Mendeley Data Showcase

Research Description This repository contains datasets from three linked studies examining biomass-dependent methylmercury (MeHg) toxicity, temperature-modulated toxicity, and thermal growth responses in marine phytoplankton. The objective was to determine how biomass and temperature regulate growth and contaminant sensitivity at the base of marine food webs. Research Hypotheses We tested three hypotheses: (1) Apparent EC₅₀ values increase with biomass due to density-dependent buffering and therefore overestimate intrinsic cellular sensitivity. (2) Temperature alters intrinsic MeHg toxicity and shifts community-level protection thresholds. (3) Phytoplankton growth follows canonical exponential (Eppley-type) scaling at suboptimal temperatures but diverges among species in full thermal performance curves. Experimental Design Experiments used axenic cultures of Phaeodactylum tricornutum, Emiliania huxleyi, Synechococcus elongatus, Thalassiosira pseudonana, and Cricosphaera carterae. For toxicity assays, cultures were exposed to graded MeHg concentrations across multiple initial biomass levels measured as optical density (OD₇₅₀). Experiments were performed at 20°C, 25°C, and 30°C under 48 h and 96 h exposures. Growth rates were calculated from OD changes, dose–response curves were fit using Hill models, and EC₅₀ values were estimated. Intrinsic toxicity was derived by regressing log₁₀(EC₅₀) against OD₇₅₀ and extrapolating to OD = 0 to remove biomass-buffering effects. Unified regression models quantified species × temperature × biomass interactions. Species sensitivity distributions (SSDs) were constructed from intrinsic EC₅₀ values to derive HC₅ and Final Acute Value benchmarks. For thermal physiology experiments, three species were incubated across six temperatures (20–35°C). Growth was modeled using quadratic thermal performance curves to estimate optimal temperature (Tₒₚₜ). Log-linear regressions and ANCOVA tested conformity with the canonical Eppley exponent (b = 0.0631). Key Findings and Reuse Biomass strongly inflated apparent EC₅₀ values; once corrected to OD = 0, intrinsic MeHg sensitivity converged across taxa. Temperature significantly modulated toxicity, particularly under longer exposure, with warming generally increasing intrinsic EC₅₀ values. Thermal growth experiments showed unimodal curves with optima near 30–32°C. Although suboptimal scaling aligned with Eppley predictions, species differed in curvature and high-temperature decline. The dataset includes raw OD measurements, growth rates, dose–response fits, EC₅₀ estimates, regression outputs, ANCOVA results, and SSD-derived benchmarks. It supports climate-sensitive ecotoxicological modeling and trait-based analyses of phytoplankton responses in a warming ocean.

This repository contains the dataset collected in the project entitled “Burnout, emotional symptoms, and work engagement among Brazilian psychologists: the influence of job demands and psychological flexibility.” Two waves of data collection were conducted. The first involved Brazilian workers and aimed to adapt the Personalized Psychological Flexibility Index (PPFI) to Brazilian Portuguese. The second was conducted with Brazilian psychologists in order to test structural models examining the relationships among job demands and resources, burnout, emotional symptoms, psychological flexibility, and work engagement. Both datasets, along with a variable codebook, are provided below.

