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Shallow groundwater quality and level across the low-lying coastal city of Christchurch, New Zealand were surveyed at a high spatial resolution (1.3 piezometers/km2) in the spring of 2020. The groundwater quality parameters recorded across 99 piezometers include specific conductance, temperature, pH, and dissolved oxygen, following the pumping of approximately three bore volumes. Additionally, 27 out of 99 piezometers were analysed for chloride concentration and alkalinity as calcium carbonate. This dataset is useful to explore shallow groundwater conditions and how these might impact co-existing subsurface infrastructure and ecosystems. Furthermore, this dataset provides a valuable point of comparison against future changes, for example due to increased seawater intrusion, pollution events, or groundwater level rise.An online survey was conducted to evaluate public perceptions towards an emerging transportation technology, namely the flying car, which is expected to join the existing traffic fleet within the following decades. click here Responses from 692 survey participants were collected. Approximately 84% of the participants were from the United States, and the remaining 16% were from the rest of the world. The data resulting from the survey include several aspects of public perceptions towards flying cars, as for example willingness to use and pay for flying cars; willingness to use and pay for flying taxi services; perceptions towards potential benefits and concerns arising from the future use of flying cars; perceptions towards considering residence relocation; and perceptions towards potential security measures to improve operational safety of flying cars. In addition, information relating to several dimensions of driving and travel behaviours and habits, and socio-demographic information of the participants were also collected. The dataset can be used as a baseline to design future surveys on Advanced Air Mobility (AAM) and flying cars, and to compare consumer perceptions across different regions and during different time periods.We present data on the stated preference for the adoption of variable rate technologies from 418 crop farmers in Switzerland. The online survey was conducted online in spring 2021. It consisted of two parts 1) a choice experiment and 2) questions about farmers' characteristics, expectations and beliefs, as well as their risk preferences. In the choice experiment, farmers were presented with eight consecutive choice tasks. Each task consisted of three alternatives, two hypothetical scenarios for variable rate technologies adoption and the status quo option. We used a split-sample approach and varied the additional profit margin gained through higher yields, label premiums or subsidies for one subsample (focussing on the willigness to accept) and additional cost (acquisition, maintenance and other costs) for the other subsample (focussing on the willigness to pay). Non-monetary attributes include 1) ownership of the technology; 2) potential to increase nitrogen use efficiency and thus reduce nitrogen losses to the environment; 3) uncertainties about the actual impact of the technology on yields and profits (reliability); 4) support in case of technical difficulties. We also collected data on farmers' experiences, attitudes and goals, as well as their risk preferences. Additionally, the survey data were matched with data from the cantonal farm census, which contains information on farm characteristics.This Data in Brief article presents a novel flow cytometric assay used to acquire and process the data presented and discussed in the research paper by Mestrum et al., co-submitted to Leukemia Research, entitled "Integration of the Ki-67 proliferation index into the Ogata score improves its diagnostic sensitivity for low-grade myelodysplastic syndromes." [1]. The dataset includes the gated fractions of the different myeloid populations in bone marrow (BM) aspirates (total BM cells, CD34 positive blast cells, erythroid cells, granulocytes and monocytes. The raw data is hosted in FlowRepository, while the analyzed data of 1) the fractions of the different myeloid cell populations and 2) the Ki-67 proliferation indices of these myeloid cell populations are provided in tabular form to allow comparison and reproduction of the data when such analyses are performed in a different setting. BM cells from aspirates of 50 myelodysplastic syndrome (MDS) patients and 20 non-clonal cytopenic controls were stained using speture research regarding stem-/progenitor cell resistance against anti-cancer therapies for myeloid malignancies, diagnostics of myeloid malignancies and prognosis of myeloid malignancies. Therefore, these data are of relevance to internist-hematologists, clinical chemists with sub-specialization of hematology and hemato-oncology oriented researchers.RDE is becoming a necessary element of the emissions certification of automotive vehicles. Real Driving Emissions (RDE) helps to ensure that the regular operation of a car, or heavy vehicle, is still within the acceptable emissions standards while driving under normal conditions. RDE is monitored by connecting a Portable Emissions Measurement System (PEMS) to the exhaust of the tested vehicle, which measures the pollutant concentrations as the car or truck drives along a standardised route. The data described in this paper is the raw, detailed PEMS records of a heavy goods vehicle, recorded at a rate of 1Hz, over multiple trips on an urban route in South Africa. The data includes the pollutant concentrations of CO, CO 2 , NO and NO 2 , ambient conditions, and vehicle diagnostics collected from different sensors mounted to the vehicle during the field tests. We performed no additional analysis on the data. The value of the data is in allowing researchers to (a) develop and test machine learning algorithms that predict the instantaneous pollutant