Searches as data: archiving and sharing search strategies using an institutional data repository.

Background: By defining search strategies and related database exports as code/scripts and data, librarians and information professionals can expand the mandate of research data management (RDM) infrastructure to include this work. This new initiative aimed to create a space in McGill University's institutional data repository for our librarians to deposit and share their search strategies for knowledge syntheses (KS). Case Presentation: The authors, a health sciences librarian and an RDM specialist, created a repository collection of librarian-authored knowledge synthesis (KS) searches in McGill University's Borealis Dataverse collection. We developed and hosted a half-day "Dataverse-a-thon" where we worked with a team of health sciences librarians to develop a standardized KS data management plan (DMP), search reporting documentation, Dataverse software training, and howto guidance for the repository. Conclusion: In addition to better documentation and tracking of KS searches at our institution, the KS Dataverse collection enables sharing of searches among colleagues with discoverable metadata fields for searching within deposited searches. While the initial creation of the DMP and documentation took about six hours, the subsequent deposit of search strategies into the institutional data repository requires minimal effort (e.g., 5-10 minutes on average per deposit). The Dataverse collection also empowers librarians to retain intellectual ownership over search strategies as valuable stand-alone research outputs and raise the visibility of their labor. Overall, institutional data repositories provide specific benefits in facilitating compliance both with PRISMA-S guidance and with RDM best practices.

High-throughput whole-slide scanning to enable large-scale data repository building.

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high-throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher-quality imaging data using fewer personnel. Here we review several practical considerations for deploying high-throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high-throughput scanning realizable to laboratories with limited resources. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology.

The 2022 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 15 invited reviews on research areas of growing importance in pathology. This year, the articles include those that focus on digital pathology, employing modern imaging techniques and software to enable improved diagnostic and research applications to study human diseases. This subject area includes the ability to identify specific genetic alterations through the morphological changes they induce, as well as integrating digital and computational pathology with 'omics technologies. Other reviews in this issue include an updated evaluation of mutational patterns (mutation signatures) in cancer, the applications of lineage tracing in human tissues, and single cell sequencing technologies to uncover tumour evolution and tumour heterogeneity. The tissue microenvironment is covered in reviews specifically dealing with proteolytic control of epidermal differentiation, cancer-associated fibroblasts, field cancerisation, and host factors that determine tumour immunity. All of the reviews contained in this issue are the work of invited experts selected to discuss the considerable recent progress in their respective fields and are freely available online (https://onlinelibrary.wiley.com/journal/10969896). © 2022 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Does universal insurance influence disparities in high-quality hospital use for inpatient pediatric congenital heart defect care within the first year of diagnosis?

BACKGROUND: Healthcare disparities are an issue in the management of Congenital Heart Defects (CHD) in children. Although universal insurance may mitigate racial or socioeconomic status (SES) disparities in CHD care, prior studies have not examined these effects in the use of High-Quality Hospitals (HQH) for inpatient pediatric CHD care in the Military Healthcare System (MHS). To assess for racial and SES disparities in inpatient pediatric CHD care that may persist despite universal insurance coverage, we performed a cross-sectional study of the HQH use for children treated for CHD in the TRICARE system, a universal healthcare system for the U.S. Department of Defense. In the present work we evaluated for the presence of disparities, like those seen in the civilian U.S. healthcare system, among military ranks (SES surrogate) and races and ethnicities in HQH use for pediatric inpatient admissions for CHD care within a universal healthcare system (MHS). METHODS: We conducted a cross-sectional study using claims data from the U.S. MHS Data Repository from 2016 to 2020. We identified 11,748 beneficiaries aged 0 to 17 years who had an inpatient admission for CHD care from 2016 to 2020. The outcome variable was a dichotomous indicator for HQH utilization. In the sample, 42 hospitals were designated as HQH. Of the population, 82.9% did not use an HQH at any point for CHD care and 17.1% used an HQH at some point for CHD care. The primary predictor variables were race and sponsor rank. Military rank has been used as an indicator of SES status. Patient demographic information at the time of index admission post initial CHD diagnosis (age, gender, sponsor marital status, insurance type, sponsor service branch, proximity to HQH based on patient zip code centroid, and provider region) and clinical information (complexity of CHD, common comorbid conditions, genetic syndromes, and prematurity) were used as covariates in multivariable logistic regression analysis. RESULTS: After controlling for demographic and clinical factors including age, gender, sponsor marital status, insurance type, sponsor service branch, proximity to HQH based on patient zip code centroid, provider region, complexity of CHD, common comorbid conditions, genetic syndromes, and prematurity, we did not find disparities in HQH use for inpatient pediatric CHD care based upon military rank. After controlling for demographic and clinical factors, lower SES (Other rank) was less likely to use an HQH for inpatient pediatric CHD care; OR of 0.47 (95% CI of 0.31 to 0.73). CONCLUSIONS: We found that for inpatient pediatric CHD care in the universally insured TRICARE system, historically reported racial disparities in care were mitigated, suggesting that this population benefitted from expanded access to care. Despite universal coverage, SES disparities persisted in the civilian care setting, suggesting that universal insurance alone cannot sufficiently address differences in SES disparities in CHD care. Future studies are needed to address the pervasiveness of SES disparities and potential interventions to mitigate these disparities such as a more comprehensive patient travel program.

