About ADSAADSA's mission is to advance the uptake of data science best practices in academia and promote conversation about the ethical implications of data science and its methods in higher education and beyond. Our vision is that data science is integrated into university research and training across disciplines, and that all data scientists openly engage with the ethical implications of their work. To accomplish this mission, we: Connect, engage, and empower data science researchers and educators to discover, build, and share resources and opportunities.Academic Data Science AllianceADSA_2b8d7944-7934-11ee-bfbf-3d202b83ea00A community network for academic data science leaders, practitioners, and educatorsCommunity InitiativesThe Academic Data Science Alliance is a project of Community Initiatives, a 501(c)(3) nonprofit organization.Data Science LeadersData Science PractitionersData Science EducatorsHigher EducationThe Academic Data Science Alliance (ADSA) is a community of leaders, practitioners, and educators who thoughtfully integrate data science and AI best practices in higher education. Our members connect and share their data-intensive approaches and responsible applications. Help us ensure data science is built by and for everyone!ADSA TeamMicaela ParkerMicaela Parker is the Founder and Executive Director of the Academic Data Science Alliance (ADSA). Before launching ADSA, Micaela worked for the Moore-Sloan Data Science Environments and was an Executive Director for the University of Washington’s eScience Institute. At eScience, she developed research and training programs, and participated in strategic planning and fiscal oversight. Based on her years of experience, Micaela now offers consulting for emerging data science initiatives in academia. Prior to her entry into data science, Micaela was a research scientist for 10 years in the University of Washington's School of Oceanography where she managed the Center for Human Health and Ocean Studies. She continues to hold the title of eScience Data Science Fellow and she is a Research Scholar with the Ronin Institute for Independent Scholarship. Micaela is an avid skier and enjoys mountain biking and snorkeling/SCUBA diving. She is terrible at baking and dancing, but continues to do both when no one is watching.Megan AtkinsonMegan Atkinson is the Program Associate for the Academic Data Science Alliance (ADSA). Prior to that she worked for the National Academies Keck Futures Initiative (NAKFI) from 2004 until the conclusion of the program in 2018. While with NAKFI she provided program and administrative support for multiple and complex programs / projects. Performed all administrative functions for the grants program, committee coordination, pre-conference, onsite, and post-conference logistics. She also played an integral part in wrapping up the program by launching the first ever NAKFI Challenge which awarded 1.5 million to alumni of the program to develop a project that would carry on NAKFI’s legacy of supporting interdisciplinary research into the future. She studied Communications at San Diego State University and has spent most her life in Southern California. In her free time she loves to go on long walks with her dog and cook for her friends and family.Steve Van TuylSteve Van Tuyl is the Program Manager for the Academic Data Science Alliance, where he is responsible for identifying and shepherding special projects within the ADSA community. Before joining ADSA, he was variously a digital repository librarian, research data librarian, and computer science librarian at Oregon State University and Carnegie Mellon University. He was also an active member of the Samvera open source repository community, helping coordinate resources for and development of the Hyrax repository solution bundle. Steve was trained as an ecologist at Colorado College and Oregon State University, and worked for many years with the US Forest Service researching the effects of natural and human disturbances on terrestrial carbon cycles. Steve likes to bake bread and tries to run far.Stella MinStella Min is the Community Coordinator for ADSA. Prior to joining ADSA, she analyzed health economics and outcomes data as a data scientist for IBM Watson Health's Life Science team. She was also a communications and operations specialist for small businesses that make a big impact on their community. Stella earned her bachelors in economics and sociology from the University of Colorado Denver. She earned her PhD in sociology with an emphasis in demography from Florida State University, where her research and training was supported by the National Science Foundation's Graduate Research Fellowship. Stella enjoys hiking, reading, cooking, and board games.Veronica WoodliefVeronica Woodlief is the Communications Coordinator for ADSA. Before joining ADSA, she was the Business and Digital