Public sharing of research datasets: a pilot study of associations

Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BACKGROUND

Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.

OBJECTIVE

This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.

METHODS

The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.

RESULTS

This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

OBJECTIVES

To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability.

DESIGN

Systematic review with meta-analysis of individual participant data.

DATA SOURCES

Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022.

REVIEW METHODS

Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis.

RESULTS

The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively.

CONCLUSIONS

The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework doi:10.17605/OSF.IO/7SX8U.

A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders

AIMS

To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD).

BACKGROUND

ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research.

METHODS

We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers.

RESULTS

Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented.

CONCLUSION

There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information.

Factors affecting digital technology access in vocational education

If policies are not thoroughly designed, technology integration may fail. As a result, users' perceptions of technology, especially access to digital technology, are critical for technology integration in education. This study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. The study also reports the structural model of the path analysis and tests of differences based on geographical areas. A scale adapted from prior studies was established, validated, and examined for its validity and reliability. A total of 1355 responses were measurable; partial least squares structural equation modeling (PLS-SEM) and t-test procedures were applied for the data analysis. The findings informed that the scale was valid and reliable. For the structural model, the strongest relationship emerged between motivational access and skills access, while the lowest existed between material access and skills access. However, motivational access has an insignificant effect on instructional use. The t-test results show that geographical areas were significantly different regarding all involved variables.

Is open science a double-edged sword?: data sharing and the changing citation pattern of Chinese economics articles

Data sharing is an important part of open science (OS), and more and more institutions and journals have been enforcing open data (OD) policies. OD is advocated to help increase academic influences and promote scientific discovery and development, but such a proposition has not been elaborated on well. This study explores the nuanced effects of the OD policies on the citation pattern of articles by using the case of Chinese economics journals. China Industrial Economics (CIE) is the first and only Chinese social science journal so far to adopt a compulsory OD policy, requiring all published articles to share original data and processing codes. We use the article-level data and difference-in-differences (DID) approach to compare the citation performance of articles published in CIE and 36 comparable journals. Firstly, we find that the OD policy quickly increased the number of citations, and each article on average received 0.25, 1.19, 0.86, and 0.44 more citations in the first four years after publication respectively. Furthermore, we also found that the citation benefit of the OD policy rapidly decreased over time, and even became negative in the fifth year after publication. In conclusion, this changing citation pattern suggests that an OD policy can be double edged sword, which can quickly increase citation performance but simultaneously accelerate the aging of articles.

Attenuated total reflection FTIR dataset for identification of type 2 diabetes using saliva

Diabetes is one of the top 5 non-communicable diseases that occur worldwide according to the World Health Organization. Despite not being a fatal disease, a late diagnosis as well as poor control can cause a fatal outcome, because of that, several studies have been carried out with the aim of proposing additional techniques to the gold standard to assist in the diagnosis and control of this disease in a non-invasive way. Considering the above, and in order to provide a solid starting point for future researches, we share a primary research dataset with 1040 saliva samples obtained by Fourier Transform Infrared Spectroscopy considering the Attenuated Total Reflectance method. Database include: gender, age, individuals (patients) with/without diabetes, the glucose value, and the result to the A1C test for the diabetic population. We believe that sharing dataset as is could increase experimentation, research, and analysis of spectra through different strategies broaden its range of applicability by chemists, doctors, physicists, computer scientists, among others, to identify the effects that the virus causes in the body and to propose possible clinical treatments as well as to develop devices that allow us to assist in the characterization of possible carriers.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection. DIANA orchestrates activity beginning with post-acquisition study discovery and ending with online notifications of findings. For AI applications, system latency (exam completion to system report time) was quantified and compared to that of clinicians (exam completion to initial report creation time). Mean DIANA latency was 9.04 ± 3.83 and 20.17 ± 10.16 min compared to clinician latency of 51.52 ± 58.9 and 65.62 ± 110.39 min for BA and ICH, respectively, with DIANA latencies being significantly lower (p < 0.001). DIANA's capabilities were also explored and found effective in retrieving and anonymizing protected health information for "big-data" medical imaging research and analysis. Mean per-image retrieval times were 1.12 ± 0.50 and 0.08 ± 0.01 s across x-ray and computed tomography studies, respectively. The data herein demonstrate that DIANA can flexibly integrate into existing hospital infrastructure and improve the process by which researchers/clinicians access imaging repository data. This results in a simplified workflow for large data retrieval and clinical integration of AI models.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases

BACKGROUND

The content of the clinical notes that have been continuously collected along patients' health history has the potential to provide relevant information about treatments and diseases, and to increase the value of structured data available in Electronic Health Records (EHR) databases. EHR databases are currently being used in observational studies which lead to important findings in medical and biomedical sciences. However, the information present in clinical notes is not being used in those studies, since the computational analysis of this unstructured data is much complex in comparison to structured data.

