變更
On 2024年8月23日 上午11:41:25 [+0800], nchuadmin:
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Updated description of Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 from
# 檔案內容 1. 01_OriginImage 原始稻穗影像圖片 Original images 2. 02_PanicleImage 去背後的稻穗影像 remove background panicle images 3. 03_ColorChecker 校正色卡影像(50x50) color checker images 4. 1082-1122_GMC_Survey.csv 2019年第二期作至2023年第二期作的調查資料 survey dataset # 各期作資料數量 1. 2019-2 ,515 2. 2020-1 ,1593 3. 2020-2 ,1734 4. 2021-1 ,2013 5. 2021-2 ,1842 6. 2022-1 ,718 7. 2022-2 ,178 8. 2023-1 ,397 9. 2023-2 ,559 10. 總數 ,9549 # 圖片瀏覽 ## 01_OriginImage: 多期作的水稻稻穗影像示意圖 ![原始圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_1.png) ## 02_PanicleImage: 經過前處理完成背景去除的稻穗影像 ![稻穗圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_2.png) ## 03_ColorChecker: 經過偵測結果截取的校正色卡影像 ![校正色卡圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_3.png) # 1082-1122_GMC_Survey.csv: 資料欄位說明 ## 樣本屬性 1. image_name: 圖片名稱 image name 2. Period: 期作別(民國年) Period (Year of the Republic of China) 3. Date: 影像拍攝日期 Image taken date 4. Area: 農試所內或外 Inside or outside the TARI 5. Field: 田區編號 Field ID 6. Speices: 水稻品種 Rice speices 7. Device: 手機作業系統 OS of mobile 8. HMC: 收穫含水量數值 Harvest moisture content ## 顏色特徵 1. R: 紅光波段 Red band 2. G: 綠光波段 Green band 3. B: 藍光波段 Blue band 4. H1: HSV的H(色調) Hue 5. S1: HSV的S(飽和度) Saturation 6. V1: HSV的V(明度) Value 7. L2: HLS的L(亮度) Lightness 8. S2: HLS的S(飽和度) Saturation 9. L: Lab的L(亮度) Lightness 10. a: Lab的a(綠色到紅色的色度變化) color among green to red 11. b: Lab的b(藍色到黃色的色度變化) color among blue to yellow 12. Y: YCrCb的Y(亮度) lightness 13. Cr: YCrCb的Cr(紅色分量) color values represent the difference among red and lightness 14. Cb: YCrCb的Cb(藍色分量) color values represent the difference among blue and lightness 15. NDI: 歸一化差異指數 normalized difference index 16. GI: 綠色指數 green index 17. RGRI: 紅綠波比例指數 red-green ratio index ## 透過對比校正、GAMMA校正、去除陰影等前處理之後的特徵 1. RC: 校正後的R R values after correction 2. GC: 校正後的G G values after correction 3. BC: 校正後的B B values after correction 4. H1C: 校正後的H1 H1 values after correction 5. S1C: 校正後的S1 S1 values after correction 6. V1C: 校正後的V1 V1 values after correction 7. L2C: 校正後的L2 L2 values after correction 8. S2C: 校正後的S2 S2 values after correction 9. LC: 校正後的L L values after correction 10. aC: 校正後的a a values after correction 11. bC: 校正後的b b values after correction 12. YC: 校正後的Y Y values after correction 13. CrC: 校正後的Cr Cr values after correction 14. CbC: 校正後的Cb Cb values after correction 15. NDIC: 校正後的NDI NDI values after correction 16. GIC: 校正後的GI GI values after correction 17. RGRIC: 校正後的RGRI RGRI values after correction ## 校正色卡顏色特徵 1. B_RGB_avg: 黑色校正色卡的平均數值 mean of black color checker 2. G_RGB_avg: 灰色校正色卡的平均數值 mean of gray color checker 3. W_RGB_avg: 白色校正色卡的平均數值 mean of white color checker 4. B_RGB_R: 黑色校正色卡的紅色波段平均值 mean of red band in black color checker 5. B_RGB_G: 黑色校正色卡的綠色波段平均值 mean of green band in black color checker 6. B_RGB_B: 黑色校正色卡的藍色波段平均值 mean of blue band in black color checker 7. G_RGB_R: 灰色校正色卡的紅色波段平均值 mean of red band in gray color checker 8. G_RGB_G: 灰色校正色卡的綠色波段平均值 mean of green band in gray color checker 9. G_RGB_B: 灰色校正色卡的藍色波段平均值 mean of blue band in gray color checker 10. W_RGB_R: 白色校正色卡的紅色波段平均值 mean of red band in white color checker 11. W_RGB_G: 白色校正色卡的綠色波段平均值 mean of green band in white color checker 12. W_RGB_B: 白色校正色卡的藍色波段平均值 mean of blue band in white color checker # 參考文獻 1. [Assessment of grain harvest moisture content using machine learning on smartphone images for optimal harvest timing](https://www.mdpi.com/1424-8220/21/17/5875) Yang, M. D., Hsu, Y. C., Tseng, W. C., Lu, C. Y., Yang, C. Y., Lai, M. H., & Wu, D. H. (2021). Assessment of grain harvest moisture content using machine learning on smartphone images for optimal harvest timing. Sensors, 21(17), 5875.
to# 檔案內容 1. 01_OriginImage 原始稻穗影像圖片 Original images 2. 02_PanicleImage 去背後的稻穗影像 remove background panicle images 3. 03_ColorChecker 校正色卡影像(50x50) color checker images 4. 1082-1122_GMC_Survey.csv 2019年第二期作至2023年第二期作的調查資料 survey dataset 5. 1131_GMC_Survey.csv 2024年第一期作的調查資料 survey dataset # 各期作資料數量 1. 2019-2 ,515 2. 2020-1 ,1593 3. 2020-2 ,1734 4. 2021-1 ,2013 5. 2021-2 ,1842 6. 2022-1 ,718 7. 2022-2 ,178 8. 2023-1 ,397 9. 2023-2 ,559 10. 2024-1 ,412 11. 總數 ,9961 # 圖片瀏覽 ## 01_OriginImage: 多期作的水稻稻穗影像示意圖 ![原始圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_1.png) ## 02_PanicleImage: 經過前處理完成背景去除的稻穗影像 ![稻穗圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_2.png) ## 03_ColorChecker: 經過偵測結果截取的校正色卡影像 ![校正色卡圖](https://uav-fly.nchu.edu.tw/bot/static/dataset_3.png) # 1082-1122_GMC_Survey.csv: 資料欄位說明 ## 樣本屬性 1. image_name: 圖片名稱 image name 2. Period: 期作別(民國年) Period (Year of the Republic of China) 3. Date: 影像拍攝日期 Image taken date 4. Area: 農試所內或外 Inside or outside the TARI 5. Field: 田區編號 Field ID 6. Speices: 水稻品種 Rice speices 7. Device: 手機作業系統 OS of mobile 8. HMC: 收穫含水量數值 Harvest moisture content ## 顏色特徵 1. R: 紅光波段 Red band 2. G: 綠光波段 Green band 3. B: 藍光波段 Blue band 4. H1: HSV的H(色調) Hue 5. S1: HSV的S(飽和度) Saturation 6. V1: HSV的V(明度) Value 7. L2: HLS的L(亮度) Lightness 8. S2: HLS的S(飽和度) Saturation 9. L: Lab的L(亮度) Lightness 10. a: Lab的a(綠色到紅色的色度變化) color among green to red 11. b: Lab的b(藍色到黃色的色度變化) color among blue to yellow 12. Y: YCrCb的Y(亮度) lightness 13. Cr: YCrCb的Cr(紅色分量) color values represent the difference among red and lightness 14. Cb: YCrCb的Cb(藍色分量) color values represent the difference among blue and lightness 15. NDI: 歸一化差異指數 normalized difference index 16. GI: 綠色指數 green index 17. RGRI: 紅綠波比例指數 red-green ratio index ## 透過對比校正、GAMMA校正、去除陰影等前處理之後的特徵 1. RC: 校正後的R R values after correction 2. GC: 校正後的G G values after correction 3. BC: 校正後的B B values after correction 4. H1C: 校正後的H1 H1 values after correction 5. S1C: 校正後的S1 S1 values after correction 6. V1C: 校正後的V1 V1 values after correction 7. L2C: 校正後的L2 L2 values after correction 8. S2C: 校正後的S2 S2 values after correction 9. LC: 校正後的L L values after correction 10. aC: 校正後的a a values after correction 11. bC: 校正後的b b values after correction 12. YC: 校正後的Y Y values after correction 13. CrC: 校正後的Cr Cr values after correction 14. CbC: 校正後的Cb Cb values after correction 15. NDIC: 校正後的NDI NDI values after correction 16. GIC: 校正後的GI GI values after correction 17. RGRIC: 校正後的RGRI RGRI values after correction ## 校正色卡顏色特徵 1. B_RGB_avg: 黑色校正色卡的平均數值 mean of black color checker 2. G_RGB_avg: 灰色校正色卡的平均數值 mean of gray color checker 3. W_RGB_avg: 白色校正色卡的平均數值 mean of white color checker 4. B_RGB_R: 黑色校正色卡的紅色波段平均值 mean of red band in black color checker 5. B_RGB_G: 黑色校正色卡的綠色波段平均值 mean of green band in black color checker 6. B_RGB_B: 黑色校正色卡的藍色波段平均值 mean of blue band in black color checker 7. G_RGB_R: 灰色校正色卡的紅色波段平均值 mean of red band in gray color checker 8. G_RGB_G: 灰色校正色卡的綠色波段平均值 mean of green band in gray color checker 9. G_RGB_B: 灰色校正色卡的藍色波段平均值 mean of blue band in gray color checker 10. W_RGB_R: 白色校正色卡的紅色波段平均值 mean of red band in white color checker 11. W_RGB_G: 白色校正色卡的綠色波段平均值 mean of green band in white color checker 12. W_RGB_B: 白色校正色卡的藍色波段平均值 mean of blue band in white color checker # 參考文獻 1. [Assessment of grain harvest moisture content using machine learning on smartphone images for optimal harvest timing](https://www.mdpi.com/1424-8220/21/17/5875) Yang, M. D., Hsu, Y. C., Tseng, W. C., Lu, C. Y., Yang, C. Y., Lai, M. H., & Wu, D. H. (2021). Assessment of grain harvest moisture content using machine learning on smartphone images for optimal harvest timing. Sensors, 21(17), 5875.
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Moved Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 from https://aidata.nchu.edu.tw/smarter/dataset/smarter_07_gmc001_0_rgmc_20240321_data_108112 to https://aidata.nchu.edu.tw/smarter/dataset/smarter_07_gmc001_0_rgmc_20240321_data_108113
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Changed value of field
nchu_text_dataset_period
to2019-10-14~2024-06-06
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
Changed value of field
nchu_text_dataset_name_org_eng_shortname
to2023nchuaicontest
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_text_dataset_casenum
to2
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_text_dataset_itemnum
to9961
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_text_dataset_authorize_file
to水稻跨期作之稻穗影像與穀粒含水量資料集_FAIR自評表.docx
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
Changed value of field
nchu_text_dataset_annotation_progress_1_note
to9961例
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_text_dataset_name_6serial
to108113
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_text_dataset_capacity
to108.04GB
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
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nchu_texarea_dataset_abstract
to跨期作稻穗影像資料集,包含原圖、萃取後的稻穗、黑、灰、白校正色卡影像,以及透過初級法測量之穀粒含水量數值與樣本屬性,共9961筆。 Inter-temporal cropping rice panicle image dataset, including original images, extracted rice ears, black, gray, and white corrected color card images, as well as grain moisture content values and sample attributes measured by the primary method, a total of 9549 entries.
