1
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Greenwood A, Woodruff ER, Nguyen C, Piper C, Clauset A, Brubaker LW, Behbakht K, Bitler BG. Early Ovarian Cancer Detection in the Age of Fallopian Tube Precursors: A Systematic Review. Obstet Gynecol 2024; 143:e63-e77. [PMID: 38176019 PMCID: PMC10922166 DOI: 10.1097/aog.0000000000005496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/30/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE To determine biomarkers other than CA 125 that could be used in identifying early-stage ovarian cancer. DATA SOURCES Ovid MEDLINE ALL, EMBASE, Web of Science Core Collection, ScienceDirect, Clinicaltrials.gov , and CAB Direct were searched for English-language studies between January 2008 and April 2023 for the concepts of high-grade serous ovarian cancer, testing, and prevention or early diagnosis. METHODS OF STUDY SELECTION The 5,523 related articles were uploaded to Covidence. Screening by two independent reviewers of the article abstracts led to the identification of 245 peer-reviewed primary research articles for full-text review. Full-text review by those reviewers led to the identification of 131 peer-reviewed primary research articles used for this review. TABULATION, INTEGRATION, AND RESULTS Of 131 studies, only 55 reported sensitivity, specificity, or area under the curve (AUC), with 36 of the studies reporting at least one biomarker with a specificity of 80% or greater specificity or 0.9 or greater AUC. CONCLUSION These findings suggest that although many types of biomarkers are being tested in ovarian cancer, most have similar or worse detection rates compared with CA 125 and have the same limitations of poor detection rates in early-stage disease. However, 27.5% of articles (36/131) reported biomarkers with better sensitivity and an AUC greater than 0.9 compared with CA 125 alone and deserve further exploration.
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Affiliation(s)
- Ashley Greenwood
- Divisions of Reproductive Sciences and Gynecologic Oncology, Department of Obstetrics and Gynecology, and the Strauss Library, University of Colorado Denver, Anschutz Medical Campus, Aurora, and the Department of Computer Science and the BioFrontiers Institute, University of Colorado, Boulder, Colorado; and the Santa Fe Institute, New Mexico
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2
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He X, Ghasemian A, Lee E, Clauset A, Mucha PJ. Sequential stacking link prediction algorithms for temporal networks. Nat Commun 2024; 15:1364. [PMID: 38355612 PMCID: PMC10866871 DOI: 10.1038/s41467-024-45598-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.
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Affiliation(s)
- Xie He
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Amir Ghasemian
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA.
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3
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Van Kleunen L, Ahmadian M, Post MD, Wolsky RJ, Rickert C, Jordan K, Hu J, Richer JK, Marjon NA, Behbakht K, Sikora MJ, Bitler BG, Clauset A. The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma. bioRxiv 2024:2024.01.26.577350. [PMID: 38352574 PMCID: PMC10862769 DOI: 10.1101/2024.01.26.577350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.
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Affiliation(s)
- Lucy Van Kleunen
- Department of Computer Science, University of Colorado, Boulder, USA
| | - Mansooreh Ahmadian
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Miriam D Post
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Rebecca J Wolsky
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Christian Rickert
- Department of Immunology and Microbiology, The University of Colorado Anschutz Medical Campus
| | - Kimberly Jordan
- Department of Immunology and Microbiology, The University of Colorado Anschutz Medical Campus
| | - Junxiao Hu
- Department of Pediatrics, Cancer Center Biostatistics Core, University of Colorado Anschutz Medical Campus, CO, USA
| | - Jennifer K. Richer
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Nicole A. Marjon
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Kian Behbakht
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Matthew J. Sikora
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Benjamin G. Bitler
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
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4
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Smith HJ, Ray LB, Yeston JS, Norton ML, Clauset A, Szuromi P, Stajic J, Alderton G, Jiang D, Stern P, Grocholski B, Lavine MS, Vignieri S, Wong W, Olingy C. In Science Journals. Science 2023; 382:781-783. [PMID: 37972192 DOI: 10.1126/science.adm9262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Highlights from the Science family of journals.
