Dealing with data: a case study on information and data management literacy

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

Introducing immunohistochemistry to the molecular biology laboratory

Widely used in research laboratories, immunohistochemistry (IHC) is a transferable skill that prepares undergraduate students for a variety of careers in the biomedical field. We have developed an inquiry-based learning IHC laboratory exercise, which introduces students to the theory, procedure, and data interpretation of antibody staining. Students are tasked with performing IHC using an "unknown" antibody and then asked to identify the cells or molecular structures within the nervous systems specific for that unknown antibody. In two lab sessions, students are exposed to handling of delicate brain slices, fluorescent microscopy, and data analysis using the Allen Brain Atlas (ABA), an online freely accessible database of mRNA transcript expression patterns in the brain. Here, we present guidelines for easy implementation in the classroom and assess learning gains achieved by the students upon completion of the IHC laboratory module. Students clearly displayed an increase in knowledge in data interpretation, procedural knowledge, and theory surrounding IHC. Thus, this module works as an inquiry-based learning based method to introduce IHC principles to undergraduate students.

Algorithmic literacy in medical students - results of a knowledge test conducted in Germany

The impact of algorithms on everyday life is ever increasing. Medicine and public health are not excluded from this development - algorithms in medicine do not only challenge, change and inform research (methods) but also clinical situations. Given this development, questions arise concerning the competency level of prospective physicians, thus medical students, on algorithm related topics. This paper, based on a master's thesis in library and information science written at Humboldt-Universität zu Berlin, gives an insight into this topic by presenting and analysing the results of a knowledge test conducted among medical students in Germany. F. J.

Data literacy and management of research data – a prerequisite for the sharing of research data

Purpose

The purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research.

Design/methodology/approach

This is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% (N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data.

Findings

The main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods.

Research limitations/implications

The survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it.

Practical implications

Awareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management.

Originality/value

The study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management.

Research data sharing in the Australian national science agency: Understanding the relative importance of organisational, disciplinary and domain-specific influences

This study delineates the relative importance of organisational, research discipline and application domain factors in influencing researchers' data sharing practices in Australia's national scientific and industrial research agency. We surveyed 354 researchers and found that the number of data deposits made by researchers were related to the openness of the data culture and the contractual inhibitors experienced by researchers. Multi-level modelling revealed that organisational unit membership explained 10%, disciplinary membership explained 6%, and domain membership explained 4% of the variance in researchers' intentions to share research data. However, only the organisational measure of openness to data sharing explained significant unique variance in data sharing. Thus, whereas previous research has tended to focus on disciplinary influences on data sharing, this study suggests that factors operating within the organisation have the most powerful influence on researchers' data sharing practices. The research received approval from the organisation's Human Research Ethics Committee (no. 014/18).

Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine

Artificial intelligence-driven anesthesiology and perioperative care may just be around the corner. However, its promises of improved safety and patient outcomes can only become a reality if we take the time to examine its technical, ethical, and moral implications. The aim of perioperative medicine is to diagnose, treat, and prevent disease. As we introduce new interventions or devices, we must take care to do so with a conscience, keeping patient care as the main objective, and understanding that humanism is a core component of our practice. In our article, we outline key principles of artificial intelligence for the perioperative physician and explore limitations and ethical challenges in the field.

Long-term preservation of biomedical research data

Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning.

We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier.

The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.

Data literacy for researchers and data librarians

This paper describes data literacy and emphasizes its importance. Data literacy is vital for researchers who need to become data literate science workers and also for (potential) data management professionals. Its important characteristic is a close connection and similarity to information literacy. To support this argument, a review of literature was undertaken on the importance of data, and the data-intensive paradigm of scientific research, researchers’ expected and real behaviour, the nature of research data management, the possible roles of the academic library, data quality and data citation, Besides describing the nature of data literacy and enumerating the related skills, the application of phenomenographic approaches to data literacy and its relationship to the digital humanities have been identified as subjects for further investigation.

Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3-4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.

What drives academic data sharing?

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 as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

Collaborative mining and interpretation of large-scale data for biomedical research insights

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

On the reproducibility of science: unique identification of research resources in the biomedical literature

Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.

Text-mining and neuroscience

The wealth and diversity of neuroscience research are inherent characteristics of the discipline that can give rise to some complications. As the field continues to expand, we generate a great deal of data about all aspects, and from multiple perspectives, of the brain, its chemistry, biology, and how these affect behavior. The vast majority of research scientists cannot afford to spend their time combing the literature to find every article related to their research, nor do they wish to spend time adjusting their neuroanatomical vocabulary to communicate with other subdomains in the neurosciences. As such, there has been a recent increase in the amount of informatics research devoted to developing digital resources for neuroscience research. Neuroinformatics is concerned with the development of computational tools to further our understanding of the brain and to make sense of the vast amount of information that neuroscientists generate (French & Pavlidis, 2007). Many of these tools are related to the use of textual data. Here, we review some of the recent developments for better using the vast amount of textual information generated in neuroscience research and publication and suggest several use cases that will demonstrate how bench neuroscientists can take advantage of the resources that are available.