How to obtain citation counts per year or exportable data from Google Scholar?

Question

To me, it seems that "Fully Convolutional Networks for Semantic Segmentation" (http://arxiv.org/abs/1411.4038v2) has quite a bit of impact. However, I would like to support this feeling with data.

On Google Scholar, I can see that the paper was cited by 116 publications. However, I would like to plot a curve showing when it was cited. So I would like to export this data to a CSV with a "date" column and a "title" column. (Other columns like authors, how often the other paper was cited, journal, ... would be nice, too).

Are there other ways to quickly analyze the impact of a paper?

Answer 1

Are you referring to this type of plot, which can be found through an author page containing that paper (in this case Evan Shelhamer), by clicking on the title:

At the Google scholar page of that paper you can also hover over the bars to see e.g. that the paper has 109 citations from 2015. I don't know about a way to directly export data from Google scholar. You could probably html scrape the data though, or by manually using "custom range" here to get citation counts for each year.

Another option is to use the Publish or Perish software (which functions as a front-end to Google scholar), which allows you to use the "Lookup citations" feature to get an exportable list of all the citing papers found through Google Scholar. This list will contain year, title, authors, citation count etc for all the citing papers.

An excerpt of the data for this particular paper is included below. In this particular case (since most of the citations are coming from arXiv) you can actually get a better temporal resolution of the citations after some post-processing of the exported data, since it includes the arXiv identifier which contains the month of publication (posting to Arxiv). That plot looks like this, for the 77 arXiv papers citing "Fully Convolutional Networks for Semantic Segmentation":

Data excerpt:

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"
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Answer 2

You can do it with scholar package. Follow the steps

1) Find the article in scholar.google.de 2) Click on one the authors registered in google scholar, this will show the page with all articles of that author 3) Click on the title of the article you want, this will show the profile for the article. Copy the complete address (url) showed behind that page 4) From the url get the id for author to get the publication list, and the the pub_id for the article

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

5) Get the data for the article and plot the data

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()