Academic literature on the topic 'Statistical data science'
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Journal articles on the topic "Statistical data science"
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Shanmugam, Ramalingam. "Statistical data science." Journal of Statistical Computation and Simulation 90, no. 9 (June 12, 2019): 1733. http://dx.doi.org/10.1080/00949655.2019.1628902.
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Cai, Junhui, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, and Linda Zhao. "Statistical Theory Powering Data Science." Statistical Science 34, no. 4 (November 2019): 669–91. http://dx.doi.org/10.1214/19-sts754.
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GRANT, ROBERT. "STATISTICAL LITERACY IN THE DATA SCIENCE WORKPLACE." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 17–21. http://dx.doi.org/10.52041/serj.v16i1.207.
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Statistical literacy, the ability to understand and make use of statistical information including methods, has particular relevance in the age of data science, when complex analyses are undertaken by teams from diverse backgrounds. Not only is it essential to communicate to the consumers of information but also within the team. Writing from the perspective of a statistician who later taught himself about data visualisation and machine learning, I consider some pitfalls for ommunication and drivers of behaviour within the team. Recruiters and managers also play a part in creating a workplace where speed and novelty are sometimes over-valued. Statisticians have a duty to educate and shape this exciting new workplace. First published May 2017 at Statistics Education Research Journal Archives
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Blei, David M., and Padhraic Smyth. "Science and data science." Proceedings of the National Academy of Sciences 114, no. 33 (August 7, 2017): 8689–92. http://dx.doi.org/10.1073/pnas.1702076114.
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Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.
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Morse-Gagne, E. E. "Culturomics: Statistical Traps Muddy the Data." Science 332, no. 6025 (March 31, 2011): 35. http://dx.doi.org/10.1126/science.332.6025.35-b.
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EFRON, B., and R. TIBSHIRANI. "Statistical Data Analysis in the Computer Age." Science 253, no. 5018 (July 12, 1991): 390–95. http://dx.doi.org/10.1126/science.253.5018.390.
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Vansteelandt, Stijn. "Statistical Modelling in the Age of Data Science." Observational Studies 7, no. 1 (2021): 217–28. http://dx.doi.org/10.1353/obs.2021.0013.
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Chen, Yuxin, Yuejie Chi, Jianqing Fan, and Cong Ma. "Spectral Methods for Data Science: A Statistical Perspective." Foundations and Trends® in Machine Learning 14, no. 5 (2021): 566–806. http://dx.doi.org/10.1561/2200000079.
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MacGillivray, Helen. "Data science, statistical investigations, team sport, and assessment." Teaching Statistics 41, no. 1 (January 24, 2019): 1–2. http://dx.doi.org/10.1111/test.12189.
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Reid, Nancy. "Statistical science in the world of big data." Statistics & Probability Letters 136 (May 2018): 42–45. http://dx.doi.org/10.1016/j.spl.2018.02.049.
Full textDissertations / Theses on the topic "Statistical data science"
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Alarcón, Soto Yovaninna. "Data science in HIV : statistical approaches for therapeutic HIV vaccine data." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672179.
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The present dissertation contributes to Data Science in the Human lmmunodeficiency Virus (HIV) field, addressing specific issues related to the modelling of data coming from three different clinical trials based on the development of HIV therapeutic vaccines. The biological questions that these studies raise are identify biomarkers that predict HIV viral rebound; explain the time to viral rebound as a consequence of antiretroviral therapy (cART) stop considering the variability of data sources; and find the relationship between spot size and spot count from Enzyme-Linked lmmunosorbent spot (ELISpot) assays data. To handle these problems from a statistical perspective, in this thesis we: adapt the elastic net penalization to the accelerated failure time model with interval-censored data, fit a mixed effects Cox model with interval-censored data, and improve statistical methodologies to deal with ELISpot assays data and a binary response, respectively.
In order to address the variable selection among a vast number of predictors to explain the time to viral rebound, we consideran elastic-net penalization approach within the accelerated failure time model. Elastic-net regularization considers a possible correlation structure among covariates, which is the case of messenger RNA (mRNA) data. For this purpose, we derive the expression of the penalized log-likelihood function for the special case of the interval-censored response (time to viral rebound).
Following, we maximize this function using distinct approaches and optimization methods. Finally, we apply these approaches to the Dendritic Cell-Based Vaccine clinical trial, and we discuss different numerical methods for the maximization of the log-likelihood.
To explain the time to viral rebound in the context of another study with data from several clinical trials, we use a mixed effects Cox model to account for the data heterogeneity. This model allows us to handle the heterogeneity between the Analytical Treatment lnterruption (ATI) studies and the fact that the patients had different number of ATI episodes. Our method proposes the use of a multiple imputation approach based on a truncated Weibull distribution to replace the interval-censored by imputed survival times. Our simulation studies show that our method has desirable properties in terms of accuracy and precision of the estimators of the fixed effects parameters. Concerning the clinical results, the higher the pre-cART VL, the larger the instantaneous risk of a viral rebound. Our method could be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect.
We finally address two different issues that have arisen when analyzing the BCN02 clinical trial. On one hand, we fit univariate log-binomial models as an alternative to the usual logistic regression. On the other hand, we use one/two- way unbalanced ANOVA to analyze the variability of the main outcomes from the ELISpot assays across time. Although these assays are widely used in the context of the HIV study, the relationship between spot size or spot count and other variables has not been studied until now.