Marketing Manager for a health and wellness company, and has done communications for a variety of academic programs. She earned her degree in Geoenvironmental Studies at Shippensburg University of Pennsylvania, and spent years researching the impacts of climate change-induced sea level rise on barrier islands in Virginia, Maryland and Florida. In her free time, Veronica teaches yoga and is an avid beachcomber, hiker, paddler, and bookworm.Tiana CurryFormer Team Member ~ Tiana Curry was the Community and Communications Assistant for the Academic Data Science Alliance (ADSA). She also served as a Data Science Fellow for the Environmental Data Science Inclusion Network (EDSIN). She is now working on a Masters degree in Computer Science with Data Science concentration in the USC Viterbi School of Engineering. Before joining ADSA, she was a research assistant for the Clean Energy Transformations Lab at UC Santa Barbara. While attending UC Santa Barbara she was also a research assistant for Dr. Shuji Nakamura’s lab and for the Global Environmental Justice Project. As a research assistant she was responsible for developing Python scripts for data cleaning, data visualizations and for conducting exploratory analysis. She found her passion for data science and research while interning at the Smithsonian Institution Data Science Lab in 2019. Tiana earned her degree in Mathematics and was awarded the Alyce Marita Whitted Memorial Award from UC Santa Barbara in 2020. Tiana loves collecting house plants, is a foodie and loves taking dance classes.ADSA BoardWe are grateful for the support, advice, and thoughtful contributions of our Board:Phil BourneUniversity of Virginia, School of Data ScienceNairanjana (Jan) DasguptaWashington State UniversityDoug HagueUniversity of North Carolina, CharlotteH.V. JagadishChair | University of Michigan Institute for Data Science, MIDASChristine KirkpatrickSan Diego Supercomputer Center, San DiegoPurush PapatlaUniversity of Wisconsin-MilwaukeeTalitha WashingtonAtlanta University Consortium, Data Science InitiativeADSA SponsorsWe are supported fiscally and financially by the following organizations. Their contributions to our efforts make all the difference.Ronin InstituteAlfred P Sloan FoundationNSFGordon and Betty Moore FoundationInstitution MembersAmerican Statistical AssociationThe American Statistical Association is the world's largest community of statisticians and data scientists. It is the second-oldest, continuously operating professional association in the country. Since it was founded in Boston in 1839, the ASA has supported excellence in the development, application, and dissemination of statistical science through meetings, publications, membership services, education, accreditation, and advocacy.The American University of ParisChartered as a liberal arts college in 1962, American University of Paris is today an urban, independent, international university. Data science at AUP spans topics such as machine learning, artificial intelligence, algorithms, statistics and geographical information systems. Teaching and research cover a wide variety of application fields such as law, astrophysics, and literature.Boston University Faculty of Computing & Data SciencesFounded in 2019, the Boston University Faculty of Computing & Data Sciences is a transdisciplinary, degree-granting academic unit that augments existing programs who remain under the traditional university organizational structure. We are propelling data sciences into the future and are actively recruiting faculty, admitting students, and moving to a new state-of-the-art building.Carnegie-Mellon UniversityLearn more about the Carnegie Mellon University Department of Statistics and Data ScienceColumbia University Data Science InstituteThe Columbia University Data Science Institute advances the state-of-the-art in data science; transforms all fields, professions, and sectors through the application of data science; and ensures the responsible use of data to benefit society. We train data scientists, develop innovative technology, foster collaborations, and work with industry to bring promising ideas to market.Florida A&M UniversityThe interdisciplinary Data Science and Engineering Program at Florida A&M University has program coordinators in Computer and Information Science, Mathematics, and Biology, and an MS and BS in Data Science are planned for Fall 2023. Current research is in Cancer, Neuro-degenerative diseases, and viral diseases.Georgia Tech Institute for Data Engineering and ScienceThe Institute for Data Engineering and Science at Georgia Institute of Technology supports research in data science foundations and data-driven discovery. Foundational areas of focus include machine learning, artificial intelligence, high-performance computing, algorithms, statistics, and optimization. The institute supports data-driven