National Center for Interprofessional Practice and Education IPE core data set and information exchange for knowledge generation.

Since 2012, the National Center for Interprofessional Practice and Education has worked with over 70 sites implementing over 100 interprofessional education and collaborative practice (IPECP) programs in the United States (U.S.). Program leaders have contributed data and information to the National Center to inform an approach to advancing the science of interprofessional practice and education (IPE), called IPE Knowledge Generation. This paper describes how the evolution of IPE Knowledge Generation blends traditional research and evaluation approaches with the burgeoning field of health informatics and big data science. The goal of IPE Knowledge Generation is to promote collaboration and knowledge discovery among IPE program leaders who collect comparable, sharable data in an information exchange. This data collection then supports analysis and knowledge generation. To enable the approach, the National Center uses a structured process for guiding IPE program design and implementation in practice settings focused on learning and the Quadruple Aim outcomes while collecting the IPE core data set and the contribution of contemporary big data science.

A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters.

The lack of machine-readable data is a major obstacle in the application of nuclear magnetic resonance (NMR) in artificial intelligence (AI). As a way to overcome this, a procedure for capturing primary NMR spectroscopic instrumental data annotated with rich metadata and publication in a Findable, Accessible, Interoperable and Reusable (FAIR) data repository is described as part of an undergraduate student laboratory experiment in a chemistry department. This couples the techniques of chemical synthesis of a never before made organic ester with illustration of modern data management practices and serves to raise student awareness of how FAIR data might improve research quality and replicability. Searches of the registered metadata are shown, which enable actionable finding and accessing of such data. The potential for re-use of the data in AI applications is discussed.

The Dementias Platform UK (DPUK) Data Portal.

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

Panorama Public: A Public Repository for Quantitative Data Sets Processed in Skyline.

To address the growing need for a centralized, community resource of published results processed with Skyline, and to provide reviewers and readers immediate visual access to the data behind published conclusions, we present Panorama Public (https://panoramaweb.org/public.url), a repository of Skyline documents supporting published results. Panorama Public is built on Panorama, an open source data management system for mass spectrometry data processed with the Skyline targeted mass spectrometry environment. The Panorama web application facilitates viewing, sharing, and disseminating results contained in Skyline documents via a web-browser. Skyline users can easily upload their documents to a Panorama server and allow other researchers to explore uploaded results in the Panorama web-interface through a variety of familiar summary graphs as well as annotated views of the chromatographic peaks processed with Skyline. This makes Panorama ideal for sharing targeted, quantitative results contained in Skyline documents with collaborators, reviewers, and the larger proteomics community. The Panorama Public repository employs the full data visualization capabilities of Panorama which facilitates sharing results with reviewers during manuscript review.

Global Health Observatory Data Repository.

The Global Health Observatory Data Repository is the publicly available interface for the World Health Organization's health-related statistics for the 194 countries that are Member States. It includes statistics for over 1,000 indicators including mortality, child nutrition, maternal health, HIV/AIDS, environmental health, equity, and more. This overview explains the variety of ways that users can access and browse WHO's health-related statistics.

Heritable disorders of connective tissue: Description of a data repository and initial cohort characterization.

We describe a data repository on heritable disorders of connective tissue (HDCT) assembled by the National Institutes of Health's National Institute on Aging (NIA) Intramural Research Program between 2001 and 2013. Participants included affected persons with a wide range of heritable connective tissue phenotypes, and unaffected family members. Elements include comprehensive history and physical examination, standardized laboratory data, physiologic measures and imaging, standardized patient-reported outcome measures, and an extensive linked biorepository. The NIA made a commitment to make the repository available to extramural investigators and deposited samples at Coriell Tissue Repository (N = 126) and GenTAC registry (N = 132). The clinical dataset was transferred to Penn State University College of Medicine Clinical and Translational Science Institute in 2016, and data elements inventoried. The consented cohort of 1,009 participants averaged 39 ± 18 years (mean ± SD, range 2-95) at consent; gender distribution is 71% F and 83% self-report Caucasian ethnicity. Diagnostic categories include Ehlers-Danlos syndrome (classical N = 50, hypermobile N = 99, vascular N = 101, rare types and unclassified N = 178), Marfan syndrome (N = 33), Stickler syndrome (N = 60), fibromuscular dysplasia (N = 135), Other HDCT (N = 72). Unaffected family members (N = 218) contributed DNA for the molecular archive only. We aim to develop further discrete data from unstructured elements, analyze multisymptom HDCT manifestations, encourage data use by other researchers and thereby better understand the complexity of these high-morbidity conditions and their multifaceted effects on affected persons.

