Search for: ScienceDirect 2022 reference works count life sciences health sciences by subdomain data statistics

Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in the next few decades. Human presence in space, especially for extended periods of time, will therefore steeply increase. However, despite more than 60 years of spaceflight, the mechanisms underlying the effects of the space environment on human physiology are still not fully understood. Animals, ranging in complexity from flies to monkeys, have played a pioneering role in understanding the (patho)physiological outcome of critical environmental factors in space, in particular altered gravity and cosmic radiation. The use of animals in biomedical research is increasingly being criticized because of ethical reasons and limited human relevance. Driven by the 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, major efforts have been focused in the past decades on the development of alternative methods that fully bypass animal testing or so-called new approach methodologies. These new approach methodologies range from simple monolayer cultures of individual primary or stem cells all up to bioprinted 3D organoids and microfluidic chips that recapitulate the complex cellular architecture of organs. Other approaches applied in life sciences in space research contribute to the reduction of animal experimentation. These include methods to mimic space conditions on Earth, such as microgravity and radiation simulators, as well as tools to support the processing, analysis or application of testing results obtained in life sciences in space research, including systems biology, live-cell, high-content and real-time analysis, high-throughput analysis, artificial intelligence and digital twins. The present paper provides an in-depth overview of such methods to replace or reduce animal testing in life sciences in space research. 

Abstract This paper presents a new statistical method that enables the use of systematic errors in the maximum-likelihood regression of integer-count Poisson data to a parametric model. The method is primarily aimed at the characterization of the goodness-of-fit statistic in the presence of the over-dispersion that is induced by sources of systematic error, and is based on a quasi-maximum-likelihood method that retains the Poisson distribution of the data. We show that the Poisson deviance, which is the usual goodness-of-fit statistic and that is commonly referred to in astronomy as the Cash statistics, can be easily generalized in the presence of systematic errors, under rather general conditions. The method and the associated statistics are first developed theoretically, and then they are tested with the aid of numerical simulations and further illustrated with real-life data from astronomical observations. The statistical methods presented in this paper are intended as a simple general-purpose framework to include additional sources of uncertainty for the analysis of integer-count data in a variety of practical data analysis situations. 

Abstract This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies. 

ABSTRACT While long recognized in the humanities, there is growing recognition in the sciences and social sciences that primary sources—as diverse as manuscripts, photographs, cultural belongings, and specimens—hold vast data about scientific and human knowledge for use in scholarship, community research, and global knowledge. Yet, data embedded in these sources are largely disconnected from the systems of discovery, access, and structured data that support reuse and insights across globally dispersed repositories. In this paper, we share select findings of a systematic review to explore the use of primary sources, and the data embedded in them, via linked data across the sciences and social sciences. Our results confirm the use of a variety of primary source data across diverse disciplines, particularly those requiring longitudinal studies and data integration from diverse repositories and contexts. We highlight how linked data are understood to: connect collections to communities; support highly granular credit, attribution, and assessment of impact; and interrelate diverse sources of knowledge. While these results suggest the value of linked data for the specific research needs of anthropology, the effectiveness of linked data in achieving these objectives and the suitability of this approach for a diversity of institutions and communities need further study. 

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes cumulative stem biomass carbon data (from pre-treatment in 2012 until 2022) and annual stem biomass growth rates (not cumulative) for 2015-2022 for the red maple trees at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibration relies on a reference set of labeled inlier data to control the type-I error rate. However, obtaining a perfectly labeled inlier reference set is often unrealistic, and a more practical scenario involves access to a contaminated reference set containing a small fraction of outliers. This paper analyzes the impact of such contamination on the validity of conformal methods. We prove that under realistic, non-adversarial settings, calibration on contaminated data yields conservative type-I error control, shedding light on the inherent robustness of conformal methods. This conservativeness, however, typically results in a loss of power. To alleviate this limitation, we propose a novel, active data-cleaning framework that leverages a limited labeling budget and an outlier detection model to selectively annotate data points in the contaminated reference set that are suspected as outliers. By removing only the annotated outliers in this ``suspicious'' subset, we can effectively enhance power while mitigating the risk of inflating the type-I error rate, as supported by our theoretical analysis. Experiments on real datasets validate the conservative behavior of conformal methods under contamination and show that the proposed data-cleaning strategy improves power without sacrificing validity. 

Biologists often set out to find relevant data in an ever-changing landscape of interesting databases. While leading journals publish descriptions of databases, they are usually not recent and do not frequently update the list that discards defunct or poor-quality databases. These indices usually include databases that are proactively requested to be included by their authors. The challenge for individual biologists, then, is to discover, explore, and select databases of interest from a large unorganized collection and effectively use them in their analysis without too large of an investment. The advocation of the FAIR data principle to improve searching, finding, accessing, and inter-operating among these diverse information sources in order to increase usability is proving to be a difficult proposition and consequently, a large number of data sources are not FAIR-compliant. Since linked open data do not guarantee FAIRness, biologists are now left to individually search for information in open networks. In this paper, we propose SoDa, for intelligent data foraging on the internet by biologists. SoDa helps biologists to discover resources based on analysis requirements and generate resource access plans, as well as storing cleaned data and knowledge for community use. SoDa includes a natural language-powered resource discovery tool, a tool to retrieve data from remote databases, organize and store collected data, query stored data, and seek help from the community when things do not work as anticipated. A secondary search index is also supported for community members to find archived information in a convenient way to enable its reuse. The features supported in SoDa endows biologists with data integration capabilities over arbitrary linked open databases and construct powerful computational pipelines using them, capabilities that are not supported in most contemporary biological workflow systems, such as Taverna or Galaxy. 

During the 2024 Ocean Sciences Meeting (OSM24), The Oceanography Society’s Justice, Equity, Diversity, and Inclusion Committee hosted a town hall on “Scientific Societies’ Roles in Building Inclusive Communities.” The town hall aimed to assess ongoing efforts to improve belonging, accessibility, justice, equity, diversity, and inclusion (BAJEDI) within ocean sciences, promote community building and discussions surrounding BAJEDI topics, and highlight the role of scientific societies in equity efforts. Here, we summarize the resultant communal discussions, which focused on effective models for increasing participation in ocean sciences, how to make ocean science careers more accessible, and strategies to build a more equitable community culture. Discussions highlighted several professional societies working to increase BAJEDI within the field and offered tangible action items to increase accessibility and equity at all career stages. An optional survey was distributed to OSM24 attendees to assess their lived experiences. Survey results highlighted that although knowledge of BAJEDI issues and training opportunities have increased, bullying and discrimination are still common. We recommend action items, including increased standardization and public accessibility of demographic data, to continue improving BAJEDI within ocean sciences. 

Abstract Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.