research in many areas including astrophysics, chemistry, biology, medicine, materials science, energy, and smart cities.Center of Applied Data Science and AnalyticsThe Center of Applied Data Science and Analytics (CADSA) at Howard University coordinates and facilitates interdisciplinary programs in data science, collaborates with other institutes and centers internal and external to Howard University, and expands research and educational linkages that will include internship and placement programs with sponsoring corporations and government agencies.ICSIThe International Computer Science Institute (ICSI) is an independent non-profit research institute and an affiliate of UC Berkeley. With a focus on scientific excellence and social impact, our work transcends disciplinary boundaries and brings academia, government, industry, and non-profit organizations together to inspire breakthroughs.James Madison UniversityThe data science program at James Madison University is in its infancy but the intended goal is to focus on networks and climate science. To this end, JMU has data science or data analytics incorporated in various forms in many departments. Starting in Fall 2023 the Mathematics & Statistics department has been tasked with building degree and other credentialed programs in data science at JMU.Middle Tennessee Data ScienceMiddle Tennessee State University offers Data Science degrees at all levels (BS, MS, and PhD) that emphasize the interdisciplinary nature of Data Science. MTSU also contains the Data Science Institute, which is an applied research focused entity that strives to help its partners solve complex problems.Mississippi State UniversityEstablished in May of 2022, the Data Science Program at Mississippi State University prepares students to meet the growing demand for data science experts in the context of ongoing digital transformation. The program supports and expands data science within the university under the governance of an intercollege faculty committee.Institute for Data ScienceHeaded by Distinguished Professor David Bader, the Institute for Data Science at the New Jersey Institute of Technology focuses on cutting-edge interdisciplinary research and development in all areas pertinent to digital data. Beyond academic research, the institute interacts closely with the outside world to identify and solve important problems in the modern data-driven economy.Center for Data ScienceThe Center for Data Science is the focal point for New York University's university-wide efforts in Data Science. CDS was established to advance NYU's goal of creating a world-leading Data Science training and research facility, and arming researchers and professionals with the tools to harness the power of Big Data.NC State University Data Science AcademyThe NC State University Data Science Academy offers 1-credit project-based courses at three levels: no-prerequisite, some programming prerequisite and advanced topics. In partnership with the libraries, we offer robust data science consulting services provided by graduate research assistants from across the university. We are developing bespoke education for alumni, extension agents and industry partners. We also run summer programs that involve K-12, undergraduate and graduate students.NorthwesternThe mission of the Northwestern Institute on Complex System is to incubate innovative collaborations that leverage complexity, networks, and data science to address societal challenges. Faculty initiatives nurtured by NICO are transforming areas as disparate as synthetic and quantitative biology, sustainability and resilience engineering, computational social sciences, business, and the law.The Ohio State University Translational Data Analytics InstituteThe Translational Data Analytics Institute at The Ohio State University is a community of researchers at the forefront of interdisciplinary, data-enabled science, scholarship and creative expression with an emphasis on significant societal impact. Its 200+ core faculty and affiliates collaborate across 50+ disciplines to innovate data science and analytics solution for the greater good.Penn StateThe diverse Data Science portfolio at Penn State includes research and educational programs at many of the University's 24 campuses and 16 academic colleges. Academic units offer degree programs aligned with their disciplinary focus, and the interdisciplinary Institute for Computational and Data Sciences (ICDS) provides University-wide data sciences research support.St. Petersburg CollegeLearn more about the data science at St. Petersburg College.Texas A&M Institute of Data ScienceThe Texas A&M Institute of Data Science pursues new approaches to Data Science research, education, operations and partnership. These approaches cross college boundaries to connect elements of Data Science from engineering, technology, science