cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102533
title: Supporting data for "spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics"

description: Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
DOI: 10.6084/m9.figshare.24407785.v2
title: E. sideroxylon genes

description: Eucalyptus sideroxylon genes, as predicted by BRAKER2 using gene homolgy.gtf file of transcript locationsAmino acid transcipt sequencesNucleotide transcipt sequences
DOI: 10.6084/m9.figshare.24407806.v2
title: E. melliodora genes

description: Eucalyptus melliodora genes, as predicted by BRAKER2 using gene homolgy.gtf file of transcript locationsAmino acid transcipt sequencesNucleotide transcipt sequences
DOI: 10.6084/m9.figshare.24407833.v2
title: E. sideroxylon v's E. melliodora synteny and rearrangements

description: tsv file describing all syntenic, rearranged, and not-aligned regions bewteen E. milliodora (reference) and E. sideroxylon (query).These are the results of aligning the genomes with nucmer (MUMmer) and analysiing alignments with SyRI.
DOI: 10.6084/m9.figshare.24407848.v2
title: Eucalyptus melliodora repeats

description: bed file describing repeats for Eucalyptus melliodora
DOI: 10.6084/m9.figshare.24407869.v2
title: Eucalyptus sideroxylon repeats

description: bed file describing repeats for Eucalyptus sideroxylon
DOI: 10.6084/m9.figshare.24602694.v2
title: BUSCO results

description: BUSCO (version 5) results for E. melliodora and E.sideroxylon
DOI: 10.6084/m9.figshare.24602700.v2
title: Fixation index (F<sub>ST</sub>)

description: Fixation index (F<sub>ST</sub>) for E. melliodora and E. sideroxylon.SNP datasets were combined for each reference genome, and subsequently F<sub>ST</sub> was calculated for each SNP using PLINK [65] (version: 1.9).
DOI: 10.6084/m9.figshare.24602706.v2
title: Recombination rate estimates (ρ)

description: Recombination rate estimates (ρ) for E.melliodora and E. siceroxylon.SNPs were phased using WhatsHap (version: 1.7) and SHAPEIT4 (version: 4.2.2), before ρ was calculated with LDJump (version: 0.3.1). LDJump made use of LDHat (version: 2.2a).
DOI: 10.6084/m9.figshare.24616131.v2
title: EggNog gene annotations

description: EggNog annotations for E. melliodora and E. sideroxylon genes.Annotations include:eggNOG_OGKEGG_koKEGG_PathwayKEGG_ModuleKEGG_ReactionKEGG_rclassBRITEKEGG_TCCAZyBiGG_ReactionPFAMs
DOI: 10.6084/m9.figshare.24625431.v2
title: Phased SNPs

description: SNPs for E. melliodora and E. sideroxylon.Reads were aligned with bwa mem, variants called with bcftools, SNPs phased using WhatsHap & SHAPEIT4.
DOI: 10.15468/dl.6pkqva
title: Occurrence Download

description: A dataset listing the 19 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Material sample",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aquilegia L."
 ]
}

 The dataset's 19 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001989-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5524/102491
title: Supporting data for "Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios"

description: Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modelled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. Modelled habitat suitability maps use information about known occurrences of species and predict suitable areas for species using climate, soil and landscape information. Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3 and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants at 5km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species and 44% of vascular plant species. We also include tabular data which includes summaries of total quality-weighted habitat area of species under different climate scenarios and time periods. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.
DOI: 10.5524/102495
title: Supporting data for "A reference genome of Commelinales provides insights into the commelinids evolution and global spread of water hyacinth (Pontederia crassipes)"

description: Commelinales belongs to the commelinids clade which also comprises Poales that includes the most important monocot species, such as rice, wheat, and maize. No reference genome of Commelinales is current available. Water hyacinth (<i>Pontederia crassipes</i>), a member of Commelinales, is one of the devastating aquatic weeds although it is also grown as an ornamental and medical plant. Here, we present a chromosome-scale reference genome of the tetraploid water hyacinth with a total length of 1.22Gb (over 95% of the estimated size) across eight pseudochromosome pairs. With the representative genomes, we reconstructed phylogeny of the commelinids, which supported Zingiberales and Commelinales being sister lineages of Arecales and shed lights on the controversial relationship of the orders. We also reconstructed ancestral karyotypes of the commelinids clade and confirmed the ancient commelinids genome have eight chromosomes, not five as previously reported. Gene family analysis revealed contraction of disease-resistance genes during polyploidization of water hyacinth, likely a result of fitness requirement for its role as a weed. Genetic diversity analysis using nine water hyacinth lines from three continents (South America, Asia and Europe) revealed very closely related nuclear genomes and almost identical chloroplast genomes of the materials, and demonstrated the global water hyacinth having a common origin in Brazil. The genomic resources of <i>P. crassipes</i> reported here contribute a crucial missing link of the commelinids species and offer novel insights into their phylogeny.
DOI: 10.5524/102500
title: Supporting data for "An improved chromosome-level genome assembly of perennial ryegrass (<i>Lolium perenne</i> L.)"