The research study aimed to investigate sense of presence mechanism by comparing the impact of different tasks performed in a virtual environment while controlling for the effect of participants' executive functions and spatial abilities. The independent variable was the Task Type, with active and passive conditions. The dependent variable was the Sense of Presence, assessed through general presence, spatial presence, involvement, and realness. Cognitive Control was included as a covariate, and it comprised inhibition, flexibility, working memory, visuospatial ability, and mental imagery. The table is organised into three parts: data obtained from the socio-demographic questionnaire, results obtained from tests measuring cognitive abilities, and scores on the scale measuring the sense of presence after completing each task in the virtual environment. 1) Socio-demographic data IT and Prog: the data corresponds to computer and programming skills. 1 equates to no knowledge, and 7 to very good knowledge Game: the scores correspond to the number of hours per week spent playing video games, where 7 equates to more than 6 hours of gaming per week. VR: corresponds to the number of times the person has used a virtual reality device. W_dead: is a question corresponding to an exclusion criterion, where 0 means that the person has never played the video game The Walking Dead: Saints and Sinners. 3D: the person had to indicate whether they had knowledge of 3D modelling, where 1 indicates yes. 2) Cognitive abilities Each column corresponds to the result obtained after evaluation of a cognitive ability. Inhib_RT: corresponds to the reaction time obtained in the Flanker Task measuring inhibition. Flex_RT: corresponds to the reaction time obtained in the Trail Making Test measuring flexibility. Wrk_Mem_RT and Wrk_Mem_ACC: correspond to the reaction time and accuracy score obtained in the N-back task measuring working memory. Mtl_Img: corresponds to the score obtained on the ‘Vividness of Visual Imagery Questionnaire’ scale measuring mental imagery, where 5 corresponds to a mental image that is ‘Perfectly realistic, as vivid as real scenery’. The minimum score is 32 and the maximum score is 160. Mtl_Rot_RT and Mtl_Rot_ACC: correspond to the reaction time and accuracy score obtained in the Mental Rotation task measuring visuospatial ability. 3) Sense of presence The scale used was the Igroup Presence Questionnaire. Responses ranged from 1 to 7. To better understand the data, here is an explanation: P_G_A corresponds to the ‘Presence’ score for the ‘General Presence’ sub-dimension after performing the active task. Thus, SP corresponds to ‘Spatial Presence’, INV to “Involvement”, REAL to ‘Realness’, “A” to Active and “P” to Passive.

This dataset contains a carefully selected collection of 2147 high-resolution tiff images of ancient and mediaeval coins from the Northeastern regions of India with a total of 51 classes from all four regions. The images of this dataset have been taken from Assam, Tripura, Koch Bihar, and Jaintiapur. This dataset will support research of numismatics, archaeology, cultural heritage digitisation, and image classification with artificial intelligence. The coins have been collected from verified private collections and five reputed auction houses: Marudhar Arts, Oswal Auction, Classical Numismatic Gallery, David Feldman, and Jain Auction House with their permissions. Each coin was examined visually and authenticated using standard numismatic references to confirm its dynasty, denomination, motif type, and inscription style. The coins' inscriptions are written in Assamese, Bengali, Sanskrit, Arabic, and various local scripts. To enable machine learning applications, all images went through standard preprocessing steps, including noise reduction, and resizing to 512 × 512 pixels. The front and back design was kept perfect for each image. The dataset comes with a detailed annotation file that documents metadata, including class labels and numismatic features. It delivers a systematic hierarchical model covering 51 defined coin classes across four regions of the Northeastern Indian dataset.

Supplemental Table I for "Exclusively extrafacial granuloma faciale: Case report and literature review" containing all case reports and series on PubMed and Embase containing the terms "granuloma faciale" and "extrafacial" without limitations on language or dates, with a a total of 53 articles, containing 81 unique extrafacial granuloma faciale patients. Data includes summary of patient demographics, lesion distribution, treatments, and outcomes.

This dataset contains qualitative data collected between October 2023 and March 2024 in Ondo State, Nigeria, as part of a doctoral research project examining food loss at the pre-consumption stage of the food supply chain. The study focuses on how valuation arrangements, such as infrastructures, financial rules, and institutional practices, shape agricultural decision-making and configure food loss among arable crop farmers. The dataset comprises 42 participants occupying different positions within the food supply chain. These include rice, maize, and cassava farmers; agricultural extension agents; agronomists; representatives from federal, state, and local agricultural agencies; media representatives; and statisticians. Data were generated through one focus group discussion (FGD) with nine farmers (five women and four men) and individual semi-structured interviews with 12 additional farmers and 21 non-farmer actors across institutional levels. Participants were purposively selected with the support of agricultural extension officers, who facilitated contact with registered farmers and relevant institutional actors. The farmers involved in the FGD had an average age of approximately 40 years and a minimum of 10 years of farming experience. They cultivated rice, maize, and cassava, the focal crops of the study. Data collection methods included face-to-face semi-structured interviews, a focus group discussion, and field observations during farm visits. Interviews were conducted in English and Yoruba. The FGD was conducted primarily in Yoruba and later translated into English. All interviews were audio-recorded and transcribed verbatim. Field observations were documented in written fieldnotes and supplemented with photographs and short video recordings for contextual reference. The dataset includes anonymised interview transcripts, and translated FGD transcripts. Participants were assigned pseudonyms to ensure confidentiality. Identifying details have been removed or masked in accordance with ethical research standards. The data capture participants’ accounts of farming practices, market access, transport and storage conditions, credit arrangements, extension interactions, and input sourcing. Attention was given to how actors evaluate crop viability, timing, pricing, infrastructure, and risk. The dataset therefore, provides rich empirical material for examining agrarian market organisation, valuation practices, infrastructural constraints, and food system inequalities in a Global South context. This repository supports transparency and reproducibility by making available the qualitative materials underlying the published analysis of valuation arrangements and food loss in Nigeria’s pre-consumption food system.