and the humanities, and inform wider social challenges.Data Intensive Studies CenterThe Data Intensive Studies Center at Tufts University is a cross-disciplinary center focused on the integration, acquisition and application of data-intensive research, scholarship and education.University of Amsterdam Data Science CentreThe University of Amsterdam Data Science Centre's mission is to enhance the university’s research by developing, sharing and applying data science methods and technologies. As a coordinating hub within the UvA Library, the centre is uniquely positioned to facilitate knowledge exchange as well as training in data-driven research.The University of ArizonaThe University of Arizona is a globally recognized research leader. Collaboratively enabling expertise and specialists with students, the Data Science Institute coordinates Data Science throughout campus. Data Sciences Academy provides academic programming. The Institute for the Future of Data and Computing expands the base of experiential learning.Berkeley Institute for Data ScienceThe Berkeley Institute for Data Science (BIDS) at the University of California, Berkeley, is a central hub of data-intensive research, open source research software, and data science training programs at the University of California, Berkeley. BIDS facilitates interdisciplinary collaborations across an increasingly diverse data science community of domain and methodological experts from across campus and beyond.Donald Bren School of Information and Computer SciencesUCI Master of Data Science - Empowering Innovation through Data Science ~ As the only computational-focused school in the UC System, the UC Irvine Donald Bren School of Information and Computer Sciences has a unique perspective on the information technology disciplines that allows us a broad foundation from which to build educational programs and research initiatives.University of California San Diego - Halicioglu Data Science InstituteFounded in 2018 as a fully independent academic unit, the mission of the Halıcıoğlu Data Science Institute (HDSI) at the University of California, San Diego is to establish the scientific foundations of data science, develop new methods and infrastructure, and train students and partners to use data science to solve the world’s most pressing problems.The University of Chicago Center for Translational Data ScienceThe Center for Translational Data Science at the University of Chicago is developing the discipline of translational data science and applying it to tackle challenging problems in biology, medicine, healthcare and the environment.University of Delaware Data Science InstituteThe University of Delaware Data Science Institute aims to accelerate research in data science, serving as a nucleating effort to catalyze interdisciplinary research collaborations across fields impacting our society.Illinois NCSA - National Center for Supercomputing ApplicationsThe National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign is a campus-wide interdisciplinary center. Since 1986, we’ve been at the epicenter of supercomputing research, pioneering innovations in computing and data. Our advanced cyberinfrastructure and expertise provide a hub for transdisciplinary research for both academia and industry.UMBCUMBC is a dynamic, R1, public research university integrating teaching, research and service to benefit the citizens of Maryland and the world. Our UMBC community redefines excellence in higher education through an inclusive culture that connects innovative teaching and learning, research across disciplines, and civic engagement.Center for Data Science College of Information & Computer ScienceThe University of Massachusetts, Amherst Center for Data Science fosters research, education, industry collaboration, and public service to make UMass Amherst a destination and partner-of-choice for research in data science. We help companies and organizations meet their growing demand for well-trained data scientists, promote economic development, and support working data scientists in Western Massachusetts.Michigan Institute for Data Science - University of MichiganThe Michigan Institute for Data Science strengthens University of Michigan's research capacity in Data Science and Artificial Intelligence, and enables their transformative use for scientific discovery and lasting societal impact. Its faculty community includes 420 methodologists and domain scientists from all schools and colleges at the Ann Arbor campus, and Dearborn and Flint campuses.UNC School of Data Science and SocietyAt the University of North Carolina School of Data Science and Society, we envision a world made healthy, safe and prosperous for all through data-informed decisions. Our school’s focus on society will allow us to utilize innovative ways to use foundational and translational data science for the