3,436,415 research outputs found
hyemin-han/BayesFactorFMRI: BayesFactorFMRI V1.0.0
No full textBayesFactorFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis of fMRI data and Bayesian meta-analysis of fMRI images with multiprocessing. This tool was developed to expedite computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFactorFMRI is available via or GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while saving time by distributing analysis tasks into multiple processes.
Please refer to and cite these articles when you use BayesFactorFMRI:
Journal of Open Research Software paper. Bayesian multiple comparison correction: Han, H. (in press). Implementation of Bayesian multiple comparison correction in the second-level analysis of fMRI data: With pilot analyses of simulation and real fMRI datasets based on voxelwise inference. Cognitive Neuroscience, 11(3), 157-169. http://bit.ly/2S6Uka2 Bayesian meta-analysis: Han, H., & Park, J. (2019). Bayesian meta-analysis of fMRI image data. Cognitive Neuroscience, 10(2), 66-76. http://bit.ly/2RCbxZ
DIETARY CONDITIONS AND DIFFERENTIAL ACCESS TO FOOD RESOURCES AMONG THE VARIOUS CLASSES DURING THE HAN PERIOD
Full text linkIn this thesis, I study how food resources and dietary conditions were determined by social and economic status during the Han period in China, B.C. 206~A.D.220. Even though earlier scholars have published research concerning the Chinese food culture of this period, these studies were limited in that they only illustrated the dietary culture of the upper class or the available food resources in one geographic area. Also, without any persuasive data, it has been assumed by these earlier scholars that there were big differences in food resources and food consumption between the upper and lower classes. In this thesis, for comparison among the classes, I divide the social and economic classes into five stratified groups: nobles, officials, peasants, soldiers and convicts. After a brief introduction of the nature of each social class, I examine the food resources and nutritional condition of each group using information such as the wealth and income of each group, the market price of food resources, the agricultural products of peasants, and the amount of food distribution to soldiers and convicts. I found these data from archaeological remains, received historical records and pictorial data, and excavated texts. This research shows a broader view of Chinese dietary condition focusing not only on the variety of food resources of nobles, but also on the different food accessibilities among the officials, and the food deficiencies of peasants. It also deals with the situations of food supply for soldiers and convicts in an effort to reveal the true dietary consumption and nutritional conditions for all Chinese. This research proves that the various classes during the Han period in China had different food resources and dietary conditions
Yu zhu shang han lun yi: si juan. v.1
No full text柯韻伯著 ; 能靜居士評閱 ; 余景和重訂 ; 埽閒居士校刊.綫裝.框18.8x13.2公分, 10行25字. 下黑口, 左右雙邊, 單黑魚尾. 版心上鐫題名, 中鐫小題, 下鐫葉次及卷次. 眉端刻註解.書名頁刻"傷寒論翼, 余聽鴻先生註, 施覺盦題", 又有紅色戳記"蘇州綠蔭堂福記精造書籍章". 背頁牌記刻"蘇州綠蔭堂藏板, 上海文瑞樓發行"前有光緖十九年[1893]埽閒居士孫思恭序及光緖癸己余景和書序. 目錄末及卷四末刻有"蘇城郡廟東首謝文翰齋刊印"《中國中醫古籍總目》01082著錄光緖十九年[1893]會稽掃閑居士孫思恭刻本, 蘇州謝文翰齋刻本, 蘇州綠蔭堂刻本及上海文瑞樓石印本.鈐"莊兆祥印", "莊兆祥"Xian zhuang.Kuang 18.8 x 13.2 gong fen, 10 hang 25 zi. Xia hei kou, zuo you shuang bian, dan hei yu wei. Ban xin shang juan ti ming, zhong juan xiao ti, xia juan ye ci ji juan ci. Mei duan ke zhu jie.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Ke Yunbo zhu ; Nengjing ju shi ping yue ; Yu Jinghe chong ding ; Saoxian ju shi jiao kan.Qian "Zhuang Zhaoxiang yin", "Zhuang Zhaoxiang
hyemin-han/BayesFMRI: The first release of BayesFMRI
No full textBayesFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis of fMRI data and Bayesian meta-analysis of fMRI images with multiprocessing. This tool was developed to expedite computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFMRI is available via or GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while saving time by distributing analysis tasks into multiple processes.
Please refer to and cite these articles when you use BayesFMRI:
Bayesian multiple comparison correction: Han, H. (in press). Implementation of Bayesian multiple comparison correction in the second-level analysis of fMRI data: With pilot analyses of simulation and real fMRI datasets based on voxelwise inference. Cognitive Neuroscience. http://bit.ly/2S6Uka2
Bayesian meta-analysis: Han, H., & Park, J. (2019). Bayesian meta-analysis of fMRI image data. Cognitive Neuroscience, 10(2), 66-76. http://bit.ly/2RCbxZY
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Han tombs
No full text1. Panda -- 2. Han tombs.Possibly reproduced from other commercial recording or radio broadcast (Pending for review)Unknown narrators.Electronic reproduction from Rulan Chao Pian Audio Cassette Collection.Spoken in English
Ji Han xiang chen zhuan
No full text[V.1-2]. 前漢相臣傳 : 十二卷 -- [v.3-5]. 後漢相臣傳 : 十六卷 -- [v.6]. 季漢相臣傳 : 六卷.[V.1-2]. Qian Han xiang chen zhuan : shi er juan -- [v.3-5]. Hou Han xiang chen zhuan : shi liu juan -- [v.6]. Ji Han xiang chen zhuan : liu juan.魏顯國纂述 ; 魏一鵬編次.綫裝, 1函.框22x14.3公分, 10行20字, 白口, 單黑魚尾, 四周單邊, 版心上鐫題名, 中鐫卷次, 下鐫葉次及小題.刻書年據《四庫全書存目叢書》鈐有"元鑑齋", "潤州笪重光鑒定印", "毛氏收藏子孫永保"印.Library's copy: 本館只存《歷代相臣傳》中《前漢》, 《後漢》, 《季漢》共六冊.Xian zhuang, 1 han.Kuang 22 x 14.3 gong fen, 10 hang 20 zi, bai kou, dan hei yu wei, si zhou dan bian, ban xin shang juan ti ming, zhong juan juan ci, xia juan ye ci ji xiao ti.Ke shu nian ju "Si ku quan shu cun mu cong shu"Wei Xianguo zuan shu ; Wei Yipeng bian ci.Qian you "Yuan jian zhai", "Runzhou Da Chongguang jian ding yin", "Mao shi shou cang zi sun yong bao" yin.Library's copy: ben guan zhi cun "Li dai xiang chen zhuan" zhong "Qian Han", "Hou Han", "Ji Han" gong liu ce
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dataset to support the article "High-resolution 𝜙-OFDR using phase unwrap and nonlinearity suppression"
No full textThis dataset is used for realizing high resolution of phase-sensitive Optical Frequency Domain Reflectometer. It is associated with the research paper:
Guo Z, Yan J, Han G, Yu Y, Greenwood D and Marco J (2023) "High-Resolution φ-OFDR Using Phase Unwrap and Nonlinearity Suppression". Journal of Lightwave Technology, 41 (9), 2885-2891. (https://doi.org/10.1109/JLT.2023.3236775).
The data is presented as an excel file:
High_resolution_OFDR_using_phase_unwrap_and_nonlinearity_suppression.xlsx
This work was funded by High Value Manufacturing Catapult and the Engineer and Physical Sciences Research Council - EPSRC EP/V000624/1. The author Gaoce Han would like to acknowledge the China Scholarship Council for sponsoring.</span
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