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
Changed value of field
nchu_texarea_dataset_other_note1
to9961筆影像資料皆包括除穀粒含水量以外的其他屬性資料以及顏色特徵萃取結果範例資料,已供資料使用者作為穀粒含水量推估模型訓練基礎 The 9549 pieces of image data all include other attribute data besides grain moisture content and sample data of color feature extraction results, which have been provided to data users as the basis for training the grain moisture content estimation model.
in Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集 -
新增資源 1131稻穗影像與穀粒含水量資料集.zip 至 Rice panicle images and moisture content dataset 水稻跨期作之稻穗影像與穀粒含水量資料集
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49 | Autonomy\", \"serial\": \"1-1\", \"question\": | 49 | Autonomy\", \"serial\": \"1-1\", \"question\": | ||
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52 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 52 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
53 | Fall-back Plans and Reproducibility\", \"serial\": \"2-17\", | 53 | Fall-back Plans and Reproducibility\", \"serial\": \"2-17\", | ||
54 | \"question\": | 54 | \"question\": | ||
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57 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | 57 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | ||
58 | \"serial\": \"2-12\", \"question\": | 58 | \"serial\": \"2-12\", \"question\": | ||
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61 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 61 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
62 | Universal Design\", \"serial\": \"5-6\", \"question\": | 62 | Universal Design\", \"serial\": \"5-6\", \"question\": | ||
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65 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 65 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
66 | \"serial\": \"7-3\", \"question\": | 66 | \"serial\": \"7-3\", \"question\": | ||
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68 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 68 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
69 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | 69 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | ||
70 | Well-being\", \"serial\": \"6-1\", \"question\": | 70 | Well-being\", \"serial\": \"6-1\", \"question\": | ||
71 | 6703\\u6709\\u6f5b\\u5728\\u7684\\u8ca0\\u9762\\u5f71\\u97ff\\uff1f\", | 71 | 6703\\u6709\\u6f5b\\u5728\\u7684\\u8ca0\\u9762\\u5f71\\u97ff\\uff1f\", | ||
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73 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | 73 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | ||
74 | \"serial\": \"1-7\", \"question\": | 74 | \"serial\": \"1-7\", \"question\": | ||
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76 | \"answer\": | 76 | \"answer\": | ||
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78 | (HOTL)\\u65b9\\u5f0f\\u76e3\\u7763|\\u4ee5Human-in-command | 78 | (HOTL)\\u65b9\\u5f0f\\u76e3\\u7763|\\u4ee5Human-in-command | ||
79 | (HIC)\\u65b9\\u5f0f\\u76e3\\u7763|\\u4e0d\\u9069\\u7528\", | 79 | (HIC)\\u65b9\\u5f0f\\u76e3\\u7763|\\u4e0d\\u9069\\u7528\", | ||
80 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 80 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
81 | Skills\", \"serial\": \"6-3\", \"question\": | 81 | Skills\", \"serial\": \"6-3\", \"question\": | ||
82 | 985e\\u7684\\u8077\\u696d\\u53ca\\u5de5\\u4f5c\\u5b89\\u6392\\uff1f\", | 82 | 985e\\u7684\\u8077\\u696d\\u53ca\\u5de5\\u4f5c\\u5b89\\u6392\\uff1f\", | ||
83 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 83 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
84 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Explainability\", | 84 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Explainability\", | ||
85 | \"serial\": \"4-3\", \"question\": | 85 | \"serial\": \"4-3\", \"question\": | ||
86 | 2f\\u5426\\u4e86\\u89e3AI\\u7cfb\\u7d71\\u7684\\u6c7a\\u7b56\\uff1f\", | 86 | 2f\\u5426\\u4e86\\u89e3AI\\u7cfb\\u7d71\\u7684\\u6c7a\\u7b56\\uff1f\", | ||
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88 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 88 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
89 | \"serial\": \"7-3-1\", \"question\": | 89 | \"serial\": \"7-3-1\", \"question\": | ||
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92 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Auditability\", | 92 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Auditability\", | ||
93 | \"serial\": \"7-2\", \"question\": | 93 | \"serial\": \"7-2\", \"question\": | ||
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96 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | 96 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | ||
97 | \"serial\": \"1-8\", \"question\": \"\\u5c0d\\u65bc\\u4ee5HITL | 97 | \"serial\": \"1-8\", \"question\": \"\\u5c0d\\u65bc\\u4ee5HITL | ||
98 | \\u3001HOTL | 98 | \\u3001HOTL | ||
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101 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 101 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
102 | Attack and Security\", \"serial\": \"2-2\", \"question\": | 102 | Attack and Security\", \"serial\": \"2-2\", \"question\": | ||
103 | 7b26\\u5408\\u7279\\u5b9a\\u7684\\u5b89\\u5168\\u6a19\\u6e96\\uff1f\", | 103 | 7b26\\u5408\\u7279\\u5b9a\\u7684\\u5b89\\u5168\\u6a19\\u6e96\\uff1f\", | ||
104 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 104 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
105 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 105 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
106 | \"serial\": \"2-7-1\", \"question\": | 106 | \"serial\": \"2-7-1\", \"question\": | ||
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108 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 108 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
109 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | 109 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | ||
110 | at large or Democracy\", \"serial\": \"6-8-1\", \"question\": | 110 | at large or Democracy\", \"serial\": \"6-8-1\", \"question\": | ||
111 | 93e\\u6703\\u6574\\u9ad4)\\u7684\\u793e\\u6703\\u5f71\\u97ff\\uff1f\", | 111 | 93e\\u6703\\u6574\\u9ad4)\\u7684\\u793e\\u6703\\u5f71\\u97ff\\uff1f\", | ||
112 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 112 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
113 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | 113 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | ||
114 | \"serial\": \"2-13\", \"question\": | 114 | \"serial\": \"2-13\", \"question\": | ||
115 | 8868\\u6240\\u90e8\\u7f72\\u7684\\u7cfb\\u7d71\\u74b0\\u5883\\uff1f\", | 115 | 8868\\u6240\\u90e8\\u7f72\\u7684\\u7cfb\\u7d71\\u74b0\\u5883\\uff1f\", | ||
116 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 116 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
117 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 117 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
118 | Fall-back Plans and Reproducibility\", \"serial\": \"2-17-1\", | 118 | Fall-back Plans and Reproducibility\", \"serial\": \"2-17-1\", | ||
119 | \"question\": | 119 | \"question\": | ||
120 | \\u78ba\\u5b9a\\u7fa9\\u7684\\u6d41\\u7a0b\\uff0c\\u4ee5\\u76e3\\u63a7 | 120 | \\u78ba\\u5b9a\\u7fa9\\u7684\\u6d41\\u7a0b\\uff0c\\u4ee5\\u76e3\\u63a7 | ||
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122 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 122 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
123 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Privacy\", | 123 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Privacy\", | ||
124 | \"serial\": \"3-2\", \"question\": | 124 | \"serial\": \"3-2\", \"question\": | ||
125 | 7cfb\\u7d71\\u7684\\u96b1\\u79c1\\u76f8\\u95dc\\u554f\\u984c\\uff1f\", | 125 | 7cfb\\u7d71\\u7684\\u96b1\\u79c1\\u76f8\\u95dc\\u554f\\u984c\\uff1f\", | ||
126 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 126 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
127 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | 127 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | ||
128 | \"serial\": \"3-4\", \"question\": | 128 | \"serial\": \"3-4\", \"question\": | ||
129 | 84\\u5f37\\u5236\\u6027\\u63aa\\u65bd\\uff1f(\\u53ef\\u8907\\u9078)\", | 129 | 84\\u5f37\\u5236\\u6027\\u63aa\\u65bd\\uff1f(\\u53ef\\u8907\\u9078)\", | ||
130 | \"answer\": | 130 | \"answer\": | ||
131 | 9(\\u5305\\u542b\\u7279\\u7a2e\\u8cc7\\u6599)|\\u4e0d\\u9069\\u7528\", | 131 | 9(\\u5305\\u542b\\u7279\\u7a2e\\u8cc7\\u6599)|\\u4e0d\\u9069\\u7528\", | ||
132 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | 132 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | ||
133 | \"serial\": \"4-1-1\", \"question\": | 133 | \"serial\": \"4-1-1\", \"question\": | ||
134 | 38\\u5165AI\\u7cfb\\u7d71\\u8cc7\\u6599\\u7684\\u54c1\\u8cea\\uff1f\", | 134 | 38\\u5165AI\\u7cfb\\u7d71\\u8cc7\\u6599\\u7684\\u54c1\\u8cea\\uff1f\", | ||
135 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 135 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
136 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | 136 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | ||
137 | \"serial\": \"4-5\", \"question\": | 137 | \"serial\": \"4-5\", \"question\": | ||
138 | 76ee\\u7684\\u3001\\u6a19\\u6e96\\u8207\\u4fb7\\u9650\\u6027\\uff1f\", | 138 | 76ee\\u7684\\u3001\\u6a19\\u6e96\\u8207\\u4fb7\\u9650\\u6027\\uff1f\", | ||
139 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 139 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
140 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 140 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
141 | Bias\", \"serial\": \"5-2\", \"question\": | 141 | Bias\", \"serial\": \"5-2\", \"question\": | ||
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143 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 143 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
144 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 144 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
