dplyr: A Grammar of Data Manipulation

Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism

Scheiman, J., Luber, J. M., Chavkin, T. A., MacDonald, T., Tung, A., Pham, L.-D., Wibowo, M. C., Wurth, R. C., Punthambaker, S., Tierney, B. T., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0485-4 · Cited by 892

netmeta : An R Package for Network Meta-Analysis Using Frequentist Methods

Balduzzi, S., Rücker, G., Nikolakopoulou, A., Papakonstantinou, T., Salanti, G., Efthimiou, O., & Schwarzer, G. (2023) · Journal of Statistical Software · doi:10.18637/jss.v106.i02 · Cited by 532

Extensive Heterogeneity and Intrinsic Variation in Spatial Genome Organization

Finn, E. H., Pegoraro, G., Brandão, H. B., Valton, A.-L., Oomen, M. E., Dekker, J., Mirny, L., & Misteli, T. (2019) · Cell · doi:10.1016/j.cell.2019.01.020 · Cited by 459

Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes

Rahim, M. K., Okholm, T. L. H., Jones, K. B., McCarthy, E. E., Liu, C. C., Yee, J. L., Tamaki, S. J., Marquez, D. M., Tenvooren, I., Wai, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.021 · Cited by 455

Factors associated with increases in US health care spending, 1996-2013.

Dieleman, J. L., Squires, E., Bui, A. L., Campbell, M., Chapin, A., Hamavid, H., Horst, C., Li, Z., Matyasz, T., Reynolds, A., et al. (2017) · JAMA · doi:10.1001/jama.2017.15927 · Cited by 347

Elevation alters ecosystem properties across temperate treelines globally

Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément, J.-C., Fajardo, A., Lavorel, S., Sundqvist, M. K., Bahn, M., Chisholm, C., et al. (2017) · Nature · doi:10.1038/nature21027 · Cited by 325

Sex differences in the elevated plus-maze test and large open field test in adult Wistar rats.

Knight, P., Chellian, R., Wilson, R., Behnood-Rod, A., Panunzio, S., & Bruijnzeel, A. W. (2021) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2021.173168 · Cited by 252

Gut feelings: a randomised, triple-blind, placebo-controlled trial of probiotics for depressive symptoms

Chahwan, B., Kwan, S., Isik, A., van Hemert, S., Burke, C., & Roberts, L. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.097 · Cited by 256

Enhancer viruses for combinatorial cell-subclass-specific labeling

Graybuck, L. T., Daigle, T. L., Sedeño-Cortés, A. E., Walker, M., Kalmbach, B., Lenz, G. H., Morin, E., Nguyen, T. N., Garren, E., Bendrick, J. L., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.011 · Cited by 187

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R., & Gresham, D. (2020) · eLife · doi:10.7554/elife.51254 · Cited by 174

The Impact of Trade on Inequality in Developing Countries

Pavcnik, N. (2017) · doi:10.3386/w23878 · Cited by 169

Modelling monotonic effects of ordinal predictors in Bayesian regression models

Bürkner, P., & Charpentier, E. (2020) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12195 · Cited by 160

Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing

Koechli, C., Campbell, A. N., Pepe-Ranney, C., & Buckley, D. H. (2019) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2018.12.013 · Cited by 108

Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting

Meinlschmidt, G., Lee, J.-H., Stalujanis, E., Belardi, A., Oh, M., Jung, E. K., Kim, H.-C., Alfano, J., Yoo, S.-S., & Tegethoff, M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01112 · Cited by 68

Childhood trauma, suicide risk and inflammatory phenotypes of depression: insights from monocyte gene expression

Schiweck, C., Claes, S., Van Oudenhove, L., Lafit, G., Vaessen, T., de Beeck, G. O., Berghmans, R., Wijkhuijs, A., Müller, N., Arolt, V., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00979-z · Cited by 59

Pervasive duplication of tumor suppressors in Afrotherians during the evolution of large bodies and reduced cancer risk

Vazquez, J. M., & Lynch, V. J. (2021) · eLife · doi:10.7554/elife.65041 · Cited by 61

Spatially resolved transcriptomics reveals innervation-responsive functional clusters in skeletal muscle

D’Ercole, C., D’Angelo, P., Ruggieri, V., Proietti, D., Virtanen, L., Parisi, C., Riera, C. S., Renzini, A., Macone, A., Marzullo, M., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111861 · Cited by 48

Retention of participants recruited to a multi-year longitudinal study via Prolific

Kothe, E. J., & Ling, M. (2019) · doi:10.31234/osf.io/5yv2u · Cited by 42

Transmission and Age Impact the Risk of Developing Febrile Malaria in Children with Asymptomatic Plasmodium falciparum Parasitemia

Wamae, K., Wambua, J., Nyangweso, G., Mwambingu, G., Osier, F., Ndung’u, F., Bejon, P., & Ochola-Oyier, L. I. (2019) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiy591 · Cited by 35

