Barrpower0259

The three datasets described in this paper were collected from online experiments distributed via Prolific.co participant system. Together, the three datasets comprise 9720 text responses of unexpected events participants predicted for everyday scenarios such as going shopping or preparing breakfast. Selleckchem XMU-MP-1 Each event was labelled by at least two independent, human raters on their topic or category (relative to their initial scenario), the valence or sentiment of the event, and whether or not the event mentions words related to the goal stated in the initial scenario. We also include summary data from a pre- and post-test conducted in the course of these experiments, as well as the analysis code in the form of Jupyter Notebooks. We provide this data and relevant code for transparency and reproducibility alongside our Cognition paper. The dataset could be useful in training machine learning models on valence/sentiment of everyday unexpected events.This dataset presents data collected from three surveys, each among researchers, research administrators and policymakers across the six geopolitical zones in Nigeria. The data were collected from 513 researchers, 118 research administrators and 60 policymakers drawn from randomly selected organizations that are implicated in Social Science Research (SSR) in Nigeria, which include 53 universities; 5 research institutes; 17 government Ministries, Departments and Agencies (MDAs) and donors; 9 private consultancies; 26 civil society organisations, private consultancies; and 7 Houses of Assembly. The surveys assessed several factors that impart the production, dissemination and uptake of social science research (SSR) in Nigeria, including research personnel, funding, infrastructure, mentoring, communication practices and products, policy-friendliness, among many others. The data are important in understanding the status of SSR and its potential to influence sustainable development in a typical developing country like Nigeria. The usefulness of the data is many folds as every stakeholder in the research-policy-development nexus is implicated. Ultimately, the data is useful in characterizing SSR system and formulating policies to boost its status and potential.We describe here a multiproxy dataset (grain size, environmental magnetism, stable carbon isotope, total nitrogen, and total organic carbon) generated on a ~116 cm long trench profile from the high altitude alpine Badnikund lake in the Central Himalaya. The dataset also includes environmental magnetic and organic geochemistry data on catchment soils of the Bednikund lake. The presented data is related to the research article "Middle Holocene Indian summer monsoon variability and its impact on cultural changes in the Indian subcontinent" [1]. The chronology of the Bednikund lake trench (BBK) profile is well established with seven AMS 14C dates. The multiproxy data is provided in tabular format in an excel file along with ages in Mendeley Data Repository. The multiproxy data can be significantly utilized for regional correlation of Indian summer monsoon (ISM) variability during the middle Holocene as well as for correlation of global climatic events. The data can also be reutilized in paleoclimate modelling for precipitation change over the past ~6000 years.This data article describes a dataset of data breaches in US listed firms over a ten-year period. Data breaches represent major events that pose serious challenges to organisations. The number of incidents has been on the increase over the last decade and this has attracted the interest of the media, consumers and regulators. While there is a well-established literature on cybersecurity in Computer Science and Information Systems journals, studies exploring the economic and business impacts of data breaches represent a relatively recent phenomenon. There is a nascent but fast-growing literature in accounting, finance and economics that focuses on the financial impacts of data breaches and this dataset provides a useful resource for future studies in this space. By providing data on the company identifier, the type of breach, the dates of breach disclosure, and relates these dates to the company's fiscal year, the dataset can be merged quickly with existing accounting and finance datasets. The dataset includes data on 506 incidents over a ten-year period thereby enabling cross-sectional and longitudinal analyses.Moricandia is a genus belonging to the family Brassicaceae. C3 and C3-C4 photosynthesis Moricandia species exist in a close phylogenetic proximity, as well as to Brassica crops. Here, we performed PacBio genome sequencing on M. moricandioides and M. arvensis. The genomes were assembled using Flye assembler, then polished with Illumina reads and reduced duplication with Purge Haplotigs. The total length of genome assemblies of M. moricandioides and M. arvensis was 498 Mbp and 759 Mbp, respectively. These data will be useful for studies of the genetic control of C3-C4 characteristics, therefore gaining new insights into the early evolutionary steps of C4 photosynthesis. Further, it can be integrated into Brassica crop breeding. The data can be accessed at ENA under the project number PRJEB39764.This article presents data on characteristics of waste foundry dust (WFD), sorbent obtained before and after batch sorption tests using As(III) and Cr(VI) aqueous solutions, by performing X-ray Diffraction (XRD), field-emission scanning electron