public good.RENCIRENCI is a highly collaborative research institute at UNC-Chapel Hill that develops and deploys advanced technologies to enable innovative research and discovery in high performance computing (HPC), informatics, data mining, data linking, and secure computing.School of Data Science at UNC CharlotteThe School of Data Science at UNC Charlotte commits to excellence in education, research, community engagement, and inclusion to shape and lead the future of data science education. We teach students to be responsible and ethical data science practitioners, leaders, and researchers in an increasingly data-driven and global society.Data Institute for Societal Challenges - The University of OklahomaData Institute for Societal Challenges at the University of Oklahoma is creating innovations in data science, artificial intelligence (AI), machine learning (ML), and data-enabled research. DISC develops and grows convergent research teams dedicated to solving local to global-scale challenges.Penn DDDThe University of Pennsylvania'sSchool of Arts and Sciences launched the Data Driven Discovery Initiative in 2021 to promote the development and use of data science in the physical sciences, life sciences, social sciences, and humanities.Goergen Institute for Data ScienceEstablished in 2015, the Goergen Institute for Data Science (GIDS) serves as University of Rochester’s integrative data science hub. The Institute offers a variety of data science degree programs, supports interdisciplinary data science research, and fosters industry-academia data science collaborations.USC ViterbiLocated in the heart of Los Angeles, the University of Southern California is a global center for arts, technology and entrepreneurship that connects students from 64 countries. The USC Viterbi School of Engineering Data Science Program ranges from advanced computing curricula to courses that are accessible to students with no programming or computer science background. USC Viterbi is consistently in the top 10 graduate engineering programs in the U.S. News and World Report rankings.University of Texas at El PasoThe University of Texas at El Paso - Data Science - College of Science ~ The Department of Mathematical Sciences at the University of Texas at El Paso (UTEP) houses data science degrees at the bachelors, masters, and doctoral levels. The programs emphasize moving from theory to practice in data science for working in interdisciplinary settings involving data-intensive analysis.UTSA School of Data ScienceThe School of Data Science (SDS) is a cornerstone of the University of Texas at San Antonio's vision to reach new levels of excellence in serving our students and society. At SDS, we are inspiring and preparing a generation of diverse data scientists to make the world more equitable, informed, and secure.University of TorontoThe University of Toronto Data Sciences Institute (DSI) is a hub and incubator for data science research, training, and partnerships. Our mission is to accelerate the impact of data sciences across disciplines to address pressing societal questions. We facilitate collaboration and the development and application of new methodologies and tools.One Utah Data Science HubThe One Utah Data Science Hub at the University of Utah is a university-wide effort designed to enhance research and infrastructure in data and data-enabled science. Led by Faculty Directors from across the university, the Hub facilitates interdisciplinary research focused on data science through two initiatives and in alignment with the Center for Data Science.University of Virginia School of Data ScienceThe University of Virginia School of Data Science -- the first of its kind in the nation -- is guided by common goals: to further discovery, share knowledge, and make a positive impact on society through collaborative, open, and responsible data science research and education.University of Washington eScience InstituteThe University of Washington eScience Institute was founded in 2008 with the mission to empower researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data. From inception, the eScience Institute has functioned beyond departmental and college walls to exemplify the interdisciplinary breadth and complex dimensions of data science. Today, the Institute has grown to a mature organization with sustained positive impact through our education, research, and community building programs.University of Wisconsin Data ScienceData is reshaping our world, and data science @ uw strives to bring the power of fundamental and applied data science to all fields of study at UW-Madison and beyond. We are committed to fostering an inclusive culture in data science that fuels creativity and discovery.University of Wisconsin MilwaukeeData science at the University of Wisconsin Milwaukee is built on an inclusive vision. We believe you can apply