Potential Dangers with Open Access Data Files in the Expanding Open Data Movement

2015 ◽  
Vol 31 (4) ◽  
pp. 298-305 ◽  
Author(s):  
Jaime A. Teixeira da Silva ◽  
Judit Dobránszki
Keyword(s):  
Open Access ◽  
Open Data ◽  
Data Movement ◽  
Open Access Data ◽  
Data Files ◽  
Open Data Movement ◽  
Access Data

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., 2017; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), the current paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals Born-Open Data for E-Prime

2020 ◽  
Author(s):  
Denis Cousineau
Keyword(s):  
Open Access ◽  
Open Data ◽  
Open Science ◽  
Research Community ◽  
Data Repository ◽  
Data Frame ◽  
Science Practices ◽  
Data Practices ◽  
Data Files ◽  
Access Data

Born-Open Data experiments are encouraged for better open science practices. To be adopted, Born-Open data practices must be easy to implement. Herein, I introduce a package for E-Prime such that the data files are automatically saved on a GitHub repository. The BornOpenData package for E-Prime works seamlessly and performs the upload as soon as the experiment is finished so that there is no additional steps to perform beyond placing a package call within E-Prime. Because E-Prime files are not standard tab-separated files, I also provide an R function that retrieves the data directly from GitHub into a data frame ready to be analyzed. At this time, there are no standards as to what should constitute an adequate open-access data repository so I propose a few suggestions that any future Born-Open data system could follow for easier use by the research community.

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals USE OF OPEN DATA AND APPLICATIONS IN THE EDUCATION PROCESS

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Dušan Jovanović
Keyword(s):  
Open Access ◽  
Spectral Resolution ◽  
Open Data ◽  
Problem Based Learning ◽  
Learning Approach ◽  
Study Program ◽  
Open Access Data ◽  
Novi Sad ◽  
Access Data ◽  
Education Process

This paper aims to show experience in education using open access data, especially sattelite images. We describe different satellite platforms with different spatial and spectral resolution. We also discuss applicability of these data in education process especially with students on study program Geodesy and Geomatics on Faculty of technical sciences, University of Novi Sad. Some practical examples are also shown. At the end, the paper also describes problems from practical view of teaching and recommendation how to solve those problems using Problem based learning approach.

scholarly journals The impact of the National Heart, Lung, and Blood Institute data: analyzing published articles that used BioLINCC open access data

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 30
Author(s):  
Saif Aldeen AlRyalat ◽  
Osama El Khatib ◽  
Ola Al-qawasmi ◽  
Hadeel Alkasrawi ◽  
Raneem al Zu’bi ◽  
...  
Keyword(s):  
Open Access ◽  
Open Data ◽  
Blood Institute ◽  
Open Access Data ◽  
Pubmed Search ◽  
Number Of Publications ◽  
Almost All ◽  
Number Of Citations ◽  
The Impact ◽  
Access Data

Background: Data sharing is now a mandatory prerequisite for several major funders and journals, where researchers are obligated to deposit the data resulting from their studies in an openly accessible repository. Biomedical open data are now widely available in almost all disciplines, where researchers can freely access and reuse these data in new studies. We aim to study the BioLINCC datasets, number of publications that used BioLINCC open access data, and the impact of these publications through the citations they received. Methods: As of July 2019, there was a total of 194 datasets stored in BioLINCC repository and accessible through their portal. We requested the full list of publications that used these datasets from BioLINCC, and we also performed a supplementary PubMed search for other publications. We used Web of Science (WoS) to analyze the characteristics of publications and the citations they received. Results: 1,086 published articles used data from BioLINCC repository for 79 (40.72%) datasets, where 115 (59.28%) datasets didn’t have any publications associated with it. Of the total publications, 987 (90.88%) articles were WoS indexed. The number of publications has steadily increased since 2002 and peaked in 2018 with a total number of 138 publications on that year. The 987 open data publications received a total of 34,181 citations up to 1st October 2019. The average citation per item for the open data publications was 34.63. The total number of citations received by open data publications per year has increased from only 2 citations in 2002, peaking in 2018 with 2361 citations. Conclusion: Majority of BioLINCC datasets were not used in secondary publications. Despite that, the datasets used for secondary publications yielded publications in WoS indexed journals and are receiving an increasing number of citations.

scholarly journals Understanding the open access data movement

2007 ◽  
Vol 54 (11) ◽  
pp. 949-950
Author(s):  
Donald R. Miller ◽  
D. John Doyle
Keyword(s):  
Open Access ◽  
Data Movement ◽  
Open Access Data ◽  
Access Data

