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Background: As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada’s data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers’ data-sharing practices to best practices for research data management and sharing. Methods: We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse. Results: Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse. Interpretation: Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable. more...

PurposeThis study examined how the qualities of both data and documents of existing datasets can contribute to researchers' satisfaction of data reuse, and how it affects their data reuse intentions mediated by attitudinal and normative beliefs of data reuse.Design/methodology/approachA combined theoretical framework integrating IS (Information Systems) Success Model and the Theory of Planned Behavior (TPB) was utilized to develop the research model of researchers' data reuse, which was evaluated using structural equation modeling based on 820 survey responses from STEM disciplines in the US.FindingsThis study found that both data and document qualities significantly contribute to researchers' satisfaction of data reuse. Then, their satisfaction significantly increases perceived usefulness and subjective norm of data reuse, and it decreases perceived risk associated with data reuse. Finally, both perceived usefulness and subjective norm significantly increases their data reuse intentions.Research limitations/implicationsThe combined theoretical framework integrating IS success model and TPB provides a new theoretical lens in understanding researchers' data reuse behaviors affected by the qualities of both data and documents.Practical implicationsThe findings of this study provided several practical implications in promoting and facilitating researchers' data reuse behaviors by improving data and document qualities of existing datasets.Originality/valueThis is one of the initial studies focusing on the roles of data and document qualities in researchers' data reuse, and it provides a systematic view of how data and document qualities influence researchers' data reuse mediated by their satisfaction of data reuse and attitudinal and normative beliefs. more...

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a ‘data dump’ that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on ‘long-tail’ preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields. more...

This study first compares the related policies of OECD, EU, USA, and South Korea. Second, policy implications for South Korea will be derived from the comparison results. Through these discussions, the author examined the problems and policy directions in opening and utilizing the government data. And in an attempt to examine the problems of open government data based on the current status of South Korea, the author examined the related problems of open government data including the increase/decrease by three subcategories; the step by step status of provided data format; the status of government data opening by provider; the status of government data opening by category; the ratio of use cases to the number of cases provided; the relationship between information and content areas in the public sector and data reuse, and the reasons for not using open government data. Through this study, it is necessary to clarify obvious beneficiaries of the open government data initiative that can meet the demand of the citizen. First, the right to use open government data should be guaranteed. Regulations related to the protection of privacy information are necessary. And it is also necessary to open judicial records to the public. more...

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. more...

Public library users’ expectations of library resources and services related to open data as well as their own estimation of their data literacy skills are examined. Library users were surveyed fro... more...

Clinical dashboards summarize indicators of high-volume patient data in a concise, user-friendly visual format. There are few studies of the use of dashboards to improve professional practice in anesthesiology. The objective of the present study was to describe the user-centered development, implementation and preliminary evaluation of clinical dashboards dealing with anesthesia unit management and quality assessment in a French university medical center. User needs and technical requirements were identified in end user interviews and then synthesized. Several representations were then developed (according to good visualization practice) and submitted to end users for appraisal. Lastly, dashboards were implemented and made accessible for everyday use via the medical center’s network. After a period of use, end user feedback on the dashboard platform was collected as a system usability score (range 0 to 100). Seventeen themes (corresponding to 29 questions and 42 indicators) were identified. After prioritization and feasibility assessment, 10 dashboards were ultimately implemented and deployed. The dashboards variously addressed the unit’s overall activity, compliance with guidelines on intraoperative hemodynamics, ventilation and monitoring, and documentation of the anesthesia procedure. The mean (standard deviation) system usability score was 82.6 (11.5), which corresponded to excellent usability. We developed clinical dashboards for a university medical center’s anesthesia units. The dashboards’ deployment was well received by the center’s anesthesiologists. The dashboards’ impact on activity and practice after several months of use will now have to be assessed. Electronic supplementary material The online version of this article (10.1007/s10877-020-00522-x) contains supplementary material, which is available to authorized users. more...

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. more...

Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues. more...

Background The implementation of an electronic health record (EHR) with structured and standardized recording of patient data can improve data quality and reusability. Whether and how users perceive these advantages may depend on the preimplementation situation. Objective To determine whether the influence of implementing a structured and standardized EHR on perceived EHR use, data quality, and data reuse differed for users working with paper-based records versus a legacy EHR before implementation. Methods We used an electronic questionnaire to measure users' perception before implementation (2014), expected change, and perceived change after implementation (2016) on three themes. We included all health care professionals in two university hospitals in the Netherlands. Before jointly implementing the same structured and standardized EHR, one hospital used paper-based records and the other a legacy EHR. We compared perceptions before and after implementation for both centers. Additionally, we compared expected benefit with perceived benefit. Results We received 7,611 responses (4,537 before and 3,074 after implementation) of which 5,707 (75%) were from professionals reading and recording patient data. A total of 975 (13%) professionals responded to both before and after implementation questionnaires. In the formerly paper-based center staff perceived improvement in all themes after implementation. The legacy EHR center experienced deterioration of perceived EHR use and data reuse, and only one improvement in EHR use. In both centers, for half of the aspects at least 45% of responders experienced results worse than expected preimplementation. Conclusion Our results indicate that the preimplementation recording practice impacts the perceived effect of the implementation of a structured and standardized EHR. For almost half of the respondents the new EHR did not meet their expectations. Especially legacy EHR centers need to investigate the expectations as these might be different and less clear cut than those in paper-based centers. These expectations need to be addressed appropriately to achieve a successful implementation. more...