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5
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Spoon K, LaBerge N, Wapman KH, Zhang S, Morgan AC, Galesic M, Fosdick BK, Larremore DB, Clauset A. Gender and retention patterns among U.S. faculty. Sci Adv 2023; 9:eadi2205. [PMID: 37862417 PMCID: PMC10588949 DOI: 10.1126/sciadv.adi2205] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/14/2023] [Indexed: 10/22/2023]
Abstract
Women remain underrepresented among faculty in nearly all academic fields. Using a census of 245,270 tenure-track and tenured professors at United States-based PhD-granting departments, we show that women leave academia overall at higher rates than men at every career age, in large part because of strongly gendered attrition at lower-prestige institutions, in non-STEM fields, and among tenured faculty. A large-scale survey of the same faculty indicates that the reasons faculty leave are gendered, even for institutions, fields, and career ages in which retention rates are not. Women are more likely than men to feel pushed from their jobs and less likely to feel pulled toward better opportunities, and women leave or consider leaving because of workplace climate more often than work-life balance. These results quantify the systemic nature of gendered faculty retention; contextualize its relationship with career age, institutional prestige, and field; and highlight the importance of understanding the gendered reasons for attrition rather than focusing on rates alone.
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Affiliation(s)
- Katie Spoon
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | - Nicholas LaBerge
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | - K. Hunter Wapman
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | - Sam Zhang
- Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA
| | - Allison C. Morgan
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | | | - Bailey K. Fosdick
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA
| | - Daniel B. Larremore
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
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6
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Karimi F, Clauset A. Abolish ageism in early-career research awards. Nature 2023; 620:492. [PMID: 37582878 DOI: 10.1038/d41586-023-02567-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
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7
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Wapman KH, Zhang S, Clauset A, Larremore DB. Author Correction: Quantifying hierarchy and dynamics in US faculty hiring and retention. Nature 2023:10.1038/s41586-023-06379-9. [PMID: 37407828 DOI: 10.1038/s41586-023-06379-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Affiliation(s)
- K Hunter Wapman
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
| | - Sam Zhang
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Aaron Clauset
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
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8
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Abstract
Despite the special role of tenure-track faculty in society, training future researchers and producing scholarship that drives scientific and technological innovation, the sociodemographic characteristics of the professoriate have never been representative of the general population. Here we systematically investigate the indicators of faculty childhood socioeconomic status and consider how they may limit efforts to diversify the professoriate. Combining national-level data on education, income and university rankings with a 2017-2020 survey of 7,204 US-based tenure-track faculty across eight disciplines in STEM, social science and the humanities, we show that faculty are up to 25 times more likely to have a parent with a Ph.D. Moreover, this rate nearly doubles at prestigious universities and is stable across the past 50 years. Our results suggest that the professoriate is, and has remained, accessible disproportionately to the socioeconomically privileged, which is likely to deeply shape their scholarship and their reproduction.
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Affiliation(s)
- Allison C Morgan
- Department of Computer Science, University of Colorado, Boulder, CO, USA.
| | - Nicholas LaBerge
- Department of Computer Science, University of Colorado, Boulder, CO, USA
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | | | - Jennie E Brand
- Department of Sociology, University of California, Los Angeles, CA, USA
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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9
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Zhang S, Wapman KH, Larremore DB, Clauset A. Labor advantages drive the greater productivity of faculty at elite universities. Sci Adv 2022; 8:eabq7056. [PMID: 36399560 PMCID: PMC9674273 DOI: 10.1126/sciadv.abq7056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/21/2022] [Indexed: 05/31/2023]
Abstract
Faculty at prestigious institutions dominate scientific discourse, producing a disproportionate share of all research publications. Environmental prestige can drive such epistemic disparity, but the mechanisms by which it causes increased faculty productivity remain unknown. Here, we combine employment, publication, and federal survey data for 78,802 tenure-track faculty at 262 PhD-granting institutions in the American university system to show through multiple lines of evidence that the greater availability of funded graduate and postdoctoral labor at more prestigious institutions drives the environmental effect of prestige on productivity. In particular, greater environmental prestige leads to larger faculty-led research groups, which drive higher faculty productivity, primarily in disciplines with group collaboration norms. In contrast, productivity does not increase substantially with prestige for faculty publications without group members or for group members themselves. The disproportionate scientific productivity of elite researchers can be largely explained by their substantial labor advantage rather than inherent differences in talent.
