Individual Data Sets Research Articles

From admissions to licensure: education data associations from a multi-centre undergraduate medical education collaboration.

This paper reports the findings of a Canada based multi-institutional study designed to investigate the relationships between admissions criteria, in-program assessments, and performance on licensing exams. The study's objective is to provide valuable insights for improving educational practices across different institutions. Data were gathered from six medical schools: McMaster University, the Northern Ontario School of Medicine University, Queen's University, University of Ottawa, University of Toronto, and Western University. The dataset includes graduates who undertook the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1) between 2015 and 2017. The data were categorized into five distinct sections: demographic information as well as four matrices: admissions, course performance, objective structured clinical examination (OSCE), and clerkship performance. Common and unique variables were identified through an extensive consensus-building process. Hierarchical linear regression and a manual stepwise variable selection approach were used for analysis. Analyses were performed on data set encompassing graduates of all six medical schools as well as on individual data sets from each school. For the combined data set the final model estimated 32% of the variance in performance on licensing exams, highlighting variables such as Age at Admission, Sex, Biomedical Knowledge, the first post-clerkship OSCE, and a clerkship theta score. Individual school analysis explained 41-60% of the variance in MCCQE1 outcomes, with comparable variables to the analysis from of the combined data set identified as significant independent variables. Therefore, strongly emphasising the need for variety of high-quality assessment on the educational continuum. This study underscores the importance of sharing data to enable educational insights. This study also had its challenges when it came to the access and aggregation of data. As such we advocate for the establishment of a common framework for multi-institutional educational research, facilitating studies and evaluations across diverse institutions. This study demonstrates the scientific potential of collaborative data analysis in enhancing educational outcomes. It offers a deeper understanding of the factors influencing performance on licensure exams and emphasizes the need for addressing data gaps to advance multi-institutional research for educational improvements.

Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data

In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating multiple sensors for predicting aboveground biomass (AGB) is important for achieving high accuracy, reducing the dataset size by eliminating redundant and irrelevant spectral features is essential for enhancing the performance of machine learning algorithms. This accelerates the learning process, thereby developing simpler and more efficient models. Our results indicate that compared individual sensor datasets, the random forest (RF) classification approach using recursive feature elimination (RFE) increased the accuracy based on F score by 82.86 % and 26.19 respectively. The mutual information regression (MIR) method shows a slight increase in accuracy when considering individual sensor datasets, but its accuracy decreases when all features are taken into account for all models. Overall, the combination of features from the Landsat 8, ALOS PALSAR backscatter, and elevation data selected based on RFE provided the best AGB estimation for the RF and XGBoost models. In contrast to the k-nearest neighbors (KNN) and support vector machines (SVM), no significant improvement in AGB estimation was detected even when RFE and MIR were used. The effect of parameter optimization was found to be more significant for RF than for all the other methods. The AGB maps show patterns of AGB estimates consistent with those of the reference dataset. This study shows how prediction errors can be minimized based on feature selection using different ML classifiers.

Factors influencing smartwatch use and comfort with health data sharing: a sequential mixed-method study protocol

IntroductionSmartwatches have become ubiquitous for tracking health metrics. These data sets hold substantial potential for enhancing healthcare and public health initiatives; it may be used to track chronic health conditions,...

Operationalizing open and restricted-access data—Formulating verifiable criteria for the openness of data sets mentioned in biomedical research articles

Abstract Increasing the availability of research data sets is a goal of many stakeholders in science, and monitoring related practices requires definitions of the entity in question. There are several, largely overlapping, definitions for open data. However, they have so far not been translated into operationalizations that would allow us to detect, in a structured and reproducible way, whether, for a specific research article, the underlying data have been shared. Here, we propose a detailed set of criteria to enable such assessments, focusing on biomedical research. We have used these criteria to distribute performance-oriented funding at a large university hospital and to monitor data sharing practices in a dashboard. In addition to fully open data, we include separate criteria for data sets with restricted access, which we also reward. The criteria are partly inspired by the FAIR principles, particularly findability and accessibility, but do not map onto individual principles. The criteria attribute open data status in a binary fashion, both to individual data sets and, ultimately, articles with which they were shared. The criteria allow a verifiable assessment, based on automated and manual screening steps, which we have implemented and validated, as described elsewhere. Here, we focus conceptually on assessing the presence of shared data.

Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace

Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.

Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance

ABSTRACT During the last two decades, a great number of global products about evapotranspiration ( E ) and terrestrial water storage ( S ) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of E and S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of E product while various S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing E , offset by decreasing S . Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins.

Using Routine Data to Improve Lesbian, Gay, Bisexual, and Transgender Health.

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Bacteroidales-Specific Antimicrobial Genes Can Influence the Selection of the Dominant Fecal Strain of Bacteroides vulgatus and Bacteroides uniformis from the Gastrointestinal Tract Microbial Community.

Bacteroides vulgatus and Bacteroides uniformis are known to be abundant in the human fecal microbial community. Although these strains typically remain stable over time in humans, disruption of this microbial community following antibiotics resulted in the transient change to new strains suggesting that a complex, dynamic strain community exists in humans. To further study the selection of dominant fecal microbial strains from the gastrointestinal tract (GIT) community, we analyzed three longitudinal metagenomic sequencing data sets using BLAST+ to identify genes encoding Bacteroidales-specific antimicrobial proteins (BSAP) that have known functions to restrict species-specific replication of B. uniformis (BSAP-2) or B. vulgatus (BSAP-3) and have been postulated to provide a competitive advantage in microbial communities. In the HMP (Human Microbiome Project) data set, we found fecal samples from individuals had B. vulgatus or B. uniformis with either complete or deleted BSAP genes that did not change over time. We also examined fecal samples from two separate longitudinal data sets of individuals who had been given either single or multiple antibiotics. The BSAP gene pattern from most individuals given either single or multiple antibiotics recovered to be the same as the pre-antibiotic strain. However, in a few individuals, we found incomplete BSAP-3 genes at early times during the recovery that were replaced by B. vulgatus with the complete BSAP-3 gene, consistent with the function of the BSAP to specifically restrict Bacteroides spp. The results of these studies provide insights into the fluxes that occur in the Bacteroides spp. GIT community following perturbation and the dynamics of the selection of a dominant fecal strain of Bacteroides spp.

Therapeutic radiographer prescribing practices in the United Kingdom: Questionnaire survey

IntroductionNon-medical professionals in the United Kingdom (UK) have been granted prescribing rights to improve patient care quality and cost-effectiveness. There is limited evidence on how therapeutic radiographer prescribers have impacted medicine management or patient services. MethodsAn online survey was conducted amongst non-medical prescribing therapeutic radiographers in the UK between 2019 and 2022. The study teams initially analysed the individual data sets, subsequently combined, and secondary analysis was performed to provide a UK perspective, to understand the services provided and identify areas for improvement. Data was analysed using descriptive statistics from Microsoft Excel® and SPSS®. Results74 non-medical prescribing therapeutic radiographers who were predominantly over 40 years old and in full-time work participated. The main job categories were consultant radiographers (n = 23, 31.1%) and advanced practice practitioners (n = 18, 24.3%). Many use their prescribing qualifications (87.5%, n = 62), issuing a mean of 15 independent and seven items by supplementary and prescribing per week. Most received assessment and diagnostic skills training before prescribing courses (91.6%, n = 67). Respondents prescribed from a median of six areas, with the highest being in GI (82%), skin (68%), infections (58%), urinary tract disorders (55%) and ear, nose, and oropharynx conditions (54%). ConclusionThis study presents the first report on therapeutic radiographers prescribing in the UK, offering insights into current practices and highlighting the success of non-medical prescribing. Therapeutic radiographers’ roles continue to expand into advanced practice and medicine-prescribing responsibilities, contributing to holistic and patient-centred care. Implications for practiceThe results are relevant for nations grappling with oncology workforce shortages and contemplating similar roles for therapeutic radiographers. The study can be a valuable resource for policymakers and healthcare organisations worldwide.