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients. more...

International audience; We collected user needs to define a process for setting up Federated Learning in a network of hospitals. We identified seven steps: consortium definition, architecture implementation, clinical study definition, data collection, initialization, model training and results sharing. This process adapts certain steps from the classical centralized multicenter framework and brings new opportunities for interaction thanks to the architecture of the Federated Learning algorithms. It is open for completion to cover a variety of scenarios. more...

Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industry. However, medical data characteristics such as relatively small datasets for rare diseases or imbalance in class distribution for rare conditions significantly restrains their adoption and reuse. Imbalanced datasets lead to difficulties in learning and obtaining accurate predictive models. This paper follows the FAIR paradigm and proposes a technique for the alignment of class distribution, which enables improving image classification performance in imbalanced data and ensuring data reuse. The experiments on the acne disease dataset support that the proposed framework outperforms the baselines and enable to achieve up to 5% improvement in image classification. more...

This paper describes a machine learning approach for annotating and analyzing data curation work logs at ICPSR, a large social sciences data archive. The systems we studied track curation work and coordinate team decision-making at ICPSR. Repository staff use these systems to organize, prioritize, and document curation work done on datasets, making them promising resources for studying curation work and its impact on data reuse, especially in combination with data usage analytics. A key challenge, however, is classifying similar activities so that they can be measured and associated with impact metrics. This paper contributes: 1) a schema of data curation activities; 2) a computational model for identifying curation actions in work log descriptions; and 3) an analysis of frequent data curation activities at ICPSR over time. We first propose a schema of data curation actions to help us analyze the impact of curation work. We then use this schema to annotate a set of data curation logs, which contain records of data transformations and project management decisions completed by repository staff. Finally, we train a text classifier to detect the frequency of curation actions in a large set of work logs. Our approach supports the analysis of curation work documented in work log systems as an important step toward studying the relationship between research data curation and data reuse., Comment: 10 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible more...

The reduction in energy consumption is key for deep neural networks (DNNs) to ensure usability and reliability, whether they are deployed on low-power end-nodes with limited resources or high-performance platforms that serve large pools of users. Leveraging the over-parametrization shown by many DNN models, convolutional neural networks (ConvNets) in particular, energy efficiency can be improved substantially preserving the model accuracy. The solution proposed in this work exploits the intrinsic redundancy of ConvNets to maximize the reuse of partial arithmetic results during the inference stages. Specifically, the weight-set of a given ConvNet is discretized through a clustering procedure such that the largest possible number of inner multiplications fall into predefined bins; this allows an off-line computation of the most frequent results, which in turn can be stored locally and retrieved when needed during the forward pass. Such a reuse mechanism leads to remarkable energy savings with the aid of a custom processing element (PE) that integrates an associative memory with a standard floating-point unit (FPU). Moreover, the adoption of an approximate associative rule based on a partial bit-match increases the hit rate over the pre-computed results, maximizing the energy reduction even further. Results collected on a set of ConvNets trained for computer vision and speech processing tasks reveal that the proposed associative-based hw-sw co-design achieves up to 77% in energy savings with less than 1% in accuracy loss. more...

In order to facilitate the reusing of open data from open data platforms’ catalogs, Web Application Programming Interfaces (APIs) are an important mechanism for reusers. However, there is a lack of suitable Web APIs to access data from open data platforms. Moreover, in most cases, the currently available APIs only allow to access catalog’s metadata or to download entire data resources (i.e. coarse-grain access to data), hampering the reuse of data. Therefore, we propose a model-based approach to automatically generate Web APIs from open data. Our generated Web APIs facilitate the access and reuse of specific data (i.e., providing fine-grain or query-level access to data), which will result in many societal and economic benefits such as transparency and innovation. With this approach we address open data publishers which will be able to include a Web API within their data, but also open data reusers in case of missing APIs. This APIfication process, which means the creation of APIs for every available dataset, is based on automatic, generic and standardised generation mechanisms. The performance and functioning of this approach is validated with different datasets, which successfully generates Web APIs that facilitate the reuse of data. This work has been funded by the National Foundation for Research, Technology and Development and the project TIN2016-78103-C2-2-R of the Spanish Ministry of Economy, Industry and Competitiveness. César González-Mora has a contract for predoctoral training with the Generalitat Valenciana and the European Social Fund by the grant ACIF/2019/044. more...