In this thesis, we propose, develop, and apply different statistical approaches that contributes to answer diverse clinical questions that are relevant in several clinical trials. We have tried to highlight that to be able to choose the appropriate methodology, make correct clinical interpretations and contribute to a meaningful scientific progress, a narrow collaboration with scientists is necessary. We expect that the original results from this thesis will contribute to the path of development and evaluation of a therapeutic HIV vaccine, helping to improve the way of living of HIV-infected people.La presente tesis contribuye a la ciencia de datos abordando problemas biológicos relevantes en el desarrollo de vacunas terapéuticas para el Virus de Inmunodeficiencia Humana (VIH) mediante la modelización de datos procedentes de tres ensayos clínicos diferentes. Algunas de las cuestiones suscitadas en estos estudios y que esta tesis aborda son: identificar biomarcadores para estudiar los factores de riesgo del rebote viral del VIH, explicar el tiempo transcurrido hasta el rebote viral como consecuencia del cese de la terapia antirretroviral (cART) considerando la variabilidad de las fuentes de datos y estudiar la relación entre las variables spot size y spot count en ensayos inmunoabsorbentes (ELISpot). Para abordar cada uno de estos interrogantes desde una perspectiva estadística, en esta tesis hemos adaptado una penalización de red elástica para el modelo de vida acelerada (AFT) con datos censurados en un intervalo, ajustado un modelo de Cox de efectos mixtos con datos censurados en un intervalo y mejorado las metodologías estadísticas existentes para tratar los datos de los ensayos ELISpot y de respuesta binaria, respectivamente. En primer lugar, hemos abordado el problema de tener más de cinco mil ARN mensajeros (ARNm) para explicar el tiempo hasta el rebote viral. Para ello, hemos considerado un enfoque de penalización de red elástica para el modelo de vida acelerada. Esta regularización considera una posible estructura de correlación entre las covariables, como sucede con los ARNm. Para este objetivo, primero derivamos la expresión de la función de verosimilitud penalizada considerando una respuesta censurada en un intervalo (tiempo hasta el rebote viral). A continuación, maximizamos esta función utilizando distintos enfoques y métodos de optimización. Finalmente, aplicamos estos métodos al ensayo clínico DCV2 y discutimos sobre diferentes enfoques numéricos para la maximización de la verosimilitud. En segundo lugar, para explicar el tiempo hasta el rebote viral proponemos ajustar un modelo de Cox de efectos mixtos. Dado que el tiempo hasta el rebote viral está censurado en un intervalo utilizamos imputación múltiple basada en una distribución de Weibull truncada. Este modelo nos permite controlar la heterogeneidad entre los estudios de interrupción analítica del tratamiento (ATI) y el hecho de que los pacientes tengan diferente número de episodios ATI. Según el estudio de simulación que realizamos, nuestro método tiene propiedades deseables en términos de exactitud y precisión de los estimadores de los parámetros de efectos fijos. Finalmente abordamos dos problemas diferentes dentro del ensayo clínico BCN02. Por un lado, ajustamos modelos log-binomiales univariados como alternativa a la clásica regresión logística. Por otro lado, utilizamos un modelo ANOVA no balanceado para analizar la variabilidad de los resultados principales de los ensayos ELISpot a lo largo del tiempo. Aunque los ensayos ELISpot se usan a menudo en el estudio del VIH, la relación entre variables como el spot size, spot count y otras no se había estudiado hasta ahora. En esta tesis hemos propuesto y desarrollado diferentes enfoques estadísticos que han dado respuesta a preguntas biológicas planteadas en tres ensayos clínicos. En este trabajo se destaca la importancia de que los distintos miembros de un equipo científico-multidisciplinar colaboren estrechamente, para así poder determinar la metodología apropiada, hacer correctas interpretaciones clínicas de los resultados de éste y, de esta forma, contribuir a un progreso científico significativo. Esperamos que los resultados originales de esta tesis contribuyan al desarrollo y la evaluación de una vacuna terapéutica del VIH, lo cual ayudaría notablemente a mejorar la calidad de vida de las personas infectadas por VIH.
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Bruno, Rexanne Marie. "Statistical Analysis of Survival Data." UNF Digital Commons, 1994. http://digitalcommons.unf.edu/etd/150.
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The terminology and ideas involved in the statistical analysis of survival data are explained including the survival function, the probability density function, the hazard function, censored observations, parametric and nonparametric estimations of these functions, the product limit estimation of the survival function, and the proportional hazards estimation of the hazard function with explanatory variables.
In Appendix A these ideas are applied to the actual analysis of the survival data for 54 cervical cancer patients.
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Ramaboa, Kutlwano K. K. M. "A comparative evaluation of data mining classification techniques on medical trauma data." Master's thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/5973.
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Includes bibliographical references (leaves 109-113).The purpose of this research was to determine the extent to which a selection of data mining classification techniques (specifically, Discriminant Analysis, Decision Trees, and three artifical neural network models - Backpropogation, Probablilistic Neural Networks, and the Radial Basis Function) are able to correctly classify cases into the different categories of an outcome measure from a given set of input variables (i.e. estimate their classification accuracy) on a common database.
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Yuan, Yinyin. "Statistical inference from large-scale genomic data." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/1066/.
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This thesis explores the potential of statistical inference methodologies in their applications in functional genomics. In essence, it summarises algorithmic findings in this field, providing step-by-step analytical methodologies for deciphering biological knowledge from large-scale genomic data, mainly microarray gene expression time series. This thesis covers a range of topics in the investigation of complex multivariate genomic data. One focus involves using clustering as a method of inference and another is cluster validation to extract meaningful biological information from the data. Information gained from the application of these various techniques can then be used conjointly in the elucidation of gene regulatory networks, the ultimate goal of this type of analysis. First, a new tight clustering method for gene expression data is proposed to obtain tighter and potentially more informative gene clusters. Next, to fully utilise biological knowledge in clustering validation, a validity index is defined based on one of the most important ontologies within the Bioinformatics community, Gene Ontology. The method bridges a gap in current literature, in the sense that it takes into account not only the variations of Gene Ontology categories in biological specificities and their significance to the gene clusters, but also the complex structure of the Gene Ontology. Finally, Bayesian probability is applied to making inference from heterogeneous genomic data, integrated with previous efforts in this thesis, for the aim of large-scale gene network inference. The proposed system comes with a stochastic process to achieve robustness to noise, yet remains efficient enough for large-scale analysis. Ultimately, the solutions presented in this thesis serve as building blocks of an intelligent system for interpreting large-scale genomic data and understanding the functional organisation of the genome.
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Guo, Danni. "Contributions to spatial uncertainty modelling in GIS : small sample data." Doctoral thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/19031.
