295 title = {Dataset: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20295}, abstract = {Extended figure 4: Dali search using the yeast and human AlphaFold Utp25 structures robustly identifies known DEAD-box helicases.}, year = {2022}, } @data{10.22002/D1.20294 title = {InteractiveResource: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20294}, abstract = {Extended figure 3: The yeast and human AlphaFold Utp25 structures are conserved. ChimeraX file of the aligned yeast (P40498) and human (Q68CQ4) AlphaFold Utp25 structures shows that they are clearly superimposable and thus conserved in structure.}, year = {2022}, } @data{10.22002/D1.20293 title = {Image: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20293}, abstract = {Extended figure 2: The high pLDDT domains 1 and 2 of the AlphaFold yeast Utp25 align well to eIF4A. Structural alignment of the individual RecA-like domains 1 and 2 of the AlphaFold yeast Utp25 (P40498) and yeast eIF4A (1FUU; (Caruthers et al. 2000)) crystal structure. The AlphaFold yeast Utp25 is coloured based on pLDDT score, from very low confidence in red to very high confidence in blue (key in bottom right) whereas the yeast eIF4A crystal structure is shown in brown. In domain 1, six regions in the AlphaFold yeast Utp25 are coloured cyan (70 < pLDDT < 90; confident) as opposed to blue (pLDDT > 90, very high confidence). Two of these areas overlap with helicase motifs Q and Ib. Similarly, there are 7 confident areas in domain 2 (cyan), with one overlapping helicase motif VI. The remainder of the helicase motifs are in high confidence areas. The location of the helicase motifs is indicated along with the coordinates of the regions used in the structural alignments of isolated domains 1 and 2.}, year = {2022}, } @data{10.22002/D1.20292 title = {Image: The SSU Processome Component Utp25p is a Pseudohelicase}, author = {J. Michael Charette}, doi = {10.22002/D1.20292}, abstract = {Extended figure 1: The N-terminal region of the yeast and human Utp25 is disordered. (Top) PONDR prediction of an N-terminal IDR (aa 1 to ~160) in the yeast Utp25. (Bottom) PONDR prediction of an N-terminal IDR (aa 1 to ~185) in the human UTP25.}, year = {2022}, } @data{10.22002/D1.20291 title = {Data and code for figures of “Nano-electromechanical spatial light modulator enabled by asymmetric resonant dielectric metasurfaces” by Hyounghan Kwon et al}, author = {Hyounghan Kwon}, doi = {10.22002/D1.20291}, abstract = {

This upload contains the main dataset and codes represented in the figures of the following publication:

“Nano-electromechanical spatial light modulator enabled by asymmetric resonant dielectric metasurfaces” by Hyounghan Kwon et al.}, year = {2022}, } @data{10.22002/D1.20290 title = {sbmlteam/libsbml: Release 5.19.7}, author = {Michael Hucka}, doi = {10.22002/D1.20290}, abstract = { ## What’s Changed * FbcModelPlugin C API for createObjective and createGeneProduct by @exaexa in https://github.com/sbmlteam/libsbml/pull/252 * Remove the three ‘Robin’ boundary conditions. by @luciansmith in https://github.com/sbmlteam/libsbml/pull/263 * Relax annot by @skeating in https://github.com/sbmlteam/libsbml/pull/265 * Fbc v3 fixes by @skeating in https://github.com/sbmlteam/libsbml/pull/264 * As Mattias noticed, diffusion and advection can apply to parameters. by @luciansmith in https://github.com/sbmlteam/libsbml/pull/267 * #268 provide detailed error message on failure by @fbergmann in https://github.com/sbmlteam/libsbml/pull/269 * - bump to 5.19.7 by @fbergmann in https://github.com/sbmlteam/libsbml/pull/270 Full Changelog: https://github.com/sbmlteam/libsbml/compare/v5.19.6…v5.19.7 LibSBML is a native library for reading, writing and manipulating files and data streams containing the Systems Biology Markup Language (SBML). It offers language bindings for C, C++, C#, Java, JavaScript, MATLAB, Perl, PHP, Python, R and Ruby.