Online Repository Research Articles

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data.

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

The (scarce and circumscribed) culinary content in food-based dietary guidelines around the world: 1991-2021.

To evaluate the culinary content of key messages contained in food-based dietary guidelines (FBDG) available at the global online repository of the FAO of the UN. Document analysis was conducted in August 2021 with data extraction of key messages explicitly related to cooking present in FBDG. Data were analysed inductively using thematic analysis. The FAO's global repository of FBDG. Not applicable. Just over half (n 39; 53·4 %) of the seventy-three FBDG analysed included at least one key message about cooking. The Latin American and Caribbean FBDG presented the greatest amount and variety of content about cooking in the key messages, whereas the Near East and North America placed less emphasis on cooking. We identified three themes: (i) healthy food preparation (n 35; 61·4 % of the fifty-seven culinary key messages identified); (ii) food hygiene (n 14; 24·6 %) and (iii) the promotion of culinary practices (n 7; 12·3 %). Albania's key message covered two themes (food hygiene and healthy food preparation) (n 1; 1·8 %). FBDG are official documents that express recommendations for a healthy diet. As most of these recommendations throughout the world include foods that must be cooked prior to consumption, culinary content should gain more visibility and be presented more broadly in these official documents.

The digital archive as storyteller

Theatre in South Africa is deeply entrenched in the art of storytelling. We live, speak and move our stories in our embodied performance practices. While performance and its development are by their nature ephemeral, how can they be captured as data? And how can the data develop digital outputs that eventually tell their own stories? This paper will outline the way in which the rehearsal process of Antigone (not quite/quiet), the first of three practice-based research productions of the ReTAGS project, was documented and curated into an online repository. As data stewards, we will share the journey undertaken to migrate the ephemeral process of theatre devising into interactive and searchable data on UCT’s digital collections platform, Ibali. We chronicle the processes and jobs undertaken to capture, catalogue, document, enhance, curate and showcase the material. We explore how an online repository can be constructed not only in order to share the multiple stories present in the rehearsal process, but to encourage further engagements to advance a rigorous living archive. Through this we aim to give precedence to the way artist-researchers can use performance-based methods as reputable means by which to produce data, in order to drive academic research.

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Digitizing Wildlife: The Case of a Reptile 3-D Virtual Museum.

In this article, we design and develop a 3-D virtual museum with holistic metadata documentation and a variety of reptile behaviors and movements. First, we reconstruct the reptile's mesh in high resolution, and then create its rigged/skinned digital counterpart. We acquire the movement of two subjects using an optical motion capture system, accelerometers, and RGB-vision cameras; these movements are then segmented and annotated to various behaviors. The 3-D environment, virtual reality (VR), and augmented reality (AR) functionalities of our online repository serve as tools for interactively educating the public on animals, which are difficult to observe and study in their natural environment. It also reveals important information regarding animals' intangible characteristics (e.g., behavior), that is critical for the preservation of wildlife. Our museum is publicly accessible, enabling motion data reusability, and facilitating learning applications through gamification. We conducted a user study that confirms the naturalness and realism of our reptiles, along with the ease of use and usefulness of our museum.

Growth at risk: Methodology and applications in an open-source platform

This article describes the construction of an open-source growth-at-risk (GaR) model. The model provides a flexible analytical tool for policymakers and researchers aiming to use the GaR approach to characterize the probability density of GDP growth conditional on domestic and international macrofinancial variables. This article, together with its related online repository, aims to foster an understanding of macrofinancial risk factors both in advanced and emerging economies.

Blockchain technology applications in the health domain: a multivocal literature review.

