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514 results for “principles”
Dataset Dental research data availability and quality according to FAIR principles
<p>This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached. </p>
Effect of Fe-doping on VS2 monolayer: A first-principles study
<p><span>This dataset includes the DFT results used to investigate Figures 3–7 of our study on the Effect of Fe-doping on VS2 monolayer: A first-principles study. The .dat files include band structure calculations from GGA+U (Figure 3) and GGA+U+SOC (Figure 4), magnetic anisotropy energy (MAE) data (Figure 5), dielectric constant from the optical properties (Figure 6), and absorption (Figure 7). All data were calculated using the QuantumATK code package. This dataset supports reuse and additional magnetic and optical behavior analysis in this work.</span></p>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
INVEST Principles
<p>INVEST stands for Independent, Negotiable, Valuable, Scalable, and Testable.</p> <p>Whatever the negotiation process looks like, a story represents the melting point of an aspect of the system. Such an aspect ideally forms a thin functional slice through of the system. This makes the story self-contained. It provides full value for the end user as, once implemented, the described problem is solved and enfolds immediate value to the user.</p> <p>In addition, self-contained stories are better plannable as they are separated from other stories. Providing stories at this level let you decide on-the-go which story to implement next. This makes your planning quite flexible. It is not needed to follow (probably hidden) dependencies before reordering the stories.</p> <p>User stories should contain only the essence of all discussions and decisions to keep them short and clear. Details have to be avoided in the first place. It is recommended to start with a narrow scope and scale later when it becomes necessary. This helps to stay focused and avoid contradiction.</p> <p>To ensure a user story has been finished it should contain a list of acceptance criteria which the implementation could be (ideally) tested against. Testable criteria are very helpful when creating a solution for a problem. Each will support to understand the problem and could be used to validate an implementation against.</p> <p>In addition, there should also be an agreement on when implementing a story is actually done. The team normally agrees on a definition-of-done checklist which has to be worked through for each user story. In general, the definition-of-done includes an agreed set of tasks which have to be part of the result (e.g., unit-tests, documentation, ...).</p>
Dataset do DH2020 [The Lusophone Digital Humanities and What they (we) are doing from the South: textual corpus analysis and FAIR principles to tackle Hegemony]
<p>Planilha de dados recuperados do Google Scholar utilizado na análise e apresentação da pesquisa empírica intitulada - <strong>The Lusophone Digital Humanities and What they (we) are doing from the South: textual corpus analysis and FAIR principles to tackle Hegemony </strong>- no evento <strong>DH2020 Ottawa</strong>: <a href="https://hcommons.org/deposits/item/hc:32051/">https://hcommons.org/deposits/item/hc:32051/</a></p>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
The Parelli vs. ISES Principles dataset
<p>This dataset represents observation frequency scores (4 observers) for behaviours in 'An ethogram for Equitation Science First Principles of Horse Training.pdf' (see the'IPTEK ISES Principles Training Evaluation Kit' for methodological detail).</p> <p>The video materials of Parelli Natural Horsemanship are compared with the ISES (International Society for Equitation Science) First Principles of Horse Training.</p>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Systems-level design principles of metabolic rewiring in an animal
<p><strong>Description: </strong></p> <p>This repository contains all source codes, necessary input and output data, and raw figures and tables for reproducing all figures and results published in the following study: </p> <p>Xuhang Li#, Hefei Zhang#, Thomas Hodder, Wen Wang, L. Safak Yilmaz, Chad L. Myers, Albertha J.M. Walhout. Systems-level Worm Perturb-Seq reveals design principles of metabolic rewiring. (2025)<em> Nature, </em>in press (# equal contribution)</p> <p><strong>Files:</strong></p> <p>This repository contains the entire project working directory for this publication. To deposit into Zenodo, we have individually zipped each subfolder of the root directory. To reproduce our study, it is advised that one should download the entire Zenodo repository and unzip every zip file to produce the corresponding subfolders. The "root.zip" should be directly unzipped into the root directory of this project (not your system root!!). </p> <p>The recovered working directory should be like this: </p> <ul> <li><strong>10_revision</strong>/</li> <li><strong>1_QC_dataCleaning</strong>/</li> <li><strong>2_DE</strong>/</li> <li><strong>3_imageAnalysis</strong>/</li> <li><strong>4_networkAnalysis</strong>/</li> <li><strong>5_GSA</strong>/</li> <li><strong>7_FBA_modeling</strong>/</li> <li><strong>data_and_tables_to_publish</strong>/</li> <li><strong>input_data</strong>/</li> <li>generate_all_tables_for_publication.R</li> <li>prepareTbls_met12.R</li> <li>prepareTbls_metVali1_OCC.R</li> <li>prepareTbls_metVali2_b12.R</li> <li>prepareTbls_metVali3_detox.R</li> <li>prepareTbls.R</li> <li>prepareTbls_SPECIAL.R</li> <li>REWIRING_FIGURE_LOOKUP.xlsx</li> </ul> <p>Please be advised that this repository contains raw codes and data that are not directly related to a figure in our paper. However, they may be useful to generate input used in the analysis of a figure, or to reproduce tables in our manuscript. It may also contain unpublished analyses and figures, which we did not intentionally delete and kept for records. </p> <p><strong>Usage: </strong></p> <p>Please refer to the table in below to locate a specific file for reproducing a figure of interest (also availabe in the <em>REWIRING_FIGURE_LOOKUP.xlsx </em>under the root directory). </p> <table> <tbody> <tr> <td>Figure</td> <td>File</td> <td>Lines<sup>a</sup></td> <td>Notes</td> </tr> <tr> <td>Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-190</td> <td> </td> </tr> <tr> <td>Fig. 1c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-83</td> <td> </td> </tr> <tr> <td>Fig. 1d</td> <td>11_additional analysis/special RNAi.