}, year = {2022}, } @data{10.22002/D1.20289 title = {WennbergLab/py-ginput: ginput v1.1.7 release}, author = {Joshua (Josh) Laughner}, doi = {10.22002/D1.20289}, abstract = {Installing with pip install or python setup.py install will now correctly copy the files in ginput/data to the installation directory. Installing in develop mode through make install is still the recommended way of setting up ginput for normal use; however, if this is part of a larger project and you want the code to live alongside your other dependencies, that works now. Two notes: 1. ginput does cache look up tables in the data directory within the package. If you install it into your environment (rather than using develop mode), you will need to ensure that the install directory is writable by the process that will run ginput (or you disable the caching). 2. If installing with python setup.py install or pip install, it is still recommended that you use the environment-py36.yml or environment-py310.yml file to install the necessary dependencies with conda. The setup.py requirements are deliberately permissive so that it can work with either the Python 3.6 or 3.10 requirements. If you rely on pip to install the dependencies and get errors, please try to reproduce the errors in a conda environment based on the environment-py36.yml or environment-py310.yml file before opening an issue. ## What’s Changed * Setup update for pip install by @rocheseb in https://github.com/WennbergLab/py-ginput/pull/1 Full Changelog: https://github.com/WennbergLab/py-ginput/compare/v1.1.6…v1.1.7 Public repository for the GINPUT priors generator}, year = {2022}, } @data{10.22002/D1.20288 title = {CO2 simulations for “Remote-sensing derived trends in gross primary production explain increases in the CO2 seasonal cycle amplitude”}, author = {He, Liyin}, doi = {10.22002/D1.20288}, abstract = {simulated surface CO2 concentrations (unit: ppm) from 2001 to 2018 used in paper “Remote-sensing derived trends in gross primary production explain increases in the CO2 seasonal cycle amplitude” (Article DOI: 10.1029/2021GB007220).}, year = {2022}, } @data{10.22002/D1.20287 title = {Indy Old Records}, author = {Camila Suarez}, doi = {10.22002/D1.20287}, abstract = {indy data before Venice change. Book 1 and Book 2 page 73}, year = {2022}, } @data{10.22002/D1.20286 title = {justinbois/iqplot: 0.3.2}, author = {Justin S. Bois}, doi = {10.22002/D1.20286}, abstract = { - Bug fixes iqplot: Bokeh plots with one quantitative variable}, year = {2022}, } @data{10.22002/D1.20285 title = {WennbergLab/py-ginput: ginput v1.1.6 release}, author = {Joshua (Josh) Laughner}, doi = {10.22002/D1.20285}, abstract = {This release includes (1) a license change and (2) fixes to allow ginput to run with Python 3.10. License change The license associated with ginput has changed from LGPL to Apache 2.0. If you download this software after 8 Sept 2022, the Apache 2.0 license is in effect. Python update The code has been updated to work with Python 3.10. If you install with make install or ./install.sh, a Python 3.10 conda environment is created instead of a Python 3.6 one. If you need to work with Python 3.6, the environment-py36.yml file contains the previous 3.6 environment configuration, and can be use with conda env create -f to manually create a Python 3.6 environment. Public repository for the GINPUT priors generator}, year = {2022}, } @data{10.14291/tccon.ggg2020.indianapolis01.R1 title = {TCCON data from Indianapolis (US), Release GGG2020.R1}, author = {Iraci, L. T. and Podolske, J. R. and Hillyard, P. W. and Roehl, C. and Wennberg, P. O. and Blavier, J.-F. and Landeros, J. and Allen, N. and Wunch, D. and Zavaleta, J. and Quigley, E. and Osterman, G. B. and Barrow, E. and Barney, J.