this work and these skills to all facets of life, be it art, business, health, weather or countless other areas. That’s why our data science programs build an appreciation of these broad applications rather than concentrating solely on methods and techniques. The same vision guides how we approach research in data science.Vanderbilt UniversityThe Vanderbilt Data Science Institute accelerates data-driven research, promotes collaboration, and trains future leaders. The institute brings together experts in data science with leaders in all academic fields to spark new discoveries. The institute educates students in data science to become leaders in industry, government, academia and the nonprofit sector.Virginia Commonwealth UniversityData science at the Virginia Commonwealth University is decentralized across five schools and departments, with academic and research foci aligned with the host department disciplines. VCU is an urban public research institution dedicated to the success and well-being of our students, patients, faculty, staff and community. Wake Technical Institute logoThe Data Science and Programming Support Services program at Wake Technical Community College prepares learners to design and develop desktop/web applications with an emphasis on business logic and data‐driven applications.William and MaryThe Data Science program at William & Mary comprises academic offerings at the undergraduate and graduate levels and a growing research portfolio. Through these activities, we prepare students for careers that explore patterns in large data sets and identify potential trends and insights, and advance the state of science in this pursuit.Winston-Salem State University - the Center for Applied Data SciencesFounded in 1892, Winston-Salem State University, home of the Center for Applied Sciences, is a public Historically Black College and University (HBCU) and an University of North Carolina (UNC) institution, where 76% of the student population are African Americans or Hispanic/Latino, 73% are female, and 23% are first generation college students.Research Lab and Small Research Institute MembersBrown DSIThe mission of Brown's Data Science Initiative is to stimulate innovation and support people aspiring to improve lives in our data-driven world. We work with partners in all disciplines to promote data science research and education.The Data Science Collaboratory at Colgate UniversityThe Data Science Collaboratory at Colgate University is a data analysis and collaboration network focused exclusively on smaller colleges and universities in New York State.Harvard Data Science InitiativeThe Harvard Data Science Initiative (HDSI) unites leading computer scientists, statisticians, and domain experts from law, business, public policy, education, medicine, public health, and myriad academic disciplines to derive meaningful and actionable insights that shape the new science of data. Its research drives data-driven policy and analyzes implications of big data for human society.Lehigh UniversityThe Institute for Data, Intelligent Systems, and Computation at Lehigh University builds upon the foundation of Lehigh research expertise in areas such as machine learning, optimization, probabilistic modeling, data-driven decision making, high-performance & data-intensive computing, statistical signal and image processing, data representation & management, modeling & simulation, robotics & computer vision, business & management technology, and privacy & security.UCSB Bren SchoolThe Bren School of Environmental Science & Management Master of Environmental Data Science (MEDS) is a professional degree program at UC Santa Barbara. The 11-month program trains environmental professionals in data science skills.Washington State UniversityThe Data Science program at Washington State University provides training in concentrated domain knowledge, advanced statistical, data, and computer science skills. This combination enables WSU graduate to effectively work in teams and easily communicate with colleagues and managers to solve problems.ADSA PeersWe are honored to share the data science community space with a number of fellow groups who share common goals and common values with us. Please check them out!US-RSEthe U.S. Research Software Engineer AssociationRSEUK-based - the U.K. Society of Research Software EngineeringURSSIUS Research Software Sustainability InstituteReSAResearch Software AllianceRDAResearch Data AllianceCS&SCode for Science & SocietyEDSIN Environmental Data Science Inclusion NetworkA community of researchers and educators who take responsibility for a just, equitable future where data science approaches are thoughtfully applied in all domains for the benefit of all._2b8d7a66-7934-11ee-bfbf-3d202b83ea00To advance the uptake of data science best practices in academia and promote conversation about the ethical