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 1e</td> <td>2_DE/output/metabolic_GRN_layout2.cys</td> <td>open in Cytoscape</td> </tr> <tr> <td>Fig. 1f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-71</td> <td> </td> </tr> <tr> <td>Fig. 1g</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-145</td> <td> </td> </tr> <tr> <td>Fig. 2a</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>1-235</td> <td> </td> </tr> <tr> <td>Fig. 2b</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>232-287</td> <td> </td> </tr> <tr> <td>Fig. 2c</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Fig. 2d</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-181</td> <td> </td> </tr> <tr> <td>Fig. 2e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-338</td> <td> </td> </tr> <tr> <td>Fig. 2f</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 3b</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2539</td> <td> </td> </tr> <tr> <td>Fig. 3c</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Fig. 3d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2183-2235</td> <td> </td> </tr> <tr> <td>Fig. 3e</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>1751-2159</td> <td> </td> </tr> <tr> <td>Fig. 3g</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Fig. 3h</td> <td>11_additional analysis/glycine tracing/glycine RNAi tracing.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Fig. 3j</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Fig. 3k</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>853-881</td> <td>run lines 1-100 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/CR_model_final_run/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Fig. 4b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_gspd_1_example_objHeatmap.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Fig. 4d</td> <td>7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randFBA.R, 7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randGRN.R</td> <td>entire file</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" or "fitted_CR_interaction_diagram_randGRN.pdf" to locate the code for making the figure (two randomizations, respectively), and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all four files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 4f</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-154</td> <td>numbers are in variable "model_explained"</td> </tr> <tr> <td>Fig. 4g (left)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 342-345</td> </tr> <tr> <td>Fig. 4g (right)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 5a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Fig. 5b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/3_DEG_modeling_edge_direction_test_randFBA.R</td> <td>entire file and see notes</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" to locate the code for making the figure, and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all two files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 5c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 343-346</td> </tr> <tr> <td>Fig. 5d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_DEG_cutoff_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1a</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>193-219</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-91</td> <td>the published figure was remade manually for prettier representations, the plotting function for which is not provided in this code. However, the raw data stays the same.</td> </tr> <tr> <td>Extended Data Fig. 1c</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>280-379</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1d</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1e</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1f</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-257</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1g</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1h</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1i</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-61</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-180</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-152</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2d</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-397</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2e</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-383</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-264</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2g</td> <td>5_GSA/1_DE_annotation_pipeline.R</td> <td>1389-1522</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 3</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Extended Data Fig. 4a</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>1-254</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4b</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>258-303</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4c</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-283</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4d</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>725-813</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-589</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4f</td> <td>11_additional analysis/regulators_nhr_sbp_tor_GEP_mGRN.