Initiating FAIR geothermal data in Indonesia

2021 ◽  
Author(s):  
Dasapta Erwin Irawan
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
Open Data ◽  
Data Reuse ◽  
Data Availability ◽  
Scientific Development ◽  
For Profit ◽  
Deep Well ◽  
Data Schema ◽  
Corporate Social

<p>One of the main keys to scientific development is data availability. Not only the data is easily discovered and downloaded, there's also needs for the data to be easily reused. Geothermal researchers, research institutions and industries are the three main stakeholders to foster data sharing and data reuse. Very expensive deep well datasets as well as advanced logging datasets are very important not only for exploitation purposes but also for the community involved eg: for regional planning or common environmental analyses. In data sharing, we have four principles of F.A.I.R data. Principle 1 Findable: data uploaded to open repository with proper data documentations and data schema, Principle 2 Accessible: removed access restrictions such as user id and password for easy downloads. In case of data from commercial entities, embargoed data is permitted with a clear embargo duration and data request procedure, Principle 3 Interoperable: all data must be prepared in a manner for straightforward data exchange between platforms, Principle 4 Reusable: all data must be submitted using common conventional file format, preferably text-based file (eg `csv` or `txt`) therefore it can be analyzed using various software and hardware. The fact that geothermal industries are packed with for-profit motivations and capital intensive would give even more reasons to embrace data sharing. It would be a good way for them to share their role in supporting society. The contributions from multiple stakeholders are the most essential part in science development. In the context of the commercial industry, data sharing is a form of corporate social responsibility (CSR). It shouldn't be defined only as giving out funding to support local communities.</p><p><strong>Keywords</strong>: open data, FAIR data, data sharing </p><p> </p>

scholarly journals Data Dentistry: How Data Are Changing Clinical Care and Research

2021 ◽  
pp. 002203452110202
Author(s):  
F. Schwendicke ◽  
J. Krois
Keyword(s):  
Health Care ◽  
Data Sharing ◽  
Clinical Care ◽  
Open Data ◽  
User Interaction ◽  
Data Availability ◽  
Related Data ◽  
Data User ◽  
Regulatory Data ◽  
Consumer Data

Data are a key resource for modern societies and expected to improve quality, accessibility, affordability, safety, and equity of health care. Dental care and research are currently transforming into what we term data dentistry, with 3 main applications: 1) medical data analysis uses deep learning, allowing one to master unprecedented amounts of data (language, speech, imagery) and put them to productive use. 2) Data-enriched clinical care integrates data from individual (e.g., demographic, social, clinical and omics data, consumer data), setting (e.g., geospatial, environmental, provider-related data), and systems level (payer or regulatory data to characterize input, throughput, output, and outcomes of health care) to provide a comprehensive and continuous real-time assessment of biologic perturbations, individual behaviors, and context. Such care may contribute to a deeper understanding of health and disease and a more precise, personalized, predictive, and preventive care. 3) Data for research include open research data and data sharing, allowing one to appraise, benchmark, pool, replicate, and reuse data. Concerns and confidence into data-driven applications, stakeholders’ and system’s capabilities, and lack of data standardization and harmonization currently limit the development and implementation of data dentistry. Aspects of bias and data-user interaction require attention. Action items for the dental community circle around increasing data availability, refinement, and usage; demonstrating safety, value, and usefulness of applications; educating the dental workforce and consumers; providing performant and standardized infrastructure and processes; and incentivizing and adopting open data and data sharing.

scholarly journals A survey of researchers' needs and priorities for data sharing

2021 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
James Harney ◽  
Lauren Cadwallader
Keyword(s):  
Data Sharing ◽  
Research Impact ◽  
Open Science ◽  
Research Data ◽  
Data Reuse ◽  
Data Availability ◽  
Data Repositories ◽  
Use Of Data ◽  
Share Data ◽  
Do So

PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities - unmet researcher needs - in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.

scholarly journals A descriptive analysis of the data availability statements accompanying medRxiv preprints and a comparison with their published counterparts

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250887
Author(s):  
Luke A. McGuinness ◽  
Athena L. Sheppard
Keyword(s):  
Data Sharing ◽  
Descriptive Analysis ◽  
Open Data ◽  
System Change ◽  
Research Data ◽  
Data Availability ◽  
Published Data ◽  
Editorial Policies ◽  
Journal Editors ◽  
Closed Data