The challenge of constructing an international XAFS database.

The present state of XAFS databases, particularly in Japan, and proposals for future directions are presented. International collaboration is important for enlarging the database for further development of XAFS spectroscopy.

Total energy expenditure in adults aged 65 years and over measured using doubly-labelled water: international data availability and opportunities for data sharing.

BACKGROUND: Increasing population lifespan necessitates a greater understanding of nutritional needs in older adults (65 year and over). A synthesis of total energy expenditure in the older population has not been undertaken and is needed to inform nutritional requirements. We aimed to establish the extent of the international evidence for total energy expenditure (TEE) using doubly-labelled water (DLW) in older adults (65 years and over), report challenges in obtaining primary data, and make recommendations for future data sharing. METHODS: Four databases were searched to identify eligible studies; original research of any study design where participant level TEE was measured using DLW in participants aged ≥65 years. Once studies were identified for inclusion, authors were contacted where data were not publicly available. RESULTS: Screening was undertaken of 1223 records; the review of 317 full text papers excluded 170 records. Corresponding or first authors of 147 eligible studies were contacted electronically. Participant level data were publicly available or provided by authors for 45 publications (890 participants aged ≥65 years, with 248 aged ≥80 years). Sixty-seven percent of the DLW data in this population were unavailable due to authors unable to be contacted or declining to participate, or data being irretrievable. CONCLUSIONS: The lack of data access limits the value of the original research and its contribution to nutrition science. Openly accessible DLW data available through publications or a new international data repository would facilitate greater integration of current research with previous findings and ensure evidence is available to support the needs of the ageing population. TRIAL REGISTRATION: The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42016047549 .

Sharing longitudinal, non-biological birth cohort data: a cross-sectional analysis of parent consent preferences.

BACKGROUND: Mandates abound to share publicly-funded research data for reuse, while data platforms continue to emerge to facilitate such reuse. Birth cohorts (BC) involve longitudinal designs, significant sample sizes and rich and deep datasets. Data sharing benefits include more analyses, greater research complexity, increased opportunities for collaboration, amplification of public contributions, and reduced respondent burdens. Sharing BC data involves significant challenges including consent, privacy, access policies, communication, and vulnerability of the child. Research on these issues is available for biological data, but these findings may not extend to BC data. We lack consensus on how best to approach these challenges in consent, privacy, communication and autonomy when sharing BC data. We require more stakeholder engagement to understand perspectives and generate consensus. METHODS: Parents participating in longitudinal birth cohorts completed a web-based survey investigating consent preferences for sharing their, and their child's, non-biological research data. Results from a previous qualitative inquiry informed survey development, and cognitive interviewing methods (n = 9) were used to improve the question quality and comprehension. Recruitment was via personalized email, with email and phone reminders during the 14-day window for survey completion. RESULTS: Three hundred and forty-six of 569 parents completed the survey in September 2014 (60.8%). Participants preferred consent processes for data sharing in future independent research that were less-active (i.e. no consent or opt-out). Parents' consent preferences are associated with their communication preferences. Twenty percent (20.2%) of parents generally agreed that their child should provide consent to continue participating in research at age 12, while 25.6% felt decision-making on sharing non-biological research data should begin at age 18. CONCLUSIONS: These finding reflect the parenting population's preference for less project-specific permission when research data is non-biological and de-identified and when governance practices are highly detailed and rigourous. Parents recognize that children should become involved in consent for secondary data use, but there is variability regarding when and how involvement occurs. These findings emphasize governance processes and participant notification rather than project-specific consent for secondary use of de-identified, non-biological data. Ultimately, parents prefer general consent processes for sharing de-identified, non-biological research data with ultimate involvement of the child.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data.

Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

OBJECTIVE: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. METHOD: We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). RESULTS: Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset. CONCLUSION: It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases.

Imbalanced target prediction with pattern discovery on clinical data repositories.

