scholarly journals Measuring the Value of Research Data: A Citation Analysis of Oceanographic Data Sets

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e92590 ◽  
Author(s):  
Christopher W. Belter
Keyword(s):  
Citation Analysis ◽  
Research Data ◽  
Data Sets ◽  
Oceanographic Data

Improvements for research data repositories: The case of text spam

2021 ◽  
pp. 016555152199863
Author(s):  
Ismael Vázquez ◽  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
José Ramón Méndez ◽  
...  
Keyword(s):  
Web Application ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Software Applications ◽  
Public Data ◽  
Protection Mechanisms ◽  
Experimental Protocols ◽  
Learning Research ◽  
Processing Steps

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML ( Computer Science/ Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study.

scholarly journals PS2-54: Best Practices: Improving Quality and Reliability in Research Data Sets

2013 ◽  
Vol 11 (3) ◽  
pp. 157-157
Author(s):  
L. McFarland ◽  
J. Richter ◽  
C. Bredfeldt
Keyword(s):  
Best Practices ◽  
Research Data ◽  
Data Sets ◽  
Quality And Reliability

Data Cleaning in Occupational Therapy Research

1994 ◽  
Vol 14 (3) ◽  
pp. 144-156
Author(s):  
Marcel P. J. M. Dijkers ◽  
Cynthia L. Creighton
Keyword(s):  
Occupational Therapy ◽  
Data Cleaning ◽  
Research Data ◽  
Data Sets ◽  
Processing Data ◽  
Research Findings

Errors in processing data prior to analysis can cause significant distortion of research findings. General principles and specific techniques for cleaning data sets are presented. Strategies are suggested for preventing errors in transcribing, coding, and keying research data.

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.

Detecting Subgroups in Survey Research

1986 ◽  
Vol 59 (2) ◽  
pp. 751-760
Author(s):  
Todd McLin Davis
Keyword(s):  
Cluster Analysis ◽  
Survey Research ◽  
Potential Interaction ◽  
Research Data ◽  
The Other ◽  
Data Sets ◽  
Statistical Procedures

A problem often not detected in the interpretation of survey research is the potential interaction between subgroups within the sample and aspects of the survey. Potentially interesting interactions are commonly obscured when data are analyzed using descriptive and univariate statistical procedures. This paper suggests the use of cluster analysis as a tool for interpretation of data, particularly when such data take the form of coded categories. An example of the analysis of two data sets with known properties, one random and the other contrived, is presented to illustrate the application of cluster procedures to survey research data.

EMODnet Chemistry new and consolidated large scale cooperation actions for 2020 and beyond

2021 ◽  
Author(s):  
Alessandra Giorgetti ◽  
Chiara Altobelli ◽  
François Galgani ◽  
Georg Hanke ◽  
Neil Holdsworth ◽  
...  
Keyword(s):  
Data Sets ◽  
Marine Research ◽  
Marine Litter ◽  
Agenda 2030 ◽  
Data Portal ◽  
Beach Litter ◽  
Oceanographic Data ◽  
Marine Data ◽  
The Eu

