Facilitate Data Discovery Research Articles

Defining the target population to make marine image-based biological data FAIR

Marine imaging studies have unique constraints on the data collected requiring a tool for defining the biological scope to facilitate data discovery, quality evaluation, sharing and reuse. Defining the ‘target population’ is way of scoping biological sampling or observations by setting the pool of organisms to be observed or sampled. It is used in survey design and planning, to determine statistical inference, and is critical for data interpretation and reuse (both images and derived data). We designed a set of attributes for defining and recording the target population in biological studies using marine photography, incorporating ecological and environmental delineation and marine imaging method constraints. We describe how this definition may be altered and recorded at different phases of a project. The set of attributes records the definition of the target population in a structured metadata format to enhance data FAIRness. It is designed as an extension to the image FAIR Digital Objects metadata standard, and we map terms to other biological data standards where possible. This set of attributes serves a need to update ecological metadata to align with new remotely-sensed data, and can be applied to other remotely-sensed ecological image data.

Keep It Simple: Using README Files to Advance Standardization in Chronobiology.

Standardization plays a crucial role in ensuring the reliability, reproducibility, and interoperability of research data in the biomedical sciences. Metadata standards are one foundation for the FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. They facilitate data discovery, understanding, and reuse. However, the adoption of metadata standards in biological research lags in practice. Barriers such as complexity, lack of incentives, technical challenges, resource constraints, and resistance to change hinder widespread adoption. In the field of chronobiology, standardization is essential but faces particular challenges due to the longitudinal nature of experimental data, diverse model organisms, and varied measurement techniques. To address these challenges, we propose an approach that emphasizes simplicity and practicality: the development of README templates tailored for particular data types and species. Through this opinion article, our intention is to initiate a dialogue and commence a community-driven standardization process by engaging potential contributors and collaborators.

Register animal-tracking tags to boost conservation.

The COVID-19 pandemic highlighted the value of pooling animal-tracking data — a global tag registry would facilitate data discovery and collaboration. The COVID-19 pandemic highlighted the value of pooling animal-tracking data — a global tag registry would facilitate data discovery and collaboration.

Community Data Mobilization in Wikidata: A paleontology perspective

Wikidata offers a centralized, accessible platform for working collaboratively to disambiguate entities, e.g., people associated with biodiversity collections, and to mobilize information about them. This shared information can be used to improve connections across institutions and individuals, to augment local data records, and to encourage expertise-sharing. Over the past year, the Paleo Data Working Group has organized several events to bring the Wikidata movement to paleontological collections. This talk will share key products and findings generated by these efforts, and note parallels to disciplines beyond paleontology. In the global collections community, momentum has been building to leverage linked data principles and facilitate data discovery using the Wikidata platform. An example of this is the increasing adoption of Wikidata specifically for the purpose of storing biographical information about people associated with collections, because doing so facilitates discovery about who is doing what work across multiple institutions, and with what impact (Groom et al. 2020, Güntsch et al. 2021). In March 2022, thirty participants gathered virtually for a participatory workshop, Using Wikidata to capture and share information about people in paleontology. This workshop was an introduction to finding, editing, and using data in Wikidata, focusing on people associated with paleontology collections (e.g., collectors, researchers, collections staff) as subjects. Together, workshop participants created or enhanced Wikidata records for around 100 individuals from a shared list, including a dozen female collectors previously known only by their husbands’ names, e.g., “Mrs. Paul E. Drez,” who we now know is Nancy Sue Drez. At the conclusion of the workshop, participants collaborated on an open-access document to share the findings. The goal of Guidelines for Using Wikidata to Mobilize Information about People in Collections: A Paleontology Perspective (see Bauer et al. 2022 for repository) is to further encourage uptake in the paleontological and collections communities. The first workshop was focused almost exclusively on the people connected to paleontological collections. A follow-up workshop in October 2022 entitled, Using Wikidata to capture and share information about paleontological collecting sites, significantly broadens this scope by exploring how Wikidata can be used to mobilize community knowledge about specimen collecting events, such as sites or expeditions. Paleontological collecting sites pose an information management challenge because they tend to be detail rich, attached to local but stable identifier schemes, and associated with specimens curated in multiple institutions. Participants in this workshop established a defined scope of information associated with a paleontological collection site–including flagging aspects of the data that may be problematic to share publicly–and subsequently were able to assess how these information needs might, or might not, fit into a linked data model on Wikidata. These findings are particularly relevant to the global collections community where we see a trend towards sharing specimen data from an event-based perspective (Schindel and Cook 2018, Robertson et al. 2019), as has long been the norm for paleontological specimens.

The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project.

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery.

