On the Reuse of Scientific Data

State of open science in cancer research

PURPOSE

This study has been focused on assessing the Open Science scenario of cancer research during the period 2011-2021, in terms of the derived scientific publications and raw data dissemination.

METHODS

A cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion.

RESULTS

50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository.

CONCLUSIONS

There has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations.

An archival perspective on pretraining data

Alongside an explosion in research and development related to large language models, there has been a concomitant rise in the creation of pretraining datasets-massive collections of text, typically scraped from the web. Drawing on the field of archival studies, we analyze pretraining datasets as informal archives-heterogeneous collections of diverse material that mediate access to knowledge. We use this framework to identify impacts of pretraining data creation and use beyond directly shaping model behavior and reveal how choices about what is included in pretraining data necessarily involve subjective decisions about values. In doing so, the archival perspective helps us identify opportunities for researchers who study the social impacts of technology to contribute to confronting the challenges and trade-offs that arise in creating pretraining datasets at this scale.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine

RATIONALE AND OBJECTIVES

Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science.

MATERIALS AND METHODS

A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus.

RESULTS

Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001).

CONCLUSION

Overall rates of availability for items were poor, leaving room for substantial improvement.

Open data-set identifier for open innovation and knowledge management

Purpose

This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.

Design/methodology/approach

Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).

Findings

This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.

Originality/value

From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.

Opportunities for improving data sharing and FAIR data practices to advance global mental health

It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.

Andre S, Wagner T, Bell JA. Attitudes and uses of archival materials among science-based anthropologists

While archival user studies have largely focused on humanities (and adjacent) scholars, this paper focuses on anthropologists engaged in scientific research. Based on qualitative results from an open-ended survey, we investigate how science-based anthropologists perceive and use archives in their work. We ask: How are science-based anthropologists and archaeologists reusing archival data in their research? What difficulties or barriers do they encounter in reusing archival data in scientific contexts? What attitudes or understandings about archival research are held by science-based anthropologists and archaeologists? Our findings primarily add to the body of literature about user experience in archives and more broadly to the emerging literature on archival data reuse. Major findings include (1) barriers and gatekeeping legacies that impact archival research and the ability of researchers to reuse data and (2) mixed perceptions about archives among researchers. We also discuss suggestions made by these communities of practice, and the ways that barriers to archival data reuse may stem from a lack of knowledge about core archival and information infrastructures among researcher communities. Together, this research showcases possible (re)uses of important primary source data in archives among scientific communities but highlights that barriers to access and misperceptions create a gap in exploiting that potential. We argue for a "re-imagining" of anthropological archives as relevant to contemporary communities and scientific pursuits toward a richer scientific research environment.

Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control

Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing.

Data management strategy for a collaborative research center

The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.

A Method to Derive the Characteristic and Kinetic Parameters of 1,1-Bis(tert-butylperoxy)cyclohexane from DSC Measurements

A differential scanning calorimetry (DSC) experiment was carried out to determine the thermal characteristics of harmful substances. Most experimenters only use the results of measurement and rarely conduct in-depth research on the variety of information behind the measurement. This study used Wolfram’s Mathematica as a DSC measurement research tool to plot the peak curve and derive the characteristic parameters graphically for 1,1-Bis(tert-butylperoxy)cyclohexane. The research steps included raw data cleansing, peak curve normalization, characteristic parameter derivation, and total reaction heat calculation. The kinetic parameters of individual data were derived through the Borchardt and Daniels method, and the autocatalytic model was also verified. We applied the derived characteristic parameters to simulate the peak curve through the Gaussian curve model, which can be used for estimating the peak curve of other heating rates. The derived kinetic parameters were used to observe the effects on the peak curve. The simulation can be used to plan the test results at other rates in a similar temperature range and can also be used to explore the influence of different kinetic parameters on the configuration of the shape of the peak curve and a preliminary model test of materials for materials DSC research.

Key Factors Affecting User Adoption of Open-Access Data Repositories in Intelligence and Security Informatics: An Affordance Perspective

Rich, diverse cybersecurity data are critical for efforts by the intelligence and security informatics (ISI) community. Although open-access data repositories (OADRs) provide tremendous benefits for ISI researchers and practitioners, determinants of their adoption remain understudied. Drawing on affordance theory and extant ISI literature, this study proposes a factor model to explain how the essential and unique affordances of an OADR (i.e., relevance, accessibility, and integration) affect individual professionals' intentions to use and collaborate with AZSecure, a major OADR. A survey study designed to test the model and hypotheses reveals that the effects of affordances on ISI professionals' intentions to use and collaborate are mediated by perceived usefulness and ease of use, which then jointly determine their perceived value. This study advances ISI research by specifying three important affordances of OADRs; it also contributes to extant technology adoption literature by scrutinizing and affirming the interplay of essential user acceptance and value perceptions to explain ISI professionals' adoptions of OADRs.