Older data in paper or analog format (e.g., field/lab notebooks, photos, maps) held in labs, offices, and archives across research institutions are an often overlooked resource for potential reuse in new scientific studies. However, there are few mechanisms to help researchers find existing analog data in order to reuse it. Yet, in the literature, reuse of historical data is particularly important in studies of biodiversity and climate change. We surveyed life science researchers at the University of Minnesota to understand and explore current and potential future use of historical data, attitudes around sharing and reusing data, and preservation of the data. Large amounts of historical data existed on our campus. Most researchers had reused or shared it, and many continued to add to their data sets. Some data had been scanned, over half of researchers have re-keyed some of their data into machine-readable format, and nearly all that were converted to a digital format were stored on unstable platforms and legacy formats. Researchers also expressed concerns about long-term preservation plans, or who to contact for assistance in planning for the future of the data, since much of these data are at risk for loss. Currently produced digital data sets are subject to guidelines and requirements developed at a national level. Solutions for historical analog data could benefit from a similar high-level treatment, and it will take experts from various fields to lead this effort. Given libraries’ expertise in data management and preservation, librarians are in a position to collaborate on devising cross-disciplinary solutions. more...

After 16.5 years the Spitzer Space Telescope was decommissioned on 30Jan2020. We present a look at the legacy of Spitzer: the 9200+ papers that have used data from the telescope and are catalogued in the Spitzer Bibliographical Database. Over the lifetime of this Great Observatory, cryogenic depletion and budget constraints brought on operational changes that in turn impacted the publication rates. This paper looks into the differences in publication rates between the Spitzer cryogenic and warm missions, and identifies those fields on the sky with especially high data reuse rates and many papers. In addition it provides a look into the citations of Spitzer fundamental papers, as well as how well authors identified the data they used. From data that were used once, to data that were used many times; the legacy of the Spitzer mission continues to grow even after the data collection has finished, and its full impact will not be known for years to come. more...

The Intergovernmental Panel on Climate Change (IPCC) currently prepares its Sixth Assessment Report (AR6). Its authors assess peer-reviewed scientific literature and recent climate datasets to info... more...

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 challenges, limitations and risks associated with it. 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 and use selected examples of such reuse from different disciplines to illustrate the enormous potential of the practice, while acknowledging their 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 the practice as a norm has the potential to benefit all stakeholders in the life sciences. more...

Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy. more...

In response to a need for diverse computing support for translational science teams, the Informatics Institute at the University of Alabama at Birmingham (UAB) has developed a prototype platform called UAB Biomedical Research Infrastructure Technology Enhancement (U-BRITE). This platform provides project management functionality, high-volume data storage, access to clinical data, processing of data through custom pipelines, and high-performance computing in an environment that is compliant with privacy regulations. The project was designed and developed with the help of four biomedical sciences teams, each with their own -omics data, clinical data, and research questions. This paper describes U-BRITE’s architecture (accessible at https://ubrite.org/) and the experience of the members of four teams who were its initial users. Our experience provides useful guidance for future data reuse and an open science model of collaborative biomedical research. more...

The number of processing elements (PEs) in a fixed-sized systolic accelerator is well matched for large and compute-bound DNNs; whereas, memory-bound DNNs suffer from PE underutilization and fail to achieve peak performance and energy efficiency. To mitigate this, specialized dataflow and/or micro-architectural techniques have been proposed. However, due to the longer development cycle and the rapid pace of evolution in the deep learning fields, these hardware-based solutions can be obsolete and ineffective in dealing with PE underutilization for state-of-the-art DNNs. In this work, we address the challenge of PE underutilization at the algorithm front and propose data reuse aware co-optimization (DRACO). This improves the PE utilization of memory-bound DNNs without any additional need for dataflow/micro-architecture modifications. Furthermore, unlike the previous co-optimization methods, DRACO not only maximizes performance and energy efficiency but also improves the predictive performance of DNNs. To the best of our knowledge, DRACO is the first work that resolves the resource underutilization challenge at the algorithm level and demonstrates a trade-off between computational efficiency, PE utilization, and predictive performance of DNN. Compared to the state-of-the-art row stationary dataflow, DRACO achieves 41.8% and 42.6% improvement in average PE utilization and inference latency (respectively) with negligible loss in predictive performance in MobileNetV1 on a $64\times64$ systolic array. DRACO provides seminal insights for utilization-aware DNN design methodologies that can fully leverage the computation power of systolic array-based hardware accelerators., Comment: Accepted as a conference paper in the IEEE Computer Society Annual Symposium on VLSI (ISVLSI). Limassol, CYPRUS, July 6-8, 2020 more...