Speech and language therapy services for autistic children in Munster: An interpretative phenomenological analysis of parental experiences and expectations

BACKGROUND: From a medical perspective, autism is characterised by a dyad of impairments in social communication and interaction, and restricted, repetitive behaviours. However, the neurodiversity movement sees autism as a difference and not as a disability. Autistic individuals account for 1–2% of the population and many autistic children and their families access Speech & Language Therapy (SLT). In Ireland, SLT is provided across primary care, childrens’ disability network teams, as part of the progressing disability services for children and young people, and private providers. However, little is known about parents’ experiences accessing SLT services for autistic children. METHODS: This study aimed to investigate the parental experiences of SLT services for autistic children in the province of Munster, Ireland. Interpretative Phenomenological Analysis was used as a methodological framework. Semi-structured online interviews were carried out with six participants to identify their experiences of receiving SLT services for their autistic children. Data analysis followed a structured process, starting with in-depth explorations of individual datasets before cross-case analysis of the whole sample. FINDINGS: Participants expressed a variety of experiences and expectations of SLT provision, which centred on three thematic journeys: 1) Parenting an autistic child, 2) Experiencing and navigating SLT services for autistic children and 3) Evolving expectations of SLT service changes. CONCLUSIONS: Findings provide insight into parents’ individual and shared experiences of SLT services for autistic children, which may enrich service providers’ understanding of parents’ perceptions of SLT services. Future research should focus on exploring the experiences of autistic individuals and Speech and Language Therapists providing services for autistic children.2

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals.

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

A marine controlled-source electromagnetic application using towed and seafloor-based receivers capable of mapping seafloor and embedded massive sulfides

Deep-sea massive sulfide deposits formed by hydrothermal fluid circulation are potential metal resources. They can exist not only as mound manifestations on the seafloor (seafloor massive sulfides) but also as embedded anomalies buried beneath the seafloor (embedded massive sulfides). The distribution of embedded massive sulfides is largely unknown, despite their expected high economic value. Recent drilling surveys have revealed a complex model suggesting embedded massive sulfides coexist beneath seafloor massive sulfides. In the coexisting case, geophysical methods are required to distinguish and map seafloor and embedded massive sulfides for accurate resource estimation. Marine controlled-source electromagnetic (CSEM) methods are useful for mapping massive sulfides because they exhibit higher electrical conductivity compared with the surrounding host rock. However, CSEM applications capable of distinguishing and mapping the massive sulfides are lacking. We use a towed electric dipole transmitter with two types of receivers: stationary ocean-bottom electric (OBE) and short-offset towed receivers. This combination uses differences in sensitivity: the towed receiver data are sensitive to seafloor massive sulfides, and the stationary OBE receiver data are sensitive to embedded massive sulfides. Our synthetic data example demonstrates that the combined inversion of towed and OBE data can recover resistivities and positions of the massive sulfides more accurately than existing inversion methods using individual applications. We perform the combined inversion of measured CSEM data obtained from the middle Okinawa Trough. The inversion models demonstrate that a combined inversion can map the location and shape of embedded massive sulfides identified during drilling more accurately than the inversion of individual data sets.

Multi-source precipitation estimation using machine learning: Clarification and benchmarking

Multi-source precipitation estimation techniques, including interpolation, bias correction, and merging, are essential for acquiring high-precision precipitation datasets. Machine learning (ML) methods have achieved inspiring success in this field, but current studies utilizing ML often blur the boundaries of interpolation, bias correction, and merging because of the utilization of uniform input–output frameworks. The ambiguity in classification (interpolation, bias correction and merging) results in inconsistent and inappropriate comparative benchmarking among studies. A widespread example is the tendency to select easy-to-beat benchmarks, which can magnify the relative performance of ML methods. We propose to reclarify ML-based precipitation estimation by conducting a series of experiments with designed feature combinations to unveil the essence of ML-based methods. Our findings suggest that with different feature combinations, there are no essential distinctions between models for merging, bias correction and interpolation. Ground measurements, as the reference in supervised learning, play a more vital role than different features in precipitation estimation. This means that the ML-based multi-source merging or bias correction should be treated as gauge data-based interpolation with various auxiliary predictors. Moreover, the improvements of ML estimates are more significant compared to low-standard benchmarks like individual precipitation datasets and simple merging methods, but less preeminent compared to high-standard benchmarks such as a sophisticated interpolation method (e.g., Geographically Weighted Regression). Therefore, we call for positioning such ML applications as interpolation and adhering to high-standard benchmarks whenever possible, which can illustrate the true potential and promote further advancements of ML in geospatial estimation.

