We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems.We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida} ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels.We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

We address fast image stitching for large image collections while being robust to drift due to chaining transformations and minimal overlap between images. We focus on scientific applications where ground-truth accuracy is far more important than visual appearance or projection error, which can be misleading. For common large-scale image stitching use cases, transformations between images are often restricted to similarity or translation. When homography is used in these cases, the odds of being trapped in a poor local minimum and producing unnatural results increases. Thus, for transformations up to affine, we cast stitching as minimizing reprojection error globally using linear least-squares with a few, simple constraints. For homography, we observe that the global affine solution provides better initialization for bundle adjustment compared to an alternative that initializes with a homography-based scaffolding and at lower computational cost. We evaluate our methods on a very large translation dataset with limited overlap as well as four drone datasets. We show that our approach is better compared to alternative methods such as MGRAPH in terms of computational cost, scaling to large numbers of images, and robustness to drift. We also contribute ground-truth datasets for this endeavor.

Author(s): Sahneh, Faryad; Balk, Meghan A; Kisley, Marina; Chan, Chi-kwan; Fox, Mercury; Nord, Brian; Lyons, Eric; Swetnam, Tyson; Huppenkothen, Daniela; Sutherland, Will; Walls, Ramona L; Quinn, Daven P; Tarin, Tonantzin; LeBauer, David; Ribes, David; Birnie, Dunbar P; Lushbough, Carol; Carr, Eric; Nearing, Grey; Fischer, Jeremy; Tyle, Kevin; Carrasco, Luis; Lang, Meagan; Rose, Peter W; Rushforth, Richard R; Roy, Samapriya; Matheson, Thomas; Lee, Tina; Brown, C Titus; Teal, Tracy K; Papeș, Monica; Kobourov, Stephen; Merchant, Nirav | Editor(s): Schwartz, Russell

The Global Research Activity Map (GRAM) is an interactive web-based system for visualizing and analyzing worldwide scholarship activity as represented by research topics. The underlying data for GRAM is obtained from Google Scholar academic research profiles and is used to create a weighted topic graph. Nodes correspond to self-reported research topics and edges indicate co-occurring topics in the profiles. The GRAM system supports map-based interactive features, including semantic zooming, panning, and searching. Map overlays can be used to compare human resource investment, displayed as the relative number of active researchers in particular topic areas, as well scholarly output in terms of citations and normalized citation counts. Evaluation of the GRAM system, with the help of university research management stakeholders, reveals interesting patterns in research investment and output for universities across the world (USA, Europe, Asia) and for different types of universities. While some of these patterns are expected, others are surprising. Overall, GRAM can be a useful tool to visualize human resource investment and research productivity in comparison to peers at a local, regional and global scale. Such information is needed by university administrators to identify institutional strengths and weaknesses and to make strategic data-driven decisions.

As cloud resources become more available as an execution platform, the need to transition applications between HPC and the cloud becomes a necessity. However, because of the complex setup and system specific demands of these applications, transition is difficult and may not scale as desired. Jetstream is a NSF funded cloud service that is aiming to provide these services for users in a dynamical allocated nature. In this work we look at three key areas to focus on when transitioning between resources: providing a portable reproducible environment, scaling between local and remote resources, and using feedback to the user for informing configuration and runtime decisions. Building on the MAKER bioinformatic application, we have deployed WQ-MAKER on the Jetstream cloud platform, helping to annotate over 30 genomes and accelerating performance from days to hours and weeks to days.

Programming in the large allows composition of processes executing code written using programming in the small. Traditionally, systems supporting programming in the large have included interpreters of OS command languages, but today, with the emergence of collaborative “big data” science, these systems also include cyberinfrastructures, which allow computations to be carried out on remote machines in the “cloud”. The rationale for these systems, even the traditional command interpreters, is human-centric computing, as they are designed to support quick, interactive development and execution of process workflows. Some cyberinfrastructures extend this human-centricity by also providing manipulation of visualizations of these workflows. To further increase the human-centricity of these systems, we have started a new project on cyber training - instruction in the use of command languages and visual components of cyberinfrastructures. Our objective is to provide scalable remote awareness of trainees' progress and difficulties, as well as collaborative and automatic resolution of their difficulties. Our current plan is to provide awareness based on a subway workflow metaphor, allow a trainer to collaborate with multiple trainees using a single instance of a command interpreter, and combine research in process and interaction workflows to support automatic help. These research directions can be considered an application of the general principle of integrating programming in the small and large.

BACKGROUND: With the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed "PGen", an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way. RESULTS: We have developed both a Linux version in GitHub ( https://github.com/pegasus-isi/PGen-GenomicVariations-Workflow ) and a web-based implementation of the PGen workflow integrated within the Soybean Knowledge Base (SoyKB), ( http://soykb.org/Pegasus/index.php ). Using PGen, we identified 10,218,140 single-nucleotide polymorphisms (SNPs) and 1,398,982 indels from analysis of 106 soybean lines sequenced at 15X coverage. 297,245 non-synonymous SNPs and 3330 copy number variation (CNV) regions were identified from this analysis. SNPs identified using PGen from additional soybean resequencing projects adding to 500+ soybean germplasm lines in total have been integrated. These SNPs are being utilized for trait improvement using genotype to phenotype prediction approaches developed in-house. In order to browse and access NGS data easily, we have also developed an NGS resequencing data browser ( http://soykb.org/NGS_Resequence/NGS_index.php ) within SoyKB to provide easy access to SNP and downstream analysis results for soybean researchers. CONCLUSION: PGen workflow has been optimized for the most efficient analysis of soybean data using thorough testing and validation. This research serves as an example of best practices for development of genomics data analysis workflows by integrating remote HPC resources and efficient data management with ease of use for biological users. PGen workflow can also be easily customized for analysis of data in other species.

Summary We demonstrate a flexible genome‐wide association study platform built upon the iPlant Collaborative Cyber‐infrastructure. The platform supports big data management, sharing, and large‐scale study of both genotype and phenotype data on clusters. End users can add their own analysis tools and create customized analysis workflows through the graphical user interfaces in both iPlant Discovery Environment and BioExtract server. Copyright © 2014 John Wiley & Sons, Ltd.

We demonstrated how a flexible Genome Wide Association Study (GWAS) platform was built using the iPlant Collaborative Cyber-infrastructure. The platform is open for end users to add additional analysis tools. With the platform, customized GWAS workflows can be built in both iPlant Discovery Environment and BioExtract server.

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.