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Includes bibliographical references.Environmental data is very costly and difficult to collect and are often vague (subjective) or imprecise in nature (e.g. hazard level of pollutants are classified as "harmful for human beings"). These realities in practise (fuzziness and small datasets) leads to uncertainty, which is addressed by my research objective: "To model spatial environmental data with .fuzzy uncertainty, and to explore the use of small sample data in spatial modelling predictions, within Geographic Information System (GIS)." The methodologies underlying the theoretical foundations for spatial modelling are examined, such as geostatistics, fuzzy mathematics Grey System Theory, and (V,·) Credibility Measure Theory. Fifteen papers including three journal papers were written in contribution to the developments of spatial fuzzy and grey uncertainty modelling, in which I have a contributed portion of 50 to 65%. The methods and theories have been merged together in these papers, and they are applied to two datasets, PM10 air pollution data and soil dioxin data. The papers can be classified into two broad categories: fuzzy spatial GIS modelling and grey spatial GIS modelling. In fuzzy spatial GIS modelling, the fuzzy uncertainty (Zadeh, 1965) in environmental data is addressed. The thesis developed a fuzzy membership grades kriging approach by converting fuzzy subsets spatial modelling into membership grade spatial modelling. As this method develops, the fuzzy membership grades kriging is put into the foundation of the credibility measure theory, and approached a full data-assimilated membership function in terms of maximum fuzzy entropy principle. The variable modelling method in dealing with fuzzy data is a unique contribution to the fuzzy spatial GIS modelling literature. In grey spatial GIS modelling, spatial predictions using small sample data is addressed. The thesis developed a Grey GIS modelling approach, and two-dimensional order-less spatially observations are converted into two one-dimensional ordered data sequences. The thesis papers also explored foundational problems within the grey differential equation models (Deng, 1985). It is discovered the coupling feature of grey differential equations together with the help of e-similarity measure, generalise the classical GM( 1,1) model into more classes of extended GM( 1,1) models, in order to fully assimilate with sample data information. The development of grey spatial GIS modelling is a creative contribution to handling small sample data.
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Smith, Jeremy Stewart. "A statistical approach to automated detection of multi-component radio sources." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/32986.
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Advances in radio astronomy are allowing for deeper and wider areas of the sky to be observed than ever before. Source counts of future radio surveys are expected to number in the tens of millions. Source finding techniques are used to identify sources in a radio image, however, these techniques identify single distinct sources and are challenged to identify multi-component sources, that is to say, where two or more distinct sources belong to the same underlying physical phenomenon, such as a radio galaxy. Identification of such phenomena is an important step in generating catalogues from surveys on which much of the radio astronomy science is based. Historically, identifying multi-component sources was conducted by visual inspection, however, the size of future surveys makes manual identification prohibitive. An algorithm to automate this process using statistical techniques is proposed. The algorithm is demonstrated on two radio images. The output of the algorithm is a catalogue where nearest neighbour source pairs are assigned a probability score of being a component of the same physical object. By applying several selection criteria, pairs of sources which are likely to be multi-component sources can be determined. Radio image cutouts are then generated from this selection and may be used as input into radio source classification techniques. Successful identification of multi-component sources using this method is demonstrated.
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Rao, Ashwani Pratap. "Statistical information retrieval models| Experiments, evaluation on real time data." Thesis, University of Delaware, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1567821.
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We are all aware of the rise of information age: heterogeneous sources of information and the ability to publish rapidly and indiscriminately are responsible for information chaos. In this work, we are interested in a system which can separate the "wheat" of vital information from the chaff within this information chaos. An efficient filtering system can accelerate meaningful utilization of knowledge. Consider Wikipedia, an example of community-driven knowledge synthesis. Facts about topics on Wikipedia are continuously being updated by users interested in a particular topic. Consider an automatic system (or an invisible robot) to which a topic such as "President of the United States" can be fed. This system will work ceaselessly, filtering new information created on the web in order to provide the small set of documents about the "President of the United States" that are vital to keeping the Wikipedia page relevant and up-to-date. In this work, we present an automatic information filtering system for this task. While building such a system, we have encountered issues related to scalability, retrieval algorithms, and system evaluation; we describe our efforts to understand and overcome these issues.
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Dikkala, Sai Nishanth. "Statistical inference from dependent data : networks and Markov chains." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127016.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 259-270).
In recent decades, the study of high-dimensional probability has taken centerstage within many research communities including Computer Science, Statistics and Machine Learning. Very often, due to the process according to which data is collected, the samples in a dataset have implicit correlations amongst them. Such correlations are commonly ignored as a first approximation when trying to analyze statistical and computational aspects of an inference task. In this thesis, we explore how to model such dependences between samples using structured high-dimensional distributions which result from imposing a Markovian property on the joint distribution of the data, namely Markov Random Fields (MRFs) and Markov chains. On MRFs, we explore a quantification for the amount of dependence and we strengthen previously known measure concentration results under a certain weak dependence condition on an MRF called the high-temperature regime. We then go on to apply our novel measure concentration bounds to improve the accuracy of samples computed according to a certain Markov Chain Monte Carlo procedure. We then show how to extend some classical results from statistical learning theory on PAC-learnability and uniform convergence to training data which is dependent under the high temperature condition. Then, we explore the task of regression on data which is dependent according to an MRF under a stronger amount of dependence than is allowed by the high-temperature condition. We then shift our focus to Markov chains where we explore the question of testing whether a certain trajectory we observe corresponds to a chain P or not. We discuss what is a reasonable formulation of this problem and provide a tester which works without observing a trajectory whose length contains multiplicative factors of the mixing or covering time of the chain P. We finally conclude with some broad directions for further research on statistical inference under data dependence.
by Sai Nishanth Dikkala.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Hennessey, Anthony. "Statistical shape analysis of large molecular data sets." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52088/.