145 | Autonomy\", \"serial\": \"1-1-1\", \"question\": | 145 | Autonomy\", \"serial\": \"1-1-1\", \"question\": | ||
146 | \\u5426\\u4f7f\\u7d42\\u7aef\\u4f7f\\u7528\\u8005\\u6216\\u4e3b\\u9ad4 | 146 | \\u5426\\u4f7f\\u7d42\\u7aef\\u4f7f\\u7528\\u8005\\u6216\\u4e3b\\u9ad4 | ||
147 | 3ef\\u80fd\\u4e0d\\u77e5\\u9053\\u6216\\u611f\\u5230\\u56f0\\u60d1)\", | 147 | 3ef\\u80fd\\u4e0d\\u77e5\\u9053\\u6216\\u611f\\u5230\\u56f0\\u60d1)\", | ||
148 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 148 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
149 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 149 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
150 | Skills\", \"serial\": \"6-4\", \"question\": | 150 | Skills\", \"serial\": \"6-4\", \"question\": | ||
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152 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 152 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
153 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | 153 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | ||
154 | Well-being\", \"serial\": \"6-1-1\", \"question\": | 154 | Well-being\", \"serial\": \"6-1-1\", \"question\": | ||
155 | 5225\\u51fa\\u54ea\\u4e9b\\u6f5b\\u5728\\u7684\\u5f71\\u97ff\\uff1f\", | 155 | 5225\\u51fa\\u54ea\\u4e9b\\u6f5b\\u5728\\u7684\\u5f71\\u97ff\\uff1f\", | ||
156 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 156 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
157 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 157 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
158 | Universal Design\", \"serial\": \"5-7\", \"question\": | 158 | Universal Design\", \"serial\": \"5-7\", \"question\": | ||
159 | 88ab\\u6392\\u65a5\\u5728\\u5916\\u7684\\u4eba\\u4f7f\\u7528\\uff1f\", | 159 | 88ab\\u6392\\u65a5\\u5728\\u5916\\u7684\\u4eba\\u4f7f\\u7528\\uff1f\", | ||
160 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 160 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
161 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 161 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
162 | Attack and Security\", \"serial\": \"2-3\", \"question\": | 162 | Attack and Security\", \"serial\": \"2-3\", \"question\": | ||
163 | 8def\\u653b\\u64ca\\u7684\\u98a8\\u96aa\\u6709\\u591a\\u5927\\uff1f\", | 163 | 8def\\u653b\\u64ca\\u7684\\u98a8\\u96aa\\u6709\\u591a\\u5927\\uff1f\", | ||
164 | \"answer\": | 164 | \"answer\": | ||
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166 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | 166 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | ||
167 | \"serial\": \"1-9\", \"question\": | 167 | \"serial\": \"1-9\", \"question\": | ||
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170 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 170 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
171 | \"serial\": \"7-4\", \"question\": | 171 | \"serial\": \"7-4\", \"question\": | ||
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174 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | 174 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | ||
175 | \"serial\": \"2-14\", \"question\": | 175 | \"serial\": \"2-14\", \"question\": | ||
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178 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 178 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
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183 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 183 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
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187 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | 187 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | ||
188 | \"serial\": \"3-4-1\", \"question\": | 188 | \"serial\": \"3-4-1\", \"question\": | ||
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191 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | 191 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | ||
192 | \"serial\": \"4-1-2\", \"question\": | 192 | \"serial\": \"4-1-2\", \"question\": | ||
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195 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 195 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
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199 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | 199 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | ||
200 | \"serial\": \"4-5-1\", \"question\": | 200 | \"serial\": \"4-5-1\", \"question\": | ||
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203 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 203 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
204 | Autonomy\", \"serial\": \"1-1-2\", \"question\": | 204 | Autonomy\", \"serial\": \"1-1-2\", \"question\": | ||
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207 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | 207 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | ||
208 | Well-being\", \"serial\": \"6-2\", \"question\": | 208 | Well-being\", \"serial\": \"6-2\", \"question\": | ||
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211 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 211 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
212 | Bias\", \"serial\": \"5-2-1\", \"question\": | 212 | Bias\", \"serial\": \"5-2-1\", \"question\": | ||
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215 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 215 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
216 | \"serial\": \"2-7-2\", \"question\": | 216 | \"serial\": \"2-7-2\", \"question\": | ||
217 | 0cAI\\u73fe\\u6709\\u6216\\u6f5b\\u5728\\u7684\\u98a8\\u96aa\\uff1f\", | 217 | 0cAI\\u73fe\\u6709\\u6216\\u6f5b\\u5728\\u7684\\u98a8\\u96aa\\uff1f\", | ||
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219 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | 219 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | ||
220 | at large or Democracy\", \"serial\": \"6-8-2\", \"question\": | 220 | at large or Democracy\", \"serial\": \"6-8-2\", \"question\": | ||
221 | 5728\\u793e\\u6703\\u5371\\u5bb3\\u964d\\u5230\\u6700\\u4f4e\\uff1f\", | 221 | 5728\\u793e\\u6703\\u5371\\u5bb3\\u964d\\u5230\\u6700\\u4f4e\\uff1f\", | ||
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223 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | 223 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | ||
224 | \"serial\": \"1-10\", \"question\": | 224 | \"serial\": \"1-10\", \"question\": | ||
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226 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 226 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
227 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 227 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
228 | Autonomy\", \"serial\": \"1-2\", \"question\": | 228 | Autonomy\", \"serial\": \"1-2\", \"question\": | ||
229 | 07AI\\u4e92\\u52d5\\u300d\\uff0c\\u7522\\u751f\\u6df7\\u6dc6\\uff1f\", | 229 | 07AI\\u4e92\\u52d5\\u300d\\uff0c\\u7522\\u751f\\u6df7\\u6dc6\\uff1f\", | ||
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231 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | 231 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Environmental | ||
232 | Well-being\", \"serial\": \"6-2-1\", \"question\": | 232 | Well-being\", \"serial\": \"6-2-1\", \"question\": | ||
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235 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | 235 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | ||
236 | \"serial\": \"3-4-2\", \"question\": | 236 | \"serial\": \"3-4-2\", \"question\": | ||
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239 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | 239 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | ||
240 | \"serial\": \"4-5-2\", \"question\": | 240 | \"serial\": \"4-5-2\", \"question\": | ||
241 | 176\\u6e96\\u78ba\\u5ea6\\u8207/\\u6216\\u932f\\u8aa4\\u7387\\uff1f\", | 241 | 176\\u6e96\\u78ba\\u5ea6\\u8207/\\u6216\\u932f\\u8aa4\\u7387\\uff1f\", | ||
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243 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | 243 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | ||
244 | \"serial\": \"4-1-3\", \"question\": | 244 | \"serial\": \"4-1-3\", \"question\": | ||
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247 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 247 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
248 | Bias\", \"serial\": \"5-2-2\", \"question\": | 248 | Bias\", \"serial\": \"5-2-2\", \"question\": | ||
249 | 3001\\u6a21\\u578b\\u8207\\u6027\\u80fd\\u7684\\u7406\\u89e3\\uff1f\", | 249 | 3001\\u6a21\\u578b\\u8207\\u6027\\u80fd\\u7684\\u7406\\u89e3\\uff1f\", | ||
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251 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 251 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
252 | \"serial\": \"2-8\", \"question\": | 252 | \"serial\": \"2-8\", \"question\": | ||
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254 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 254 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
255 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 255 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
256 | \"serial\": \"7-4-1\", \"question\": | 256 | \"serial\": \"7-4-1\", \"question\": | ||
257 | 28\\u65bcAI\\u7cfb\\u7d71\\u7684\\u6cd5\\u5f8b\\u898f\\u7bc4\\uff1f\", | 257 | 28\\u65bcAI\\u7cfb\\u7d71\\u7684\\u6cd5\\u5f8b\\u898f\\u7bc4\\uff1f\", | ||
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259 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | 259 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | ||
260 | \"serial\": \"2-15\", \"question\": | 260 | \"serial\": \"2-15\", \"question\": | ||
261 | a13\\u7df4\\u7684\\u8cc7\\u6599\\u6216\\u5047\\u8a2d\\u5931\\u6548?\", | 261 | a13\\u7df4\\u7684\\u8cc7\\u6599\\u6216\\u5047\\u8a2d\\u5931\\u6548?\", | ||