Objective To determine whether medRxiv data availability statements describe open or closed data—that is, whether the data used in the study is openly available without restriction—and to examine if this changes on publication based on journal data-sharing policy. Additionally, to examine whether data availability statements are sufficient to capture code availability declarations. Design Observational study, following a pre-registered protocol, of preprints posted on the medRxiv repository between 25th June 2019 and 1st May 2020 and their published counterparts. Main outcome measures Distribution of preprinted data availability statements across nine categories, determined by a prespecified classification system. Change in the percentage of data availability statements describing open data between the preprinted and published versions of the same record, stratified by journal sharing policy. Number of code availability declarations reported in the full-text preprint which were not captured in the corresponding data availability statement. Results 3938 medRxiv preprints with an applicable data availability statement were included in our sample, of which 911 (23.1%) were categorized as describing open data. 379 (9.6%) preprints were subsequently published, and of these published articles, only 155 contained an applicable data availability statement. Similar to the preprint stage, a minority (59 (38.1%)) of these published data availability statements described open data. Of the 151 records eligible for the comparison between preprinted and published stages, 57 (37.7%) were published in journals which mandated open data sharing. Data availability statements more frequently described open data on publication when the journal mandated data sharing (open at preprint: 33.3%, open at publication: 61.4%) compared to when the journal did not mandate data sharing (open at preprint: 20.2%, open at publication: 22.3%). Conclusion Requiring that authors submit a data availability statement is a good first step, but is insufficient to ensure data availability. Strict editorial policies that mandate data sharing (where appropriate) as a condition of publication appear to be effective in making research data available. We would strongly encourage all journal editors to examine whether their data availability policies are sufficiently stringent and consistently enforced.

scholarly journals Paving the Way to Open Data

2019 ◽  
Vol 1 (4) ◽  
pp. 368-380 ◽  
Author(s):  
Yan Wu ◽  
Elizabeth Moylan ◽  
Hope Inman ◽  
Chris Graf
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Data Availability ◽  
Peer Reviews ◽  
Open Research ◽  
Research Communities ◽  
The Way

It is easy to argue that open data are critical to enabling faster and more effective research discovery. In this article, we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data (Findable, Accessible, Interoperable and Reusable) with the implementation of four data policies: “Encourages”, “Expects”, “Mandates” and “Mandates and Peer Reviews Data”. We describe the rationale for these policies and levels of adoption so far. In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations. With this information, we'll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve, and we hope to showcase researchers from those communities leading in open research.

scholarly journals Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Author(s):  
Tom Elis Hardwicke ◽  
Maya B Mathur ◽  
Kyle Earl MacDonald ◽  
Gustav Nilsonne ◽  
George Christopher Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive, and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (“analytic reproducibility”). To investigate, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition. Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification, and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Vol 5 (8) ◽  
pp. 180448 ◽  
Author(s):  
Tom E. Hardwicke ◽  
Maya B. Mathur ◽  
Kyle MacDonald ◽  
Gustav Nilsonne ◽  
George C. Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (‘analytic reproducibility’). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition . Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals Data Reuse and the Social Capital of Open Science

2016 ◽  
Author(s):  
Bradly Alicea
Keyword(s):  
Game Theory ◽  
Social Capital ◽  
Data Sharing ◽  
Open Data ◽  
Open Science ◽  
Payoff Matrix ◽  
Reciprocal Relationship ◽  
Data Reuse ◽  
Data Set ◽  
Shared Data

ABSTRACTParticipation in open data initiatives require two semi-independent actions: the sharing of data produced by a researcher or group, and a consumer of shared data. Consumers of shared data range from people interested in validating the results of a given study to people who actively transform the available data. These data transformers are of particular interest because they add value to the shared data set through the discovery of new relationships and information which can in turn be shared with the same community. The complex and often reciprocal relationship between producers and consumers can be better understood using game theory, namely by using three variations of the Prisoners’ Dilemma (PD): a classical PD payoff matrix, a simulation of the PD n-person iterative model that tests three hypotheses, and an Ideological Game Theory (IGT) model used to formulate how sharing strategies might be implemented in a specific institutional culture. To motivate these analyses, data sharing is presented as a trade-off between economic and social payoffs. This is demonstrated as a series of payoff matrices describing situations ranging from ubiquitous acceptance of Open Science principles to a community standard of complete non-cooperation. Further context is provided through the IGT model, which allows from the modeling of cultural biases and beliefs that influence open science decision-making. A vision for building a CC-BY economy are then discussed using an approach called econosemantics, which complements the treatment of data sharing as a complex system of transactions enabled by social capital.