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Affiliation(s)
- Sam Zhang
- Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA
| | - K. Hunter Wapman
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | - Daniel B. Larremore
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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10
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Vignieri S, Vinson V, Ferrarelli LK, Stajic J, Jiang D, Clauset A, Purnell BA, Hines PJ, Sugden AM, Erkes DA, Malo CS, Szuromi P. In Science Journals. Science 2022; 375:398-400. [PMID: 35084983 DOI: 10.1126/science.ada0328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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11
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Hodges K, McNutt M, Clauset A, Jackson J, Machlis G, Naeem S. The Fine Art of Scientific Advocacy: A Tribute to Tom Lovejoy. Sci Adv 2022; 8:eabn9704. [PMID: 35020430 DOI: 10.1126/sciadv.abn9704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Kip Hodges
- Deputy Editor, Science Advances; Foundation Professor, School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA.
| | - Marcia McNutt
- Former Editor-in-Chief, AAAS; President, National Academy of Sciences, 500 Fifth Street NW, Washington, DC 20001, USA.
| | - Aaron Clauset
- Deputy Editor, Science Advances; Department of Computer Science University of Colorado, Boulder, Boulder, CO 80309; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| | - Jeremy Jackson
- Deputy Editor, Science Advances; Center for Biodiversity and Conservation, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024-5192, USA
| | - Gary Machlis
- Former Deputy Editor, Science Advances; University Professor of Environmental Sustainability, Clemson University, Clemson, SC 29634, USA
| | - Shahid Naeem
- Deputy Editor, Science Advances; Department of Ecology, Evolution, and Environmental Biology, Director, Earth Institute Center for Environmental Sustainability, Columbia University, 1200 Amsterdam Ave., 10th Floor Schermerhorn Extension, MC5557, New York, NY 10027, USA
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Vinson V, Malo CS, Clauset A, Ash C, Jiang D, Stajic J, Grocholski B, Purnell BA, Suleymanov Y, Smith KT, Stern P, Wong W, Ray LB, Williams I. In Science Journals. Science 2021; 374:950-953. [PMID: 34793233 DOI: 10.1126/science.acx9638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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13
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Clauset A. Improved disease tracking with AI. Science 2021. [DOI: 10.1126/science.372.6548.1300-d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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14
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Clauset A, Ray LB, Scanlon ST, Norton M, Nusinovich Y, Smith HJ, Olingy C, VanHook AM, Vignieri S, Szuromi P, Jiang D, Stajic J, Rai TS, Osborne IS. This Week in Science. Science 2021. [DOI: 10.1126/science.2021.372.6548.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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15
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Clauset A, Behbakht K, Bitler BG. Decoding the dynamic tumor microenvironment. Sci Adv 2021; 7:7/23/eabi5904. [PMID: 34088677 PMCID: PMC8177696 DOI: 10.1126/sciadv.abi5904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/13/2021] [Indexed: 05/03/2023]
Abstract
An opportunity to improve cancer outcomes with machine learning.
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Affiliation(s)
- Aaron Clauset
- Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Kian Behbakht
- University of Colorado Cancer Center, Aurora, CO 80045, USA
- Department of OB/GYN, Division of Gynecologic Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of OB/GYN, Division of Reproductive Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Benjamin G Bitler
- University of Colorado Cancer Center, Aurora, CO 80045, USA.
