Как получить количество цитирований за год или данные для экспорта из Google Scholar?

Мне кажется, что «Полностью сверточные сети для семантической сегментации» ( http://arxiv.org/abs/1411.4038v2 ) имеют довольно большое влияние. Однако я хотел бы подтвердить это ощущение данными.

В Google Scholar я вижу, что статью цитировали 116 публикаций. Тем не менее, я хотел бы построить кривую, показывающую, когда она была процитирована. Поэтому я хотел бы экспортировать эти данные в CSV со столбцом «дата» и столбцом «название». (Другие столбцы, такие как авторы, как часто цитируется другая статья, журнал, ... тоже было бы неплохо).

Есть ли другие способы быстро проанализировать влияние статьи?

Ссылка на гугл ученого не работает
В этом конкретном случае имейте в виду, что большинство библиографических метаданных не предоставляют надежной информации на более конкретной основе, чем год публикации, поэтому вы вряд ли получите очень полезную диаграмму «когда она была процитирована» для статьи, опубликованной на конец 2014...
@EnergyNumbers Интересно, спасибо за ссылку. Почему вы не связали статью Манипулирование Google Scholar Citations и Google Scholar Metrics: просто, легко и заманчиво, на которой, кажется, основана статья?

Ответы (2)

Вы имеете в виду этот тип сюжета, который можно найти на странице автора, содержащей эту статью (в данном случае Эвана Шелхамера ), нажав на заголовок:

введите описание изображения здесь

На странице ученого Google этой статьи вы также можете навести курсор на столбцы, чтобы увидеть, например, что в статье 109 ссылок с 2015 года. Я не знаю, как напрямую экспортировать данные из ученого Google. Вы, вероятно, могли бы очистить данные html или вручную, используя « пользовательский диапазон » , чтобы получить количество цитирований за каждый год.

Другим вариантом является использование программного обеспечения Publish or Perish (которое работает как внешний интерфейс для Google Scholar), которое позволяет вам использовать функцию « Поиск цитат », чтобы получить экспортируемый список всех цитирующих статей, найденных через Google Scholar. Этот список будет содержать год, название, авторов, число цитирований и т. д. для всех цитирующих статей.

Ниже приводится выдержка из данных для этой конкретной статьи. В этом конкретном случае (поскольку большинство ссылок исходит из arXiv) вы действительно можете получить лучшее временное разрешение цитат после некоторой постобработки экспортированных данных, поскольку они включают идентификатор arXiv, который содержит месяц публикации (публикация в Архив). Этот график выглядит следующим образом для 77 статей arXiv со ссылкой на « Полностью сверточные сети для семантической сегментации »:

введите описание изображения здесь

Выдержка из данных:

