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NASA NTRSDate not supplied

Tracking Community Building in Open Science

Open Science is enabled by a vibrant community of researchers who regularly engage with the data, from its production to its organization, curation, archiving, dissemination, analysis, and publication. This presentation will examine community building in open science. The NASA Open Science Data Repository (OSDR) makes data available to the public following the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. OSDR takes open science further with the OS Analysis Working Groups (AWGs) that facilitate community development and promotion. The primary activity of each AWG is to establish and validate analytical processes to generate higher-order data from data housed in OSDR. There are a number of these groups on various topics, including the Animal AWG, Plant AWG, Microbial AWG, Multi-Omics AWG, AI/ML AWG, and the Ames Life Sciences Data Archive (ALSDA) AWG. The international volunteers participating in these AWGs come from academia, citizen science initiatives, industry, and government. They include researchers, principal investigators, professors, trained hobbyists, and students from various domains and disciplines. Anyone may request to join the AWGs, and membership requests are vetted monthly by the group organizers before granting admission. Core to membership is demonstrated expertise through records of training, integrity, work in the professed domain(s), and good community standing. Regular virtual meetings are held for each AWG, with a varying cadence depending on the group's needs and goals. AWG communities share their expertise in research including cutting edge tools, software, frameworks, data formats, and libraries accelerating research collectively. This collaborative approach helps community members cross technology gaps and identify emerging challenges. These diverse communities encompass a wide range of individuals hailing from various sectors within the Science Mission Directorate and beyond. They serve as a means to promote and enhance transparency, accessibility, and inclusion. An annual AWG Symposium brings contributors together in person. Participation in AWGs can be synchronous or asynchronous, with some groups performing most of their work in off hours. Participants gain valuable skills and connections that allow them to add value to their communities and new organizations that they join, resulting in an expanded return on investment for the space life science community. Open science is increasingly a federal mandate and initiatives like NASA's Transform to Open Science and instruments like the Decadal Survey of Biological and Physical Sciences in Space demonstrate the need to carefully consider best practices in this domain. Here, we present greater detail about the makeup and participation metrics of the various AWGs affiliated with OSDR and details of successful peer-reviewed publication campaigns.

Christina M Johnson↗
NASA NTRSDate not supplied

AI Curation Methods for NASA Scientific Data

The NASA Open Science Data Repository (OSDR) serves as a central hub for sharing and accessing NASA's vast collection of scientific data, supporting researchers across diverse fields. To enhance the efficiency, accuracy, and accessibility of this data, we are leveraging advanced artificial intelligence (AI) techniques as part of the AI for Curation project. By integrating large language models (LLMs) into our data curation workflow, we aim to streamline the entire process—from data submission to user interaction. This initiative focuses on improving key areas, including data ingestion, curation, and user engagement with curated datasets, impacting multiple domains and a wide user base. First, we are developing tools that can automatically parse data in various formats, using LLMs to convert unstructured data into structured, standardized formats. This reduces the manual effort required for curation, allowing curators to focus on more critical scientific analyses. Additionally, AI and machine learning (ML) models are being implemented to automate data validation and verification, ensuring the highest standards of data quality and reliability. Finally, we are creating a conversational AI agent to interact with the curated scientific studies in OSDR, helping users easily navigate the repository and access relevant data. By enhancing data discoverability and accessibility, these advancements will foster new research opportunities and promote the principles of open science.

Walter Alvarado↗
NASA NTRSDate not supplied

Enabling Open and Interoperable Science: Multi-Omics Data Processing Platform with NASA GeneLab Standardized Bioinformatics Workflows for Space and Earth Research

Multi-omics biological data continues to be generated at an astounding pace. Genomics, transcriptomics, metabolomics, and proteomics, or collectively known as multi-omics data, are used to assess biological functions, and provide invaluable insights into human, animal, plant, and environmental health both on Earth and in Space. Despite the abundance of these valuable data, the need for bioinformatics expertise, particularly as it relates to the niche filed of space biology, and a lack of accessible resources for processing these data limit their usefulness in deriving biological insights. The NASA Open Science Data Repository (OSDR) provides access to omics data from various spaceflight and analog studies. To enhance the accessibility and reusability of these data, GeneLab (part of OSDR) designs and implements standardized, community-driven, open-source bioinformatics workflows to transform raw omics data into standardized processed data. Currently, GeneLab-processed data from hundreds of space studies have been reused for meta-analyses. This has led to new insights and scientific publications that extend beyond the initial research, thereby enriching our understanding of molecular-scale biological responses to the space environment. To make these bioinformatics workflows open and accessible, GeneLab teamed up with DOE-funded initiatives, including the National Microbiome Data Collaborative (NMDC), to create the NASA EDGE [Empowering the Development of Genomics Expertise] Bioinformatics web-based platform. NASA EDGE utilizes shared compute resources to run the GeneLab standardized bioinformatics workflows, which eliminates the need for researchers to have their own high performance computing cluster. The web-based platform makes complicated biological analyses incredibly easy to perform, thus expanding the reach of these analyses to bioinformatics novices, students, and even citizen scientists enabling them to contribute to scientific discoveries and progress. The authors will demonstrate how the NASA EDGE platform can be used to process microbial omics data hosted on OSDR as well as user-generated omics datasets using GeneLab’s standard workflows.