- Department of OB/GYN, Division of Reproductive Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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Abstract
Background Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Results We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data. Conclusions Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology. Supplementary Information The online version supplementary material available at 10.1186/s12859-021-04075-x.
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Affiliation(s)
- Andrew J Kavran
- Department of Biochemistry, University of Colorado, Boulder, CO, USA.,BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Aaron Clauset
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA. .,Department of Computer Science, University of Colorado, Boulder, CO, USA. .,Santa Fe Institute, Santa Fe, NM, USA.
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17
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Morgan AC, Way SF, Hoefer MJD, Larremore DB, Galesic M, Clauset A. The unequal impact of parenthood in academia. Sci Adv 2021; 7:7/9/eabd1996. [PMID: 33627417 PMCID: PMC7904257 DOI: 10.1126/sciadv.abd1996] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 02/05/2021] [Indexed: 05/19/2023]
Abstract
Across academia, men and women tend to publish at unequal rates. Existing explanations include the potentially unequal impact of parenthood on scholarship, but a lack of appropriate data has prevented its clear assessment. Here, we quantify the impact of parenthood on scholarship using an extensive survey of the timing of parenthood events, longitudinal publication data, and perceptions of research expectations among 3064 tenure-track faculty at 450 Ph.D.-granting computer science, history, and business departments across the United States and Canada, along with data on institution-specific parental leave policies. Parenthood explains most of the gender productivity gap by lowering the average short-term productivity of mothers, even as parents tend to be slightly more productive on average than nonparents. However, the size of productivity penalty for mothers appears to have shrunk over time. Women report that paid parental leave and adequate childcare are important factors in their recruitment and retention. These results have broad implications for efforts to improve the inclusiveness of scholarship.
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Affiliation(s)
- Allison C Morgan
- Department of Computer Science, University of Colorado, Boulder, CO, USA.
| | - Samuel F Way
- Department of Computer Science, University of Colorado, Boulder, CO, USA
| | - Michael J D Hoefer
- Department of Computer Science, University of Colorado, Boulder, CO, USA
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | | | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
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18
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Purnell BA, Nusinovich Y, Hines PJ, Szuromi P, Grocholski B, Williams I, Yeston J, Clauset A, Kelly PN, VanHook AM, Vignieri S, Zahn LM. This Week in Science. Science 2020. [DOI: 10.1126/science.2020.370.6512.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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19
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Clauset A. ADHD exerts lifelong impairment. Science 2020. [DOI: 10.1126/science.370.6512.70-o] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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20
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Jordan KR, Sikora MJ, Slansky JE, Minic A, Richer JK, Moroney MR, Hu J, Wolsky RJ, Watson ZL, Yamamoto TM, Costello JC, Clauset A, Behbakht K, Kumar TR, Bitler BG. The Capacity of the Ovarian Cancer Tumor Microenvironment to Integrate Inflammation Signaling Conveys a Shorter Disease-free Interval. Clin Cancer Res 2020; 26:6362-6373. [PMID: 32928797 DOI: 10.1158/1078-0432.ccr-20-1762] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/22/2020] [Accepted: 09/10/2020] [Indexed: 01/08/2023]
Abstract
PURPOSE Ovarian cancer has one of the highest deaths to incidence ratios across all cancers. Initial chemotherapy is effective, but most patients develop chemoresistant disease. Mechanisms driving clinical chemo-response or -resistance are not well-understood. However, achieving optimal surgical cytoreduction improves survival, and cytoreduction is improved by neoadjuvant chemotherapy (NACT). NACT offers a window to profile pre- versus post-NACT tumors, which we used to identify chemotherapy-induced changes to the tumor microenvironment. EXPERIMENTAL DESIGN We obtained matched pre- and post-NACT archival tumor tissues from patients with high-grade serous ovarian cancer (patient, n = 6). We measured mRNA levels of 770 genes (756 genes/14 housekeeping genes, NanoString Technologies), and performed reverse phase protein array (RPPA) on a subset of matched tumors. We examined cytokine levels in pre-NACT ascites samples (n = 39) by ELISAs. A tissue microarray with 128 annotated ovarian tumors expanded the transcriptional, RPPA, and cytokine data by multispectral IHC. RESULTS The most upregulated gene post-NACT was IL6 (16.79-fold). RPPA data were concordant with mRNA, consistent with elevated immune infiltration. Elevated IL6 in pre-NACT ascites specimens correlated with a shorter time to recurrence. Integrating NanoString (n = 12), RPPA (n = 4), and cytokine (n = 39) studies identified an activated inflammatory signaling network and induced IL6 and IER3 (immediate early response 3) post-NACT, associated with poor chemo-response and time to recurrence. CONCLUSIONS Multiomics profiling of ovarian tumor samples pre- and post-NACT provides unique insight into chemo-induced changes to the tumor microenvironment. We identified a novel IL6/IER3 signaling axis that may drive chemoresistance and disease recurrence.
