cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102437
title: Supporting data for "Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps"

description: Genotyping-by-Sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by PCR duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. <br>We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset-dependent) and others produce consistent advantageous results among them (dataset-independent). <br>We set as default in the Reads2Map workflows the approaches that showed to be dataset-independent for GBS datasets according to our results. This reduces the number required of tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.
DOI: 10.5524/102393
title: Supporting data for "EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses"

description: Polygenic risk score (PRS) analyses are now routinely applied across biomedical research. However, as PRS studies grow in size, there is an increased risk of sample overlap between the genome-wide association study (GWAS) from which the PRS is derived and the target sample, in which PRS are computed and hypotheses are tested. Despite the wide recognition of the sample overlap problem, its potential impact on the results from PRS studies has not yet been quantified and no analytical solution has been provided. Here, we first conduct a comprehensive investigation into the scale of the sample overlap problem, finding that PRS results can be substantially inflated even in the presence of minimal overlap. Next, we introduce a method and software, EraSOR (Erase Sample Overlap and Relatedness), which eliminates the inflation caused by sample overlap (and close relatedness) in almost all settings tested here. EraSOR could be useful in PRS studies (with target sample &gt; 1k) similar to those investigated here, either: (i) to mitigate the potential effects of known or unknown inter-cohort overlap and close relatedness, or (ii) as a sensitivity tool to highlight the possible presence of sample overlap before its direct removal, when possible, or else to provide a lower bound on PRS analysis results after accounting for potential sample overlap.<br>This research has been conducted using the UK Biobank Resource under application 18177 (Dr OReilly).
DOI: 10.5524/102403
title: Supporting data for "GADMA2: more efficient and flexible demographic inference from genetic data"

description: Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several inference engines and provides global parameters optimization based on a genetic algorithm. <br>Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new inference engines, an updated optimization algorithm and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported inference engines on simulated data and demonstrate an example of GADMA2 usage on two empirical datasets. <br>We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2s inference engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for two empirical datasets of inbred species.
DOI: 10.5524/102575
title: Supporting data for “PhageGE: An interactive web platform for exploratory analysis and visualisation of bacteriophage genomes”

description: Antimicrobial resistance is a serious threat to global health. Due to the stagnant antibiotic discovery pipeline, bacteriophages (phages) have been proposed as an alternative therapy for the treatment of infections caused by multidrug-resistant (MDR) pathogens. Genomic features play an important role in phage pharmacology. However, our knowledge of phage genomics is sparse and the use of existing bioinformatic pipelines and tools requires considerable bioinformatic expertise. These challenges have substantially limited the clinical translation of phage therapy.<br> We have developed PhageGE (Phage Genome Explorer), a user-friendly graphical interface application for the interactive analysis of phage genomes. PhageGE enables users to perform key analyses, including phylogenetic analysis, visualisation of phylogenetic trees, prediction of phage lifecycle, and comparative analysis of phage genome annotations. The new R Shiny webserver, PhageGE, integrates existing R packages and combines them with several newly developed functions to facilitate these analyses. Additionally, the webserver provides interactive visualisation capabilities and allows users to directly export publication-quality images.<br> PhageGE is a valuable tool that simplifies the analysis of phage genome data and may expedite the development and clinical translation of phage therapy. PhageGE is publicly available at http://phagege.com
DOI: 10.15468/dl.987nuv
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (ef807e08-c954-487f-9e70-3cc75f173812, 36ddf48a-380c-4e24-8179-190808fa8e94, 90fd6680-349f-11d8-aa2d-b8a03c50a862, 8e1a97a0-3ca8-11d9-8439-b8a03c50a862, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, 33d232b5-5cc2-4521-8bd6-121c5bb41c32, 6ba9a8cc-513a-4a51-bf93-6f5de8040a96, 253fc67c-c7fb-4c85-88f5-43ac6d9dc8a9)",
 "TaxonKey is Laguncularia racemosa (L.) C.F.Gaertn."
 ]
}

