Making experimental data tables in the life sciences more FAIR: a pragmatic approach
Ontology highlight
ABSTRACT:
SUBMITTER: Jacob D
PROVIDER: S-EPMC7736789 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The paper proposes a new method for conducting a literature review by structured data of more than 2200 scientific articles and 1300 researchers on SSHPA (Social Sciences and Humanities Peer Awards), an open database of Vietnamese social scientists' scientific productivity. Based on the logical structure of SSHPA, the authors create a specialized database for the literature review: SDA (SSHPA Data Analysis). Combining expert's caliber and computational algorithms, SDA is expected to offer an immensely efficient and analytical based method of scanning data, hence ameliorating the traditional approach to conducting a literature review.•A specialized database for literature review is created using the scientific articles and author profiles from SSHPA, an open database of Vietnamese social scientists' productivity.•The review database assigns values of topics or methodological attributes to articles sourced from SSHPA.•Then, the authors can query comprehensive data tables, graphs, or diagrams to use for literature review.
| S-EPMC7115160 | biostudies-literature
Project description:Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.
| S-EPMC7594074 | biostudies-literature
Project description:Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.
| S-EPMC9203546 | biostudies-literature
Project description:Species checklists are a crucial source of information for research and policy. Unfortunately, many traditional species checklists vary wildly in their content, format, availability and maintenance. The fact that these are not open, findable, accessible, interoperable and reusable (FAIR) severely hampers fast and efficient information flow to policy and decision-making that are required to tackle the current biodiversity crisis. Here, we propose a reproducible, semi-automated workflow to transform traditional checklist data into a FAIR and open species registry. We showcase our workflow by applying it to the publication of the Manual of Alien Plants, a species checklist specifically developed for the Tracking Invasive Alien Species (TrIAS) project. Our approach combines source data management, reproducible data transformation to Darwin Core using R, version control, data documentation and publication to the Global Biodiversity Information Facility (GBIF). This checklist publication workflow is openly available for data holders and applicable to species registries varying in thematic, taxonomic or geographical scope and could serve as an important tool to open up research and strengthen environmental decision-making.
| S-EPMC7661093 | biostudies-literature
Project description:Observer bias and other "experimenter effects" occur when researchers' expectations influence study outcome. These biases are strongest when researchers expect a particular result, are measuring subjective variables, and have an incentive to produce data that confirm predictions. To minimize bias, it is good practice to work "blind," meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. Here, using text mining and a literature review, we find evidence that blind protocols are uncommon in the life sciences and that nonblind studies tend to report higher effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols in mind.
| S-EPMC4496034 | biostudies-literature
Project description:Data from high throughput experiments often produce far more results than can ever appear in the main text or tables of a single research article. In these cases, the majority of new associations are often archived either as supplemental information in an arbitrary format or in publisher-independent databases that can be difficult to find. These data are not only lost from scientific discourse, but are also elusive to automated search, retrieval and processing. Here, we use the nanopublication model to make scientific assertions that were concluded from a workflow analysis of Huntington's Disease data machine-readable, interoperable, and citable. We followed the nanopublication guidelines to semantically model our assertions as well as their provenance metadata and authorship. We demonstrate interoperability by linking nanopublication provenance to the Research Object model. These results indicate that nanopublications can provide an incentive for researchers to expose data that is interoperable and machine-readable for future use and preservation for which they can get credits for their effort. Nanopublications can have a leading role into hypotheses generation offering opportunities to produce large-scale data integration.
| S-EPMC4603842 | biostudies-literature
Project description:Experimental protocols are key when planning, performing and publishing research in many disciplines, especially in relation to the reporting of materials and methods. However, they vary in their content, structure and associated data elements. This article presents a guideline for describing key content for reporting experimental protocols in the domain of life sciences, together with the methodology followed in order to develop such guideline. As part of our work, we propose a checklist that contains 17 data elements that we consider fundamental to facilitate the execution of the protocol. These data elements are formally described in the SMART Protocols ontology. By providing guidance for the key content to be reported, we aim (1) to make it easier for authors to report experimental protocols with necessary and sufficient information that allow others to reproduce an experiment, (2) to promote consistency across laboratories by delivering an adaptable set of data elements, and (3) to make it easier for reviewers and editors to measure the quality of submitted manuscripts against an established criteria. Our checklist focuses on the content, what should be included. Rather than advocating a specific format for protocols in life sciences, the checklist includes a full description of the key data elements that facilitate the execution of the protocol.
| S-EPMC5978404 | biostudies-literature
Project description:Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
| S-EPMC6069748 | biostudies-literature
Project description:OBJECTIVES:To provide cancer patients and clinicians with more accurate estimates of a patient's life expectancy with respect to noncancer mortality, we estimated comorbidity-adjusted life tables and health-adjusted age. STUDY DESIGN AND SETTING:Using data from the Surveillance Epidemiology and End Results-Medicare database, we estimated comorbidity scores that reflect the health status of people who are 66 years of age and older in the year before cancer diagnosis. Noncancer survival by comorbidity score was estimated for each age, race, and sex. Health-adjusted age was estimated by systematically comparing the noncancer survival models with US life tables. RESULTS:Comorbidity, cancer status, sex, and race are all important predictors of noncancer survival; however, their relative impact on noncancer survival decreases as age increases. Survival models by comorbidity better predicted noncancer survival than the US life tables. The health-adjusted age and national life tables can be consulted to provide an approximate estimate of a person's life expectancy, for example, the health-adjusted age of a black man aged 75 years with no comorbidities is 67 years, giving him a life expectancy of 13 years. CONCLUSION:The health-adjusted age and the life tables adjusted by age, race, sex, and comorbidity can provide important information to facilitate decision making about treatment for cancer and other conditions.
| S-EPMC3934002 | biostudies-literature
Project description:Evidence suggests that women in academia are hindered by conscious and unconscious biases, and often feel excluded from formal and informal opportunities for research collaboration. In addition to ensuring fairness and helping to redress gender imbalance in the academic workforce, increasing women's access to collaboration could help scientific progress by drawing on more of the available human capital. Here, we test whether researchers tend to collaborate with same-gendered colleagues, using more stringent methods and a larger dataset than in past work. Our results reaffirm that researchers co-publish with colleagues of the same gender more often than expected by chance, and show that this 'gender homophily' is slightly stronger today than it was 10 years ago. Contrary to our expectations, we found no evidence that homophily is driven mostly by senior academics, and no evidence that homophily is stronger in fields where women are in the minority. Interestingly, journals with a high impact factor for their discipline tended to have comparatively low homophily, as predicted if mixed-gender teams produce better research. We discuss some potential causes of gender homophily in science.
| S-EPMC6485756 | biostudies-literature