How do properties of data, their curation, and their funding relate to reuse?

Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter‐university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.

Toward a definition of digital object reuse

Purpose

The purpose of this paper is to present conceptual definitions for digital object use and reuse. Typically, assessment of digital repository content struggles to go beyond traditional usage metrics such as clicks, views or downloads. This is problematic for galleries, libraries, archives, museums and repositories (GLAMR) practitioners because use assessment does not tell a nuanced story of how users engage with digital content and objects.

Design/methodology/approach

This paper reviews prior research and literature aimed at defining use and reuse of digital content in GLAMR contexts and builds off of this group’s previous research to devise a new model for defining use and reuse called the use-reuse matrix.

Findings

This paper presents the use-reuse matrix, which visually represents eight categories and numerous examples of use and reuse. Additionally, the paper explores the concept of “permeability” and its bearing on the matrix. It concludes with the next steps for future research and application in the development of the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT).

Practical implications

The authors developed this model and definitions to inform D-CRAFT, an Institute of Museum and Library Services National Leadership Grant project. This toolkit is being developed to help practitioners assess reuse at their own institutions.

Originality/value

To the best of the authors’ knowledge, this paper is one of the first to propose distinct definitions that describe and differentiate between digital object use and reuse in the context of assessing digital collections and data.

The search for Schizosaccharomyces fission yeasts in environmental meta-transcriptomes

Fission yeast is an important model organism in evolutionary genetics and cell biology research. Nevertheless, most research is limited to a single laboratory strain and knowledge of its natural occurrence is limited, which reduces our understanding of its life history and hinders isolation of new strains from nature. Understanding the natural diversity of fission yeast can provide insight into its genetic and phenotypic diversity and the evolutionary processes that shaped these. Here we aimed to identify candidate natural habitats of fission yeasts by searching through a large collection of publicly available environmental metatranscriptomic datasets. Using a custom pipeline, we processed over 13,000 NCBI SRA accessions, from a wide range of 34 different environmental categories. Overall, we found a very low abundance of putative yeast transcripts, with most fission yeast signatures coming from the categories of 'food' and 'terrestrial arthropods'. Additionally, a signal could be found in a variety of marine and fresh aquatic habitats. Our results do not provide a conclusive answer on the natural habitat of fission yeasts, but our analysis further narrows the range of locations where fission yeasts naturally occur.

The non-linear impact of data handling on network diffusion models

Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the challenge of errors in data collection have been analyzed in the literature, here we highlight the importance of data handling in the modeling and simulation process, and how particular data handling errors can lead to errors in model output. We develop a framework for assessing the impact of potential data errors for models of spreading processes on networks, a broad class of models that capture many important real-world phenomena (e.g., epidemics, rumor spread, etc.). We focus on the susceptible-infected-removed (SIR) and Threshold models and examine how systematic errors in data handling impact the predicted spread of a virus (or information). Our results demonstrate that data handling errors can have significant impact on model conclusions especially in critical regions of a system.

Encoding Collective Knowledge, Instructing Data Reusers: The Collaborative Fixation of a Digital Scientific Data Set

This article provides a novel perspective on the use and reuse of scientific data by providing a chronological ethnographic account and analysis of how a team of researchers prepared an astronomical catalogue (a table of measured properties of galaxies) for public release. Whereas much existing work on data reuse has focused on information about data (such as metadata), whose form or lack has been described as a hurdle for reusing data successfully, I describe how data makers tried to instruct users through the processed data themselves. The fixation of this catalogue was a negotiation, resulting in what was acceptable to team members and coherent with the diverse data uses pertinent to their completed work. It was through preparing their catalogue as an ‘instructing data object’ that this team seeked to encode its members’ knowledge of how the data were processed and to make it consequential for users by devising methodical ways to structure anticipated uses. These methods included introducing redundancies that would help users to self-correct mistaken uses, selectively deleting data, and deflecting accountability through making notational choices. They dwell on an understanding of knowledge not as exclusively propositional (such as the belief in propositions), but as embedded in witnessable activities and the products of these activities. I discuss the implications of this account for philosophical notions of collective knowledge and for theorizing coordinative artifacts in CSCW. Eventually, I identify a tension between ‘using algorithms’ and ‘doing science’ in preparing data sets and show how it was resolved in this case.

Data and its (dis)contents: A survey of dataset development and use in machine learning research

In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases.

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

Purpose

The purpose of this study is to explore current practices, challenges and technological needs of different data repositories.

Design/methodology/approach

An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey.

Findings

In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%).

Originality/value

This study identifies the current challenges and needs for improving data repository functionalities and user experiences.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204

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.