 The dataset includes 100 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001988-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8273049
title: Fruit-bearing plant species observations in Brazilian cities

description: This data set, extracted from iNaturalist, compiles observations from the capitals of all 27 Brazilian federative units and specifically focuses on the species listed on https://doi.org/10.5281/zenodo.10212850. A backup of this data set was obtained from iNaturalist on August 22nd, 2023. The dataset features 47 columns, capturing details such as observation time, location, license, and taxonomic identification. It provides an extensive taxonomic breakdown, covering classifications from kingdom and phylum down to species, subspecies, and variety (in some cases).
DOI: 10.5524/102499
title: Supporting data for "Citizen Science Data on Urban Forageable Plants: A Case Study in Brazil"

description: This paper presents two key data sets derived from the <i>Pomar Urbano</i> project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced in Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study encompasses data from all 27 Brazilian state capitals, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. We expect the data sets to serve as a resource for further studies in urban foraging, food security, cultural ecosystem services, and environmental sustainability.
DOI: 10.5524/102507
title: Supporting data for "Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups"

description: Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole-genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole-genome and matched phenome data to conduct further population-genome studies and develop healthcare services based on population genomics.<br>Here, we present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency &gt; 0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new geno-phenotype associations which were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories.<br>Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole-genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.
DOI: 10.5524/102496
title: Supporting data for "Enhanced Bovine Genome Annotation Through Integration of Transcriptomics and Epi-Genetics Datasets Facilitates Genomic Biology"

description: A total number of 160,820 unique transcripts (50% protein-coding) representing 34,882 unique genes (60% protein-coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio Iso-seq data, ONT-seq data, de novo assembled transcripts from RNA-seq data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as WTTS-seq, RAMPAGE, ChIP-seq, and ATAC-seq. A large proportion of identified transcripts (69%) were un-annotated, of which 86% were produced by annotated genes and 14% by un-annotated genes. A median of two 5 untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. <br>Furthermore, we identified 3,744 genes that functioned as non-coding genes in fetal tissues, but as protein coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available QTL data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. These validated results show significant improvement over current bovine genome annotations.
DOI: 10.34740/kaggle/dsv/11959531
title: Paper Helicopter experiment.
DOI: 10.15468/dl.gwcy9w
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus maculosus Smith, 1934"
 ]
}

 The dataset includes 194 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001990-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15520714
title: Dataset for Publication: "Nitro-Group π System Drives the Interaction of RRx-001 with Electrons in Solution"

description: Raw experimental data and description file.
DOI: 10.5281/zenodo.15520715
title: Dataset for Publication: "Nitro-Group π System Drives the Interaction of RRx-001 with Electrons in Solution"

description: Raw experimental data and description file.
DOI: 10.15468/dl.2wyqvu
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus torquatus madrensis Olson, 1986"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001991-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dm9cdh
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Botanical Database of Southern Africa (BODATSA): Botanical Collections",
 "Country is Croatia"
 ]
}

 The dataset includes 17 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001992-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3x5uzq
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is LACM Malacology",
 "Country is Croatia"
 ]
}

 The dataset includes 29 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001993-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4pssdc
title: Occurrence Download

description: A dataset containing 14088 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus magister Hallowell, 1854"
 ]
}

 The dataset includes 14088 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001994-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13970575
title: Fully differentiable, fully distributed River Discharge Prediction: data sets

description: This repository contains the data sets used in: Scholz et al. (2025). Fully differentiable, fully distributed River Discharge Prediction.



dem_1000.h5 based on EU-DEM v1.1, reprojected to RADOLAN grid: https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50

efas.h5 based on EFAS historical: https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview

era5_ssrd_neckar*.nc based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

radolan_neckar_*.h5 based on RADOLAN rw product provided by the Deutsche Wetterdienst: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/


Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (https://grdc.bafg.de/), and then preprocessed with the provided bafg_parser.py python script. We use the following stations in our work:



