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  • Impacts of the Covid-19 Pandemic on Life of Higher Education Students: Global Survey Dataset from the First Wave
    The Covid-19 pandemic caused by the novel coronavirus SARS-CoV-2 has completely reshaped the lives of people around the world, including higher education students. Beyond serious health consequences for a proportion of those directly affected by the virus, the pandemic holds important implications for the life and work of higher education students, considerably affecting their physical and mental well-being. To capture how students perceived the first wave of the pandemic’s impact, one of the most comprehensive and large-scale online surveys across the world was conducted. Carried out between 5 May 2020 and 15 June 2020, the survey came at a time when most countries were experiencing the arduous lockdown restrictions. The online questionnaire was prepared in seven different languages (English, Italian, North Macedonian, Portuguese, Romanian, Spanish, Turkish) and covered various aspects of higher education students’ life, including socio-demographic and academic characteristics, academic life, infrastructure and skills for studying from home, social life, emotional life and life circumstances. Using the convenience sampling method, the online questionnaire was distributed to higher education students (aged 18 and over) and enrolled in a higher education institution. The final dataset consisted of 31,212 responses from 133 countries and 6 continents. The data may prove useful for researchers studying the pandemic’s impacts on various aspects of student life. Policymakers can utilize the data to determine the best solutions as they formulate policy recommendations and strategies to support students during this and any future pandemic. Acknowledgments: The extensive dataset could not be collected without the numerous international partners who provided the exceptional assistance with questionnaire translation and/or data collection. This work also acknowledges the international partners, who may have been unintentionally omitted from authorship due to the snowball recruitment technique. Special thanks go also to anonymous global survey participants for their valuable insights into the lives of students, which they shared selflessly. The authors also acknowledge the CovidSocLab project (http://www.covidsoclab.org/) as a working platform for international collaboration. Funding: The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P5-0093).
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  • Dataset 'Governance and performance attributes of American and European small-scale fisheries'
    This repository includes the input data for the analyses conducted by Ouréns et al. (2021): 'Governance and performance of American and European small-scale fisheries: a quantitative assessment from stakeholders' perceptions'. Please cite this work if it this material is referenced in any publication. For comments or questions please email Dr. Rosana Ourens: rosana.ourens@cefas.co.uk The data contains 15 characteristics of fisheries governance, 5 contextual variables, and 4 indicators of fisheries performances for 303 small-scale fisheries in the Americas and Europe. The data was obtained from a survey completed by stakeholders with different backgrounds. The methodology to derive the characteristics values from survey questions is detailed in the paper.
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  • Table S1
    Feeds for Saanen goat
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  • Application of automated and robotically deployed in-situ X-ray fluorescence analysis for nuclear waste management.
    Data used for the "Application of automated and robotically deployed in-situ X-ray fluorescence analysis for nuclear waste management." paper.
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  • Bogazici University Smartphone Accelerometer Sensor Dataset
    Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Smartphones have built-in motion sensors such as accelerometer, gyroscope and orientation sensors. Recent researches on smartphones show that behavioral biometrics can be obtained from the smartphone motion sensors. In this context, we develop an Android application that collects accelerometer sensor data while user playing a game. This application records all accelerometer data and touch event information while users touch the screen. We perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users whose age vary from 4 to 11, and 100 adult users whose age are between 16 and 55. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users aged 17-57 for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. We use popular Android smartphones in the experiments and they have all 100 Hz sampling rate. These data can be used for behavioral biometric analyses such as user age group and gender detection, user identification and authentication or tap event detection.
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  • Data for: Stress and the brain transcriptome: identifying commonalities and clusters in standardized data from published experiments
    Supplementary Dataset 1. The dataset contains all retrieved data (full preprocessed dataset), information concerning experimental design and final result of re-annotation and gene name standardization. Supplementary Table 1. Summary of experiments included in the analysis. It also contains numerical codes used for identification of individual studies and transcriptomic comparisons concerning genotype, sex and stress-sensitivity of tested subjects, applied stress procedures, and analyzed brain areas. Supplementary Table 2. Ranking of stress-responsive genes. Supplementary Table 3. Referential list of GC-responsive genes. Supplementary Table 4. Ranking of genes depending on duration of stress. Supplementary Table 5. Ranking of genes with altered expression in animals that are vulnerable and resistant to stress. Supplementary Table 6. Sex-specific genes that are responsive to stress. Supplementary Table 7. Ranking of genes that are found both in animal models of stress and human PTSD patients. Supplementary Table 8. Calculation of gene expression enrichment in various cell types associated with the mouse nervous system based on data retrieved from http://mousebrain.org/genesearch.html. The analysis is restricted to genes belonging to choroid, meningeal and opalin cluster. Supplementary Cluster Files 1. Cluster analysis of stress-responsive genes. Use Java Treeview software (https://sourceforge.net/projects/jtreeview/files/) to visualize data following guidelines provided in Supplementary Fig 2. List of experiments and their codes is available in Supplementary Table 1. Supplementary Cluster Files 2. Cluster analysis restricted to amygdala. Supplementary Cluster Files 3. Cluster analysis restricted to hippocampus. Supplementary Cluster Files 4. Cluster analysis restricted to nucleus accumbens. Supplementary Cluster Files 5. Cluster analysis restricted to prefrontal cortex. Supplementary Cluster Files 6. Cluster analysis restricted to mice. Supplementary Cluster Files 7. Cluster analysis restricted to rats. Supplementary Cluster Files 8. Cluster analysis restricted to acute stress. Supplementary Cluster Files 9. Cluster analysis restricted to prolonged stress (longer than 1 week). Supplementary Cluster Files 10. Cluster analysis restricted to stress of medium duration (from 2 to 7 days). Supplementary Cluster Files 11. Cluster analysis restricted to stress resilient animals Supplementary Cluster Files 12. Cluster analysis restricted to stress vulnerable animals.