An ensemble technique to predict Parkinson's disease using machine learning algorithms

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting motor and non-motor symptoms. Its symptoms develop slowly, making early identification difficult. Machine learning has a significant potential to predict Parkinson's disease on features hidden in voice data. This work aimed to identify the most relevant features from a high-dimensional dataset, which helps accurately classify Parkinson's Disease with less computation time. Three individual datasets with various medical features based on voice have been analyzed in this work. An Ensemble Feature Selection Algorithm (EFSA) technique based on filter, wrapper, and embedding algorithms that pick highly relevant features for identifying Parkinson's Disease is proposed, and the same has been validated on three different datasets based on voice. These techniques can shorten training time to improve model accuracy and minimize overfitting. We utilized different ML models such as K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Gradient Boosting. Each of these models was fine-tuned to ensure optimal performance within our specific context. Moreover, in addition to these established classifiers, we proposed an ensemble classifier is found on a high optimal majority of the votes. Dataset-I achieves classification accuracy with 97.6 %, F1-score 97.9 %, precision with 98 % and recall with 98 %. Dataset-II achieves classification accuracy 90.2 %, F1-score 90.2 %, precision 90.2 %, and recall 90.5 %. Dataset-III achieves 83.3 % accuracy, F1-score 83.3 %, precision 83.5 % and recall 83.3 %. These results have been taken using 13 out of 23, 45 out of 754, and 17 out of 46 features from respective datasets. The proposed EFSA model has performed with higher accuracy and is more efficient than other models for each dataset.

On data set tensions and signatures of new cosmological physics

ABSTRACT Can new cosmic physics be uncovered through tensions amongst data sets? Tensions in parameter determinations amongst different types of cosmological observation, especially the ‘Hubble tension’ between probes of the expansion rate, have been invoked as possible indicators of new physics, requiring extension of the ΛCDM paradigm to resolve. Within a fully Bayesian framework, we show that the standard tension metric gives only part of the updating of model probabilities, supplying a data co-dependence term that must be combined with the Bayes factors of individual data sets. This shows that, on its own, a reduction of data set tension under an extension to ΛCDM is insufficient to demonstrate that the extended model is favoured. Any analysis that claims evidence for new physics solely on the basis of alleviating data set tensions should be considered incomplete and suspect. We describe the implications of our results for the interpretation of the Hubble tension.

Gender norms and the child penalty in China

We utilize a comprehensive individual panel dataset from China and employ an event study approach to investigate the influence of childbirth on gender disparities across various labor market outcomes, emphasizing the role of gender norms in shaping the child penalty. Our findings show a pronounced decline in individual earnings, labor force participation and working hours for women after childbirth, though labor force participation returns to pre-childbirth level in the long term. Such trends are not observed among men. These gender differentiated outcomes are driven by a key factor: gender norms, specifically women’s and their husbands’ views toward gender roles. Parents’ or in-laws’ views play a smaller role. Women suffer a larger child penalty in individual earnings if they or their spouses hold traditional views compared to those who, or whose spouses, hold more egalitarian views. Potential channels are that traditional views may predispose mothers to focus more on household activities and/or engage in informal jobs that are more flexible and family friendly.

Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection

Detecting OOD inputs is crucial to deploy machine learning models to the real world safely. However, existing OOD detection methods require an in-distribution (ID) dataset to retrain the models. In this paper, we propose a Deep Generative Models (DGMs) based transferable OOD detection that does not require retraining on the new ID dataset. We first establish and substantiate two hypotheses on DGMs: DGMs exhibit a predisposition towards acquiring low-level features, in preference to semantic information; the lower bound of DGM's log-likelihoods is tied to the conditional entropy between the model input and target output. Drawing on the aforementioned hypotheses, we present an innovative image-erasing strategy, which is designed to create distinct conditional entropy distributions for each individual ID dataset. By training a DGM on a complex dataset with the proposed image-erasing strategy, the DGM could capture the discrepancy of conditional entropy distribution for varying ID datasets, without re-training. We validate the proposed method on the five datasets and show that, without retraining, our method achieves comparable performance to the state-of-the-art group-based OOD detection methods. The project codes will be open-sourced on our project website.