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263 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 263 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
264 | Attack and Security\", \"serial\": \"2-3-1\", \"question\": | 264 | Attack and Security\", \"serial\": \"2-3-1\", \"question\": | ||
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267 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | 267 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Society | ||
268 | at large or Democracy\", \"serial\": \"6-8-3\", \"question\": | 268 | at large or Democracy\", \"serial\": \"6-8-3\", \"question\": | ||
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271 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 271 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
272 | Fall-back Plans and Reproducibility\", \"serial\": \"2-18\", | 272 | Fall-back Plans and Reproducibility\", \"serial\": \"2-18\", | ||
273 | \"question\": | 273 | \"question\": | ||
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275 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 275 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
276 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 276 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
277 | Universal Design\", \"serial\": \"5-7-2\", \"question\": | 277 | Universal Design\", \"serial\": \"5-7-2\", \"question\": | ||
278 | 4e3b\\u9ad4\\uff0c\\u4f86\\u53c3\\u8207\\u6216\\u8aee\\u8a62\\uff1f\", | 278 | 4e3b\\u9ad4\\uff0c\\u4f86\\u53c3\\u8207\\u6216\\u8aee\\u8a62\\uff1f\", | ||
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280 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 280 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
281 | Skills\", \"serial\": \"6-5-1\", \"question\": | 281 | Skills\", \"serial\": \"6-5-1\", \"question\": | ||
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283 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 283 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
284 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 284 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
285 | Skills\", \"serial\": \"6-6\", \"question\": | 285 | Skills\", \"serial\": \"6-6\", \"question\": | ||
286 | 2f\\u5426\\u6709\\u52de\\u52d5\\u529b\\u53bb\\u6280\\u8853\\u5316(risk | 286 | 2f\\u5426\\u6709\\u52de\\u52d5\\u529b\\u53bb\\u6280\\u8853\\u5316(risk | ||
287 | of de-skilling of the workforce)\\u7684\\u98a8\\u96aa\\uff1f\", | 287 | of de-skilling of the workforce)\\u7684\\u98a8\\u96aa\\uff1f\", | ||
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289 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 289 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
290 | Universal Design\", \"serial\": \"5-8\", \"question\": | 290 | Universal Design\", \"serial\": \"5-8\", \"question\": | ||
291 | ff0c\\u8003\\u616e\\u901a\\u7528\\u8a2d\\u8a08\\u539f\\u5247(Universal | 291 | ff0c\\u8003\\u616e\\u901a\\u7528\\u8a2d\\u8a08\\u539f\\u5247(Universal | ||
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294 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | 294 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Oversight\", | ||
295 | \"serial\": \"1-11\", \"question\": | 295 | \"serial\": \"1-11\", \"question\": | ||
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298 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | 298 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | ||
299 | \"serial\": \"3-5\", \"question\": | 299 | \"serial\": \"3-5\", \"question\": | ||
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302 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | 302 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | ||
303 | \"serial\": \"4-1-4\", \"question\": | 303 | \"serial\": \"4-1-4\", \"question\": | ||
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306 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 306 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
307 | Autonomy\", \"serial\": \"1-2-1\", \"question\": | 307 | Autonomy\", \"serial\": \"1-2-1\", \"question\": | ||
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310 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 310 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
311 | Bias\", \"serial\": \"5-2-3\", \"question\": | 311 | Bias\", \"serial\": \"5-2-3\", \"question\": | ||
312 | ee3\\u8868\\u6027)\\u800c\\u5c0e\\u81f4\\u7684\\u504f\\u898b\\uff1f\", | 312 | ee3\\u8868\\u6027)\\u800c\\u5c0e\\u81f4\\u7684\\u504f\\u898b\\uff1f\", | ||
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314 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | 314 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Communication\", | ||
315 | \"serial\": \"4-5-3\", \"question\": | 315 | \"serial\": \"4-5-3\", \"question\": | ||
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318 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 318 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
319 | \"serial\": \"2-8-1\", \"question\": | 319 | \"serial\": \"2-8-1\", \"question\": | ||
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322 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | 322 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accuracy\", | ||
323 | \"serial\": \"2-16\", \"question\": | 323 | \"serial\": \"2-16\", \"question\": | ||
324 | 40\\u671f\\u5f85\\u7684AI\\u6e96\\u78ba\\u6027\\u7a0b\\u5ea6\\uff1f\", | 324 | 40\\u671f\\u5f85\\u7684AI\\u6e96\\u78ba\\u6027\\u7a0b\\u5ea6\\uff1f\", | ||
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326 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 326 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
327 | Attack and Security\", \"serial\": \"2-3-2\", \"question\": | 327 | Attack and Security\", \"serial\": \"2-3-2\", \"question\": | ||
328 | 28\\u7684\\u653b\\u64ca\\u9ede\\u70ba\\uff1a(\\u53ef\\u8907\\u9078)\", | 328 | 28\\u7684\\u653b\\u64ca\\u9ede\\u70ba\\uff1a(\\u53ef\\u8907\\u9078)\", | ||
329 | \"answer\": \"\\u8907\\u9078^\\u8cc7\\u6599\\u6c61\\u67d3\\uff08Data | 329 | \"answer\": \"\\u8907\\u9078^\\u8cc7\\u6599\\u6c61\\u67d3\\uff08Data | ||
330 | poisoning, | 330 | poisoning, | ||
331 | 3\\u7df4\\u8cc7\\u6599\\uff09|\\u6a21\\u578b\\u898f\\u907f\\uff08Model | 331 | 3\\u7df4\\u8cc7\\u6599\\uff09|\\u6a21\\u578b\\u898f\\u907f\\uff08Model | ||
332 | evasion, | 332 | evasion, | ||
333 | 6\\u985e\\uff09|\\u6a21\\u578b\\u9006\\u5411\\u653b\\u64ca\\uff08Model | 333 | 6\\u985e\\uff09|\\u6a21\\u578b\\u9006\\u5411\\u653b\\u64ca\\uff08Model | ||
334 | inversion, | 334 | inversion, | ||
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336 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 336 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
337 | Fall-back Plans and Reproducibility\", \"serial\": \"2-18-1\", | 337 | Fall-back Plans and Reproducibility\", \"serial\": \"2-18-1\", | ||
338 | \"question\": | 338 | \"question\": | ||
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340 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 340 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
341 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 341 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
342 | \"serial\": \"7-5\", \"question\": | 342 | \"serial\": \"7-5\", \"question\": | ||
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344 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 344 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
345 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 345 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
346 | Autonomy\", \"serial\": \"1-3\", \"question\": | 346 | Autonomy\", \"serial\": \"1-3\", \"question\": | ||
347 | 800c\\u5f71\\u97ff\\u4eba\\u985e\\u7684\\u81ea\\u4e3b\\u6027\\uff1f\", | 347 | 800c\\u5f71\\u97ff\\u4eba\\u985e\\u7684\\u81ea\\u4e3b\\u6027\\uff1f\", | ||
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349 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | 349 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Impact on Work and | ||
350 | Skills\", \"serial\": \"6-6-1\", \"question\": | 350 | Skills\", \"serial\": \"6-6-1\", \"question\": | ||
351 | 7de9\\u89e3\\u53bb\\u6280\\u8853\\u5316\\u7684\\u98a8\\u96aa\\uff1f\", | 351 | 7de9\\u89e3\\u53bb\\u6280\\u8853\\u5316\\u7684\\u98a8\\u96aa\\uff1f\", | ||
352 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 352 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
353 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | 353 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Data Governance\", | ||
354 | \"serial\": \"3-6\", \"question\": | 354 | \"serial\": \"3-6\", \"question\": | ||
355 | \u7cfb\\u7d71\\u7b26\\u5408\\u76f8\\u95dc\\u6a19\\u6e96(\\u4f8b\\u5982 | 355 | \u7cfb\\u7d71\\u7b26\\u5408\\u76f8\\u95dc\\u6a19\\u6e96(\\u4f8b\\u5982 | ||
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358 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 358 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
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362 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 362 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
363 | Fall-back Plans and Reproducibility\", \"serial\": \"2-19\", | 363 | Fall-back Plans and Reproducibility\", \"serial\": \"2-19\", | ||
364 | \"question\": | 364 | \"question\": | ||
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368 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 368 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
369 | \"serial\": \"7-6\", \"question\": | 369 | \"serial\": \"7-6\", \"question\": | ||
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372 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 372 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
373 | \"serial\": \"2-8-2\", \"question\": | 373 | \"serial\": \"2-8-2\", \"question\": | ||
374 | 83c\\u5b8c\\u6574\\u6027\\uff1a\\u8eab\\u5fc3\\u5b8c\\u6574\\u6027)\", | 374 | 83c\\u5b8c\\u6574\\u6027\\uff1a\\u8eab\\u5fc3\\u5b8c\\u6574\\u6027)\", | ||
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376 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 376 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