Blockchain technology has been changing the nature of several businesses, from supply chain management to electronic record management systems and copyright management to healthcare applications. It provides a resilient and secure platform for modifications due to its distributed and shared nature and cryptographic functions. Each new technology, however, comes with its challenges alongside its opportunities. Previously, we performed a systematic literature review (SLR) to explore how blockchain technology potentially benefits health domain applications. The previous SLR included 27 formal literature papers from 2016 to 2020. Noticing that blockchain technology is rapidly growing, we extended the previous SLR with a multivocal literature review (MLR) approach to present the state of the art in this study. We focused on understanding to what degree blockchain could answer the challenges inherited in the health domain and whether blockchain technology may bring new challenges to health applications. The MLR consists of 78 sources of formal literature and 23 sources of gray literature from 2016 to 2021. As a result of this study, we specified 17 health domain challenges that can be categorized into four groups: (i) meeting regulatory requirements and public health surveillance, (ii) ensuring security and privacy, (iii) ensuring interoperability, and (iv) preventing waste of resources. The analysis shows that blockchain makes significant contributions to the solutions of these challenges. However, 10 new pitfalls come with adopting the technology in the health domain: the inability to delete sensitive data once it is added to a chain, limited ability to keep large-scale data in a blockchain, and performance issues. The data we extracted during the MLR is available in a publicly accessible online repository.

Genetic epidemiology of human neutrophil antigen variants suggests significant global variability.

Human neutrophil antigens possess significant clinical implications especially in the fields of transfusion and transplantation medicine. Efforts to estimate the prevalence of genetic variations underpinning the antigenic expression are emerging. However, there lacks a precise capture of the global frequency profiles. Our article emphasizes the potential utility of maintaining an organized online repository of evidence on neutrophil antigen-associated genetic variants from published literature and reports. This, in our opinion, is an emerging area and would significantly benefit from the awareness and understanding of population-level diversities.

Evidence-Based Interventions for Reducing Breast Cancer Disparities: What Works and Where the Gaps Are?

Simple SummaryBreast cancer is the most common cancer in women in the United States and most countries around the world. There are many breast cancer risk factors that are amenable to intervention. This includes long-established risk factors (physical activity, obesity, alcohol consumption, breastfeeding), as well as emerging risk factors (tobacco smoke in early life, environmental chemicals). To inform future prevention, we inventoried existing evidence-based cancer control programs (EBCCP) in the National Cancer Institute’s online repository. We found that there are no existing EBCCPs for alcohol, breastfeeding, or environmental chemicals. While there are EBCCPs for physical activity, obesity, and early life tobacco control, only three programs were identified as high-quality, multilevel programs that were developed for populations that face breast cancer disparities. There thus remains a need for evidence-based interventions that can reduce breast cancer disparities.The National Cancer Institute (NCI) has established an online repository of evidence-based cancer control programs (EBCCP) and increasingly calls for the usage of these EBCCPs to reduce the cancer burden. To inventory existing EBCCPs and identify remaining gaps, we summarized NCI’s EBCCPs relevant to reducing breast cancer risk with an eye towards interventions that address multiple levels of influence in populations facing breast cancer disparities. For each program, the NCI EBCCP repository provides the following expert panel determined summary metrics: (a) program ratings (1–5 scale, 5 best) of research integrity, intervention impact, and dissemination capability, and (b) RE-AIM framework assessment (0–100%) of program reach, effectiveness, adoption, and implementation. We quantified the number of EBCCPs that met the quality criteria of receiving a score of ≥3 for research integrity, intervention impact, and dissemination capability, and receiving a score of ≥50% for available RE-AIM reach, effectiveness, adoption, and implementation. For breast cancer risk reduction, we assessed the presence and quality of EBCCPs related to physical activity (PA), obesity, alcohol, tobacco control in early life, breastfeeding, and environmental chemical exposures. Our review revealed several major gaps in EBCCPs for reducing the breast cancer burden: (1) there are no EBCCPs for key breast cancer risk factors including alcohol, breastfeeding, and environmental chemical exposures; (2) among the EBCPPs that exist for PA, obesity, and tobacco control in early life, only a small fraction (24%, 17% and 31%, respectively) met all the quality criteria (≥3 EBCCP scores and ≥50% RE-AIM scores) and; (3) of those that met the quality criteria, only two PA interventions, one obesity, and no tobacco control interventions addressed multiple levels of influence and were developed in populations facing breast cancer disparities. Thus, developing, evaluating, and disseminating interventions to address important risk factors and reduce breast cancer disparities are needed.

