Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Data files for Competitive ability depends on mating system and ploidy level across Capsella species

The three data files associated with the article: Competitive ability depends on mating system and ploidy level across Capsella species. Annals Of Botany 2022: doi.org/10.1093/aob/mcac044 See README for details on each files

opencc-by-4.0Jul 2024View details →

STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset

This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators of monitoring system". The indicators are organised by category, principles, criteria, requirements and references.

opencc-by-4.0Sep 2022View details →

Data supporting: Microscopic observation of two-level systems in a metallic glass model

Dataset of double well potentials sampled from energy landscape exploration of a ternary Lennard-Jones model supporting: "Microscopic observation of two-level systems in a metallic glass model" Thermalised configurations of the ternary Lennard-Jones model are given in the archive (configs.zip) of 1200 atoms at \(T_f\) 0.488, 0.509, 0.558 and 0.617 in the lammps (https://www.lammps.org/) data file format (https://docs.lammps.org/read_data.html). The two datasets each provided as (.zip) archives named dataset1.zip and dataset2.zip Datafiles (.csv) are named nebdf_{:3.3f}_{:05d}.csv where the float is \(T_f\) and the integer is \(\tilde{m}\).  columns of each .csv file are: 'transitions', 'forward barriers', 'reverse barriers', 'asymmetry', 'barrier', 'euclidean distance', 'distance along string', 'n_intermediates', 'deltas', 'splittings', 'delta_zeroes', 'gammas', 'PR', 'glass', 'omegas1', 'omegas2', 'omegasts', 'Index 1', 'Index 2', 'Frequency 1>2', 'Frequency 2>1', 'e_1', 'e_2', 'dc', 'Tprep' 'glass' is the index of the glassy metabasin sampled omegas1', 'omegas2', 'omegasts' are the curvatures of the minimum energy oaths near the first minimum, second minimum and transition state 'e_1', 'e_2' are the energy per atom of the two glass minima  'dc' is the typical particle displacement corresponding to \(\sqrt{\dfrac{d^2}{PR}}\)

opencc-by-4.0Sep 2023View details →

Time Series of Water Levels in a Coastal Barrier-Lagoon System, NW Spain (2009-2012)

This repository contains the data recorded by water-level loggers (survey-pressure transducers) deployed in a barrier-lagoon coastal system, which were used in the study by R. González-Villanueva, M. Pérez-Arlucea, and S. Costas titled 'Lagoon Water-Level Oscillations Driven by Rainfall and Wave Climate,' published in Coastal Engineering, Volume 130, 2017, Pages 34-45, ISSN 0378-3839, available at <a href="https://doi.org/10.1016/j.coastaleng.2017.09.013">https://doi.org/10.1016/j.coastaleng.2017.09.013</a> The repository consists of three text files: <ol> <li>lagoon_water_level.txt</li> <li>sea_level.txt</li> <li>phreatic_level.txt</li> </ol> Each file includes a header with metadata and information for each column in the data file, as follows: <ul> <li>pt_id: ID of the individual record</li> <li>pt: instrument used</li> <li>lat: Latitude in WGS84</li> <li>long: Longitude in WGS84</li> <li>units: Indicates the measurement unit for the water level recordings</li> <li>temporal resolution: Indicates the time interval between two consecutive measurements</li> <li>column 1: Description of the data contained in column 1</li> <li>column 2: Description of the data contained in column 2</li> <li>column n: Description of the data contained in column n</li> </ul>

openmit-licenseMar 2025View details →

Systems-level design principles of metabolic rewiring in an animal

Description: 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:  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) Nature, in press (# equal contribution) Files: 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!!).  The recovered working directory should be like this:  <ul> <li>10_revision/</li> <li>1_QC_dataCleaning/</li> <li>2_DE/</li> <li>3_imageAnalysis/</li> <li>4_networkAnalysis/</li> <li>5_GSA/</li> <li>7_FBA_modeling/</li> <li>data_and_tables_to_publish/</li> <li>input_data/</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> 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.  Usage:  Please refer to the table in below to locate a specific file for reproducing a figure of interest (also availabe in the REWIRING_FIGURE_LOOKUP.xlsx under the root directory).  <table> <tbody> <tr> <td>Figure</td> <td>File</td> <td>Linesa</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> 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.  Notice: 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.   Contact: For any questions, please contact Xuhang (Hang) Li at Xuhang.Li@umassmed.edu. (we plan to deposit an updated version with better code annotations, but cannot complete that currently due to time restirctions)

opencc-by-4.0Jun 2022View details →

Optimal elevated agrivoltaic system design and key performance indicators across Europe based on three crop light levels