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5a</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5b</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-92</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6a</td> <td>2_DE/6_1_biCluster_heatmaps_PCCtree.R (top row); 2_DE/6_2_rewiring_summary_map_PCCtree.R (bottom row)</td> <td>top row: 210-219; 229-238; bottom row: 1-162; 162-229</td> <td>These two scripts produced the last two figures in the first row and the first two figures in the second row. The remaining two figures are repetitive with other main/extended data figures, whose source code were specified there</td> </tr> <tr> <td>Extended Data Fig. 6b</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>1-184</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6c</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>189-319</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6d</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>326-480</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 7a-e</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-189</td> <td>The annotation bar plot is in 2_DE/SUPP_extra_figures_for_rewiring.R lines 394-715</td> </tr> <tr> <td>Extended Data Fig. 8a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-753</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-785</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8c</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>759-764</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Extended Data Fig. 8e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8f</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>870-874</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8g</td> <td>11_additional analysis/uPhe tracing/uphe RNAi.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9a</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9b</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>691-714</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9c</td> <td>11_additional analysis/uGlucose tracing/uGlu rewiring.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9d</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>607-629</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>636-685</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9f</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 10a</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td>This figure is a subset of fig. 2f</td> </tr> <tr> <td>Extended Data Fig. 10c</td> <td>7_FBA_modeling/REVISION/1_cluster_CR_model.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_excludeMultiObjGenes.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-225</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-340</td> </tr> <tr> <td>Extended Data Fig. 11d</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 352-355</td> </tr> <tr> <td>Extended Data Fig. 11e</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 362-365</td> </tr> <tr> <td>Extended Data Fig. 11f</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>486-742</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>300-329</td> <td>run lines 1-101 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/REVISION/human_perturb_seq/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Extended Data Fig. 12b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/1_EDA_of_human_perturb_seq.R</td> <td>1-176</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12e</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12f</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-341</td> </tr> <tr> <td>Extended Data Fig. 12g</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 353-356</td> </tr> <tr> <td>Extended Data Fig. 12h</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 363-366</td> </tr> <tr> <td>Extended Data Fig. 12i</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2a</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>467-572</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2b</td> <td>2_DE/6_4_Wald_heatmap_each_RNAi_Cluster.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2c</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-389</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2d</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-308</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2e</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-234</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2f</td> <td>12_FLEX_benchmark/run_FLEX_eval.sh</td> <td>entire file</td> <td>the bash scripts calls corresponding R scripts for reproducing the figures. You will need to install the FLEX package to run these codes. </td> </tr> <tr> <td>Supplementary Data Fig. 3, 4</td> <td>N/A</td> <td>N/A</td> <td>No coding involved as these figures were made manually</td> </tr> <tr> <td>Supplementary Data Fig. 5</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 6</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>274-578</td> <td>please load the dependency starting from line 1</td> </tr> </tbody> </table> <p><em><strong>a: The lines indicate the chunk of codes to reproduce the corresponding figure; The figure is reproduced at the end of the referred codes. Please note that you may have to run the codes above the referred chunk (i.e., from the first line) to load dependent variables to execute the referred codes. However, you should be able to reproduce the figure only with the codes within the referred script. </strong></em></p> <p><strong>Notice:</strong></p> <p>We advise you download the entire project working directory (including all zip files and unzip them into corresponding folders), for reproducing any analysis. If you only download the zip file relevant to your figure of interest, you may or may not run into issues due to the missing files in another folder. </p> <p><strong>Contact:</strong></p> <p><strong>For any questions, please contact Xuhang (Hang) Li at Xuhang.Li@umassmed.edu. </strong><strong>(<em>we plan to deposit an updated version with better code annotations, but cannot complete that currently due to time restirctions</em>)</strong></p> <p> </p>
A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset
<p>A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (<a href="https://doi.org/10.5281/zenodo.6374598">Martinez et al., 2022</a>) and can be downloaded and cited from this landing page.</p> <p><strong>The list is open for additions by the community via comments directly to this <a href="https://docs.google.com/spreadsheets/d/1pMWEyadkGW22zYaBl3LsNYoQVk9AW711-2Ogyi8UZZo/edit#gid=0">link</a>. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the <a href="http://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong></p> <p>This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the <a href="https://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>; however, these still support their implementation.</p> <p>The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports.</p> <p> </p> <p><strong>Data collection</strong></p> <p>This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!</p>
Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries
<p>This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties.</p> <p>The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-qz055">https://doi.org/10.26434/chemrxiv-2022-qz055</a>.</p> <p>All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP).</p> <p>The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>) are stored, is provided in the following table:</p> <table> <tbody> <tr> <td>Convergence_study</td> <td>Figure S1</td> </tr> <tr> <td>Bulk_properties</td> <td>Table S3</td> </tr> <tr> <td>Surface_properties</td> <td>Table 1, Table 2, Figure 1, Table S5</td> </tr> <tr> <td>Adsorption_properties</td> <td>Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9</td> </tr> <tr> <td>Diffusion_properties</td> <td>Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9</td> </tr> </tbody> </table> <p> </p>
Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease
<p>Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.</p>
First-principles investigation of phase stability in substoichiometric zirconium carbide under high pressure
<p>This is a data set that supports a first-principles investigation into the phase stability of zirconium carbide under high pressure. High throughput density function theory data is used to train a cluster expansion. The trained model is used to sample the phase space of the substoichiometric ZrC system into high pressures. This sampling sheds insight on how pressure affects the stability and partial vacancy ordering in the ZrC system.</p>
First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions
<p>Documentation for the Dataset used in the publication entitled "First-principles prediction of the Co–Al phase diagram including configurational, vibrational and magnetic contributions" <br>** These datasets comprise all configurations used in Co-Al system and their formation enthalpies at different temperatures, where configurational, vibrational and magnetic contributions were considered. Hcp Co and fcc Al were used as reference states. **<br>** More details about the methodology can be found in the paper "First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions, Journal of Materials Research and Technology, 2024" **</p> <p>1. bcc-Co-Al.zip<br>- Description: bcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>2. fcc-Co-Al.zip<br>- Description: fcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. hcp-Co-Al.zip<br>- Description: hcp-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with hcp lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>4. Formation enthalpies of bcc-Co-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-Co-Al.zip corresponds to a configuration.<br> 2- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT) following eq.(18) in the paper.<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 7- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 8- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE. <br> 10- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 11- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 12- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 14- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 15- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 16- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.<br> 18- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 19- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 20- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K fitted by CE.</p> <p><br>5. Formation enthalpies of fcc Co-Al.xlsx<br>- Description: Formation enthalpies of fcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Co-Al.xlsx.</p> <p><br>6. Formation enthalpies of hcp-Co-Al.xlsx<br>- Description: Formation enthalpies of hcp lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation energies of bcc-Co-Al.xlsx.</p> <p><br>7. ECIs of bcc-Co-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>8. ECIs of fcc-Co-Al at different temperatures.txt<br>- Description: ECIs of fcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>9. ECIs of hcp-Co-Al at different temperatures.txt<br>- Description: ECIs of hcp lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>10. Clusters of bcc-Co-Al.txt<br>- Description: Cluster information of bcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>11. Clusters of fcc-Co-Al.txt<br>- Description: Cluster information of fcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>12. Clusters of hcp-Co-Al.txt<br>- Description: Cluster information of hcp lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions
<p>Documentation for the Dataset used in the publication entitled "First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions" <br>** These datasets comprise all configurations used in Al-Li system and their formation enthalpies at different temperatures, where both configurational and vibrational contribution were considered. Bcc Li and fcc Al were used as reference state. **<br>** More details about the methodology can be found in the paper "Wei Shao, Sha Liu, Javier LLorca, First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions, Computational Materials Science, 2023"**</p> <p>1. bcc-Al-Li.zip<br>- Description: bcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>2. fcc-Al-Li.zip<br>- Description: fcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. Formation enthalpies of bcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in bcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns:<br> 1-(Folder nam) - type: numerical (integer)<br> Description: Each folder name in the bcc-Al-Li.zip corresponds to a configuration.<br> 2- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT).<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration fitted by CE at 0 K. <br> 6- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 7- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K calculated by DFT and bond length vs. bond stiffness relationship (L-S).