377 | Bias\", \"serial\": \"5-2-4\", \"question\": | 377 | Bias\", \"serial\": \"5-2-4\", \"question\": | ||
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380 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | 380 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Traceability\", | ||
381 | \"serial\": \"4-1-5\", \"question\": | 381 | \"serial\": \"4-1-5\", \"question\": | ||
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384 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 384 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
385 | Universal Design\", \"serial\": \"5-9\", \"question\": | 385 | Universal Design\", \"serial\": \"5-9\", \"question\": | ||
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388 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | 388 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Resilience to | ||
389 | Attack and Security\", \"serial\": \"2-5\", \"question\": | 389 | Attack and Security\", \"serial\": \"2-5\", \"question\": | ||
390 | \\u662f\\u5426\\u5df2\\u5c0d\\u7cfb\\u7d71\\u9032\\u884c\\u7d05\\u968a | 390 | \\u662f\\u5426\\u5df2\\u5c0d\\u7cfb\\u7d71\\u9032\\u884c\\u7d05\\u968a | ||
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393 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | 393 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"General Safety\", | ||
394 | \"serial\": \"2-9\", \"question\": | 394 | \"serial\": \"2-9\", \"question\": | ||
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397 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 397 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
398 | Autonomy\", \"serial\": \"1-3-1\", \"question\": | 398 | Autonomy\", \"serial\": \"1-3-1\", \"question\": | ||
399 | 42\\u7aef\\u4f7f\\u7528\\u8005\\u904e\\u5ea6\\u4f9d\\u8cf4AI\\uff1f\", | 399 | 42\\u7aef\\u4f7f\\u7528\\u8005\\u904e\\u5ea6\\u4f9d\\u8cf4AI\\uff1f\", | ||
400 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | 400 | \"answer\": \"\\u55ae\\u9078^\\u662f|\\u5426|\\u4e0d\\u9069\\u7528\", | ||
401 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | 401 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Reliability, | ||
402 | Fall-back Plans and Reproducibility\", \"serial\": \"2-20\", | 402 | Fall-back Plans and Reproducibility\", \"serial\": \"2-20\", | ||
403 | \"question\": | 403 | \"question\": | ||
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406 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 406 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
407 | Bias\", \"serial\": \"5-3\", \"question\": | 407 | Bias\", \"serial\": \"5-3\", \"question\": | ||
408 | 6642\\uff0c\\u53ef\\u80fd\\u6ce8\\u5165\\u7684\\u504f\\u898b\\uff1f\", | 408 | 6642\\uff0c\\u53ef\\u80fd\\u6ce8\\u5165\\u7684\\u504f\\u898b\\uff1f\", | ||
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410 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | 410 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Accessibility and | ||
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475 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 475 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
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511 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 511 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
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519 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 519 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
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523 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | 523 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Risk Management\", | ||
524 | \"serial\": \"7-8\", \"question\": | 524 | \"serial\": \"7-8\", \"question\": | ||
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527 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 527 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
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531 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | 531 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Human Agency and | ||
532 | Autonomy\", \"serial\": \"1-6-2\", \"question\": | 532 | Autonomy\", \"serial\": \"1-6-2\", \"question\": | ||
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535 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | 535 | \"pkg_answer\": \"\", \"note\": \"\"}, {\"cat\": \"Avoidance of Unfair | ||
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544 | Bias\", \"serial\": \"5-5-4\", \"question\": | 544 | Bias\", \"serial\": \"5-5-4\", \"question\": | ||
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547 | \"pkg_answer\": \"\", \"note\": \"\"}]", | 547 | \"pkg_answer\": \"\", \"note\": \"\"}]", | ||
548 | "annotationinfos1": "[]", | 548 | "annotationinfos1": "[]", | ||
549 | "annotationinfos2": "[]", | 549 | "annotationinfos2": "[]", | ||
550 | "author": "Hank Huang \u9ec3\u701a\u8f1d", | 550 | "author": "Hank Huang \u9ec3\u701a\u8f1d", | ||
551 | "author_email": "balimon@hotmail.com", | 551 | "author_email": "balimon@hotmail.com", | ||
552 | "caseinfos": "[]", | 552 | "caseinfos": "[]", | ||
553 | "coauthors": "[]", | 553 | "coauthors": "[]", | ||
554 | "creator_user_id": "df121c90-3280-41f7-8a0d-004c9993ec31", | 554 | "creator_user_id": "df121c90-3280-41f7-8a0d-004c9993ec31", | ||
555 | "deidinfos": "[]", | 555 | "deidinfos": "[]", | ||
556 | "fairfiles": "[{\"cat\": \"Findable\", \"serial\": \"RDA-F1-01M\", | 556 | "fairfiles": "[{\"cat\": \"Findable\", \"serial\": \"RDA-F1-01M\", | ||
557 | \"eval\": | 557 | \"eval\": | ||
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879 | 8b49\\u548c\\u6388\\u6b0a\\u53d6\\u7528\\u5354\\u5b9a\\u53d6\\u7528\", | 879 | 8b49\\u548c\\u6388\\u6b0a\\u53d6\\u7528\\u5354\\u5b9a\\u53d6\\u7528\", | ||
880 | \"eval_desc\": | 880 | \"eval_desc\": | ||
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882 | \"eval_prompt\": | 882 | \"eval_prompt\": | ||
883 | 96c6\\u524d\\u9700\\u8981\\u5148\\u767b\\u5165\\u5e33\\u865f\\u3002\", | 883 | 96c6\\u524d\\u9700\\u8981\\u5148\\u767b\\u5165\\u5e33\\u865f\\u3002\", | ||
884 | \"answer\": \"\\u5168\\u9762\\u5be6\\u65bd\", \"note\": | 884 | \"answer\": \"\\u5168\\u9762\\u5be6\\u65bd\", \"note\": | ||
885 | e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\"}, | 885 | e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\"}, | ||
886 | {\"cat\": \"Accessible\", \"serial\": \"RDA-A2-01M\", \"eval\": | 886 | {\"cat\": \"Accessible\", \"serial\": \"RDA-A2-01M\", \"eval\": | ||
887 | 6599\\u4e0d\\u518d\\u53ef\\u7528\\u5f8c\\u4ecd\\u7136\\u53ef\\u7528\", | 887 | 6599\\u4e0d\\u518d\\u53ef\\u7528\\u5f8c\\u4ecd\\u7136\\u53ef\\u7528\", | ||
888 | \"eval_desc\": | 888 | \"eval_desc\": | ||
889 | 5e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\", | 889 | 5e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\", | ||
890 | \"eval_prompt\": | 890 | \"eval_prompt\": | ||
891 | 8aaa\\u660e\\u7db2\\u9801\\u5c07\\u4ecd\\u7136\\u5b58\\u5728\\u3002\", | 891 | 8aaa\\u660e\\u7db2\\u9801\\u5c07\\u4ecd\\u7136\\u5b58\\u5728\\u3002\", | ||
892 | \"answer\": \"\\u5168\\u9762\\u5be6\\u65bd\", \"note\": | 892 | \"answer\": \"\\u5168\\u9762\\u5be6\\u65bd\", \"note\": | ||
893 | e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\"}, | 893 | e73\\u53f0\\u5f8c\\u5373\\u7b26\\u5408\\u6b64\\u9805\\u6e96\\u5247\"}, | ||
894 | {\"cat\": \"Interoperable\", \"serial\": \"RDA-I3-04M\", \"eval\": | 894 | {\"cat\": \"Interoperable\", \"serial\": \"RDA-I3-04M\", \"eval\": | ||
895 | 5c0d\\u5176\\u4ed6\\u8cc7\\u6599\\u7684\\u9650\\u5b9a\\u5f15\\u7528\", | 895 | 5c0d\\u5176\\u4ed6\\u8cc7\\u6599\\u7684\\u9650\\u5b9a\\u5f15\\u7528\", | ||
896 | \"eval_desc\": | 896 | \"eval_desc\": | ||
897 | 5c0d\\u5176\\u4ed6\\u8cc7\\u6599\\u7684\\u9650\\u5b9a\\u5f15\\u7528\", | 897 | 5c0d\\u5176\\u4ed6\\u8cc7\\u6599\\u7684\\u9650\\u5b9a\\u5f15\\u7528\", | ||
898 | \"eval_prompt\": \"\\u4f8b\\u5982: | 898 | \"eval_prompt\": \"\\u4f8b\\u5982: | ||
899 | 7528\\u548c\\u6b64\\u8cc7\\u6599\\u96c6\\u7684\\u95dc\\u4fc2\\u3002\", | 899 | 7528\\u548c\\u6b64\\u8cc7\\u6599\\u96c6\\u7684\\u95dc\\u4fc2\\u3002\", | ||
900 | \"answer\": \"\\u4e0d\\u9069\\u7528\", \"note\": | 900 | \"answer\": \"\\u4e0d\\u9069\\u7528\", \"note\": | ||
901 | \"\\u4e0d\\u5305\\u542b\"}]", | 901 | \"\\u4e0d\\u5305\\u542b\"}]", | ||
902 | "groups": [], | 902 | "groups": [], | ||
903 | "id": "165d06a7-9df1-4407-8823-b8232a58c23d", | 903 | "id": "165d06a7-9df1-4407-8823-b8232a58c23d", | ||
904 | "isopen": false, | 904 | "isopen": false, | ||
905 | "license_id": "", | 905 | "license_id": "", | ||
906 | "license_title": "", | 906 | "license_title": "", | ||
907 | "maintainer": "Hank Huang \u9ec3\u701a\u8f1d", | 907 | "maintainer": "Hank Huang \u9ec3\u701a\u8f1d", | ||
908 | "maintainer_email": "balimon@hotmail.com", | 908 | "maintainer_email": "balimon@hotmail.com", | ||
909 | "metadata_created": "2024-03-23T15:16:58.434915", | 909 | "metadata_created": "2024-03-23T15:16:58.434915", | ||
n | 910 | "metadata_modified": "2024-03-26T04:23:12.564132", | n | 910 | "metadata_modified": "2024-08-23T03:41:24.872023", |
911 | "name": "smarter_07_gmc001_0_rgmc_20240321_data_108112", | 911 | "name": "smarter_07_gmc001_0_rgmc_20240321_data_108113", | ||
912 | "nchu_checkbox_dataset_annotation_method_3": "1", | 912 | "nchu_checkbox_dataset_annotation_method_3": "1", | ||
913 | "nchu_checkbox_dataset_remark_method_2": "1", | 913 | "nchu_checkbox_dataset_remark_method_2": "1", | ||
914 | "nchu_ddl_dataset_project": [ | 914 | "nchu_ddl_dataset_project": [ | ||
915 | "\u667a\u6167\u8fb2\u696d" | 915 | "\u667a\u6167\u8fb2\u696d" | ||
916 | ], | 916 | ], | ||
917 | "nchu_ddl_dataset_status": [ | 917 | "nchu_ddl_dataset_status": [ | ||
918 | "\u5df2\u5be9\u6838" | 918 | "\u5df2\u5be9\u6838" | ||
919 | ], | 919 | ], | ||
920 | "nchu_ddl_dataset_status_ori": "['\u5f85\u5be9\u6838']", | 920 | "nchu_ddl_dataset_status_ori": "['\u5f85\u5be9\u6838']", | ||
921 | "nchu_ddl_dataset_type": [ | 921 | "nchu_ddl_dataset_type": [ | ||
