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Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting
<p>The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal <em>Scientific Reports</em>. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x.</p> <p><strong>Below is the legend for the Supplementary Information</strong>, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in <em>Scientific Reports</em>.</p> <p><strong>Supplementary 1</strong> – <em>UpperPaleoDietV4.html</em> – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions.</p> <p><strong>Supplementary 2</strong> – <em>Support Information 2.docx</em> – Word document containing supplementary tables 2 and 3.</p> <p><strong>Supplementary 3</strong> – <em>ArchaeoloigcalDataset_v8.csv</em> – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 4</strong> – <em>EuroUpperPaleoFaunas_v6.csv</em> – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 5 </strong>– <em>Lupo2016.csv</em> – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 6</strong> – <em>PushkinaRaia_FaunaWeights.csv</em> – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 7</strong> – <em>environmental_BG.csv</em> – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1.</p> <p>For more information on the data, methods, and results, please see the main paper. </p> <p> </p> <p> </p>
Structural Inheritance in the Eastern Cordillera, NW Argentina: Low‐Temperature Thermochronology of the Cianzo Basin - Supporting Information
<p>Supporting information accompanying the publication "Structural Inheritance in the Eastern Cordillera, NW Argentina: Low‐Temperature Thermochronology of the Cianzo Basin" published in Tectonics. The dataset contains (U-Th-Sm)/He and apatite fission track data from the Cianzo Basin, Jujuy, Argentina, and accompanying figures.</p> <p>Table S1 contains full single-grain results from apatite (AHe) and zircon (ZHe) (U-Th-Sm)/He analyses. Table S2 and S3 contain AFT results including full counting data from apatite fission track (AFT) analyses.</p> <p>Figure S1 supports AHe and ZHe data with plots showing relationships between cooling ages, eU, Ft and ESR. Figures S2–S4 support AFT data with radial plots.</p>
Project files provided as supporting information to the manuscript "A deep learning approach to the structural analysis of proteins"
<p><strong>README file to the project files provided as supporting information to the manuscript “A deep learning approach to the structural analysis of proteins”</strong></p> <p>Dec. 30, 2018</p> <p>Authors: Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p> </p> <p>- datasets.zip: archive containing five .csv files, namely:</p> <p> - decoys_cm.csv : all the data for 10728 protein decoys, training set</p> <p> - evaluation_cm.csv : all data for 146 proteins in the evaluation set</p> <p> - random_CG.csv : 1200 Coulomb matrices. 100 CG models for each protein with 120 amino acids</p> <p> - 1e5g_centered_sphere.csv : 100 CG models in which the central atoms in 1e5g are not removed</p> <p> - 1e5g_random_sphere.csv : 10 CG models for 10 different (random) locations for the sphere that includes atoms that have to be retained. 100 CG models in total</p> <p> </p> <p>- decoys_labels.lab containing the labels associated to the 10728 decoys present in the training set</p> <p>- evaluation_labels.lab containing the labels associated to the 146 pdb files in the evaluation set</p> <p>- random_CG_labels.lab containing the labels associated to the 6 proteins with 120 amino acids</p> <p>- network_development_training: a python script that performs cross validation and full training of the model</p> <p>- saved_networks.zip FOLDER containing 10 networks: the architecture is included in .json files while weight parameters are inside .hs files</p> <p> </p> <p>- pdb_files.zip FOLDER containing the PDB files that have been employed in the project, namely:</p> <p> - pdb_files_len100 : pdb files with 100 amino acids</p> <p> - pdb_files_len101-110 : pdb files with a number of amino acids between 101 and 110</p> <p> - decoys : decoys of length 100 extracted from the above folder: name syntax == PDBNAME_decoy_STARTRES_ENDRES.pdb</p> <p> EXAMPLE 6gsp.pdb will give rise to 6gsp_decoy_0_100.pdb , 6gsp_decoy_1_101.pdb , 6gsp_decoy_2_102.pdb , 6gsp_decoy_3_103.pdb , 6gsp_decoy_4_104.pdb</p> <p> - pdb_files_len100 : 6 pdb files with 120 amino acids</p> <p> </p>
