398 research outputs found
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Vocabulary learning through technology-enhanced learning approaches
Full text linkThe dataset consists of second language (English) learning data, collected from 132 Chinese learners aged 13 to 14 in two schools in China over a period of approximately 6 months. Learners were divided into four experimental groups with different technology-enhanced learning conditions (video enhancement, video enhancement plus self-regulation mechanism, digital flashcards, and digital flashcards plus self-regulation mechanism) and one control group with no intervention. The data was collected at three time points. At Time 1, quantitative data were collected through a pre-existing vocabulary knowledge test, a target vocabulary pre-test (measuring written form recognition, aural form recognition and meaning recall), and a self-regulation questionnaire. At Time 2, at the end of each learning intervention (a 5-week intervention, one session per week), quantitative data were collected through an immediate post-test of the target words. At time 3, quantitative data were collected one month after the last intervention session through a delayed post-test regarding the influence of repetitions under different technology-enhanced learning conditions
Interviews with Saudi English teachers
Full text linkInterviews with EFL writing teachers to investigate their knowledge about the MD markers and their use in argumentative writing. There are 10 interviews with the EFL writing teachers who have been asked about the meaning of argumentative writing and how to use MD markers in the argumentative writing
Reflections on the journey towards outstanding: developing positive orientations to diversity in an urban primary school
Full text linkThe data are three interviews with staff members of a primary school which had moved from OFSTED requires improvement to OFSTED outstanding grading between 2019 and 2023
Data supporting: "Anhedonia mediates the relationship between stigma and Major Depressive Disorder (MDD): A longitudinal path analysis"
No full textMajor Depressive Disorder (MDD) is one of the most common mental health conditions affecting young people and is linked to major impairments in functioning, quality of life, and suicide risk. Adolescence and emerging adulthood are particularly vulnerable periods due to ongoing brain and social development, making young people more sensitive to stress and stigma. Internalised stigma is a key psychosocial stressor that can intensify MDD symptoms, yet the mechanisms explaining this link remain poorly understood and have rarely been tested in clinically depressed youth.
Data was collected from young people aged 16–25 years with clinically significant MDD symptoms (Mood and Feelings Questionnaire) who completed measures of MDD (Mood and Feelings Questionnaire), anhedonia (Anhedonia Scale for Adolescents), and stigma (Internalized Stigma of Mental Illness Inventory-9) at baseline (N = 445) and six-month follow-up (N = 343). The study investigated whether anhedonia and its subcomponents mediate the association between stigma and MDD symptoms, both cross-sectionally and longitudinally.
By combining validated clinical measures with a longitudinal design, this dataset provides a rare opportunity to examine how stigma contributes to the persistence and worsening of MDD in youth. This is the first study to demonstrate that anhedonia is a central mechanism linking stigma to MDD over time, advancing theoretical understanding, highlighting a novel intervention target, and informing the development of stigma-reduction and early intervention strategies for young people.
The dataset includes participants’ demographic information alongside responses to questionnaires assessing MDD symptoms, stigma, anhedonia, and motivation at both baseline and follow-up
Data supporting: 'Impact of mental health stigma on anhedonic experiences in young people with clinical depression'
No full textClinical depression, also known as major depressive disorder (MDD), is a common and serious condition affecting many young people, often leading to persistent sadness, loss of interest, and emotional numbness. In youth, MDD can significantly impact academic performance, relationships, and overall life satisfaction. While the symptoms of depression are well understood, the role of stigma in exacerbating these symptoms, particularly anhedonia - such as reduced motivation and social withdrawal - remains unclear. Stigma can amplify feelings of isolation, shame, and disconnection, potentially worsening symptoms.
This study aimed to explore how depression stigma influences symptoms like anhedonia, focusing on its impact on motivation, physical activity, and social participation. By examining personal experiences with stigma, the study seeks to understand how stigma contributes to a cycle of disengagement, ultimately helping to develop strategies to support recovery.
These findings provide insight into how stigma may contribute to social withdrawal, reduced motivation, and emotional disengagement, which can further exacerbate depressive symptoms.