922 | "\u9700\u63d0\u51fa\u7533\u8acb_\u516c\u958b" | 922 | "\u9700\u63d0\u51fa\u7533\u8acb_\u516c\u958b" | ||
923 | ], | 923 | ], | ||
924 | "nchu_ddl_dataset_update_frequency": "\u5b9a\u671f", | 924 | "nchu_ddl_dataset_update_frequency": "\u5b9a\u671f", | ||
925 | "nchu_radio_dataset_annotation_progress": "1", | 925 | "nchu_radio_dataset_annotation_progress": "1", | ||
926 | "nchu_radio_dataset_connect_otherdb": "2", | 926 | "nchu_radio_dataset_connect_otherdb": "2", | ||
927 | "nchu_radio_dataset_deid_method": "1", | 927 | "nchu_radio_dataset_deid_method": "1", | ||
928 | "nchu_radio_dataset_is_aimodelset": "0", | 928 | "nchu_radio_dataset_is_aimodelset": "0", | ||
929 | "nchu_radio_dataset_legitimate_basis": "1", | 929 | "nchu_radio_dataset_legitimate_basis": "1", | ||
930 | "nchu_texarea_dataset_abstract": | 930 | "nchu_texarea_dataset_abstract": | ||
n | 931 | 23\u672c\u5c6c\u6027\uff0c\u51719549\u7b46\u3002\r\n\r\nInter-temporal | n | 931 | 23\u672c\u5c6c\u6027\uff0c\u51719961\u7b46\u3002\r\n\r\nInter-temporal |
932 | cropping rice panicle image dataset, including original images, | 932 | cropping rice panicle image dataset, including original images, | ||
933 | extracted rice ears, black, gray, and white corrected color card | 933 | extracted rice ears, black, gray, and white corrected color card | ||
934 | images, as well as grain moisture content values \u200b\u200band | 934 | images, as well as grain moisture content values \u200b\u200band | ||
935 | sample attributes measured by the primary method, a total of 9549 | 935 | sample attributes measured by the primary method, a total of 9549 | ||
936 | entries.", | 936 | entries.", | ||
937 | "nchu_texarea_dataset_other": | 937 | "nchu_texarea_dataset_other": | ||
938 | c6\u3001\u570b\u5bb6\u5b89\u5168\u53ca\u6838\u5fc3\u79d1\u6280\u3002", | 938 | c6\u3001\u570b\u5bb6\u5b89\u5168\u53ca\u6838\u5fc3\u79d1\u6280\u3002", | ||
939 | "nchu_texarea_dataset_other_note1": | 939 | "nchu_texarea_dataset_other_note1": | ||
940 | u6c34\u91cf\u63a8\u4f30\u6a21\u578b\u8a13\u7df4\u57fa\u790e\r\n\r\nThe | 940 | u6c34\u91cf\u63a8\u4f30\u6a21\u578b\u8a13\u7df4\u57fa\u790e\r\n\r\nThe | ||
941 | 9549 pieces of image data all include other attribute data besides | 941 | 9549 pieces of image data all include other attribute data besides | ||
942 | grain moisture content and sample data of color feature extraction | 942 | grain moisture content and sample data of color feature extraction | ||
943 | results, which have been provided to data users as the basis for | 943 | results, which have been provided to data users as the basis for | ||
944 | training the grain moisture content estimation model.", | 944 | training the grain moisture content estimation model.", | ||
945 | "nchu_texarea_dataset_purpose": | 945 | "nchu_texarea_dataset_purpose": | ||
946 | 0\u6a21\u578b\u70ba\u76ee\u7684\u4e4b\u8cc7\u6599\u3002\r\n\r\nCollect | 946 | 0\u6a21\u578b\u70ba\u76ee\u7684\u4e4b\u8cc7\u6599\u3002\r\n\r\nCollect | ||
947 | data that can be used for the purpose of developing a multi-temporal | 947 | data that can be used for the purpose of developing a multi-temporal | ||
948 | grain moisture content estimation model using mobile devices.", | 948 | grain moisture content estimation model using mobile devices.", | ||
949 | "nchu_texarea_dataset_usage": | 949 | "nchu_texarea_dataset_usage": | ||
950 | u7a3b\u6536\u5272\u63a1\u6536\u7684\u61c9\u7528\u3002\r\n\r\nEstablish | 950 | u7a3b\u6536\u5272\u63a1\u6536\u7684\u61c9\u7528\u3002\r\n\r\nEstablish | ||
951 | models for rice ear segmentation, calibrated color card detection, and | 951 | models for rice ear segmentation, calibrated color card detection, and | ||
952 | grain moisture content estimation to estimate grain moisture content | 952 | grain moisture content estimation to estimate grain moisture content | ||
953 | by taking images of rice ears with mobile devices, providing | 953 | by taking images of rice ears with mobile devices, providing | ||
954 | applications for rice harvesting.", | 954 | applications for rice harvesting.", | ||
955 | "nchu_texarea_dataset_value": | 955 | "nchu_texarea_dataset_value": | ||
956 | \u91cd\u6e2c\u5f97\uff0c\u76f8\u7576\u5f97\u4f86\u4e0d\u6613\r\n\r\nIt | 956 | \u91cd\u6e2c\u5f97\uff0c\u76f8\u7576\u5f97\u4f86\u4e0d\u6613\r\n\r\nIt | ||
957 | includes images taken by mobile devices that were produced over time. | 957 | includes images taken by mobile devices that were produced over time. | ||
958 | Investigators need to take samples outdoors under the sun. In | 958 | Investigators need to take samples outdoors under the sun. In | ||
959 | addition, the moisture content of rice ears and grains obtained | 959 | addition, the moisture content of rice ears and grains obtained | ||
960 | through the primary method must be wetted by precision balances and | 960 | through the primary method must be wetted by precision balances and | ||
961 | oven scales. Measuring the weight and dry weight is quite | 961 | oven scales. Measuring the weight and dry weight is quite | ||
962 | hard-earned.", | 962 | hard-earned.", | ||
963 | "nchu_text_dataset_annotation_format": "csv", | 963 | "nchu_text_dataset_annotation_format": "csv", | ||
964 | "nchu_text_dataset_annotation_method_2_note": "", | 964 | "nchu_text_dataset_annotation_method_2_note": "", | ||
965 | "nchu_text_dataset_annotation_method_3_note": | 965 | "nchu_text_dataset_annotation_method_3_note": | ||
966 | e5\u7a40\u7c92\u542b\u6c34\u91cf\u6578\u503c\u4f5c\u70ba\u6a19\u8a18", | 966 | e5\u7a40\u7c92\u542b\u6c34\u91cf\u6578\u503c\u4f5c\u70ba\u6a19\u8a18", | ||
n | 967 | "nchu_text_dataset_annotation_progress_1_note": "9549\u4f8b", | n | 967 | "nchu_text_dataset_annotation_progress_1_note": "9961\u4f8b", |
968 | "nchu_text_dataset_annotation_progress_2_note": "", | 968 | "nchu_text_dataset_annotation_progress_2_note": "", | ||
969 | "nchu_text_dataset_annotation_progress_3_note": "", | 969 | "nchu_text_dataset_annotation_progress_3_note": "", | ||
n | 970 | "nchu_text_dataset_authorize_file": "", | n | 970 | "nchu_text_dataset_authorize_file": |
971 | 7c92\u542b\u6c34\u91cf\u8cc7\u6599\u96c6_FAIR\u81ea\u8a55\u8868.docx", | ||||
971 | "nchu_text_dataset_capacity": "107GB", | 972 | "nchu_text_dataset_capacity": "108.04GB", | ||
972 | "nchu_text_dataset_casenum": "1", | 973 | "nchu_text_dataset_casenum": "2", | ||
973 | "nchu_text_dataset_contact": "Hank Huang \u9ec3\u701a\u8f1d", | 974 | "nchu_text_dataset_contact": "Hank Huang \u9ec3\u701a\u8f1d", | ||
974 | "nchu_text_dataset_deid_method_2_note": | 975 | "nchu_text_dataset_deid_method_2_note": | ||
975 | 7121\u53bb\u8b58\u5225\u5316\u8655\u7406\u4e4b\u5fc5\u8981\r\n\r\nThis | 976 | 7121\u53bb\u8b58\u5225\u5316\u8655\u7406\u4e4b\u5fc5\u8981\r\n\r\nThis | ||
976 | dataset DOES NOT contain personal sensitive and private information, | 977 | dataset DOES NOT contain personal sensitive and private information, | ||
977 | so there is no need to de-identify it.", | 978 | so there is no need to de-identify it.", | ||
978 | "nchu_text_dataset_format": "png and csv", | 979 | "nchu_text_dataset_format": "png and csv", | ||
n | 979 | "nchu_text_dataset_itemnum": "9549", | n | 980 | "nchu_text_dataset_itemnum": "9961", |
980 | "nchu_text_dataset_legitimate_basis_1_note": "", | 981 | "nchu_text_dataset_legitimate_basis_1_note": "", | ||
981 | "nchu_text_dataset_legitimate_basis_2_note": "", | 982 | "nchu_text_dataset_legitimate_basis_2_note": "", | ||
982 | "nchu_text_dataset_legitimate_basis_3_note": "", | 983 | "nchu_text_dataset_legitimate_basis_3_note": "", | ||
983 | "nchu_text_dataset_name_4code": "rgmc", | 984 | "nchu_text_dataset_name_4code": "rgmc", | ||
984 | "nchu_text_dataset_name_4serial": "data", | 985 | "nchu_text_dataset_name_4serial": "data", | ||
n | 985 | "nchu_text_dataset_name_6serial": "108112", | n | 986 | "nchu_text_dataset_name_6serial": "108113", |
986 | "nchu_text_dataset_name_id": "gmc001", | 987 | "nchu_text_dataset_name_id": "gmc001", | ||
n | 987 | "nchu_text_dataset_name_org_eng_shortname": "07", | n | 988 | "nchu_text_dataset_name_org_eng_shortname": "2023nchuaicontest", |
988 | "nchu_text_dataset_name_project": "smarter", | 989 | "nchu_text_dataset_name_project": "smarter", | ||
989 | "nchu_text_dataset_name_yyyymmdd": "20240321", | 990 | "nchu_text_dataset_name_yyyymmdd": "20240321", | ||
990 | "nchu_text_dataset_other_note": | 991 | "nchu_text_dataset_other_note": | ||
991 | \u65bc\u4e0a\u8ff0\u9069\u6cd5\u6027\u57fa\u790e\u985e\u5225\u3000This | 992 | \u65bc\u4e0a\u8ff0\u9069\u6cd5\u6027\u57fa\u790e\u985e\u5225\u3000This | ||
992 | data set does not contain personal sensitive and privacy information | 993 | data set does not contain personal sensitive and privacy information | ||
993 | and therefore may not apply to the above legal basis categories.", | 994 | and therefore may not apply to the above legal basis categories.", | ||
n | 994 | "nchu_text_dataset_period": "2019-10-14~2023-11-06", | n | 995 | "nchu_text_dataset_period": "2019-10-14~2024-06-06", |
995 | "nchu_text_dataset_remark_method_4_note": "", | 996 | "nchu_text_dataset_remark_method_4_note": "", | ||
996 | "nchu_text_dataset_remark_tool": | 997 | "nchu_text_dataset_remark_tool": | ||
997 | "\u624b\u52d5\u8f38\u5165\u6578\u503c\u3000Manually enter values", | 998 | "\u624b\u52d5\u8f38\u5165\u6578\u503c\u3000Manually enter values", | ||
998 | "nchu_text_dataset_serial": "20240321175117", | 999 | "nchu_text_dataset_serial": "20240321175117", | ||
999 | "nchu_text_dataset_source": "self", | 1000 | "nchu_text_dataset_source": "self", | ||
1000 | "nchu_text_dataset_tag": | 1001 | "nchu_text_dataset_tag": | ||
1001 | 7d1a\u6cd5\u6aa2\u6e2c\u4e4b\u7a40\u7c92\u542b\u6c34\u91cf\u6578\u503c | 1002 | 7d1a\u6cd5\u6aa2\u6e2c\u4e4b\u7a40\u7c92\u542b\u6c34\u91cf\u6578\u503c | ||
1002 | Grain moisture content measured by primary method", | 1003 | Grain moisture content measured by primary method", | ||
1003 | "nchu_text_dataset_type": "RGB images and csv data file", | 1004 | "nchu_text_dataset_type": "RGB images and csv data file", | ||
