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Replication materials for "A Temporal Comparability Audit of EM-DAT Heatwave Records in Europe"
July, 2026 • Software
Zhu, Wei
This research compendium accompanies “A Temporal Comparability Audit of EM-DAT
Heatwave Records in Europe.” It provides project-authored analysis and
acquisition scripts, tests, pinned env…
This research compendium accompanies “A Temporal Comparability Audit of EM-DAT
Heatwave Records in Europe.” It provides project-authored analysis and
acquisition scripts, tests, pinned environment metadata, aggregate results,
manuscript source, final figures, source-version metadata, and cryptographic
manifests. It supports rebuilding the manuscript from archived aggregate
artifacts and auditing the reported results without redistributing
provider-controlled records.
The archive does not include EM-DAT workbooks, reversible EM-DAT row-level
derivatives, restricted E-OBS or WHO source files, third-party papers, or
machine-local environments. Recomputing EM-DAT-dependent matching, event
families, and entry batches requires an independently authorised provider
workbook. The included CC3D sensitivity subset remains attributed under CC BY
4.0.
This repository contains the supplementary datasets, computational notebooks and structural prediction files generated for the study "A TSS-aware catalogue of microRNA-encoded peptides in tomato (Sola…
This repository contains the supplementary datasets, computational notebooks and structural prediction files generated for the study "A TSS-aware catalogue of microRNA-encoded peptides in tomato (Solanum lycopersicum)". The dataset supports the identification, annotation, structural characterization and comparative analysis of candidate microRNA-encoded peptides (miPEPs) associated with tomato pri-miRNAs.
The repository is organised into the following files:
TableS1.xlsx: Metadata and sequence features of the curated tomato miPEP catalog.
TableS2.xlsx: Physicochemical values and statistical summaries used to support Figures 5 and 6.
TableS3.xlsx: Predicted ORFs associated with miRNA loci in seven plant species.
TableS4.xlsx: Exact amino-acid k-mer matches detected between tomato miPEPs and peptide datasets from the surveyed non-tomato species.
TableS5.xlsx: Association between cross-species detectability of tomato miPEPs and miPEP metadata features.
TableS6.xlsx: Strand-aware RNA-seq support for MIR loci screened for miPEP prediction in tomato leaves and roots.
TableS7.xlsx: Exploratory analysis of the association between genomic context and miPEP features in tomato.
description_tables_full.txt: full description of the tables included.
eggplant_premiR_candidates_BLAST_EN.ipynb: Homology-based identification of putative eggplant pre-miRNA loci through BLAST alignment of known Solanaceae pre-miRNAs, followed by extraction of candidate genomic regions.
generic_pri_miRNA_upstream_from_GFF3_chr_to_NC_FIXED.ipynb: Extraction of strand-aware upstream sequences from annotated pri-miRNA loci using genome and GFF3 files, with automated chromosome-to-genome identifier matching.
miPEPs_ORFs_kmer_overlap_colab.ipynb: Detection of exact amino-acid k-mer matches (6–30 aa) between predicted ORF peptides and the curated tomato miPEP dataset for comparative conservation analyses.
orfs_from_fasta_ATG_6_200aa_v2_EN.ipynb: Identification and extraction of all ATG-initiated ORFs (6–200 amino acids) from DNA FASTA sequences, including optional reverse-complement strand analysis and peptide FASTA generation.
upstream_from_miRBase_hairpin_list_noGFF3_EN.ipynb: Extraction of upstream genomic regions from miRBase hairpin sequences when genomic annotations (GFF3 files) are unavailable, using genome mapping to infer precursor coordinates.
IGV_robust_transcripts_panels.zip: strand-separated IGV/JBrowse-type coverage plots for the 21 robust loci detected overall.
mipeps_alphafold_prediction.zip: AlphaFold2 structural predictions for all 107 tomato miPEPs, including structure files, confidence metrics and pLDDT-coloured structural visualizations generated from the AlphaFold2 models.