Amanda M. Saravia-Butler↗
NASA NTRSDate not supplied

Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach

Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.

Jonathan Oribello↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗
NASA NTRSDate not supplied

HRP Data Management Plan

The purpose of Human Research Program Data Management Plan (DMP) is to define the processes and activities required for the overall management of the research data collected and managed by HRP throughout their life cycle. New updates to the Data Management Plan in 2023 include 1. CAPABILITIES AND SERVICES Data Repositories. Principal Investigators (PIs) funded by HRP may be asked to submit data to one of several NASA data repositories. HRP archives data in the NASA Life Sciences Portal (NLSP) that it considers to be unique and high value. This includes data from human subjects in space flight (ISS and commercial flights) and ground analogs to spaceflight; spaceflight tech demos involving humans; human omics data including the microbiome; parabolic flight studies; and the NASA Space Radiation Laboratory (NSRL). The Open Science Data Repository (OSDR) includes The Ames Life Sciences Data Archive (ALSDA), used to archive non-human biological data (e.g., animal) generated by the Human Research program, and GeneLab, available to HRP PIs to archive non-human omics data. Catalog for search and retrieval. A catalog of non-human HRP life science experiments, with all associated descriptions (mission, payload, hardware, and personnel related information), and biospecimens is provided on the NLSP public web site for search and retrieval. 2. IRB ROLE IN RETURN OF INDIVIDUAL RESEARCH RESULTS The NASA IRB manages the process for incidental findings and for returning results to subjects for studies for which NASA IRB is the IRB of record. Omics data, especially genomics data, may generate information significant to the health of or risk to a research subject. These data potentially hold the keys to understand lifetime risks of chronic diseases, such as cancer, as well as risks associated with exposures common in space flight. 3. UPDATE OF TERMS – IDENTIFIABLE AND ATTRIBUTABLE DATA HRP now follows Federal and NASA policy by using “identifiable” instead of “attributable” for Personally Identifiable Information (PII). 4. POLICY ABOUT INTERNAL NON-RESEARCH USE OF DATA The HRP Chief Scientist grants access to data from HRP-funded research for non-research internal use that includes program management, customer facilitation, strategic planning, and risk research planning. Typical HRP personnel granted access to HRP research data for internal use include the Element Scientist, Subject Matter Experts (SME), and Data/bioinformatics Scientists. If data accessed for Internal Use is provided to an intramural or extramural scientist for hypothesis driven research, all Federal and NASA regulations (e.g., IRB review) regarding human subject research apply.

Data Management Plan↗
NASA NTRSDate not supplied

Optimizing a Small RNAseq Analysis Pipeline for NASA GeneLab Using Open-Source Tools and Libraries

Small RNA sequencing (small RNAseq) is a powerful tool for studying the regulation of gene expression in various organisms. Small RNAseq has been leveraged in space biology research to study how expression of small RNAs, e.g. micro RNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), change upon exposure to the space environment. NASA GeneLab currently hosts small RNAseq raw data derived from space-relevant experiments on the Open Science Data Repository (OSDR). To maximize the accessibility of these data to the scientific community, in addition to hosting raw data, which is only interpretable by bioinformaticians, GeneLab plans to process all small RNAseq datasets and make those processed data available to the scientific community via the OSDR. In this study, we present the development of the GeneLab standardized pipeline for processing small RNAseq datasets. Using human, plant, and synthetic small RNAseq datasets, we interrogate various open-source software and publicly available databases to evaluate their accuracy and reproducibility in each step of the pipeline. For quality control and adapter detection and trimming, we evaluated TrimGalore!, FASTX, SeqKit, and DNApi methods to optimize alignment to reference genomes. We compared BWA, Bowtie, and Bowtie2 to determine the optimal alignment tool. For each alignment tool we also assessed various reference databases, including Ensembl reference genomes and different types of small RNA reference databases, including genome, hairpin, and miRNA references from the miRbase and MirGeneDB databases. To quantify the aligned data, we compared SAMtools, HTSeq, and RSEM for counting alignment events from each alignment tool used. Finally, we evaluated various tools, including DESeq2 and EdgeR, for data normalization and subsequent differential expression analysis. We will present the results from our comparative analyses for each pipeline step and propose a consensus pipeline for processing small RNAseq data derived from various organisms exposed to the space environment.