description: This work is an update and extension of the previously published article Ultralong Oxford Nanopore Reads Enable the Development of a Reference-Grade Perennial Ryegrass Genome Assembly, by Frei et al.. The published genome assembly of the doubled haploid perennial ryegrass (<i>Lolium perenne</i> L.) genotype Kyuss (Kyuss v1.0) marked a milestone for forage grass research and breeding. However, order and orientation errors may exist in the pseudo-chromosomes of Kyuss, since barley (<i>Hordeum vulgare</i> L.), which diverged 30 million years ago from perennial ryegrass, was used as the reference to scaffold Kyuss. To correct for structural errors possibly present in the published Kyuss assembly, we <i>de novo</i> assembled the genome again and generated 50-fold coverage high-throughput chromosome conformation capture (Hi-C) data to assist pseudo-chromosome construction. The resulting new chromosome-level assembly Kyuss v2.0 showed improved quality with high contiguity (contig N50 = 120 Mb), high completeness (total BUSCO score = 99%), high base-level accuracy (QV = 50) and correct pseudo-chromosome structure (validated by Hi-C contact map). This new assembly will serve as a better reference genome for <i>Lolium</i> spp. and greatly benefit the forage and turf grass research community.
DOI: 10.5524/102501
title: Supporting data for "Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models"

description: The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relationships, diagnose diseases, and develop biomarkers using various data sources like magnetic resonance imaging and electroencephalography. The primary objective of ML is to build models that can make accurate predictions on unseen data. Researchers aim to prove the existence of such generalizable models by evaluating performance using techniques such as cross-validation (CV), which uses systematic subsampling to estimate the generalization performance. Choosing a CV scheme and evaluating an ML pipeline can be challenging and, if used improperly, can lead to overestimated results and incorrect interpretations. We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering common pitfalls. In this manuscript, we present the rationale behind julearns design, its core features, and showcase three examples of previously-published research projects that can be easily implemented using this novel library. Julearn aims to simplify the entry into the ML world by providing an easy-to-use environment with built in guards against some of the most common ML pitfalls. With its design, unique features and simple interface, it poses as a useful Python-based library for research projects.
DOI: 10.5524/102528
title: Supporting data for "Genome evolution and transcriptome plasticity associated with adaptation to monocot and dicot plants in <i>Colletotrichum</i> fungi."

description: <i>Colletotrichum</i> is an important plant pathogen and a model system to study evolution in fungi. We investigate the adaptation to different cell wall compositions combining genomics and transcriptomics. Our results suggest that the ancestral <i>Colletotrichum</i> were associated with dicot plants and certain branches progressively adapted to different monocot hosts, reshaping the gene content and its regulation.
DOI: 10.5524/102534
title: Supporting data for "Unveiling Vertebrate Development Dynamics in Frog <i>Xenopus laevis</i> using Micro-CT Imaging"