implications of data science and its methods in higher education and beyond._2b8d7b42-7934-11ee-bfbf-3d202b83ea00DataWe believe in a just, equitable future where data science approaches are thoughtfully applied in all domains for the benefit of all.ScienceData science is rapidly becoming a new paradigm for research and discovery, integrating approaches from computer science, statistics, applied mathematics, visualization and communication, and many application domains. Data science seeks to extract knowledge and insight from datasets that are often large and/or messy. Innovations in the methods for analyzing, visualizing, and interpreting data (such as Artificial Intelligence, AI) are core to extracting these insights. The far-reaching possibilities of data science and AI have highlighted how critical the field is to data-intensive discovery across all research domains.ResponsibilityResponsible data science means systematically reflecting on and addressing the ethical and societal implications of every decision in the data life cycle, including but not limited to power, bias, privacy and security concerns. Development and use of responsible data science approaches are still limited by two main elements: 1) the general lack of integration of trained socio-technical data scientists and social science concepts into data science research and education, and 2) the need for foundational changes to how we "do science," from how we recognize intellectual contributions to how we infuse responsible, ethical practice in every aspect of data science teaching and research. If you are interested in learning more, check out Catherine D'Ignazio and Lauren F. Klein's book Data Feminism and a recent talk they gave for the Turing Institute [YouTube link].Artificial IntelligenceArtificial Intelligence is a powerful tool that data scientists use to solve research, business, and operational problems. While AI has been around for many decades, recent advances have brought AI to the forefront, and data scientists are paying attention. ADSA and the academic data science community engage with AI in its many forms, for applications in medicine, physical and biological sciences, and beyond. We are also committed to understanding the ethical dimensions of using AI tools in data science, and examining how data scientists can advocate for ethical applications of their research.EmpathyWE ARE AN OPEN AND WELCOMING COMMUNITY. We are a supportive community with empathy and understanding for one another. We embrace open-mindedness and collaboration over competition.Open-MindednessCollaborationLearningWe foster and promote continuous learning, recognizing we all grow personally and professionally throughout our lives.JusticeADSA values and advocates for justice, diversity, equity, and inclusion of all backgrounds and lived experiences in data science and more broadly in academia (including, but certainly not limited to: race, ethnicity, nationality, gender identity and expression, sexual orientation, disability, age, socioeconomic status, religion)DiversityEquityInclusionCommunityWE BRING PEOPLE AND ORGANIZATIONS TOGETHER. We believe data science is built by and for all disciplines, and that this interdisciplinarity contributes to the evolution of the field. ADSA creates opportunities for collaboration and facilitates exchanges among individuals, organizations, and domains to advance the field of data science. These collaborations strengthen our community and communities around us. We promote the inclusion of people and research that inform data science in the context of societal perspectives and impacts on marginalized communities.PerspectiveWE VALUE THE PERSPECTIVES AND EXPERIENCES OF OUR COMMUNITY. We respect and recognize individual and organizational differences, and believe that the inclusion of diverse perspectives in collaborations is beneficial to all of us. ADSA encourages community members to share their perspectives and incorporate the perspectives of others in their work. These conversations allow us to inform and transform best practices in data science.ExperienceHumanityWE ADVOCATE FOR THE HUMANS IN DATA SCIENCE. ADSA encourages reflections on societal contexts in the development of data-driven tools and approaches, especially when these analyses drive policy decisions. ADSA advocates for the inclusion of human impact considerations throughout the data lifecycle. We believe this is best achieved through collaborations across domains and sectors, including with scholars in the social sciences and humanities.RecognitionADSA also advocates for meaningful recognition of the people who create the software and data-intensive workflows that drive research on academic campuses. We believe their work is not sufficiently valued by the metrics that define success in academia. Both faculty and non-faculty data science research and support