427,"K Simonyan, A Zisserman","Very deep convolutional networks for large-scale image recognition",2014,"arXiv preprint arXiv:1409.1556","arxiv.org","http://arxiv.org/abs/1409.1556","http://scholar.google.com/scholar?cites=15993525775437884507&as_sdt=2005&sciodt=0,5&hl=en&num=20",1,2015-11-18,""
51,"LC Chen, G Papandreou, I Kokkinos, K Murphy…","Semantic image segmentation with deep convolutional nets and fully connected crfs",2014,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1412.7062","http://scholar.google.com/scholar?cites=12556287530133233148&as_sdt=2005&sciodt=0,5&hl=en&num=20",2,2015-11-18,""
31,"B Hariharan, P Arbeláez, R Girshick, J Malik","Hypercolumns for object segmentation and fine-grained localization",2014,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1411.5752","http://scholar.google.com/scholar?cites=338188405356970854&as_sdt=2005&sciodt=0,5&hl=en&num=20",3,2015-11-18,""
24,"S Zheng, S Jayasumana, B Romera-Paredes…","Conditional random fields as recurrent neural networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1502.03240","http://scholar.google.com/scholar?cites=4680896688857314530&as_sdt=2005&sciodt=0,5&hl=en&num=20",12,2015-11-18,""
20,"J Dai, K He, J Sun","Convolutional feature masking for joint object and stuff segmentation",2014,"arXiv preprint arXiv:1412.1283","arxiv.org","http://arxiv.org/abs/1412.1283","http://scholar.google.com/scholar?cites=3867986733742388443&as_sdt=2005&sciodt=0,5&hl=en&num=20",4,2015-11-18,""
18,"G Papandreou, LC Chen, K Murphy…","Weakly-and semi-supervised learning of a DCNN for semantic image segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1502.02734","http://scholar.google.com/scholar?cites=12298732919189295864&as_sdt=2005&sciodt=0,5&hl=en&num=20",6,2015-11-18,""
14,"J Dai, K He, J Sun","Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation",2015,"arXiv preprint arXiv:1503.01640","arxiv.org","http://arxiv.org/abs/1503.01640","http://scholar.google.com/scholar?cites=10583411756105923851&as_sdt=2005&sciodt=0,5&hl=en&num=20",9,2015-11-18,""
13,"AG Schwing, R Urtasun","Fully connected deep structured networks",2015,"arXiv preprint arXiv:1503.02351","arxiv.org","http://arxiv.org/abs/1503.02351","http://scholar.google.com/scholar?cites=9137941562147447673&as_sdt=2005&sciodt=0,5&hl=en&num=20",10,2015-11-18,""
13,"G Lin, C Shen, I Reid","Efficient piecewise training of deep structured models for semantic segmentation",2015,"arXiv preprint arXiv:1504.01013","arxiv.org","http://arxiv.org/abs/1504.01013","http://scholar.google.com/scholar?cites=1420854562551446027&as_sdt=2005&sciodt=0,5&hl=en&num=20",25,2015-11-18,""
12,"S Ren, K He, R Girshick, J Sun","Faster r-cnn: Towards real-time object detection with region proposal networks",2015,"arXiv preprint arXiv:1506.01497","arxiv.org","http://arxiv.org/abs/1506.01497","http://scholar.google.com/scholar?cites=16436232259506318906&as_sdt=2005&sciodt=0,5&hl=en&num=20",5,2015-11-18,""
10,"G Bertasius, J Shi, L Torresani","DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection",2014,"arXiv preprint arXiv:1412.1123","arxiv.org","http://arxiv.org/abs/1412.1123","http://scholar.google.com/scholar?cites=2089551699301366907&as_sdt=2005&sciodt=0,5&hl=en&num=20",8,2015-11-18,""
9,"D Pathak, E Shelhamer, J Long, T Darrell","Fully convolutional multi-class multiple instance learning",2014,"arXiv preprint arXiv:1412.7144","arxiv.org","http://arxiv.org/abs/1412.7144","http://scholar.google.com/scholar?cites=6242051221514792488&as_sdt=2005&sciodt=0,5&hl=en&num=20",11,2015-11-18,""
