This dataset contains quality-controlled SNP and InDel genotype calls from whole-genome resequencing of 299 purebred Large White pigs. The dataset includes 100 individuals sequenced at 30× depth (merged genome-wide VCFs) and 199 individuals sequenced at 10× depth (chromosome-split VCFs). All variants were called against the *Sus scrofa* Sscrofa11.1 reference genome and filtered using standard GATK hard-filtering criteria: QD < 2.0, QUAL < 30.0, FS > 200.0, ReadPosRankSum < -20.0, call rate < 90%, MAF < 0.05. This data supports the findings of the study "Whole-genome resequencing identifies RASAL2 as a candidate gene for feed efficiency in Large White pigs".
Pretrained weights for the hybrid Transformer‑Biophysical Fusion architecture of EpiRNA. Combines a frozen DNA‑BERT backbone with a cross‑scale biophysical CNN. Trained on 11,844 human m⁶A sites (GSE63753, miCLIP) and an equal number of DRACH‑containing negative windows. Achieves AUROC 0.80, specificity 0.83, sensitivity 0.61 on the held‑out test set. Intended for offline genome‑wide prediction; the live web tool uses a faster CNN variant.
Complete RNA-Seq analysis for single-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
Ivani, Jessica K. · Nogina, Alexandra · Zakharko, Taras
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Dataset for the paper Agent-oriented modality in the languages of North America, published in the Proceedings of the 28th Workshop on American Indigenous Languages (WAIL 28), May 1-2, University of California Santa Barbara ( https://www.wailconference.org/home )
Gene expression data for comparing 3 time points following injuries to the human mandible. The whole peripheral blood cells were processed for RNA sequencing (Novogene).
Raw qPCR quantification summaries (per-well Cq values and run metadata) for HOXA-10, HOXA-11, HOXA-13, and ACTB, exported directly from the BioRad CFX-96 / CFX Maestro software. These are the raw amplification run records (runs of 13-14 May 2024) underlying the relative-expression (RQ) values reported in the associated study. Each target is provided as a separate sheet (Well, Fluor, Target, Content, Sample, Cq), with a README sheet describing the contents. Standard-curve efficiency values were not generated as permanent records for these runs.
Complete RNA-Seq analysis for paired-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
This archive contains a single Wolfram Language (.wl) file, including all expressions of short-distance coefficients and the cross sections supporting the associated PRD article.
Head, Taylor · Bresnahan, Sean · Chang, Yung-Han · et al.
This dataset contains nominal cis-eQTL summary statistics from QTLtools (v1.3.1) applied to GTEx v8 short-read RNA-seq data across 48 tissues. Results are provided for three GENCODE annotation versions (v27, v38, and v45) and two quantification methods (Salmon and STAR+featureCounts). For each tissue-annotation-method combination, the dataset includes nominal association results for all cis-SNPs within 1 Mb of each tested gene with gene expression rank-normalized prior to QTL mapping. These data accompany the manuscript "Quantification method affects replicability of eQTL analysis, colocalization, and TWAS" ( Head et al., biorXiv 2025 ), which provides detail on statistical methods and demonstrates that quantification method and transcriptomic annotation choice substantially affect eGene detection, expression prediction, colocalization, and TWAS results. Files: Summary statistics are organized by tissue, and each tissue folder contains nominal QTL results for each annotation-quantification method combination. Columns : Each gzipped .txt file has the following column format: 1 phe_id The gene ID (phenotype ID) 2 phe_chr The phenotype chromosome 3 phe_from Start position of the phenotype 4 phe_to End position of the phenotype 5 phe_strd The phenotype strand 6 n_var_in_cis The number variants in the cis window for this phenotype. 7 dist_phe_var The distance between the variant and the phenotype start positions. 8 var_id The variant ID. 9 var_chr The variant chromosome. 10 var_from The start position of the variant. 11 var_to The end position of the variant. 12 nom_pval The nominal p-value of the association between the variant and the phenotype. 13 r_squared The r squared of the linear regression. 14 slope The beta (slope) of the linear regression. 15 best_hit Whether this varint was the best hit for this phenotype.
