ArticlePLoS biology2019
Developing a modern data workflow for regularly updated data.
Article in PLoS biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Ten quick tips to build a Model Life Cycle.PLoS computational biology · 2025Article
- Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production.PloS one · 2025Article
- A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.Proceedings. Biological sciences · 2024Article
- How do you do the things that you do? Ethological approach to the description of robot behaviour.Biologia futura · 2023Review
- Data rescue: saving environmental data from extinction.Proceedings. Biological sciences · 2022Article
- Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact.Prevention science : the official journal of the Society for Prevention Research · 2022Article
- CAN-SAR: A database of Canadian species at risk information.Scientific data · 2022Article
- Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package.PloS one · 2022Article
- Provoking a Cultural Shift in Data Quality.Bioscience · 2021Article
- Streamlining data-intensive biology with workflow systems.GigaScience · 2021Article
- Article
- Automated data-intensive forecasting of plant phenology throughout the United States.Ecological applications : a publication of the Ecological Society of America · 2020Article
- Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.GigaScience · 2019Article
- A parasite's perspective on data sharing.GigaScience · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow leverages tools from software development, including version control and continuous integration, to create a modern data management system that automates the pipeline.
Indexed as
Identifiers
What OpenQuestion holds
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.