Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Computational reproducibility"'
Autor:
Samuel, Sheeba, Mietchen, Daniel
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 2, Iss 2, Pp 4:1-4:24 (2024)
The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration and inter
Externí odkaz:
https://doaj.org/article/713f167755dc4db5b62813c6c8798cf4
Publikováno v:
SoftwareX, Vol 28, Iss , Pp 101938- (2024)
We present the first major release of the Classification Algorithms Comparison Pipeline (CACP). The proposed software enables one to compare newly developed classification algorithms in Python with other classifiers to evaluate classification perform
Externí odkaz:
https://doaj.org/article/da55067caad3457da35460d543c3387b
Autor:
Meghan A. Balk, John Bradley, M. Maruf, Bahadir Altintaş, Yasin Bakiş, Henry L. Bart Jr, David Breen, Christopher R. Florian, Jane Greenberg, Anuj Karpatne, Kevin Karnani, Paula Mabee, Joel Pepper, Dom Jebbia, Thibault Tabarin, Xiaojun Wang, Hilmar Lapp
Publikováno v:
Methods in Ecology and Evolution, Vol 15, Iss 6, Pp 1129-1145 (2024)
Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These inc
Externí odkaz:
https://doaj.org/article/5bc57536d4894554b05c8091604369d4
Autor:
Luis Marone
Publikováno v:
Ecología Austral, Vol 34, Iss 1 (2024)
The lack of reproducibility of scientific results is jeopardizing the trust in science. An effort to inform the dynamic, but non-arbitrary, nature of scientific evidence is required along with strengthening the reliability of published results. Conce
Externí odkaz:
https://doaj.org/article/51a8c789096c4e6883ccb22caebc7f7b
Publikováno v:
Data Science Journal, Vol 23, Pp 23-23 (2024)
Machine learning (ML) and advanced computational methods are powerful tools for processing and deriving value from large data volumes. These methods are being developed and deployed rapidly, but best practices are still evolving regarding code and da
Externí odkaz:
https://doaj.org/article/8e5d6ede7aaa432a9e65e14932a1a040
Autor:
Murat Çalışkan, Berk Anbaroğlu
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101498- (2023)
The widespread use of Global Navigation Satellite System (GNSS) receivers for monitoring people, vehicles, and animals has generated large amounts of space–time point data. One of the important analyses of such data is hot spot detection. This coul
Externí odkaz:
https://doaj.org/article/0fa9d0f57eab40158b2904de2a21025f
Autor:
Mine Dogucu, Mine Çetinkaya-Rundel
Publikováno v:
Journal of Statistics and Data Science Education, Vol 30, Iss 3, Pp 251-260 (2022)
AbstractIt is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices
Externí odkaz:
https://doaj.org/article/858df027ce8942519d40ecd7c3ce6a0c
Publikováno v:
Journal of Statistics and Data Science Education, Vol 30, Iss 3, Pp 209-218 (2022)
ABSTRACTThis article synthesizes ideas that emerged over the course of a 10-week symposium titled “Teaching Reproducible Research: Educational Outcomes” https://www.projecttier.org/fellowships-and-workshops/2021-spring-symposium that took place i
Externí odkaz:
https://doaj.org/article/1e13bb34606b468e9a6122dbee1ca408
Publikováno v:
Psych, Vol 3, Iss 4, Pp 836-867 (2021)
Computational reproducibility is the ability to obtain identical results from the same data with the same computer code. It is a building block for transparent and cumulative science because it enables the originator and other researchers, on other c
Externí odkaz:
https://doaj.org/article/83be34c84a7341c0a8e89d0e9e61b7b2
Publikováno v:
SoftwareX, Vol 19, Iss , Pp 101134- (2022)
This paper presents a Classification Algorithms Comparison Pipeline (CACP) for comparing newly developed classification algorithms in Python with other commonly used classifiers to evaluate classification performance, reproducibility, and statistical
Externí odkaz:
https://doaj.org/article/653f48dd4b514fa291d3742c94d39b3d