BACKGROUND: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. METHODS: We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. RESULTS: We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. CONCLUSIONS: Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

Feasibility of Combining Common Data Elements Across Studies to Test a Hypothesis.

PURPOSE: The purpose of this article is to describe the outcomes of a collaborative initiative to share data across five schools of nursing in order to evaluate the feasibility of collecting common data elements (CDEs) and developing a common data repository to test hypotheses of interest to nursing scientists. This initiative extended work already completed by the National Institute of Nursing Research CDE Working Group that successfully identified CDEs related to symptoms and self-management, with the goal of supporting more complex, reproducible, and patient-focused research. DESIGN: Two exemplars describing the group's efforts are presented. The first highlights a pilot study wherein data sets from various studies by the represented schools were collected retrospectively, and merging of the CDEs was attempted. The second exemplar describes the methods and results of an initiative at one school that utilized a prospective design for the collection and merging of CDEs. METHODS: Methods for identifying a common symptom to be studied across schools and for collecting the data dictionaries for the related data elements are presented for the first exemplar. The processes for defining and comparing the concepts and acceptable values, and for evaluating the potential to combine and compare the data elements are also described. Presented next are the steps undertaken in the second exemplar to prospectively identify CDEs and establish the data dictionaries. Methods for common measurement and analysis strategies are included. FINDINGS: Findings from the first exemplar indicated that without plans in place a priori to ensure the ability to combine and compare data from disparate sources, doing so retrospectively may not be possible, and as a result hypothesis testing across studies may be prohibited. Findings from the second exemplar, however, indicated that a plan developed prospectively to combine and compare data sets is feasible and conducive to merged hypothesis testing. CONCLUSIONS: Although challenges exist in combining CDEs across studies into a common data repository, a prospective, well-designed protocol for identifying, coding, and comparing CDEs is feasible and supports the development of a common data repository and the testing of important hypotheses to advance nursing science. CLINICAL RELEVANCE: Incorporating CDEs across studies will increase sample size and improve data validity, reliability, transparency, and reproducibility, all of which will increase the scientific rigor of the study and the likelihood of impacting clinical practice and patient care.

The Image and Data Archive at the Laboratory of Neuro Imaging.

The LONI Image and Data Archive (IDA)(1) is a repository for sharing and long-term preservation of neuroimaging and biomedical research data. Originally designed to archive strictly medical image files, the IDA has evolved over the last ten years and now encompasses the storage and dissemination of neuroimaging, clinical, biospecimen, and genetic data. In this article, we report upon the genesis of the IDA and how it currently securely manages data and protects data ownership.

Sharing data in the global alzheimer's association interactive network.

The Global Alzheimer's Association Interactive Network (GAAIN) aims to be a shared network of research data, analysis tools, and computational resources for studying the causes of Alzheimer's disease. Central to its design are policies that honor data ownership, prevent unauthorized data distribution, and respect the boundaries of contributing institutions. The results of data queries are displayed in graphs and summary tables, which protects data ownership while providing sufficient information to view trends in aggregated data and discover new data sets. In this article we report on our progress in sharing data through the integration of geographically-separated and independently-operated Alzheimer's disease research studies around the world.

Pain and Interoception Imaging Network (PAIN): A multimodal, multisite, brain-imaging repository for chronic somatic and visceral pain disorders.

The Pain and Interoception Imaging Network (PAIN) repository (painrepository.org) is a newly created NIH (NIDA/NCCAM) funded neuroimaging data repository that aims to accelerate scientific discovery regarding brain mechanisms in pain and to provide more rapid benefits to pain patients through the harmonization of efforts and data sharing. The PAIN Repository consists of two components, an Archived Repository and a Standardized Repository. Similar to other 'open' imaging repositories, neuroimaging researchers can deposit any dataset of chronic pain patients and healthy controls into the Archived Repository. Scans in the Archived Repository can be very diverse in terms of scanning procedures and clinical metadata, complicating the merging of datasets for analyses. The Standardized Repository overcomes these limitations through the use of standardized scanning protocols along with a standardized set of clinical metadata, allowing an unprecedented ability to perform pooled analyses. The Archived Repository currently includes 741 scans and is rapidly growing. The Standardized Repository currently includes 433 scans. Pain conditions currently represented in the PAIN repository include: irritable bowel syndrome, vulvodynia, migraine, chronic back pain, and inflammatory bowel disease. Both the PAIN Archived and Standardized Repositories promise to be important resources in the field of chronic pain research. The enhanced ability of the Standardized Repository to combine imaging, clinical and other biological datasets from multiple sites in particular make it a unique resource for significant scientific discoveries.