The Structure of Sub-nm Platinum Clusters at Elevated Temperatures (Supplementary Information)
<p><strong><em>This dataset consists of raw data and denoised scanning transmission electron microscopy videos of sub-nm sized clusters of Pt on a carbon substrate. The data is used in the article "The Structure of Sub-nm Platinum Clusters at Elevated Temperatures" published in Angewandte Chemie International Edition, 2019, DOI:10.1002/anie.201911068</em></strong><strong><em> </em></strong></p> <p><strong>Video S1.</strong> A typical sub-nm amorphous cluster at room temperature. 0.5 nm scale bar.</p> <p><strong>Video S2.</strong> Two typical crystalline sub-nm clusters at 350°C. 0.5 nm scale bar.</p> <p><strong>Video S3. </strong>In this high-speed recording at 147 fps, the high beam current required for this fast imaging has suppressed the crystallinity of the cluster, despite the temperature of 350°C. 0.5 nm scale bar.</p> <p><strong>Video S4. </strong>The unusually stable 13-atom cluster at the bottom forms an fcc cuboid, and can be seen rotating at three orientations, as shown by the inset model and in Fig. 3a-c. 0.5 nm scale bar.</p> <p><strong>Video S5. </strong>This 15-atom cluster initially forms an fcc cube, then transforms into multiple hcp structures. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p> <p><strong>Video S6. </strong>The cluster in this movie is a 22-atom truncated rectangular cuboid. 0.5 nm scale bar.</p> <p><strong>Video S7. </strong>In the center and bottom, two 6-atom octagons are rotating (shown in Fig. S4) as they add onto their larger neighboring clusters. The 13-atom cluster in the top forms an unusually stable fcc cuboctahedron from frame 219. 0.5 nm scale bar.</p> <p><strong>Video S8. </strong>The cluster on the bottom left forms a fleeting icosahedron-like structure. 0.5 nm scale bar.</p> <p><strong>Video S9. </strong>This cluster shows fcc structures, despite being recorded at 200°C, but with a very low beam dose. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p>
Suppl. Information to "The tropical coral Pocillopora acuta displays an unusual chromatin structure and shows histone H3 clipping plasticity upon bleaching"
<p><strong>Supplementary File 1:</strong> Multiple alignment for protein sequences of core histones with Pocillopora acuta, Pocillopora damicornis, Acropora digitifera, Nematostella vectensis, Hydra vulgaris, Schistosoma mansoni and Mus musculus. A. Histone H2A; B. Histone H2B; C. Histone H3; D. Histone H4. An asterisk (*) means that the amino acid is conserved between all species.</p> <p><strong>Supplementary File 2</strong>: Original (uncropped and unedited) images used for Figures 1 to 4.</p> <p><strong>Supplementary File 3:</strong> <em>P. acuta</em> nuclei and <em>Symbiodinium</em> count on a Thoma cell counting chamber done over three different nuclei extractions. For each extraction, two counts were performed. P. acuta nuclei were stained with Hoechst 33342 and display a blue fluorescence at 350 nm. Symbiodinium are not damaged by our extraction method and are not permeable to Hoechst. They display a red fluorescence because of their chlorophyl content. Observations were done on a Leica DMLB with objective PL Fluotar 40x and 100x. A text version of the data in the Excel file below.</p> <p>Extraction #1 replicate 1: 102 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 112 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 42 <em>P. acuta </em>nuclei (Blue) ; 0 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 55 <em>P. acuta </em>nuclei (Blue) ; 1 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 215 <em>P. acuta </em>nuclei (Blue) ; 3 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 257 <em>P. acuta</em> nuclei (Blue) ; 5 <em>Symbiodinium</em> (Red)</p> <p>Made at IHPE.</p>
NewSOC, supplementary information to WT2.5.4 "Cells with honeycomb structured oxygen electrodes": electrochemical and post-mortem data sets.