METHODS

We propose a two-stage workflow for solving an existing gap in Extraction, Transformation and Loading (ETL) procedures regarding observational databases. The first stage of the workflow extracts prescriptions present in patient's clinical notes, while the second stage harmonises the extracted information into their standard definition and stores the resulting information in a common database schema used in observational studies.

RESULTS

We validated this methodology using two distinct data sets, in which the goal was to extract and store drug related information in a new Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database. We analysed the performance of the used annotator as well as its limitations. Finally, we described some practical examples of how users can explore these datasets once migrated to OMOP CDM databases.

CONCLUSION

With this methodology, we were able to show a strategy for using the information extracted from the clinical notes in business intelligence tools, or for other applications such as data exploration through the use of SQL queries. Besides, the extracted information complements the data present in OMOP CDM databases which was not directly available in the EHR database.

Open Data Policies among Library and Information Science Journals

Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record. The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies. This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors. Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies.

The sharing of research data facing the COVID-19 pandemic

During the previous Ebola and Zika outbreaks, researchers shared their data, allowing many published epidemiological studies to be produced only from open research data, to speed up investigations and control of these infections. This study aims to evaluate the dissemination of the COVID-19 research data underlying scientific publications. Analysis of COVID-19 publications from December 1, 2019, to April 30, 2020, was conducted through the PubMed Central repository to evaluate the research data available through its publication as supplementary material or deposited in repositories. The PubMed Central search generated 5,905 records, of which 804 papers included complementary research data, especially as supplementary material (77.4%). The most productive journals were The New England Journal of Medicine, The Lancet and The Lancet Infectious Diseases, the most frequent keyword was pneumonia, and the most used repositories were GitHub and GenBank. An expected growth in the number of published articles following the course of the pandemics is confirmed in this work, while the underlying research data are only 13.6%. It can be deduced that data sharing is not a common practice, even in health emergencies, such as the present one. High-impact generalist journals have accounted for a large share of global publishing. The topics most often covered are related to epidemiological and public health concepts, genetics, virology and respiratory diseases, such as pneumonia. However, it is essential to interpret these data with caution following the evolution of publications and their funding in the coming months.

Does open data boost journal impact: evidence from Chinese economics

To encourage research transparency and replication, more and more journals have been requiring authors to share original datasets and analytic procedures supporting their publications. Does open data boost journal impact? In this article, we report one of the first empirical studies to assess the effects of open data on journal impact. China Industrial Economics (CIE) mandated authors to open their research data in the end of 2016, which is the first to embrace open data among Chinese journals and provides a natural experiment for policy evaluation. We use the data of 37 Chinese economics journals from 2001 to 2019 and apply synthetic control method to causally estimate the effects of open data, and our results show that open data has significantly increased the citations of journal articles. On average, the current- and second-year citations of articles published with CIE have increased by 1 ~ 4 times, and articles published before the open data policy also benefited from the spillover effect. Our findings suggest that journals can leverage compulsory open data to develop reputation and amplify academic impacts.

Oh, Behave!: PRESIDENTIAL ADDRESS, XXth International Conference on Infant Studies New Orleans, LA, US May 2016

Behavior is essential for understanding infant learning and development. Although behavior is transient and ephemeral, we have the technology to make it tangible and enduring. Video uniquely captures and preserves the details of behavior and the surrounding context. By sharing videos for documentation and data reuse, we can exploit the tremendous opportuni-ties provided by infancy research and overcome the important challenges in studying behavior. The Datavyu video coding software and Databrary digital video library provide tools and infrastructure for mining and sharing the richness of video. This article is based on my Presidential Address to the International Congress on Infant Studies in New Orleans, May 22, 2016 (Video 1 at https://www.databrary.org/volume/955/slot/39352/-?asset=190106. Given that the article de-scribes the power of video for understanding behavior, I use video clips rather than static images to illustrate most of my points, and the videos are shared on the Databrary library.

Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions

This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research. The purpose of the study was to identify the habits and current experiences of Spanish researchers in the health sciences in relation to the management and sharing of raw research data. Method: An electronic questionnaire composed of 40 questions divided into three blocks was designed. The three Section s contained questions on the following aspects: (A) personal information; (B) creation and reuse of data; and (C) preservation of data. The questionnaire was sent by email to a list of universities in Spain to be distributed among their researchers and professors. A total of 1063 researchers completed the questionnaire. More than half of the respondents (54.9%) lacked a data management plan; nearly a quarter had storage systems for the research group; 81.5% used personal computers to store data; “Contact with colleagues” was the most frequent means used to locate and access other researchers’ data; and nearly 60% of researchers stated their data were available to the research group and collaborating colleagues. The main fears about sharing were legal questions (47.9%), misuse or interpretation of data (42.7%), and loss of authorship (28.7%). The results allow us to understand the state of data sharing among Spanish researchers and can serve as a basis to identify the needs of researchers to share data, optimize existing infrastructure, and promote data sharing among those who do not practice it yet.

Citations to chemical databases in scholarly articles: to cite or not to cite?

Purpose

Chemical databases have had a significant impact on the way scientists search for and use information. The purpose of this paper is to spark informed discussion and fuel debate on the issue of citations to chemical databases.

Design/methodology/approach

A citation analysis to four major chemical databases was undertaken to examine resource coverage and impact in the scientific literature. Two commercial databases (SciFinder and Reaxys) and two public databases (PubChem and ChemSpider) were analyzed using the “Cited Reference Search” in the Science Citation Index Expanded from the Web of Science (WoS) database. Citations to these databases between 2000 and 2016 (inclusive) were evaluated by document types and publication growth curves. A review of the distribution trends of chemical databases in peer-reviewed articles was conducted through a citation count analysis by country, organization, journal and WoS category.

Findings

In total, 862 scholarly articles containing a citation to one or more of the four databases were identified as only steadily increasing since 2000. The study determined that authors at academic institutions worldwide reference chemical databases in high-impact journals from notable publishers and mainly in the field of chemistry.

Originality/value

The research is a first attempt to evaluate the practice of citation to major chemical databases in the scientific literature. This paper proposes that citing chemical databases gives merit and recognition to the resources as well as credibility and validity to the scholarly communication process and also further discusses recommendations for citing and referencing databases.

The formation of research ethics for data sharing by biological scientists: an empirical analysis

Purpose

The purpose of this paper is to investigate how biological scientists form research ethics for data sharing, and what the major factors affecting biological scientists’ formation of research ethics for data sharing are.

Design/methodology/approach

A research model for data sharing was developed based on the consequential theorists’ perspective of ethics. An online survey of 577 participants was administered, and the proposed research model was validated with a structural equation modeling technique.

Findings

The results show that egoism factors (perceived reputation, perceived risk, perceived effort), utilitarianism factors (perceived community benefit and perceived reciprocity) and norm of practice factors (perceived pressure by funding agency, perceived pressure by journal and norm of data sharing) all contribute to the formation of research ethics for data sharing.

Research limitations/implications

This research employed the consequentialist perspective of ethics for its research model development, and the proposed research model nicely explained how egoism, utilitarianism and norm of practice factors influence biological scientists’ research ethics for data sharing, which eventually leads to their data sharing intentions.

Practical implications

This research provides important practical implications for examining scientists’ data sharing behaviors from the perspective of research ethics. This research suggests that scientists’ data sharing behaviors can be better facilitated by emphasizing their egoism, utilitarianism and normative factors involved in research ethics for data sharing.

Originality/value

The ethical perspectives in data sharing research has been under-studied; this research sheds light on biological scientists’ formation of research ethics for data sharing, which can be applied in promoting scientists’ data sharing behaviors across different disciplines.