SmallRNAseq, NASA GeneLab, quality control, adapte↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Analysis Toolkit for Space Radiation Measurements Relevant to Space Radiation Biology

RadLab, a new addition to the NASA Open Science Data Repository (OSDR), is a public platform for space radiation data relevant to human space exploration. RadLab consists of a database, a submission portal, and user-friendly visualization and data analysis tools, including a graphical user interface (GUI) and an application programming interface (API). Investigators from ISS partners including Germany, Italy, Canada, Hungary, the Czech Republic, Russia, Japan have committed to providing data from their instruments. RadLab will also include data from other spacecraft in LEO: the Space Shuttle, the Mir space station, biosatellites; and beyond LEO: the lunar and the Martian surface, the heliocentric orbit at 1 AU, Mars orbit, and Earth-Mars space. Once fully operational, RadLab will provide open, centralized access to space radiation physics data relevant to human space exploration; a platform for submission of data by agencies and research institutions responsible for radiation detectors deployed in space; analysis tools to facilitate detector and dataset intercomparison to better understand space habitat radiation environments; capabilities for space biology investigators to determine the radiation environment to which samples were exposed. A RadLab Working Group (RLWG) has been formed, modeled on the GeneLab Analysis Working Groups and comprised of data contributors and users. RLWG tasks include identifying data sources, normalizing data from diverse detectors, expanding the analysis toolkit and, perhaps most importantly, sharing ideas for research exploiting capabilities of RadLab. We will provide an overview of RadLab data and capabilities and discuss examples of its potential as a resource for open science.

radiation↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from spaceflight biological and health studies are increasingly being made findable, accessible, interoperable, and reusable for the scientific public. These data, as well as space science-relevant biospecimens, are available through NASA’s Open Science Data Repository (OSDR), which is the new umbrella grouping of NASA GeneLab, the Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection (NBISC). The quality of data is underpinned by datasets having rich metadata (determined through Analysis Working Group members), processing pipelines to enable data reuse standards, and ontologies specifying terminology semantics (e.g., the Radiation Biology Ontology).

space biology↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, to foster collaborations with equity. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within the Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate with community members and set new standards for space-relevant data and metadata. The AWGs welcome any ASGSR members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are encouraged to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions rely on plants and crops for crew and ecosystem health. Access to space plant data enables scientists to gain a deeper understanding of biological responses to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, and altered photoperiods. Open Science is the practice of making research available to all, while respecting diverse cultures, fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) mission designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) developed by NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. OSDR started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository (GeneLab), providing detailed metadata on investigation, sample, and assay levels. Today, GeneLab hosts 62 plant datasets which have led to 5 published peer-reviewed meta-analysis publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs are welcoming any ASPB members interested in providing plant expertise for space biology. The addition of ALSDA to OSDR is also expanding analysis capability beyond ‘omics. Now is the time to get involved as a Subject Matter Expert as we establish the framework for modern plant data archiving through the AWGs. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, and fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs welcome any ASPB members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRSDate not supplied

GL4U: Using Space Biology Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant multi-omics data via the Open Science Data Repository (OSDR) that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct (training students) and indirect (training educators) approaches. The GL4U pilot programs were conducted in June 2021 (direct training) and 2022 (indirect training). During the pilots, students and educators at Historically Black Colleges and Universities (HBCUs) and Minority Serving Institutions (MSIs) participated in a week-long (direct training) or two-week-long (indirect training) bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze space biology RNA sequencing data from OSDR. During the educator pilot, participants received materials, training, and the necessary compute resources to enable them to run the bootcamp at their home institutions, thereby extending the reach of this initiative. In July 2023, GeneLab is partnering with JPL to expand GL4U to include amplicon sequencing (Amp-Seq) analysis training. During the GL4U Amp-Seq bootcamp, student and educator participants will receive training on how to analyze and interpret Amp-Seq data using the NASA GeneLab data processing pipeline. All bootcamp material, including instructions for requesting compute resources, will be made publicly available on GitHub for educators to teach the GL4U content in subsequent semesters. GL4U provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. We present results from pre- and post-training surveys completed by all participants of the Amp-Seq bootcamp.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets

RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Lauren M. Sanders↗