concentrations or (b) studying the variance of pollutant concentrations that occurs under typical driving conditions.A hypervirulent pathotype of A. hydrophila (vAh) is responsible for Motile Aeromonas Septicemia (MAS) and causes mass mortalities among farmed carp and catfish species in the USA and China. One unique phenotype for vAh among other A. hydrophila strains is the ability to utilize myo-inositol as a sole carbon source. While screening for Aeromonas isolates from diseased fish that can grow using myo-inositol as a sole carbon source, A. dhakensis 1P11S3 was isolated from the spleen of striped catfish (Pangasianodon hypopthalmus) displaying clinical MAS symptoms from a freshwater farm in Malaysia. Aeromonas dhakensis is also an important pathogen in aquaculture, and in this study, we report the draft genome sequence for A. dhakensis 1P11S3, that utilize myo-inositol as a sole carbon source.Contamination of aquatic ecosystems with anthropogenic pollutants, including pharmaceutical drugs, is a major concern worldwide. Aquatic organisms such as fish are particularly at risk of exposure to pollutants. The surface of fish is the first point of contact with pollutants, but few studies have considered the impact of pollutants on the skin-scale barrier. The present proteome data are the basis of the findings discussed in the associated research article "Proteomics of sea bass skin-scales exposed to the emerging pollutant fluoxetine compared to estradiol" [1]. Juvenile sea bass were exposed by intraperitoneal injections to a) the antidepressant fluoxetine (FLX), a widely prescribed psychotropic drug and an emerging pollutant; b) the natural estrogen 17β-estradiol (E2) and c) the vehicle, coconut oil (control). The scale proteome of fish exposed to these compounds for 5 days was analysed using quantitative label-free proteomics technology SWATH-MS (sequential windowed data-independent acquisition of the total high-resolution-mass spectra). The proteome data generated was submitted to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD020983. LC-MS data from pooled protein extracts from the scales of all experimental groups was acquired using information-dependent acquisition (IDA) and 1,254 proteins were identified by searching against the sea bass genome database. 715 proteins were quantified by SWATH acquisition, and 213 proteins had modified levels (p less then 0.05) between the E2- or FLX-exposed fish compared to the control. The main biological processes and KEGG pathways affected by E2 or FLX treatments were identified using Cytoscape/ClueGO enrichment analyses.This data article presents a tripartite dataset that formed the empirical basis for a comprehensive bibliometric analysis of the use of city labels denoting sustainable urbanism in the scientific literature (Schraven, 2021). The tripartite dataset was generated using the abstract and citation database Scopus (Elsevier). Dataset A lists 148 city labels denoting different approaches to urban planning and development. It was used to select 35 city labels that specifically address sustainable urbanism ('sustainable city', 'smart city', 'compact city' etc.). Dataset B references 11,337 journal and review articles spanning the period 1990-2019. All retrieved articles contain at least one of the 35 city labels in the title, abstract, and author keywords. This database was used to calculate the frequency of the selected city labels across time, and to analyze the co-occurrences of city labels. It was further used to calculate the future trajectory of scientific outputs using the Logistic Growth Model (LGM). Dataset C entails 22,820 author keywords extracted from across the 11,337 articles. This was used to analyze the co-occurrences of keywords with city labels. The data article describes the methods of data collection and curation, the analysis performed, and the potential for reusing the data for further research. The comprehensiveness of the bibliometric corpus - spanning three decades and 35 city labels - lends itself to further investigation of how sustainable urban development has evolved as a topic in the scientific literature since the 1990s. Furthermore, the robust methodology developed could be adapted to other scientific repositories and, indeed, other research problems and questions.This data article describes a dataset that allows exploring the determinants of superstars' sentiment in tournaments. It consists of 1,284 press conferences of Tiger Woods in the PGA Tour between 1996 and 2020. We used natural language processing, a form of artificial intelligence, to extract and encode in a quantitative form the sentiment in Tiger Woods press conferences both before the tournament and after the rounds played. Additionally, the dataset provides a series of variables that describe Tiger Woods' scoring and performance momentum in each round and variables that describe health-related and off-the-course issues that could affect his performance on the course. This data can be useful to understand the sentiment that superstars go through before important tournaments, their sentiment following a major victory or defeat, how that sentiment evolves throughout their athletic career, and how sentiment is associated with performance momentum.This article describes a dataset that allows to explore the determinants and moderators of athletes' decision to enter in tournaments endowed with a monetary prize. Specifically, the dataset contains variables that describe athlete's short-term momentum (i.e., performance streak in the tournaments recently entered) and long-term momentum (i.e., performance streak in the same tournament across seasons), which permits an in-depth analysis of how past performance trajectory drives self-selection into tournaments. The dataset consists of 54,915 self-selection decisions that golfers have taken over an eleven-year period (1996-2006) when deciding to participate in PGA Tour tournaments.