With the increased density and the technology scaling, flash memory is more vulnerable to noise effects, overwhelmingly lowering the data retention time. The periodic data refresh technique is commonly used to retain the long-term data integrity. However, the frequent refresh requests also introduce the increased access conflict and severe write amplification, leading to sub-optimal performance and lifetime improvement. In this paper, we propose ApproxRefresh, which enables the uncorrectable data reuse with the assistance of approximate-computing applications in order to reduce the refresh costs. Specifically, we design a lightweight remapping-free refresh technique, called RFR, to periodically correct, compute an enhanced ECC and only remap new ECC parity bits, which dramatically reduces the refresh costs. Then, with approximate-read and precise-read hotness awareness, ApproxRefresh selectively adopts RFR or the traditional remapping-based refresh technique to reduce the refresh costs and the read disturbance. In addition, a corresponding ApproxRefresh-aware garbage collection algorithm is proposed to complete the design. Evaluations show that ApproxRefresh reduces the refresh latency by 35.09% over the state-of-the-art. more...

This paper builds on the investigation of data reuse in creative processes to discuss ‘epistemic pluralism’ and data ‘alter-value’ in research data management. Focussing on a specific non-designated community, we conducted semi-structured interviews with five artists in relation to five works. Data reuse is a critical component of all these works. The qualitative content analysis brings to light agonistic-antagonistic practices in data reuse and shows multiple deconstructions of the notion of data value as it is portrayed in the data reuse literature. Finally, the paper brings to light the benefits of including such practices in the conceptualization of data curation. more...

BackgroundThe benefits from responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for clinical trialists and sponsors. Recently, the International Committee of Medical Journal Editors (ICMJE) required a data sharing statement (DSS) from submissions reporting clinical trials effective July 1, 2018. We set out to evaluate the implementation of the policy in three leading medical journals (JAMA, Lancet, and New England Journal of Medicine (NEJM)).MethodsA MEDLINE/PubMed search of clinical trials published in the three journals between July 1, 2018 and April 4, 2020 identified 487 eligible trials (JAMA n = 112, Lancet n = 147, NEJM n = 228). Two reviewers evaluated each of the 487 articles independently. Captured outcomes were declared data availability, data type, access, conditions and reasons for data (un)availability, and funding sources.Findings334 (68.6%, 95% confidence interval (CI), 64.1%–72.5%) articles declared data sharing, with non-industry NIH-funded trials exhibiting the highest rates of declared data sharing (88.9%, 95% CI, 80.0%–97.8) and industry-funded trials the lowest (61.3%, 95% CI, 54.3%–68.3). However, only two IPD datasets were actually deidentified and publicly available as of April 10, 2020. The remaining were supposedly accessible via request to authors (42.8%, 143/334), repository (26.6%, 89/334), and company (23.4%, 78/334). Among the 89 articles declaring to store IPD in repositories, only 17 articles (19.1%) deposited data, mostly due to embargo and regulatory approval. Embargo was set in 47.3% (158/334) of data-sharing articles, and in half of them the period exceeded 1 year or was unspecified.InterpretationMost trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make clinical data available. However, a wide gap between declared and actual data sharing exists. To improve transparency and data reuse, journals should promote the use of unique pointers to dataset location and standardized choices for embargo periods and access requirements. All data, code, and materials used in this analysis are available on OSF at https://osf.io/s5vbg/. more...

The freely available European Parliament Proceedings Parallel Corpus, or Europarl, is one of the largest multilingual corpora available to date. Surprisingly, bibliometric analyses show that it has... more...

To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues. To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues. more...

Scholars in our field, Operations and Supply Chain Management (OSCM), are under high pressure to show research productivity. At most schools, this productivity is measured by the number of journal articles published. One possible response to such pressure is to improve research efficiency: publishing more journal articles from each data collection effort. In other words, using one dataset for multiple publications. As long as each publication makes a sufficient contribution, and authors ensure transparency in methods and consistency across publications, generating more than one publication from one data collection effort is possible. The aim of this Notes and Debates article, however, is to draw attention to inappropriate reuse of empirical data in OSCM research, to explain its implications and to suggest ways in which to promote research quality and integrity. Based on two cases of extensive data reuse in OSCM, eighteen problematic practices associated with the reuse of data across multiple journal articles are identified. Recommendations on this issue of data reuse are provided for authors, reviewers, editors and readers. more...

Purpose We explore the challenges of the secondary use of data in clinical information systems which critical care units in the National Health Service (England) are facing. Methods We conducted an online survey of critical care units in England regarding their practices in collecting and using clinical information systems and data. Results Critical care units use clinical information systems typically independently of hospital information systems and focus mainly on using data for auditing, management reporting and research. Respondents reported that extracting data from their clinical information system was difficult and that they would use stored data more if it were easier to access. Data extraction takes time and who extracts data, the training they receive and the tools they use affect the extraction and use of data. Conclusion A number of key challenges affect the secondary use of data in critical care: a lack of integration of information systems within critical care and across departments; barriers to accessing data; mismatched data tools and user requests. Data are predominantly used for reporting and research with less emphasis on using data to inform clinical practice. more...