Alpha_fold_prediction_panels_all.pdf: pdf with all Alpha_fold_prediction images for the 107miPEPs, corresponding to the best prediction for each.
mipepORFS_mappingdata.zip: BAM alignment files and their associated BAI index files generated and used for visual inspection in IGV (Integrative Genomics Viewer) of genomic regions corresponding to putative miPEPs investigated in this study. The dataset includes files from four leaf samples and four root samples.The BAM files contain sequencing reads aligned to the reference genome used in the analysis. These files preserve the information required to visualize:
the genomic position of the reads,
the coverage across each locus,
the orientation of the alignments
File use
To correctly visualize the alignments from mipepORFS_mappingdata.zip, the files should be opened in IGV together with:
the corresponding reference genome: Solanum lycopersicum reference genome sequence, assembly SL3.0
the mipep annotation track used in the study: mipeps_prediction_SL3.0.gtf, included in the file
For improved visualization and interpretation in IGV, the gene annotation file Solanum_lycopersicum.SL3.0.57.chr.gtf can also be loaded together with the BAM and BAI files.
The tomato reference genome sequence (Solanum lycopersicum assembly SL3.0) and the corresponding gene annotation file can be downloaded from Ensembl Plants.
This software archive contains R scripts used to identify and characterize MYOD1-related loop-chained enhancer modules in WT and SMCHD1-KO cells.
The workflow includes:
1. Identification of …
This software archive contains R scripts used to identify and characterize MYOD1-related loop-chained enhancer modules in WT and SMCHD1-KO cells.
The workflow includes:
1. Identification of directly MYOD1-bound super/stitched enhancers and loop-connected related super/stitched enhancers within TADs.2. Annotation of enhancer activity and stitched- or super-enhancer classification.3. Characterization of super/stitched enhancer–enhancer nexus connectivity and loop-hop structure.4. Identification of loop-supported super/stitched enhancer–promoter connections.
The repository README provides requirements and usage instructions.
EU-Citizen.Science is an online platform for sharing knowledge, tools, training, and resources for citizen science. The platform has grown significantly since its launch approximately four years ago. …
EU-Citizen.Science is an online platform for sharing knowledge, tools, training, and resources for citizen science. The platform has grown significantly since its launch approximately four years ago. This report evaluates the platform’s resource-sharing branch, focusing on the effectiveness of its resource sorting and presentation system. The assessment was conducted through a combination of manual review and quantitative methods, utilizing web scraping, API access, and analytical tools such as Python and Google Analytics. After identifying issues with the current tagging system, potential underlying causes were examined. Based on these findings, this report presents recommendations to improve the platform’s usability and accessibility. Additionally, within the scope of the ECS Project, the suggestions aim to further support the development of the ECS Academy.
Citizen sciencePlatformsResourcesTraining resourcesTagging systems
Reproducibility repository for Convergence of entropy-conservative summation-by-parts discretizations to smooth solutions of hyperbolic conservation laws
July, 2026 • Software
Ranocha, Hendrik
Convergence of entropy-conservative summation-by-parts discretizations to smooth solutions of hyperbolic conservation laws
EBoD-FL estimates of the environmental burden of disease, 2013-2022
July, 2026 • Dataset
Pauwels, Arno, Vandeputte, Emilie, Demoury, Claire, De Clercq, Eva M, Devleesschauwer, Brecht
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the environmental burden of disease
EBoD-FL assesses population exposure to environmental risk factors in Flanders and ap…
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the environmental burden of disease
EBoD-FL assesses population exposure to environmental risk factors in Flanders and applies comparative risk assessment to estimate the proportion of the disease burden attributable to these risks. The environmental burden of disease results build on the estimates of causes of death and disability from the Belgian National Burden of Disease Study.
Air pollution
Poor air quality is caused by pollution with particulate matter (PM2.5) and nitrogen dioxide (NO2), among other substances. Population exposure to PM2.5 and NO2 is based on modelled air quality produced by IRCEL-CELINE and population data provided by Statbel.
Environmental noise
Environmental noise is the result of traffic, including noise from road traffic. Population exposure is based on the numbers reported for the European Environmental Noise Directive (published by the EEA), interpolated with a generalised linear model.