This dataset contains the raw and processed experimental data supporting the study entitled “Magnetic Fe₃O₄@Cr-MOF Hybrid Adsorbent for Efficient Organic Dye Separation: Mechanistic Insights and Process Optimization.” The data include adsorption isotherm and kinetic measurements, thermodynamic parameters, response surface methodology (RSM) optimization results, and characterization data (XRD, FTIR, BET surface area, and SEM analysis). These data support the evaluation of adsorption performance, mechanistic interpretation, and process optimization for organic dye removal from aqueous systems.s

This dataset consists of images of two types of bacterial strains streaked on MacConkey agar plates. The images of the bacterial colonies were taken under two different shooting conditions, “controlled” and “uncontrolled” as described in “steps to reproduce” section. These bacterial strains are: 1- Escherichia coli (E. coli): the number of images under controlled conditions is (168) and the number of images under uncontrolled conditions is (3532). 2- Klebsiella pneumoniae (K. pneumoniae): the number of images under controlled conditions is (152) and the number of images under uncontrolled conditions is (3513). IN THE REPOSITORY, YOU WILL FIND: Group 1 consists of images taken from 39 and 36 plates of K. pneumoniae and E. coli respectively, under controlled and uncontrolled conditions. Group2 consists of images taken from 25 plates of K. pneumoniae and E. coli, under controlled and uncontrolled conditions. An excel sheet detailing the numbers of images in the folders. NOTABLE FINDING: Baseline CNNs trained on this data achieved high accuracy, indicating that phone images provide sufficient discriminative signal without expert inspection. Please refer to: 1) S. A. Nagro et al., "Automatic Identification of Single Bacterial Colonies Using Deep and Transfer Learning," in IEEE Access, vol. 10, pp. 120181-120190, 2022, DOI: https://doi.org/10.1109/ACCESS.2022.3221958 2) M. Kutbi et al., "Leveraging Smartphone Imaging and Deep Transfer Learning for Bacterial Colony Classification: From Uncontrolled to Controlled Settings," in IEEE Access, doi: https://doi.org/10.1109/ACCESS.2025.3625648 HOW THIS DATASET CAN BE USED: This dataset can be utilized in any research interested in recognizing different features of bacterial colonies. The dataset can also be used to train and evaluate deep learning models for colony classification, while also supporting studies on robustness, generalization, and practical deployment, to advance computer vision and AI applications in microbiology. By combining clinically important bacteria with controlled and uncontrolled imaging, the dataset offers a realistic and accessible resource for researchers interested in developing AI methods that perform reliably in laboratory and non-laboratory environments. IMPORTANT NOTE: This dataset was expanded to include different types of bacterial strains and culture media which can be found in [DOI: 10.17632/v54x8jdx5x.1] IF YOU USE THIS DATASET, PLEASE REFERENCE THE FOLLOWING: 1. DOI: https://doi.org/10.1109/ACCESS.2022.3221958 2. DOI: https://doi.org/10.1109/ACCESS.2025.3625648 3. DOI: https://doi.org/10.17632/kx6gz3wmcf.1 4. DOI: https://doi.org/10.17632/v54x8jdx5x.1