Ten simple rules for building an antiracist lab.

Demographics of the science, technology, engineering, and mathematics (STEM) workforce and student body in the US and Europe continue to show severe underrepresentation of Black, Indigenous, and people of color (BIPOC). Among the documented causes of the persistent lack of diversity in STEM are bias, discrimination, and harassment of members of underrepresented minority groups (URMs). These issues persist due to continued marginalization, power imbalances, and lack of adequate policies against misconduct in academic and other scientific institutions. All scientists can play important roles in reversing this trend by shifting the culture of academic workplaces to intentionally implement equitable and inclusive policies, set norms for acceptable workplace conduct, and provide opportunities for mentorship and networking. As scientists are increasingly acknowledging the lack of racial and ethnic diversity in science, there is a need for clear direction on how to take antiracist action. Here we present 10 rules to help labs develop antiracists policies and action in an effort to promote racial and ethnic diversity, equity, and inclusion in science.

Interdisciplinarity research based on NSFC-sponsored projects: A case study of mathematics in Chinese universities.

We investigate the interdisciplinarity of mathematics based on an analysis of projects sponsored by the NSFC (National Natural Science Foundation of China). The motivation of this study lies in obtaining an efficient method to quantify the research interdisciplinarities, revealing the research interdisciplinarity patterns of mathematics discipline, giving insights for mathematics scholars to improve their research, and providing empirical supports for policy making. Our data set includes 6147 NSFC-sponsored projects implemented by 3225 mathematics professors in 177 Chinese universities with established mathematics departments. We propose the weighted-mean DIRD (diversity of individual research disciplines) to quantify interdisciplinarity. In addition, we introduce the matrix computation method, discover several properties of such a matrix, and make the computation cost significantly lower than the bitwise computation method. Finally, we develop an automatic DIRD computing system. The results indicate that mathematics professors at top normal universities in China exhibit strong interdisciplinarity; mathematics professors are most likely to conduct interdisciplinary research involving information science (research department), computer science (research area), computer application technology (research field), and power system bifurcation and chaos (research direction).

Gender Representation on Journal Editorial Boards in the Mathematical Sciences.

We study gender representation on the editorial boards of 435 journals in the mathematical sciences. Women are known to comprise approximately 15% of tenure-stream faculty positions in doctoral-granting mathematical sciences departments in the United States. Compared to this group, we find that 8.9% of the 13067 editorships in our study are held by women. We describe group variations within the editorships by identifying specific journals, subfields, publishers, and countries that significantly exceed or fall short of this average. To enable our study, we develop a semi-automated method for inferring gender that has an estimated accuracy of 97.5%. Our findings provide the first measure of gender distribution on editorial boards in the mathematical sciences, offer insights that suggest future studies in the mathematical sciences, and introduce new methods that enable large-scale studies of gender distribution in other fields.

Modelización matemática de la propagación de enfermedades infecciosas: de dónde venimos y hacia dónde vamos / Mathematical modelling of the propagation of infectious diseases: where we came from, and where we are going

Es de indudable importancia dotar al gestor sanitario de herramientas que permitan predecir el comportamiento de la propagación de una enfermedad infecciosa, de manera que a partir de las mismas se pueden establecer y simular estrategias de control. El desarrollo de tales herramientas es de carácter pluridisciplinar siendo la base de las mismas, algoritmos o modelos matemáticos que son implementados computacionalmente. En este trabajo se muestra el funcionamiento de estos modelos matemáticos y se detalla su clasificación atendiendo a diferentes factores. Además se lleva a cabo un estudio cuantitativo de los mismos haciendo uso de las principales bases de datos relacionadas con el tema de estudio (Medline y Web of Science). De este trabajo se concluye el importante papel que pueden jugar los modelos matemáticos a la hora de simular los procesos infecciosos y, asimismo, se proponen futuras y eficaces vías de investigación en la modelización matemática de enfermedades infecciosas(AU)

This work deals with the study of the use of mathematical models to simulate the spreading of infectious diseases. There is no doubt about the importance of the use of computational tools that allow the health staff to model and predict the spreading of an infectious disease. Using such tools one can establish and simulate disease control strategies. The development of such technologies is a multidisciplinary issue; in this sense, the mathematical algorithms –that must be computationally implemented- play a central role. The main goal of this work is to highlight among health community the increasing importance of the use of mathematical models for epidemic disease spreading. Consequently, the main features of such models are introduced and their classification is stated taking into account the behavior, the basic population unit or the mathematical objects used. An exhaustive search of related papers through the most important databases (Medline and Web of Science) are performed. The main conclusion obtained from this work is the central role that mathematical models can play in the simulation of epidemic spreading; moreover, some ideas about the future research are stated(AU)

Un modelo estructural de variables cognitivo-motivacionales explicativas del rendimiento académico en Lengua Española y Matemáticas / A structural model of cognitive-motivational variables as explanatory factors of academic achievement in Spanish Language and Mathematics

En los últimos años se vienen desarrollando diversos modelos explicativos que tratan de analizar el valor predictivo de distintos factores en relación al rendimiento escolar, así como los efectos directos e indirectos que se producen entre ellos. Nuestro objetivo es contrastar un modelo estructural que integra algunas variables de tipo cognitivo y motivacional que influyen en el rendimiento de los alumnos en las dos áreas instrumentales básicas de nuestro curriculo: Lengua Española y Matemáticas. Entre estas variables incluimos las aptitudes diferenciales, el autoconcepto específico, las orientaciones de meta, el esfuerzo y las estrategias de aprendizaje. La muestra está compuesta por 341 alumnos españoles de primer curso de Educación Secundaria Obligatoria. Empleamos distintas pruebas y cuestionarios para la evaluación cada una de ellas y aplicamos SEM para contrastar las relaciones del modelo inicial. El modelo propuesto obtiene un ajuste satisfactorio en las dos áreas estudiadas, siendo significativas la totalidad de las relaciones hipotetizadas. La aptitud matemática y verbal es la variable con mayor poder explicativo sobre el rendimiento académico. También destaca la influencia directa del autoconcepto específico sobre el rendimiento, las orientaciones de meta y el esfuerzo, así como la capacidad mediadora del esfuerzo y de las estrategias de aprendizaje entre las metas académicas y el rendimiento final (AU)

In recent years, several explanatory models have been developed which attempt to analyse the predictive worth of various factors in relation to academic achievement, as well as the direct and indirect effects that they produce. The aim of this study was to examine a structural model incorporating various cognitive and motivational variables which influence student achievement in the two basic core skills in the Spanish curriculum: Spanish Language and Mathematics. These variables included differential aptitudes, specific self-concept, goal orientations, effort and learning strategies. The sample comprised 341 Spanish students in their first year of Compulsory Secondary Education. Various tests and questionnaires were used to assess each student, and Structural Equation Modelling (SEM) was employed to study the relationships in the initial model. The proposed model obtained a satisfactory fit for the two subjects studied, and all the relationships hypothesised were significant. The variable with the most explanatory power regarding academic achievement was mathematical and verbal aptitude. Also notable was the direct influence of specific self-concept on achievement, goal-orientation and effort, as was the mediatory effect that effort and learning strategies had between academic goals and final achievement (AU)