scholarly journals The reproducibility challenge – what researchers need

2017 ◽  
Author(s):  
Federica Rosetta
Keyword(s):  
Scientific Discovery ◽  
Open Data ◽  
Science Research ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Scientific Practices ◽  
Data Citation ◽  
Materials Used ◽  
High Level

Watch the VIDEO here.Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

Big data and Ag-Analytics

2016 ◽  
Vol 76 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Joshua Woodard
Keyword(s):  
Design Methodology ◽  
Social Science Research ◽  
Open Data ◽  
Science Research ◽  
Research Community ◽  
Research Data ◽  
Agricultural Finance ◽  
Content Type ◽  
High Level ◽  
Practical Implications

Purpose – The purpose of this paper is to provide a brief and necessarily partial overview of the design, motivation, and use of the Ag-Analytics platform (ag-analytics.org), focussing on integration and warehousing of publicly available research data for broad communities of researchers, including those in the area of agricultural finance. Design/methodology/approach – The paper walks the reader through an overview of the layout and utilization of the Ag-Analytics platform, including a few example applications of some of the tools and web API’s. Findings – Much of the data researchers routinely use in agricultural and environmental finance and related fields are often – strictly speaking – publicly available; however the form in which they are distributed leads to great inefficiencies in data sourcing and processing which can be greatly improved. The goal of the Ag-Analytics open data/open source platform is to help researchers centralize and share in such efforts. Development of systems for disseminating, documenting, and automating the processing of such data can lead to more transparency in research, better routes for validation, and a more robust research community. Practical implications – Some of the tools and methods are discussed, as well as practical issues in data sourcing and automation for research. A few high level introductory examples and applications are illustrated. Originality/value – Development and adoption of such systems and data resources remains seriously lacking in social science research, particularly in the economics, natural resource, environmental, and agricultural finance spheres. This brief provides an overview of one such system which should be of value to researchers in this field and many others.

scholarly journals Incentivising research data sharing: a scoping review

2021 ◽  
Vol 6 ◽  
pp. 355
Author(s):  
Helen Buckley Woods ◽  
Stephen Pinfield
Keyword(s):  
Data Sharing ◽  
Cultural Change ◽  
Scoping Review ◽  
Good Practice ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Data Practices

Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.

Sharing Open Data in Agriculture

2021 ◽  
pp. 244-266
Author(s):  
Liah Shonhe
Keyword(s):  
Agricultural Sector ◽  
Open Data ◽  
Research Data ◽  
Data Sets ◽  
Research Activity ◽  
African Countries ◽  
Data Set ◽  
Data Repositories ◽  
Bibliographic Data ◽  
Prolific Authors

The main focus of the study was to explore the practices of open data sharing in the agricultural sector, including establishing the research outputs concerning open data in agriculture. The study adopted a desktop research methodology based on literature review and bibliographic data from WoS database. Bibliometric indicators discussed include yearly productivity, most prolific authors, and enhanced countries. Study findings revealed that research activity in the field of agriculture and open access is very low. There were 36 OA articles and only 6 publications had an open data badge. Most researchers do not yet embrace the need to openly publish their data set despite the availability of numerous open data repositories. Unfortunately, most African countries are still lagging behind in management of agricultural open data. The study therefore recommends that researchers should publish their research data sets as OA. African countries need to put more efforts in establishing open data repositories and implementing the necessary policies to facilitate OA.

Opportunities for Adopting Open Research Data in Learning Analytics

2021 ◽  
pp. 29-60
Author(s):  
Katarzyna Biernacka ◽  
Niels Pinkwart
Keyword(s):  
Learning Analytics ◽  
Open Data ◽  
Publishing Research ◽  
Open Science ◽  
Research Data ◽  
Research Institutions ◽  
Open Research ◽  
Main Emphasis ◽  
Published Research ◽  
Made In

The relevance of open research data is already acknowledged in many disciplines. Demanded by publishers, funders, and research institutions, the number of published research data increases every day. In learning analytics though, it seems that data are not sufficiently published and re-used. This chapter discusses some of the progress that the learning analytics community has made in shifting towards open practices, and it addresses the barriers that researchers in this discipline have to face. As an introduction, the movement and the term open science is explained. The importance of its principles is demonstrated before the main focus is put on open data. The main emphasis though lies in the question, Why are the advantages of publishing research data not capitalized on in the field of learning analytics? What are the barriers? The authors evaluate them, investigate their causes, and consider some potential ways for development in the future in the form of a toolkit and guidelines.

scholarly journals Smoothing surfaces and attributes

2014 ◽  
Vol 33 (2) ◽  
pp. 128-129 ◽  
Author(s):  
Matt Hall
Keyword(s):  
Open Access ◽  
Programming Languages ◽  
Open Data ◽  
Data Sets ◽  
Proprietary Software ◽  
High Level

Welcome to this new column. Every two months, a geoscientist will present a brief exploration of a geophysical topic. The idea is to take a tour bus around a subject and point out some of the sights, perhaps stopping briefly at an exemplary problem or instructive viewpoint. So far it's useful, but maybe not remarkable. The remarkable thing, I hope, is that the tour will be open access. The tutors will use only open data sets that anyone can download. There will be no proprietary software. I will strongly encourage the use of Octave, R, or Python, all high-level (that is, easy-to-learn) programming languages for scientists, and the important parts of the code will be shared. I've tried to give a flavor of all this in today's tutorial, using Python. If you are new to Python, IPython is a great place to start—visit ipython.org/install .