Open-access policy and data-sharing practice in UK academia

Data sharing can be defined as the release of research data that can be used by others. With the recent open-science movement, there has been a call for free access to data, tools and methods in academia. In recent years, subject-based and institutional repositories and data centres have emerged along with online publishing. Many scientific records, including published articles and data, have been made available via new platforms. In the United Kingdom, most major research funders had a data policy and require researchers to include a ‘data-sharing plan’ when applying for funding. However, there are a number of barriers to the full-scale adoption of data sharing. Those barriers are not only technical, but also psychological and social. A survey was conducted with over 1800 UK-based academics to explore the extent of support of data sharing and the characteristics and factors associated with data-sharing practice. It found that while most academics recognised the importance of sharing research data, most of them had never shared or reused research data. There were differences in the extent of data sharing between different gender, academic disciplines, age and seniority. It also found that the awareness of Research Council UK’s (RCUK) Open-Access (OA) policy, experience of Gold and Green OA publishing, attitudes towards the importance of data sharing and experience of using secondary data were associated with the practice of data sharing. A small group of researchers used social media such as Twitter, blogs and Facebook to promote the research data they had shared online. Our findings contribute to the knowledge and understanding of open science and offer recommendations to academic institutions, journals and funding agencies.

A Systems Approach to Evaluate One Health Initiatives

Challenges calling for integrated approaches to health, such as the One Health (OH) approach, typically arise from the intertwined spheres of humans, animals, and ecosystems constituting their environment. Initiatives addressing such wicked problems commonly consist of complex structures and dynamics. As a result of the EU COST Action (TD 1404) "Network for Evaluation of One Health" (NEOH), we propose an evaluation framework anchored in systems theory to address the intrinsic complexity of OH initiatives and regard them as subsystems of the context within which they operate. Typically, they intend to influence a system with a view to improve human, animal, and environmental health. The NEOH evaluation framework consists of four overarching elements, namely: (1) the definition of the initiative and its context, (2) the description of the theory of change with an assessment of expected and unexpected outcomes, (3) the process evaluation of operational and supporting infrastructures (the "OH-ness"), and (4) an assessment of the association(s) between the process evaluation and the outcomes produced. It relies on a mixed methods approach by combining a descriptive and qualitative assessment with a semi-quantitative scoring for the evaluation of the degree and structural balance of "OH-ness" (summarised in an OH-index and OH-ratio, respectively) and conventional metrics for different outcomes in a multi-criteria-decision-analysis. Here, we focus on the methodology for Elements (1) and (3) including ready-to-use Microsoft Excel spreadsheets for the assessment of the "OH-ness". We also provide an overview of Element (2), and refer to the NEOH handbook for further details, also regarding Element (4) (http://neoh.onehealthglobal.net). The presented approach helps researchers, practitioners, and evaluators to conceptualise and conduct evaluations of integrated approaches to health and facilitates comparison and learning across different OH activities thereby facilitating decisions on resource allocation. The application of the framework has been described in eight case studies in the same Frontiers research topic and provides first data on OH-index and OH-ratio, which is an important step towards their validation and the creation of a dataset for future benchmarking, and to demonstrate under which circumstances OH initiatives provide added value compared to disciplinary or conventional health initiatives.

Data Sharing in Psychology: A Survey on Barriers and Preconditions

Despite its potential to accelerate academic progress in psychological science, public data sharing remains relatively uncommon. In order to discover the perceived barriers to public data sharing and possible means for lowering them, we conducted a survey, which elicited responses from 600 authors of articles in psychology. The results confirmed that data are shared only infrequently. Perceived barriers included respondents’ belief that sharing is not a common practice in their fields, their preference to share data only upon request, their perception that sharing requires extra work, and their lack of training in sharing data. Our survey suggests that strong encouragement from institutions, journals, and funders will be particularly effective in overcoming these barriers, in combination with educational materials that demonstrate where and how data can be shared effectively.

Incorporating data sharing to the reward system of science

Purpose

It is widely recognized that sharing data is beneficial not only for science but also for the common good, and researchers are increasingly expected to share their data. However, many researchers are still not making their data available, one of the reasons being that this activity is not adequately recognized in the current reward system of science. Since the attribution of data sets to individual researchers is necessary if we are to include them in research evaluation processes, the purpose of this paper is to explore the feasibility of linking data set records from DataCite to the authors of articles indexed in the Web of Science.