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  • Original data of "CCL3 in the bone marrow microenvironment causes bone loss and bone marrow adiposity in aged mice"
    Original data of "CCL3 in the bone marrow microenvironment causes bone loss and bone marrow adiposity in aged mice"
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  • The effect of extreme temperatures on soil organic matter decomposition from Atlantic oak forest ecosystems.(ISCIENCE-D-21-01516R1)
    This paper focuses on the soil organic matter, SOM, sensitivity to extreme temperatures and intends to relate it to soil organic matter properties which could be attached to the recalcitrance and lability of the organic substrates. The soil samples were collected in oak forests ecosystems in Ireland (Samples DC, G and K) and UK (Samples ROG, BW and NF) and represents the organic matter from different soil horizons: LF samples from the soil surface; the mineral soil under, M samples; and in the case of the samples from Ireland, an intermediate H layer in between LF and M horizons (H samples). SOM thermal properties were provided by simultaneous thermogravimetry (TG) and differential scanning calorimetry (DSC) (TG-DSC) in an attempt of parametrizing SOM recalcitrance by the red-ox state. This is connected to the response of the soil samples to increasing and decreasing temperatures, measured by calorimetry as the heat rate of SOM decomposition. The calorimeter used is a model TAM III that allows the direct monitoring of the heat rates at different temperatures. The simultaneous TG-DSC gave the different soil thermal fractions of the samples and their heat of combustion, QSOM, values, connected to the degree of reduction of organic substrates by well-known principles. Here we attach the raw tabulated data from the TG-DSC measurements for all the samples used in this work as supplementary material S1. They are Excel files with columns representing, from left to right, time in seconds, temperature in Celsius degrees, the heat released in mW and the weight lost in mg. TAM III allows us to design a calorimetric heat wave and to monitor the heat rate from the soil samples at increasing temperatures from 20 ºC to 30, 40, 50 and 60 ºC, and to decreasing temperatures from 60ºC to 40 and 20 ºC. By this procedure we can compare which samples resist or do not resist the heat wave and to link it to their red-ox state by different statistical analysis. All the raw tabulated data from the calorimetric design with TAM III are provided here too, together with their baselines, as supplementary material S2. The Excel files show the heat flow rate in watts and joules at the different temperatures of the measurement. The files S2a are the data from UK LF samples, S2b the ones from UK Mineral samples, S2c are the calorimetric data from Irish LF samples, S2d the Irish H samples, and S2e the Irish M samples. Samples S2f and S2g are the baselines with the empty ampoules used in the calorimetric measurements, detailed for every experiment in the first folder of the Excel files. All the details dealing with the samples are explained in the paper.
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  • Dataset for 16 parameters of ten thunderstorm ground enhancements (TGEs) allowing recovery of electron energy spectra and estimation the structure of the electric field above earth’s surface
    Dataset for 16 parameters of ten thunderstorm ground enhancements (TGEs) allowing recovery of electron energy spectra and estimation the structure of the electric field above earth’s surface A.Chilingarian G.Hovsepyan The atmospheric electric field not only initiates lightning flashes but also originates huge fluxes of electrons and gamma rays incident on the earth’s surface. To reach a complete understanding of both phenomena and to find new and easily measured indicators of the global change in the climate system, the monitoring of the atmospheric electric fields is vitally demanded. Commercially available electric field meters are monitoring the near-surface (NS) atmospheric electric field worldwide in different weather conditions. Special sensors are designed for airborne measurements of the electric field profile with meteorological balloon systems. However, balloon measurements are rare and very slow (20-40 minutes to traverse a storm), performing measurements along the uncontrolled and random flight path, balloons are often taken away by the wind or occasionally destroyed by a lightning flash. However, till now balloon soundings provide the only available data to sample the depth of a storm. Based on these measurements overall understanding was reached that a strong electric field above 1.0 kV/cm started on heights above 1-2 km from the earth's surface. Large electric fields were measured well above 2 km over the ground. Although we cannot expect the same behavior at different destinations, the overall understanding in the atmospheric physics community is that strength of the electric field at altitudes below 2 km above ground is well below the critical energy necessary for a runaway process (accelerating and multiplying electrons from the ambient population of cosmic rays). The electron energy spectra measured at Aragats at an altitude of 3200, in this concern, are attributed to the Compton scattered electrons originated by a “gamma ray beam”, from the relativistic electron-gamma ray cascades unleashed high in the atmosphere. In the posted dataset we demonstrate that, at Aragats, a strong accelerating electric field can be continued almost to the earth’s surface. We present 10 TGE events observed in 2018-2021 allowing recovering electron energy spectra and estimating the heights of the termination of the strong electric field above the ground. The estimates vary from 10 to 150 m above ground, thus the electric field can reach ≈ 2.0 kV/cm at altitudes 3200 – 3350 m.
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  • TIME HAS STARTED LONG BEFORE BIGBANG
    THIS HYPOTHESIS COULD NOT BE IGNORED UNLESS AND UNTIL WE ARE ABLE TO PEEP THROUGH THE HORIZON OF OUR OWN UNIVERSE. THE CONCEPT OF DARK ENERGY WILL BE WHIMSICAL IF THIS CONCEPT WHICH GIVES A NEW DEFINITION OF UNIVERSE IS ACCOUNTED.
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