Mendes, Ana · Agonia Pereira, Luís · Vairinhos, Valter

1 files · 83 KB · zipdeclared

Dataset and commented Python program that reproduce, end to end, all the quantitative and lexical analyses of a study on study habits, self-regulation and well-being in hybrid higher education (n = 69). The associated article is currently under review; its full reference will be added upon acceptance. The package includes the anonymised questionnaire data, the reproduction script (fixed random seed), the exact library versions (requirements.txt) and full documentation of the composite indices.

Vasic, Iva · Reitemeyer, Benedikt · Fill, Hans-Georg

1 files · 849 KB · zipdeclared

These are code and supplementary material for the BIR 2026 conference paper on formal evaluation of LLM-generated conceptual models. Title: Evaluating LLM-Generated Conceptual Models: A Theoretical Approach for Formalizing the Calculation of Metrics on the Meta Level The work was financially supported by the Smart Living Lab , a joint project funded by the University of Fribourg, EPFL, and HEIA-FR.

Bunn, Andrew G.

1 files · 27 MB · zipdeclared

An applied, R-based introduction to time series analysis for environmental scientists and ecologists. It covers autocorrelation and stationarity, ARMA models, cross-correlation, regression with autocorrelated errors, trend detection, forecasting and reconstruction, and frequency-domain methods including wavelets, with an emphasis on building intuition and getting things done in R rather than mathematical derivation. The book originated as graduate course notes at Western Washington University.

Newman-Norlund, Roger

1 files · 51 MB · zipdeclared

brainWhiz is a static, single-page Three.js viewer for multi-atlas neuroimaging figures. It renders brain parcellations in 3D, colors regions by per-region CSV values or voxelwise .nii statistical maps (auto-matching a CSV to its atlas), draws DTI / resting-state connectivity, shows native-resolution slices and mosaics, and composes publication-ready figure panels — entirely in the browser. Bundled atlases, NeuroQuery task maps, and connectivity are third-party data with their own terms, for non-commercial research; cite the original sources.

Jin, Yinuo · Myers, Joshua · Rajbhandari, Presha · et al.

1 files · 20 GB · zipdeclared

Dataset overview Pre-aligned multi-modal liver dataset for sample NIH_F5 with 2D (single tissue section) and a 3D (serial sections section_01...08 ) variants. Each variant pairs two spatially co-registered modalities - Xenium (spatial transcriptomics / RNA) and DESI (mass-spectrometry imaging / metabolomics), with cross-modal aligned spatial coordinates stored in the SpatialData .zarr stores ( obsm/xenium_map , obsm/desi_map ). data/LYNX_liver_dataset/ ├── NIH_F5_2D/ # single 2D section │ ├── xenium/ # Xenium (RNA) │ │ └── cell_feature_matrix.h5 # cell × gene counts (.h5ad) │ └── DESI/ # DESI (metabolomics) │ ├── NIH_F5.h5 # procesed pixel × ion intensity matrix │ └── NIH_F5.ome.tif # raw ion-image file │ └── NIH_F5_3D/ # serial 3D stack ├── xenium/ │ └── section_{}/ # one Xenium bundle per section │ └── sdata.zarr └── DESI/ └── section_{}_sdata.zarr # aligned DESI SpatialData store per section Formats: .zarr - SpatialData/OME-Zarr stores (images + AnnData tables with aligned spatial / xenium_map / desi_map coordinates); .h5 - cell×gene (Xenium) or pixel×ion (DESI) matrices; .ome.tif - morphology / DESI ion images.