Design/methodology/approach

DataCite and WoS records are linked together based on the similarity between the names of the data sets’ creators and the articles’ authors, as well as the similarity between the noun phrases in the titles of the data sets and the titles and abstract of the articles.

Findings

The authors report that a large number of DataCite records can be attributed to specific authors in WoS, and the authors demonstrate that the prevalence of data sharing varies greatly depending on the research discipline.

Originality/value

It is yet unclear how data sharing can provide adequate recognition for individual researchers. Bibliometric indicators are commonly used for research evaluation, but to date no large-scale assessment of individual researchers’ data sharing activities has been carried out.

Do usage counts of scientific data make sense? An investigation of the Dryad repository

Purpose

The purpose of this paper is to evaluate the impact of scientific data in order to assess the reliability of data to support data curation, to establish trust between researchers to support reuse of digital data and encourage researchers to share more data.

Design/methodology/approach

The authors compared the correlations between usage counts of associated data in Dryad and citation counts of articles in Web of Science in different subject areas in order to assess the possibility of using altmetric indicators to evaluate scientific data.

Findings

There are high positive correlations between usage counts of data and citation counts of associated articles. The citation counts of article’s shared data are higher than the average citation counts in most of the subject areas examined by the authors.

Practical implications

The paper suggests that usage counts of data could be potentially used to evaluate scholarly impact of scientific data, especially for those subject areas without special data repositories.

Originality/value

The study examines the possibility to use usage counts to evaluate the impact of scientific data in a generic repository Dryad by different subject categories.

Research data management in academic institutions: A scoping review

OBJECTIVE

The purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions.

MATERIALS AND METHODS

We conducted a scoping review by searching forty literature databases encompassing a broad range of disciplines from inception to April 2016. We included all study types and data extracted on study design, discipline, data collection tools, and phase of the research data lifecycle.

RESULTS

We included 301 articles plus 10 companion reports after screening 13,002 titles and abstracts and 654 full-text articles. Most articles (85%) were published from 2010 onwards and conducted within the sciences (86%). More than three-quarters of the articles (78%) reported methods that included interviews, cross-sectional, or case studies. Most articles (68%) included the Giving Access to Data phase of the UK Data Archive Research Data Lifecycle that examines activities such as sharing data. When studies were grouped into five dominant groupings (Stakeholder, Data, Library, Tool/Device, and Publication), data quality emerged as an integral element.

CONCLUSION

Most studies relied on self-reports (interviews, surveys) or accounts from an observer (case studies) and we found few studies that collected empirical evidence on activities amongst data producers, particularly those examining the impact of research data management interventions. As well, fewer studies examined research data management at the early phases of research projects. The quality of all research outputs needs attention, from the application of best practices in research data management studies, to data producers depositing data in repositories for long-term use.

Reproducible and reusable research: are journal data sharing policies meeting the mark?

Background

There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature.

Methods

The online author’s instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal’s data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal.

Results

A total of 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. A total of 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. A total of 9.1% of journals mentioned data sharing indirectly, and only 14.8% addressed protein, proteomic, and/or genomic data sharing. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing criteria. Open access journals were not more likely to require data sharing than subscription journals.

Discussion

Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable.

A brief assessment of researchers’ perceptions towards research data in India

The present paper dwells upon the importance of raw data for the development of science and research. The study includes an interview of 40 researchers and faculty members to understand their perception towards the raw data. It has suggested that the libraries can play a pivotal role in extending support to the researchers for organizing, archiving and preserving raw data for future use. Libraries may evolve a system at the university level wherein the researchers and faculty members be encouraged to deposit their raw research data in the institutional repositories, which most of the university libraries have developed.

Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis

Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis.

Has open data arrived at the British Medical Journal (BMJ)? An observational study

OBJECTIVE

To quantify data sharing trends and data sharing policy compliance at the British Medical Journal (BMJ) by analysing the rate of data sharing practices, and investigate attitudes and examine barriers towards data sharing.

DESIGN

Observational study.

SETTING

The BMJ research archive.

PARTICIPANTS

160 randomly sampled BMJ research articles from 2009 to 2015, excluding meta-analysis and systematic reviews.

MAIN OUTCOME MEASURES

Percentages of research articles that indicated the availability of their raw data sets in their data sharing statements, and those that easily made their data sets available on request.