Garriga-Alonso, Núria · Laromaine, Anna · Alonso-Pernas, Pol · et al.
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This dataset contains Caenorhabditis elegans ( C. elegans ) RNA sequencing data, to assess the effects of in vivo EMF exposure on C. elegans gene expression. There are 27 samples: 3 different EMF exposure conditions (Exp, Sham, Neg) and 3 C. elegans generations (G1, G2, G3), and each with 3 biological replicates. There are two FASTQ files per sample, as it is data from a paired-end sequencing. This dataset contains the following files: Sham.zip: raw FASTQ files for Sham samples Neg.zip: raw FASTQ files for Neg samples Exp.zip: raw FASTQ files for Exp samples Expression_Profile.WBcel235.gene: raw and normalized gene counts Samples_Metadata: metadata of the 27 samples
Agreement Between Large Language Models and Humans in Research Proposal Review - Data and Code This repository contains the data and code required to reproduce the analyses, statistical results, and figures presented in the associated manuscript. Files are organized by function and described below. All research proposals are anonymized and labeled with non-identifying identifiers (A, B, C, ...). Reviewer identities were never provided to the authors. To prevent inadvertent disclosure, all free-text review content from both human reviewers and large language models (LLMs) has been removed; only the numerical evaluation data required to reproduce the reported analyses are included. Data files Human_raw_scores.csv Individual numerical scores assigned by human reviewers, one row per reviewer × proposal × criterion. Used to compute panel-level summary statistics and the inter-reviewer reliability metrics reported in the manuscript. Human.csv Proposal-level human reference scores (one row per proposal) used as the human benchmark against which LLM scores and rankings are compared. LLM_data_combined_clean_filtered.csv All numerical scores generated by the evaluated LLMs. Preprocessed to remove evaluations in which a model failed to return one or more required numerical scores (see Data provenance and known limitations for counts). review_criteria.txt The evaluation criteria and rating scales. See the note under Data provenance regarding the 2023 vs. 2024 criterion naming. Data dictionary Human_raw_scores.csv Column Description Applicant Anonymized proposal identifier (A, B, C, ...). Cycle Review cycle the proposal belongs to (2023 or 2024). Label Scoring criterion: Intellectual merit , Potential for impact , Collaborative Potential , or Overall ranking . Reviewer_Seq Reviewer index within a proposal (1, 2, 3, ...). Identities are unknown; this index only links a single reviewer's ratings across criteria for the same proposal, in source-file order. It is not consistent across proposals (reviewer 1 for proposal A is not reviewer 1 for proposal B). Rating Numerical score. Criterion ratings use a 1-5 scale; Overall ranking uses a 1-3 scale (3 = fund, 2 = fund with modifications, 1 = do not fund). Human.csv Column Description Name Anonymized proposal identifier (matches Applicant above). Cycle Review cycle (2023 or 2024). IM , Impact , Collab , Overall Panel-mean scores for the four criteria. Score Weighted composite panel score, computed as 0.4·IM + 0.3·Impact + 0.3·Collab , matching the weighting applied to the LLM composite scores. LLM_data_combined_clean_filtered.csv Column Description Name Anonymized proposal identifier (matches Human.csv ). Type Input given to the model: Abstract or Full_Proposal . Model Model identifier. For models with controllable reasoning depth, the tier is appended as a suffix ( _low , _medium , _high ). The portion before the first underscore is the root model. Prompt Prompting strategy ( OneShot or CoT ). Seed Requested random seed. For models that did not support seed specification at the time of execution (the reasoning models listed in the Methods), the API ignored this value and it functions only as a replicate index; output is not reproducible from it for those models. Temp Sampling temperature (0.1, 0.5, 0.9). IM , Impact , Collab Criterion ratings (1-5 scale). Overall Overall recommendation (1-3 scale). See limitation note on out-of-scale values. Score