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  • Matlab scripts used to analyze data associated with the manuscript entitled "A single cell atlas of the human liver tumor microenvironment". *please used Matlab 2019b to run the following m files. Files: inputData.mat: mat contains all raw and preprocessed data used in the study Create_Interactions_Network.m: Matlab script used to calculate Ligand-Receptor interaction score between different cell types. The script creates panels of Figure 3 and Table S5. Hepatocytes_Reconstruction.m: Matlab script used to reconstruct human hepatocytes zonation along the lobule axis. The script creates panel 'c' of Figure 4, Figure S4, and Table S7. Cancer_Cells_Spatial_Analysis.m: Matlab script used to calculate differential gene expression between malignant cells found at different zones (malignant border, malignant core, and fibrotic zone) captured by laser microdissection. The script creates panel 'd' of Figure 4 helperFunctions.zip: This folder contains required functions used by the m files.
    Data Types:
    • Software/Code
  • This contains the scripts we used while mining git repositories to quantify and characterize three different types of "Ghost Commits": MG 1, MG 2, and FG.
    Data Types:
    • Software/Code
  • The replication package of the paper "A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts" including the dataset, results and tools
    Data Types:
    • Dataset
    • File Set
  • # Installation conda create -n deep_texture python=3.6 source activate deep_texture conda install numpy pillow conda install keras-gpu conda install keras # if GPUs are not available pip install git+https://github.com/keras-team/keras-applications.git@d506dc82d0 # downgrade keras-application ## usage import deep_texture (prep, dnn) = deep_texture.setup_texture(arch = 'nasnet', layer = 'normal_concat_11', cbp_dir = '/tmp') dtr = deep_texture.calc_features_file("./test.png", prep, dnn)
    Data Types:
    • Other
    • Software/Code
  • Planning for power systems with high penetrations of variable renewable energy requires higher spatial and tempo- ral granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155.
    Data Types:
    • Dataset
    • File Set
  • Integrating data from multiple sources with the aim to identify records that correspond to the same entity is required in many real-world applications including healthcare, national security, and businesses. However, privacy and confidentiality concerns impede the sharing of personal identifying values to conduct linkage across different organizations. Privacy-preserving record linkage (PPRL) techniques have been developed to tackle this problem by performing clustering based on the similarity between encoded record values, such that each cluster contains (similar) records corresponding to one single entity. When employing PPRL on databases from multiple parties, one major challenge is the prohibitively large number of similarity comparisons required for clustering, especially when the number and size of databases are large. While there have been several private blocking methods proposed to reduce the number of comparisons, they fall short in providing an efficient and effective solution for linking multiple large databases. Further, all of these methods are largely dependent on data. In this paper, we propose a novel private blocking method for efficiently linking multiple databases by exploiting the data characteristics in the form of probabilistic signatures and introduce a local blocking evaluation step for validating blocking methods without knowing the ground-truth. Experimental results show the efficacy of our method in comparison to several state-of-the-art methods.
    Data Types:
    • Other
    • Software/Code
  • This are the R codes used to generate the research clusters and its respective plots in the paper "What are the ingredients for food systems change towards sustainability? - Insights from the literature" by Weber, Hanna; Poeggel, Karoline; Eakin, Hallie; Fischer, Daniel; Lang, Daniel; von Wehrden, Henrik; Wiek, Arnim, which is currently under revision. The code groups publications into different clusters based on co-abundancy of words. The main code is the file “using_ginko_explained”, which sources the codes “statistics” and “scopus”, as well as the csv-file "Conceptual_Vocabulary".
    Data Types:
    • Software/Code
  • This code enables the mapping of single-molecule m6A methylations.
    Data Types:
    • Software/Code
  • Snapshot of peer-evaluated artifact corresponding to the published conference paper [1]. [1] Sandeep Dasgupta, Sushant Dinesh, Deepan Venkatesh, Vikram S. Adve, and Christopher W. Fletcher 2020. Scalable Validation of Binary Lifters. In Proceedings of the 2020 ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM. https://doi.org/10.1145/3385412.3385964
    Data Types:
    • Software/Code
  • aaa
    Data Types:
    • Software/Code