Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ten simple rules for effective computational research.

ABSTRACT:

SUBMITTER: Osborne JM

PROVIDER: S-EPMC3967918 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Ten simple rules for effective computational research.

Osborne James M JM Bernabeu Miguel O MO Bruna Maria M Calderhead Ben B Cooper Jonathan J Dalchau Neil N Dunn Sara-Jane SJ Fletcher Alexander G AG Freeman Robin R Groen Derek D Knapp Bernhard B McInerny Greg J GJ Mirams Gary R GR Pitt-Francis Joe J Sengupta Biswa B Wright David W DW Yates Christian A CA Gavaghan David J DJ Emmott Stephen S Deane Charlotte C

PLoS computational biology 20140327 3

PMID: 24675742

Similar Datasets

Ten simple rules for providing effective bioinformatics research support.

Project description:Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.

| S-EPMC7098546 | biostudies-literature

Ten simple rules for innovative dissemination of research.

Project description:How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.

| S-EPMC7161944 | biostudies-literature

Ten simple rules for writing a PLOS ten simple rules article.

Project description: Not available

| S-EPMC4207461 | biostudies-literature

Ten simple rules for making research software more robust.

Project description:Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution or even off the primary developer's computer. We present ten simple rules to make such software robust enough to be run by anyone, anywhere, and thereby delight your users and collaborators.

| S-EPMC5390961 | biostudies-literature

Ten simple rules for using public biological data for your research.

Project description:With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

| S-EPMC9815577 | biostudies-literature

Ten simple rules for getting ahead as a computational biologist in Academia.

Project description: Not available

| S-EPMC3017106 | biostudies-literature

Ten "simple" rules for non-Indigenous researchers engaging Indigenous communities in Arctic research.

Project description: Not available

| S-EPMC11210771 | biostudies-literature

Ten simple rules for creating a sense of belonging in your research group.

Project description: Not available

| S-EPMC9731414 | biostudies-literature

Ten simple (empirical) rules for writing science.

Project description: Not available

| S-EPMC4415812 | biostudies-literature

Ten simple rules for implementing open and reproducible research practices after attending a training course.

Project description:Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things; however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices; (2) managing problems that arise during implementation; and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.

| S-EPMC9815586 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data