description: <i>Xenopus laevis</i>, the African clawed frog, is a versatile vertebrate model organism employed across various biological disciplines, prominently in developmental biology to elucidate the intricate processes underpinning body plan reorganization during metamorphosis. Despite its widespread utility, a notable gap exists in the availability of comprehensive datasets encompassing <i>Xenopus</i> late developmental stages. <br><br>In the present study, we harnessed micro-computed tomography (micro-CT), a non-invasive 3D imaging technique utilizing X-rays to examine structures at a micrometer scale, to investigate the developmental dynamics and morphological changes of this crucial vertebrate model. Our approach involved generating high-resolution images and computed 3D models of developing <i>Xenopus</i> specimens, spanning from premetamorphosis tadpoles to fully mature adult frogs. This extensive dataset enhances our understanding of vertebrate development and is adaptable for various analyses. For instance, we conducted a thorough examination, analyzing body size, shape, and morphological features, with a specific emphasis on skeletogenesis, teeth, and organs like the brain at different stages. Our analysis yielded valuable insights into the morphological changes and structure dynamics in 3D space during <i>Xenopus</i> development, some of which were not previously documented in such meticulous detail. This implies that our datasets effectively capture and thoroughly examine <i>Xenopus</i> specimens. Thus, these datasets hold the solid potential for additional morphological and morphometric analyses, including individual segmentation of both hard and soft tissue elements within <i>Xenopus</i>. <br><br>Our repository of micro-CT scans represents a significant resource that can enhance our understanding of <i>Xenopus</i> development and the associated morphological changes. The widespread utility of this amphibian species, coupled with the exceptional quality of our scans, which encompass a comprehensive series of developmental stages, opens up extensive opportunities for their broader research application. Moreover, these scans have the potential for use in virtual reality, 3D printing, and educational contexts, further expanding their value and impact.
DOI: 10.5281/zenodo.14916550
title: A machine learning approach to light-induced order-disorder  phase transitions: large-scale long-time simulations with it ab initio accuracy.

description: Data for the paper 'Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy' DOI https://doi.org/10.1038/s41524-025-01614-5

The folder contains:

- a folder 'figures' with python scripts to reproduce all Figures and Supplementary Figures of the paper.

- a folder 'software' with the list of all software used and their respective versions.

- a folder 'GAP_potentials' with all the GAP training sets, input files and potentials used in the paper.

- a folder 'example_files' containing some examples of input and output files for the various calculations that we have performed in the paper (ab initio force calculations, ab initio phonon calculations, GAP phonon calculations, molecular dynamics)
DOI: 10.5281/zenodo.14916551
title: A machine learning approach to light-induced order-disorder  phase transitions: large-scale long-time simulations with it ab initio accuracy.

description: Data for the paper 'Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy' DOI https://doi.org/10.1038/s41524-025-01614-5

The folder contains:

- a folder 'figures' with python scripts to reproduce all Figures and Supplementary Figures of the paper.

- a folder 'software' with the list of all software used and their respective versions.

- a folder 'GAP_potentials' with all the GAP training sets, input files and potentials used in the paper.

- a folder 'example_files' containing some examples of input and output files for the various calculations that we have performed in the paper (ab initio force calculations, ab initio phonon calculations, GAP phonon calculations, molecular dynamics)
DOI: 10.5524/102451
title: Supporting data for "A Database of Restriction Maps to Expand the Utility of Bacterial Artificial Chromosomes"

description: While Bacterial Artificial Chromosomes were once a key resource for the genomic community, they have been obviated, for sequencing purposes, by long-read technologies. Such libraries may now serve as a valuable resource for manipulating and assembling large genomic constructs. To enhance accessibility and comparison, we have developed a BAC restriction map database. Using information available from the National Center for Biotechnology Informations cloneDB ftp site, a database containing the restriction maps for both uniquely placed and insert-sequenced BACs from 11 libraries covering available restriction enzyme recognition sequences was constructed. Along with the database, a set of python functions are available for reconstructing the database and more easily accessing information within. This data is valuable for researchers simply using BACs, as well as working with larger sections of the genome in terms of synthetic genes, large-scale editing, and mapping.
DOI: 10.5524/102466
title: Genome data of <i>Scrobipalpa atriplicella</i>

description: <i>Scrobipalpa atriplicella</i>, commonly known as the goosefoot groundling moth, is a small moth in the family Gelechiidae. It is native to Europe and northern Asia, but was introduced into the US and is now found commonly in North America. The larvae are leafminers and prefer feeding on plants in the goosefoot genus (<i>Chenopodium</i>), which also includes quinoa. <br>Understanding the genotype of pest species provides an important baseline for designing integrated pest management (IPM) strategies. We sequenced ~37x coverage with PacBio HiFi reads and assembled the genome of one larva of <i>S. atriplicella</i>, resulting in a 301.53 Mb genome in 7,092 contigs with N50 0.05 Mb, and a BUSCO completeness score of 73.3%.
DOI: 10.5524/102468
title: Genome data of <i>Keiferia lycopersicella</i>

