Metadata Template Research Articles

FAIR data station for lightweight metadata management and validation of omics studies.

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

How to meet the data publication requirement for L&O Letters

Limnology and Oceanography Letters was conceived as a fully open science journal where all articles and their underlying data are freely available online (Soranno et al. 2021). The data publication requirement, described in the author guidelines (https://aslopubs.onlinelibrary.wiley.com/hub/journal/23782242/about/author-guidelines#data), ensures that the data set(s) analyzed in each study, and the metadata describing them, are easily accessible to support the authors' conclusions. This requirement is a journal commitment to the FAIR principles of improving Findability, Accessibility, Interoperability, and Reuse of scientific data (Wilkinson et al. 2016). Data publication benefits authors by fostering reproducibility, enhancing the credibility of your results, and increasing the likelihood that your paper will be cited (Christensen et al. 2019). Although the journal's online author guidelines explain the data-publication requirement, we receive submissions where that requirement is not fully met. Those submissions are returned unreviewed to authors, delaying the editorial process. As a guide for authors, I provide here a checklist of the five steps taken at the editorial office to verify that the data-publication requirements are fully met so your paper can be forwarded for review. 1. Have the authors published their data and metadata in a freely accessible repository with a permanent identifier and a URL for accessing both? Authors are required to publish the data file(s) they analyzed with metadata in a publicly accessible repository that issues a new DOI, including unique identifiers and other critical identifying components. Founding Editor Patricia Soranno provides a step-by-step guide for publishing data to meet this requirement (Soranno 2019). The published data can be embargoed during the review process, but the embargo must be removed once an article is accepted for publication. To be clear, the journal's requirement is not met by placing your data in supplementary material, by making data available on request, or by citing the DOI of a larger data set from which the analyzed data were extracted. Most data sets analyzed in this journal are built from original data produced by the authors, subsets of published data, merging data from multiple sources, and/or manipulating data. The intent is transparency and reproducibility of your work by requiring direct access to the exact version of data you created, compiled, and analyzed to support your conclusions. You can include ancillary data if they are given an identifier to distinguish them from the data you analyzed. The one exception to the data-publication requirement is for unusually large data sets that exceed the repository's maximum limit. In these cases, you can meet the requirement by publishing the code written to produce the data file(s) analyzed in your article. 2. If data and metadata have not been published before submission, do the authors provide a plan for meeting this requirement? The journal's policy allows authors to publish their data while their manuscript is under review. If your data have not been published at the time of manuscript submission, you will be asked to specify the repository where your data will be published and to include in your submission a metadata file following the L&O Letters Metadata Template (available in author guidelines). Submission of a metadata file is not necessary if you intend to publish your data in a repository that requires comprehensive documentation and metadata comparable to that in the L&O Letters Template (e.g., EDI, Zenodo, BCO-DMO, Pangea, and ScienceBase). In this case, you can simply provide the general URL to the data repository. 3. Have the authors included a Data Availability Statement? Authors must include a Data Availability Statement in their manuscript, just after the Author Contribution Statement. For an original submission where your data have not yet been published, insert the statement: (Data and metadata will be made available in the XXX data repository). For a revision, insert the statement: (Data and metadata are available at [insert the URL where your data are available]). 4. For a revised manuscript, do the authors provide the URL link for the published data that includes the metadata file? Revised manuscripts will be reviewed only after the data publication requirement is met, including publication of both data and metadata. 5. Do authors cite their data publication? Authors must include a citation of their data publication in the References section of their manuscript and cite this publication in the main body of the text (typically in Methods section). This step is essential for tracking the use of data sets and to ensure that data producers receive credit for their work as a citation.