1004 | "nchu_textarea_dataset_annotation_mapping": "", | 1005 | "nchu_textarea_dataset_annotation_mapping": "", | ||
1005 | "nchu_textarea_dataset_approve_note": | 1006 | "nchu_textarea_dataset_approve_note": | ||
1006 | "\u8cc7\u6599\u5b8c\u6574\uff0c\u5be9\u6838\u901a\u904e | 1007 | "\u8cc7\u6599\u5b8c\u6574\uff0c\u5be9\u6838\u901a\u904e | ||
1007 | 6\u6b04\u4f4d\u539f\u6709\u932f\u5b57\u5df2\u76f4\u63a5\u4fee\u6b63)", | 1008 | 6\u6b04\u4f4d\u539f\u6709\u932f\u5b57\u5df2\u76f4\u63a5\u4fee\u6b63)", | ||
1008 | "notes": "# \u6a94\u6848\u5167\u5bb9\r\n1. 01_OriginImage | 1009 | "notes": "# \u6a94\u6848\u5167\u5bb9\r\n1. 01_OriginImage | ||
1009 | \u539f\u59cb\u7a3b\u7a57\u5f71\u50cf\u5716\u7247 Original images | 1010 | \u539f\u59cb\u7a3b\u7a57\u5f71\u50cf\u5716\u7247 Original images | ||
1010 | \r\n2. 02_PanicleImage | 1011 | \r\n2. 02_PanicleImage | ||
1011 | \u53bb\u80cc\u5f8c\u7684\u7a3b\u7a57\u5f71\u50cf remove background | 1012 | \u53bb\u80cc\u5f8c\u7684\u7a3b\u7a57\u5f71\u50cf remove background | ||
1012 | panicle images \r\n3. 03_ColorChecker | 1013 | panicle images \r\n3. 03_ColorChecker | ||
1013 | \u6821\u6b63\u8272\u5361\u5f71\u50cf(50x50) color checker images | 1014 | \u6821\u6b63\u8272\u5361\u5f71\u50cf(50x50) color checker images | ||
1014 | \r\n4. 1082-1122_GMC_Survey.csv | 1015 | \r\n4. 1082-1122_GMC_Survey.csv | ||
1015 | \u81f32023\u5e74\u7b2c\u4e8c\u671f\u4f5c\u7684\u8abf\u67e5\u8cc7\u6599 | 1016 | \u81f32023\u5e74\u7b2c\u4e8c\u671f\u4f5c\u7684\u8abf\u67e5\u8cc7\u6599 | ||
n | n | 1017 | survey dataset \r\n5. 1131_GMC_Survey.csv | ||
1018 | 2024\u5e74\u7b2c\u4e00\u671f\u4f5c\u7684\u8abf\u67e5\u8cc7\u6599 | ||||
1016 | survey dataset \r\n\r\n# | 1019 | survey dataset \r\n\r\n# | ||
1017 | \u5404\u671f\u4f5c\u8cc7\u6599\u6578\u91cf\r\n1. 2019-2\t,515 \r\n2. | 1020 | \u5404\u671f\u4f5c\u8cc7\u6599\u6578\u91cf\r\n1. 2019-2\t,515 \r\n2. | ||
1018 | 2020-1\t,1593 \r\n3. 2020-2\t,1734 \r\n4. 2021-1\t,2013 \r\n5. | 1021 | 2020-1\t,1593 \r\n3. 2020-2\t,1734 \r\n4. 2021-1\t,2013 \r\n5. | ||
1019 | 2021-2\t,1842 \r\n6. 2022-1\t,718 \r\n7. 2022-2\t,178 \r\n8. | 1022 | 2021-2\t,1842 \r\n6. 2022-1\t,718 \r\n7. 2022-2\t,178 \r\n8. | ||
n | 1020 | 2023-1\t,397 \r\n9. 2023-2\t,559 \r\n10. \u7e3d\u6578\t,9549 | n | 1023 | 2023-1\t,397 \r\n9. 2023-2\t,559 \r\n10. 2024-1 ,412 \r\n11. |
1021 | \r\n\r\n\r\n# \u5716\u7247\u700f\u89bd\r\n## 01_OriginImage: | 1024 | \u7e3d\u6578\t,9961 \r\n\r\n\r\n# \u5716\u7247\u700f\u89bd\r\n## | ||
1025 | 01_OriginImage: | ||||
1022 | u5716](https://uav-fly.nchu.edu.tw/bot/static/dataset_1.png)\r\n\r\n## | 1026 | u5716](https://uav-fly.nchu.edu.tw/bot/static/dataset_1.png)\r\n\r\n## | ||
1023 | 02_PanicleImage: | 1027 | 02_PanicleImage: | ||
1024 | 7406\u5b8c\u6210\u80cc\u666f\u53bb\u9664\u7684\u7a3b\u7a57\u5f71\u50cf | 1028 | 7406\u5b8c\u6210\u80cc\u666f\u53bb\u9664\u7684\u7a3b\u7a57\u5f71\u50cf | ||
1025 | u5716](https://uav-fly.nchu.edu.tw/bot/static/dataset_2.png)\r\n\r\n## | 1029 | u5716](https://uav-fly.nchu.edu.tw/bot/static/dataset_2.png)\r\n\r\n## | ||
1026 | 03_ColorChecker: | 1030 | 03_ColorChecker: | ||
1027 | 6e2c\u7d50\u679c\u622a\u53d6\u7684\u6821\u6b63\u8272\u5361\u5f71\u50cf | 1031 | 6e2c\u7d50\u679c\u622a\u53d6\u7684\u6821\u6b63\u8272\u5361\u5f71\u50cf | ||
1028 | https://uav-fly.nchu.edu.tw/bot/static/dataset_3.png)\r\n\r\n\r\n\r\n# | 1032 | https://uav-fly.nchu.edu.tw/bot/static/dataset_3.png)\r\n\r\n\r\n\r\n# | ||
1029 | 1082-1122_GMC_Survey.csv: \u8cc7\u6599\u6b04\u4f4d\u8aaa\u660e | 1033 | 1082-1122_GMC_Survey.csv: \u8cc7\u6599\u6b04\u4f4d\u8aaa\u660e | ||
1030 | \r\n\r\n## \u6a23\u672c\u5c6c\u6027\r\n1. image_name: | 1034 | \r\n\r\n## \u6a23\u672c\u5c6c\u6027\r\n1. image_name: | ||
1031 | \u5716\u7247\u540d\u7a31 image name \r\n2. Period: | 1035 | \u5716\u7247\u540d\u7a31 image name \r\n2. Period: | ||
1032 | \u671f\u4f5c\u5225(\u6c11\u570b\u5e74) Period (Year of the Republic of | 1036 | \u671f\u4f5c\u5225(\u6c11\u570b\u5e74) Period (Year of the Republic of | ||
1033 | China) \r\n3. Date: \u5f71\u50cf\u62cd\u651d\u65e5\u671f Image taken | 1037 | China) \r\n3. Date: \u5f71\u50cf\u62cd\u651d\u65e5\u671f Image taken | ||
1034 | date \r\n4. Area: \u8fb2\u8a66\u6240\u5167\u6216\u5916 Inside or | 1038 | date \r\n4. Area: \u8fb2\u8a66\u6240\u5167\u6216\u5916 Inside or | ||
1035 | outside the TARI \r\n5. Field: \u7530\u5340\u7de8\u865f Field ID | 1039 | outside the TARI \r\n5. Field: \u7530\u5340\u7de8\u865f Field ID | ||
1036 | \r\n6. Speices: \u6c34\u7a3b\u54c1\u7a2e Rice speices \r\n7. Device: | 1040 | \r\n6. Speices: \u6c34\u7a3b\u54c1\u7a2e Rice speices \r\n7. Device: | ||
1037 | \u624b\u6a5f\u4f5c\u696d\u7cfb\u7d71 OS of mobile \r\n8. HMC: | 1041 | \u624b\u6a5f\u4f5c\u696d\u7cfb\u7d71 OS of mobile \r\n8. HMC: | ||
1038 | \u6536\u7a6b\u542b\u6c34\u91cf\u6578\u503c Harvest moisture content | 1042 | \u6536\u7a6b\u542b\u6c34\u91cf\u6578\u503c Harvest moisture content | ||
1039 | \r\n\r\n## \u984f\u8272\u7279\u5fb5\r\n1. R: \u7d05\u5149\u6ce2\u6bb5 | 1043 | \r\n\r\n## \u984f\u8272\u7279\u5fb5\r\n1. R: \u7d05\u5149\u6ce2\u6bb5 | ||
1040 | Red band \r\n2. G: \u7da0\u5149\u6ce2\u6bb5 Green band \r\n3. B: | 1044 | Red band \r\n2. G: \u7da0\u5149\u6ce2\u6bb5 Green band \r\n3. B: | ||
1041 | \u85cd\u5149\u6ce2\u6bb5 Blue band \r\n4. H1: | 1045 | \u85cd\u5149\u6ce2\u6bb5 Blue band \r\n4. H1: | ||
1042 | HSV\u7684H(\u8272\u8abf) Hue \r\n5. S1: | 1046 | HSV\u7684H(\u8272\u8abf) Hue \r\n5. S1: | ||
1043 | HSV\u7684S(\u98fd\u548c\u5ea6) Saturation \r\n6. V1: | 1047 | HSV\u7684S(\u98fd\u548c\u5ea6) Saturation \r\n6. V1: | ||
1044 | HSV\u7684V(\u660e\u5ea6) Value \r\n7. L2: HLS\u7684L(\u4eae\u5ea6) | 1048 | HSV\u7684V(\u660e\u5ea6) Value \r\n7. L2: HLS\u7684L(\u4eae\u5ea6) | ||
1045 | Lightness \r\n8. S2: HLS\u7684S(\u98fd\u548c\u5ea6) Saturation | 1049 | Lightness \r\n8. S2: HLS\u7684S(\u98fd\u548c\u5ea6) Saturation | ||
1046 | \r\n9. L: Lab\u7684L(\u4eae\u5ea6) Lightness \r\n10. a: | 1050 | \r\n9. L: Lab\u7684L(\u4eae\u5ea6) Lightness \r\n10. a: | ||
1047 | b\u7684a(\u7da0\u8272\u5230\u7d05\u8272\u7684\u8272\u5ea6\u8b8a\u5316) | 1051 | b\u7684a(\u7da0\u8272\u5230\u7d05\u8272\u7684\u8272\u5ea6\u8b8a\u5316) | ||
1048 | color among green to red \r\n11. b: | 1052 | color among green to red \r\n11. b: | ||
1049 | b\u7684b(\u85cd\u8272\u5230\u9ec3\u8272\u7684\u8272\u5ea6\u8b8a\u5316) | 1053 | b\u7684b(\u85cd\u8272\u5230\u9ec3\u8272\u7684\u8272\u5ea6\u8b8a\u5316) | ||
1050 | color among blue to yellow \r\n12. Y: YCrCb\u7684Y(\u4eae\u5ea6) | 1054 | color among blue to yellow \r\n12. Y: YCrCb\u7684Y(\u4eae\u5ea6) | ||
1051 | lightness \r\n13. Cr: YCrCb\u7684Cr(\u7d05\u8272\u5206\u91cf) color | 1055 | lightness \r\n13. Cr: YCrCb\u7684Cr(\u7d05\u8272\u5206\u91cf) color | ||
1052 | values represent the difference among red and lightness \r\n14. Cb: | 1056 | values represent the difference among red and lightness \r\n14. Cb: | ||
1053 | YCrCb\u7684Cb(\u85cd\u8272\u5206\u91cf) color values represent the | 1057 | YCrCb\u7684Cb(\u85cd\u8272\u5206\u91cf) color values represent the | ||
1054 | difference among blue and lightness \r\n15. NDI: | 1058 | difference among blue and lightness \r\n15. NDI: | ||
1055 | \u6b78\u4e00\u5316\u5dee\u7570\u6307\u6578 normalized difference | 1059 | \u6b78\u4e00\u5316\u5dee\u7570\u6307\u6578 normalized difference | ||
1056 | index \r\n16. GI: \u7da0\u8272\u6307\u6578 green index \r\n17. | 1060 | index \r\n16. GI: \u7da0\u8272\u6307\u6578 green index \r\n17. | ||
1057 | RGRI: \u7d05\u7da0\u6ce2\u6bd4\u4f8b\u6307\u6578 red-green ratio | 1061 | RGRI: \u7d05\u7da0\u6ce2\u6bd4\u4f8b\u6307\u6578 red-green ratio | ||
1058 | index \r\n\r\n## | 1062 | index \r\n\r\n## | ||
1059 | 9670\u5f71\u7b49\u524d\u8655\u7406\u4e4b\u5f8c\u7684\u7279\u5fb5\r\n1. | 1063 | 9670\u5f71\u7b49\u524d\u8655\u7406\u4e4b\u5f8c\u7684\u7279\u5fb5\r\n1. | ||
1060 | RC: \u6821\u6b63\u5f8c\u7684R R values after correction \r\n2. GC: | 1064 | RC: \u6821\u6b63\u5f8c\u7684R R values after correction \r\n2. GC: | ||
1061 | \u6821\u6b63\u5f8c\u7684G G values after correction \r\n3. BC: | 1065 | \u6821\u6b63\u5f8c\u7684G G values after correction \r\n3. BC: | ||
1062 | \u6821\u6b63\u5f8c\u7684B B values after correction \r\n4. H1C: | 1066 | \u6821\u6b63\u5f8c\u7684B B values after correction \r\n4. H1C: | ||
1063 | \u6821\u6b63\u5f8c\u7684H1 H1 values after correction \r\n5. S1C: | 1067 | \u6821\u6b63\u5f8c\u7684H1 H1 values after correction \r\n5. S1C: | ||
1064 | \u6821\u6b63\u5f8c\u7684S1 S1 values after correction \r\n6. V1C: | 1068 | \u6821\u6b63\u5f8c\u7684S1 S1 values after correction \r\n6. V1C: | ||
1065 | \u6821\u6b63\u5f8c\u7684V1 V1 values after correction \r\n7. L2C: | 1069 | \u6821\u6b63\u5f8c\u7684V1 V1 values after correction \r\n7. L2C: | ||
1066 | \u6821\u6b63\u5f8c\u7684L2 L2 values after correction \r\n8. S2C: | 1070 | \u6821\u6b63\u5f8c\u7684L2 L2 values after correction \r\n8. S2C: | ||
1067 | \u6821\u6b63\u5f8c\u7684S2 S2 values after correction \r\n9. LC: | 1071 | \u6821\u6b63\u5f8c\u7684S2 S2 values after correction \r\n9. LC: | ||
1068 | \u6821\u6b63\u5f8c\u7684L L values after correction \r\n10. aC: | 1072 | \u6821\u6b63\u5f8c\u7684L L values after correction \r\n10. aC: | ||
1069 | \u6821\u6b63\u5f8c\u7684a a values after correction \r\n11. bC: | 1073 | \u6821\u6b63\u5f8c\u7684a a values after correction \r\n11. bC: | ||
1070 | \u6821\u6b63\u5f8c\u7684b b values after correction \r\n12. YC: | 1074 | \u6821\u6b63\u5f8c\u7684b b values after correction \r\n12. YC: | ||
1071 | \u6821\u6b63\u5f8c\u7684Y Y values after correction \r\n13. CrC: | 1075 | \u6821\u6b63\u5f8c\u7684Y Y values after correction \r\n13. CrC: | ||
1072 | \u6821\u6b63\u5f8c\u7684Cr Cr values after correction \r\n14. CbC: | 1076 | \u6821\u6b63\u5f8c\u7684Cr Cr values after correction \r\n14. CbC: | ||
1073 | \u6821\u6b63\u5f8c\u7684Cb Cb values after correction \r\n15. NDIC: | 1077 | \u6821\u6b63\u5f8c\u7684Cb Cb values after correction \r\n15. NDIC: | ||
1074 | \u6821\u6b63\u5f8c\u7684NDI NDI values after correction \r\n16. GIC: | 1078 | \u6821\u6b63\u5f8c\u7684NDI NDI values after correction \r\n16. GIC: | ||