7,"H Noh, S Hong, B Han","Learning Deconvolution Network for Semantic Segmentation",2015,"arXiv preprint arXiv:1505.04366","arxiv.org","http://arxiv.org/abs/1505.04366","http://scholar.google.com/scholar?cites=4896002303003783815&as_sdt=2005&sciodt=0,5&hl=en&num=20",80,2015-11-18,""
5,"M Jaderberg, K Simonyan, A Zisserman…","Spatial transformer networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.02025","http://scholar.google.com/scholar?cites=1662293494062093494&as_sdt=2005&sciodt=0,5&hl=en&num=20",7,2015-11-18,""
5,"R Girshick, J Donahue, T Darrell, J Malik","Region-based Convolutional Networks for Accurate Object Detection and Segmentation",0,"ieeexplore.ieee.org","","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7112511","http://scholar.google.com/scholar?cites=2674763949973029385&as_sdt=2005&sciodt=0,5&hl=en&num=20",42,2015-11-18,""
4,"S Ren, K He, R Girshick, X Zhang, J Sun","Object Detection Networks on Convolutional Feature Maps",2015,"arXiv preprint arXiv:1504.06066","arxiv.org","http://arxiv.org/abs/1504.06066","http://scholar.google.com/scholar?cites=8299550676813721451&as_sdt=2005&sciodt=0,5&hl=en&num=20",21,2015-11-18,""
4,"S Xie, Z Tu","Holistically-Nested Edge Detection",2015,"arXiv preprint arXiv:1504.06375","arxiv.org","http://arxiv.org/abs/1504.06375","http://scholar.google.com/scholar?cites=18154299256265143241&as_sdt=2005&sciodt=0,5&hl=en&num=20",112,2015-11-18,""
3,"W Liu, A Rabinovich, AC Berg","Parsenet: Looking wider to see better",2015,"arXiv preprint arXiv:1506.04579","arxiv.org","http://arxiv.org/abs/1506.04579","http://scholar.google.com/scholar?cites=11105541992267753132&as_sdt=2005&sciodt=0,5&hl=en&num=20",13,2015-11-18,""
3,"A Dosovitskiy, T Brox","Inverting convolutional networks with convolutional networks",2015,"arXiv preprint arXiv:1506.02753","arxiv.org","http://arxiv.org/abs/1506.02753","http://scholar.google.com/scholar?cites=3843085858101673825&as_sdt=2005&sciodt=0,5&hl=en&num=20",14,2015-11-18,""
2,"S Hong, H Noh, B Han","Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation",2015,"arXiv preprint arXiv:1506.04924","arxiv.org","http://arxiv.org/abs/1506.04924","http://scholar.google.com/scholar?cites=15385340253531275638&as_sdt=2005&sciodt=0,5&hl=en&num=20",15,2015-11-18,""
2,"O Russakovsky, AL Bearman, V Ferrari…","What's the point: Semantic segmentation with point supervision",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.02106","http://scholar.google.com/scholar?cites=14456480836534501375&as_sdt=2005&sciodt=0,5&hl=en&num=20",16,2015-11-18,""
2,"Z Xie, K Xu, W Shan, L Liu, Y Xiong…","Projective Feature Learning for 3D Shapes with Multi-View Depth Images",2015,"Computer Graphics …","Wiley Online Library","http://onlinelibrary.wiley.com/doi/10.1111/cgf.12740/full","http://scholar.google.com/scholar?cites=16653555319690091022&as_sdt=2005&sciodt=0,5&hl=en&num=20",17,2015-11-18,""
2,"P Wang, X Shen, Z Lin, S Cohen, B Price…","Joint Object and Part Segmentation using Deep Learned Potentials",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.00276","http://scholar.google.com/scholar?cites=4342156029683513177&as_sdt=2005&sciodt=0,5&hl=en&num=20",48,2015-11-18,""
2,"B Yang, J Yan, Z Lei, SZ Li","Convolutional Channel Features For Pedestrian, Face and Edge Detection",2015,"arXiv preprint arXiv:1504.07339","arxiv.org","http://arxiv.org/abs/1504.07339","http://scholar.google.com/scholar?cites=6994455475312011326&as_sdt=2005&sciodt=0,5&hl=en&num=20",59,2015-11-18,""
2,"S Gidaris, N Komodakis","Object detection via a multi-region & semantic segmentation-aware CNN model",2015,"arXiv preprint arXiv:1505.01749","arxiv.org","http://arxiv.org/abs/1505.01749","http://scholar.google.com/scholar?cites=17076919334968493616&as_sdt=2005&sciodt=0,5&hl=en&num=20",65,2015-11-18,""
