This dataset contains all raw data used to generate the plots from Minden et al. 2023 - MDPI genes. Full title: "Mimicked Mixing Heterogeneities of Industrial Bioreactors Stimulate Long-Lasting Adaption Programs in Ethanol-Producing Yeasts" (2023)

Raw data from the Forests paper: "Seed sourcing strategies considering climate change forecasts: a practical test in Scots pine"

Forests. 2020; 11(11):1222. https://doi.org/10.3390/f11111222

It comprises data from a multisite (5) provenance test: height (measurements years:1995, 2000 and 2005), dbh (measurements years:1995, 2000 and 2005) and survival (measurements years: 2000 and 2005)

This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values).

The data was collected in a 4-floor university building in Tampere, Finland, during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints.

The dataset and/or the associated software are to be cited as follows:

E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798

A detailed description of our data can be found here:

Lohan, Elena Simona, Torres-Sospedra, Joaquín, Leppäkoski, Helena, Richter, Philipp, Peng, Zhe, Huerta, Joaquín
"Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning", MDPI Data journal, 16 pages, doi:10.3390/data2040032, http://www.mdpi.com/2306-5729/2/4/32

This dataset contains all raw data used to generate the plots from Minden et al. 2022 - MDPI metabolites. Full title: "Monitoring intracellular metabolite dynamics in Saccharomyces cerevisiae during industrially relevant famine stimuli"

The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel.

Both the datasets comments are divided into seven categories:-

Label 1- Gratitude

Label 2- About the recipe

Label 3- About the video

Label 4- Praising

Label 5- Hybrid

Label 6- Undefined

Label 7- Suggestions and queries

All the labelling has been done manually.

Nisha Madhulika dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

Kabita Kitchen dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

There are two separate datasets file of each channel named as preprocessing and main file .

The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:

• Id
• Comment text
• Labels

• Count of stop-words
• Uppercase words
• Hashtags
• Word count
• Char count
• Average words
• Numeric

The main file includes:

• Id
• comment text
• Labels

Please cite the paper

https://www.mdpi.com/2504-2289/3/3/37

MDPI and ACS Style

Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. Big Data Cogn. Comput. 2019, 3, 37.

These data represent the underlying figures and tables of the manuscript.

This dataset is associated with the following publication: Yuan, L., and K.J. Forshay. Using SWAT to Evaluate Streamflow and Lake Sediment Loading in the Xinjiang River Basin with Limited Data. WATER. MDPI AG, Basel, SWITZERLAND, 12(1, 39): 1-20, (2019).

These are the data associated with the manuscript "https://www.mdpi.com/2073-4441/15/1/15." They are available under "Supplementary Materials" on the left side of the webpage, under the Table of Contents. This dataset is associated with the following publication: Krause, J.R., M.E. Gannon, A.J. Oczkowski, M.J. Schwartz, L.K. Champlin, D. Steinmann, M. Maxwell-Doyle, E. Pirl, V. Allen, and E.B. Watson. Tidal Flushing Rather Than Non-Point Source Nitrogen Pollution Drives Nutrient Dynamics in A Putatively Eutrophic Estuary. WATER. MDPI, Basel, SWITZERLAND, 15(1): 15, (2023).

This is the raw data used for analysis. This experiment examined the genetic parameters of a novel, direct measure of resilience to water deficit in tall fescue (Lolium arundinaceum [Schreb.] Darbysh.). Heritability, genetic correlations, and predicted gain from selection were estimated for average productivity, resilience, and stability based on forage mass of a tall fescue half-sib population grown under a linesource irrigation system with five different water levels (WL).

This dataset includes the account information of 1,872,677 Goodreads users. The information has been anonymized (for more detail please take a look at our paper). The details for each individual includes:* Anonymized user ID* Friend count (number)* Group count (number)* Review count (number)* Detected Binary Gender (based on name)* Detected country-level location (based on the location provided by the user. We have removed more detailed location indicators to preserve the anonymity of users)* Anonymized list of favorite authors * Numerical length of the user’s about section * Joined date (the year and month which the user joined the website)* Last active date (the year and month the user was last active. As the dataset was collected in 2020, we do not know if the users were active after the date of collection)Please cite the corresponding paper:Nazanin Sabri, Ingmar Weber: A Global Book Reading Dataset. MDPI Data, 2021.

The files provided here are the supporting data and code files for the analyses presented in "Impact of data temporal resolution on quantifying residential end uses of water", an article submitted to the Water journal (https://www.mdpi.com/journal/water). The journal paper assessed how the temporal resolution at which water use data are collected impacts our ability to identify water end use events, calculate features of individual events, and classify events by end use. Additionally, we also explored implications for data management associated with collecting this type of data as well as methods and tools for analyzing and extracting information from it. The data were collected in the cities of Logan and Providence, Utah, USA in 2022 and are included in this resource. The code and data included in this resource allow replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.

An investigation was performed on each of the individual CASMI datasets to identify whether the chemicals were present in DSSTox, the database underlying the Dashboard (see Methods for details). As our identification workflow relies on database presence, the presence or absence of a chemical in the database is clearly highly influential in terms of overall performance. Results from the dataset assembly analysis from each CASMI year dataset are presented in Table 1 and described in detail by dataset year below (complete datasets are provided in Supplemental File 2 and available as lists on the Dashboard).