Weighted composite, 0.4·IM + 0.3·Impact + 0.3·Collab . Category Reasoning/architecture category of the model. Year Evaluation wave in which the run was performed (proposals were re-evaluated as new model generations were released); this is not the proposal's submission cycle. Use Cycle in the human files for submission cycle. Data provenance and known limitations We document the following so that users can interpret the data accurately. Two review cycles, combined. The 28 proposals come from two internal seed grant cycles: 15 from 2023 and 13 from 2024 ( Cycle column). For 2024 proposals, proposal-level means in Human.csv are the official institute panel means; for 2023 proposals they are computed from the individual ratings in Human_raw_scores.csv . Reviewers per proposal. Panels ranged from 3 to 6 reviewers. Reviewer identities were never provided; the human inter-reviewer reliability is therefore estimated with a one-way random-effects model (ICC(1,1)), which is the appropriate model when each proposal is rated by a different, unidentified set of reviewers. Six 2024 reviews not present at the individual level. For six 2024 proposals (G, R, T, W, X, Z), one reviewer's scores were submitted without written comments and are not included in Human_raw_scores.csv . For these proposals, Human.csv carries the official institute panel means, so the panel mean in Human.csv and the mean recomputed from Human_raw_scores.csv differ slightly. The reproducibility check in 06_Human_data.ipynb confirms exact agreement for all proposals with complete individual records and reports the expected small differences for these six. Out-of-scale LLM Overall ratings. A small number of responses (≈1.2% of reviews, almost entirely from gpt-3.5-turbo) rated the overall recommendation on a 1-5 scale rather than the requested 1-3 scale, in a format the parser could not distinguish. The analysis code masks values outside the valid range before computing any Overall -based result; the composite Score does not use Overall and is unaffected. Excluded LLM evaluations. Evaluations in which a model failed to return one or more required numerical scores were removed prior to analysis. The file provided here is the post-exclusion (analyzed) dataset. Code notebooks Two groups of notebooks are provided: (i) the LLM evaluation pipeline and (ii) statistical analysis and figure generation. LLM evaluation pipeline (Notebooks 1-4) Documentation of the methodology used to generate the LLM evaluations. These use synthetic examples and contain no confidential data, API credentials, or real proposal text. 01_pipeline_overview.ipynb - architecture, configuration, criteria, output format, evaluation matrix. 02_prompting_strategies.ipynb - one-shot and chain-of-thought prompting; example selection; text vs. vision input. 03_response_parsing.ipynb - regex extraction of ratings and comments; error handling; decimal ratings. 04_example_evaluation.ipynb - end-to-end workflow on synthetic data. Statistical analysis and figures (Notebooks 5-6) 05_Data_Processing.ipynb - ANOVA and effect sizes; empirical absolute score differences; ICC(2,1) and Spearman correlations vs. the human panel; Monte Carlo comparisons; scatter, slope, and bias figures. 06_Human_data.ipynb - ICC(1,1)/ICC(1,k) for human reviewers with bootstrap confidence intervals; Monte Carlo single-reviewer vs. leave-one-out panel rank agreement; consistency check of Human.csv against Human_raw_scores.csv . Reproducibility Running 05_Data_Processing.ipynb and 06_Human_data.ipynb against the included data reproduces the statistical results and figures in the manuscript. Notebooks 1-4 document the evaluation pipeline using synthetic examples. Requirements pandas numpy scipy statsmodels scikit-learn matplotlib The LLM evaluation pipeline notebooks (1-4) additionally use the packages listed in requirements.txt . Citation If you use this data or code, please cite the associated manuscript and this archive: Gorski, C., Leo, N., Gayah V. Agreement Between Large Language Models and Humans in Research Proposal Review - Data and Scripts. 2026. Zenodo. https://doi.org/10.5281/zenodo.18187034 License Data are released under CC BY 4.0; code is released under the MIT License.