2,"G Papandreou, I Kokkinos…","Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection",2015,"Proceedings of the IEEE …","cv-foundation.org","","http://scholar.google.com/scholar?cites=372687354279680428&as_sdt=2005&sciodt=0,5&hl=en&num=20",84,2015-11-18,"PDF"
2,"X Zhang, J Zou, K He, J Sun","Accelerating Very Deep Convolutional Networks for Classification and Detection",2015,"arXiv preprint arXiv:1505.06798","arxiv.org","http://arxiv.org/abs/1505.06798","http://scholar.google.com/scholar?cites=11183077033015235296&as_sdt=2005&sciodt=0,5&hl=en&num=20",89,2015-11-18,""
1,"A Khosla, AS Raju, A Torralba, A Oliva","Understanding and predicting image memorability at a large scale",2015,"","people.csail.mit.edu","","http://scholar.google.com/scholar?cites=4151583339195604249&as_sdt=2005&sciodt=0,5&hl=en&num=20",18,2015-11-18,"PDF"
1,"B Shuai, Z Zuo, W Gang","Quaddirectional 2D-Recurrent Neural Networks For Image Labeling",2015,"","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7118156","http://scholar.google.com/scholar?cites=13720065868238901658&as_sdt=2005&sciodt=0,5&hl=en&num=20",19,2015-11-18,""
1,"Z Liang, S Ding, L Lin","Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks",2015,"arXiv preprint arXiv:1507.03409","arxiv.org","http://arxiv.org/abs/1507.03409","http://scholar.google.com/scholar?cites=12791133750001877582&as_sdt=2005&sciodt=0,5&hl=en&num=20",20,2015-11-18,""
1,"D Pathak, P Krähenbühl, T Darrell","Constrained Convolutional Neural Networks for Weakly Supervised Segmentation",2015,"arXiv preprint arXiv:1506.03648","arxiv.org","http://arxiv.org/abs/1506.03648","http://scholar.google.com/scholar?cites=18113115400192563138&as_sdt=2005&sciodt=0,5&hl=en&num=20",22,2015-11-18,""
1,"C Ionescu, O Vantzos, C Sminchisescu","Matrix Backpropagation for Deep Networks with Structured Layers",2015,"","maths.lth.se","","http://scholar.google.com/scholar?cites=17387807402435828231&as_sdt=2005&sciodt=0,5&hl=en&num=20",23,2015-11-18,"PDF"
1,"V Badrinarayanan, A Kendall, R Cipolla","SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation",2015,"arXiv preprint arXiv:1511.00561","arxiv.org","http://arxiv.org/abs/1511.00561","http://scholar.google.com/scholar?cites=18037094217443794526&as_sdt=2005&sciodt=0,5&hl=en&num=20",24,2015-11-18,""
1,"K Lee, A Zlateski, A Vishwanathan…","Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1508.04843","http://scholar.google.com/scholar?cites=17230449095463437923&as_sdt=2005&sciodt=0,5&hl=en&num=20",26,2015-11-18,""
1,"B Pepik, R Benenson, T Ritschel, B Schiele","What Is Holding Back Convnets for Detection?",2015,"Pattern Recognition","Springer","http://link.springer.com/chapter/10.1007/978-3-319-24947-6_43","http://scholar.google.com/scholar?cites=1468500825478747183&as_sdt=2005&sciodt=0,5&hl=en&num=20",27,2015-11-18,""
1,"C Ionescu, O Vantzos, C Sminchisescu","Training Deep Networks with Structured Layers by Matrix Backpropagation",2015,"arXiv preprint arXiv:1509.07838","arxiv.org","http://arxiv.org/abs/1509.07838","http://scholar.google.com/scholar?cites=8704018611282114837&as_sdt=2005&sciodt=0,5&hl=en&num=20",28,2015-11-18,""
1,"Z Liu, X Li, P Luo, CC Loy, X Tang","Semantic Image Segmentation via Deep Parsing Network",2015,"arXiv preprint arXiv:1509.02634","arxiv.org","http://arxiv.org/abs/1509.02634","http://scholar.google.com/scholar?cites=18281955767933637624&as_sdt=2005&sciodt=0,5&hl=en&num=20",29,2015-11-18,""
1,"X Gibert, VM Patel, R Chellappa","Deep Multi-task Learning for Railway Track Inspection",2015,"arXiv preprint arXiv:1509.05267","arxiv.org","http://arxiv.org/abs/1509.05267","http://scholar.google.com/scholar?cites=16444267879523298138&as_sdt=2005&sciodt=0,5&hl=en&num=20",30,2015-11-18,""