Optimal elevated (stilted) agrivoltaic system design (PV coverage ratio) is given on a European gridded level (25km grid and NUTS3 regions) based on three light levels: shade-loving crops (daily light integral (DLI) of 12 mol/m²day), shade-tolerant crops (DLI of 12 mol/m²day) and shade-intolerant crops (DLI of 25 mol/m²day) Estimations of other performance indicators are given: power capacity (kWp/ha), energy production (MWh/ha), levelized cost of electricity (€/MWh) and land equivalent ratio (LER -). The assumptions and methodology of this dataset can be found in the article "Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level." Interactive maps can be found on https://iiw.kuleuven.be/apps/agrivoltaics/maps.html

openmit-licenseJul 2019View details →

Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets

This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++.  The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets.  The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well.  This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 .   The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906. Format of the data: Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality.  The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset.  The pre-processing set is CassavaSlices.  The output set for Soybean is SoybeanResultsJul11.  The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C.  _B is the largest, and only contains the results overwritten on the original X-Ray images.  Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.

opencc-by-4.0Jul 2024View details →

STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.3 Content Level Matrix dataset

This dataset contains the final set of indicators selected for the content level of the new monitoring system. It is part of the D3.2 "Report on additional indicators of monitoring system" of the STAR4BBS Project (Appendix 6.3). The list of indicators are accompanied by guidance notes, the specific sector and value chain to which they apply as well as potential examples of indicators are suggested.

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Primary production estimates from 14C uptake (in situ), determined by the incorporation of inorganic carbon into particulate organic carbon (POC) due to photosynthesis at selected light levels from CCE LTER process cruises in the California Current System, 2006 - 2021 (ongoing).

Primary productivity samples of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process crusies (since 2006, ongoing). Light penetration below the surface is estimated from the Secchi disk depth. Niskin bottles from depths with ambient light intensities corresponding to light levels simulated by on-deck incubators are identified and sampled. Primary production is estimated from 14C uptake using this simulated in situ technique (followed by filtering) by which the assimilation of dissolved inorganic carbon by phytoplankton yields a measure (in µg/L/day) of the rate of photosynthetic primary production (particulate organic carbon, POC) at selected light levels in the euphotic zone within the CCE study area.

openCC0Jun 2023View details →

opencc-zeroJun 2017View details →

A Collection of Novice Interactions with the OCaml Top-Level System

See the included README.md for details on the file format.

opencc-by-4.0Dec 2023View details →

Groundwater level data, aquifer system boundaries, and supplementary tables associated with Jasechko, S. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, doi.org/10.1038/s41586-023-06879-8 (2024).

Groundwater level data, aquifer system boundaries, and Supplementary Tables associated with Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R.G., Fallatah, O., Kirchner, J.W. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, https://doi.org/10.1038/s41586-023-06879-8 (2024).

opencc-by-4.0Dec 2023View details →

Supplementary Material for "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control"