Technological advances in imaging and data acquisition are leading to the development of petabyte-scale neuroscience image datasets. These large-scale volumetric datasets pose unique challenges since analyses often span the entire volume, requiring a unified platform to access it. In this paper, we describe the Brain Observatory Storage Service and Database (BossDB), a cloud-based solution for storing and accessing petascale image datasets. BossDB provides support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API). A key feature is the scalable indexing of spatial data and automatic and manual annotations to facilitate data discovery. Our project is open source and can be easily and cost effectively used for a variety of modalities and applications, and has effectively worked with datasets over a petabyte in size.

Data-sharing practices in publications funded by the Canadian Institutes of Health Research: a descriptive analysis.

Background:As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada’s data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers’ data-sharing practices to best practices for research data management and sharing.Methods:We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse.Results:Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse.Interpretation:Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable.

NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

PubChem BioAssay: 2017 update.

PubChem's BioAssay database (https://pubchem.ncbi.nlm.nih.gov) has served as a public repository for small-molecule and RNAi screening data since 2004 providing open access of its data content to the community. PubChem accepts data submission from worldwide researchers at academia, industry and government agencies. PubChem also collaborates with other chemical biology database stakeholders with data exchange. With over a decade's development effort, it becomes an important information resource supporting drug discovery and chemical biology research. To facilitate data discovery, PubChem is integrated with all other databases at NCBI. In this work, we provide an update for the PubChem BioAssay database describing several recent development including added sources of research data, redesigned BioAssay record page, new BioAssay classification browser and new features in the Upload system facilitating data sharing.

A Volunteered Geographic Information Framework to Enable Bottom-Up Disaster Management Platforms

Recent disasters, such as the 2010 Haiti earthquake, have drawn attention to the potential role of citizens as active information producers. By using location-aware devices such as smartphones to collect geographic information in the form of geo-tagged text, photos, or videos, and sharing this information through online social media, such as Twitter, citizens create Volunteered Geographic Information (VGI). To effectively use this information for disaster management, we developed a VGI framework for the discovery of VGI. This framework consists of four components: (i) a VGI brokering module to provide a standard service interface to retrieve VGI from multiple resources based on spatial, temporal, and semantic parameters; (ii) a VGI quality control component, which employs semantic filtering and cross-referencing techniques to evaluate VGI; (iii) a VGI publisher module, which uses a service-based delivery mechanism to disseminate VGI, and (iv) a VGI discovery component to locate, browse, and query metadata about available VGI datasets. In a case study we employed a FOSS (Free and Open Source Software) strategy, open standards/specifications, and free/open data to show the utility of the framework. We demonstrate that the framework can facilitate data discovery for disaster management. The addition of quality metrics and a single aggregated source of relevant crisis VGI will allow users to make informed policy choices that could save lives, meet basic humanitarian needs earlier, and perhaps limit environmental and economic damage.

The center for expanded data annotation and retrieval.

The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments.

A Minimum Data Set for Sharing Biobank Samples, Information, and Data: MIABIS

Numerous successful scientific results have emerged from projects using shared biobanked samples and data. In order to facilitate the discovery of underutilized biobank samples, it would be helpful if a global biobank register containing descriptive information about the samples existed. But first, for shared data to be comparable, it needs to be harmonized. In compliance with the aim of BBMRI (Biobanking and Biomolecular Resources Research Infrastructure), to harmonize biobanking across Europe, and the conclusion that the move towards a universal information infrastructure for biobanking is directly connected to the issues of semantic interoperability through standardized message formats and controlled terminologies, we have developed an updated version of the minimum data set for biobanks and studies using human biospecimens. The data set called MIABIS (Minimum Information About BIobank data Sharing) consists of 52 attributes describing a biobank's content. The aim is to facilitate data discovery through harmonization of data elements describing a biobank at the aggregate level. As many biobanks across Europe possess a tremendous amount of samples that are underutilized, this would help pave the way for biobank networking on a national and international level, resulting in time and cost savings and faster emergence of new scientific results.

Ontological Approach for Information Discovery in Internet Databases

The Internet has solved the age-old problem of network connectivity and thus enabling the potential access to, and data sharing among large numbers of databases. However, enabling users to discover useful information requires an adequate metadata infrastructure that must scale with the diversity and dynamism of both users' interests and Internet accessible databases. In this paper, we present a model that partitions the information space into a distributed, highly specialized domain ontologies. We also introduce inter-ontology relationships to cater for user-based interests across ontologies defined over Internet databases. We also describe an architecture that implements these two fundamental constructs over Internet databases. The aim of the proposed model and architecture is to eventually facilitate data discovery and sharing for Internet databases.