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Protein classification databases are widely used in the prediction of protein structure and function, and amongst these databases the manually-curated Structural Classification of Proteins database (SCOP) is considered to be a gold standard. In SCOP, functional relationships are described by hyperfamily and superfamily categories and structural relationships are described by family, species and protein categories. We present a method to calculate a difference measure between pairs of proteins that can be used to reproduce SCOP2 structural relationship classifications, and that can also be used to reproduce a subset of functional relationship classifications at the superfamily level. Calculating the difference measure requires first finding the best correspondence between atoms in two protein configurations. The problem of finding the best correspondence is known as the unlabelled, partial matching problem. We consider the unlabelled, partial matching problem through a detailed analysis of the approach presented in Green and Mardia (2006). Using this analysis, and applying domain-specific constraints, we develop a new algorithm called GProtA for protein structure alignment. The proposed difference measure is constructed from the root mean squared deviation of the aligned protein structures and a binary similarity measure, where the binary similarity measure takes into account the proportions of atoms matching from each configuration. The GProtA algorithm and difference measure are applied to protein structure data taken from the Protein Data Bank. The difference measure is shown to correctly classify 62 of a set of 72 proteins into the correct SCOP family categories when clustered. Of the remaining 9 proteins, 2 are assigned incorrectly and 7 are considered indeterminate. In addition, a method for deriving characteristic signatures for categories is proposed. The signatures offer a mechanism by which a single comparison can be made to judge similarity to a particular category. Comparison using characteristic signatures is shown to correctly delineate proteins at the family level, including the identification of both families for a subset of proteins described by two family level categories.
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Chaudhuri, Abon. "Geometric and Statistical Summaries for Big Data Visualization." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1382235351.
Full textBooks on the topic "Statistical data science"
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Statistical learning and data science. Boca Raton: CRC Press, 2012.
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Ismay, Chester, and Albert Y. Kim. Statistical Inference via Data Science. Boca Raton : Taylor and Francis, 2019. | Series: Chapman & hall/crc the r series: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780367409913.
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Statistical data analysis. Oxford: Clarendon Press, 1998.
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Chen, Ding-Geng, Jiahua Chen, Xuewen Lu, Grace Y. Yi, and Hao Yu, eds. Advanced Statistical Methods in Data Science. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5.
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Petrucci, Alessandra, Filomena Racioppi, and Rosanna Verde, eds. New Statistical Developments in Data Science. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21158-5.
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D, Maddy, Brew J. S, and Quaternary Research Association, eds. Statistical modelling of quaternary science data. Cambridge: Quaternary Research Association, 1995.
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Commission, United Nations Statistical. Statistical data editing: Impact on data quality. New York: United Nations, 2006.
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Analyzing social science data. London: SAGE, 2002.
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Just plain data analysis: Finding, presenting, and interpreting social science data. 2nd ed. Lanham: Rowman & Littlefield Publishers, 2012.
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Statistics and data analysis for social science. Boston: Allyn & Bacon, 2012.
Find full textBook chapters on the topic "Statistical data science"
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Timbers, Tiffany, Trevor Campbell, and Melissa Lee. "Statistical inference." In Data Science, 315–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003080978-10.
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Rose, Doug. "Applying Statistical Analysis." In Data Science, 27–38. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2253-9_4.
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Dettling, Marcel, and Andreas Ruckstuhl. "Statistical Modelling." In Applied Data Science, 181–203. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11821-1_11.
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Simmons, Jeffrey P., Lawrence F. Drummy, Charles A. Bouman, and Marc De Graef. "Materials Science vs. Data Science." In Statistical Methods for Materials Science, 3–12. Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781315121062-1.
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Mane, Deepa, and Sachin Shelke. "Role of Statistical Methods in Data Science." In Data Science, 21–31. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003283249-2.
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Agresti, Alan, and Maria Kateri. "Introduction to Statistical Science." In Foundations of Statistics for Data Scientists, 1–28. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003159834-1.
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Villalobos Alva, Jalil. "Statistical Data Analysis." In Beginning Mathematica and Wolfram for Data Science, 209–42. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6594-9_6.
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Yang, Ching-Chi, and Lih-Yuan Deng. "Statistical Learning Approaches." In Dimensionality Reduction in Data Science, 169–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_8.
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Yang, Ching-Chi, Max Garzon, and Lih-Yuan Deng. "Conventional Statistical Approaches." In Dimensionality Reduction in Data Science, 79–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_4.
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Agresti, Alan, and Maria Kateri. "Statistical Science: A Historical Overview." In Foundations of Statistics for Data Scientists, 333–40. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003159834-9.
Full textConference papers on the topic "Statistical data science"
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Gibbs, Alison L., and Alex Stringer. "The Fundamental Role of Computation in Teaching Statistical Theory." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.rmcxl.
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What skills, knowledge and habits of mind does a statistician require in order to contribute effectively as an inhabitant of the data science ecosystem? We describe a new course in statistical theory that was developed as part of our consideration of this question. The course is a core requirement in a new curriculum for undergraduate students enrolled in statistics programs of study. Problem solving and critical thinking are developed through both mathematical and computational thinking and all ideas are motivated through questions to be answered from large, open and messy data. Central to the development of the course is the tenet that the use of computation is as fundamental to statistical thinking as the use of mathematics. We describe the course, including its connection to the learning outcomes of our new statistics program of study, and the multiple ways we use and integrate computation.
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Ponomarenko, Alexey. "Reformatting statistical education in Russia: changes in classifications, standards, and programs." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17314.
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In the centrally planned economy the main function of official statistics was monitoring of plans’ execution. Hence, official statisticians had to be experts in economics and bookkeeping, like tax inspectors. Russian statistical education was oriented mostly to official needs and statistics was included in the same educational group as economics. Currently, professional requirements for statisticians have changed. Official statistics lost its control function, and the old reporting system is being replaced by sample surveys that are less onerous for respondents and also less expensive. The statistical agency needs more professionals in survey methodology, as well as statistical managers and mathematicians. In 2015, the new professional standard "statistician" was accepted in Russia in line with ISCO 2008. It consists of set of competences for professionals in data collection, processing, analysis and methodology in any field of activity, including business, finance, science, medicine. In 2016, the educational classification of statistics was changed to the same group as mathematics. A new educational standard for statistics was also accepted. This presentation describes part of the reconstructive process.