<br> 8- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K fitted by CE. <br> 10- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 11- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K fitted by CE. <br> 14- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 15- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE.<br> 18- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 19- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE.<br> 22- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 23- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K calculated by DFT and L-S.<br> 24- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K fitted by CE. <br> 26- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 27- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 28- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE.<br> 30- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 31- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K calculated by DFT and L-S.<br> 32- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K fitted by CE.<br> 34- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 35- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT and L-S.<br> 36- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 38- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 39- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 40- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 42- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 43- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K calculated by DFT and L-S.<br> 44- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K fitted by CE.</p> <p>4. Formation enthalpies fcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in fcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Al-Li.xlsx.</p> <p><br>5. ECIs of bcc-Al-Li at different temperatures.txt<br>- Description: ECIs of bcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>6. ECIs of fcc-Al-Li at different temperatures.txt<br>- Description: ECIs of fcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>7. Clusters of bcc-Al-Li.txt<br>- Description: Cluster information of bcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>8. Clusters of fcc-Al-Li.txt<br>- Description: Cluster information of fcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
Data and code for "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling"
<p>The data includes atomic structures, time-dependent dipole moments, and photoabsorption spectra of the systems modeled and analyzed in the article "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling" by Tuomas P. Rossi, Timur Shegai, Paul Erhart, and Tomasz J. Antosiewicz.</p> <p>The input scripts for reproducing the data are also included. The time-dependent density-functional theory calculations use the LCAOTDDFT module of <a href="https://wiki.fysik.dtu.dk/gpaw/">the GPAW code</a>, and the atomic structures are created with <a href="https://wiki.fysik.dtu.dk/ase/">the ASE code</a>.</p> <p>See <em>README.md</em> in the archive for a detailed description.</p>
Replication package for paper: Insights on the Use of Software Design Principles in Machine Learning Pipelines
<p>This is the replication package of the paper "Insights on the Use of Software Design Principles in Machine Learning Pipelines".</p> <p>This replication package contains two files:</p> <ul> <li><a href="../api/records/13828806/draft/files/Data%20extraction.xlsx/content" target="_blank" rel="noopener noreferrer">Data extraction.xlsx</a>: file containing the details of the extracted data for each single ML project. </li> <li><a href="../api/records/13828806/draft/files/Source%20Code%20and%20Metadata.zip/content" target="_blank" rel="noopener noreferrer">Source Code and Metadata.zip</a>: zip file including the source code local copy analyzed and the repository metadata (.json) provided by GitHub API for each ML project .repository </li> </ul> <p>Reference: [1] Lidia López, Cristina Gómez, and Claudia Ayala. Insights on the Use of Software Design Principles in Machine Learning Pipelines. <em>Accepted </em>in the 2024 edition of the International Conference on Product-Focused Software Process Improvement (PROFES 2024).</p> <p><strong>Note</strong>: The licence is applicable to the excel file. "Source Code and Metadata.zip" file contains source code repositories downloaded from GitHub, the license for each repository is defined in the corresponding GitHub repository by their authors.</p>
Data and code for "First Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface"
<p>This is the complete release of data and code for publication "First Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface", <a href="https://chemrxiv.org/engage/chemrxiv/article-details/66ffec1acec5d6c1422377f8" target="_blank" rel="noopener">10.26434/chemrxiv-2024-w17ws-v2</a></p> <p>Contains VASP inputs (POSCAR, KPOINTS, INCAR) and partial data (large VASP output data files not included), post processed numpy files for plotting, python and jupyter scripts for processing and plotting, and workflows. Jupyter scripts that lead to figures in publication are labeled with figure number. Requires python packages of "<a href="https://github.com/caizefeng/vaspvis.git">https://github.com/caizefeng/vaspvis.git</a>" and "https://github.com/DerekDardzinski/OgreInterface" to plot and make structures respectively.</p> <p>Zip files each contain the different sections for the data, with bulk calculations, single material slab calculations, SM-SM bilayers, SM-Al bilayers, ZnTe and CdSe trilayers, interface matching and optimization, and OGRE structure provided.</p>
Enhanced Catalytic Performance of a Single-Atom Cu on Mo2C toward the CO2/CO Hydrogenation to Methanol: A First-Principles Study_dataset
<p>Dataset of inputs and outputs concerning mechanisms, bader charge analysis, frequency analysis and model used in the study namely: Enhanced Catalytic Performance of a Single-Atom Cu on Mo2C toward the CO2/CO Hydrogenation to Methanol: A First-Principles Study published on <em>Cat. Sci. Technol. </em>(DOI: 10.1039/d4cy00703d).</p>
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</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.