Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions.

ABSTRACT: Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).

SUBMITTER: Benureau FCY

PROVIDER: S-EPMC5758530 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions.

Benureau Fabien C Y FCY Rougier Nicolas P NP

Frontiers in neuroinformatics 20180104

Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-ru ...[more]

PMID: 29354046

Similar Datasets

Patterns of text reuse in a scientific corpus.

Project description:We consider the incidence of text "reuse" by researchers via a systematic pairwise comparison of the text content of all articles deposited to arXiv.org from 1991 to 2012. We measure the global frequencies of three classes of text reuse and measure how chronic text reuse is distributed among authors in the dataset. We infer a baseline for accepted practice, perhaps surprisingly permissive compared with other societal contexts, and a clearly delineated set of aberrant authors. We find a negative correlation between the amount of reused text in an article and its influence, as measured by subsequent citations. Finally, we consider the distribution of countries of origin of articles containing large amounts of reused text.

| S-EPMC4291616 | biostudies-literature

A survey of researchers' code sharing and code reuse practices, and assessment of interactive notebook prototypes.

Project description:This research aimed to understand the needs and habits of researchers in relation to code sharing and reuse; gather feedback on prototype code notebooks created by NeuroLibre; and help determine strategies that publishers could use to increase code sharing. We surveyed 188 researchers in computational biology. Respondents were asked about how often and why they look at code, which methods of accessing code they find useful and why, what aspects of code sharing are important to them, and how satisfied they are with their ability to complete these tasks. Respondents were asked to look at a prototype code notebook and give feedback on its features. Respondents were also asked how much time they spent preparing code and if they would be willing to increase this to use a code sharing tool, such as a notebook. As a reader of research articles the most common reason (70%) for looking at code was to gain a better understanding of the article. The most commonly encountered method for code sharing-linking articles to a code repository-was also the most useful method of accessing code from the reader's perspective. As authors, the respondents were largely satisfied with their ability to carry out tasks related to code sharing. The most important of these tasks were ensuring that the code was running in the correct environment, and sharing code with good documentation. The average researcher, according to our results, is unwilling to incur additional costs (in time, effort or expenditure) that are currently needed to use code sharing tools alongside a publication. We infer this means we need different models for funding and producing interactive or executable research outputs if they are to reach a large number of researchers. For the purpose of increasing the amount of code shared by authors, PLOS Computational Biology is, as a result, focusing on policy rather than tools.

| S-EPMC9406794 | biostudies-literature

A large dataset of scientific text reuse in Open-Access publications.

Project description:We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publications and include a variety of reuse types, ranging from boilerplate text to verbatim copying to quotations and paraphrases. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. The Webis-STEREO-21 does not indicate if a reuse case is legitimate or not, as its focus is on the general study of text reuse in science, which is legitimate in the vast majority of cases. It allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.

| S-EPMC9879940 | biostudies-literature

The dynamic control of kiss-and-run and vesicular reuse probed with single nanoparticles.

Project description:Vesicular secretion of neurotransmitter is essential for neuronal communication. Kiss-and-run is a mode of membrane fusion and retrieval without the full collapse of the vesicle into the plasma membrane and de novo regeneration. The importance of kiss-and-run during efficient neurotransmission has remained in doubt. We developed an approach for loading individual synaptic vesicles with single quantum dots. Their size and pH-dependent photoluminescence change allowed us to distinguish kiss-and-run from full-collapse fusion and to track single vesicles through multiple rounds of kiss-and-run and reuse, without perturbing vesicle cycling. Kiss-and-run dominated at the beginning of stimulus trains, reflecting the preference of vesicles with high release probability. Its incidence was increased by rapid firing, a response appropriate to shape the kinetics of neurotransmission during a wide range of firing patterns.

| S-EPMC2696197 | biostudies-literature

Channeling Community Contributions to Scientific Software: A Sprint Experience.

Project description:In 2014, the khmer software project participated in a two-day global sprint coordinated by the Mozilla Science Lab. We offered a mentored experience in contributing to a scientific software project for anyone who was interested. We provided entry-level tasks and worked with contributors as they worked through our development process. The experience was successful on both a social and a technical level, bringing in 13 contributions from 9 new contributors and validating our development process. In this experience paper we describe the sprint preparation and process, relate anecdotal experiences, and draw conclusions about what other projects could do to enable a similar outcome. The khmer software is developed openly at http://github.com/dib-lab/khmer/.

| S-EPMC5104275 | biostudies-other

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code.

Project description:NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

| S-EPMC5487483 | biostudies-other

Trypanosoma brucei re-run

Project description:Sequencing of progeny from a Trypanosome brucei genetic crosses between T. brucei 247 and T. b. gambiense 386

| PRJEB2010 | ENA

Trypanosoma brucei re-run

Project description:Sequencing of progeny from a Trypanosome brucei genetic crosses between T. brucei 247 and T. brucei 927

| PRJEB2011 | ENA

Trypanosoma brucei re-run

Project description:Sequencing of progeny from a Trypanosome brucei genetic crosses between T. brucei 927 and T. brucei 927

| PRJEB2012 | ENA

Scientific contributions of citizen science applied to rare or threatened animals.

Project description:Citizen science is filling important monitoring gaps and thus contributing to the conservation of rare or threatened animals. However, most researchers have used peer-reviewed publications to evaluate citizen science contributions. We quantified a larger spectrum of citizen science's contributions to the monitoring of rare or threatened animals, including contributions to the peer-reviewed publications, gray literature and to conservation measures (i.e., actions taken as a direct result of citizen science monitoring). We sought to provide broad information on how results of studies of citizen science monitoring is used. We also evaluated factors associated with success of citizen science projects. We conducted a web search to find citizen science projects focusing on rare and threatened species and surveyed citizen science project managers about their contributions and factors influencing their success. The number of projects increased rapidly after 2010. Almost one-half of the citizen science projects produced at least 1 peer-reviewed publication, 64% produced at least 1 gray literature publication, and 64% resulted in at least 1 conservation measure. Conservation measures covered a wide range of actions, including management and mitigation plans, modification of threat status, identification and establishment of protected areas, habitat restoration, control of invasive species, captive breeding programs, and awareness campaigns. Longevity, data quality, and collaboration type best explained quantities of all types of scientific contributions of citizen science. We found that citizen science contributed substantially to knowledge advancement and conservation, especially when programs were long term and had rigorous data collection and management standards, and multidisciplinary or transdisciplinary collaborations.

| S-EPMC10092489 | 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