microscopy (FE-SEM) coupled with energy dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS) analyses. Data are related to a research article "Waste foundry dust (WFD) as a reactive material for removing As(III) and Cr(VI) from aqueous solutions" [1]. The data provide information obtained from various analytical methods to investigate mechanisms of As(III) and Cr(VI) removal from aqueous solutions by WFD, an industrial by-product. These data can be of interest to researchers studying contaminant removal mechanisms by reactive materials, in particular industrial by-products.We gathered total organic carbon (%) and relative abundances of benthic foraminifera in intertidal areas and transitional waters from the English Channel/European Atlantic Coast (587 samples) and the Mediterranean Sea (301 samples) regions from published and unpublished datasets. This database allowed to calculate total organic carbon optimum and tolerance range of benthic foraminifera in order to assign them to ecological groups of sensitivity. Optima and tolerance range were obtained by mean of the weighted-averaging method. The data are related to the research article titled "Indicative value of benthic foraminifera for biomonitoring assignment to ecological groups of sensitivity to total organic carbon of species from European intertidal areas and transitional waters" [1].This research aimed to present data on the effect of social capital on business performance in the Nigerian informal economy. Primary data collection was carried out through a cross-sectional survey of 600 informal business owners within Ikeja Local Government Area (LGA), Lagos State, Nigeria. A simple sampling technique was further adopted in selecting the sample size of the study, and a close-ended questionnaire was adopted for the data collection process. Descriptive statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 23. This data has the potential to be reused for full empirical research relating to social capital and business performance in emerging economies.Extensive use of the internet has enabled easy access to many different sources, such as news and social media. Content shared on the internet cannot be fully fact-checked and, as a result, misinformation can spread in a fast and easy way. Recently, psychologists and economists have shown in many experiments that prior beliefs, knowledge, and the willingness to think deliberately are important determinants to explain who falls for fake news. Many of these studies only rely on self-reports, which suffer from social desirability. We need more objective measures of information processing, such as eye movements, to effectively analyze the reading of news. To provide the research community the opportunity to study human behaviors in relation to news truthfulness, we propose the FakeNewsPerception dataset. FakeNewsPerception consists of eye movements during reading, perceived believability scores, questionnaires including Cognitive Reflection Test (CRT) and News-Find-Me (NFM) perception, and political orientation, collected from 25 participants with 60 news items. Initial analyses of the eye movements reveal that human perception differs when viewing true and fake news.A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.The worldwide spread of the COVID-19 pandemic has unpredictably changed the way people live, by influencing their behaviors and beliefs. This article presents the raw data that have been used to investigate how the pandemic affected people's beliefs and expectations about their future. A total of 3991 participants (18-85 years old) were recruited through an online survey using the Qualtrics platform. The data collection was carried out during the Italian lockdown, between April 1st and April 20th, 2020. This survey collected information about psychological and socioeconomic variables related to the COVID-19 emergency. Respondents filled out a battery of questionnaires that included five measures. Three of the measures were specifically developed by the authors 1. Expected repercussions of COVID-19; 2. Forethought scale; and 3. Perceived financial resources. The two other measures were standardized questionnaires the Zimbardo Time Perspective Inventory Short Version (ZTPI-short) and the Positive and Negative As, and experimental materials that have been used to expand our knowledge in the study of time perspective, beliefs, and emotions.Cytosine-phosphate-guanine (CpG) oligonucleotides are commonly-used vaccine adjuvants to promote the activation of antigen-presenting cells (APCs). To mount an effective immune response, CpG needs to be internalized and bind to its endosomal Toll-like receptor 9 (TLR-9) inside the APCs. Using flow cytometry and fluorescence microscopy, this article presents the cellular uptake data of the amino-dextran nanoparticle (aDNP) and aDNP loaded with CpG immobilized on its surface by either electrostatic adsorption or covalent conjugation. The uptake of fluorescently-labelled aDNPs by murine splenic dendritic cells and macrophages was determined by flow cytometry and uptake by murine bone-marrow-derived dendritic cells was evaluated by fluorescence microscopy. The data presented in this paper correlates with the in vitro immune-stimulatory activity observed for the two different CpG loading methods in the research article "Nanoparticle system based on amino-dextran as a drug delivery vehicle immune-stimulatory CpG-oligonucleotide loading and delivery" (Nguyen et al.