In order to evaluate negative citations in articles and comments on PubPeer as mechanisms for correcting science, we have built up a corpus of articles associated with retraction or correction notices.
As we are interested in citations, we focus here on articles with at least 1 citation according to Scite database, that is 45,811 articles.
Mass Spectrometry Imaging datasets used as validation of the functionality of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using MALDI TOF/TOF ultrafleXtreme. Referred to as Dataset 1-10 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957). Datasets 1 and 2: Mouse Pancreatic Tissue. Dataset 3: Mouse Kidney Tissue. Datasets 4-10: Mouse Brain Tissue.
The data set contains Hadamard matrices of order 28 in machine-readable form, convenient for use in programs.
Data taken from the site
This data set complements following ones
Ukhalov, Alexey; Nevskii, Mikhail (2018), “Functions for checking necessary conditions for maximality of 0/1-determinant and example”, Mendeley Data, v1 http://dx.doi.org/10.17632/sm3x4xrb42.1
Ukhalov, Alexey (2019), “Matrices having the largest known determinant in machine-readable form”, Mendeley Data, v1 http://dx.doi.org/10.17632/hzf94h43c5.1
Data is presented in three formats: Wolfram Mathematica Notebook, PDF, and Plain Text.
Contributors:Rebecca L Hansen, Maria Emilia Dueñas, Young Jin Lee
Mass Spectrometry Imaging dataset of B73 inbred root used in the validation of of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using Thermo Finnigan™ MALDI-LTQ-Orbitrap Discovery. The datasets are referred to as Dataset 13 and Dataset 14 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957).
Contributors:Kerem Özkap, Ertan Peksen, Ismail Kaplanvural, Deniz Çaka
This data and code are associated with the article "3D Scanner Technology Implementation to Numerical Modeling of GPR" by the same authors. The 3D scanner data and Matlab code used in the article are provided with other necessary files. The Readme file comprises detailed descriptions of the data files and formats.
Please see the publication for more information about this data set.
Contributors:Javier Pastor-Galindo, Mattia Zago, Pantaleone Nespoli, Sergio Lopez, Alberto Huertas Celdrán, Manuel Gil Pérez, José A. Ruipérez-Valiente, Gregorio Martinez Perez, Felix Gomez Marmol
While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.
The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
Data have been exported in three formats to provide the maximum flexibility
- MongoDB Dump BSONs: To import these data, please refer to the official MongoDB documentation.
- JSON Exports: Both the users and the tweets collections have been exported as canonical JSON files.
- CSV Exports (only tweets): The tweet collection has been exported as plain CSV file with comma separators.
This data was collected during a study on the perceptions of RAS-produced fish in Germany.
In the discussion of expanding sustainable aquaculture production in the EU, recirculating aquaculture systems (RAS) adopt an increasingly important role. While capable of mitigating certain environmental externalities commonly found with other forms of aquaculture and enabling the intensive production of safe and healthy aquatic products, RAS are also characterized by higher investment and operating costs which require higher sales prices. Incorporating principles from the theory of planned behavior, multi-attribute attitude models and a taste test, this research investigates consumers’ and fine dining chefs’ perception of RAS-produced fish using the example of pikeperch (Sander lucioperca). While perception and stated purchase intention of RAS-produced pikeperch is generally positive, results reveal a comparably small portion of consumers willing to pay above average prices. Healthiness related aspects are of greatest relevance to both investigated groups and regression analysis shows that personal norms and moral obligation are significant positive predictors of consumers’ purchase intention.