Scientific Data

We release an enhanced FIB-SEM volume and associated neuron, mitochondria, synapse and glia automated segmentations for the complete, gap- and artifact-free central nervous system of the first instar female larval Drosophila melanogaster. Segmentation data is largely unproofread besides select neurons in the premotor and proprioceptive system and select subvolumes where synapses have been manually annotated, serving as training and evaluation data. The dataset represents the first complete central nervous system of the larval fly, and now awaits substantial improvement of machine vision approaches that effectively leverage domain knowledge for the automated yet data-guided proofreading, or substantial and costly human proofreading. All the data and methods are publicly available for further improvement and analysis.

Japanese macaque (Macaca fuscata) is used in biomedical and neurobiology research, yet transcriptomic resources for the brain are limited. We present a hybrid RNA sequencing dataset and a prefrontal cortex transcriptome assembly from two healthy 6-year-old animals. Short-read Illumina ({approx}70 million paired-end reads per sample) and long-read Oxford Nanopore direct RNA sequencing ({approx}2.5 million reads per sample) were combined. Reads were quality controlled, aligned to the macFus_1.0 reference genome, and assembled with StringTie2. Transcripts were annotated using Trinotate and eggNOG-mapper, and open reading frames were predicted with TransDecoder. The released data package includes raw reads (NCBI SRA BioProject PRJNA1295993), transcript sequences and structural annotation files, predicted coding sequences and proteins, functional annotation tables, and transcript abundance estimates (TPM). Technical validation includes read-level QC and protein-level comparisons to expressed gene sets from human, rhesus macaque and chimpanzee prefrontal cortex. These resources enable reuse for transcript-level expression studies, isoform characterization and comparative primate neurogenomics.

Dissolved organic matter (DOM) is a central component of biogeochemical cycling, but molecular observations generated by ultrahigh-resolution mass spectrometry remain fragmented across studies, limiting quantitative comparisons among ecosystems. Here, we compile and harmonize a global dataset of DOM molecular traits comprising 3,999 samples from 337 studies published between 2005 and 2026. The dataset includes eight intensity-weighted molecular traits: hydrogen-to-carbon (H/C), oxygen-to-carbon (O/C), nitrogen-to-carbon (N/C), and sulfur-to-carbon (S/C) ratios; molecular mass; double-bond equivalents (DBE); modified aromaticity index (AImod); and nominal oxidation state of carbon (NOSC). Samples span aquatic, terrestrial, atmospheric, plant-associated, petroleum-related, and other environments, and are linked, where available, to geographic, physicochemical, nutrient, and climatic metadata. Molecular data were harmonized using common definitions and restricted to negative-mode electrospray-ionization FT-ICR MS to improve cross-study comparability. This resource enables cross-system comparisons of DOM molecular traits and supports analyses of environmental gradients, geographic coverage, and global DOM chemogeography.

State Cancer Profiles (statecancerprofiles.cancer.gov), maintained by the National Cancer Institute with the Centers for Disease Control and Prevention, is a widely used source of county- and state-level cancer statistics in the United States, used for cancer-center catchment-area surveillance and for geographic studies of cancer burden, screening, and access to care. The site offers no API, no bulk download, and no archive of prior estimates: its sole machine-readable export returns one statistical stratum per HTTP request, and when the underlying data are updated the previous estimates are overwritten and become unrecoverable. This resource provides the complete national county- and state-level extract of all four State Cancer Profiles data topics (incidence, mortality, screening and risk factors, and demographics) as typed, analysis-ready files with the stratifying dimensions as columns, published under pinned, citable version DOIs on Zenodo (concept DOI 10.5281/zenodo.11098814). One version DOI is minted per distinct upstream data vintage, the set of values the site served between successive replacements. Three vintages have been captured to date; at each observed vintage boundary roughly 97% of estimate values changed, so which vintage an analysis draws on affects its results. From the 2026-08-24 release forward, cells that the upstream site suppresses are retained as typed nulls with an explicit suppression-reason column. Capture has been automated on an approximately monthly cadence since February 2025, and each future upstream revision will be preserved as a new vintage.

