From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences

Unified and pluralistic ideals for data sharing and reuse in biodiversity

How should billions of species observations worldwide be shared and made reusable? Many biodiversity scientists assume the ideal solution is to standardize all datasets according to a single, universal classification and aggregate them into a centralized, global repository. This ideal has known practical and theoretical limitations, however, which justifies investigating alternatives. To support better community deliberation and normative evaluation, we develop a novel conceptual framework showing how different organizational models, regulative ideals and heuristic strategies are combined to form shared infrastructures supporting data reuse. The framework is anchored in a general definition of data pooling as an activity of making a taxonomically standardized body of information available for community reuse via digital infrastructure. We describe and illustrate unified and pluralistic ideals for biodiversity data pooling and show how communities may advance toward these ideals using different heuristic strategies. We present evidence for the strengths and limitations of the unification and pluralistic ideals based on systemic relationships of power, responsibility and benefit they establish among stakeholders, and we conclude the pluralistic ideal is better suited for biodiversity data.

Promoting data quality and reuse in archaeology through collaborative identifier practices

Investments in data management infrastructure often seek to catalyze new research outcomes based on the reuse of research data. To achieve the goals of these investments, we need to better understand how data creation and data quality concerns shape the potential reuse of data. The primary audience for this paper centers on scientific domain specialists that create and (re)use datasets documenting archaeological materials. This paper discusses practices that promote data quality in support of more open-ended reuse of data beyond the immediate needs of the creators. We argue that identifier practices play a key, but poorly recognized, role in promoting data quality and reusability. We use specific archaeological examples to demonstrate how the use of globally unique and persistent identifiers can communicate aspects of context, avoid errors and misinterpretations, and facilitate integration and reuse. We then discuss the responsibility of data creators and data reusers to employ identifiers to better maintain the contextual integrity of data, including professional, social, and ethical dimensions.

Framework for entity extraction with verification: application to inference of data set usage in research publications

Purpose

The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

Design/methodology/approach

The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module.

Findings

This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization.

Originality/value

Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Past and future uses of text mining in ecology and evolution

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.