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von Bing, Yap. "What can statistics education offer to data science?" In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.ljrkt.
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Data science relies heavily on statistical ideas, though it seems more concerned with prediction than statistics, which is more focused on modeling the data production process. This article will argue that the data scientist will do well to pay more attention to the likely disconnect between the chosen statistical model and the process it tries to emulate. Three learning goals are proposed and illustrated with elementary examples to help students grasp the idea. The disconnect is relevant to the replication crisis, yet is inadequately discussed in statistical communities. The lessons here are applicable to the education of statisticians.
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Gould, Robert, Suyen Machado, Christine Ong, Terri Johnson, James Molyneux, Steve Nolen, Hongsuda Tangmunarunkit, LeeAnn Trusela, and Linda Zanontian. "Teaching data science to secondary students: the mobilize introduction to data science curriculum." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16402.
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Making sense of data is complex, and the knowledge and skills required to understand "Big Data" - and many open data sources - go beyond those taught in traditional introductory statistics courses. The Mobilize project has created and implemented a course for secondary students, Introduction to Data Science (IDS), that aims to develop computational and statistical thinking skills so that students can access and analyze data from a variety of traditional and non-traditional sources. Although the course does not directly address open source data, such data are used in the curriculum, and an outcome of the project is to develop skills and habits of mind that allow students to use open source data to understand their community. This paper introduces the course and describes some of the challenges in its implementation.
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Puloka, Malia S., Stephanie Budgett, and Maxine Pfannkuch. "Statistical education and official statistics - training future data scientists." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.lciru.
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EMOS (The European Master in Official Statistics) was set up to strengthen the collaboration within academia and producers of official statistics and help develop professionals able to work with European official data at different levels in the fast-changing production system of the 21st century. In this paper we address the need for training in Official Statistics, particularly in current times, where new skill sets and competencies are necessary. In particular, the needs for new data sources currently used by national statistical systems require the development of new methodologies. For that purpose, we do a matching between National Statistical Offices (NSO) needs and the offer from universities.
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Silva, Maria Eduarda, and Pedro Campos. "Statistical education and official statistics - training future data scientists." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.tqrje.
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EMOS (The European Master in Official Statistics) was set up to strengthen the collaboration within academia and producers of official statistics and help develop professionals able to work with European official data at different levels in the fast-changing production system of the 21st century. In this paper we address the need for training in Official Statistics, particularly in current times, where new skill sets and competencies are necessary. In particular, the needs for new data sources currently used by national statistical systems require the development of new methodologies. For that purpose, we do a matching between National Statistical Offices (NSO) needs and the offer from universities.
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Lampropoulos, Panos. "Statistical inversion of the LOFAR EoR data." In ISKAF2010 Science Meeting. Trieste, Italy: Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.112.0071.
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Schwab-McCoy, Aimee. "Developing a first-year seminar course in statistics and data science." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16307.
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Statistical literacy is an increasingly important skill for today’s students. Undergraduate enrollments in statistics at both the introductory and advanced level have skyrocketed, and statistics education researchers have done much to modernize the curricula and increase student engagement. The course describes in this paper explores an alternative approach to statistical literacy and data science: a discussion-oriented, first-year seminar course. The seminar course emphasizes real data problems, student-led discussions and critiques, and the use of statistics in media and policy decisions. This paper will discuss the structure and justification, the content, and challenges instructors may face when adapting the course to their own institution.
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McKinney, Wes. "Data Structures for Statistical Computing in Python." In Python in Science Conference. SciPy, 2010. http://dx.doi.org/10.25080/majora-92bf1922-00a.
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Bantilan, Niels. "pandera: Statistical Data Validation of Pandas Dataframes." In Python in Science Conference. SciPy, 2020. http://dx.doi.org/10.25080/majora-342d178e-010.
Full textReports on the topic "Statistical data science"
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Bates, C. Richards, Melanie Chocholek, Clive Fox, John Howe, and Neil Jones. Scottish Inshore Fisheries Integrated Data System (SIFIDS): Work package (3) final report development of a novel, automated mechanism for the collection of scallop stock data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23449.
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[Extract from Executive Summary] This project, aimed at the development of a novel, automated mechanism for the collection of scallop stock data was a sub-part of the Scottish Inshore Fisheries Integrated Data Systems (SIFIDS) project. The project reviewed the state-of-the-art remote sensing (geophysical and camera-based) technologies available from industry and compared these to inexpensive, off-the -shelf equipment. Sea trials were conducted on scallop dredge sites and also hand-dived scallop sites. Data was analysed manually, and tests conducted with automated processing methods. It was concluded that geophysical acoustic technologies cannot presently detect individual scallop but the remote sensing technologies can be used for broad scale habitat mapping of scallop harvest areas. Further, the techniques allow for monitoring these areas in terms of scallop dredging impact. Camera (video and still) imagery is effective for scallop count and provide data that compares favourably with diver-based ground truth information for recording scallop density. Deployment of cameras is possible through inexpensive drop-down camera frames which it is recommended be deployed on a wide area basis for further trials. In addition, implementation of a ‘citizen science’ approach to wide area recording is suggested to increase the stock assessment across the widest possible variety of seafloor types around Scotland. Armed with such data a full, statistical analysis could be completed and data used with automated processing routines for future long-term monitoring of stock.
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Bozek, Michael, and Tani Hubbard. Greater Yellowstone Network amphibian monitoring protocol science review: A summary of reviewers’ responses. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293614.