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Affiliation(s)
- Kimberly R Jordan
- Department of Immunology and Microbiology, University of Colorado, School of Medicine, Aurora, Colorado
| | - Matthew J Sikora
- Department of Pathology, The University of Colorado Anschutz Medical Campus, Aurora, Colorado.,University of Colorado Comprehensive Cancer Center, Aurora, Colorado
| | - Jill E Slansky
- Department of Immunology and Microbiology, University of Colorado, School of Medicine, Aurora, Colorado
| | - Angela Minic
- Department of Immunology and Microbiology, University of Colorado, School of Medicine, Aurora, Colorado
| | - Jennifer K Richer
- Department of Pathology, The University of Colorado Anschutz Medical Campus, Aurora, Colorado.,University of Colorado Comprehensive Cancer Center, Aurora, Colorado
| | - Marisa R Moroney
- Division of Gynecologic Oncology, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Junxiao Hu
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado
| | - Rebecca J Wolsky
- Department of Pathology, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Zachary L Watson
- University of Colorado Comprehensive Cancer Center, Aurora, Colorado.,Division of Reproductive Sciences, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Tomomi M Yamamoto
- Division of Reproductive Sciences, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - James C Costello
- University of Colorado Comprehensive Cancer Center, Aurora, Colorado.,Department of Pharmacology, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Aaron Clauset
- Department of Computer Science, The University of Colorado, Boulder, Colorado.,Santa Fe Institute, Santa Fe, New Mexico.,BioFrontiers Institute, The University of Colorado, Boulder, Colorado
| | - Kian Behbakht
- University of Colorado Comprehensive Cancer Center, Aurora, Colorado.,Division of Gynecologic Oncology, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado.,Division of Reproductive Sciences, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - T Rajendra Kumar
- Division of Reproductive Sciences, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Benjamin G Bitler
- University of Colorado Comprehensive Cancer Center, Aurora, Colorado. .,Division of Reproductive Sciences, Department of OB/GYN, The University of Colorado Anschutz Medical Campus, Aurora, Colorado
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Clauset A. Resilient misinformation in a crisis. Science 2020. [DOI: 10.1126/science.367.6477.522-f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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22
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Zahn LM, Alderton G, Mao S, VanHook AM, Kiberstis PA, Kelly PN, Szuromi P, Hurtley SM, Grocholski B, Ash C, Yeston J, Clauset A, Stern P, Smith KT, Pujanandez L. This Week in Science. Science 2020. [DOI: 10.1126/science.2020.367.6477.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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23
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Clauset A. Symbolic behavior in Neanderthals. Science 2019. [DOI: 10.1126/science.366.6465.583-f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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24
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Maroso M, Smith KT, Ray LB, Szuromi P, Kiberstis PA, VanHook AM, Clauset A, Ash C, Vignieri S, Lavine MS, Zahn LM, Alderton G, Williams I, Funk MA, Stern P, Vinson V. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.366.6465.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Hines PJ, Williams E, Grocholski B, Czajka C, Vinson V, Clauset A, Fogg CN, Rai TS, Szuromi P, Scanlon ST, Ash C, Hurtley SM, Zahn LM, Yeston J, Sugden AM. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.366.6464.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Clauset A. Measuring street protest events. Science 2019. [DOI: 10.1126/science.366.6464.440-c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Osborne IS, Alderton G, Yeston J, Clauset A, Mao S, Szuromi P, Stern P, Williams E, Stajic J, Smith KT, Funk MA, Vignieri S, Vinson V, Czajka C. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.365.6458.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Clauset A. Assessing U.S. support for gene drives. Science 2019. [DOI: 10.1126/science.365.6458.1131-h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Funk MA, Nusinovich Y, Zahn LM, Williams I, Szuromi P, Osborne IS, Stajic J, Vinson V, Lavine MS, Mao S, Wong W, Ash C, Vignieri S, Smith KT, Ray LB, Alderton G, Clauset A. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.365.6457.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Clauset A. Animal behavior analysis at scale. Science 2019. [DOI: 10.1126/science.365.6457.995-h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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31