1,"X Gibert, VM Patel, R Chellappa","Material classification and semantic segmentation of railway track images with deep convolutional neural networks,”",2015,"IEEE International Conference …","researchgate.net","","http://scholar.google.com/scholar?cites=13942078593779597868&as_sdt=2005&sciodt=0,5&hl=en&num=20",31,2015-11-18,"PDF"
1,"H Xu, S Venugopalan, V Ramanishka…","A Multi-scale Multiple Instance Video Description Network",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.05914","http://scholar.google.com/scholar?cites=1990066366497434516&as_sdt=2005&sciodt=0,5&hl=en&num=20",33,2015-11-18,""
1,"F Liu, C Shen, G Lin, I Reid","Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields",2015,"arXiv preprint arXiv:1502.07411","arxiv.org","http://arxiv.org/abs/1502.07411","http://scholar.google.com/scholar?cites=2562418167496300062&as_sdt=2005&sciodt=0,5&hl=en&num=20",52,2015-11-18,""
1,"PD Vo, A Ginsca, H Le Borgne, A Popescu","Effective Training of Convolutional Networks using Noisy Web Images",0,"comupedia.org","","","http://scholar.google.com/scholar?cites=12447971813084759439&as_sdt=2005&sciodt=0,5&hl=en&num=20",70,2015-11-18,"PDF"
1,"Z Zhang, AG Schwing, S Fidler, R Urtasun","Monocular Object Instance Segmentation and Depth Ordering with CNNs",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.03159","http://scholar.google.com/scholar?cites=7431213548054053779&as_sdt=2005&sciodt=0,5&hl=en&num=20",78,2015-11-18,""
1,"S Tsogkas, I Kokkinos, G Papandreou…","Semantic Part Segmentation with Deep Learning",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.02438","http://scholar.google.com/scholar?cites=16300824466121812385&as_sdt=2005&sciodt=0,5&hl=en&num=20",79,2015-11-18,""
1,"G Bertasius, J Shi, L Torresani","High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision",2015,"arXiv preprint arXiv:1504.06201","arxiv.org","http://arxiv.org/abs/1504.06201","http://scholar.google.com/scholar?cites=6429592123688911770&as_sdt=2005&sciodt=0,5&hl=en&num=20",86,2015-11-18,""
1,"M Havaei, A Davy, D Warde-Farley, A Biard…","Brain Tumor Segmentation with Deep Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.03540","http://scholar.google.com/scholar?cites=4159936825454045654&as_sdt=2005&sciodt=0,5&hl=en&num=20",91,2015-11-18,""
1,"P Fischer, A Dosovitskiy, E Ilg, P Häusser…","FlowNet: Learning Optical Flow with Convolutional Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1504.06852","http://scholar.google.com/scholar?cites=4399198863370102461&as_sdt=2005&sciodt=0,5&hl=en&num=20",111,2015-11-18,""
0,"CA Brust, S Sickert, M Simon, E Rodner, J Denzler","Efficient Convolutional Patch Networks for Scene Understanding",0,"hera.inf-cv.uni-jena.de","","","http://scholar.google.com/scholar?q=related:G0POBdhSJIsJ:scholar.google.com/&hl=en&num=20&as_sdt=0,5&sciodt=0,5",32,2015-11-18,"PDF"
0,"J Vašícek, M Hradiš, F Radenovic, O Chum","Camera Elevation Estimation from a Single Mountain Landscape Photograph",0,"cmp.felk.cvut.cz","","","",34,2015-11-18,"PDF"
0,"A Dubrovina, P Kisilev, B Ginsburg, S Hashoul…","Computational Mammography using Deep Neural Networks",0,"cs.technion.ac.il","","","",35,2015-11-18,"PDF"
0,"DL Richmond, D Kainmueller, MY Yang…","Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1507.07583","",36,2015-11-18,""
0,"X Liang, Y Wei, X Shen, J Yang, L Lin, S Yan","Proposal-free Network for Instance-level Object Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1509.02636","",37,2015-11-18,""
0,"M Xie, N Jean, M Burke, D Lobell, S Ermon","Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1510.00098","",38,2015-11-18,""
