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Wien),2019,dissertation,scientific_computing,2,9.0,10.0,222.03389830508473,13,,True,True,baseline,222.03389830508473,landmark,True102888532099,10.3847/1538-3881/aabc4f,,The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *,The Astronomical Journal,2018,article,scientific_computing,1,459.0,1025.0,193.46314218749401,7687,234.9501,True,True,baseline,193.46314218749401,landmark,True111494474111,10.1515/9783110666991-007,,7 Linear algebra applications,De Gruyter eBooks,2020,book-chapter,scientific_computing,2,184.0,190.0,163.70790263421142,482,30.6361,True,False,baseline,163.70790263421142,landmark,True127154488692,10.1016/j.engappai.2026.114843,,Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation,Engineering Applications of Artificial Intelligence,2026,article,scientific_computing,1,45.0,45.0,33.579175704989154,45,110.7417,False,False,post,159.98492606958206,landmark,True132913606428,10.3758/s13428-018-01193-y,,PsychoPy2: Experiments in behavior made easy,Behavior Research Methods,2019,article,scientific_computing,1,351.0,816.0,124.07724923810785,5166,170.3746,True,True,baseline,124.07724923810785,landmark,True144408339746,10.1007/s44379-025-00015-1,2410.13228,From PINNs to PIKANs: recent advances in physics-informed machine learning,Machine learning for computational science and engineering,2025,article,scientific_computing,3,239.0,239.0,51.33385214007782,239,102.1338,False,True,post,120.99586631516542,landmark,True154417522354,10.1038/s43588-026-01009-6,,NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements,Nature Computational Science,2026,article,scientific_computing,1,34.0,34.0,26.21830985915493,34,44.9009,False,True,post,120.87749969701756,landmark,True163038025793,10.1016/j.cpc.2021.108033,,VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code,Computer Physics Communications,2021,article,scientific_computing,1,947.0,2087.0,120.59877093864604,7856,267.5871,False,True,baseline,120.59877093864604,landmark,True173143496104,10.1201/9781315107042-14,,Numerical Optimization,CRC Press eBooks,2018,book-chapter,scientific_computing,2,225.0,235.0,109.14754610815879,335,14.0895,True,False,baseline,109.14754610815879,landmark,True184407925765,10.1016/j.cossms.2025.101214,2503.09814,A practical guide to machine learning interatomic potentials – Status and future,Current Opinion in Solid State and Materials Science,2025,review,scientific_computing,1,214.0,214.0,3.7709251101321586,214,9.2721,False,True,post,108.3393949432862,landmark,True194383218913,10.1038/s41586-023-06185-3,,Accurate medium-range global weather forecasting with 3D neural networks,Nature,2023,article,scientific_computing,1,511.0,1104.0,106.38333772739257,1634,181.7259,True,True,post,106.38333772739257,landmark,True204386373863,10.7717/peerj-cs.1516,,"PyMC: a modern, and comprehensive probabilistic programming framework in Python",PeerJ Computer Science,2023,article,scientific_computing,4,390.0,626.0,100.85192909280501,870,132.4344,True,True,post,100.85192909280501,landmark,True214242546620,10.1007/978-1-4842-6876-6_1,,DPLYR,Apress eBooks,2021,book-chapter,scientific_computing,1,185.0,286.0,96.29548332629801,367,143.0004,True,False,baseline,96.29548332629801,landmark,True222962853966,10.1007/s10915-018-0757-z,1511.06324,Global Convergence of ADMM in Nonconvex Nonsmooth Optimization,Journal of Scientific Computing,2018,article,scientific_computing,2,246.0,390.0,92.76320659281009,1268,89.8329,False,True,baseline,92.76320659281009,landmark,True234211084734,10.1017/9781108594967.028,,Positive Definite Matrices,Cambridge University Press eBooks,2019,book-chapter,scientific_computing,1,102.0,110.0,89.28544845552662,166,22.481,True,False,baseline,89.28544845552662,landmark,True244245687930,10.1201/9781003090601-06,,Chemical Reaction Engineering,CRC Press eBooks,2020,book-chapter,scientific_computing,2,90.0,102.0,81.77618941255305,322,24.1009,True,False,baseline,81.77618941255305,landmark,True257128919969,10.1016/j.advengsoft.2026.104124,,SA-PINN: A self-attention enhanced physics-informed neural network for multiaxial fatigue life prediction with small samples,Advances in Engineering Software,2026,article,scientific_computing,2,23.0,23.0,25.467432950191572,23,46.775,False,False,post,81.77007332445305,landmark,True264415338867,10.1137/25m1738863,2503.00768,Nonlinear Model Reduction by 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Chemistry,2024,article,computational_biology,2,536.0,1064.0,176.8137166729525,1064,208.5864,True,False,post,176.8137166729525,landmark,True504388522045,10.1371/journal.pone.0294236,,SRplot: A free online platform for data visualization and graphing,PLoS ONE,2023,article,computational_biology,2,537.0,1676.0,174.5922042929299,2536,232.667,True,True,post,174.5922042929299,landmark,True517139978436,10.1093/bioinformatics/btag129,,Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2,Bioinformatics,2026,article,computational_biology,2,48.0,48.0,64.58550316678397,48,73.0121,True,True,post,170.6505878075542,landmark,True527140287209,10.1038/s41586-026-10265-5,,Towards end-to-end automation of AI