}, doi = {10.14291/tccon.ggg2020.indianapolis01.R1}, abstract = {The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the GGG2020 data release of observations from the TCCON station at Indianapolis, Indiana, USA}, year = {2022}, } @data{10.22002/D1.20283 title = {justinbois/iqplot: v0.3.1}, author = {Justin S. Bois}, doi = {10.22002/D1.20283}, abstract = {Hot fix of bug involved with using swarm_kwargs in strip plots. iqplot: Bokeh plots with one quantitative variable}, year = {2022}, } @data{10.22002/D1.20282 title = {First steps for presentation and analysis of calcium imaging data}, author = {Andrey Andreev and Desiderio Ascencio}, doi = {10.22002/D1.20282}, abstract = {Calcium imaging is a widely used tool in neuroscience, and our community now has access to multiple computational tools to process and analyze these data. However, significant portion of work with imaging data relies on much simpler approaches than offered in these software packages. Correlation with stimuli or behavior, detection of periodic activity, dimensionality reduction, and other basic approaches can be straight-forward for researchers seasoned in data analysis, but unfamiliar to larger community of experimental neuroscientists. Here we provide examples of such analysis using Python language together with sample open-access datasets. We discuss questions addressed with standard algorithms and offer documented Jupiter notebooks ready to be adapted for other data.}, year = {2022}, } @data{10.22002/D1.20281 title = {Crystallization Kinetics of Magnesium Sulfate Hydrate on Europa}, author = {Johnson, Paul}, doi = {10.22002/D1.20281}, abstract = {

Data behind figures for “Crystallization Kinetics of Vitreous Magnesium Sulfate Hydrate and Implications for Europa’s Surface,” submitted to Geophysical Research Letters

}, year = {2022}, } @data{10.22002/D1.20280 title = {caltechlibrary/cell-atlas: Release 2.3}, author = {Oikonomou, Catherine and Jensen, Grant and Morrell, Thomas E}, doi = {10.22002/D1.20280}, abstract = {This release adds minor updates to the video and text content of “The Atlas of Bacterial & Archaeal Cell Structure” site, including seven new scientist profiles. Cell atlas}, year = {2022}, } @data{10.22002/D1.20279 title = {Surveying metal antimonate photoanodes for solar fuel generation}, author = {Zhou, Lan and Gregoire, John M.}, doi = {10.22002/D1.20279}, abstract = {Data .zip file containing data tables used to generate the figures of the associated manuscript, raw photoelectrochemistry data, and .udi files that combined composition and XRD data. See the enclosed readme.txt}, year = {2022}, } @data{10.22002/D1.20278 title = {PDF version the Atlas of Bacterial and Archaeal Cell Structure}, author = {Oikonomou, Catherine and Jensen, Grant}, doi = {10.22002/D1.20278}, abstract = {PDF format of version 2.3 of “The Atlas of Bacterial & Archaeal Cell Structure” by Catherine M. Oikonomou & Grant J. Jensen}, year = {2022}, } @data{10.14291/tccon.ggg2020.hefei01.R0 title = {TCCON data from Hefei (PRC), Release GGG2020.R0}, author = {Liu, Cheng and Wang, Wei and Sun, Youwen and Shan, Changgong}, doi = {10.14291/tccon.ggg2020.hefei01.R0}, abstract = {The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the GGG2020 data release of observations from the TCCON station at Hefei, China}, year = {2022}, } @data{10.22002/D1.20275 title = {caltechlibrary/epxml_to_datacite: M1 support and general fixes}, author = {Morrell, Thomas E}, doi = {10.22002/D1.20275}, abstract = {This release includes M1 support for Macs, as well as a number of other bug fixes. It enhances the caltechauthors script with support for text file inputs and improvements to series and numbering information transfer. It also fixes a bug with accepted date transfer. Full Changelog: https://github.com/caltechlibrary/epxml_to_datacite/compare/v1.1…v1.2 Transform Eprints XML to DataCite XML}, year = {2022}, } @data{10.22002/D1.20274 title = {Atlas of Bacterial and Archaeal Cell Structure - Offline Version 2.3}, author = {Oikonomou, Catherine and Jensen, Grant}, doi = {10.22002/D1.20274}, abstract = {The offline version 2.3 of “The Atlas of Bacterial & Archaeal Cell Structure” by Catherine M. Oikonomou & Grant J. Jensen}, year = {2022}, } @data{10.14291/tccon.ggg2020.edwards01.R0 title = {TCCON data from Edwards (US), Release GGG2020.R0}, author = {Iraci, L. T. and Podolske, J. R. and Roehl, C. and Wennberg, P. O. and Blavier, J.-F. and Allen, N. and Wunch, D. and Osterman, G. B.}, doi = {10.14291/tccon.ggg2020.edwards01.R0}, abstract = {The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the GGG2020 data release of observations from the TCCON station at Armstrong Flight Research Center, Edwards, CA, USA}, year = {2022}, }