RESULTS

3 articles contained the data in the article. 50 out of 157 (32%) remaining articles indicated the availability of their data sets. 12 used publicly available data and the remaining 38 were sent email requests to access their data sets. Only 1 publicly available data set could be accessed and only 6 out of 38 shared their data via email. So only 7/157 research articles shared their data sets, 4.5% (95% CI 1.8% to 9%). For 21 clinical trials bound by the BMJ data sharing policy, the per cent shared was 24% (8% to 47%).

CONCLUSIONS

Despite the BMJ's strong data sharing policy, sharing rates are low. Possible explanations for low data sharing rates could be: the wording of the BMJ data sharing policy, which leaves room for individual interpretation and possible loopholes; that our email requests ended up in researchers spam folders; and that researchers are not rewarded for sharing their data. It might be time for a more effective data sharing policy and better incentives for health and medical researchers to share their data.

Research data explored: an extended analysis of citations and altmetrics

In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data "impacts" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics "foot-prints" is even lower (4-9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.

Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data's reuse potential and compliance with journal data policies.

To Share or Not to Share? A Survey of Biomedical Researchers in the U.S. Southwest, an Ethnically Diverse Region

Background

Cancer health disparities research depends on access to biospecimens from diverse racial/ethnic populations. This multimethodological study, using mixed methods for quantitative and qualitative analysis of survey results, assessed barriers, concerns, and practices for sharing biospecimens/data among researchers working with biospecimens from minority populations in a 5 state region of the United States (Arizona, Colorado, New Mexico, Oklahoma, and Texas). The ultimate goals of this research were to understand data sharing barriers among biomedical researchers; guide strategies to increase participation in biospecimen research; and strengthen collaborative opportunities among researchers.

Methods and Population

Email invitations to anonymous participants (n = 605 individuals identified by the NIH RePORT database), resulted in 112 responses. The survey assessed demographics, specimen collection data, and attitudes about virtual biorepositories. Respondents were primarily principal investigators at PhD granting institutions (91.1%) conducting basic (62.3%) research; most were non-Hispanic White (63.4%) and men (60.6%). The low response rate limited the statistical power of the analyses, further the number of respondents for each survey question was variable.

Results

Findings from this study identified barriers to biospecimen research, including lack of access to sufficient biospecimens, and limited availability of diverse tissue samples. Many of these barriers can be attributed to poor annotation of biospecimens, and researchers’ unwillingness to share existing collections. Addressing these barriers to accessing biospecimens is essential to combating cancer in general and cancer health disparities in particular. This study confirmed researchers’ willingness to participate in a virtual biorepository (n = 50 respondents agreed). However, researchers in this region listed clear specifications for establishing and using such a biorepository: specifications related to standardized procedures, funding, and protections of human subjects and intellectual property. The results help guide strategies to increase data sharing behaviors and to increase participation of researchers with multiethnic biospecimen collections in collaborative research endeavors

Conclusions

Data sharing by researchers is essential to leveraging knowledge and resources needed for the advancement of research on cancer health disparities. Although U.S. funding entities have guidelines for data and resource sharing, future efforts should address researcher preferences in order to promote collaboration to address cancer health disparities.

Norms of data sharing in biological sciences: The roles of metadata, data repository, and journal and funding requirements

Institutional environments, comprising regulative pressures by funding agencies and journal publishers, and institutional resources, including the availabilities of data repositories and standards for metadata, function as important determinants in scientists’ data-sharing norms, attitudes and behaviours. This research investigates how these functions influence biological scientists’ data-sharing norms and how the data-sharing norms influence their data-sharing behaviours mediated by attitudes towards data sharing. The research model was developed based on the integration of institutional theory and theory of planned behaviour. The proposed research model was validated based on a total of 608 responses from a national survey conducted in the USA. The Partial Least Squares (PLS) was employed to analyse the survey data. Results show how institutional pressures by funding agencies and journals and the availabilities of data repository and metadata standards all have significant influences on data-sharing norms, which have significant influences on data-sharing behaviours, as mediated by attitudes towards data sharing.

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.

Mandated data archiving greatly improves access to research data

The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, structure. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000-fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with ∼8 d delay and some disagreement with authors. Given the long-term benefits of data accessibility to the academic community, we believe that journal-based mandatory data archiving policies and mandatory data availability statements should be more widely adopted.