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A consumer survey was conducted in eastern India in 2017 to understand the heterogeneity of consumers’ food choice. Face-to-face interviews were conducted among urban and rural consumers from low- and middle-income households in Odisha and West Bengal, eastern India, using a structured questionnaire. A multi-stage sampling procedure was implemented with stratified random sampling as the first stage and systematic sampling as the second stage. The survey data comprise responses from 501 respondents who have active involvement in grocery purchase decision-making and/or in meal planning or cooking for the household. The survey generated a dataset that was used to unravel five sources of heterogeneity (5Ws) in gastronomic systems that affect consumers’ diets (i) socioeconomic characteristics of the target population (who); (ii) food environments (where); (iii) eating occasions (when); (iv) consumed dishes (what); and (v) ingredient attributes and consumer attitudes towards food (why). The approach and analyses are elaborated in the article “Unraveling heterogeneity of consumers’ food choice Implications for nutrition interventions in eastern India”. Data from the survey can be further used to design behavioral experiments and interactive food choice tablet applications to elicit behavioral intentions in food choice.In this work, the sphingosine-1-phosphate receptor modulator fingolimod was assessed as a preclinical candidate for the treatment of acute ischaemic stroke according to the Stroke Therapy Academic Industry Roundtable (STAIR) preclinical recommendations. Young (15-17 weeks), aged (72-73 weeks), and ApoE-/- mice (20-21 weeks) fed a high fat diet (all C57BL/6 mice) underwent permanent electrocoagulation of the left middle cerebral artery. Mice received either saline or fingolimod (0.5 mg/kg or 1 mg/kg) at 2-, 24-, and 48-hours post-ischaemia via intraperitoneal (i.p.) injection. Another cohort of young mice (8-9 and 17-19 weeks) received short-term (5 days) or long-term (10 days) fingolimod (0.5 mg/kg) treatment in a treatment duration study. For young, aged, and ApoE-/- mice, motor behavioural tests (cylinder and grid-walking) were performed at days 0, 3, and 7 post-ischaemia to evaluate neurobehavioural recovery. In the treatment duration study, the grid-walking test was performed at days 0, 2, 5 and 10 post-ischaemia. Brain tissue sections were stained with haematoxylin and eosin (H&E), and NeuN to quantify tissue damage. read more Flow cytometry was used to quantify T cell populations in blood, spleen, and lymph nodes. The data presented in this article improves our understanding of the potential neuroprotective and immunomodulatory effects of fingolimod in a mouse model of brain ischaemia. Such data may be significant in the design of future preclinical and clinical stroke studies for fingolimod.This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. Image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under real-world conditions using a digital camera and with the help of a pathologist. JMuBEN dataset contains three compressed folders with images inside. The first file contains 7682 images of Cerscospora, the second contains 8337 images of rust and the last one contains 6572 images of Phoma. JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https//data.mendeley.com/datasets/tgv3zb82nd/1 and https//data.mendeley.com/datasets/t2r6rszp5c/1 respectively.We compiled data from histological sources on the formation ages for human mandibular and maxillary permanent first molars, lateral and central incisors and canines. From this we summarised the data by reporting weighted means for cusp initiation, crown completion and apex completion. This provides a reference for bioarchaeological and medical studies investigating early childhood. More specifically, this reference is a crucial element in the study of early childhood nutrition and morbidity from osteological analysis and stable isotope analyses of teeth and their growth increments.Sjögren’s syndrome is an autoimmune disease that can also occur in children. The disease is not well defined and there is limited information on the presence of chemokines, cytokines, and biomarkers (CCBMs) in the saliva of children that could improve their disease diagnosis. In a recent study [1], we reported a large dataset of 105 CCBMs that were associated with both lymphocyte and mononuclear cell functions [2] in the saliva of 11 children formally diagnosed with Sjögren’s syndrome and 16 normal healthy children. Here, we extend those findings and use the Mendeley dataset [2] to identify CCBMs that have predictive power for Sjögren’s syndrome in female children. Datasets of CCBMs from all saliva samples and female children saliva samples were standardized. We used machine learning methods to select Sjögren’s syndrome associated CCBMs and assessed the predictive power of selected CCBMs in these two datasets using receiver operating characteristic (ROC) curves and associated areas under curve (AUC) as metrics. We used eight classifiers to identify 16 datasets that contained from 2 to 34 CCBMs with AUC values ranging from 0.91 to 0.94.This data article describes 34 datasets, compiled into one table, describing guest attendance at lunch meal servings in Swedish public schools and preschools. Fifteen of the schools and all 16 of the preschools covered belong to one municipality, while the remaining three schools belong to two other municipalities, all located in central Sweden. Data on number of plates was used as a proxy of the number of guests eating lunch. Number of used plates was recorded from late August 2010 to early June 2020, i.e. covering the period both before and during the initial phase of the Covid-19 pandemic, so that making possible to evaluate changes in guest attendance during the pandemic. Since these were real data, all data elements pertaining to exact canteens or staff identity have been removed. There is a scarcity of real business data for scientific and educational purposes, so these datasets can play an important role in research and education within catering management, consumption pattern analysis, machine learning, data mining and other fields.