Mendeley Data Showcase

This dataset contains a PowerPoint presentation on the fundamentals of blockchain technology. It provides an introductory overview of blockchain principles, decentralization, smart contracts, consensus mechanisms, and more.

Raw data of the published paper - https://doi.org/10.1016/j.apsusc.2024.161098

Research Hypothesis, Findings, and Data Interpretation We hypothesized that shorter harvest intervals would improve the postharvest quality and storability of rabbiteye blueberries (Vaccinium virgatum cv. ‘Brightwell’). Specifically, frequent harvesting (every 2–3 days) was expected to reduce berry damage, weight loss, and firmness deterioration while maintaining optimal sugar and acid content. In contrast, longer harvest intervals (seven days) were anticipated to lead to greater fruit deterioration due to prolonged field exposure. To test this, blueberries were harvested at 2-day (Trt A), 3-day (Trt B), and 7-day (Trt C) intervals during the 2023 and 2024 seasons in Georgia, USA. After harvest, fruit was stored at 1°C and 85% relative humidity for 21 days, with quality assessments at 0, 7, 14, and 21 days. Parameters measured included berry damage (%), berry diameter (mm), weight loss (%), firmness (g/mm), total soluble solids (°Brix), titratable acidity (% citric acid), and anthocyanin concentration (mg cyanidin-3-glucoside/g FW). Key Findings Berry Damage and Weight Loss: The 7-day harvest interval (Trt C) resulted in significantly higher berry damage and weight loss compared to 2-day and 3-day intervals. Berries from shorter harvest intervals maintained a better overall appearance. Firmness Retention: The 7-day interval led to the most significant firmness decline during storage. The 2-day and 3-day intervals better preserved fruit texture. Total Soluble Solids and Titratable Acidity: Sugar content remained stable across treatments. Acidity levels declined more in the 7-day interval, likely due to extended field exposure. Anthocyanin Concentration: The 7-day interval berries had the highest anthocyanin content, suggesting extended ripening increased pigment accumulation. However, this came at the expense of storability and texture. Data Interpretation and Practical Implications Optimal Harvest Interval: A 3-day harvest interval (Trt B) provided the best balance between minimizing postharvest losses and maintaining quality, making it a practical option for growers. Postharvest Storage Considerations: Longer harvest intervals resulted in higher deterioration, requiring additional interventions (e.g., rapid cooling, packaging) to mitigate losses. Anthocyanin Trade-off: While extended field exposure increased anthocyanin levels, it negatively impacted firmness and storability, suggesting that quality cannot be defined by pigment accumulation alone. Conclusion Our findings confirm that shorter harvest intervals significantly improve postharvest blueberry quality. A 3-day interval is recommended to optimize fruit storability while maintaining efficient harvesting operations. These insights provide valuable guidance for blueberry growers in warm climates seeking to extend marketability and reduce postharvest losses.

The dataset consists of tables of refractive index values versus wavelength and temperature n(lambda, T) for 4 hygroscopic organic liquids. Each table corresponds to one liquid: (i) HEG – hexaethylene glycol, 96% abcr GmbH lot 1508060; (ii) PEG – pentaethylene glycol, 98.0%, Alfa Aesar lot 10207595 and pentaethylene glycol, 97.0%, abcr GmbH lot 1509179 (no differences in measurements were found) ; (iii) TPG – tripropylene glycol, ≥99%, Alfa Aesar lot H25W018; (iv) DPG – dipropylene glycol, mixture of isomers, 99%, Alfa Aesar lot 10201331. The measurements cover the range from 390 to 1070 nm in wavelength and from 1 to 45ºC in temperature. Each data point corresponds to a mean value of several measurements. Outlier measurement series were discarded. The accuracy of the refractive index measurement is estimated to be ±0.0003, while the accuracy of the wavelength measurement is ~1%, and the accuracy of the temperature measurement is ±0.5 K. The measurements were performed with a modified Abbe refractometer. The calibration was performed with water at each temperature from 5 to 45ºC and with ethylene glycol at 1ºC. The details of experimental setup and procedures can be found in [Jakubczyk et al., Chromatic dispersion and thermal coefficients of hygroscopic liquids: 5 glycols and glycerol, Scientific Data, (2023) 10:894, DOI: 10.1038/s41597-023-02819-3]. The SketchUp file (.skp, 2017 format) contains the corresponding 3D model, while the .mp4 file contains a walk-around video of the setup, generated from the model.

Correction for calcium interference in the 40Ar/39Ar dating method

The dataset in this study focuses on the burden of mental health illness, specifically anxiety and depression, across 149 countries over a 26-year period. The research combines national-level data on mental health prevalence (NAP) sourced from the World Health Organization (WHO), which tracks global mental illness data. To analyze the potential drivers of mental health outcomes, the study includes independent variables related to knowledge creation and transfer, such as publication data from Clarivate's Journal Citation Report on science, technology, engineering, mathematics, arts, and humanities. Additionally, it incorporates control variables from organizations like SIPRI, the World Bank, and the Heritage Foundation, which provide macro-level data on population size, economic freedom, internet penetration, R&D investment, political corruption, military spending, economic status, and literacy rates. The dataset, which integrates these variables, helps reduce the limitations found in previous studies regarding sample selection and regional focus, offering a comprehensive framework for understanding the complex relationships between mental health and societal factors.