0,"S Gupta, J Hoffman, J Malik","Cross Modal Distillation for Supervision Transfer",2015,"arXiv preprint arXiv:1507.00448","arxiv.org","http://arxiv.org/abs/1507.00448","",39,2015-11-18,""
0,"H Chu, H Mei, M Bansal, MR Walter","Accurate Vision-based Vehicle Localization using Satellite Imagery",2015,"arXiv preprint arXiv:1510.09171","arxiv.org","http://arxiv.org/abs/1510.09171","",40,2015-11-18,""
0,"D Dai, Y Wang, Y Chen, L Van Gool","How Useful Is Image Super-resolution to Other Vision Tasks?",2015,"arXiv preprint arXiv:1509.07009","arxiv.org","http://arxiv.org/abs/1509.07009","",41,2015-11-18,""
0,"A Raj, D Maturana, S Scherer","Multi-Scale Convolutional Architecture for Semantic Segmentation",2015,"","ri.cmu.edu","","",43,2015-11-18,"PDF"
0,"F Xia, J Zhu, P Wang, A Yuille","Pose-Guided Human Parsing with Deep Learned Features",2015,"arXiv preprint arXiv:1508.03881","arxiv.org","http://arxiv.org/abs/1508.03881","",44,2015-11-18,""
0,"J Xie, M Kiefel, MT Sun, A Geiger","Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer",2015,"arXiv preprint arXiv:1511.03240","arxiv.org","http://arxiv.org/abs/1511.03240","",45,2015-11-18,""
0,"J Kim, V Pavlovic","Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks",2015,"arXiv preprint arXiv:1506.09174","arxiv.org","http://arxiv.org/abs/1506.09174","",46,2015-11-18,""
0,"C Sun, M Paluri, R Collobert, R Nevatia…","ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03776","",47,2015-11-18,""
0,"LC Chen, JT Barron, G Papandreou, K Murphy…","Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03328","",49,2015-11-18,""
0,"N van Noord, E Postma","Exploring the influence of scale on artist attribution",2015,"arXiv preprint arXiv:1506.05929","arxiv.org","http://arxiv.org/abs/1506.05929","",50,2015-11-18,""
0,"A Kendall, V Badrinarayanan, R Cipolla","Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding",2015,"arXiv preprint arXiv:1511.02680","arxiv.org","http://arxiv.org/abs/1511.02680","",51,2015-11-18,""
0,"C Wang, X Yan, M Smith, K Kochhar…","A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks",2015,"… in Medicine and …","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7318881","",53,2015-11-18,""
0,"PH Liu","Novel Convolutional Neural Networks for Deep Learning and Its Applications to General Image Classification",2015,"","pc01.lib.ntust.edu.tw","http://pc01.lib.ntust.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708115-214445","",54,2015-11-18,""
0,"S Bittel, V Kaiser, M Teichmann, M Thoma","Pixel-wise Segmentation of Street with Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.00513","",55,2015-11-18,""
0,"D Ravìa, M Bober, GM Farinella, M Guarnera…","Semantic Segmentation of Images Exploiting DCT Based Features and Random Forest",2015,"Pattern Recognition","Elsevier","","",56,2015-11-18,"HTML"
0,"H Nam, B Han","Learning Multi-Domain Convolutional Neural Networks for Visual Tracking",2015,"arXiv preprint arXiv:1510.07945","arxiv.org","http://arxiv.org/abs/1510.07945","",57,2015-11-18,""
0,"LC Chen, Y Yang, J Wang, W Xu, AL Yuille","Attention to Scale: Scale-aware Semantic Image Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03339","",58,2015-11-18,""
0,"A Seff, L Lu, A Barbu, H Roth, HC Shin…","Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection",2015,"… Image Computing and …","Springer","http://link.springer.com/chapter/10.1007/978-3-319-24571-3_7","",60,2015-11-18,""
0,"AL Jones","Segmenting Microarrays with Deep Neural Networks",2015,"bioRxiv","biorxiv.org","http://biorxiv.org/content/early/2015/06/03/020404.abstract","",61,2015-11-18,""
0,"Y Wang, J Liu, Y Li, H Lu","Semi-and Weakly-Supervised Semantic Segmentation with Deep Convolutional Neural Networks",2015,"Proceedings of the 23rd Annual ACM …","dl.acm.org","http://dl.acm.org/citation.cfm?id=2806322","",62,2015-11-18,""
