data reuse
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2021 ◽  
Author(s):  
Katherine E. O. Todd-Brown ◽  
Rose Z. Abramoff ◽  
Jeffrey Beem-Miller ◽  
Hava K. Blair ◽  
Stevan Earl ◽  
...  

Abstract. In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources- remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.


Author(s):  
Jeremy Logan ◽  
Greeshma Agasthya ◽  
Heidi Hanson ◽  
Matthew Wolf ◽  
Heechan Lee ◽  
...  

Author(s):  
Jose-Norberto Mazon ◽  
Rob Brennan ◽  
Markus Helfert
Keyword(s):  

Pharmacy ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 198
Author(s):  
Navila Talib Chaudhry ◽  
Bryony Dean Franklin ◽  
Salmaan Mohammed ◽  
Jonathan Benn

Objectives: To conduct a systematic review and narrative synthesis of interventions based on secondary use of data (SUD) from electronic prescribing (EP) and electronic hospital pharmacy (EHP) systems and their effectiveness in secondary care, and to identify factors influencing SUD. Method: The search strategy had four facets: 1. Electronic databases, 2. Medication safety, 3. Hospitals and quality/safety, and 4. SUD. Searches were conducted within EMBASE, Medline, CINAHL, and International Pharmaceutical Abstracts. Empirical SUD intervention studies that aimed to improve medication safety and/or quality, and any studies providing insight into factors affecting SUD were included. Results: We identified nine quantitative studies of SUD interventions and five qualitative studies. SUD interventions were complex and fell into four categories, with ‘provision of feedback’ the most common. While heterogeneous, the majority of quantitative studies reported positive findings in improving medication safety but little detail was provided on the interventions implemented. The five qualitative studies collectively provide an overview of the SUD process, which typically comprised nine steps from data identification to analysis. Factors influencing the SUD process were electronic systems implementation and level of functionality, knowledge and skills of SUD users, organisational context, and policies around data reuse and security. Discussion and Conclusion: The majority of the SUD interventions were successful in improving medication safety, however, what contributes to this success needs further exploration. From synthesis of research evidence in this review, an integrative framework was developed to describe the processes, mechanisms, and barriers for effective SUD.


2021 ◽  
pp. 146879412110522
Author(s):  
Kahryn Hughes ◽  
Vibeke A Frank ◽  
Maria D Herold ◽  
Esben Houborg

This research note reports on five online workshops by an international team of scholars, the authors, with shared interests in drug (mis)use. The workshops comprise a novel form of collective international qualitative secondary analysis (iQSA) exploring the possibilities for, and value of, qualitative data reuse across international contexts. These preparatory workshops comprise the preliminary stages of a longer programme of methodological development of iQSA, and we used them to identify what challenges there may be for translating evidence across international contexts, what strategies might be best placed to support or facilitate analytical engagement in this direction, and if possible, what empirical value such exchange might have. We discuss how working across international contexts involved the authors in new 'translational' work to address the challenges of establishing and sharing meaning. Such ‘translation’ entailed a modest degree of empirical engagement, namely, the casing of empirical examples from our datasets that supported an articulation of our various research studies, a collective interrogation of how, why and which such cases could be used for best translational effect and a collective reflexive engagement with how these cases generated new and novel questions that in turn re-engaged us with our own data in new ways. Descriptions of our datasets, therefore, emerged as multifaceted assemblages of ‘expertise’ and comprised the evidential bases for new empirical insights, research questions and directions.


2021 ◽  
Vol 40 ◽  
pp. 103188
Author(s):  
Bryony Moody ◽  
Tom Dye ◽  
Keith May ◽  
Holly Wright ◽  
Caitlin Buck
Keyword(s):  

2021 ◽  
Author(s):  
Tetiana Biloborodova ◽  
Inna Skarga-Bandurova ◽  
Mark Koverha ◽  
Illia Skarha-Bandurov ◽  
Yelyzaveta Yevsieieva

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.


2021 ◽  
Author(s):  
Martin Courtois ◽  
Alexandre Filiot ◽  
Gregoire Ficheur

The use of international laboratory terminologies inside hospital information systems is required to conduct data reuse analyses through inter-hospital databases. While most terminology matching techniques performing semantic interoperability are language-based, another strategy is to use distribution matching that performs terms matching based on the statistical similarity. In this work, our objective is to design and assess a structured framework to perform distribution matching on concepts described by continuous variables. We propose a framework that combines distribution matching and machine learning techniques. Using a training sample consisting of correct and incorrect correspondences between different terminologies, a match probability score is built. For each term, best candidates are returned and sorted in decreasing order using the probability given by the model. Searching 101 terms from Lille University Hospital among the same list of concepts in MIMIC-III, the model returned the correct match in the top 5 candidates for 96 of them (95%). Using this open-source framework with a top-k suggestions system could make the expert validation of terminologies alignment easier.


2021 ◽  
pp. 27-29
Author(s):  
Takashiro Akitsu ◽  
Shintaro Suda ◽  
Natsuki Katsuumi

We faced an example of re-reporting of the crystal structure, which was studied from another perspective. With the development of data-driven science, the efficiency of all researchers may be improved if the rules of data "reuse", which are different from "novelty", are established. In this context, the crystal structure of a copper(II) complex with 2,6-pyridine dicarboxylic acid, C14H8O8CuN2.H2O (monohydrate), was re-determined by us again. It has a different number of crystalline water molecules in a crystal of the same copper(II) complex previously reported (trihydrate). Interestingly, both crystal structures have been reported again and again by many researchers for a long time. What’s novelty for each report?


2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Sara Mannheimer

Objective: Big social data (such as social media and blogs) and archived qualitative data (such as interview transcripts, field notebooks, and diaries) are similar, but their respective communities of practice are under-connected. This paper explores shared challenges in qualitative data reuse and big social research and identifies implications for data curation. Methods: This paper uses a broad literature search and inductive coding of 300 articles relating to qualitative data reuse and big social research. The literature review produces six key challenges relating to data use and reuse that are present in both qualitative data reuse and big social research—context, data quality, data comparability, informed consent, privacy & confidentiality, and intellectual property & data ownership. Results: This paper explores six key challenges related to data use and reuse for qualitative data and big social research and discusses their implications for data curation practices. Conclusions: Data curators can benefit from understanding these six key challenges and examining data curation implications. Data curation implications from these challenges include strategies for: providing clear documentation; linking and combining datasets; supporting trustworthy repositories; using and advocating for metadata standards; discussing alternative consent strategies with researchers and IRBs; understanding and supporting deidentification challenges; supporting restricted access for data; creating data use agreements; supporting rights management and data licensing; developing and supporting alternative archiving strategies. Considering these data curation implications will help data curators support sounder practices for both qualitative data reuse and big social research.


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