Cohort profile: the Men and Parenting Pathways (MAPP) Study: a longitudinal Australian cohort study of men's mental health and well-being at the normative age for first-time fatherhood

PURPOSE

The Men and Parenting Pathways (MAPP) Study is a prospective investigation of men's mental health and well-being across the normative age for transitioning to fatherhood. This includes trajectories and outcomes for men who do and do not become fathers across five annual waves of the study.

PARTICIPANTS

Australian resident, English-speaking men aged 28-32 years at baseline were eligible. Recruitment was over a 2-year period (2015-2017) via social and traditional media and through engagement with study partners. Eight hundred and eighteen eligible men consented to participate. Of these, 664 men completed the first online survey of whom 608 consented to ongoing participation. Of the ongoing sample, 83% have participated in at least two of the first three annual online surveys.

FINDINGS TO DATE

Three waves of data collection are complete. The first longitudinal analysis of MAPP data, published in 2020, identified five profiles that characterise men's patterns of depressive symptom severity and presentations of anger. Profiles indicating pronounced anger and depressive symptoms were associated with fathers' lack of perceived social support, and problems with coparenting and bonding with infants. In a second study, MAPP data were combined with three other Australian cohorts in a meta-analysis of associations between fathers' self-reported sleep problems up to 3 years postpartum and symptoms of depression, anxiety and stress. Adjusted meta-analytic associations between paternal sleep and mental health risk ranged from 0.25 to 0.37.

FUTURE PLANS

MAPP is an ongoing cohort study. Waves 4 and 5 data will be ready for analyses at the end of 2021. Future investigations will include crossed-lagged and trajectory analyses that assess inter-relatedness and changing social networks, mental health, work and family life. A nested study of COVID-19 pandemic-related mental health and coping will add two further waves of data collection in a subsample of MAPP participants.

Metadata Made Easy: Develop and Use Domain-Specific Metadata Schemes by following the dmdScheme approach

Metadata plays an essential role in the long-term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain-specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co-development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet.We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain-specific scheme. We describe how to create a domain-specific scheme from this template, including the iterative co-development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation.The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML.Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain-specific schemes. A near-future priority is to provide methods to interface domain-specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.

A study of the roles of metadata standard and data repository in science, technology, engineering and mathematics researchers' data reuse

Purpose

This research investigates how the availabilities of both metadata standards and data repositories influence researchers' data reuse intentions either directly or indirectly as mediated by the norms of data reuse and their attitudes toward data reuse.

Design/methodology/approach

The theory of planned behavior (TPB) was employed to develop the research model of researchers' data reuse intentions, focusing on the roles of metadata standards, data repositories and norms of data reuse. The proposed research model was evaluated using the structural equation modeling (SEM) method based on the survey responses received from 811 STEM (science, technology, engineering and mathematics) researchers in the United States.

Findings

This research found that the availabilities of both metadata standards and data repositories significantly affect STEM researchers' norm of data reuse, which influences their data reuse intentions as mediated by their attitudes toward data reuse. This research also found that both the availability of data repositories and the norm of data reuse have a direct influence on data reuse intentions and that norm of data reuse significantly increases the effect of attitude toward data reuse on data reuse intention as a moderator.

Research limitations/implications

The modified model of TPB provides a new perspective in apprehending the roles of resource facilitating conditions such as the availabilities of metadata standards and data repositories in an individual's attitude, norm and their behavioral intention to conduct a certain behavior.

Practical implications

This study suggests that scientific communities need to develop more supportive metadata standards and data repositories by considering their roles in enhancing the community norm of data reuse, which eventually lead to data reuse behaviors.

Originality/value

This study sheds light on the mechanism of metadata standard and data repository in researchers' data reuse behaviors through their community norm of data reuse; this can help scientific communities and academic institutions to better support researchers in their data sharing and reuse behaviors.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0431

On Earth as It Is in Heaven: Proxy Measurements to Assess Sustainable Development Goals at the Company Level through CSR Indicators

In recent years, we have witnessed dramatic changes in the following two regards: First, Corporate Social Responsibility (CSR) practices have become ubiquitous in companies, and second, the challenge that Sustainable Development (SD) presents to society and to the planet has been illustrated in the United Nations’ Sustainable Development Goals (SDGs). In this context, this study aims to identify the extent to which companies address the SDGs through their CSR practices. We conducted the present research by first performing a content analysis to determine the communalities between CSR practices and the SDGs. Then, we performed a quantitative analysis to assess the performance of various companies in terms of common aspects of CSR and SDGs. The main findings are as follows: (1) Not all SDGs are related to CSR practices; (2) companies perform differently in their CSR practices and, consequently, make different contributions to the SDGs; and (3) there is little difference among company profiles regarding their performances in CSR practices towards the SDGs. The main contributions of this study are, first, that it provides a new perspective on the relationship between CSR and SD and, second, the creation of a list of what can be considered the minimum requirements for CSR practices if the SDGs are to be reached.