This study explores the factors that influence the data reuse behaviors of scientists and identifies the generalized patterns that occur in data reuse across various disciplines. This research employed an integrated theoretical framework combining institutional theory and the theory of planned behavior. The combined theoretical framework can apply the institutional theory at the individual level and extend the theory of planned behavior by including relevant contexts. This study utilized a survey method to test the proposed research model and hypotheses. Study participants were recruited from the Community of Science's (CoS) Scholar Database, and a total of 1,528 scientists responded to the survey. A multilevel analysis method was used to analyze the 1,237 qualified responses. This research showed that scientists' data reuse intentions are influenced by both disciplinary level factors (availability of data repositories) and individual level factors (perceived usefulness, perceived concern, and the availability of internal resources). This study has practical implications for promoting data reuse practices. Three main areas that need to be improved are identified: Educating scientists, providing internal supports, and providing external resources and supports such as data repositories. more...

Geometrical variation is present in any production process and needs to be controlled, to ensure compliance with design requirements and minimize costs. Tolerancing of injected plastic parts (IPPs) is mostly performed in practice without using consistent criteria, many times resulting in tolerances which are unnecessarily narrow and incompatible with the respective manufacturing process capabilities. In this paper, a new method is proposed to support IPPs dimensional production tolerancing in early design phases, based on historical measurement data reuse and similarity analysis. Rule-based reasoning has been employed together with a scoring system to identify reliable and similar cases stored in a database, involving attributes of both the parts and the dimensional characteristics. Two different approaches for data recovery were considered, providing the ability to choose between a simplified and a more accurate analysis. The method feasibility is demonstrated by a case study involving 20 different IPPs and 24 dimensional characteristics from three suppliers. The proposed method outperformed the use of standard DIN 16742 to estimate production tolerances. Comparisons between the estimated production tolerances and the respective process capabilities resulted in maximum mean absolute deviation of 10% and Pearson’s r of about 0.6. more...

Many disciplines within the social sciences have a dynamic culture of sharing and reusing data. Because social science data differ from data in the hard sciences, it is necessary to explicitly examine social science data reuse. This study explores the data reuse behaviors of social scientists in order to better understand both the factors that influence those social scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing social scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that social scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated social scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced social scientists' intentions to reuse data by reducing the perceived effort involved. more...

Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin f\"ur Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements. more...

Climate change data and information is among those of the highest interest for cross-domain researchers, policy makers and the general public. Serving climate projection data to these diverse users requires detailed and accessible documentation.Thus, the CMIP6 (Coupled Model Intercomparison Project Phase 6) data infrastructure consists not only of the ESGF (Earth System Grid Federation) as the data dissemination component but additionally of ES-DOC (Earth System Documentation) and the Citation Service for describing the provenance of the data. These services provide further information on the data creation process (experiments, models, …) and data reuse (data references and licenses) and connect the data to other external resources like research papers.The contribution will present documentation of the climate change workflow around the furtherInfoURL page serving as an entry point. The challenges are to collect quality-controlled information from the international research community in different infrastructure components and to display them seamlessly alongside on the furtherInfoURL page. References / Links:CMIP6: https://pcmdi.llnl.gov/CMIP6/ ES-DOC: https://es-doc.org/ Citation Service: http://cmip6cite.wdc-climate.de more...

Pressing environmental and societal challenges demand the reuse of data on a much larger scale. Central to improvements on this front are approaches that support structured and detailed data descriptions of published data. In general, the reusability of scientific datasets such as measurements generated by instruments, observations collected in the field, and model simulation outputs, require information about the contexts through which they were produced. These contexts include the instrumentation, methods, and analysis software used. In current data curation practice, data providers often put a significant effort in capturing descriptive metadata about datasets. Nonetheless, metadata about instruments and methods provided by data authors are limited, and in most cases are unstructured.The ‘Interoperability’ principle of FAIR emphasizes the importance of using formal vocabularies to enable machine-understandability of data and metadata, and establishing links between data and related research entities to provide their contextual information (e.g., devices and methods). To support FAIR data, PANGAEA is currently elaborating workflows to enrich instrument information of scientific datasets utilizing internal as well as third party services and ontologies and their identifiers. This abstract presents our ongoing development within the projects FREYA and FAIRsFAIR as follows:Integrating the AWI O2A (Observations to Archives) framework and associated suite of tools within PANGAEA’s curatorial workflow as well as semi-automatized ingestion of observatory data. Linking data with their observation sources (devices) by recording the persistent identifiers (PID) from the O2A sensor registry system (sensor.awi.de) as part of the PANGAEA instrumentation database. Enriching device and method descriptions of scientific data by annotating them with appropriate vocabularies such as the NERC device type and device vocabularies or scientific methodology classifications. In our contribution we will also outline the challenges to be addressed in enabling FAIR vocabularies of instruments and methods. This includes questions regarding reliability and trustworthiness of third party ontologies and services. Further, challenges in content synchronisation across linked resources and implications on FAIRness levels of data sets such as dependencies on interlinked data sources and vocabularies.We will show in how far adapting, harmonizing and controlling the used vocabularies, as well as identifier systems between data provider and data publisher, improves the findability and re-usability of datasets , while keeping the curational overhead a slow as possible. This use case is a valuable example of how improving interoperability through harmonization efforts, though initially problematic and labor intensive, can benefits to a multitude of stakeholders in the long run: data users, publishers, research institutes, and funders. more...