<p>These are data set, related to participation of the IEN in project NewSOC. It includes results of the SEM-EDS analysis of the cells with hexagonal current collecting net and electrochemical performance data, both EIS and C-V characteristics. Results are grouped in zip archives, named according to cell design, “infill-net”.</p> <p>Compositions:</p> <p>LNF – LaNi<sub>0.6</sub>Fe<sub>0.4</sub>O<sub>3</sub> (net)</p> <p>LSC – La<sub>0.6</sub>Sr<sub>0.4</sub>CoO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSF – La<sub>0.5</sub>Sr<sub>0.5</sub>FeO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSCCF – La<sub>0.6</sub>Sr<sub>0.4</sub>Co<sub>0.15</sub>Cu<sub>0.05</sub>Fe<sub>0.8</sub>O<sub>3-</sub><sub>d </sub>(net)</p> <p>PCM – Pr<sub>0.5</sub>Ca<sub>0.5</sub>MnO<sub>3 </sub>(net)</p> <p>BSCFM – Ba<sub>0.5</sub>Sr<sub>0.5</sub>Co<sub>0.725</sub>Fe<sub>0.2</sub>Mo<sub>0.075</sub>O<sub>3-</sub><sub>d</sub> (infill)</p> <p> </p> <p><strong>Data presentation. </strong></p> <p><em>C-V characteristics:</em></p> <p>This is text files, generated by Zahner galvanostat, with self-decriptional titles.</p> <p>“05iv_650c_100h2+100h2o_500air.txt” – measurement at 650°C, 100 mL/min H<sub>2</sub> and 100 mL/min H<sub>2</sub>O on fuel side, 500 mL/min of air on air side.</p> <p><em>EIS data:</em></p> <p>EIS results were extracted from proprietary binary files, generated by Zahner galvanostat, and raw data is generally meaningless except the owners of such hardware. So, extracted EIS can be found in Excel files, used in analysis, in sheet “Experimental”. Other sheets in xlsx include some metadata (“info”), results of the equivalent circuit fit (“fit”) and some plots. Fit results might not be relevant. </p> <p><em>SEM</em></p> <p>Post-mortem results presented as SEM images (tif or jpg files) and pdf files with results of the EDS analysis.</p> <p><strong>LSC-LNF </strong></p> <p><em>(air flow 1 L/min, current density 0.25 A/cm<sup>2</sup>)</em></p> <p>test_1_07: </p> <p>03_eis20200917142808.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>05_eis20200917123105.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>08_eis20200917123857.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>10_eis20200917131537.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>test_1_08:</p> <p>03_eis20200917125707.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>09_eis20200917130019.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.09 H<sub>2</sub>+ 0.21 H<sub>2</sub>O;</p> <p>10_eis20200917130600.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>12_eis20200917130737.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.12 H<sub>2</sub>+ 0.28 H<sub>2</sub>O;</p> <p>test_3_14:</p> <p>01_eis20210517102042.xlsx– EIS, SOFC, 700°C, Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>; 05_eis20210517102625.xlsx– EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>07_eis20210517102305.xlsx– EIS, SOEC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem after test_1_07)</em></p> <p>ogniwo_310_2020 ****.jpg - surface</p> <p> </p> <p><strong>BSCMF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_2_14</p> <p>01eis_700c_cc4a_100h2+100n2_500air_eqc20220110112836.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02eis_700c_cc4a_100h2+100h2o_500ai_eqc20220110112724.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_650c_cc3a_100h2+100h2o_500ai_eqc20220110112503.xlsx–650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06eis_650c_cc3a_100h2+100n2_500air_eqc20220110112332.xlsx –650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07eis_625c_cc3a_100h2+100n2_500air_eqc20220110112214.xlsx –625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08eis_625c_cc3a_100h2+100h2o_500ai_eqc20220110111834.xlsx–625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>test_1_67</p> <p>01_eqc20220831134628.xlsx–700°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831134724.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220831135442.xlsx–650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831135622.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831135801.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>09_eqc20220831135933.xlsx –650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_14)</em></p> <p>493-2021-1.pdf, 493-2021-1i.pdf, 493-2021-2.pdf, 493-2021-2-2.pdf, 493-2021-2-3.pdf –cross-sections with EDS</p> <p>493_2021_*_**.tif - cross-sections</p> <p> </p> <p><strong>BSCMF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p> test_2_08</p> <p>01_eis20211104144342.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>03eis_700c__eis20211108102241.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_700c__eis20211108102401.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08eis__eis20211110094642.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>10eis__eis20211110094945.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>11eis__eis20211110101358.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>13eis__eis20211110100837.xlsx- 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p> test_2_26</p> <p>04_eqc20220817114103.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220817121529.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>07_eqc20220802084119.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1</p> <p>08_eqc20220802084616.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220802150705.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>11_eqc20220802150108.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_26)</em></p> <p>661_BSCMF_LSCCF_p.pdf – cross-section with EDS</p> <p>661_BSCMF_LSCCF_PM.pdf - surface with EDS</p> <p>661_BSCMF_LSCCF_p_01.tif, 661_BSCMF_LSCCF_p_02.tif, 661_BSCMF_LSCCF_p_03.tif, 661_BSCMF_LSCCF_p_04.tif - cross-section, infill zone</p> <p>661_BSCMF_LSCCF_p_05.tif, 661_BSCMF_LSCCF_p_06.tif - cross-section, net zone zone</p> <p>661_BSCMF_LSCCF_PM_**.tif - surface</p> <p> </p> <p><strong>LSF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_1_65</p> <p>01_eqc20220816105347.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>02_eqc20220816105941.