We performed 10X Genomics single-cell RNAsequencing of human prepheral blood mononuclear cells from healthy control, PD and RBD patients. This dataset contains raw FASTQ files. Sequencing was performed using NovaSeq 6000 S4 PE 100bp. Reads were processed using the 10X Genomics Cell Ranger Single Cell 2.0.0 pipeline. FASTQs generated from sequencing output were aligned to the human GRCh38 reference genome using STAR algorithm 2.7.3a.
The development of social media has changed the way individuals express emotions and communicate with their social environment. One emerging phenomenon is the use of TikTok's repost feature as a means of indirect communication through the re-sharing of content perceived to represent the user's personal feelings or experiences. This study aims to determine the behavior of using TikTok's repost feature as an indirect communication medium for expressing emotions among Indonesian women aged 18-20 years. The study used a quantitative method with a descriptive design through an online survey of 200 respondents selected using a purposive sampling technique. Data were collected using a five-point Likert-based questionnaire structured based on the dimensions of the repost feature and emotional expression. The results showed that the level of utilization of TikTok's repost feature was in the high category with an average value of 76.97. In addition, the results of the hypothesis test showed a significance value of 0.000 (p < 0.05), indicating that Indonesian women aged 18-20 years significantly utilize TikTok's repost feature as an indirect communication medium for expressing emotions. These findings indicate that the repost feature not only functions as a means of content distribution, but also as a symbolic communication medium to convey emotional messages, build digital identity, gain social validation, and maintain self-image in the digital environment.
This Supplementary Material provides additional technical details supporting the main manuscript. It contains detailed proofs of the main results, as well as several technical lemmas together with their complete proofs. It also includes additional tables, a detailed description of the gene expression data used in the study, and a complementary analysis of these data providing further interpretation of the results. The document further presents extended algorithmic descriptions and additional implementation details that could not be included in the main text due to space limitations. In particular, we provide a complete description of the computational complexity of the proposed framework, detailed specifications of the kernel-based affinity functions, as well as additional simulation studies used to assess the robustness of the method. Moreover, this document includes detailed pseudocode and implementation guidelines for the proposed Gamma-kernel-based functional PCA (FPCA/MDS) approach, along with extended discussions on hyperparameter selection and numerical stability considerations. It also contains auxiliary technical results that complement the main theoretical developments and provide additional justification for certain methodological steps. The results presented in this Supplementary Material aim to ensure full reproducibility of the proposed methodology and to facilitate its implementation in related applications involving high-dimensional gene expression data or functional data. Throughout this document, equation numbers and section references are consistent with the main manuscript unless otherwise specified.
This dataset includes TMY files for the 5000 largest cities and towns in the EU, based on CERRA data from 2006-2020, and using a TMY ISO methodology. The files represents a statistically typical year meant for energy calculations of buildings, and are therefore not suited for evaluating extreme conditions. The repository contains: EPW-files - zipped collection of .epw-files The most common file format for climate data for energy simulations applicable to most programs Datasheets - a zip files of individual datasheets A collection of datasheets containing a comparison of TMYs with the data source as well as referance data The datasheets contain Norwegian text Error metrics - ZIP file Error metrics mapped to a map of Norway, for visualization purposes As well as the metrics that are mapped Selected years - a comma seperated file the selected typical meteorological months for each location Changelog - a txt file a overview of changes in the dataset since 1.0 The contents of the repository is produced by The Norwegian University og Life Science (NMBU), Department of Building- and Environmental Technology. The data is provided 'as is', without warranty of any kind, express or implied. In no event shall the authors be liable for any claim, damages or other liability. An interactive map containing the TMY3 data, but not the TMY ISO, can be found at https://www.climatedataforbuildings.eu/ If you use the datasets or tools in your work, please reference the relevant article that document the methods and data: Petersen & Thiis (2025), " Continental-Scale Assessment of Typical Meteorological Years in a Changing Climate " , Energy and Buildings, DOI: 10.1016/j.enbuild.2025.116814
Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.
