The Carpentries project comprises Software Carpentry and Data Carpentry, communities of Instructors, Trainers, Maintainers, helpers, and supporters who share a mission to teach foundational computational and data science skills to researchers. The Carpentries subscribes to a shared Code of Conduct. 

Credit: NSF/Stephen Voss

NSF INCLUDES is a comprehensive initiative to enhance U.S. leadership in science and engineering discovery and innovation by proactively seeking and effectively developing science, technology, engineering and mathematics (STEM) talent from all sectors and groups in our society. By facilitating partnerships, communication and cooperation, NSF aims to build on and scale up what works in broadening participation programs to reach underserved populations nationwide.

Three researchers and early National Ecological Observatory Network (NEON) users talk about how NEON will enable new discoveries and collaborations in the field of ecology and beyond.

Search firms serve two roles in diversifying senior leadership. The first is to partner with organizations to identify what they are looking for in a new hire. The second is to help organizations move beyond their own networks to find the best candidates. Despite the relatively high proportion of well-educated people of color in the United States, diversity among management and leadership in a variety of sectors remains limited. In the environmental sector, particularly, people of color comprise only 12 to 16 percent of staff at environmental organizations and agencies (Taylor 2014). Organizations are increasingly turning to executive search firms to assist them in hiring for senior level positions, and often express interest in finding more diverse candidates, thereby, making search firms the gatekeepers of the networks that impact the movement of talent (Faulconbridge, Beaverstalk, Hall and Hewitson 2009).

The increasingly diverse demographics of the United States and the rising share of educational and consequent financial capital possessed by people of color are beginning to force organizations across sectors to rethink models of success and how to ensure sustainability in the future. In the environmental sector, organizations are turning attention to diversifying management and leadership to better reflect the constituencies they serve. In order to do this effectively, mainstream environmental organizations must institute readiness, recruitment, and retention (3Rs) practices that integrate diversity, equity and inclusion into their mission and work. In terms of diversity, equity and inclusion, readiness refers to an organization’s capacity and preparedness to foster diverse viewpoints, support employees and partner organizations through inclusive and equitable practices and culture. Recruitment means the active procurement of diverse talent pools, and retention means building meaningful pathways to promotion and building affinity within the organization so that all differences are valued. 3R best practices are the tools by which an organization meets its diversity challenges, especially at the highest levels, and transforms into a truly inclusive work culture. 3R practices are critical to organizations remaining relevant and developing sustainable solutions to our most pressing environmental problems.

Environmental institutions have been working on diversity efforts for the better part of five decades. This report discusses the findings of a study of three types of environmental institutions: 191 conservation and preservation organizations, 74 government environmental agencies, and 28 environmental grant-making foundations. It also reports the findings of interviews conducted with 21 environmental professionals who were asked to reflect on the state of diversity in environmental institutions. The study focuses primarily on gender, racial, and class diversity in these institutions as it pertains to the demographic characteristics of their boards and staff. It examines the recruitment and hiring of new workers as well as the types of diversity initiatives undertaken by the organizations. The report also discusses other kinds of diversities such as cultural, sexual orientation, intergenerational, and rural-urban.

The Inclusive Ecology section was established in the summer of 2016 in order to provide resources and support for all ecologists, regardless of physical/mental ability, gender identity, ethnicity, socioeconomic status, culture, nationality, family status, politics, religion, level of education, or age. During its establishment, concerns were raised that the Ecological Society of America (ESA) and its sections already strive to be inclusive, and that there are already several programs in place to address issues of diversity. Emerging patterns in our national socio-political climate have rendered establishment of this section especially timely. In the spirit of our mission, we are coordinating efforts to engage people from myriad perspectives in collegial conversation with a common goal of advancing ecological science.

The Women in Data Science (WiDS) initiative aims to inspire and educate data scientists worldwide, regardless of gender, and support women in the field.  WiDS started as a conference at Stanford in November 2015. Now, WiDS includes a global conference, with 150+ regional events worldwide; a datathon, encouraging participants to hone their skills; and a podcast, featuring leaders in the field talking about their work, and their journeys.