Dataset Reuse: Toward Translating Principles to Practice

The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse.

The reuse of public datasets in the life sciences: potential risks and rewards

The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.

Use and users of a social science research data archive

This study focuses on the use and users of Finnish social science research data archive. Study is based on enriched user data of the archive from years 2015–2018. Study investigates the number and type of downloaded datasets, the number of citations for data, the demographics of data downloaders and the purposes data are downloaded for. Datasets were downloaded from the archive 10346 times. Majority of the downloaded datasets are quantitative. Quantitative datasets are also more often cited, but the number of citations vary and does not always correlate with the number of downloads. Use of the archive varies by user’s country, organization, and discipline. Datasets from the archive were downloaded most often for study work, bachelor’s and master’s theses, and research purposes. It is likely that reusing research data will increase in the near future as more data will become available, scholars are more informed about research data management, and data citation practices are established.

A dataset describing data discovery and reuse practices in research

This paper presents a dataset produced from the largest known survey examining how researchers and support professionals discover, make sense of and reuse secondary research data. 1677 respondents in 105 countries representing a variety of disciplinary domains, professional roles and stages in their academic careers completed the survey. The results represent the data needs, sources and strategies used to locate data, and the criteria employed in data evaluation of these respondents. The data detailed in this paper have the potential to be reused to inform the development of data discovery systems, data repositories, training activities and policies for a variety of general and specific user communities.

Measurement(s)	data discovery behaviours • data reuse behaviours • data management
Technology Type(s)	Survey • Self-Report

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12445034

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

Towards reproducible computational drug discovery

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

FAIR Data Reuse – the Path through Data Citation

One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.

HastaLaVista, a web-based user interface for NMR-based untargeted metabolic profiling analysis in biomedical sciences: towards a new publication standard

Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral analysis and may increase the pace of new discovery. Improving the existing portfolio of open software may increase the fraction of shared data by decreasing the amount of effort required to publish them in a manner that is useful to others. However, a weakness of open software, when compared to commercial ones, is the lack of user-friendly graphical interface that may discourage inexperienced researchers. Here, a web-browser-oriented solution is presented and demonstrated for metabolic profiling analysis that combines the power of R for back-end statistical analyses and of JavaScript for front-end visualisations and user interactivity. This unique combination of statistical programming and web-browser visualisation brings enhanced data interoperability and interactivity into the open source realm. It is exemplified by characterizing the extent to which bariatric surgery perturbs the metabolisms of rats, showing the value of the approach in iterative analysis by the end-user to establish a deeper understanding of the system perturbation. HastaLaVista is available at: (https://github.com/jwist/hastaLaVista, 10.5281/zenodo.3544800) under MIT license. The approach described in this manuscript can be extended to connect the interface to other scripting languages such as Python, and to create interfaces for other types of data analysis.

Cyberecoethnopharmacolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Development of a new term which describes the contemporary, composite, constituent sciences of ethnopharmacology.

AIM OF THE STUDY

To discuss the polysyllabic term cyberecoethnopharmacolomics in the context of the future of ethnopharmacology in global health care.

MATERIALS AND METHODS

Literature background and assessment from the prior literature, diverse databases, and personal discussions.

RESULTS

The profiles and literature background with contemporary and future thoughts regarding the concepts and practices of cyber-, eco-, ethno-, pharmacol-, and -omics, and their impact in ethnopharmacology for the future are presented in the context of integrated health care systems.

CONCLUSIONS

Ethnopharmacology has a major role to play in global health care if the relevant sciences and cutting-edge technologies can coalesce synergistically as a responsive, evidence-based health care practice.

Sharing and reusing cell image data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.

Attitudes and norms affecting scientists' data reuse

The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour. To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves). Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations. We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value. We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.

ACCESS climate data management

Building on achievements and experience gained through the EU project DAMOCLES and international data management during the International Polar Year, ACCESS, data management was implemented using the same platform as used for DAMOCLES. A metadata-driven approach through which all datasets are properly described with discovery and use metadata was chosen in order to simplify data management and data usage. The system provides automated submission and checking of datasets, search and download as well as visualisation and transformation on user demand and metadata export. Long-term management of ACCESS climate datasets is done within the context of the Arctic Data Centre. This ensures visibility of ACCESS datasets in the context of WMO and GEOSS catalogues. Challenges with ACCESS data management have mainly been cultural with the consequence that the system has been underutilised within the duration of the project duration.

Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science

What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, "data for good" projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multiresearcher field sites, we develop a set of concepts for analyzing and advancing the practice of data science and improving critical data studies, including (1) communication is central to the data science endeavor; (2) making sense of data is a collective process; (3) data are starting, not end points, and (4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical, and better, ways of knowing in increasingly datum-saturated societies.