<h2>Supplementary material for the paper "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control" </h2>The paper is submitted to "International Conference on Dependable Systems and Networks, 2024". Author names withheld for double-blind reviewing.<ul><li>The file proofs_and_hyperparameters.pdf contains proofs of Theorem 1--2 and Corollary 1 in the paper. It also includes formulas for computing the belief state (Eq. 4) and for computing the curves in Fig. 6. It also includes a complete list of hyperparameters used for all experiments detailed in the paper.</li><li>The file ids_alerts_statistics.json contains the statistics used to produce Fig. 10 in the paper and to define the parameter Z for the experiments in section VIII.<ul><li>The JSON file contains a single object with the following keys: 'conditionals_counts', 'conditionals_kl_divergences', 'conditionals_probs', 'conditions', 'descr', 'emulation_name', 'id', 'initial_distributions_counts', 'initial_distributions_probs', 'initial_maxs', 'initial_means', 'initial_mins', 'initial_stds', 'maxs', 'means', 'metrics', 'mins', 'num_conditions', 'num_measurements', 'num_metrics', 'stds'. </li><li>The key "conditionals_counts" leads to another object with the following keys: 'A:CVE-2010-0426 exploit_D:Continue_M:[]', 'A:CVE-2015-3306 exploit_D:Continue_M:[]', 'A:CVE-2015-5602 exploit_D:Continue_M:[]', 'A:CVE-2016-10033 exploit_D:Continue_M:[]', 'A:Continue_D:Continue_M:[]', 'A:DVWA SQL Injection Exploit_D:Continue_M:[]', 'A:FTP dictionary attack for username=pw_D:Continue_M:[]', 'A:Ping Scan_D:Continue_M:[]', 'A:SSH dictionary attack for username=pw_D:Continue_M:[]', 'A:Sambacry Explolit_D:Continue_M:[]', 'A:ShellShock Explolit_D:Continue_M:[]', 'A:TCP SYN (Stealth) Scan_D:Continue_M:[]', 'A:Telnet dictionary attack for username=pw_D:Continue_M:[]', 'intrusion', 'no_intrusion'</li><li>The above keys correspond to different types of intrusions, see Table 6 in the paper.</li><li>Each of the keys listed above leads to a new object with 1551 keys which correspond to different types of metrics collected from the infrastructure. The metric used for produce Fig. 10 in the paper is called "alerts_weighted_by_priority". This key leads to another object where the keys correspond to the number of alerts weighted by priority and the values correspond to the measurements from the system.</li></ul></li><li>The file intrusion_traces.zip contains 6400 intrusion traces. Each trace contains a list of attacker actions and the corresponding measurements from the system. When unzipped, it is a directory with 64 files which take up 1500GB. Each file contains 100 traces in JSON format.</li><li>The file source_code_and_docker_files.zip contains the source code and the docker containers used for the experiments. It is a system we have developed for 3 years. It includes 225,000 lines of Python, 40,000 lines of JavaScript, 3000 lines of Dockerfiles, 2500 lines of Makefile, and 1800 lines of Bash. When unzipped one can find documentation about the source code in a file called "documentation.pdf" and in the README file.</li></ul>

opencc-by-4.0Nov 2024View details →

Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario

The database is built over three main sources <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset. Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset. The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above.  Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.

opencc-by-4.0Aug 2017View details →

On the use of envelope following responses to estimate peripheral level compression in the auditory system

Dataset containing the envelope following responses (EFR) recordings related to the manuscript "Can envelope following responses be used to estimate level compression in the auditory system?". Files content and structure: EFR: <ul> <li>1_data__efr_nh.csv:                 EFR recordings from the normal-hearing (NH) listeners</li> <li>2_data__efr_hi.csv:                  EFR recordings from the hearing-impaired (HI) listeners</li> <li>3_data__efr_nh_repeat.csv:     Repeated EFR recordings from the NH listeners</li> </ul> The files regarding the EFR recordings contain: <ul> <li>subject:                     Listener id</li> <li>lvl_vect:                    Stimulation level</li> <li>efr_magn_xxxx_hz:  EFR magnitude (dB re to 1 µV) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li>efr_bkg_xxxx_hz:     Magnitude (dB re to 1 µV) of an estimate of the background noise floor of the recording for each frequency. </li> <li>ftest_xxxx_hz:          Ftest (0 or 1) value for each frequency.</li> </ul>   Distortion-product otoacoustic emissions (DPOAE) were also recorded in the same listeners although the data was not included in the final manuscript. Details of the methodology used can be given on request. DPOAE: <ul> <li>4_data__dpoae_nh.csv:            DPOAE recordings from the NH listeners.</li> </ul> The files regarding the DPOAE recordings contain: <ul> <li>subject:                           Listener id</li> <li>lvl_vect:                          Stimulation level</li> <li>dpoae_magn_xxxx_hz:  DPOAE magnitude (dB SPL) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li>dpoae_bkg_xxxx_hz:     Magnitude (dB SPL) of an estimate of the background noise floor of the recording for each frequency. </li> <li>signif_xxxx_hz:              Satisfaction of the significance criterion (0 or 1) for each frequency.</li> </ul>

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills

Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.

opencc-by-4.0Aug 2021View details →

Dataset for: Non-Markovian effects of two-level systems in a niobium coaxial resonator with asingle-photon lifetime of 10 milliseconds

Datasets for the publication "Non-Markovian effects of two-level systems in a niobium coaxial resonator with asingle-photon lifetime of 10 milliseconds ", Physical Review Applied (2021). The upload contains the data as well as the evalationb routines to repdroduce the figures in the publication.