This paper describes the design and implementation of a 16-channel polyphase filter bank (PFB) channelizer. The PFB channelizer structure implements a resource-efficient multichannel digital receiver for a set of frequency division multiplexed (FDM) signals that exist in a single sampled data stream. The implementation is based on the Zynq® field programmable gate array (FPGA) and aims to exploit the potential for data reuse and flexibility offered by the PFB channelizer structure. General design criteria are summarized for the 16-channel polyphase filter bank channelizer. Python and Vivado simulation results of the performance and operation for the design are also presented. more...

[EN] Investments in data infrastructures, data management, data repositories, and Open Data sharing policies and recommendations are viewed as increasingly important for scientific knowledge production. One of the underlying assumptions justifying these investments is that the more available Open Data becomes, then the greater the possibilities for creating new knowledge that can advance both science and human wellbeing. Yet efforts and investments in Open Data and other ways of data sharing only have value if data are actually reused. Recent scholarly efforts have brought forth some of the challenges and facilitators related to the reuse of data, in order to inform current and future policies and investments. However, despite these efforts, we still do not know why and how some researchers are successful in reusing data, despite the challenges they face, and why some researchers abandon the process of reusing data when facing such challenges. This dissertation aims to fill this gap by focusing on a causal explanation of the data reuse process, which it understands as being nested in broader patterns of researchers' motivations, scientific goals and decision-making strategies. The dissertation is comprised of three main elements. First, it proposes a heuristic model of the scientific actor, the bounded individual horizon (BIH) model, which understands that, on the one hand, researchers' work and careers are structured by their motivation to produce scientific contributions and rewards systems that prioritizes certain types of contributions. On the other hand, researchers' struggles to achieve their objective of creating new findings that accrue recognition and rewards occur within a frame of limited information and resources, conditioned by multiple institutional, social, and other factors. Second, the study proposes a mechanistic causal theoretical explanation that enables us to understand the data reuse process and its effects (outcomes). The data-reuse mechanism as it is called, enables us to understand how the satisficing behavior that characterizes scientific decision-making applies to the specific conditions and processes of data reuse. Third, a set of ten empirical case studies of data reuse in health research were conducted and are reported in the dissertation. These cases are analyzed and interpreted using the complementary theoretical lenses of the bounded individual horizon and the data-reuse mechanism approaches. The main findings explain that there is an apparent association between the extent and types of efforts required to reuse data, researchers' contextualized motivations, and broader goal-setting and decision-making frames. Access to data is a necessary condition for the reuse of data, yet is not sufficient for the reuse to happen. Characteristics of available data, including the context of their production, the extent of the preparation and stewarding of these data and their potential value in relation to researchers' motivations to make new scientific claims or generate background knowledge are found to be essential elements for understanding why some data reuse processes persist and succeed, while others do not. The thesis concludes that efforts and investments designed to reap the benefits of data reuse should also be expanded to include training researchers in data reuse, including to efficiently recognize opportunities, navigate the challenges of the reuse process, and be aware of and acknowledge the limitations of the use of secondary data. Without such investments, the promises and expectations linked to emerging data infrastructures, data repositories, data management guidelines and open science practices are argued to be far less likely to reach their full potential., [ES] Las inversiones en infraestructuras de datos, gestión de datos, repositorios de datos y políticas y recomendaciones de intercambio de Datos Abiertos (Open Data) se consideran cada vez más importantes para la producción del conocimiento científico. Una de las razones que justifica estas inversiones es que cuanto más Datos Abiertos haya, mayores serán las posibilidades de crear nuevo conocimiento que pueda hacer avanzar tanto la ciencia como el bienestar humano. Sin embargo, los esfuerzos y la inversión en Datos Abiertos y otras formas de compartirlos sólo tienen valor si se reutilizan realmente. Recientes trabajos académicos han puesto de manifiesto algunos de los retos y factores facilitadores relacionados con la reutilización de los datos, a fin de asesorar las políticas e inversiones actuales y futuras. Sin embargo, a pesar de esos esfuerzos, todavía desconocemos por qué y cómo algunos/as investigadores/as logran reutilizar los datos, a pesar de los retos a los que enfrentan, y por qué otros/as investigadores/as abandonan el proceso de reutilización de los datos. La presente tesis tiene por objeto llenar este vacío centrándose en una explicación causal del proceso de reutilización de los datos, que se entiende está inmersa en pautas de conducta más amplias que se relacionan con las motivaciones, los objetivos científicos y las estrategias de toma de decisiones de los/as investigadores/as. Este estudio consta de tres elementos principales. En primer lugar, propone un modelo heurístico del actor científico, el modelo del horizonte individual delimitado (BIH por su nombre en inglés, bounded individual horizon). En él se entiende que, por una parte, el trabajo y la carrera de los/as investigadores/as se estructuran en función de su motivación para producir contribuciones científicas y de los sistemas de recompensa que dan prioridad a determinados tipos de contribuciones. Por otra parte, los esfuerzos de los/as investigadores/as para lograr su objetivo de crear nuevos hallazgos que acumulen reconocimiento y recompensas se producen en un marco de información y recursos limitados, condicionados por múltiples factores institucionales, sociales y de otra índole. En segundo lugar, esta tesis propone una explicación teórica causal mecanicista que permite comprender el proceso de