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Clauset A. Doesn't matter if it's fair…as long as you win. Science 2019. [DOI: 10.1126/science.365.6450.245-p] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Balasubramani A, Lavine MS, Pujanandez L, Stajic J, Vignieri S, Vinson V, Ferrarelli LK, Zahn LM, Smith HJ, Hurtley SM, Grocholski B, Alderton G, Osborne IS, Yeston J, Clauset A, Scanlon ST. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.365.6450.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Balasubramani A, Charneski CA, Osborne IS, Wong W, Smith KT, Yeston J, Ash C, Funk MA, Clauset A, Post K, Grocholski B, Hines PJ, Stern P, Lavine MS. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.364.6445.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Clauset A, Post K. Ancient usage of cannabis. Science 2019. [DOI: 10.1126/science.364.6445.1043-h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Foley JF, Fahrenkamp-Uppenbrink J, Smith HJ, Stajic J, Hines PJ, Balasubramani A, Stern P, Pujanandez L, Sugden AM, Osborne IS, Zahn LM, Clauset A, Post K, Smith KT. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.364.6444.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Szuromi P, Ray LB, Stern P, Vinson V, Funk MA, Osborne IS, Purnell BA, Hines PJ, Nusinovich Y, Yeston J, Williams I, Hurtley SM, Alderton G, Smith KT, Clauset A, Post K, Williams E. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.364.6441.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Clauset A, Post K. Machine learning improves hearing aids. Science 2019. [DOI: 10.1126/science.364.6441.645-p] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Mao S, Vinson V, Ray LB, Vignieri S, Purnell BA, Yeston J, Kiberstis PA, Fahrenkamp-Uppenbrink J, Lavine MS, Grocholski B, Clauset A, Post K, Osborne IS, Czajka C, Balasubramani A, Maroso M, Szuromi P, Smith HJ, Ferrarelli LK. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.364.6438.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Post K, Clauset A. Heart-function modeling for the masses. Science 2019. [DOI: 10.1126/science.363.6434.1411-i] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Stern P, Yeston J, Zahn LM, Stajic J, Fahrenkamp-Uppenbrink J, Alderton G, Scanlon ST, Balasubramani A, Maroso M, Sugden AM, Foley JF, Vignieri S, Kelly PN, Hurtley SM, Post K, Clauset A, Hines PJ, Vinson V. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.363.6434.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Abstract
Real-world networks are often claimed to be scale free, meaning that the fraction of nodes with degree k follows a power law k−α, a pattern with broad implications for the structure and dynamics of complex systems. However, the universality of scale-free networks remains controversial. Here, we organize different definitions of scale-free networks and construct a severe test of their empirical prevalence using state-of-the-art statistical tools applied to nearly 1000 social, biological, technological, transportation, and information networks. Across these networks, we find robust evidence that strongly scale-free structure is empirically rare, while for most networks, log-normal distributions fit the data as well or better than power laws. Furthermore, social networks are at best weakly scale free, while a handful of technological and biological networks appear strongly scale free. These findings highlight the structural diversity of real-world networks and the need for new theoretical explanations of these non-scale-free patterns. Real-world networks are often said to be ”scale free”, meaning their degree distribution follows a power law. Broido and Clauset perform statistical tests of this claim using a large and diverse corpus of real-world networks, showing that scale-free structure is far from universal.