research,Nature,2026,article,autonomous_scientific_discovery,4,94.0,94.0,104.60334399738458,94,335.9735,True,True,post,334.1907344564603,landmark,True537125546963,10.1016/j.addr.2026.115781,,Artificial intelligence and machine 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Intelligence,2025,article,autonomous_scientific_discovery,5,196.0,196.0,30.34398782343988,196,40.9323,True,True,post,99.22673555553315,landmark,True574365512576,10.1016/j.iotcps.2023.04.003,,"ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope",Internet of Things and Cyber-Physical Systems,2023,article,autonomous_scientific_discovery,6,1309.0,2124.0,76.85650367529561,2423,590.6367,True,True,post,76.85650367529561,landmark,True584414990951,10.1016/j.matt.2026.102748,2508.13197,The rise of generative AI for metal-organic framework design and synthesis,Matter,2026,article,autonomous_scientific_discovery,3,21.0,21.0,16.40625,21,28.4842,True,True,post,74.65963216580496,landmark,True594406406714,10.1038/s42256-024-00972-x,,Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning,Nature Machine 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Research,2018,article,computational_biology,3,421.0,2624.0,131.1704666044956,17675,509.3871,True,True,baseline,131.1704666044956,landmark,True832804822363,10.1093/nar/gky427,,SWISS-MODEL: homology modelling of protein structures and complexes,Nucleic Acids Research,2018,article,computational_biology,3,904.0,2454.0,122.67237997234459,13067,345.6079,True,True,baseline,122.67237997234459,landmark,True843208691473,10.1093/nar/gkab1038,,The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences,Nucleic Acids Research,2021,article,computational_biology,2,743.0,2312.0,100.20101036958255,6103,381.4154,True,True,baseline,100.20101036958255,landmark,True852938574745,10.1093/nar/gkz268,,The EMBL-EBI search and sequence analysis tools APIs in 2019,Nucleic Acids Research,2019,article,computational_biology,2,1094.0,2262.0,149.0781904899552,4162,956.3441,True,True,baseline,149.0781904899552,landmark,True863136918052,10.1093/nar/gky1049,,UniProt: a worldwide hub of protein knowledge,Nucleic Acids Research,2018,article,computational_biology,1,457.0,2078.0,103.87661189182235,7679,309.1951,True,True,baseline,103.87661189182235,landmark,True873107527779,10.1093/nar/gkaa1074,,"The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets",Nucleic Acids Research,2020,article,computational_biology,4,246.0,2031.0,115.75029377203289,8015,303.9867,True,True,baseline,115.75029377203289,landmark,True883159995035,10.1093/nar/gkab382,,MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights,Nucleic Acids Research,2021,article,computational_biology,3,1102.0,2027.0,117.64839283140344,3958,207.4321,True,True,baseline,117.64839283140344,landmark,True893095583226,10.1093/nar/gkaa913,,Pfam: The protein families database in 2021,Nucleic Acids Research,2020,article,computational_biology,1,792.0,1991.0,113.47062279670975,7261,214.3673,True,True,baseline,113.47062279670975,landmark,True903096828292,10.1093/nar/gkaa970,,KEGG: integrating viruses and cellular organisms,Nucleic Acids Research,2020,article,computational_biology,1,541.0,1955.0,111.4189189189189,4022,222.942,True,True,baseline,111.4189189189189,landmark,True912899760200,10.1093/nar/gky1106,,The PRIDE database and related tools and resources in 2019: improving support for quantification data,Nucleic Acids Research,2018,article,computational_biology,3,507.0,1805.0,74.32352941176471,6693,384.4369,True,True,baseline,74.32352941176471,landmark,True924223552703,10.1093/nar/gkac240,,Search and sequence analysis tools services from EMBL-EBI in 2022,Nucleic Acids Research,2022,article,computational_biology,2,862.0,1760.0,89.45426085909514,2267,150.397,True,True,baseline,89.45426085909514,landmark,True934368358469,10.1093/nar/gkad344,,"antiSMASH 7.0: new and improved 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Research,2018,article,computational_biology,4,660.0,1555.0,77.73249831173425,3629,185.9094,True,True,baseline,77.73249831173425,landmark,True972942829220,10.1093/nar/gkz310,,antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline,Nucleic Acids Research,2019,article,computational_biology,2,661.0,1520.0,18.891765924391507,2907,264.3601,True,True,baseline,18.891765924391507,landmark,True984384652177,10.1186/s12859-023-05385-y,,MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads,BMC Bioinformatics,2023,article,computational_biology,5,839.0,1444.0,100.66579880594335,1858,138.9013,True,True,post,100.66579880594335,landmark,True992898402099,10.1093/nar/gky995,,The Pfam protein families database in 2019,Nucleic Acids Research,2018,article,computational_biology,2,366.0,1396.0,69.7842878734283,4595,208.2751,True,True,baseline,69.7842878734283,landmark,True1003161596995,10.1093/nar/gkab335,,antiSMASH 6.0: improving cluster detection and comparison capabilities,Nucleic Acids Research,2021,article,computational_biology,1,649.0,1396.0,21.18855808523059,2562,230.4087,True,True,baseline,21.18855808523059,landmark,True1014308616064,10.1093/nar/gkac993,,InterPro in 2022,Nucleic Acids Research,2022,article,computational_biology,2,418.0,1388.0,70.5468829956955,2399,150.466,True,True,baseline,70.5468829956955,landmark,True102