1075 | \u6821\u6b63\u5f8c\u7684GI GI values after correction \r\n17. RGRIC: | 1079 | \u6821\u6b63\u5f8c\u7684GI GI values after correction \r\n17. RGRIC: | ||
1076 | \u6821\u6b63\u5f8c\u7684RGRI RGRI values after correction \r\n\r\n## | 1080 | \u6821\u6b63\u5f8c\u7684RGRI RGRI values after correction \r\n\r\n## | ||
1077 | \u6821\u6b63\u8272\u5361\u984f\u8272\u7279\u5fb5\r\n1. B_RGB_avg: | 1081 | \u6821\u6b63\u8272\u5361\u984f\u8272\u7279\u5fb5\r\n1. B_RGB_avg: | ||
1078 | \u9ed1\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | 1082 | \u9ed1\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | ||
1079 | mean of black color checker \r\n2. G_RGB_avg: | 1083 | mean of black color checker \r\n2. G_RGB_avg: | ||
1080 | \u7070\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | 1084 | \u7070\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | ||
1081 | mean of gray color checker \r\n3. W_RGB_avg: | 1085 | mean of gray color checker \r\n3. W_RGB_avg: | ||
1082 | \u767d\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | 1086 | \u767d\u8272\u6821\u6b63\u8272\u5361\u7684\u5e73\u5747\u6578\u503c | ||
1083 | mean of white color checker \r\n4. B_RGB_R: | 1087 | mean of white color checker \r\n4. B_RGB_R: | ||
1084 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1088 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1085 | mean of red band in black color checker \r\n5. B_RGB_G: | 1089 | mean of red band in black color checker \r\n5. B_RGB_G: | ||
1086 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1090 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1087 | mean of green band in black color checker \r\n6. B_RGB_B: | 1091 | mean of green band in black color checker \r\n6. B_RGB_B: | ||
1088 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1092 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1089 | mean of blue band in black color checker \r\n7. G_RGB_R: | 1093 | mean of blue band in black color checker \r\n7. G_RGB_R: | ||
1090 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1094 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1091 | mean of red band in gray color checker \r\n8. G_RGB_G: | 1095 | mean of red band in gray color checker \r\n8. G_RGB_G: | ||
1092 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1096 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1093 | mean of green band in gray color checker \r\n9. G_RGB_B: | 1097 | mean of green band in gray color checker \r\n9. G_RGB_B: | ||
1094 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1098 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1095 | mean of blue band in gray color checker \r\n10. W_RGB_R: | 1099 | mean of blue band in gray color checker \r\n10. W_RGB_R: | ||
1096 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1100 | 6821\u6b63\u8272\u5361\u7684\u7d05\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1097 | mean of red band in white color checker \r\n11. W_RGB_G: | 1101 | mean of red band in white color checker \r\n11. W_RGB_G: | ||
1098 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1102 | 6821\u6b63\u8272\u5361\u7684\u7da0\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1099 | mean of green band in white color checker \r\n12. W_RGB_B: | 1103 | mean of green band in white color checker \r\n12. W_RGB_B: | ||
1100 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | 1104 | 6821\u6b63\u8272\u5361\u7684\u85cd\u8272\u6ce2\u6bb5\u5e73\u5747\u503c | ||
1101 | mean of blue band in white color checker \r\n\r\n\r\n# | 1105 | mean of blue band in white color checker \r\n\r\n\r\n# | ||
1102 | \u53c3\u8003\u6587\u737b\r\n1. [Assessment of grain harvest moisture | 1106 | \u53c3\u8003\u6587\u737b\r\n1. [Assessment of grain harvest moisture | ||
1103 | content using machine learning on smartphone images for optimal | 1107 | content using machine learning on smartphone images for optimal | ||
1104 | harvest timing](https://www.mdpi.com/1424-8220/21/17/5875) \r\nYang, | 1108 | harvest timing](https://www.mdpi.com/1424-8220/21/17/5875) \r\nYang, | ||
1105 | M. D., Hsu, Y. C., Tseng, W. C., Lu, C. Y., Yang, C. Y., Lai, M. H., & | 1109 | M. D., Hsu, Y. C., Tseng, W. C., Lu, C. Y., Yang, C. Y., Lai, M. H., & | ||
1106 | Wu, D. H. (2021). Assessment of grain harvest moisture content using | 1110 | Wu, D. H. (2021). Assessment of grain harvest moisture content using | ||
1107 | machine learning on smartphone images for optimal harvest timing. | 1111 | machine learning on smartphone images for optimal harvest timing. | ||
1108 | Sensors, 21(17), 5875. \r\n", | 1112 | Sensors, 21(17), 5875. \r\n", | ||
n | 1109 | "num_resources": 1, | n | 1113 | "num_resources": 2, |
1110 | "num_tags": 7, | 1114 | "num_tags": 7, | ||
1111 | "organization": { | 1115 | "organization": { | ||
1112 | "approval_status": "approved", | 1116 | "approval_status": "approved", | ||
1113 | "created": "2022-05-19T11:34:42.143017", | 1117 | "created": "2022-05-19T11:34:42.143017", | ||
1114 | "description": "## \u8a08\u756b\u4ecb\u7d39 | 1118 | "description": "## \u8a08\u756b\u4ecb\u7d39 | ||
1115 | 08\u756b\u8a2d\u8a08\u9006\u5883\u5834\u57df\u7814\u767c\u9059\u6e2cAI | 1119 | 08\u756b\u8a2d\u8a08\u9006\u5883\u5834\u57df\u7814\u767c\u9059\u6e2cAI | ||
1116 | 5\u81f3\u751f\u6b96\u751f\u9577\u968e\u6bb5\uff0c\u7d50\u5408UAV\u8207 | 1120 | 5\u81f3\u751f\u6b96\u751f\u9577\u968e\u6bb5\uff0c\u7d50\u5408UAV\u8207 | ||
1117 | IoT \u8a2d\u5099\u5c0e\u5165\u5f71\u50cf\u5206\u6790\u8207 AI | 1121 | IoT \u8a2d\u5099\u5c0e\u5165\u5f71\u50cf\u5206\u6790\u8207 AI | ||
1118 | 8853\u843d\u5730\u8207\u7522\u80fd\u63d0\u5347\u4e4b\u6548\u76ca\u3002 | 1122 | 8853\u843d\u5730\u8207\u7522\u80fd\u63d0\u5347\u4e4b\u6548\u76ca\u3002 | ||
1119 | \r\n\r\n### \u6548\u76ca \r\n 1. | 1123 | \r\n\r\n### \u6548\u76ca \r\n 1. | ||
1120 | 70ba\u91cd\u8981\u8fb2\u696d\u7814\u7a76\u57fa\u790e\u5efa\u8a2d\u3002 | 1124 | 70ba\u91cd\u8981\u8fb2\u696d\u7814\u7a76\u57fa\u790e\u5efa\u8a2d\u3002 | ||
1121 | \r\n 2. | 1125 | \r\n 2. | ||
1122 | 9810\u6e2c\u8207\u9006\u5883\u98a8\u96aa\u9810\u8b66\u6a21\u5f0f\u3002 | 1126 | 9810\u6e2c\u8207\u9006\u5883\u98a8\u96aa\u9810\u8b66\u6a21\u5f0f\u3002 | ||
1123 | \r\n 3. | 1127 | \r\n 3. | ||
1124 | 4e4b\u9059\u6e2c\u8f14\u52a9\u8cc7\u6e90\u7ba1\u7406\u6a5f\u5236\u3002 | 1128 | 4e4b\u9059\u6e2c\u8f14\u52a9\u8cc7\u6e90\u7ba1\u7406\u6a5f\u5236\u3002 | ||
1125 | ", | 1129 | ", | ||
1126 | "id": "896d404d-e1d3-4ad9-900d-750d3747c119", | 1130 | "id": "896d404d-e1d3-4ad9-900d-750d3747c119", | ||
1127 | "image_url": "", | 1131 | "image_url": "", | ||
1128 | "is_organization": true, | 1132 | "is_organization": true, | ||
1129 | "name": "hort", | 1133 | "name": "hort", | ||
1130 | "state": "active", | 1134 | "state": "active", | ||
1131 | "title": "Crop digital twin for growth adversity index and | 1135 | "title": "Crop digital twin for growth adversity index and | ||
1132 | disaster damage assessment | 1136 | disaster damage assessment | ||
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1134 | "type": "organization" | 1138 | "type": "organization" | ||
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1149 | \u53bb\u80cc\u5f8c\u7684\u7a3b\u7a57\u5f71\u50cf remove background | 1153 | \u53bb\u80cc\u5f8c\u7684\u7a3b\u7a57\u5f71\u50cf remove background | ||
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1152 | \r\n4. 1082-1122_GMC_Survey.csv | 1156 | \r\n4. 1082-1122_GMC_Survey.csv | ||
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t | t | 1178 | }, | ||
1179 | { | ||||
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1182 | "created": "2024-08-22T04:43:38.563551", | ||||
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1192 | survey dataset\r\n", | ||||
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1174 | } | 1212 | } | ||
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1176 | "state": "active", | 1214 | "state": "active", | ||
1177 | "tags": [ | 1215 | "tags": [ | ||
1178 | { | 1216 | { | ||
1179 | "display_name": "grain moisture content", | 1217 | "display_name": "grain moisture content", | ||
1180 | "id": "8fb06d02-3942-4248-aae7-cb040088afa1", | 1218 | "id": "8fb06d02-3942-4248-aae7-cb040088afa1", | ||
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1183 | "vocabulary_id": null | 1221 | "vocabulary_id": null | ||
1184 | }, | 1222 | }, | ||
1185 | { | 1223 | { | ||
1186 | "display_name": "multiple periods", | 1224 | "display_name": "multiple periods", | ||
1187 | "id": "369b0e90-2556-4b79-b31e-c761218596ec", | 1225 | "id": "369b0e90-2556-4b79-b31e-c761218596ec", | ||
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1190 | "vocabulary_id": null | 1228 | "vocabulary_id": null | ||
1191 | }, | 1229 | }, | ||
1192 | { | 1230 | { | ||
1193 | "display_name": "panicle", | 1231 | "display_name": "panicle", | ||
1194 | "id": "fcd816ec-47cc-4416-8656-12df83a5fa02", | 1232 | "id": "fcd816ec-47cc-4416-8656-12df83a5fa02", | ||
1195 | "name": "panicle", | 1233 | "name": "panicle", | ||
1196 | "state": "active", | 1234 | "state": "active", | ||
1197 | "vocabulary_id": null | 1235 | "vocabulary_id": null | ||
1198 | }, | 1236 | }, | ||
1199 | { | 1237 | { | ||
1200 | "display_name": "rice", | 1238 | "display_name": "rice", | ||
1201 | "id": "6d9fd46b-45c0-4296-90a0-20c2e139e18b", | 1239 | "id": "6d9fd46b-45c0-4296-90a0-20c2e139e18b", | ||
1202 | "name": "rice", | 1240 | "name": "rice", | ||
1203 | "state": "active", | 1241 | "state": "active", | ||
1204 | "vocabulary_id": null | 1242 | "vocabulary_id": null | ||
1205 | } | 1243 | } | ||
1206 | ], | 1244 | ], | ||
1207 | "title": "Rice panicle images and moisture content dataset | 1245 | "title": "Rice panicle images and moisture content dataset | ||
1208 | 57\u5f71\u50cf\u8207\u7a40\u7c92\u542b\u6c34\u91cf\u8cc7\u6599\u96c6", | 1246 | 57\u5f71\u50cf\u8207\u7a40\u7c92\u542b\u6c34\u91cf\u8cc7\u6599\u96c6", | ||
1209 | "toolinfos": "[]", | 1247 | "toolinfos": "[]", | ||
1210 | "type": "dataset", | 1248 | "type": "dataset", | ||
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1212 | "version": null | 1250 | "version": null | ||
1213 | } | 1251 | } |