GeoLOD: A Spatial Linked Data Catalog and Recommender

The increasing availability of linked data poses new challenges for the identification and retrieval of the most appropriate data sources that meet user needs. Recent dataset catalogs and recommenders provide advanced methods that facilitate linked data search, but none exploits the spatial characteristics of datasets. In this paper, we present GeoLOD, a web catalog of spatial datasets and classes and a recommender for spatial datasets and classes possibly relevant for link discovery processes. GeoLOD Catalog parses, maintains and generates metadata about datasets and classes provided by SPARQL endpoints that contain georeferenced point instances. It offers text and map-based search functionality and dataset descriptions in GeoVoID, a spatial dataset metadata template that extends VoID. GeoLOD Recommender pre-computes and maintains, for all identified spatial classes in the Web of Data (WoD), ranked lists of classes relevant for link discovery. In addition, the on-the-fly Recommender allows users to define an uncatalogued SPARQL endpoint, a GeoJSON or a Shapefile and get class recommendations in real time. Furthermore, generated recommendations can be automatically exported in SILK and LIMES configuration files in order to be used for a link discovery task. In the results, we provide statistics about the status and potential connectivity of spatial datasets in the WoD, we assess the applicability of the recommender, and we present the outcome of a system usability study. GeoLOD is the first catalog that targets both linked data experts and geographic information systems professionals, exploits geographical characteristics of datasets and provides an exhaustive list of WoD spatial datasets and classes along with class recommendations for link discovery.

Founding Limnology & Oceanography Letters: The challenges, risks, and rewards of launching a new scientific journal

Founding <i>Limnology &amp; Oceanography Letters</i>: The challenges, risks, and rewards of launching a new scientific journal

Role of Content Analysis in Improving the Curation of Experimental Data

&#x0D; As researchers are increasingly seeking tools and specialized support to perform research data management activities, the collaboration with data curators can be fruitful. Yet, establishing a timely collaboration between researchers and data curators, grounded in sound communication, is often demanding. In this paper we propose manual content analysis as an approach to streamline the data curator workflow. With content analysis curators can obtain domain-specific concepts used to describe experimental configurations in scientific publications, to make it easier for researchers to understand the notion of metadata and for the development of metadata tools. We present three case studies from experimental domains, one related to sustainable chemistry, one to photovoltaic generation and another to nanoparticle synthesis. The curator started by performing content analysis in research publications, proceeded to create a metadata template based on the extracted concepts, and then interacted with researchers. The approach was validated by the researchers with a high rate of accepted concepts, 84 per cent. Researchers also provide feedback on how to improve some proposed descriptors. Content analysis has the potential to be a practical, proactive task, which can be extended to multiple experimental domains and bridge the communication gap between curators and researchers.&#x0D; [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]&#x0D;

Quality of physicochemical data on nanomaterials: an assessment of data completeness and variability.

Grouping and read-across has emerged as a reliable approach to generate safety-related data on nanomaterials (NMs). However, its successful implementation relies on the availability of detailed characterisation of NM physicochemical properties, which allows the definition of groups based on read-across similarity. To this end, this study assessed the availability and completeness of existing (meta)data on 11 experimentally determined physicochemical properties and 18 NMs. Data on representative NMs were mainly extracted from existing datasets stored in the eNanoMapper database, now available on the European Observatory on Nanomaterials website, while data on case-study NMs were provided by their industrial manufacturers. The extent of available (meta)data was assessed and data gaps were identified, thereby determining future testing needs. Data completeness was assessed by using the information checklists included in the templates for data logging developed by the EU-funded projects NANoREG and GRACIOUS. A completeness score (CS) between 0 and 1 was calculated for each (meta)data unit, template section, property, technique and NM. The results show a heterogeneous distribution of available (meta)data across materials and properties, with none of the selected NMs fully characterised. The average CS calculated for representative NMs (0.43) was considerably lower than for case-study NMs (0.68). The low CS was largely caused by missing information on sample preparation and standard operating procedures, and was attributed to a lack of harmonised data reporting and entry procedure. This study therefore suggests that a persistent use of well-defined and harmonised reporting schemes for experimental results is a useful tool to increase (meta)data completeness and ensure their integration and reuse.