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Affiliation(s)
- Anna D Broido
- Department of Applied Mathematics, University of Colorado, 526 UCB, Boulder, CO, 80309, USA.
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, 430 UCB, Boulder, CO, 80309, USA. .,BioFrontiers Institute, University of Colorado, 596 UCB, Boulder, CO, 80309, USA. .,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA.
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Clauset A. Concentrated news precedes legislation. Science 2019. [DOI: 10.1126/science.363.6429.831-d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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45
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Stajic J, Hurtley SM, Grocholski B, Williams I, Clauset A, Yeston J, Mao S, Szuromi P, Ferrarelli LK, Vinson V, Zahn LM, Alderton G, Pujanandez L. This Week in Science. Science 2019. [DOI: 10.1126/science.2019.363.6429.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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46
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Abstract
The composition of the scientific workforce shapes the direction of scientific research, directly through the selection of questions to investigate, and indirectly through its influence on the training of future scientists. In most fields, however, complete census information is difficult to obtain, complicating efforts to study workforce dynamics and the effects of policy. This is particularly true in computer science, which lacks a single, all-encompassing directory or professional organization. A full census of computer science would serve many purposes, not the least of which is a better understanding of the trends and causes of unequal representation in computing. Previous academic census efforts have relied on narrow or biased samples, or on professional society membership rolls. A full census can be constructed directly from online departmental faculty directories, but doing so by hand is expensive and time-consuming. Here, we introduce a topical web crawler for automating the collection of faculty information from web-based department rosters, and demonstrate the resulting system on the 205 PhD-granting computer science departments in the U.S. and Canada. This method can quickly construct a complete census of the field, and achieve over 99% precision and recall. We conclude by comparing the resulting 2017 census to a hand-curated 2011 census to quantify turnover and retention in computer science, in general and for female faculty in particular, demonstrating the types of analysis made possible by automated census construction.
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Affiliation(s)
- Allison C. Morgan
- Department of Computer Science, University of Colorado, Boulder, CO, United States of America
- * E-mail:
| | - Samuel F. Way
- Department of Computer Science, University of Colorado, Boulder, CO, United States of America
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, United States of America
- BioFrontiers Institute, University of Colorado, Boulder, CO, United States of America
- Santa Fe Institute, Santa Fe, NM, United States of America
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47
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Clauset A. Forests improve dietary diversity. Science 2018. [DOI: 10.1126/science.361.6403.657-h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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48
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Balasubramani A, Purnell BA, Kline R, Osborne IS, Smith OM, Szuromi P, Yeston J, Lavine MS, Foley JF, Clauset A, Smith HJ, Hines PJ, Grocholski B, Zahn LM, Scanlon ST. This Week in Science. Science 2018. [DOI: 10.1126/science.2018.361.6403.twis] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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49
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Grocholski B, Kelly PN, Nusinovich Y, Clauset A, Mao S, Osborne IS, Ash C, Vinson V, Balasubramani A, Szuromi P, Jasny BR, Stern P, Ferrarelli LK, Purnell BA, Smith HJ. This Week in Science. Science 2018. [DOI: 10.1126/science.2018.359.6380.twis] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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50
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Clauset A. Unscientific hunt management plans. Science 2018. [DOI: 10.1126/science.359.6380.1114-d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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