Big trefoil (Lotus pedunculatus Cav.) is a perennial forage legume that thrives on acidic, low-fertility soils and produces condensed tannins that reduce enteric methanogenesis in ruminants. Despite this agronomic potential, genomic resources for the species remain scarce, and the existing haploid assembly does not resolve the two haplotypes of this outcrossing diploid species. Here we present a haplotype-resolved, chromosome-level reference genome for L. pedunculatus genotype Lusitano29 -- the first plant genome assembled using CiFi, a long-read chromosome conformation capture method. We combined PacBio HiFi long reads with CiFi concatemers produced from DpnII and HindIII libraries; in silico digestion and combinatorial pairing of the resulting monomers yielded 790.3 M and 10.3 M pseudo-paired contacts, respectively, enabling scaffolding and manual curation to chromosome level. The 991.1 Mb assembly resolves two phased haplotypes of 500 and 491 Mb, with 96.6% of the sequence anchored in twelve pseudo-chromosomes (six per haplotype). Telomeric repeats were detected at 19 of 24 pseudo-chromosome ends, and no structural errors were detected (scaffold N50 73.8 Mb; consensus QV 64.7; k-mer completeness 99.4%; genome-mode BUSCO completeness 97.0%; CRAQ S-AQI 100.0). Annotation supported by PacBio Iso-Seq full-length transcripts predicted 38,069 and 36,484 protein-coding genes in haplotypes 1 and 2, respectively (protein-mode BUSCO completeness 96.5%), indicating a high completeness of annotated genes. This genome assembly provides a foundation for allele-aware trait dissection of proanthocyanidin biosynthesis, comparative genomics in Lotus, and population genomics and genomics-assisted breeding in L. pedunculatus.

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

Adolescence is a critical period of neurodevelopment, yet most neuroimaging datasets focus on adult populations, leaving a gap in our understanding of how the brain processes information during this formative stage. Here we present a multimodal neuroimaging dataset acquired from 41 adolescent participants aged 11-18 years, combining 3T fMRI data with concurrent eye-tracking and physiological monitoring during naturalistic movie-watching. Data are shared in BIDS-compliant format and technical validation demonstrates good data quality across participants, with low head motion and strong inter-subject neural synchronisation during movie-watching. In addition to the scanning session, participants completed remote assessments covering a broad range of self-reported developmental and mental health traits, alongside cognitive tasks targeting reward-based and social learning. This dataset offers a rich resource for studying the adolescent brain, with particular utility for research on individual differences in mental health and cognition. All data and processing code is openly available to facilitate reproducible science.

Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.

A major goal of the BRAIN Initiative Cell Atlas Network (BICAN) is to create a suite of foundational reference cell atlases and associated standards for human and non-human primate brains. Central to this goal is the creation of cross-species harmonized cellular taxonomies and structural parcellations with formal ontologies that can be mapped into 3D reference frameworks bridging neuroimaging and cellular and histological resolutions. We describe here an iterative approach, focused initially on the basal ganglia, to co-create structural and cellular ontologies in human, macaque and marmoset brains, including a Harmonized Ontology of Mammalian Brain Anatomy (HOMBA), and to map and refine structural parcellations into neuroimaging-based common coordinate frameworks. These references provide the framework for documenting and mapping all experimental sampling in BICAN, allowing analyses of cellular and molecular variation as a function of topographic position, and enabling comparisons of cellular, molecular and neuroimaging-based functional variation within and between primate species. HighlightsO_LIA hierarchical Harmonized Ontology of Mammalian Brain Anatomy (HOMBA) covering 2348 structures C_LIO_LIHOMBA-annotated 3D common coordinate frameworks (CCFs) of the basal ganglia across species C_LIO_LIHistologically informed 3D parcellation/atlas of 280 human subcortical structures indexed by HOMBA C_LIO_LIMapping and integration of structural, cellular and functional data with HOMBA and CCFs C_LI