Promoting computationally reproducible research in applied linguistics: Recommended practices and considerations

Reproducible research is an important topic that has been discussed in many fields, including applied linguistics. It helps researchers to verify findings through attempts to re-create the numeric results reported in the original studies. Researchers can then evaluate the extent to which the analysis code is accurate, the statistical values are correctly reported, and errors in these aspects of the study influence the findings. Building on and extending recommended open science research practices, we describe and demonstrate how reproducible research can be further promoted and practiced from three methodological perspectives. First, we describe advantages of sharing supplementary information in online repositories, with a particular focus on IRIS (Instrument for Research Into Second Language Learning and Teaching) and OSF (Open Science Framework). Second, we consider ways to present such information in a reproducible manner by encouraging the use of R and R Markdown, a container, and a online platform. Finally, we focus on using simulated data created from the original data, which can be particularly useful when original data cannot be shared due to various reasons. Through these recommendations and considerations, we intend to enable readers to better understand how they can apply such practices to their own research context and eventually how these practices can advance the broader applied linguistics community.

A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions.

The a priori calculation of statistical power has become common practice in behavioral and social sciences to calculate the necessary sample size for detecting an expected effect size with a certain probability (i.e., power). In multi-factorial repeated measures ANOVA, these calculations can sometimes be cumbersome, especially for higher-order interactions. For designs that only involve factors with two levels each, the paired t test can be used for power calculations, but some pitfalls need to be avoided. In this tutorial, we provide practical advice on how to express main and interaction effects in repeated measures ANOVA as single difference variables. In particular, we demonstrate how to calculate the effect size Cohen's d of this difference variable either based on means, variances, and covariances of conditions or by transforming [Formula: see text] or [Formula: see text] from the ANOVA framework into d. With the effect size correctly specified, we then show how to use the t test for sample size considerations by means of an empirical example. The relevant R code is provided in an online repository for all example calculations covered in this article.

Bootstrapping a Biodiversity Knowledge Graph

The "biodiversity knowledge graph" is a nice metaphor for connecting biodiversity data sources, but can we actually build it? Do we have sufficient linked data available? Given that a knowledge graph is an aggregation of data from multiple sources, how do we give those sources credit for that data, and how do we handle changes to that data? Given that the classic interface to a knowledge graph is an intimidatingly empty SPARQL query box, how do we make the knowledge within a graph more accessible? This talk discusses an attempt to build a knowledge graph with an eye on how to maintain the graph in the future. It adopts a model similar to Global Biodiversity Information Facility (GBIF) and CheckListBank where individual data providers make datasets available as independently citable units with Digital Object Identifiers (DOIs). Each dataset comprises linked data in the form of N-triples. To create a knowledge graph we simply download one or more such datasets and add them to a triple store. Each data source is assigned to its own named graph, such that we have provenance for each dataset, and we can update any dataset independently. Furthermore, anyone can build their own knowledge graph by mixing and matching the set of data (people, publications, taxa, etc.) most appropriate to their interests. To bootstrap this approach, exemplar datasets are created based on data harvested from ORCID, Zenodo, and taxonomic name databases. Each demonstration dataset could be replaced in the future by data published directly by those providers. In some cases there are sufficient shared identifiers (such as DOIs and ORCIDs) to form a graph, but taxonomic data typically forms isolated islands. To help the knowledge graph coalesce we need "glue" in the form of datasets that link pairs of different identifiers, such as Life Science Identifiers (LSIDs) for names to DOIs for publications. With the addition of those cross links we can start to generate bibliographies for taxa, discover communities of taxonomic expertise, and more. This model of building a knowledge graph also opens opportunities for smaller, focussed datasets to be added to the graph using the same approach (as set of N-triples archived in an online repository). In order to be useful, a knowledge graph needs to be easy to query and visualise. Simply providing a SPARQL endpoint is unlikely to be enough. As part of this project, I developed a GraphQL interface to the knowledge graph to provide a set of standard queries that can support a simple web interface to the graph. This provides a way to explore the graph as it is being developed, which in turn can highlight gaps in connectivity and coverage that need to be addressed.