A descriptive analysis of the data availability statements accompanying medRxiv preprints and a comparison with their published counterparts

McGuinness, L. A., & Sheppard, A. L. (2021) · PLOS ONE · doi:10.1371/journal.pone.0250887 · Cited by 33

A systematic characterization of microglia-like cell occurrence during retinal organoid differentiation

Bartalska, K., Hübschmann, V., Korkut-Demirbaş, M., Cubero, R. J. A., Venturino, A., Rössler, K., Czech, T., & Siegert, S. (2022) · iScience · doi:10.1016/j.isci.2022.104580 · Cited by 22

Refining and modifying the EFCAMDAT: Lessons from creating a new corpus from an existing large-scale English learner language database

Shatz, I. (2020) · International Journal of Learner Corpus Research · doi:10.1075/ijlcr.20009.sha · Cited by 20

The Role of Intestinal Microbiota in Metastatic Colorectal Cancer Patients Treated With Capecitabine

Aarnoutse, R., Ziemons, J., de Vos-Geelen, J., Valkenburg-van Iersel, L., Wildeboer, A. C. L., Vievermans, A., Creemers, G.-J. M., Baars, A., Vestjens, H. J. H. M. J., Le, G. N., et al. (2022) · Clinical Colorectal Cancer · doi:10.1016/j.clcc.2021.10.004 · Cited by 19

A meta-analysis of the impact of COVID-19 on student achievement

Storey, N., & Zhang, Q. (2024) · Educational Research Review · doi:10.1016/j.edurev.2024.100624 · Cited by 18

The rodent object-in-context task: A systematic review and meta-analysis of important variables

Sep, M. S. C., Vellinga, M., Sarabdjitsingh, R. A., & Joëls, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249102 · Cited by 17

Effectiveness of Nutrient Management on Water Quality Improvement: A Synthesis on Nitrate-Nitrogen Loss from Subsurface Drainage

Liu, W., Yuan, Y., & Koropeckyj-Cox, L. (2021) · Transactions of the ASABE · doi:10.13031/trans.14078 · Cited by 17

Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: a single-blinded crossover randomized controlled trial

Klappe, E. S., Heijmans, J., Groen, K., ter Schure, J., Cornet, R., & de Keizer, N. F. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105264 · Cited by 15

A Revised Molecular Model of Ovarian Cancer Biomarker CA125 (MUC16) Enabled by Long-read Sequencing

Wang, C.-W., Weaver, S. D., Boonpattrawong, N., Schuster-Little, N., Patankar, M., & Whelan, R. J. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0327 · Cited by 12

Decoding methane concentration in Alberta oil sands: A machine learning exploration

Sysoeva, L., Bouderbala, I., Kent, M. H., Saha, E., Zambrano-Luna, B. A., Milne, R., & Wang, H. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112835 · Cited by 11

Industrial Big Data Visualization: A Case Study Using Flight Data Recordings to Discover the Factors Affecting the Airplane Fuel Efficiency

Li, M., & Zhou, Q. (2017) · 2017 IEEE Trustcom/BigDataSE/ICESS · doi:10.1109/trustcom/bigdatase/icess.2017.322 · Cited by 9

Pelagic ecoregions: Operationalizing an ecosystem approach to fisheries management in the Atlantic Ocean

Todorović, S., Juan-Jordá, M. J., Arrizabalaga, H., & Murua, H. (2019) · Marine Policy · doi:10.1016/j.marpol.2019.103700 · Cited by 9

How adverse childhood experiences get under the skin: A systematic review, integration and methodological discussion on threat and reward learning mechanisms

Ruge, J., Ehlers, M. R., Kastrinogiannis, A., Klingelhöfer-Jens, M., Koppold, A., Abend, R., & Lonsdorf, T. B. (2023) · doi:10.31234/osf.io/nfpbj · Cited by 7

Functional genomics of trypanotolerant and trypanosusceptible cattle infected with Trypanosoma congolense across multiple time points and tissues

McHugo, G. P., Ward, J. A., Browne, J. A., O’Gorman, G. M., Meade, K. G., Hill, E. W., Hall, T. J., & MacHugh, D. E. (2025) · doi:10.1101/2025.01.31.635858 · Cited by 4

Development of a tool for measuring uncertainty of acute cytotoxicity results using the methodology of OECD Guidance Document 129

Neves, L. D. O., Garrido, B. C., Granjeiro, J. M., & Balottin, L. B. L. (2025) · NAM Journal · doi:10.1016/j.namjnl.2025.100021 · Cited by 1

Flower Feeding and Reproductive Timing in Spix's Night Monkeys ( Aotus vociferans ): Evidence From Arboreal Camera Traps

Viganò, C., Mainardi, A., Alonso‐Alonso, P., Gottstein, M., Romero, V., Samaniego, R., Uchuari, J. P., & Heer, K. (2026) · Ecology and Evolution · doi:10.1002/ece3.73918