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This dataset consists of raw sequencing snRNA-seq data and snATAC-seq data (10x Genomics Chromium Next GEM Multiome ATAC/GEX). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108 ) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors. (edited)
This dataset includes TMY files for the 5000 largest cities and towns in the EU, based on CERRA data from 1991-2020, and using a TMY ISO methodology. The files represents a statistically typical year meant for energy calculations of buildings, and are therefore not suited for evaluating extreme conditions. The repository contains: EPW-files - zipped collection of .epw-files The most common file format for climate data for energy simulations applicable to most programs Datasheets - a zip files of individual datasheets A collection of datasheets containing a comparison of TMYs with the data source as well as referance data The datasheets contain Norwegian text Error metrics - ZIP file Error metrics mapped to a map of Norway, for visualization purposes As well as the metrics that are mapped Selected years - a comma seperated file the selected typical meteorological months for each location Changelog - a txt file a overview of changes in the dataset since 1.0 The contents of the repository is produced by The Norwegian University og Life Science (NMBU), Department of Building- and Environmental Technology. The data is provided 'as is', without warranty of any kind, express or implied. In no event shall the authors be liable for any claim, damages or other liability. An interactive map containing the TMY3 data, but not the TMY ISO, can be found at https://www.climatedataforbuildings.eu/ If you use the datasets or tools in your work, please reference the relevant article that document the methods and data: Petersen & Thiis (2026), " Continental-Scale Assessment of Typical Meteorological Years in a Changing Climate " , Energy and Buildings, DOI: 10.1016/j.enbuild.2025.116814
This dataset contains bulk RNA‑seq expression matrices and sample‑level phenotype data from the Miami Alzheimer's disease cohort used in the study "Sex and insulin resistance biomarker modelling in a new large‑scale Alzheimer's disease transcriptomic resource". FASTQ files were aligned with STAR using two strategies: a standard gene‑level alignment to the reference genome and an alternative alignment restricted to genes represented on the Affymetrix GeneTitan array to enable cross‑platform replication. Gene‑level counts were obtained with featureCounts, low‑count genes were removed, and filtered matrices were size‑factor normalised with DESeq2 and log2‑transformed. Combat‑Seq was applied to filtered counts using total count deciles as pseudo‑batches, and additional neutrophil‑adjusted matrices were generated by correcting for estimated neutrophil fractions derived from immune cell deconvolution. The dataset includes two filtered raw count matrices, four corresponding processed matrices (including neutrophil‑adjusted versions), and a phenotype table linking each sample to diagnosis and key covariates, with consistent sample identifiers across all files. Please note that there are other systematic differences in the case vs. control data that have not been fully explored through ComBat correction.
This dataset includes TMY files for the 5000 largest cities and towns in the EU, based on CERRA data from 1991-2020, and using a TMY3 methodology. The files represents a statistically typical year meant for energy calculations of buildings, and are therefore not suited for evaluating extreme conditions. The repository contains: EPW-files - zipped collection of .epw-files The most common file format for climate data for energy simulations applicable to most programs Datasheets - a zip files of individual datasheets A collection of datasheets containing a comparison of TMYs with the data source as well as referance data The datasheets contain Norwegian text Error metrics - ZIP file Error metrics mapped to a map of Norway, for visualization purposes As well as the metrics that are mapped Selected years - a comma seperated file the selected typical meteorological months for each location Changelog - a txt file a overview of changes in the dataset since 1.0 The contents of the repository is produced by The Norwegian University og Life Science (NMBU), Department of Building- and Environmental Technology. The data is provided 'as is', without warranty of any kind, express or implied. In no event shall the authors be liable for any claim, damages or other liability. An interactive map containing the TMY3 data, but not the TMY ISO, can be found at https://www.climatedataforbuildings.eu/ If you use the datasets or tools in your work, please reference the relevant article that document the methods and data: Petersen & Thiis (2025), " Continental-Scale Assessment of Typical Meteorological Years in a Changing Climate " , Energy and Buildings, DOI: 10.1016/j.enbuild.2025.116814