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220816112311.xlsx - 650°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220816111640.xlsx - 650°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07_eqc20220816142436.xlsx- 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08_eqc20220816142842.xlsx-625°C, 0.625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem) </em></p> <p>663_LSF_LSCCF_p.pdf – cross-section with EDS</p> <p>663_LSF_LSCCF_PM.pdf - surface with EDS</p> <p>663_LSF_LSCCF_P_GR_**.tif – cross-section of the cell</p> <p>663_LSF_LSCCF_P_LSCCF_**.tif – surface of the LSCCF grid</p> <p>663_LSF_LSCCF_PM_**.tif - surface of the LSF infill</p> <p> </p> <p> </p> <p><strong>LSF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>tests_1_66</p> <p>01_eqc20220831133210.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831133409.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>03_eqc20220831133928.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>05_eqc20220831134048.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831134142.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831134237.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220831134412.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem)</em></p> <p>658_LSF_PCM_p.pdf – cross-section with EDS</p> <p>658_LSF_PCM_PM.pdf - surface with EDS</p> <p>658_LSF_PCM_P_LSF_**.tif – cross-section, infill zone</p> <p>658_LSF_PCM_P_pcm_**.tif - cross-section, net zone</p> <p>658_LSF_PCM_PM_**.tif - surface</p> <p><strong>description.pdf </strong>- pdf version of this information.</p>
Supplementary materials for "Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments"
<p>Supplementary materials for "Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments". These include precalculated RNA Blocks, MBRs (Matrix of Bear encoded RNA), sPSSMs (structural Position Specific Scoring Matrix), RIG (Relative Information Gain) scores, and plots calculated for 3016 Rfam 14.1 families. In particular:</p> <ul> <li><strong>alignments.zip:</strong> zipped file containing the structural alignments for each Rfam family.</li> <li><strong>RNA_Blocks.zip</strong>: zipped file containing the RNA blocks used to derive different substitution matrices.</li> <li><strong>MBRs.zip</strong>: zipped file containing the substitution matrices.</li> <li><strong>sPSSMs.zip</strong>: zipped file containing the structural Position Specific Scoring Matrices.</li> <li><strong>RIGs.zip</strong>: zipped file containing the RIG scores.</li> <li><strong>entropy.zip</strong>: zipped file containing the (rescaled) entropy.</li> <li><strong>plots.zip</strong>: zipped file containing the plots. </li> </ul> <p>All the scripts to build all these files are available at <a href="https://github.com/helmercitterich-lab/RIG">https://github.com/helmercitterich-lab/RIG</a>.</p>
Annotation dataset for the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind"
<p>This is the resulting dataset from the text annotation exercise in the article titled "<strong>On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind</strong>" that will appear in the journal Open Archeology in a special issue titled Archaeological Practice on Shifting Grounds (edited by Åsa Berggren and Antonia Davidovic-Walther). The article is accepted for publication and the annotations are final. CIDOC CRM is used for text annotations.</p>
Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information
<p>Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper.</p> <p>Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.</p>
Supporting information for "Cosolvent effects on the structure and thermoresponse of a polymer brush: PNIPAM in DMSO-water mixtures"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in "Cosolvent effects on the structure and thermoresponse of a PNIPAM brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p> <p>All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories:</p> <ul> <li>FTIR <ul> <li>FTIR transmission data of binary DMSO-water mixtures as a function of solvent composition.</li> <li>FTIR deconvolution was performed using software readily available at <a href="https://github.com/haydenrob/spec_deconv">https://github.com/haydenrob/spec_deconv</a>.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a hydrated (solid-liquid) polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips</a> repo.</li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “DMSO_6mol_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox</a> repo.</li> </ul> </li> </ul>
Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)
<p>pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a></p> <p>pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a></p> <p>Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a></p> <p> </p> <p>Network Changelog:</p> <p>V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository.</p> <p>V 0.9 - Python code updated to the release version; paper published</p> <p>V 0.8 - Python code (beta) to run models included</p> <p>V 0.7 - Original upload of development models </p> <p> </p> <p>Selected works with SIPFENN alongside DFT and experiments:</p> <p>- <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a></p> <p>- <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a></p> <p> </p> <p>SIPFENN Abstract (original publication, 2021):</p> <p>In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.<br> <br> In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.<br> <br> We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface. </p> <p> </p> <p>Contacts:</p> <p>- Adam Krajewski: ak@psu.edu</p> <p>- Prof. Zi-Kui Liu: zxl15@psu.edu</p>
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
<p>(This is the dataset used in <a href="https://doi.org/10.1101/2022.05.18.492501" rel="noopener" title="Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information">Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information</a>, Abstract below)</p> <p><span>Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using pairwise spike time statistics, co-activity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.</span></p>
Data from: Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.