reutilización de los datos y sus efectos (resultados). El mecanismo de reutilización de datos (datareuse mechanism), como se denomina, nos permite comprender cómo la toma de decisiones científicas está caracterizada por una conducta que tiende a satisfacer esos objetivos en unas condiciones y procesos específicos de reutilización de datos. En tercer lugar, este estudio incluye los resultados del estudio empírico de diez estudios de casos de reutilización de datos en ciencias de la salud. Estos casos se han analizado e interpretado utilizando el modelo teórico del horizonte individual delimitado y los enfoques del mecanismo de reutilización de datos. Los resultados principales explican que existe una aparente asociación entre el alcance el alcance y tipo de esfuerzo requerido para reutilizar datos, las motivaciones contextualizadas de los/as investigadores/as y marcos más amplios de fijación de objetivos y toma de decisiones. El acceso a los datos es una condición necesaria para su reutilización, pero no es suficiente para que ésta se produzca. Para comprender por qué algunos procesos de reutilización de datos persisten y tienen éxito, mientras que otros no,son elementos esenciales: las características de los datos disponibles, incluido el contexto de su producción; el grado de preparación y administración de esos datos; y su potencial valor en relación con las motivaciones de los investigadores para hacer nuevas afirmaciones científicas o generar conocimientos de base. Este estudio concluye que los esfuerzos e inversiones destinados a aprovechar los beneficios de la reutilización de los datos también deberían ampliarse para incluir la capacitación de los/as investigadores/as en materia de reutilización de datos. En particular, debe insistirse en la capacidad para reconocer eficientemente las oportunidades, sortear los problemas del proceso de reutilización y ser conscientes y reconocer las limitaciones de la utilización de datos secundarios. Sin estas inversiones, las promesas y expectativas vinculadas a las emergentes infraestructuras de datos, los repositorios de datos, las directrices de gestión de datos y las prácticas científicas abiertas tienen muchas menos probabilidades de alcanzar su pleno potencial., [CA] Les inversions en infraestructures de dades, gestió de dades, repositoris de dades i polítiques i recomanacions d'intercanvi de Dades Obertes (Open Data) es consideren cada vegada més importants per a la producció del coneixement científic. Un dels supòsits subjacents que justifiquen aquestes inversions és que com més disponibles siguen les Dades Obertes, majors seran les possibilitats de crear nou coneixement que pugui fer avançar tant la ciència com el benestar humà. No obstant això, els esforços i les inversions en les Dades Obertes i altres maneres de compartir dades només tenen valor si les dades es reutilitzen realment. Recents investigacions acadèmics han posat de manifest alguns dels reptes i dels factors facilitadors relacionats amb la reutilització de les dades, a fi d'informar les polítiques i inversions actuals i futures. No obstant això, encara desconeixem per què i com alguns/es investigador(e)s aconsegueixen reutilitzar les dades, malgrat els reptes als quals s’enfronten, i per què altres investigador(e)s abandonen el procés de reutilització de les dades quan s'enfronten a aquests reptes. La present tesi té com a objectiu omplir aquest buit centrant-se en una explicació causal del procés de reutilització de dades, que s'entén que està associada amb pautes més àmplies derivades de les motivacions, els objectius científics i les estratègies de presa de decisions d’els/les investigador(e)s. La tesi consta de tres elements principals. En primer lloc, proposa un model heurístic de l'actor científic, el model de l'horitzó individual delimitat (BIH pel nom anglès, bounded individual horizon), que entén que, d'una banda, el treball i la carrera d’els/les investigador(e)s s'estructuren en funció de la seua motivació per a produir contribucions científiques i dels sistemes de recompensa que prioritzen determinats tipus de contribucions. D'altra banda, els esforços d’els/les investigador(e)s per aconseguir el seu objectiu d’obtenir nous resultats que acumulin reconeixement i recompenses es produeixen en un marc d'informació i recursos limitats, condicionats per múltiples factors institucionals, socials i d'altra índole. En segon lloc, aquesta tesi proposa una explicació teòrica causal mecanicista que permet comprendre el procés de reutilització de les dades i els seus efectes (resultats). El mecanisme de reutilització de dades (data-reuse mechanism), com es denomina, ens permet comprendre com el comportament satisfactori que caracteritza la presa de decisions científiques s'aplica a les condicions i processos específics de reutilització de dades. En tercer lloc, aquesta tesi inclou l'estudi empíric d'un conjunt de deu estudis de casos de reutilització de dades en ciències de la salut, així com també els resultats d’aquest estudi.. Aquests casos s'han analitzat i interpretat utilitzant les lents teòriques complementàries de l'horitzó individual delimitat i els enfocaments del mecanisme de reutilització de dades. Les principals conclusions expliquen que existeix una aparent associació entre l'abast i els tipus d'esforços necessaris per a reutilitzar dades, les motivacions contextualitzades d’els/les investigador(e)s i els marcs més amplis de fixació d'objectius i presa de decisions. L'accés a les dades és una condició necessària per a la seua reutilització, però no és suficient perquè aquesta es produeixi. Es considera que les característiques de les dades disponibles, inclòs el context de la seua producció, el grau de preparació i administració d'aquestes dades i el seu potencial valor en relació amb les motivacions d’els/les investigador(e)s per a fer noves afirmacions científiques o generar coneixements de base, són elements essencials per a comprendre per què alguns processos de reutilització de dades persisteixen i tenen èxit, mentre que uns altres no. Aquest estudi conclou que els esforços i inversions destinats a aprofitar els beneficis de la reutilització de dades també haurien d'ampliar-se per a incloure la capacitació d’els/les investigador(e)s en matèria de reutilització de dades, en particular per a reconèixer eficientment les oportunitats, superar els problemes del procés de reutilització i ser conscients i reconèixer les limitacions de la reutilització de dades secundàries. Sense aquests esforços i inversions, les promeses i expectatives vinculades a les infraestructures, repositoris i directrius de gestió de dades i les pràctiques científiques obertes tenen moltes menys probabilitats d'aconseguir el seu ple potencial. more...