Building an Australian Reference Genome Atlas

Currently, genomics data for living species are stored in public and private repositories online. These repositories remain largely disconnected and only partially findable. The Australian Reference Genome Atlas (ARGA) Project is solving the problem of genomics data obscurity by creating an online platform where life sciences researchers can comprehensively and confidently search for data for taxa relevant to Australian research. At its most basic, ARGA is a tool for aggregating and indexing publicly available genomics (and genetics) data. We aim to improve the experience of discovering and accessing this data by building search functionality, based on features such as phenotypic traits and predicted and observed species distributions, and supporting data packaging and transfer to analysis environments. ARGA will index GenBank (National Institutes of Health (NIH), USA), the European Nucleotide Archive (EMBL-ENA), the database of Bioplatforms Australia, and selected DNA repositories in Australian faunal collections and herbaria. We will integrate these records with the occurrence records and taxonomic framework of the Atlas of Living Australia (ALA) to enrich the data and make it searchable using taxonomy, location, ecological characteristics and selected phenotypic data. The chief aims and outputs for the project are to: create a system to enable contextual metadata about a species to be used as a pointer to a variety of genomic data associated with that species; add functionality to that system to enable additional contextual information groupings, and community curation of these created groupings; create a user-facing web-accessible interface for the system; and devise a mechanism that allows the researchers searching the multiple genomic repositories, via ARGA, to select files for subsequent analysis and export them to other cloud-based analysis infrastructure. create a system to enable contextual metadata about a species to be used as a pointer to a variety of genomic data associated with that species; add functionality to that system to enable additional contextual information groupings, and community curation of these created groupings; create a user-facing web-accessible interface for the system; and devise a mechanism that allows the researchers searching the multiple genomic repositories, via ARGA, to select files for subsequent analysis and export them to other cloud-based analysis infrastructure. Our approach to ARGA incorporates: ingesting species metadata from multiple sequence repositories into a consistent data format using Darwin Core Archive (DwC-A); processing metadata using the Pipelines system developed by the Global Biodiversity Information Facility (GBIF), and as implemented in the ALA and other Living Atlases. indexing metadata using a Solr search engine; and providing a front-end web interface for users to find, select and export sequence files to a number of cloud-based analysis platforms. ingesting species metadata from multiple sequence repositories into a consistent data format using Darwin Core Archive (DwC-A); processing metadata using the Pipelines system developed by the Global Biodiversity Information Facility (GBIF), and as implemented in the ALA and other Living Atlases. indexing metadata using a Solr search engine; and providing a front-end web interface for users to find, select and export sequence files to a number of cloud-based analysis platforms. Here we will present an overview of the ARGA infrastructure and demonstrate an early prototype of the platform. We will show how ARGA can be used to interrogate DNA sequence records for taxa relevant to Australian research questions, realising a vision where genomics-based solutions to biological questions in conservation, ecology, agriculture and biosecurity can be manifested.

Inner crust equations of state for CompOSE

In this paper, we present some relativistic mean-field inner crust equations of state that have recently been uploaded in the CompOSE online repository. These equations of state fulfill experimental and microscopic constraints, and are also able to reproduce two solar-mass stars. We integrate the TOV equations to obtain the mass-radius relation, and we also calculate the tidal deformability, compactness, and effective tidal deformability to compare with the latest astrophysical data from NICER and LIGO and Virgo.

A virtual reality classroom to teach and explore crystal solid state structures

We present an educational application of virtual reality that we created to help students gain an in-depth understanding of the internal structure of crystals and related key concepts. Teachers can use it to give lectures to small groups (10-15) of students in a shared virtual environment, both remotely (with teacher and students in different locations) and locally (while sharing the same physical space). Lectures can be recorded, stored in an online repository, and shared with students who can either review a recorded lecture in the same virtual environment or can use the application for self-studying by exploring a large collection of available crystal structures. We validated our application with human subjects receiving positive feedback.

The use of the phrase "data not shown" in dental research.