A reference set of curated biomedical data and metadata from clinical case reports

Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

Construction a Standardized Metadata Template to Extract Relevant Biomedical Insights from Clinical Case Reports

Construction a Standardized Metadata Template to Extract Relevant Biomedical Insights from Clinical Case Reports

Message from the Executive Director: Interview with Pat Soranno, Editor‐in‐Chief, L&amp;O Letters

Message from the Executive Director: Interview with Pat Soranno, Editor‐in‐Chief, <i>L&amp;O Letters</i>

Tropospheric water vapour isotopologue data (H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&amp;lt;sup&amp;gt;16&amp;lt;/sup&amp;gt;O, H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;O, and HD&amp;lt;sup&amp;gt;16&amp;lt;/sup&amp;gt;O) as obtained from NDACC/FTIR solar absorption spectra

Abstract. We report on the ground-based FTIR (Fourier transform infrared) tropospheric water vapour isotopologue remote sensing data that have been recently made available via the database of NDACC (Network for the Detection of Atmospheric Composition Change; ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA/) and via doi:10.5281/zenodo.48902. Currently, data are available for 12 globally distributed stations. They have been centrally retrieved and quality-filtered in the framework of the MUSICA project (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). We explain particularities of retrieving the water vapour isotopologue state (vertical distribution of H216O, H218O, and HD16O) and reveal the need for a new metadata template for archiving FTIR isotopologue data. We describe the format of different data components and give recommendations for correct data usage. Data are provided as two data types. The first type is best-suited for tropospheric water vapour distribution studies disregarding different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type is needed for analysing moisture pathways by means of H2O, δD-pair distributions.

How to Document ‐ Ocean Acidification Data

The number of ocean acidification (OA) studies has increased significantly over the last decade. Most of this was due to studies on biological responses of organisms to OA. The lack of a protocol to document biological response OA data prevents the research community from properly archiving, discovering, accessing, and utilizing this important body of OA data sets. In this e‐Lecture, we present how to document an OA data set by explaining major components of a metadata template, which can be applied to a broad spectrum of OA studies, including those studying the biological responses to OA. The major metadata components include Investigators, Title, Abstract, Temporal coverage, Spatial coverage, Geographic names, Location of organism collection, Platforms, Variable metadata clusters, Publications describing the data set, and Supplementary information. Of these components, Variable metadata clusters (variables and their metadata sub‐elements) are treated as the focal point of the template. In addition to variable name , other metadata elements include the observation type, whether it is an in‐situ observation, manipulation condition, or response variable, biological subject, life stage of the biological subject, etc. Information about how to access the metadata template files is also stated.

A metadata template for ocean acidification data

Abstract. This paper defines the best practices for documenting ocean acidification (OA) data and presents a framework for an OA metadata template. Metadata is structured information that describes and locates an information resource. It is the key to ensuring that a data set will be accessible into the future. With the rapid expansion of studies on biological responses to OA, the lack of a common metadata template to document the resulting data poses a significant hindrance to effective OA data management efforts. In this paper, we present a metadata template that can be applied to a broad spectrum of OA studies, including those studying the biological responses to OA. The "variable metadata section", which includes the variable name, observation type, whether the variable is a manipulation condition or response variable, and the biological subject on which the variable is studied, forms the core of this metadata template. Additional metadata elements, such as investigators, temporal and spatial coverage, and data citation, are essential components to complete the template. We explain the structure of the template, and define many metadata elements that may be unfamiliar to researchers.

A model of biodiversity information integration

We proposes a model of biodiversity information integration involving data describing biodiversity entities from different layers and angles using artificial intelligence. Based on analysis of current biodiversity database systems and the demands of biodiversity researchers, this model supports decision-making on biodiversity conservation and sustainable use. According to the Dublin Core metadata template and the standard of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), the model builds up a platform, in both website and CDROM forms, to publish data in different data formats (text, image, vector data, audio, and video) from distributed databases. Data exchange could be implemented smoothly among remote databases and/or between remote and local databases according to the standard of OAI-PMH.