This dataset is associated with the following publication: McEachran, A., A. Chao, H. Al-Ghoul, C. Lowe, C. Grulke, J. Sobus, and A. Williams. Revisiting Five Years of CASMI Contests with EPA Identification Tools. Metabolites. MDPI AG, Basel, SWITZERLAND, 10(6): 260, (2020).

This study utilized the Pb- and As-contaminated soils to determine the combined effect of pH with respect to PZC and different rates of P-application on pyromorphite formation, and Pb and arsenic (As) bioaccessibility as impacted by speciation changes. Solution chemistry analysis along with synchrotron-based Pb- and As-speciation, and bioaccessibility treatment effect ratios (TERs) were conducted. This dataset is associated with the following publication: Karna, R., M. Noerpel, T. Luxton, and K. Scheckel. Point of Zero Charge: Role in Pyromorphite Formation and Bioaccessibility of Lead and Arsenic in Phosphate-Amended Soils. Soil Systems. MDPI AG, Basel, SWITZERLAND, 2(2): 22, (2018).

This collection contains data from the article published in MDPI Genes "Structural Analysis of microRNAs in Myeloid Cancer Reveals Consensus Motifs".

This repository hosts the main outputs from an analysis using the WWF Water Risk Filter to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) was published in the special issue of the MDPI journal Water: "Hydro-Meteorological Hazards under Climate Change".

This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at globaldamwatch.org . The source code used in this study is available at https://github.com/rafaexx/hydropowerClimateChange

See the interactive maps using this data at https://rcamargo.shinyapps.io/HydropowerClimateChange

This site is for us to upload the database is used for the analysis in the manuscript titled "Uneven Burdens: The Intersection of Brownfields, Pollution, and Socioeconomic Disparities in New Jersey, USA". The manuscript is has been published, and we here we provide the full version (in a shape file) of the database. We thank you for your patience. The link to the manuscript: https://www.mdpi.com/2071-1050/16/23/10535

Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))

True stress true strain curves of 51CrV4 spring steel grade Cylindrical samples  5 x 10 mm were machined, with the cylinder axis being in the rolling direction. Deformational dilatometer apparatus TA Instruments 805A/D (TA Instruments, New Castle, PA, USA) was employed for determination of true stress - true strain curves, Experiments were performed at different temperatures (1000, 1050, 1100, 1150, and 1200 °C) and strain rates (0.01, 0.1, 1, and 10 s−1). A standard non-lubricated 0.1 mm thick Mo plates are placed on sample contact surfaces. Data are firstly used in the article "https://www.mdpi.com/2075-4701/9/3/290" %%%%%%%%%%%%%%%%%%%% %Author %franci.vode@imt.si %www.IMT.si %%%%%%%%%%%%%%%%%%%%

Files "BS2V1-xxxx-y.yy.asc" are true stress true strain data for the spring steel presented in the article xxxx-temperatures - 1000, 1050, 1100, 1150 and 1200 °C y.yy-strain rates - 0.01,0.1,1,10 s-1.

For execution of four script files (starintg with OCT_*) in the directory, octave is required.

It can be downloaded and installed from "https://octave.org/" under the following conditions: Copyright © 1998-2022 John W. Eaton. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Required packages are 'control' and 'optimization'.

Within scripts provided, edit paths to filenames where required, denoted as %!!!!!!!!!!!!!!!!!!! %enter your path here

Two *.mat files in the directory are : * "SYSE_1200_0o01.mat" - transfer function G(s) for (1200°C, 0.01 s-1)saved as mat file * "SYSE_V1_1000_0_01_P4_Z3.mat - transfer function G'(s) for (1000°C, 0.01 s-1)saved as mat file with 3 zeros and 4 poles.

OpenMedText Dataset

A comprehensive biomedical text corpus consisting of MDPI journal articles and open-source medical textbooks for language model training and research.

  Dataset Summary

OpenMedText is a large-scale biomedical text dataset that includes:

Med-MDPI: 121,489 biomedical journal articles across 37 categories from MDPI journals Med-Textbooks: 29 open-source medical textbooks covering various medical disciplines

  Folder Structure

OpenMedText/ ├──… See the full description on the dataset page: https://huggingface.co/datasets/ywchoi/OpenMedText.

Simulation data of an ultralight aircraft.

Data *.mat collects in MAT files all flight data and ASSE coefficients for three manoeuvres:

stall: from 10 s to 40 s

AoS sweep: from 80 s to 110 s

3211 elevator: from 5 s to 40 s

Data *_noise.mat are the same with uncertainty levels described in https://www.mdpi.com/1436482

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water resource management. However, water-quality studies are limited by the lack of complete and reliable data sets on surface-water-quality variables. These deficiencies are particularly noticeable in developing countries.

This work focuses on surface-water-quality data from Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. Data collected at six monitoring stations are publicly available at https://www.dinama.gub.uy/oan/datos-abiertos/calidad-agua/. The high temporal and spatial variability that characterizes water-quality variables and the high rate of missing values (between 50% and 70%) raises significant challenges.

To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)).

IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases.

In this dataset, we include the original and imputed values for the following variables:

Water temperature (Tw)

Dissolved oxygen (DO)

Electrical conductivity (EC)

pH

Turbidity (Turb)

Nitrite (NO2-)

Nitrate (NO3-)

Total Nitrogen (TN)

Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC].

More details about the study area, the original datasets, and the methodology adopted can be found in our paper https://www.mdpi.com/2071-1050/13/11/6318.

If you use this dataset in your work, please cite our paper: Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. https://doi.org/10.3390/su13116318