Fernández-Martínez, Carolina · Siddiqui, Muhammad Shuaib · Daza, Vanesa

1 files · 5.9 MB · zipdeclared

Dataset for "A Knowledge-Based Multi-Agent Framework for Security Control Recommendation" Authors : Carolina Fernández-Martínez (i2CAT / UPF), Shuaib Siddiqui (i2CAT), Vanesa Daza (UPF) This contains the dataset and its generating code, as used in Section 3 of the article "A Knowledge-Based Multi-Agent Framework for Security Control Recommendation", published in Elsevier's Knowledge-Based Systems in 2026. It provides a security control selection based on NIST SP 800-53 rev5 extended catalogue, Multi-Agent Influence Diagram, Game Theory and No-Regret-based utilities. Besides the dataset itself, the scripts used to correlate and curate this data from InfoSec and academic sources are provided, along with such sources and the links to their original sources. The main repository for the dataset and its code used in Section 3 as well as the code used in Section 4 can be found in GitHub . Section overview This work is structured as follows: . ├── dataset_analysis.py ├── dataset_contribution.py ├── dataset_curation.py ├── dataset_helpers.py ├── ground_truth │ ├── manual │ │ ├── csftools_stridelm.csv │ │ └── gemini3pro_secdims_impl.csv │ ├── papers │ │ ├── doi_10_1007_impl_control.csv │ │ ├── doi_10_1016_jisa_2025_104056 │ │ │ ├── doi_10_1016_jisa_2025_104056_raw.csv │ │ │ └── generation_scripts │ │ │ ├── controls_summary.xlsx │ │ │ ├── controls.xlsx │ │ │ ├── domain.py │ │ │ ├── groups.xlsx │ │ │ ├── main.py │ │ │ ├── patterns.gml │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ ├── software.xlsx │ │ │ ├── technique.xlsx │ │ │ ├── ttp-control.xlsx │ │ │ ├── ttps.xlsx │ │ │ └── utils.py │ │ ├── doi_10_1093_cybsec_tyaf020_mapping.csv │ │ └── doi_10_1093_cybsec_tyaf020_scores.csv │ └── standards │ ├── Cybersecurity_Framework_v2-0_Concept_Crosswalk_800-53_5_2_0_draft.csv │ └── NIST_SP-800-53_rev5_catalog.json ├── output │ └── dataset_curated.csv └── README.md The ground truth contains both manual mappings, academic papers and InfoSec standardised data: ground_truth : hosts sources used for the data curation. manual : data extracted manually, whether directly checking sources or iteratively requested to an LLM. csftools_stridelm.csv : manually extracted data from CSF tools indicating the contribution of each control subfamily to mitigate a given STRIDE-LM threat. Each value follows a comma-separated format (e.g. "0,2,3,4,8,12") or use -1 if there is no contribution. gemini3pro_secdims_impl.csv : LLM-parsed data from CSF tools , requesting Gemini 3 Pro to extract data on the security control subfamilies: (1) whether these can be SW-implementable, (2) their coverage to the different Security Dimensions and (3) a text-based justification regarding such coverage. papers : doi_10_1007_impl_control.csv : dataset post-processed from that provided by paper with DOI:10.1007/s10664-025-10649-7 . Basically, this CSV assigns numeric codes to the column "Related to implementation-level feature? (yes/no)" from the tab "SP800 53 rev. 3 (technical cont" of the "2) Systematic Review - Security Standards.xlsx" file in that dataset. This can tabke the following values: 0 (if not SW-implementable), 1 (if SW-implementable), -1 (if undefined in the original dataset) or -2 (if the security control subfamily is not even present in the original dataset). doi_10_1016_jisa_2025_104056 : dataset provided by paper with DOI:10.1016/j.jisa.2025.104056 . generation_scripts : minor modifications to the original scripts to generate their dataset. See README.md inside. doi_10_1093_cybsec_tyaf020_mapping.csv : dataset post-processed from that provided by paper with DOI:10.1093/cybsec/tyaf020 . This CSV contains the table from "Appendix A" of the "Appendix A - D.docx". doi_10_1093_cybsec_tyaf020_scores.csv : dataset post-processed from that provided by paper with DOI:10.1093/cybsec/tyaf020 . This CSV contains the table from "Appendix C" of the "Appendix A - D.docx". standards : Cybersecurity_Framework_v2-0_Concept_Crosswalk_800-53_5_2_0_draft.csv : NIST resource that maps CSF 2.0 subcategories to security control subfamilies from SP 800-53 rev5. NIST_SP-800-53_rev5_catalog.json : NIST SP 800-53 rev5 catalogue of security control subfamilies as obtained from the full catalogue in JSON format. The output folder contains the generated, curated dataset by default. Upon running the scripts below, more files will follow. Generating the dataset and ancillary files 1. Curated dataset The curated dataset is generated under "output/dataset_curated.csv" after running the following script. This file is required for the other scripts. python3 dataset_curation.py 2. Summaries, statistics and figures The analysis on the dataset extracts statistics (in .csv and .tex files) and generates figures (in .pdf and .png) from the dataset: output dataset_curated_ciatunp.{csv,tex} : table with number of control families contribute to each Security Dimension. dataset_curated_stridelm.{csv,tex} : table with number of control families contribute to each STRIDE-LM threat. dataset_curated_score_summary.{csv,tex} : statistics for minimum, average, mode, maximum, standard deviation and inter-quartile range per control family. figures : dataset_curated_ciatunp_contribution_implementable_cats_ids.{pdf,png} : distribution of the contribution of SW-implementable control families (axis Z) and subfamilies (axis Y) towards security dimensions (axis X). dataset_curated_ciatunp_contribution_total_cats_ids.{pdf,png} : distribution of the contribution of all kinds of control families (axis Z) and sub families (axis Y) towards security dimensions (axis X). dataset_curated_score_contribution_total_cats_ids.{pdf,png} : distribution of the score (axis X, in deciles) for all kinds of control families (axis Z) and subfamilies (axis Y). dataset_curated_stridelm_contribution_implementable_cats_ids.{pdf,png} : distribution of the contribution of SW-implementable control families (axis Z) and subfamilies (axis Y) towards mitigating types of STRIDE-LM threats (axis X). dataset_curated_stridelm_contribution_total_cats_ids.{pdf,png} : distribution of the contribution of all kinds of control families (axis Z) and subfamilies (axis Y) towards mitigating types of STRIDE-LM threats (axis X). python3 dataset_analysis.py Besides this, the following script quantifies the contribution of the academic datasets and sources used during the process, generating these files: output dataset_curated_score_summary_contribution_ds_imp_{all,top}.tex : table comparing the score of each of the top SW-implementable control subfamilies across the curated dataset ("Total" column) and the datasets used from academic papers (other columns). dataset_curated_score_summary_contribution_ds_tot_{all,top}.tex : table comparing the score of each of the top control subfamilies of all kinds across the curated dataset ("Total" column) and the datasets used from academic papers (other columns). dataset_curated_score_stats_imp_{all,top}.tex : table with statistics on the amount and average score for both all and top SW-implementable control subfamilies. dataset_curated_score_stats_mt0_imp_{all,top}.tex : table with statistics on the amount and average score for both all and the top SW-implementable control subfamilies whose score is more than 0. dataset_curated_score_stats_tot_{all,top}.tex : table with statistics on the amount and average score for both all and top control subfamilies of all kinds. dataset_curated_score_stats_mt0_tot_{all,top}.tex : table with statistics on the amount and average score for both all and the top control subfamilies of all kinds whose score is more than 0. dataset_curated_summary_{all,top}.xlsx : sheet files with multiple tabs to determine grouping and statistical data, such as the score of the top security control subfamilies and the score of their counterparts in the used datasets. Tabs "contribution_ds_tot" and "contribution_ds_imp" are the most relevant, performing these calculation for all kinds and SW-implementable security control subfamilies, respectively. In all cases, the first file considers all control subfamilies, whereas the second considers the top 20 ones. Note that this script has a specific pre-requirement that must be installed to generate the excel file. sudo apt install python3-openpyxl python3 dataset_contribution.py Licence This work is dual-licenced according to the type of resource: Datasets: CC BY-NC 4.0 Code: GNU AGPL 3.0 Funding This work was supported by the grants COALESCE-6G PID2024-163028OB-I00, funded by MICIU/AEI/10.13039/501100011033/FEDER, EU; and AEI-PID2021-128521OB-I00, funded by the Spanish Recovery, Transformation and Resilience Plan through the European Union (Next Generation).