description: <i>Keiferia lycopersicella</i>, commonly known as the tomato pinworm, is a small moth in the family Gelechiidae. It can be found in the US, Mexico, and the Caribbean. Larvae mainly feed on tomato plants, but can also feed on potato, eggplant, and other solanaceous crops. <br>Understanding the genotype of pest species provides an important baseline for designing integrated pest management (IPM) strategies. We sequenced 50x coverage of PacBio HiFi reads and assembled the genome of one larva of <i>K. lycopersicella</i>, resulting in a 443.65 Mb genome in 61 contigs with N50 14.5 Mb, and a BUSCO completeness score of 96.6%.
DOI: 10.5524/102476
title: Supporting data for "The genome assembly and annotation of the Chinese cobra, <i>Naja atra</i>"

description: In China, there are 65 types of venomous snakes, and the Chinese Cobra <i>Naja atra</i> is one of the most prevalent and a major cause of snakebite incidents to humans. Furthermore, <i>N.atra</i> has been listed as a protected animal in some areas, and has also been listed as vulnerable by the IUCN. In recent years, due to the medical value of snake venom, there has been growing interest In venomics research. With genomic resources being extremely important for understanding the molecular mechanisms of venom production. We collected a snake sample in Huangshan, Anhui, China, which was identified as <i>N.atra</i> by morphological identification. Here, we report a highly continuous genome assembly of this <i>N.atra</i>. The genome size of <i>N.atra</i> is 1.67Gb, the repeat content making up 37.8 % of this. A total of 26,432 functional genes were annotated. This data provides an important resource in the study of <i>N.atra</i> venom production and can also provide some guidance for the protection of the species.
DOI: 10.5524/102479
title: Supporting data for "The probability of edge existence due to node degree: a baseline for network-based predictions"

description: Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the networks specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degrees predictive performance diminishes when the networks used for training and testingdespite measuring the same biological relationshipswere generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).
DOI: 10.5524/102481
title: Supporting data for "A high-quality chromosomal genome assembly of the sea cucumber <i>Chiridota heheva</i> and its hydrothermal adaptation"

description: <i>Chiridota heheva</i> is a cosmopolitan holothurian well adapted to diverse deep-sea ecosystems, especially chemosynthetic environments. Besides high hydrostatic pressure and limited light, high concentrations of metal ions also represent harsh conditions in hydrothermal environments. Few holothurian species can live in such extreme conditions. Therefore, it is valuable to elucidate the adaptive genetic mechanisms of <i>C. heheva</i> in hydrothermal environments.<br> Herein, we report a high-quality reference genome assembly of <i>C. heheva</i> from the Kairei vent, which is the first chromosome-level genome of Apodida. The chromosome-level genome size was 1.43 Gb, with a scaffold N50 of 53.24 Mb and BUSCO completeness score of 94.5 %. Contig sequences were clustered, ordered, and assembled into 19 natural chromosomes. Comparative genome analysis found that the expanded gene families and positively selected genes of <i>C. heheva</i> were involved in the DNA damage repair process. The expanded gene families and the unique genes were contributed to maintaining iron homeostasis in an iron-enriched environment. The positively selected gene RFC2 with ten positively selected sites played an essential role in DNA repair under extreme environments.<br>This first chromosome-level genome assembly of <i>C. heheva</i> reveals the hydrothermal adaptation of holothurians. As the first chromosome-level genome of Order Apodida, this genome will provide the resource for investigating the evolution of Class Holothuroidea.
DOI: 10.57760/sciencedb.07077
title: A dataset of Chiridota heheva
title: A dataset of Chiridota heheva

description: A sample of C. heheva was collected from the Kairei vent in the Mid-Indian Ocean, with a depth of 2428 m. The HiFi data was generated on the PacBio Sequel II platform, while the Hi-C data and total RNA were generated on the Illumina NovaSeq-6000 sequencing platform. The chromosome-level genome assembly of C. heheva was performed using both HiFi reads and Hi-C reads. The total size of the final assembly was 1.43 Gb with N50 of 53.24 Mb, consisting 19 chromosome-level scaffolds.
description: A sample of C. heheva was collected from the Kairei vent in the Mid-Indian Ocean, with a depth of 2428 m. The HiFi data was generated on the PacBio Sequel II platform, while the Hi-C data and total RNA were generated on the Illumina NovaSeq-6000 sequencing platform. The chromosome-level genome assembly of C. heheva was performed using both HiFi reads and Hi-C reads. The total size of the final assembly was 1.43 Gb with N50 of 53.24 Mb, consisting 19 chromosome-level scaffolds.