The data deluge following the rise of Internet of Things contributes towards the creation of non-reusable data silos. Especially in the environmental sciences domain, syntactic and semantic heterogeneity hinders data re-usability as most times manual labour and domain expertise is required. Both the different syntaxes under which environmental timeseries are formatted and the implicit semantics which are used to describe them contribute to this end. Usually, the real meaning of data is obscured in a combination of short data labels, titles and various value codes, that require domain or institutional knowledge to decipher. The FAIR data principles for scientific data sharing are stewardship offer a framework based on community-adopted metadata. In this work, we present the Environmental Data Acquisition Module (EDAM) which focuses on data interoperability and reuse, and deals with syntactic and semantic heterogeneity using a template approach. Data curators draft templates to describe in an abstract fashion the syntax of the timeseries datasets they want to acquire or disseminate. They complement each template with a metadata file, which is used to annotate observables and their properties (including physical quantities and units of measurement) with terms from an ontology. EDAM employs a reasoner to infer compatibility among syntactically and semantically heterogeneous datasets, and enables timeseries, format and units of measurement transformation on-the-fly. Our approach utilizes a local ontology to store metadata about datasets, which enables EDAM to acquire and transform datasets which were originally stored with different semantics and syntaxes. We demonstrate EDAM in a case study where we transform meteorological input files of four agricultural models. Our approach, allows to cut across environmental data silos and facilitate timeseries reusability, as it enables users to (a) discover datasets in other formats, (b) transform them and (c) reuse them in their scientific workflows. This directly contributes to the toolshed for FAIR data management in environmental sciences. EDAM implementation has been released under an open-source license. more...

Data Intelligence 2(1/2), 96-107 (2020). doi:10.1162/dint_a_00032 special issue: "Special Issue: Emerging FAIR Practices / Issue Editors: Barend Mons, Erik Schultes, Annika Jacobsen", Published by MIT Press, Cambridge, MA more...

RESUMO O artigo apresenta análise exploratória das práticas e das percepções a respeito do acesso aberto a dados de pesquisa embasada em dados coletados por meio de survey, realizada com pesquisadores brasileiros. As 4.676 respostas obtidas demonstram que, apesar do grande interesse pelo tema, evidenciado pela prevalência de variáveis relacionadas ao compartilhamento e ao uso de dados e aos repositórios institucionais, não há clareza por parte dos sujeitos sobre os principais tópicos relacionados. Conclui-se que, apesar da maioria dos pesquisadores afirmar que compartilha dados de pesquisa, a disponibilização desses dados de forma aberta e irrestrita ainda não é amplamente aceita.Palavras-chave: Dados Abertos de Pesquisa; Compartilhamento de Dados; Reuso de Dados.ABSTRACT This article presents an exploratory analysis of the practices and perceptions regarding open access to research data based on information collected by a survey with Brazilian researchers. The 4,676 responses show that, despite the great interest in the topic, evidenced by the prevalence of variables related to data sharing and use and to institutional repositories, there is no clarity on the part of the subjects on the main related topics. We conclude that, although the majority of the researchers share research data, the availability of this data in an open and unrestricted way is not yet widely accepted.Keywords: Open Research Data; Data Sharing; Data Reuse. more...