A Cloud-based Science Gateway for Enabling Science as a Service to Facilitate Open Science and Reproducible Research

2020 ◽  
Author(s):  
Mohan Ramamurthy
Keyword(s):  
Cloud Computing ◽  
Information Science ◽  
Virtual Machines ◽  
Scientific Discovery ◽  
Open Data ◽  
Open Science ◽  
Reproducible Research ◽  
Data Infrastructure ◽  
Science Gateway ◽  
Access To Data

<p>The geoscience disciplines are either gathering or generating data in ever-increasing volumes. To ensure that the science community and society reap the utmost benefits in research and societal applications from such rich and diverse data resources, there is a growing interest in broad-scale, open data sharing to foster myriad scientific endeavors. However, open access to data is not sufficient; research outputs must be reusable and reproducible to accelerate scientific discovery and catalyze innovation.</p><p>As part of its mission, Unidata, a geoscience cyberinfrastructure facility, has been developing and deploying data infrastructure and data-proximate scientific workflows and analysis tools using cloud computing technologies for accessing, analyzing, and visualizing geoscience data.</p><p>Specifically, Unidata has developed techniques that combine robust access to well-documented datasets with easy-to-use tools, using workflow technologies. In addition to fostering the adoption of technologies like pre-configured virtual machines through Docker containers and Jupyter notebooks, other computational and analytic methods are enabled via “Software as a Service” and “Data as a Service” techniques with the deployment of the Cloud IDV, AWIPS Servers, and the THREDDS Data Server in the cloud. The collective impact of these services and tools is to enable scientists to use the Unidata Science Gateway capabilities to not only conduct their research but also share and collaborate with other researchers and advance the intertwined goals of Reproducibility of Science and Open Science, and in the process, truly enabling “Science as a Service”.</p><p>Unidata has implemented the aforementioned services on the Unidata Science Gateway ((http://science-gateway.unidata.ucar.edu), which is hosted on the Jetstream cloud, a cloud-computing facility that is funded by the U. S. National Science Foundation. The aim is to give geoscientists an ecosystem that includes data, tools, models, workflows, and workspaces for collaboration and sharing of resources.</p><p>In this presentation, we will discuss our work to date in developing the Unidata Science Gateway and the hosted services therein, as well as our future directions toward increasing expectations from funders and scientific communities that they will be Open and FAIR (Findable, Accessible, Interoperable, Reusable). In particular, we will discuss how Unidata is advancing data and software transparency, open science, and reproducible research. We will share our experiences in how the geoscience and information science communities are using the data, tools and services provided through the Unidata Science Gateway to advance research and education in the geosciences.</p>

scholarly journals Key Aspects of Open Data in Finnish RDI Cooperation between Higher Education and Businesses

2021 ◽  
pp. 1-13
Author(s):  
Seliina Päällysaho ◽  
Jaana Latvanen ◽  
Anttoni Lehto ◽  
Jaakko Riihimaa ◽  
Pekka Lahti ◽  
...  
Keyword(s):  
Higher Education ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
General Level ◽  
Joint Research ◽  
Research Organizations ◽  
Universities Of Applied Sciences ◽  
Key Aspects ◽  
Five Phases

The article highlights aspects that should be considered during an open research, development, and innovation (RDI) process cycle to improve the utilization of research data and foster open cooperation between higher education and businesses. The viewpoint here is in publicly funded joint research projects of the universities of applied sciences (UAS), the concept is, however, applicable in other higher education and research organizations as well. There are various challenges related to research data management in general as well as to the openness and reuse of data and results. The findings of this article are based on the results of a two-day expert workshop, and these results are interlinked with five phases of an open RDI process cycle: planning, implementation, documentation, sharing, and commercialization. Various drivers and barriers can be identified in different stages of the process. On a general level, special attention must be paid to critical factors such as ownership and sharing of data and results, confidential information and business secrets as well as following the requirements of the open science policies of the participating organizations and funders. This article also highlights several best practices that should be considered in each phase of an open RDI process cycle with businesses.

scholarly journals eLabFTW as an Open Science tool to improve the quality and translation of preclinical research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 292
Author(s):  
Michael Hewera ◽  
Daniel Hänggi ◽  
Björn Gerlach ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Data Management ◽  
Open Source ◽  
Academic Research ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Preclinical Research ◽  
Research Data Management ◽  
New Methods ◽  
Set Up

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

scholarly journals Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data

2021 ◽  
Vol 3 (1) ◽  
pp. 189-204
Author(s):  
Hua Nie ◽  
Pengcheng Luo ◽  
Ping Fu
Keyword(s):  
Data Management ◽  
Open Data ◽  
Implementation Process ◽  
Open Science ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management ◽  
University Library ◽  
Open Research ◽  
Peking University

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.

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