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Source code for simulating the cooling process of nonadecane monolayer.
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The current research examines negotiators’ deception behaviors towards unfamiliar counterparts with varying creditable reputations– specifically, proficient, friendly, and honest reputations. We primarily differentiate between the honest and friendly reputations, which are both seemingly cooperative, and often tangled in the negotiation literature. We generally hypothesized that Negotiators would deceive counterparts with honest reputations less than those with friendly (or proficient) reputations and that the attenuated deception towards counterparts with honest versus friendly (or proficient) reputations would disappear (or even backfire) in the face of in-congruency – that is, in face of counterparts' deceptive conduct. We also gained further insight into the underlying mechanisms and boundary conditions. Data was extracted from "Qualtrics". It includes raw data from our negotiation sessions (reported in Studies 1 to 4) including three preliminary studies (A, B, and C). Please note that in Studies 2 and 4, we also had a prior phase - reported in the manuscript as phase 1, which measured various individual differences, including participants' dispositional lying tendencies. Study 2 and Study 4's data files contain the main session variables (Phase 2) plus the individual differences measures collected in Phase 1 (for the same participant). The actual chat sessions (conducted via "chatplat" in Study 4) are also attached in a txt file extracted from "chatplat" platform, and are in Hebrew. SPSS data files are attached (for each Study). We added a label for each variable for further clarifications. We also attached SPSS syntax files. These files include comments demonstrating the exact filter condition (Data-> Select Cases) used before any analyses were conducted. We further report the specific SPSS analyses conducted and reported in the manuscript.
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Background Snakebite envenoming is a major neglected tropical disease that affects millions of people every year. The only effective treatment against snakebite envenoming consists of unspecified cocktails of polyclonal antibodies purified from the plasma of immunized production animals. Currently, little data exists on the molecular interactions between venom toxin epitopes and antivenom antibody paratopes. To address this issue, high-density peptide microarray (hdpm) technology has recently been adapted to the field of toxinology. However, analysis of such valuable datasets requires expert understanding and, thus, complicates its broad application within the field. Results In the present study, we developed a user-friendly, and high-throughput web application named “Snake Toxin and Antivenom Binding Profiles” (STAB Profiles), to allow straight-forward analysis of hdpm datasets. To test our tool and evaluate its performance with a large dataset, we conducted hdpm assays using all African snake toxin protein sequences available in the UniProt database at the time of study design, together with eight commercial antivenoms in clinical use in Africa, thus representing the largest venom-antivenom dataset to date. Furthermore, we introduced a novel method for evaluating raw signals from a peptide microarray experiment and a data normalization protocol enabling inter-microarray and even intra-microarray chip comparisons. Finally, these data, alongside all the data from previous similar studies by Engmark et al., were preprocessed according to our newly developed protocol and made publicly available for download through the STAB Profiles web application (https://snake.shinyapps.io/STAB_Profiles/). With these data and our tool, we were able to gain key insights into toxin-antivenom interactions and were able to differentiate the ability of different antivenoms to interact with certain toxins of interest. Conclusions The data, as well as the web application, we present in this article should be of significant value to the venom-antivenom research community. Insights gained from our current and future analyses of this dataset carry the potential to guide the improvement and optimization of current antivenoms for maximum patient benefit, as well as aid the development of next generation antivenoms.
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Name: learn.py Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Software required: python2 software stack, including numpy, optparse, pandas, keras, sklearn and matplotlib Language: python Program size: 17kB Name: out.txt Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Description: Output from learn.py when operating on cnn_data.txt Name: cnn_data.csv Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 ASCII plaintext, comma-separated values, with comment-lines indicated by a ``#''. Header precisely define the file format Size: 266MB
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Macroinvertebrate kick-sampling results on the River Beas in November 2017. Additional details are available in: https://doi.org/10.3390/w10091247 This research was funded by the UK Natural Environment Research Council, grant number NE/N016394/1.
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Dataset contains: GISAXS maps od the investigated films, optical , XRD, I-V and quantum efficinecy data
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Here we provide data and code used in the paper "Mitochondrial fission and fusion dynamics generate efficient, robust, and evenly-distributed network topologies in budding yeast cells".
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Stimulation Artifact Source Separation
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Raw data and scripts to support the article: Johannisson, W., Harnden, R., Zenkert, D., Lindbergh, G., Shape-Morphing Carbon Fiber Composite using Electrochemical Actuation.
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The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. The test data, or similar data, has been used for some publications, including: Vidal, C., Naguib, M., Gross, O., Malysz, P., Kollmeyer P. and Emadi, A. (2020). Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks. [online] Sae.org. Available at: https://www.sae.org/publications/technical-papers/content/2020-01-1181/ [Accessed 28 Jan. 2020]. C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
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