Yuan, Qianmu · Zhu, Mingming

1 files · 456 MB · zipdeclared

This ZIP file contains: 1. The DeltaCata-DB dataset preprocessing scripts and the processed dataset. 2. The source code and the trained model checkpoints of DeltaCata.

Akinshin, Andrey

1 files · 2.1 MB · zipdeclared

This manual presents a toolkit of statistical procedures that provide reliable results across diverse real-world distributions, with ready-to-use implementations and detailed explanations. The toolkit consists of renamed, recombined, and refined versions of existing methods. Written for software developers, mathematicians, and LLMs.

Seefelder, Manuel Thomas

1 files · 147 MB · zipdeclared

This record contains the derived data and analysis code accompanying the manuscript: Seefelder, M. A meta-analysis resolves the huntingtin interactome into coactivator losses and a robust proteostatic and synaptic gain network (2026). The study integrates four previously published huntingtin (HTT) affinity-proteomics datasets and contrasts wild-type and polyglutamine-expanded HTT within a single Bayesian differential-interactomics model (BayesInteractomics), assigning every protein a calibrated, condition-dependent interaction call. Of 4,338 proteins evaluated, 275 are condition-dependent, describing a bidirectional remodelling of the HTT interactome - a loss of transcription-activation coactivators (Mediator, the ASCOM H3K4-methyltransferase, CREBBP, CDK9) and a gain of proteostatic and synaptic contacts (the 26S proteasome, HSP70 chaperones, synaptic and actin-cytoskeletal networks), around an intact chaperonin-HAP40 core. Contents Source differential-interactome table (differential_results.xlsx, all.csv, HTT_unchanged.csv) - per-protein posterior interaction probabilities, differential calls and false-discovery rates for wild-type and mutant HTT. Call-specific protein lists (gene-symbol lists for each differential class). Derived over-representation / enrichment results. Per-figure source data for all main and supplementary figures. A snapshot of the analysis and figure-generation code used to produce all results and figures. A snapshot of the BayesInteractomics.jl framework version used for interaction scoring. Raw data provenance This is a re-analysis of published datasets. The primary (raw) affinity-proteomics data are not re-hosted here and remain available from the original publications and their associated repositories: Greco et al. (2022), Justice et al. (2025), Sap et al. (2021) and Gutiérrez-García et al. (2023). Reproducibility Running the deposited code against the deposited derived data regenerates the figures and tables of the manuscript. The BayesInteractomics method is developed openly at https://github.com/ma-seefelder/BayesInteractomics.jl and described in full in a companion methods paper (Seefelder, submitted).

Sánchez-Pinillos, Martina · Fortin, Marie-Josée · Messier, Christian · et al.

1 files · 173 MB · zipdeclared

Code and data for: Sánchez-Pinillos, M., Fortin, M.-J., Messier, C., Kneeshaw, D. 2026. Forecasting ecological trajectories from ecological dynamic regimes to improve resilience analysis. Methods in Ecology and Evolution.