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Science reviews are an essential cornerstone of all excellent science programs and are a requirement of monitoring programs within the Inventory and Monitoring Division of the National Park Service (NPS). Science reviews provide necessary professional critique of objectives, study design, data collection, analysis, scientific interpretation, and how effectively information is transferred to target audiences. Additionally, reviews can help identify opportunities to cooperate more effectively with interested and vested partners to expand the impacts of collective findings across larger landscapes. In December 2020, seven biologists from USGS, USFWS, and NPS provided a critical review of the Greater Yellowstone Network Amphibian Monitoring Protocol for monitoring Columbia spotted frogs (Rana luteiventris), boreal chorus frogs (Pseudacris maculata), western toads (Anaxyrus boreas), western tiger salamanders (Ambystoma mavortium), and environmental conditions at wetland sites clustered within watershed units in Yellowstone and Grand Teton national parks. This review followed sixteen years of GRYN amphibian and wetland monitoring, allowing us to evaluate the impact of the work thus far and to discuss potential improvements to the protocol. Reviewers were asked to assess the following amphibian monitoring objectives per Bennetts et al. (2013, Cooperative amphibian monitoring protocol for the Greater Yellowstone Network: Narrative, version 1.0, https://irma.nps.gov/DataStore/Reference/Profile/2194571) and to assess the degree to which GRYN is meeting the objectives based on the current sampling, analyses, and reporting: Objective 1: Estimate the proportion of catchments and wetland sites used for breeding by each of the four common, native amphibian species annually, and estimate the rate at which their use is changing over time. Objective 2: Determine the total number of wetlands within sampled catchments that are suitable for amphibian breeding (i.e., have standing water during the breeding season) annually. Objective 3: For western toads, estimate the proportion of previously identified breeding areas that are used annually, and estimate the rate at which their use may be changing over time. Generally, reviewers commended the GRYN Amphibian Monitoring Program, including the design, the statistical rigor of current analytical approaches, the large number of monitoring reports and publications, and the audiences reached. Reviewers unanimously felt that the first two objectives of this protocol are being met for two species (Columbia spotted frogs and boreal chorus frogs) in medium- and high-quality catchments, and all but one reviewer also felt these objectives are being met for western tiger salamanders. It was universally recognized that objective 3 for western toads is not being met but reviewers attributed this to issues related to funding and capacity rather than design flaws. Reviewers felt the current design provides an adequate base for parlaying additional work and offered suggestions focused on increasing efficiencies, maximizing information that can be collected in the field, strengthening analyses, and improving scientific outreach. In this document, we summarize reviewers' comments and include their full written reviews in Appendix B.
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.
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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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McGee, Steven, Randi Mcgee-Tekula, and Noelia Baez Rodriguez. Using the Science of Hurricane Resilience to Foster the Development of Student Understanding and Appreciation for Science in Puerto Rico. The Learning Partnership, March 2022. http://dx.doi.org/10.51420/conf.2022.1.
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For school age children on the island of Puerto Rico, the back-to-back hurricane strikes of Irma and Maria were their first experience with the tragedy of hurricanes in Puerto Rico. There is much concern in the general public about the ability of the Puerto Rican forests, like El Yunque, to recover. These concerns reveal common misconceptions about the dynamics of forest ecosystems. The focus of this research is Journey to El Yunque, a middle school curriculum unit that engages students in evidence-based modeling of hurricane disturbance using long-term data about population dynamics after Hurricane Hugo. Research was guided by the following research question: How does engagement in the science of disturbance ecology impact students’ understanding of and appreciation for ecosystems dynamics? Students completed pre and post assessment understanding of ecosystems dynamics and rated the teacher implementation using the Inquiry-Based Science Teaching survey. Based on a paired t-test, students statistically increased their performance from pretest to posttest with an effect size of 0.22. At the teacher level, the Inquiry-Based Instruction score was a statistically significant predictor of the posttest performance. In other words, these results provide evidence that engaging students in the practices of ecology predicted increased understanding of population dynamics.
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Downes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
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The main recommendations of the panel report can be summarised under five key headings: Building the Scottish Bronze Age: Narratives should be developed to account for the regional and chronological trends and diversity within Scotland at this time. A chronology Bronze Age Scotland: ScARF Panel Report iv based upon Scottish as well as external evidence, combining absolute dating (and the statistical modelling thereof) with re-examined typologies based on a variety of sources – material cultural, funerary, settlement, and environmental evidence – is required to construct a robust and up to date framework for advancing research. Bronze Age people: How society was structured and demographic questions need to be imaginatively addressed including the degree of mobility (both short and long-distance communication), hierarchy, and the nature of the ‘family’ and the ‘individual’. A range of data and methodologies need to be employed in answering these questions, including harnessing experimental archaeology systematically to inform archaeologists of the practicalities of daily life, work and craft practices. Environmental evidence and climate impact: The opportunity to study the effects of climatic and environmental change on past society is an important feature of this period, as both palaeoenvironmental and archaeological data can be of suitable chronological and spatial resolution to be compared. Palaeoenvironmental work should be more effectively integrated within Bronze Age research, and inter-disciplinary approaches promoted at all stages of research and project design. This should be a two-way process, with environmental science contributing to interpretation of prehistoric societies, and in turn, the value of archaeological data to broader palaeoenvironmental debates emphasised. Through effective collaboration questions such as the nature of settlement and land-use and how people coped with environmental and climate change can be addressed. Artefacts in Context: The Scottish Chalcolithic and Bronze Age provide good evidence for resource exploitation and the use, manufacture and development of technology, with particularly rich evidence for manufacture. Research into these topics requires the application of innovative approaches in combination. This could include biographical approaches to artefacts or places, ethnographic perspectives, and scientific analysis of artefact composition. In order to achieve this there is a need for data collation, robust and sustainable databases and a review of the categories of data. Wider Worlds: Research into the Scottish Bronze Age has a considerable amount to offer other European pasts, with a rich archaeological data set that includes intact settlement deposits, burials and metalwork of every stage of development that has been the subject of a long history of study. Research should operate over different scales of analysis, tracing connections and developments from the local and regional, to the international context. In this way, Scottish Bronze Age studies can contribute to broader questions relating both to the Bronze Age and to human society in general.
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Hudgens, Bian, Jene Michaud, Megan Ross, Pamela Scheffler, Anne Brasher, Megan Donahue, Alan Friedlander, et al. Natural resource condition assessment: Puʻuhonua o Hōnaunau National Historical Park. National Park Service, September 2022. http://dx.doi.org/10.36967/2293943.