Abstract 6242: A workflow execution system in a data fabric for integrative cancer analyses

Abstract Cancer researchers are increasingly conducting multi-omic research and performing integrative analyses on combinations of genomic, proteomic, transcriptomic, imaging, single cell and other data modalities. However, it is quite challenging for a researcher to effectively access, aggregate and analyze data and metadata from different data sources in a scalable and reproducible manner, as the individual datasets may be disconnected from each other, and have separate authentication and authorization requirements. We developed a workflow execution system in a data fabric called the Biomedical Research Hub (BRH) to overcome these challenges for academic researchers. The BRH is powered by the Gen3 technology, an open-source Kubernetes based software stack that allows cancer researchers to create their own data fabric and interoperate with data from multiple data sources. The workflow execution system utilizes nextflow and allows researchers to run containerized applications in the cloud in a secure and isolated environment. Data from multiple resources can be combined for analysis using convenient pay models including NIH STRIDES. We plan to demonstrate the application of our system on a scientific use case involving Clonal Hematopoiesis of Indeterminate potential (CHIP), a phenomenon that has been associated with aging, cancer, cardiovascular diseases, infection and all-cause mortality. We run containerized CHIP workflows on the cloud, utilizing two datasets accessible through BRH: i) The Genomic Data Commons, the world’s largest source of harmonized cancer data and ii) BioDataCatalyst, an NHLBI ecosystem that drives discovery and innovation for heart, lung, blood and sleep disorders. Our system is ideally suited for machine learning on large aggregated cancer datasets and federated learning tasks. Citation Format: Aarti Venkat, Pauline Ribeyre, Jawad Qureshi, Sai Shanmukha Narumanchi, J Montgomery Maxwell, Bill Winslow, Sara Volk Garcia, Chris Meyer, Tzintzuni Garcia, Peter Vassilatos, Clinton Malson, Zhenyu Zhang, Robert Grossman. A workflow execution system in a data fabric for integrative cancer analyses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6242.

Abstract 338: Unraveling layers of proteogenomic complexity in cancer through multiomic exploration of archived FFPE tissue

Abstract Formalin Fixed Paraffin Embedded (FFPE) tissues are a staple in clinical diagnostics associated with solid tumor. The preserved tumor tissue can be interrogated using classical histopathology and complemented with molecular approaches to elucidate cancerous SNPs, indels, structural variants, repeat expansion, copy number expansions, and mutational burden. Increasingly, approaches are being deployed to interrogate tissue not only through individual datasets but through integrated approaches that provide spatial information that defines the tumor microenvironment. Here, we illustrate using FFPE blocks from various tissues a multiomic workflow that allows for deep exploration of the molecular underpinnings of cancerous tissue. FFPE blocks were serially sliced into various FFPE slides with a single slide H&E stained. Individual slides were then utilized to explore the genome, epigenome, single cell RNA-seq, and digital spatial profiling. Genome information was captured using hybrid capture based approaches (whole exome sequencing and targeted cancer panel) followed by deep NGS on an Illumina platform and analyzed for a variety of variants and tumor mutational burden. DNA methylation was detailed using target capture probes targeting DNA methylation sites. Single cell approaches were applied to explore the transcriptome using 10X Genomics scRNAseq Flex kit. Digital spatial profiling was done using the NanoString for both transcriptomics and proteomics, with the GeoMx Whole Transcriptome Atlas and Human Core and Pan-Tumor protein panels. Aggregating these data illustrates the expanding landscape of information that can be extracted from FFPE derived tissue and the potential for novel discovery and diagnostic power integrating these complex data types holds. Citation Format: Andrea J. O'Hara, Yang Han, Ilaria DeVito, Laure Turner, Haythem Latif. Unraveling layers of proteogenomic complexity in cancer through multiomic exploration of archived FFPE tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 338.

The Arctic Plant Aboveground Biomass Synthesis Dataset

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.