0,"BS Riggan, C Reale, NM Nasrabadi","Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition",2015,"Access, IEEE","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7270978","",63,2015-11-18,""
0,"A Carlier, A Salvador, F Cabezas, X Giro-i-Nieto…","Assessment of crowdsourcing and gamification loss in user-assisted object segmentation",2015,"Multimedia Tools and …","Springer","http://scholar.google.comhttps://link.springer.com/content/pdf/10.1007%2Fs11042-015-2897-6.pdf","",64,2015-11-18,""
0,"P Hu, D Ramanan","Bottom-up and top-down reasoning with convolutional latent-variable models",2015,"arXiv preprint arXiv:1507.05699","arxiv.org","http://arxiv.org/abs/1507.05699","",66,2015-11-18,""
0,"SS Mukherjee, N Robertson","Deep Head Pose: gaze-direction estimation in multimodal video",2013,"","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7279167","",67,2015-11-18,""
0,"MM Cheng, VA Prisacariu, S Zheng…","DenseCut: Densely Connected CRFs for Realtime GrabCut",2015,"Computer Graphics …","Wiley Online Library","http://onlinelibrary.wiley.com/doi/10.1111/cgf.12758/full","",68,2015-11-18,""
0,"J Pan","Visual Saliency Prediction using Deep learning Techniques",2015,"","imatge.upc.edu","","",69,2015-11-18,"PDF"
0,"A Dosovitskiy, P Fischer, J Springenberg…","Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks",2015,"IEEE Transactions on …","computer.org","http://www.computer.org/csdl/trans/tp/preprint/07312476-abs.html","",71,2015-11-18,""
0,"X Wu","An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation",2015,"arXiv preprint arXiv:1506.05849","arxiv.org","http://arxiv.org/abs/1506.05849","",72,2015-11-18,""
0,"LC Chen, G Papandreou, I Kokkinos, K Murphy…","SEMANTIC IMAGE SEGMENTATION WITH DEEP CON-VOLUTIONAL NETS AND FULLY CONNECTED CRFS",0,"stat.ucla.edu","","","",73,2015-11-18,"PDF"
0,"C Frogner, C Zhang, H Mobahi, M Araya-Polo…","Learning with a Wasserstein Loss",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.05439","",74,2015-11-18,""
0,"G Bertasius, J Shi, L Torresani","Semantic Segmentation with Boundary Neural Fields",2015,"arXiv preprint arXiv:1511.02674","arxiv.org","http://arxiv.org/abs/1511.02674","",75,2015-11-18,""
0,"Y Wei, X Liang, Y Chen, X Shen, MM Cheng…","STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1509.03150","",76,2015-11-18,""
0,"H Lai, S Xiao, Z Cui, Y Pan, C Xu, S Yan","Deep Cascaded Regression for Face Alignment",2015,"arXiv preprint arXiv:1510.09083","arxiv.org","http://arxiv.org/abs/1510.09083","",77,2015-11-18,""

Вы можете сделать это с scholarпакетом. Следуйте шагам

1) Найдите статью на сайте student.google.de 2) Нажмите на одного из авторов, зарегистрированных в google Scholar, отобразится страница со всеми статьями этого автора 3) Нажмите на название статьи, которую вы хотите, это покажет профиль для статьи. Скопируйте полный адрес (url), показанный за этой страницей введите описание изображения здесь4) Из URL-адреса получите идентификатор автора, чтобы получить список публикаций, и pub_id для статьи.

id="ltRSM0AAAAJ"   #user
arta=get_publications(id, cstart = 0, pagesize = 100, flush = FALSE)
article=as.character(arta$pubid)[2] #W7OEmFMy1HYC

5) Получить данные для статьи и построить данные

art2=get_article_cite_history(id, article)
library(ggplot2)
ggplot(data=art2, aes(x=year, y=cites)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=cites), vjust=-0.3, size=3.5)+
  theme_minimal()

введите описание изображения здесь

Как получить количество цитирований за год или данные для экспорта из Google Scholar?
СпросиСетьПолитика конфиденциальности1y, 0v