NCWIT is a non-profit community that convenes, equips, and unites change leader organizations to increase the meaningful participation of all women — at the intersections of race, ethnicity, class, age, sexual orientation, and disability status — in the influential field of computing, particularly in terms of innovation and development.

Before NCWIT was chartered in 2004 by the National Science Foundation, programs focusing on women and computing existed mostly in isolation — without the benefit of shared best practices, effective resources, communication with others, or national reach. Today, these programs are part of the NCWIT community, creating a far greater impact than if institutions acted alone.

Explore in Radiant

Note: You must be a member of the BIRDD group to explore this data in Radiant.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent.

Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. https://doi.org/10.17226/25104.

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences.

Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences.

Explore in Radiant

Note: You must be a member of the BIRDD group to explore this data in Radiant.

This link takes you to the Code Resources page with the Super Easy R Stacker Script For Beginners, with only 3 lines of code -- and now, no need to mess with file paths -- is to help non- or early-R users combine the NEON site-month data that is downloaded from the NEON data portal. 

This R script allows you to combine multiple months & sites of NEON data downloaded as a .zip file from the NEON data portal (data.neonscience.org).  

This script is only for NEON data products that are delivered as zipped folders of .csv files (most OS and IS data products but not AOP data products). 

Over 6500 specimens that were measured by David Lack. These data were transcribed from records deposited at the California Academy of Sciences.

Explore in Radiant

Note: You must be a member of the BIRDD group to explore this data in Radiant.

Citation

• Lack DL (1947) Darwin's finches: an essay on the general biological theory of evolution, Cambridge University Press.
• Lack DL (1947) Data from: Darwin's finches: an essay on the general biological theory of evolution. Dryad Digital Repository. https://doi.org/10.5061/dryad.150

This is a place where statisticians, epidemiologists, informaticists, machine learning practitioners, and other research methodologists communicate with themselves and with clinical, translational, and health services researchers to discuss issues related to data...

Learn more...

Created by Frank Harrell

from the Statistical Thinking blog by Frank Harrell

This paper describes an innovative way of teaching on problem solving while using inquiry learning methods. 

ABSTRACT

We sought to test a hypothesis that systemic blind spots in active learning are a barrier both for instructors—who cannot see what every student is actually thinking on each concept in each class—and for students—who often cannot tell precisely whether their thinking is right or wrong, let alone exactly how to fix it. We tested a strategy for eliminating these blind spots by having students answer open-ended, conceptual problems using a Web-based platform, and measured the effects on student attrition, engagement, and performance. In 4 years of testing both in class and using an online platform, this approach revealed (and provided specific resolution lessons for) more than 200 distinct conceptual errors, dramatically increased average student engagement, and reduced student attrition by approximately fourfold compared with the original lecture course format (down from 48.3% to 11.4%), especially for women undergraduates (down from 73.1% to 7.4%). Median exam scores increased from 53% to 72–80%, and the bottom half of students boosted their scores to the range in which the top half had scored before the pedagogical switch. By contrast, in our control year with the same active-learning content (but without this “zero blind spots” approach), these gains were not observed.

This lab is the beginning of a 12-week project during which students will clone a gene from the ciliate Tetrahymena thermophila, fuse it to the gene encoding GFP, put the engineered gene back into Tetrahymena, and induce its expression. The gene will be cloned by PCR, so the first step is to design primers to allow the amplification of the desired gene. This lab will take students through the steps to do that, and will also demonstrate the use of other bioinformatics tools.

• Bioinformatics I
• Bioinformatics I - Instructor's Guide

This lab is the beginning of a 13-week project in which students will engineer a construct to knockout a gene from the ciliate Tetrahymena thermophila. The first step is to identify an appropriate gene homolog in Tetrahymena using the databases. Students will then design primers to amplify the gene by PCR and clone it into a plasmid vector.

• Bioinformatics II
• Bioinformatics II - Instructor's Guide

In this module, students assess the relative amount of expression of a gene of interest throughout different stages in the Tetrahymena life cycle. The life cycle is complex and involves a number of physiological changes and DNA processing events (see Tetrahymena Facts). Gene expression is evaluated through assessing the relative production of gene transcripts at different time points in the life cycle by reverse transcriptase PCR, yielding an "expression profile". (Estimated time: 5 x 4-hour laboratory periods.)