The use of phrases such as "data/results not shown" is deemed an obscure way to represent scientific findings. Our aim was to investigate how frequently papers published in dental journals use the phrases and what kind of results the authors referred to with these phrases in 2021. We searched the Europe PubMed Central (PMC) database for open-access articles available from studies published in PubMed-indexed dental journals until December 31st, 2021. We searched for "data/results not shown" phrases from the full texts and then calculated the proportion of articles with the phrases in all the available articles. From studies published in 2021, we evaluated whether the phrases referred to confirmatory results, negative results, peripheral results, sensitivity analysis results, future results, or other/unclear results. Journal- and publisher-related differences in publishing studies with the phrases in 2021 were tested with Fisher's exact test using the R v4.1.1 software. The percentage of studies with the relevant phrases from the total number of studies in the database decreased from 13% to 3% between 2010 and 2020. In 2021, out of 2,434 studies published in 73 different journals by eight publishers, 67 (2.8%) used the phrases. Potential journal- and publisher-related differences in publishing studies with the phrases were detected in 2021 (p = 0.001 and p = 0.005, respectively). Most commonly, the phrases referred to negative (n = 16, 24%), peripheral (n = 22, 33%) or confirmatory (n = 11, 16%) results. The significance of unpublished results to which the phrases referred considerably varied across studies. Over the last decade, there has been a marked decrease in the use of the phrases "data/results not shown" in dental journals. However, the phrases were still notably in use in dental studies in 2021, despite the good availability of accessible free online supplements and repositories.

CRITTERBASE, a science-driven data warehouse for marine biota

Data on marine biota exist in many formats and sources, such as published literature, data repositories, and unpublished material. Due to this heterogeneity, information is difficult to find, access and combine, severely impeding its reuse for further scientific analysis and its long-term availability for future generations. To address this challenge, we present CRITTERBASE, a publicly accessible data warehouse and interactive portal that currently hosts quality-controlled and taxonomically standardized presence/absence, abundance, and biomass data for 18,644 samples and 3,664 benthic taxa (2,824 of which at species level). These samples were collected by grabs, underwater imaging or trawls in Arctic, North Sea and Antarctic regions between the years 1800 and 2014. Data were collated from literature, unpublished data, own research and online repositories. All metadata and links to primary sources are included. We envision CRITTERBASE becoming a valuable and continuously expanding tool for a wide range of usages, such as studies of spatio-temporal biodiversity patterns, impacts and risks of climate change or the evidence-based design of marine protection policies.