<p>This data accompanies the paper entitled <strong><em>Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.</em></strong></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: <em>S. rum</em> and <em>T. mar</em> LDHs. The systems have been simulated at 315 K for <em>S. rum </em>and 340 K for <em>T. mar</em>. Final configurations of the proteins after productions are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
Open the record for dataset details and reuse information.
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p>
Armenian: Word order and information structure
<ul> <li> <p><strong>Basic word order: arguments in favour of OV and VO</strong></p> </li> <li> <p><strong>What is information structure: topic, focus</strong></p> </li> <li> <p><strong>Usual position of sentential stress and (EA) auxiliary</strong></p> </li> <li> <p><strong>‘Topical’ constituents (agent/experiencer subject, specific object): positions and properties</strong></p> </li> <li> <p><strong>Preverbal and postverbal focus</strong></p> </li> <li> <p><strong>Head-final vs. head-initial constituents</strong></p> </li> <li> <p><strong>Factors favouring VO order (cognitive, areal, typological)</strong></p> </li> </ul> <p> </p> <p>This lecture is part of the lecture series:</p> <p><em>Glottothèque: Languages of the Anatolia, Caucasus, Iran, Mesopotamia; grammatical snippets online </em>(electronic resource). Bamberg, Cambridge, Göttingen, Moskow, Nicosia, Paris: LACIM network, at https://spw.uni-goettingen.de/projects/lacim/, edited by Christiane Bulut, Anaïd Donabédian-Demopoulos, Geoffrey Haig, Geoffrey Khan, Pollet Samvelian, Stavros Skopeteas, Nina Sumbatova.</p>
Supplementary Information: Effects of cryo-EM cooling on structural ensembles
<p>This data set contains a pdb file with the ribosome-EF-Tu complex atoms used for analysis. The trajectories (xtc files) contain the ensembles of structures before cooling and after cooling with various cooling time spans.</p> <p>model3_training.zip contains the code to train and and analyse kinetic model3 as well as the rmsf quantiles obtained from MD simulations, and the temperature drop estimates used for the model.</p>
Structured Information on State and Evolution of Dockerfiles on GitHub
<p>Docker containers are standardized, self-contained units of applications, packaged with their dependencies and execution environment. The environment is defined in a Dockerfile that specifies the steps to reach a certain system state as infrastructure code, with the aim of enabling reproducible builds of the container. To lay the groundwork for research on infrastructure code, we collected structured information about the state and the evolution of Dockerfiles on GitHub and release it as a PostgreSQL database archive (over 100,000 unique Dockerfiles in over 15,000 GitHub projects). Our dataset enables answering a multitude of interesting research questions related to different kinds of software evolution behavior in the Docker ecosystem.</p>
Fire Effects Information System: FEIS Invasiveness (structured data) (750) in DwCA
The Fire Effects Information System (FEIS) provides up-to-date information about fire effects on plants, lichens, and animals. It was developed at the United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory in Missoula, Montana. The FEIS database contains literature reviews, taken from current English-language literature of about 900 plant species, 7 lichen species, about 100 wildlife species, 17 Research Project Summaries, and 16 Kuchler plant communities of North America. The emphasis of each review and summary is fire and how it affects species. Background information on taxonomy, distribution, basic biology, and ecology of each species is also included. Reviews are thoroughly documented, and each contains a complete bibliography. Managers from several land management agencies (United States Department of Agriculture, Forest Service, and United States Department of Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, and National Park Service) choose the species included in the database. Those agencies funded the original work and continue to support maintenance and updating of the database. <p></p>https://www.feis-crs.org/feis/<p></p>The Fire Effects Information System (FEIS) provides up-to-date information about fire effects on plants, lichens, and animals. It was developed at the United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory in Missoula, Montana. The FEIS database contains literature reviews, taken from current English-language literature of about 900 plant species, 7 lichen species, about 100 wildlife species, 17 Research Project Summaries, and 16 Kuchler plant communities of North America. The emphasis of each review and summary is fire and how it affects species. Background information on taxonomy, distribution, basic biology, and ecology of each species is also included. Reviews are thoroughly documented, and each contains a complete bibliography. Managers from several land management agencies (United States Department of Agriculture, Forest Service, and United States Department of Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, and National Park Service) choose the species included in the database. Those agencies funded the original work and continue to support maintenance and updating of the database. <p></p>https://www.feis-crs.org/feis/ FEIS data on EOL include invasiveness status.
Data and Scripts for the Article "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid"
<p>Data and scripts for the article titled "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid".</p> <p>For further details on the contents, see the "readme.md"-file.</p> <p>Article available at <a href="https://doi.org/10.1039/D4CP02454K">10.1039/D4CP02454K</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.