This Research Data Package contains datasets from a Systematic Literature Review (SLR) on AI for Empowering Women in AI. The data includes: Dataset_SLR.csv: All studies retrieved from Scopus using the predefined search query. Dataset_Primary_Studies.csv: The final selection of primary studies after applying inclusion/exclusion criteria and quality assessment. These datasets document the study selection process.

Data and code required for running all the analyses and plotting all the figures presented in the paper: The ontogeny of foraging in meerkat, a cooperatively breeding mongoose. Overview of files: Focal watch datasets used for all the foraging analyses in the manuscript - FocalWatches_PerFocalData: Focal level dataset used for the analyses of prey capture rate, digging efficiency, digging success, digging effort, surface prey & prey size - FocalWatches_PreyData: Per focal sums of different prey categories caught, must be joined by FocalID to the above dataset to obtain model covariates. - FocalWatches_DigBoutDur: Dig Bout dataset for analyses of the duration of individual dig bouts. Code for foraging analyses - MeerkatForagingGAMS_FocalLevel.Rmd - Code to run the models looking at foraging performance at the focal level and produce plots for the paper (Fig 1) and SIs. - MeerkatForagingGAMS_Diet.Rmd - Code to run the models looking at specific diet components and produce plots for the paper (Fig 2) and SIs. - MeerkatForagingGAMS_BoutLevel.Rmd - Code to run the models looking at duration of single digs and produce plots for the SIs. Reproductive & growth data used for Figure 3 - LH_FirstDomAge: The age first dominant, use to create Figure 3b - LH_FirstSexActivity: The first age that evidence of sexual behaviour was observed, used to create Figure 3a - Growth_FrontClaw: Front claw measurements for the fitting of the monophasic growth curve - Growth_Mass: Mass measurement for fitting the biphasic growth curve Code for LH plots and Growth models - LifeHistoryAnalyses.R - Code for producing Figure 3 and running the growth models that underlie some plots. The IDs for Meerkats, Groups and Focals are unique to the datasets present here, so while they are joinable between the datasets presented here, they will not linked to other meerkat data. Additionally, while the code provided can generate all figures in the data, additional formatting has been applied to graphics regarding labels and text size before publication. All funding details associated with these data can be found in the associated publication.

This dataset is used for training object detection models with the YOLOv9 method. The resulting model is used for implementation in pig cleaning robots. This robot is purpose to improve biosecurity on pig farms.

Data Description for the Project: "Good and Bad Classification of Egg and Bread Toast" This dataset is designed to support a machine learning project aimed at classifying eggs and bread toasts as either "Good" or "Bad" based on various quality parameters. It comprises a total of 1000 samples, evenly distributed across two categories: Egg Samples: 500 total (250 Good, 250 Bad) Bread Toast Samples: 500 total (250 Good, 250 Bad) The dataset is structured to facilitate binary classification tasks and can be applied to food quality assessment systems, automated inspection lines, and educational projects related to food safety and processing. 1. Egg Samples Total Samples: 500 Good Eggs (250 samples): Fresh eggs with optimal physical and chemical properties. Bad Eggs (250 samples): Eggs that are spoiled, stale, or not suitable for consumption. Features: Shell Characteristics: Color: Ranges from white to brown shades. Texture: Smooth (good) vs. rough or cracked (bad). Cleanliness: Clean shell (good) vs. dirty/stained shell (bad). Internal Quality Parameters: Yolk Position & Shape: Centered and round yolk (good) vs. flattened or displaced yolk (bad). Albumen Consistency: Thick and firm albumen (good) vs. watery or thin albumen (bad). Odor: Neutral smell (good) vs. sulfuric or unpleasant odor (bad). Physical Tests: Float Test Results: Sinks and lies flat (good) vs. floats in water (bad). Weight: Standard weight range for fresh eggs vs. underweight or dehydrated eggs. 2. Bread Toast Samples Total Samples: 500 Good Bread Toast (250 samples): Properly toasted slices, evenly browned, and suitable for consumption. Bad Bread Toast (250 samples): Over-toasted (burnt), under-toasted (raw), or stale toasts. Features: Color and Texture: Color Uniformity: Golden-brown (good) vs. burnt black or pale (bad). Texture: Crisp outer layer with soft inner crumb (good) vs. hard, burnt, or soggy texture (bad). Moisture Content: Optimal moisture retained in good toasts vs. excessively dry or too moist in bad toasts. Physical Dimensions: Thickness: Uniform slices in good samples vs. uneven or broken slices in bad ones. Aroma and Taste: Pleasant toasted aroma and flavor (good) vs. burnt smell or off-flavor (bad). 3. Data Collection and Preprocessing All samples were collected under controlled conditions to ensure consistency. Visual inspection, olfactory tests, and physical assessments were performed. Images of each sample were captured under standardized lighting and angles. Data preprocessing involved normalization, feature extraction (color histograms, texture analysis), and removal of outliers. 4. Potential Applications Automated sorting systems in food industries.