6335290: STEIN

6335291: GAILDORF

6335565: BAD IMNAU

6335600: ROCKENAU SKA

6335601: LAUFFEN

6335602: PLOCHINGEN

6335603: ROTTWEIL

6335604: KIRCHENTELLINSFURT

6335620: MOSBACH

6335660: PFORZHEIM

6335665: DENKENDORF

6335671: ALTENSTEIG

6335675: MURR

6335676: OPPENWEILER

6335680: SCHWABSBERG

6335681: UNTERGRIESHEIM

6335690: NEUSTADT


To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1
DOI: 10.5281/zenodo.13970576
title: Fully differentiable, fully distributed River Discharge Prediction: data sets

description: This repository contains the data sets used in: Scholz et al. (2024). Fully differentiable, fully distributed River Discharge Prediction.



dem_1000.h5 based on EU-DEM v1.1, reprojected to RADOLAN grid: https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50

efas.h5 based on EFAS historical: https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview

era5_tsr_neckar*.h5 based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

radolan_neckar*.h5 based on RADOLAN rw product provided by the Deutsche Wetterdienst: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/


Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (https://grdc.bafg.de/), and then preprocessed with the provided bafg_parser.py python script. We use the following stations in our work:



6335290: STEIN

6335291: GAILDORF

6335565: BAD IMNAU

6335600: ROCKENAU SKA

6335601: LAUFFEN

6335602: PLOCHINGEN

6335603: ROTTWEIL

6335604: KIRCHENTELLINSFURT

6335620: MOSBACH

6335660: PFORZHEIM

6335665: DENKENDORF

6335671: ALTENSTEIG

6335675: MURR

6335676: OPPENWEILER

6335680: SCHWABSBERG

6335681: UNTERGRIESHEIM

6335690: NEUSTADT


To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1
DOI: 10.5281/zenodo.14548401
title: Fully differentiable, fully distributed River Discharge Prediction: data sets

description: This repository contains the data sets used in: Scholz et al. (2024). Fully differentiable, fully distributed River Discharge Prediction.



dem_1000.h5 based on EU-DEM v1.1, reprojected to RADOLAN grid: https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50

efas.h5 based on EFAS historical: https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview

era5_tsr_neckar*.h5 based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

radolan_neckar*.h5 based on RADOLAN rw product provided by the Deutsche Wetterdienst: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/


Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (https://grdc.bafg.de/), and then preprocessed with the provided bafg_parser.py python script. We use the following stations in our work:



6335290: STEIN

6335291: GAILDORF

6335565: BAD IMNAU

6335600: ROCKENAU SKA

6335601: LAUFFEN

6335602: PLOCHINGEN

6335603: ROTTWEIL

6335604: KIRCHENTELLINSFURT

6335620: MOSBACH

6335660: PFORZHEIM

6335665: DENKENDORF

6335671: ALTENSTEIG

6335675: MURR

6335676: OPPENWEILER

6335680: SCHWABSBERG

6335681: UNTERGRIESHEIM

6335690: NEUSTADT


To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1
DOI: 10.5281/zenodo.15482198
title: Fully differentiable, fully distributed River Discharge Prediction: data sets

description: This repository contains the data sets used in: Scholz et al. (2025). Fully differentiable, fully distributed River Discharge Prediction.



dem_1000.h5 based on EU-DEM v1.1, reprojected to RADOLAN grid: https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50

efas.h5 based on EFAS historical: https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview

era5_ssrd_neckar*.nc based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

radolan_neckar_*.h5 based on RADOLAN rw product provided by the Deutsche Wetterdienst: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/


Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (https://grdc.bafg.de/), and then preprocessed with the provided bafg_parser.py python script. We use the following stations in our work:



6335290: STEIN

6335291: GAILDORF

6335565: BAD IMNAU

6335600: ROCKENAU SKA

6335601: LAUFFEN

6335602: PLOCHINGEN

6335603: ROTTWEIL

6335604: KIRCHENTELLINSFURT

6335620: MOSBACH

6335660: PFORZHEIM

6335665: DENKENDORF

6335671: ALTENSTEIG

6335675: MURR

6335676: OPPENWEILER

6335680: SCHWABSBERG

6335681: UNTERGRIESHEIM

6335690: NEUSTADT


To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1
DOI: 10.15468/dl.dc2zrn
title: Occurrence Download

description: A dataset containing 155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is NHMD Entomology Collection",
 "Country is Croatia"
 ]
}

 The dataset includes 155 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001997-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.