PhycoBank: Repository for algal novelties

The International Code of Nomenclature (ICN) for algae, fungi, and plants calls for indexing of names in nomenclatural repositories (Turland 2018, Art. 42). Scientific names, new combinations, validations, and typifications of algae are novelties tracked by PhycoBank, the registration system for algae. PhycoBank was established and institutionalized at the Botanic Garden Berlin as the repository for nomenclatural acts concerning algae. Since June 2018, PhycoBank staff have been operating the registration system permanently. All data entered into the system undergo a curatorial process to assure a high level of data quality. PhycoBank’s three main components comprise a user-friendly data entry web application available for all registered submitters (self-registration allowed) and curators, a public data access portal, and a search engine that integrates most of the larger online repositories for algae names. The latter is not only a prerequisite for reliable data curation during the registration process but also a valuable, publicly available online tool for algae names. A fourth component still under development is handling the tight integration of the registration system with the workflow of digital publishers. The resulting data are expected to be of high quality and to have been intensively checked against existing nomenclatural acts worldwide. A crucial requirement for the data entry application is thus an intensive support for data validation and curation. Besides the search index giving access to a huge number of existing external names, the data entry application offers a set of tools for quality assurance. It supports and requires the creation of mostly fully atomized data that strictly follow the rules of the ICN. Additionally, completeness for core data is mandatory. Both, atomization and completeness are a precondition to achieve the required uniqueness in the dataset. Completeness also implies that new combinations can only be registered after or together with their original name. The same applies to bi- or trinomials and the respective uni- and binomials they are built on. The publicly available data portal allows accessing published registrations by searching for scientific names, nomenclatural authors, higher ranks and for each part of a bibliographic reference (such as bibliographic author, journal, title, year) stored in PhycoBank. Being a name centric application PhycoBank is neutral with respect to taxonomic opinions. Therefore, only nomenclatural synonymies (basionym or replaced synonym relationships) are stored in the system and names are not attached to a unique classification. Instead, to facilitate search via higher ranks as required by users, classification information is stored as a directed graph of higher taxa with registered names linking to this graph. By this, searches can be performed on multiple classifications simultaneously and can return all possible matches. PhycoBank uses http-based persistent identifiers (e.g. http://phycobank.org/102170), which makes them resolvable and actionable. These identifiers link to nomenclatural acts only and not to the information in the act. That is, a PhycoBank identifier should not be used to refer to a scientific name even if this name was established in the nomenclatural act. PhycoBank makes use of the application stack offered by the EDIT Platform for Cybertaxonomy (Kohlbecker 2017). However, as registration workflows essentially differ from other taxonomic workflows, a completely new user interface for editing has been developed, which provides the users with an intuitive and fluent user experience despite the high complexity of the data. The Common Data Model (Müller 2017) is the core data model for the Platform and already covered the vast majority of data types and fields required by the registration in terms of completeness and degree of atomization. For PhycoBank, it only needed to be extended by a single data type representing the registration/nomenclatural act itself. As of July 2022, PhycoBank includes 4,332 registrations, of which 4,202 are name novelties (1,523 in preparation or ready, 239 under curation, 2,407 published, and 33 rejected); 130 registrations refer to lectotypes or epitypes of existing names. PhycoBank will apply for recognition as a repository in 2022. This is a prerequisite for a proposal to make registration of nomenclatural acts for algae mandatory. This is possible before, at or after the 20th International Botanical Congress 2024.

PyFIT3D and pyPipe3D — The new version of the integral field spectroscopy data analysis pipeline

We present a new version of the FIT3D and Pipe3D codes, two packages to derive properties of the stellar populations and the ionized emission lines from optical spectroscopy and integral field spectroscopy data respectively. The new codes have been fully transcribed to Python from the original Perl and C versions, modifying the algorithms when needed to make use of the unique capabilities of this language with the main goals of (1) respecting as much as possible the original philosophy of the algorithms, (2) maintaining a full compatibility with the original version in terms of the format of the required input and produced output files, and (3) improving the efficiency and accuracy of the algorithms, and solving known (and newly discovered) bugs. The complete package is freely distributed, with an available repository online. pyFIT3D and pyPipe3D are fully tested with data of the most recent IFS data surveys and compilations (e.g. CALIFA, MaNGA, SAMI and AMUSING++), and confronted with simulations. We describe here the code, its new implementation, its accuracy in recovering the parameters based on simulations, and a showcase of its implementation on a particular dataset.

Population-Based Screening of Newborns: Findings From the NBS Expansion Study (Part One).

Each year, through population-based newborn screening (NBS), 1 in 294 newborns is identified with a condition leading to early treatment and, in some cases, life-saving interventions. Rapid advancements in genomic technologies to screen, diagnose, and treat newborns promise to significantly expand the number of diseases and individuals impacted by NBS. However, expansion of NBS occurs slowly in the United States (US) and almost always occurs condition by condition and state by state with the goal of screening for all conditions on a federally recommended uniform panel. The Newborn Screening Translational Research Network (NBSTRN) conducted the NBS Expansion Study to describe current practices, identify expansion challenges, outline areas for improvement in NBS, and suggest how models could be used to evaluate changes and improvements. The NBS Expansion Study included a workshop of experts, a survey of clinicians, an analysis of data from online repositories of state NBS programs, reports and publications of completed pilots, federal committee reports, and proceedings, and the development of models to address the study findings. This manuscript (Part One) reports on the design, execution, and results of the NBS Expansion Study. The Study found that the capacity to expand NBS is variable across the US and that nationwide adoption of a new condition averages 9.5 years. Four factors that delay and/or complicate NBS expansion were identified. A companion paper (Part Two) presents a use case for each of the four factors and highlights how modeling could address these challenges to NBS expansion.