The dataset includes demographic information and responses to relevant questionnaires, providing details on participants' demographics and their responses related to depression, stigma, anhedonia, motivation, and social participation
GIS dataset locating suitable sites in England for high-quality Chardonnay viticulture: climate, topography, and soils (2010–2019, 2040–2059 RCP4.5)
Full text linkThis dataset provides GIS files from an assessment of site suitability for Chardonnay viticulture in England, aimed at identifying locations capable of producing premium single-varietal still wines. The modelling framework uses the Chablis region (France) as an analogue for climate and topography, with additional consideration of soil suitability. Climate conditions are presented for two periods: 2010–2019 (representing current conditions) and 2040–2059 (RCP 4.5, median and upper projections). For each scenario, mean vintage scores were calculated using the Weather Model of Biss and Ellis (2021, 2022). Land suitability is further classified into four categories – Unclassified, Village, Premier Cru, and Grand Cru – based on the combined effects of topography and soils
Soil, site and tree characteristics for acute oak decline (AOD) symptomatic and non-symptomatic oak in three UK woodlands, southern England, 2016
No full textThis dataset contains information on tree, site and soil properties from three oak woodland sites in southern England: Writtle Forest, Monks Wood, and Stratfield Brake. Data were collected in 2016 to investigate the relationship between Acute Oak Decline (AOD) symptoms and local environmental factors. The study included 10 symptomatic and 10 asymptomatic trees at each site.
Tree characteristics include latitude and longitude coordinates, species-level classification (based on morphological measurements and genetic SNP analysis confirming identity as Quercus robur, Quercus petraea, or hybrids), tree dimensions (height, diameter at 1.35 m, crown dimensions), crown condition (density reduction), and rooting depth.
Data on the surrounding site context were also collected, including depth to gleying, basal area of surrounding stands (measured at 0–20 m and 20–40 m), compound topographic index, and tree social status (e.g., dominant, codominant, suppressed).
Soil characteristics were analysed to capture both physical and chemical properties. Physical properties included texture and bulk density, while chemical analyses measured organic matter content, pH, total carbon and nitrogen, mineral nitrogen (nitrate and ammonium), ‘Olsen’ extractable phosphorus, cation exchange capacity, and concentrations of exchangeable cations.
The work was supported by the grant BB/N022831/1 (‘PuRpOsE’) with additional funding from the Woodland Trust, Bartlett’s Tree Experts and The University of Reading
Data used in the article ‘The effects of processing steps on avenanthramides, avenacosides and β-glucan content during the production of oat-based milk alternatives”
Full text linkThis dataset contains data obtained from Chapter 4 of Thesis. Experimental measurements of the concentration of beta glucan, and compounds avenanthramides and avenacoside a, in oat-based milk samples throughout various stages of production.
The data was obtained using a HP LC-MS, and an enzymatic assay followed by spectrophotometer readings, as well solid content measurements.
Data processing and preparation activities:
Data was collected in Excel files. Different tabs have been assigned for different measurements/figures
Dataset to support 'Speciation analysis of fungi by LAP-MALDI mass spectrometry'
No full textThis dataset contains raw and processed liquid atmospheric pressure matrix-assisted laser desorption/ionisation (LAP-MALDI) mass spectrometry (MS) data from two fungal species: Saccharomyces cerevisiae and Candida albicans, along with corresponding MS/MS data of lipids and proteins detected in the MS profiles.
From the lipid MS profiles, m/z values were searched against the LIPIDS MAPS database at www.lipidmaps.org/bulk_search.
Raw MS/MS of proteins data were processed using Mascot Distiller (version 2.8.5.1, 64-bit; Matrix Science, London, UK). The resulting protein fragment ion peak lists were submitted to the Mascot MS/MS Ions Search tool (version 3.1; Matrix Science) for protein characteristion and species identification. Protein database searches were conducted against the Mascot contaminants database (29 January 2016; 247 sequences; 128,130 residues) and the Swiss-Prot database (22 May 2024; 571,282 sequences; 206,678,396 residues).
BLAST searches were undertaken with the Mascot-identified amino acid sequences submitted to the BLAST search routine at www.uniprot.org/blast.
All MS and MS/MS data were acquired using a modified Synapt G2-Si instrument coupled to an in-house–built atmospheric pressure MALDI source
Dataset for PhD thesis: 'Investigating the electrical modification of fog in the UAE'
No full textThis dataset contains potential gradient, visibility, standard meteorological data, and aerosol/droplet size and number concentration measurements used in the PhD thesis 'Investigating The Electrical Modification of Fog In The UAE'. The data were collected from two locations in Dubai, United Arab Emirates (UAE): Sanad Academy and Al Marmoom (24°56' N, 55°30' E; 25°00' N, 55°30' E, respectively).
Electric field was measured by a Campbell CS110 electric field mill at 3m height (which was then converted to potential gradient using electric field = -potential gradient). Visibility was measured by a Biral SWS-100 visibility sensor on the same mast as the CS110. Standard meteorological data were initially obtained from Al Marmoom site from February 2021 to January 2022, after which it was installed in Sanad Academy. Droplet size and number concentration data were collected using a light optical aerosol counter (LOAC), which was deployed only during forecasted fog events