Extreme temperatures
Ambient temperatures that deviate from a moderate average are harmful to health in the form of heat or cold. The population’s exposure to meteorological variables was determined using interpolated data from the Royal Meteorological Institute.
Notes
For more information on the CSV format, please see https://docs.fileformat.com/spreadsheet/csv/.
Explore the estimates on https://burden.sciensano.be/shiny/ebodfl/.
FlandersBurden of diseaseRisk factorsEnvironmental exposureBeBOD
EBoD-FL estimates of the local environmental burden of disease, 2013-2022
July, 2026 • Dataset
Pauwels, Arno, Vandeputte, Emilie, Demoury, Claire, De Clercq, Eva M, Devleesschauwer, Brecht
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the local environmental fatal burden of disease
EBoD-FL assesses population exposure to environmental risk factors in Fla…
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the local environmental fatal burden of disease
EBoD-FL assesses population exposure to environmental risk factors in Flanders and applies comparative risk assessment to estimate the proportion of the disease burden attributable to these risks. The environmental burden of disease results build on the estimates of causes of death and disability from the Belgian National Burden of Disease Study. Local estimates of attributable burden are available for mortality and years of life lost.
Air pollution
Poor air quality is caused by pollution with particulate matter (PM2.5) and nitrogen dioxide (NO2), among other substances. Population exposure to PM2.5 and NO2 is based on modelled air quality produced by IRCEL-CELINE and population data provided by Statbel.
Environmental noise
Environmental noise is the result of traffic, including noise from road traffic. Population exposure is based on the numbers reported for the European Environmental Noise Directive (published by the EEA), interpolated with a generalised linear model.
Extreme temperatures
Ambient temperatures that deviate from a moderate average are harmful to health in the form of heat or cold. The population’s exposure to meteorological variables was determined using interpolated data from the Royal Meteorological Institute.
Notes
For more information on the CSV format, please see https://docs.fileformat.com/spreadsheet/csv/.
Explore the estimates on https://burden.sciensano.be/shiny/ebodfl/.
FlandersBurden of diseaseRisk factorsEnvironmental exposureBeBOD
EBoD-FL estimates of the exposure to environmental risk factors, 2013-2022
July, 2026 • Dataset
Pauwels, Arno, Vandeputte, Emilie, Demoury, Claire, De Clercq, Eva M, Devleesschauwer, Brecht
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the exposure to environmental risk factors
EBoD-FL assesses population exposure to environmental risk factors in Flanders…
Mapping the Environmental Burden of Disease in Flanders (EBoD-FL)
Estimates of the exposure to environmental risk factors
EBoD-FL assesses population exposure to environmental risk factors in Flanders and applies comparative risk assessment to estimate the proportion of the disease burden attributable to these risks. The estimation of the exposure to environmental risks in the population builds on a variety of data sources, listed separately for the risk factors considered.
Air pollution
Poor air quality is caused by pollution with particulate matter (PM2.5) and nitrogen dioxide (NO2), among other substances. Population exposure to PM2.5 and NO2 is based on modelled air quality produced by IRCEL-CELINE and population data provided by Statbel.
Environmental noise
Environmental noise is the result of traffic, including noise from road traffic. Population exposure is based on the numbers reported for the European Environmental Noise Directive (published by the EEA), interpolated with a generalised linear model.
Extreme temperatures
Ambient temperatures that deviate from a moderate average are harmful to health in the form of heat or cold. The population’s exposure to meteorological variables was determined using interpolated data from the Royal Meteorological Institute.
Notes
For more information on the CSV format, please see https://docs.fileformat.com/spreadsheet/csv/.
Explore the estimates on https://burden.sciensano.be/shiny/ebodfl/.
There is an increasing interest in upgrading the EModel, a parametric tool for speech quality estimation, to the wideband and super-wideband contexts. The
Contemporary models of Unmanned Aerial Vehicles (UAVs) are largely developed using simulators. In a typical scheme, a flight simulator is dovetailed with a
Undertaking engineering research can be compounding for beginning graduate students and thwarting even for seasoned researchers. With a wealth of academic
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