Grapevine (Vitis vinifera) is one of the worlds most economically important fruit crops and a model species for perennial fruit tree genetics and genomics. The extensive genetic diversity found in cultivated and wild Vitis species provides a valuable resource for studies of domestication, adaptation, trait evolution, and breeding. This article presents a standardized whole-genome variant dataset comprising 547 grapevine accessions maintained in the INRAE Bordeaux grapevine germplasm collection, including 397 domesticated V. vinifera cultivars and 150 wild Vitis accessions. Whole-genome sequencing data were generated at a target sequencing depth of approximately 20x, and sequence variants were identified using a standardized Genome Analysis Toolkit (GATK) workflow against the reference genome PN40024v4 (40X). Variant discovery was carried out simultaneously across the complete sample set to ensure consistent genotype calling. All accessions were sequenced using the same technology and processed using the same reference genome, sequence alignment, variant-calling, and filtering workflow to produce a standardized variant dataset comprising ca. 9.1M SNPs and 0.77M INDELs. The resulting VCF files provide a harmonized genomic resource that can be readily reused for studies of grapevine genetics, germplasm characterization, population genomics, comparative genomics, genome-wide association studies, and the development and benchmarking of bioinformatic methods. SPECIFICATIONS TABLE O_TBL View this table: org.highwire.dtl.DTLVardef@490449org.highwire.dtl.DTLVardef@1b884fborg.highwire.dtl.DTLVardef@12280bforg.highwire.dtl.DTLVardef@32bcceorg.highwire.dtl.DTLVardef@1099221_HPS_FORMAT_FIGEXP M_TBL C_TBL VALUE OF THE DATAO_LIThis dataset provides whole-genome raw sequencing for 118 wild Vitis accessions originating from North America and Asia and a sequencing-derived variant data for these accessions and 429 Vitis vinifera cultivars and wild accessions from the INRAE Bordeaux germplasm collection, previously published by Dong et al. 2023[1]. The dataset captures genetic variation across a total of 547 grapevine accessions, including domesticated and wild grapevine germplasm using a common variant-calling pipeline, facilitating direct comparisons among accessions. C_LIO_LIThe inclusion of wild Vitis species together with cultivated grapevine accessions provides a resource for studies of grapevine diversity, domestication, phylogenetic relationships, and comparative genomics. The dataset enables the investigation of genetic variation across multiple Vitis species using a standardized set of genomic variants. C_LIO_LIThe variant call format (VCF) file can be reused for population genetics, phylogenetic analyses, genetic diversity assessments, introgression analyses, and the identification of genomic regions of interest. The dataset is compatible with widely used bioinformatics software and can be integrated with other publicly available grapevine genomic resources. C_LIO_LIThis dataset constitutes a genomic resource for grapevine breeding and conservation research. Researchers can use these data to identify genetic diversity in wild relatives, compare allelic variation between cultivated and wild germplasm, investigate candidate loci associated with traits of interest, and support the management and characterization of grapevine germplasm collections. C_LI

Arthropods represent the most species-rich animal group on the planet, yet they remain largely overlooked in formal conservation planning. New Mexico is a US state with exceptional biodiversity, including numerous endemic, rare, and threatened arthropod species. Recent attention to global insect declines has highlighted the need to document possible threats to and declines of New Mexico insects and other arthropods. All terrestrial arthropods in New Mexico lacked state-level regulatory protection until 2025, and baseline data on the taxonomy, ecology, geographic distribution and conservation needs for most New Mexico arthropod species remained absent. To address this gap, we developed the New Mexico Rare Arthropod Resource (NM-RARe), a publicly accessible regional database for arthropod species and subspecies of conservation concern in New Mexico. Drawing from multiple vetted sources, we compiled an initial working list of 7,018 taxa and conducted 261 preliminary IUCN Red List assessments and 185 NatureServe state rank assessments, covering 285 total taxa. Of those assessed by IUCN Red List standards, 52 were subspecies and 209 were species level assessments, 165 of which met the criteria for inclusion and were incorporated into the NM-RARe database (https://nmrare.org/). Ninety-three of the pollinating insect species from NM-RARe were then formally incorporated into New Mexico's 2025 State Wildlife Action Plan (SWAP), representing a significant conservation milestone. Beyond its immediate regional impact, NM-RARe presents a standardized, scalable methodology adaptable to any region or taxonomic group, offering a replicable framework for arthropod conservation assessment worldwide.

Public sequencing repositories contain large amounts of gut microbiome data that could support cross-study comparison, reproducibility analysis, and microbiome foundation model development. However, the extent to which these data are structured, harmonized, and reusable at archive scale remains unclear. Here, we characterized publicly available gut microbiome sequencing metadata from the NCBI Sequence Read Archive using Google BigQuery, focusing on human gut metagenome, mouse gut metagenome, and broadly annotated gut metagenome records. We evaluated temporal growth, sequencing depth, BioSample and BioProject structure, platform and instrument use, metadata completeness, host attribution, publication linkage, and research themes from linked literature. Public gut microbiome data increased substantially over time and were dominated by human-associated datasets and Illumina sequencing platforms. Core technical metadata fields were highly complete, but biological context needed for reuse, including host identity, phenotype, study design, and disease status, was often inconsistently encoded or required recovery from BioSample attributes and linked publications. In the generic "gut metagenome" cohort, host identity could be assigned for only 13.00% of BioSamples, highlighting the limitations of broad organism annotations for automated cohort construction. Publication linkage was also incomplete at the archive level, although usable text was recovered for most linked publications. Topic modeling of SRA-linked literature showed persistent emphasis on core gut microbiota composition and increasing representation of human cohort and infant microbiome studies. Overall, these findings show that public gut microbiome data are extensive and technically rich but not uniformly analysis ready. Improved metadata harmonization, publication linkage, and biological context recovery will be necessary to support reliable large-scale reuse and AI-ready microbiome data resources.