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals The National Heart, Lung, and Blood Institute data: analyzing published articles that used BioLINCC open access data

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 30
Author(s):  
Saif Aldeen AlRyalat ◽  
Osama El Khatib ◽  
Ola Al-qawasmi ◽  
Hadeel Alkasrawi ◽  
Raneem al Zu’bi ◽  
...  
Keyword(s):  
Open Access ◽  
Open Data ◽  
Blood Institute ◽  
Open Access Data ◽  
Database Index ◽  
Pubmed Search ◽  
Number Of Publications ◽  
Almost All ◽  
Number Of Citations ◽  
Access Data

Background: Data sharing is now a mandatory prerequisite for several major funders and journals, where researchers are obligated to deposit the data resulting from their studies in an openly accessible repository. Biomedical open data are now widely available in almost all disciplines, where researchers can freely access and reuse these data in new studies. We aim to study the BioLINCC datasets, number of publications that used BioLINCC open access data, and the citations received by these publications. Methods: As of July 2019, there was a total of 194 datasets stored in BioLINCC repository and accessible through their portal. We requested the full list of publications that used these datasets from BioLINCC, and we also performed a supplementary PubMed search for other publications. We used Web of Science (WoS) to analyze the characteristics of publications and the citations they received, where WoS database index high quality articles. Results: 1,086 published articles used data from BioLINCC repository for 79 (40.72%) datasets, where 115 (59.28%) datasets did not have any publications associated with it. Of the total publications, 987 (90.88%) articles were WoS indexed. The number of publications has steadily increased since 2002 and peaked in 2018 with a total number of 138 publications on that year. The 987 open data publications (i.e., secondary publications) received a total of 34,181 citations up to 1 st October 2019. The average citation per item for the open data publications was 34.63. The total number of citations received by open data publications per year has increased from only 2 citations in 2002, peaking in 2018 with 2361 citations. Conclusion: Majority of BioLINCC datasets were not used in secondary publications. Despite that, the datasets used for secondary publications yielded publications in WoS indexed journals and are receiving an increasing number of citations.

Do open access data files represent an academic Risk?

2015 ◽  
Vol 66 (11) ◽  
pp. 2390-2391 ◽  
Author(s):  
Jaime A. Teixeira da Silva ◽  
Judit Dobránszki
Keyword(s):  
Open Access ◽  
Academic Risk ◽  
Open Access Data ◽  
Data Files ◽  
Access Data

scholarly journals The impact of the National Heart, Lung, and Blood Institute data: analyzing published articles that used BioLINCC open access data

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 30
Author(s):  
Saif Aldeen AlRyalat ◽  
Osama El Khatib ◽  
Ola Al-qawasmi ◽  
Hadeel Alkasrawi ◽  
Raneem al Zu’bi ◽  
...  
Keyword(s):  
Open Access ◽  
Open Data ◽  
Blood Institute ◽  
Open Access Data ◽  
Database Index ◽  
Pubmed Search ◽  
Number Of Publications ◽  
Number Of Citations ◽  
The Impact ◽  
Access Data

Background: Data sharing is now a mandatory prerequisite for several major funders and journals, where researchers are obligated to deposit the data resulting from their studies in an openly accessible repository. Biomedical open data are now widely available in almost all disciplines, where researchers can freely access and reuse these data in new studies. We aim to study the BioLINCC datasets, number of publications that used BioLINCC open access data, and the impact of these publications through the citations they received. Methods: As of July 2019, there was a total of 194 datasets stored in BioLINCC repository and accessible through their portal. We requested the full list of publications that used these datasets from BioLINCC, and we also performed a supplementary PubMed search for other publications. We used Web of Science (WoS) to analyze the characteristics of publications and the citations they received, where WoS database index high quality articles. Results: 1,086 published articles used data from BioLINCC repository for 79 (40.72%) datasets, where 115 (59.28%) datasets didn’t have any publications associated with it. Of the total publications, 987 (90.88%) articles were WoS indexed. The number of publications has steadily increased since 2002 and peaked in 2018 with a total number of 138 publications on that year. The 987 open data publications received a total of 34,181 citations up to 1 st October 2019. The average citation per item for the open data publications was 34.63. The total number of citations received by open data publications per year has increased from only 2 citations in 2002, peaking in 2018 with 2361 citations. Conclusion: Majority of BioLINCC datasets were not used in secondary publications. Despite that, the datasets used for secondary publications yielded publications in WoS indexed journals and are receiving an increasing number of citations.

Sign in / Sign up

Export Citation Format

Share Document