This folder contains data used to illustrate the utility of Weka detector in TrackMate.

- classifier.model: trained Weka classifier.
- image data: human dermal microvascular blood endothelial cells expressing GFP-paxillin

More detail on using these files can be found here: https://imagej.net/plugins/trackmate/trackmate-weka.

This folder contains data used to illustrate the utility of Weka detector in TrackMate.

• classifier.model: trained Weka classifier.
• MDA231 paxillin DMSO 1 min.czi - MDA231 paxillin DMSO 1 min.czi #01_t1_t40_crop.tif: example image.

More detail on using these files can be found here: https://imagej.net/plugins/trackmate/trackmate-weka.

These datasets contain a set of news articles in English, French and Spanish extracted from Medisys (i‧e. advanced search) according the following criteria: (1) Keywords (at least): COVID-19, ncov2019, cov2019, coronavirus; (2) Keywords (all words): masque (French), mask (English), máscara (Spanish) (3) Periods: March 2020, May 2020, July 2020; (4) Countries: UK (English), Spain (Spanish), France (French). A corpus by country has been manually collected (copy/paste) from Medisys. For each country, 100 snippets by period (the 1st, 10th, 15th, 20th for each month) are built. The datasets are composed of: (1) A corpus preprocessed for the BioTex tool - https://gitlab.irstea.fr/jacques.fize/biotex_python (.txt) [~ 900 texts]; (2) The same corpus preprocessed for the Weka tool - https://www.cs.waikato.ac.nz/ml/weka/ (.arff); (3) Terms extracted with BioTex according spatio-temporal criteria (*.csv) [~ 9000 terms]. Other corpora can be collected with this same method. The code in Perl in order to preprocess textual data for terminology extraction (with BioTex) and classification (with Weka) tasks is available. A new version of this dataset (December 2020) includes additional data: - Python preprocessing and BioTex code [Execution_BioTex‧tgz]. - Terms extracted with different ranking measures (i‧e. C-Value, F-TFIDF-C_M) and methods (i‧e. extraction of words and multi-word terms) with the online version of BioTex [Terminology_with_BioTex_online_dec2020.tgz],

Breast Cancer Dataset

This dataset is about books and is filtered where the book is Instant Weka how-to : implement cutting-edge data mining aspects in Weka to your applications, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Dataset

This dataset was created by Marina Grabelli

Released under Apache 2.0

Contents

Dataset

This dataset was created by Gedeon Alejo Aruni

Contents

Soybean Dataset

Weka Experimenter file.

A benchmark dataset for automated machine learning, consisting of a set of algorithms and instances, with a focus on hyperparameter optimization.

SI1_Supporting Information file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito Culex quinquefasciatus to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.

SI2_EXP1_50IRs from Liu et al (2013) SDF file presents the structures of each of the 50 IRs analyzed.

SI3_EXP2_Datasets gathers the four datasets as SDF files from Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009) used for the repellency modeling in EXP2.

SI4_EXP3_Prospective analysis provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.

SI5_QuBiLS-MIDAS MDs lists contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.

SI6_EXP1_Sensillar Modeling comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu et al. (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.

SI7_EXP2_Repellency Modeling encompasses the four datasets in the study: Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.

SI8_Virtual Hits includes the cluster analysis results and physico-chemical properties of new IR virtual leads.

Dataset

This dataset was created by Click Mintaka

Released under CC0: Public Domain

Contents

File Name: WordsSelectedByInformationGain.csv Data Preparation: Xiaoru Dong, Linh Hoang Date of Preparation: 2018-12-12 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: the file contains a list of 1655 informative words selected by applying information gain feature selection strategy. Information gain is one of the methods commonly used for feature selection, which tells us how many bits of information the presence of the word are helpful for us to predict the classes, and can be computed in a specific formula [Jurafsky D, Martin JH. Speech and language processing. London: Pearson; 2014 Dec 30].We ran Information Gain feature selection on Weka -- a machine learning tool. Notes: In order to reproduce the data in this file, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.

Explore the historical Whois records related to weka-net-territorial.com (Domain). Get insights into ownership history and changes over time.

Glass Dataset

This statistic shows data on the revenue development of the Weka Holding publishing group in Germany from 2007 to 2020. In 2020, the publishing group located in Augsburg generated a revenue of 180.7 million euros.

WEKA Group is a company. It is located in Od, Germany and was founded in 1973. The company is part of the Communication Services sector, specifically in the Media industry.

This dataset provides information about the number of properties, residents, and average property values for Wa Weka Circle cross streets in Nashville, TN.

MEKA is a multi-label/multi-target extension to Weka.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Weka-Learning-Ltd.