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Natural Resource Condition Assessments (NRCAs) evaluate current conditions of natural resources and resource indicators in national park units (parks). NRCAs are meant to complement—not replace—traditional issue- and threat-based resource assessments. NRCAs employ a multi-disciplinary, hierarchical framework within which reference conditions for natural resource indicators are developed for comparison against current conditions. NRCAs do not set management targets for study indicators, and reference conditions are not necessarily ideal or target conditions. The goal of a NRCA is to deliver science-based information that will assist park managers in their efforts to describe and quantify a park’s desired resource conditions and management targets, and inform management practices related to natural resource stewardship. The resources and indicators emphasized in a given NRCA depend on the park’s resource setting, status of resource stewardship planning and science in identifying high-priority indicators, and availability of data and expertise to assess current conditions for a variety of potential study resources and indicators. Puʻuhonua o Hōnaunau National Historical Park (hereafter Puʻuhonua o Hōnaunau NHP) encompasses 1.7 km2 (0.7 mi2) at the base of the Mauna Loa Volcano on the Kona coast of the island of Hawaiʻi. The Kona coast of Hawaiʻi Island is characterized by calm winds that increase in the late morning to evening hours, especially in the summer when there is also a high frequency of late afternoon or early evening showers. The climate is mild, with mean high temperature of 26.2° C (79.2° F) and a mean low temperature of 16.6° C (61.9° F) and receiving on average 66 cm (26 in) of rainfall per year. The Kona coast is the only region in Hawaiʻi where more precipitation falls in the summer than in the winter. There is limited surface water runoff or stream development at Puʻuhonua o Hōnaunau NHP due to the relatively recent lava flows (less than 1,500 years old) overlaying much of the park. Kiʻilae Stream is the only watercourse within the park. Kiʻilae Stream is ephemeral, with occasional flows and a poorly characterized channel within the park. A stream gauge was located uphill from the park, but no measurements have been taken since 1982. Floods in Kiʻilae Stream do occur, resulting in transport of fluvial sediment to the ocean, but there are no data documenting this phenomenon. There are a small number of naturally occurring anchialine pools occupying cracks and small depressions in the lava flows, including the Royal Fishponds; an anchialine pool modified for the purpose of holding fish. Although the park’s legal boundaries end at the high tide mark, the sense of place, story, and visitor experience would be completely different without the marine waters adjacent to the park. Six resource elements were chosen for evaluation: air and night sky, water-related processes, terrestrial vegetation, vertebrates, anchialine pools, and marine resources. Resource conditions were determined through reviewing existing literature, meta-analysis, and where appropriate, analysis of unpublished short- and long-term datasets. However, in a number of cases, data were unavailable or insufficient to either establish a quantitative reference condition or conduct a formal statistical comparison of the status of a resource within the park to a quantitative reference condition. In those cases, data gaps are noted, and comparisons were made based on qualitative descriptions. Overall, the condition of natural resources within Puʻuhonua o Hōnaunau NHP reflects the surrounding landscape. The coastal lands immediately surrounding Puʻuhonua o Hōnaunau NHP are zoned for conservation, while adjacent lands away from the coast are agricultural. The condition of most natural resources at Puʻuhonua o Hōnaunau NHP reflect the overall condition of ecological communities on the west Hawai‘i coast. Although little of the park’s vegetation...
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Mayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/guxy9244.
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Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
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Garsa, Adam, Julie K. Jang, Sangita Baxi, Christine Chen, Olamigoke Akinniranye, Owen Hall, Jody Larkin, Aneesa Motala, Sydne Newberry, and Susanne Hempel. Radiation Therapy for Brain Metasases. Agency for Healthcare Research and Quality (AHRQ), June 2021. http://dx.doi.org/10.23970/ahrqepccer242.
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Objective. This evidence report synthesizes the available evidence on radiation therapy for brain metastases. Data sources. We searched PubMed®, Embase®, Web of Science, Scopus, CINAHL®, clinicaltrials.gov, and published guidelines in July 2020; assessed independently submitted data; consulted with experts; and contacted authors. Review methods. The protocol was informed by Key Informants. The systematic review was supported by a Technical Expert Panel and is registered in PROSPERO (CRD42020168260). Two reviewers independently screened citations; data were abstracted by one reviewer and checked by an experienced reviewer. We included randomized controlled trials (RCTs) and large observational studies (for safety assessments), evaluating whole brain radiation therapy (WBRT) and stereotactic radiosurgery (SRS) alone or in combination, as initial or postoperative treatment, with or without systemic therapy for adults with brain metastases due to non-small cell lung cancer, breast cancer, or melanoma. Results. In total, 97 studies, reported in 190 publications, were identified, but the number of analyses was limited due to different intervention and comparator combinations as well as insufficient reporting of outcome data. Risk of bias varied; 25 trials were terminated early, predominantly due to poor accrual. Most studies evaluated WBRT, alone or in combination with SRS, as initial treatment; 10 RCTs reported on post-surgical interventions. The combination treatment SRS plus WBRT compared to SRS alone or WBRT alone showed no statistically significant difference in overall survival (hazard ratio [HR], 1.09; confidence interval [CI], 0.69 to 1.73; 4 RCTs; low strength of evidence [SoE]) or death due to brain metastases (relative risk [RR], 0.93; CI, 0.48 to 1.81; 3 RCTs; low SoE). Radiation therapy after surgery did not improve overall survival compared with surgery alone (HR, 0.98; CI, 0.76 to 1.26; 5 RCTs; moderate SoE). Data for quality of life, functional status, and cognitive effects were insufficient to determine effects of WBRT, SRS, or post-surgical interventions. We did not find systematic differences across interventions in serious adverse events radiation necrosis, fatigue, or seizures (all low or moderate SoE). WBRT plus systemic therapy (RR, 1.44; CI, 1.03 to 2.00; 14 studies; moderate SoE) was associated with increased risks for vomiting compared to WBRT alone. Conclusion. Despite the substantial research literature on radiation therapy, comparative effectiveness information is limited. There is a need for more data on patient-relevant outcomes such as quality of life, functional status, and cognitive effects.
