Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

LAMAS 4 IC - Laboratory approved metadata acquisition schemas for ion chromatography

Metadata are necessary to describe scientific experiments. The use of metadata schemas enable the collection of structured and standardized metadata, supporting the findability, accessibility, interoperability and reusability of the data and metadata in accordance with the FAIR data principles. This paper introduces the first metadata schemas for the analytical methods of anion and cation ion-exchange chromatography measurements. The developed schemas are aligned with the ASTM E1151:1993 norm defining terms and relationships in ion chromatography. They are implemented by using the common JSON schema standard and publicly shared for direct use and further refinement for various application fields of IC measurements. Approval of the schema in laboratory environments was achieved in the field of applied plasma science and two examples from different use cases of water analysis for plasma applications are represented. Through practical application, the introduced schemas have demonstrated their effectiveness in laboratory environments, marking a step forward in standardizing the documentation of IC measurements to support advanced research data management and the application of modern data science methods.

Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring

Aim

Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring.

Innovation

In this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada.

Main conclusions

We retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques.

Affective memories and perceived value: motivators and inhibitors of the data search-access process

Purpose

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.

Design/methodology/approach

This study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.

Findings

The results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.

Originality/value

Through the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.

Untargeted Metabolomics for Integrative Taxonomy: Metabolomics, DNA Marker-Based Sequencing, and Phenotype Bioimaging

Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods such as DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We used three thallose liverwort species Riccia glauca, R. sorocarpa, and R. warnstorfii (order Marchantiales, Ricciaceae) with Lunularia cruciata (order Marchantiales, Lunulariacea) as an outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) were integrated with DNA marker-based sequencing of the trnL-trnF region and high-resolution bioimaging. Our untargeted chemotaxonomy methodology enables us to distinguish taxa based on chemophenetic markers at different levels of complexity: (1) molecules, (2) compound classes, (3) compound superclasses, and (4) molecular descriptors. For the investigated Riccia species, we identified 71 chemophenetic markers at the molecular level, a characteristic composition in 21 compound classes, and 21 molecular descriptors largely indicating electron state, presence of chemical motifs, and hydrogen bonds. Our untargeted approach revealed many chemophenetic markers at different complexity levels that can provide more mechanistic insight into phylogenetic delimitation of species within a clade than genetic-based methods coupled with traditional morphology-based information. However, analytical and bioinformatics analysis methods still need to be better integrated to link the chemophenetic information at multiple scales.

BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain

Background

Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. To support the management and analysis of these data, two techniques from computer science are of interest, namely Named Entity Recognition (NER) and Relation Extraction (RE). While the former enables better content discovery and understanding, the latter fosters the analysis by detecting connections between entities and, thus, allows us to draw conclusions and answer relevant domain-specific questions. To automatically predict entities and their relations, machine/deep learning techniques could be used. The training and evaluation of those techniques require labelled corpora.

New information

In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Macro- and microscopic images of organisms are pivotal in biodiversity research. Despite that bioimages have manifold applications such as assessing the diversity of form and function, FAIR bioimaging data in the context of biodiversity are still very scarce, especially for difficult taxonomic groups such as bryophytes. Here, we present a high-quality reference dataset containing macroscopic and bright-field microscopic images documenting various phenotypic characters of the species belonging to the liverwort family of Scapaniaceae occurring in Europe. To encourage data reuse in biodiversity and adjacent research areas, we annotated the imaging data with machine-actionable metadata using community-accepted semantics. Furthermore, raw imaging data are retained and any contextual image processing like multi-focus image fusion and stitching were documented to foster good scientific practices through source tracking and provenance. The information contained in the raw images are also of particular interest for machine learning and image segmentation used in bioinformatics and computational ecology. We expect that this richly annotated reference dataset will encourage future studies to follow our principles.

Data sharing practices across knowledge domains: A dynamic examination of data availability statements in PLOS ONE publications

As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal’s data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities.

MITI minimum information guidelines for highly multiplexed tissue images

The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the bio text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of bio is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the bio annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.

A Test Collection for Dataset Retrieval in Biodiversity Research

Searching for scientific datasets is a prominent task in scholars' daily research practice. A variety of data publishers, archives and data portals offer search applications that allow the discovery of datasets. The evaluation of such dataset retrieval systems requires proper test collections, including questions that reflect real world information needs of scholars, a set of datasets and human judgements assessing the relevance of the datasets to the questions in the benchmark corpus. Unfortunately, only very few test collections exist for a dataset search. In this paper, we introduce the BEF-China test collection, the very first test collection for dataset retrieval in biodiversity research, a research field with an increasing demand in data discovery services. The test collection consists of 14 questions, a corpus of 372 datasets from the BEF-China project and binary relevance judgements provided by a biodiversity expert.