Over the last five years, over 13,000 tissue datasets with 200+ million cells from 20 consortia have been spatially registered into the Human Reference Atlas (HRA) common coordinate framework (CCF). The shared 3D spatial and semantic reference system enables exploration of datasets in the context of all other data across organs, assay types, and spatial scales. However, manual registration of individual samples remains resource intensive, posing feasibility challenges exacerbated by the proliferation of samples, assays, and atlasing efforts. This paper presents two approaches to scale up HRA construction: (1) projecting data across biomedical reference atlas systems and (2) using millitomes to bulk register tissue blocks into a reference organ. Both methods use the AutoMated Alignment and Projection (AMAP) pipeline to align 3D mesh models using point cloud registration. We demonstrate the evolving HRA-aligned atlas ecosystem for 6 models from the SPARC Program (heart), Gut Cell Atlas (large intestine), 500-subject consensus kidneys, and the Julich Brain Atlas. Additionally, we used AMAP to project 7 millitome models across 5 organs onto the HRA ecosystem, integrating 300+ tissue extraction sites. AMAP enables scalable tissue registration of data across atlas ecosystems enabling the construction of detailed reference maps of the human body.

Natural-sound research requires stimulus sets that combine acoustic standardization with detailed behavioural characterization. We present 1,377 two-second sounds representing 240 expert-defined source--action classes. We call this database "MaMa Sounds", as it resulted from the collaborative effort of two academic teams in Maastricht and Marseille. The sounds were manually curated, segmented, sampled at 16 kHz, and labelled with a noun identifying the source and a verb identifying the action. We release deidentified trial-level identification and familiarity data together with multiple per-sound norms (e.g., identification accuracy, confidence and agreement; familiarity), along with overall norms derived with principal component analysis. Noun, verb, and joint noun--verb norms are provided as direct means and medians with the number of contributing observations. This battery preserves process-specific information, while two principal-component scores provide compact overall behavioural-identifiability measures derived from response ease, semantic correspondence, agreement, and familiarity. The repository also contains deterministic response-cleaning code, participant and reference Word2Vec representations, and code reproducing the public sound-level tables. The resource supports stimulus selection, matching, and continuous modelling in auditory cognition and neuroscience.

High-content cell imaging approaches enable the systematic characterization of cellular function through the acquisition of multimodal information from large cohorts of live single cells. Yet, due to their scale and complexity, data acquired via such approaches are often challenging to meaningfully share across laboratories and effectively use for independent studies. Since its inception, the main purpose of TheCellVision.org repository has been to fill this gap, providing the research community with access to large-scale, multimodal single-cell datasets, in a structured, intuitive, and user-friendly way. Here, we report on the third major update of TheCellVision.org, which involves the expansion of the repository with the addition of data from two single-cell phenomics projects; the Intracellular Organization Dynamics project, which quantitatively maps changes in the morphology of 21 major subcellular structures in live yeast cells elicited by the systematic inhibition of essential genes, and the DUB Biology project, which describes changes in the concentration and localization of the budding yeast proteome in mutants of key deubiquitination enzymes (DUBs). With these additions, the repository now hosts six complementary high-content imaging projects which collectively explore the dynamics of intracellular organization and the proteome during changes in cell state and in response to environmental and genetic perturbations.

Khang Dan 18 (KD18) is an Oryza sativa L. subsp. indica rice cultivar widely cultivated in northern Vietnam and used as an experimental and breeding background in Vietnamese rice research. Although KD18 has previously been represented in low-depth population resequencing datasets, a contiguous and annotated cultivar-specific genome has not been available. Here, we report a chromosome-scale genome assembly of KD18 generated using Oxford Nanopore long-read and Illumina short-read sequencing. The 395.3-Mb assembly comprises 12 chromosome-scale pseudomolecules containing approximately 95% of the assembled sequence and 99.6% of the predicted protein-coding genes. The assembly showed 97.2% BUSCO completeness, an average Merqury quality value of 46 and a long terminal repeat assembly index of 13.21. A total of 56,546 protein-coding genes representing 71,237 transcripts were predicted, with 99% BUSCO and 98.68% OMArk completeness. These statistics are similar to those of other high-quality genome assemblies that were recently published for different Asian rice cultivars, therefore providing a cultivar-specific genomic resource for research involving KD18 and KD18-derived materials.