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Lazonick, William, Philip Moss, and Joshua Weitz. Equality Denied: Tech and African Americans. Institute for New Economic Thinking, February 2022. http://dx.doi.org/10.36687/inetwp177.
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Thus far in reporting the findings of our project “Fifty Years After: Black Employment in the United States Under the Equal Employment Opportunity Commission,” our analysis of what has happened to African American employment over the past half century has documented the importance of manufacturing employment to the upward socioeconomic mobility of Blacks in the 1960s and 1970s and the devastating impact of rationalization—the permanent elimination of blue-collar employment—on their socioeconomic mobility in the 1980s and beyond. The upward mobility of Blacks in the earlier decades was based on the Old Economy business model (OEBM) with its characteristic “career-with-one-company” (CWOC) employment relations. At its launching in 1965, the policy approach of the Equal Employment Opportunity Commission assumed the existence of CWOC, providing corporate employees, Blacks included, with a potential path for upward socioeconomic mobility over the course of their working lives by gaining access to productive opportunities and higher pay through stable employment within companies. It was through these internal employment structures that Blacks could potentially overcome barriers to the long legacy of job and pay discrimination. In the 1960s and 1970s, the generally growing availability of unionized semiskilled jobs gave working people, including Blacks, the large measure of employment stability as well as rising wages and benefits characteristic of the lower levels of the middle class. The next stage in this process of upward socioeconomic mobility should have been—and in a nation as prosperous as the United States could have been—the entry of the offspring of the new Black blue-collar middle class into white-collar occupations requiring higher educations. Despite progress in the attainment of college degrees, however, Blacks have had very limited access to the best employment opportunities as professional, technical, and administrative personnel at U.S. technology companies. Since the 1980s, the barriers to African American upward socioeconomic mobility have occurred within the context of the marketization (the end of CWOC) and globalization (accessibility to transnational labor supplies) of high-tech employment relations in the United States. These new employment relations, which stress interfirm labor mobility instead of intrafirm employment structures in the building of careers, are characteristic of the rise of the New Economy business model (NEBM), as scrutinized in William Lazonick’s 2009 book, Sustainable Prosperity in the New Economy? Business Organization and High-Tech Employment in the United States (Upjohn Institute). In this paper, we analyze the exclusion of Blacks from STEM (science, technology, engineering, math) occupations, using EEO-1 employment data made public, voluntarily and exceptionally, for various years between 2014 and 2020 by major tech companies, including Alphabet (Google), Amazon, Apple, Cisco, Facebook (now Meta), Hewlett Packard Enterprise, HP Inc., Intel, Microsoft, PayPal, Salesforce, and Uber. These data document the vast over-representation of Asian Americans and vast under-representation of African Americans at these tech companies in recent years. The data also shine a light on the racial, ethnic, and gender composition of large masses of lower-paid labor in the United States at leading U.S. tech companies, including tens of thousands of sales workers at Apple and hundreds of thousands of laborers & helpers at Amazon. In the cases of Hewlett-Packard, IBM, and Intel, we have access to EEO-1 data from earlier decades that permit in-depth accounts of the employment transitions that characterized the demise of OEBM and the rise of NEBM. Given our findings from the EEO-1 data analysis, our paper then seeks to explain the enormous presence of Asian Americans and the glaring absence of African Americans in well-paid employment under NEBM. A cogent answer to this question requires an understanding of the institutional conditions that have determined the availability of qualified Asians and Blacks to fill these employment opportunities as well as the access of qualified people by race, ethnicity, and gender to the employment opportunities that are available. Our analysis of the racial/ethnic determinants of STEM employment focuses on a) stark differences among racial and ethnic groups in educational attainment and performance relevant to accessing STEM occupations, b) the decline in the implementation of affirmative-action legislation from the early 1980s, c) changes in U.S. immigration policy that favored the entry of well-educated Asians, especially with the passage of the Immigration Act of 1990, and d) consequent social barriers that qualified Blacks have faced relative to Asians and whites in accessing tech employment as a result of a combination of statistical discrimination against African Americans and their exclusion from effective social networks.
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Evidence Synthesis and Meta-Analysis for Drug Safety. Council for International Organizations of Medical Sciences (CIOMS), 2016. http://dx.doi.org/10.56759/lela7055.
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At any point in the drug development process, systematic reviews and meta-analysis can provide important information to guide the future path of the development programme and any actions that might be needed in the post-marketing setting. This report gives the rationale for why and when a meta-analysis should be considered, all in the context of regulatory decision-making, and the tasks, data collection, and analyses that need to be carried out to inform those decisions. -- There is increasing demand by decision-makers in health care, the biopharmaceutical industry, and society at large to have access to the best available evidence on benefits and risks of medicinal products. The best strategy will take an overview of all the evidence and where it is possible and sensible, combine the evidence and summarize the results. For efficacy, the outcomes generally use the same or very similar predefined events for each of the trials to be included. Most regulatory guidance and many Cochrane Collaboration reviews have usually given more attention to assessment of benefits, while issues around combining evidence on harms have not been as well-covered. However, the (inevitably) unplanned nature of the data on safety makes the process more difficult. -- Combining evidence on adverse events (AEs), where these were not the focus of the original studies, is more challenging than combining evidence on pre-specified benefits. This focus on AEs represents the main contribution of the current CIOMS X report. The goal of the CIOMS X report is to provide principles on appropriate application of meta-analysis in assessing safety of pharmaceutical products to inform regulatory decision-making. This report is about meta-analysis in this narrow area, but the present report should also provide conceptually helpful points to consider for a wider range of applications, such as vaccines, medical devices, veterinary medicines or even products that are combinations of medicinal products and medical devices. -- Although some of the content of this report describes highly technical statistical concepts and methods (in particular Chapter 4), the ambition of the working group has been to make it comprehensible to non-statisticians for its use in clinical epidemiology and regulatory science. To that end, Chapters 3 and 4, which contain the main technical statistical aspects of the appropriate design, analysis and reporting of a meta-analysis of safety data are followed by Chapter 5 with a thought process for evaluating the findings of a meta-analysis and how to communicate these.