scholarly journals Generating partial civil information model views using a semantic information retrieval approach

2020 ◽  
Vol 25 ◽  
pp. 41-54
Author(s):  
Tuyen Le ◽  
H. David Jeong ◽  
Stephen B. Gilbert ◽  
Evgeny Chukharev-Hudilainen
Keyword(s):  
Data Exchange ◽  
Open Data ◽  
Relevant Information ◽  
Information Modeling ◽  
Retrieval Algorithm ◽  
Data Types ◽  
Specific Domain ◽  
Data Schema ◽  
Traditional Procedure ◽  
Wide Range

Open data standards (e.g. LandXML, TransXML, CityGML) are a key to addressing the interoperability issue in exchanging civil information modeling (CIM) data throughout the project life-cycle. Since these schemas include rich sets of data types covering a wide range of assets and disciplines, model view definitions (MVDs) which define subsets of a schema are required to specify what types of data to be shared in accordance with a specific exchange scenario. The traditional procedure for generating and implementing MVDs is time-consuming and laborious as entities and attributes relevant to a particular data exchange context are manually identified by domain experts. This paper presents a method that can locate relevant information from a source XML data schema for a specific domain based on the user's keyword. The study employs a semantic resource of civil engineering terms to understand the semantics of a keyword-based query. The study also introduces a novel context-based search technique for retrieving related entities and their referenced objects. The developed method was tested on a gold standard of several LandXML subschemas. The experiment results show that the semantic MVD retrieval algorithm achieves a mean average precision of nearly 90%. The research is original, being a novel method for extracting partial civil information models given a keyword from the end user. The method is expected to become a fundamental tool assisting professionals in extracting data from complex digital datasets.

scholarly journals Evaluation of Clinical Trial Data Sharing Policy in Leading Medical Journals

2020 ◽  
Author(s):  
Valentin Danchev ◽  
Yan Min ◽  
John Borghi ◽  
Mike Baiocchi ◽  
John P.A. Ioannidis
Keyword(s):  
Clinical Trials ◽  
New England ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
International Committee ◽  
Data Reuse ◽  
Data Availability ◽  
Medical Journals ◽  
Pubmed Search ◽  
Materials Used

AbstractBackgroundThe 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/.

scholarly journals Delivering on open data: Current practices in data sharing, and challenges ahead!

2018 ◽  
Author(s):  
Timon Oefelein
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Research Data ◽  
Data Reuse ◽  
Valuable Insight ◽  
Research Libraries ◽  
Critical Areas ◽  
Clear Vision ◽  
Working Together ◽  
Current Practices

Watch the VIDEO.The case for sharing research data has been strongly made in many parts of the world, but noticeably so in Europe. Open access to research data delivers more value for every funding Euro by enabling data reuse and reducing unnecessary duplication of research. Further, open data can help speed the pace of discovery and allows for reproducibility studies. The European Commission has set out a clear vision for open data in their Horizon Europe proposal. Yet in 2018 only about half of research data are shared, according to surveys of researchers, and a much smaller proportion are shared openly or in ways that maximise discoverability and reuse. Whilst policy implementation remains critical to the uptake of data sharing, this must be joined by greater support and education for researchers, and faster, easier routes to sharing data optimally. We also need to make it worth a researcher’s time to share their data. Starting with the case for better data practice, this talk showcases the findings of one of the largest author surveys of its kind on current practices, attitudes and perceptions in data-sharing at the point of scholarly publication. The survey, carried out by Springer Nature in 2018, is based on over 7700 responses from academic researchers - at various levels of their career – in Europe, Asia, America, and Australasia. Responses are from across all subject areas. The resulting data provides a valuable insight into how, where, and why data is currently shared and what the main obstacles to sharing it are.The talk identifies the most “critical areas” – as borne out of the survey findings – that need to be tackled with top priority if we are to accelerate the speed and scope of data-sharing. In closing, we therefore ask – how can we better work together across research libraries, institutions, funders, governments, and publishers, to address and action these “critical areas”?  Indeed, it is only by working together that we can unlock the huge potential of research data, namely to improve our knowledge, to address the grand societal challenges, and to help solve some of the most pressing problems in science today.

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