ArticleFrontiers in neuroinformatics2016
Proposed Training to Meet Challenges of Large-Scale Data in Neuroscience.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- The neuroscience education readiness checklist for digital tools and methods.Frontiers in computational neuroscience · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- An Advanced Undergraduate Laboratory Course: Calcium Imaging and Data Analysis - a Mini Graduate Research Experience.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2025Article
- Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.Neuroinformatics · 2024Article
- Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.Neuroinformatics · 2024Article
- Integrating Programming into Neuroscience Courses.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2024Article
- Enhancing Student Research Experiences with Open Data from the Allen Brain Map.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2022Article
- The Mismatch Between Neuroscience Graduate Training and Professional Skill Sets.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2022Article
- Teaching Computation in Neuroscience: Notes on the 2019 Society for Neuroscience Professional Development Workshop on Teaching.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2021Article
- Learning How to Code While Analyzing an Open Access Electrophysiology Dataset.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2020Article
- Non-Disposable Assignments for Remote Neuroscience Laboratory Teaching Using Analysis of Human Data.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2020Article
- Why Study the History of Neuroscience?Frontiers in behavioral neuroscience · 2019Review
- Win-win data sharing in neuroscience.Nature methods · 2017Article
- Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2017Article
- Diversity of Graduates from Bachelor's, Master's and Doctoral Degree Neuroscience Programs in the United States.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scale of data being produced in neuroscience at present and in the future creates new and unheralded challenges, outstripping conventional ways of handling, considering, and analyzing data. As neuroinformatics enters into this big data era, a need for a highly trained and perhaps unique workforce is emerging. To determine the staffing needs created by the impending era of big data, a workshop (iNeuro Project) was convened November 13-14, 2014. Participants included data resource providers, bioinformatics/analytics trainers, computer scientists, library scientists, and neuroscience educators. These individuals provided perspectives on the challenges of big data, the preparation of a workforce to meet these challenges, and the present state of training programs. Participants discussed whether suitable training programs will need to be constructed from scratch or if existing programs can serve as models. Currently, most programs at the undergraduate and graduate levels are located in Europe-participants knew of none in the United States. The skill sets that training programs would need to provide as well as the curriculum necessary to teach them were also discussed. Consistent with Vision and Change in Undergraduate Biology Education: A Call to Action, proposed curricula included authentic, hands-on research experiences. Further discussions revolved around the logistics and barriers to creating such programs. The full white paper, iNeuro Project Workshop Report, is available from iNeuro Project.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.