"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

Big data, little data, no data : scholarship in the networkedworld

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Improving reporting standards for polygenic scores in risk prediction studies

Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.

What Drives Academic Data Sharing

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Polygenic risk scores (PRS), often aggregating the results from genome-wide association studies, can bridge the gap between the initial discovery efforts and clinical applications for disease risk estimation. However, there is remarkable heterogeneity in the reporting of these risk scores. This lack of adherence to reporting standards hinders the translation of PRS into clinical care. The ClinGen Complex Disease Working Group, in a collaboration with the Polygenic Score (PGS) Catalog, have updated the Genetic Risk Prediction (GRIPS) Reporting Statement to the current state of the field and to enable downstream utility. Drawing upon experts in epidemiology, statistics, disease-specific applications, implementation, and policy, this 22-item reporting framework defines the minimal information needed to interpret and evaluate a PRS, especially with respect to any downstream clinical applications. Items span detailed descriptions of the study population (recruitment method, key demographic and clinical characteristics, inclusion/exclusion criteria, and outcome definition), statistical methods for both PRS development and validation, and considerations for potential limitations of the published risk score and downstream clinical utility. Additionally, emphasis has been placed on data availability and transparency to facilitate reproducibility and benchmarking against other PRS, such as deposition in the publicly available PGS Catalog. By providing these criteria in a structured format that builds upon existing standards and ontologies, the use of this framework in publishing PRS will facilitate translation of PRS into clinical care and progress towards defining best practices. Summary In recent years, polygenic risk scores (PRS) have increasingly been used to capture the genome-wide liability underlying many human traits and diseases, hoping to better inform an individual’s genetic risk. However, a lack of adherence to existing reporting standards has hindered the translation of this important tool into clinical and public health practice; in particular, details necessary for benchmarking and reproducibility are underreported. To address this gap, the ClinGen Complex Disease Working Group and Polygenic Score (PGS) Catalog have updated the Genetic Risk Prediction (GRIPS) Reporting Statement into the 22-item Polygenic Risk Score Reporting Statement (PRS-RS). This framework provides the minimal information expected of authors to promote the validity, transparency, and reproducibility of PRS by encouraging authors to detail the study population, statistical methods, and potential clinical utility of a published score. The widespread adoption of this framework will encourage rigorous methodological consideration and facilitate benchmarking to ensure high quality scores are translated into the clinic.

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Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

/pdf/is-useful-research-data-usually-shared-an-investigation-of-11xmxyl4pn.pdf

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Data citation and reuse practice in biodiversity - Challenges of adopting a standard citation model

The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner.

Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations.

This is an accepted manuscript of an article published by Emerald in Online Information Review on 24/08/2021, available online at: https://doi.org/10.1108/OIR-04-2021-0204 
The accepted version of the publication may differ from the final published version.

Are data repositories fettered? A survey of current practices, challenges, and future technologies

Primary data collected during a research study is increasingly shared and may be re-used for new studies. To assess the extent of data sharing in favourable circumstances and whether such checks can be automated, this article investigates the summary statistics of primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 330 primary human GWAS papers. Of these, only 10.6% reported the location of a complete set of GWAS summary data, increasing from 4.3% in 2010 to 16.8% in 2017. Whilst information about whether data was shared was usually located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong norms regarding data sharing. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

/pdf/is-useful-research-data-usually-shared-an-investigation-of-4ocrg6o9ym.pdf

Nushrat Khan

Papers

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Data citation and reuse practice in biodiversity - Challenges of adopting a standard citation model

Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations.

Are data repositories fettered? A survey of current practices, challenges, and future technologies

Is useful research data usually shared? An investigation of genome-wide association study summary statistics