Understanding how the human brain processes dynamic audiovisual events requires datasets that combine naturalistic stimulation, high temporal resolution, and systematic experimental manipulation of sensory information. Here, we introduce AVMoments-EEG, the first large-scale, high-temporal-resolution human EEG dataset designed to characterize neural responses to naturalistic audiovisual events. Across 10 participants, we recorded 64-channel scalp EEG during free viewing of 3-second naturalistic video clips derived from the Audiovisual Moments in Time dataset. The stimulus set comprised 896 training videos (56 videos in each of 16 event types), each presented three times, and 64 held-out test videos (4 videos in each of 16 types), each presented 40 times in each of three conditions: (1) intact audiovisual input, (2) intact visual input with scrambled audio, and (3) intact auditory input with scrambled visual input. This design yielded 10,368 trials per participant and combines broad stimulus coverage with highly repeated test stimuli. AVMoments-EEG enables temporally resolved investigation of naturalistic event processing and modality-specific audiovisual contributions, while providing a benchmark for video-to-EEG encoding, EEG-based decoding, and brain-model alignment. The dataset offers a resource for studying dynamic event perception, multisensory processing, and the temporal correspondence between human neural responses and artificial neural network representations.

European white oaks (Quercus section Quercus) are ecologically and economically important forest trees characterized by extensive shared genetic variation and a history of interspecific gene flow. Genomic resources remain uneven across species, limiting comparative analyses and pangenome development. Here, we present haplotype-resolved chromosome-scale genome assemblies and genome annotations for four European white oak species: Quercus robur, Q. petraea, Q. pubescens, and Q. frainetto. The assemblies were generated from PacBio HiFi sequencing data and include both phased haplotypes for each species. Genome sizes range from 779 to 817 Mb and all assemblies are organized into 12 chromosome-scale pseudomolecules with high completeness and contiguity. We additionally provide species-specific repeat annotations, structurally and functionally annotated protein-coding gene sets, and complete organellar genomes. The dataset includes the first reference genomes for Q. pubescens and Q. frainetto, together with newly generated assemblies for Q. robur and Q. petraea produced using a consistent sequencing and analysis workflow. These resources provide a standardized framework for comparative genomics, pangenome construction, genome evolution studies, and investigations of adaptation and introgression across European white oaks.

Ensuring the availability and accessibility of research data is fundamental to advancing knowledge, as codified in the FAIR principles (Findable, Accessible, Interoperable, and Reusable). Accurate metadata documentation is indispensable for meeting these principles; however, entries in deposition databases often contain inadequate, repetitive, or incomplete descriptions. Much of this metadata is captured in free-text fields, motivating the need for scalable, repository-agnostic methods to quantify metadata richness. Here, we quantify free-text metadata richness across three repositories using Natural Language Processing (NLP) methods: BioDare2, an experimental circadian rhythm database; DataShare, a domain-agnostic University of Edinburgh database; and Image Data Resource (IDR), a public repository of biological image datasets from published studies. In general, repositories exhibit distinct distributions of word count and information density, consistent with differences in scope. Named-entity recognition and information-density metrics detected significant category-level differences in BioDare2 (species) and DataShare (communities), while identifying greater consistency in the more curated IDR. The most frequent entities reflected each repository's focus: circadian terminology in BioDare2, microscopy-related entities in IDR, and community-driven terms in DataShare. We develop a scalable framework utilising word counts, named-entity recognition, and entity-derived information density to assess metadata quality across repositories, offering a broadly applicable evaluation tool.

The cynomolgus macaque is a widely used primate model in neuroscience, yet existing brain atlas resources remain limited in resolution and lack a standardized framework for integrating structural, molecular, and connectivity data within a common reference space. Here we present CynoBrain, a high-resolution multi-modal brain atlas constructed from population-averaged MRI data at 9.4 Tesla, yielding a 75 micron isotropic template with significant improvement in resolution. A co-registered whole-brain autofluorescence template resolves cytoarchitectural boundaries not visible in MRI template. Three major parcellation schemes, D99 143-area, M132 105-area, and SARM are incorporated in CynoBrain space. Spatial transcriptomic based laminar definition, reconstructed from 2D sections into volumetric common space, provides a six-layer cortical segmentation spanning the whole cortex. As a demonstration of the integrative capability of this framework, 2D retrograde tracing data is reconstructed and mapped to the Cynobrain space, enabling volumetric compilation of 2D macaque brain mapping data across subjects. An online interactive platform was constructed to allow full access to Cynobrain, establishing it as a scalable, open platform for cross-modal data integration for primate neuroscience. Overall, CynoBrain is a unified framework for cross-modal data integration for macaque brain mapping with high-resolution, multiple atlases and transcriptomic based laminar definition.