The SIParCS Program at the National Center for Atmospheric Research is aimed at university students who are interested in pursuing a career in such areas as:

• Application Optimization
• Computer Security and Formal Verification
• Data Science
• Numerical Methods
• Software Engineering
• Supercomputing Systems Operations
• Visualization

2019 Technical Projects and Program Intern Applications are now available!  Applications due January 11, 2019

These eight animated short videos explain ecological data, concepts, or techniques, including: "Ecological Forecasting: The Science of Predicting Ecosystems,"Eddy Covariance: Measuring an Ecosystem's Breath," and Mapping the Invisible: Introduction to Spectral Remote Sensing."

Students use their number sense to make observations and come up with reasonable guesses or explanations for the patterns shown. Forked version with all infectious disease graphs.

• All students should and can learn to formulate their own questions
• All educators can easily teach the skill as part of their regular practice

The Darwin's Finch Morphology database contains data, mostly morphological (e.g., beak dimensions), on individual Darwin’s finches. The data are from a number of published and unpublished sources.

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Bibliography

• Price, F. and Donovan, S. (2006). "BioQUEST Library VII: BIRDD: Beagle Investigations Return with Darwinian Data Version 2.0 (User Notes)."  BioQUEST Curriculum Consortium.
• Lack, D.L. (1945). “The Galápagos finches (Geospizinae): a study in variation.” Occasional Papers of the California Academy of Sciences 21: 1-159.
• Lack, D.L. (1947). Darwin's Finches: an essay on the general biological theory of evolution. Cambridge, England, Cambridge University Press.
• Lack, D.L. (1969). “Subspecies and sympatry in Darwin's Finches.” Evolution 23: 252-263.
• Snodgrass, R. E. and E. Heller (1904). “Papers from the Hopkins-Stanford Galapagos Expedition, 1898-99 XVI. Birds.” Proc. Wash. Acad. Sci. 5: 231-372.
• Sulloway, F. J. (1982). “The Beagle collections of Darwin's Finches (Geospizinae).” Bulletin of the British Museum (Natural History), Zoology series 43: 49-94.
• Swarth, H. S. (1931). “The avifauna of the Galapagos Islands.” Occ. Pap. Calif. Acad. Sci 18: 5-299.

The Data Science for Social Good Fellowship is a University of Chicago summer program to train aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Working closely with governments and nonprofits, fellows take on real-world problems in education, health, energy, public safety, transportation, economic development, international development, and more. For three months they learn, hone, and apply their data science, analytical, and coding skills, collaborate in a fast-paced atmosphere, and learn from mentors coming from industry and academia.

Each summer, SOARS protégés spend ten weeks conducting original research at the National Center for Atmospheric Research (NCAR) or at laboratories of other SOARS sponsors. By the end of the summer, protégés will prepare scientific papers and present their research at a colloquium. SOARS protégés can participate for up to four summers so that they can explore the breadth of geoscience and transition smoothly into graduate school.

We are an international mentorship group with a focus on helping more women become active participants and leaders in the Python open-source community. Our mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events and social gatherings.

PyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. Anyone with an interest in Python is encouraged to participate!

Data for Democracy is a worldwide community of passionate volunteers working together to promote trust and understanding in data and technology.

Data for Black Lives is a group of activists, organizers, and mathematicians committed to the mission of using data science to create concrete and measurable change in the lives of Black people.

Girls Who Code was founded with a single mission: to close the gender gap in technology.

The mission of NumFOCUS is to promote sustainable high-level programming languages, open code development, and reproducible scientific research. We accomplish this mission through our educational programs and events as well as through fiscal sponsorship of open source scientific computing projects. We aim to increase collaboration and communication within the data science and scientific computing community.

SACNAS is an inclusive organization dedicated to fostering the success of Chicanos/Hispanics and Native Americans, from college students to professionals, in attaining advanced degrees, careers, and positions of leadership in STEM.

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.

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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.