<p>EMODnet Chemistry is one of the seven thematic portals of EMODnet (European Marine Observation and Data Network), the long-term initiative aiming to ensure that European marine data are findable, accessible, interoperable and re-usable. EMODnet was launched by DG MARE in 2009 as the pillar of the Blue Growth strategy, Marine Knowledge 2020.</p><p>Eutrophication (e.g. nutrients, oxygen and chlorophyll), contaminants (e.g. hydrocarbons, pesticides, heavy metals, antifoulants) and marine litter (e.g. beach litter, seafloor litter and floating micro litter) are the main categories of quality assured marine data sets and data products made available through the EMODnet Chemistry portal.</p><p>45 marine research and monitoring institutes and oceanographic data management experts from 30 countries comprise the EMODnet Chemistry network, including National Oceanographic Data Centres (NODC), National Environmental Monitoring Agencies and Marine Research Institutes actively involved in managing, processing and providing access to data sets from European marine waters and global oceans.</p><p>During 2020 EMODnet Chemistry consolidated fundamental international collaborations and upgraded cooperation actions on the European and global level to share and harmonize data, knowledge and services, following decision-makers’ needs to implement EU directives, such as MSFD, MSPD, INSPIRE directive, and the Agenda 2030 Sustainable Development Goals of the United Nations</p><p>Main EMODnet Chemistry 2020 transnational cooperation actions are:</p><ul><li>The MSFD Technical Group on Marine Litter used the EMODnet Chemistry Marine Litter Database to compute the EU beach litter quantitative Baselines and Threshold values.</li> <li>The European Environment Agency confirmed the use of EMODnet Chemistry data for three environmental state indicators relating to eutrophication and contaminants.</li> <li>Mercator Ocean International and EMODnet Chemistry set up the first joint portfolio of products in support of the MSFD implementation. The two partners are also exploring opportunities to support the aquaculture sector.</li> <li>EMODnet -Chemistry and the In Situ Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service (CMEMS INSTAC) collaborated with ENVRI Marine European Research Infrastructures (Euro-Argo, EMSO, ICOS, Lifewatch and SeaDataNet) to enhance FAIRness of in situ data.</li> <li>Mercator Ocean international, UNDESA, SULITEST NGO and EMODnet Chemistry have been creating an awareness questionnaire to raise awareness on the Goal 14 of the UN Agenda 2030 for Sustainable Development.</li> <li>The EU asked EMODnet Chemistry to share its experience at the G20 workshop on harmonized monitoring and data compilation of marine plastic litter organized by the Ministry of the Environment, Japan.</li> <li>The international Oxygen data portal and Ocean Acidification data portal received contributions from EMODnet Chemistry and CMEMS in situ TAC for their implementation.</li> <li>The National Marine Data and Information Service of China collaborates with EMODnet to strengthen international ocean data through the EMOD-PACE project.</li> </ul>

Creating Tactile Graphs for Students With Visual Impairments

2019 ◽  
pp. 223-240
Author(s):  
Terence W. Cavanaugh ◽  
Nicholas P. Eastham
Keyword(s):  
Data Analysis ◽  
Rapid Prototyping ◽  
Visual Impairments ◽  
Research Data ◽  
Assistive Technologies ◽  
Data Sets ◽  
3D Printer ◽  
Graduate Schools ◽  
Student Research ◽  
The Cost

Educational technologists are often asked to provide assistance in the identification or creation of assistive technologies for students. Individuals with visual impairments attending graduate schools are expected to be able to work with data sets, including reading, interpreting, and sharing findings with others in their field, but due to their impairments may not be able to work with standard displays. The cost and time involved in preparing adapted graphs based on student research data for individuals with visual impairments can be prohibitive. This chapter introduces a method for the rapid prototyping of tactile graphs for students to use in data analysis through the use of spreadsheets, internet-based conversion tools, and a 3D printer.

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.

Developing a Data Management Platform for the Ocean Science Community

2018 ◽  
Vol 52 (3) ◽  
pp. 28-32 ◽  
Author(s):  
Chris Turner ◽  
Ian Gill
Keyword(s):  
Data Collection ◽  
Data Management ◽  
High Performance ◽  
Project Planning ◽  
Data Sets ◽  
Management Platform ◽  
Ocean Science ◽  
Science Community ◽  
Oceanographic Data ◽  
Data Management Platform

AbstractThe management of oceanographic data is particularly challenging given the variety of protocols for the analysis of data collection and model output, the vast range of environmental conditions studied, and the potentially enormous extent and volume of the resulting data sets and model results. Here, we describe the Research Workspace (the Workspace), a web platform designed around data management best practices to meet the challenges of managing oceanographic data throughout the research life cycle. The Workspace features secure user accounts and automatic file versioning to assist with the early stages of project planning and data collection. Jupyter Notebooks have been integrated into the Workspace to support reproducible numerical analysis and data visualization while making use of high-performance computer resources collocated with data assets. An ISO-compliant metadata editor has also been integrated into the Workspace to support data synthesis, publication, and reuse. The Workspace currently supports stakeholders across the ocean science community, from funding agencies to individual investigators, by providing a data management platform to meet the needs of big ocean data.

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