positions are essential to our research and teaching ecosystems, and the advancement of data science. We advocate for alternative metrics in hiring, promotion and tenure assessments, and the inclusion of core funding for these positions and the maintenance of their outputs.EthicsWE PROMOTE WORKING ETHICALLY, TRANSPARENTLY, AND OPENLY. We believe that data, insight, and knowledge should be accessible and co-created by and for everyone, and that barriers to access and collaboration are a detriment to the field. ADSA recognizes that knowledge should be shared widely and that working openly cultivates a trustworthy and transparent environment at all levels of engagement with the community. ADSA advocates for integrity of data science research and quality of education, communicating needs with the larger community of stakeholders and guiding policy. We know that ethics is essential to the development of data science as a field. We value and promote the incorporation of ethical decision-making in the development and application of data science tools and methods.TransparencyOpennessCommunityConnect our members and share their data-intensive approaches and responsible applications in teaching and research_2b8d7c50-7934-11ee-bfbf-3d202b83ea001The Academic Data Science Alliance (ADSA) is a network of academic data science practitioners, educators, and leaders, and academic-adjacent colleagues, who thoughtfully integrate data science best practices in higher education. Our members connect and share their data-intensive approaches and responsible applications in teaching and research. By sharing lessons learned and collaborating on research and training, our members help each other find the right path for their unique university or college environment.Career Guidebook Serve as a reference for hiring managers and administrators on the motivation, means, and strategies for building and sustaining successful research programs and rewarding career paths for data scientists and research software engineers_2b8d7d36-7934-11ee-bfbf-3d202b83ea001.1Data ScientistsData Scientist and Research Software Engineer positions are growing in academia. The changing nature of computing and research offers opportunities to recognize and support these new types of positions in research teams.Research Software EngineersHiring ManagersMuch of the guidebook is written for hiring managers, though elements are relevant to administrators, human resources employees, funding agencies, or data scientists and research software engineers themselves.AdministratorsHuman Resources EmployeesFunding AgenciesDavid BeckCHAPTER LEAD | University of WashingtonIan CosdenCHAPTER LEAD | Princeton UniversityBlake JoyceCHAPTER LEAD | University of Alabama at BirminghamJing LiuCHAPTER LEAD | University of MichiganChristina MaimoneCHAPTER LEAD | Northwestern UniversityKenton McHenryCHAPTER LEAD | National Center for Supercomputing ApplicationsMicaela ParkerCHAPTER LEAD | Academic Data Science AllianceSteve Van TuylCHAPTER LEAD | Academic Data Science AllianceNicky AgateCHAPTER CONTRIBUTOR | University of PennsylvaniaPeter AlonziCHAPTER CONTRIBUTOR | University of VirginiaArlyn BurgessCHAPTER CONTRIBUTOR | University of VirginiaSean ClevelandCHAPTER CONTRIBUTOR | University of Hawaii SystemAaron CulichCHAPTER CONTRIBUTOR | University of California, BerkeleyAlex DavisCHAPTER CONTRIBUTOR | The Ohio State UniversitySandra GesingCHAPTER CONTRIBUTOR | University of Illinois, ChicagoLauna GreerCHAPTER CONTRIBUTOR | University of ChicagoChris HoldgrafCHAPTER CONTRIBUTOR | 2i2cDaniel S. KatzCHAPTER CONTRIBUTOR | National Center for Supercomputing ApplicationsRay LevyCHAPTER CONTRIBUTOR | North Carolina State UniversityCody MarkelzCHAPTER CONTRIBUTOR | University of California, BerkeleyNirav MerchantCHAPTER CONTRIBUTOR | University of ArizonaScott MichaelCHAPTER CONTRIBUTOR | Indiana UniversityBill MillsCHAPTER CONTRIBUTOR | University of ColoradoTiffany OliverCHAPTER CONTRIBUTOR | Spelman CollegeAlessandro OrsoCHAPTER CONTRIBUTOR | Georgia Institute of TechnologyGina-Maria PomannCHAPTER CONTRIBUTOR | Duke UniversityRahim RasoolCHAPTER CONTRIBUTOR | University of ChicagoKristina RiemerCHAPTER CONTRIBUTOR | University of ArizonaTrevor SpreadburyCHAPTER CONTRIBUTOR | University of ChicagoSarah StoneCHAPTER CONTRIBUTOR | University of WashingtonJess SweeneyCHAPTER CONTRIBUTOR | University of ChicagoKaren TomkoCHAPTER CONTRIBUTOR | Ohio Supercomputer CenterDavid UminskyCHAPTER CONTRIBUTOR | University of ChicagoValeri VasquezCHAPTER CONTRIBUTOR | University of California, BerkeleyJeffrey YoungCHAPTER CONTRIBUTOR | Georgia Institute of TechnologyWill TomlinsonCHAPTER CONTRIBUTOR | Boston UniversityLiz VuCHAPTER CONTRIBUTOR | Sloan FoundationBruce WilsonCHAPTER CONTRIBUTOR | National Aeronautics and Space AdministrationThe ADSA-USRSE Career Guidebook will serve as a reference for hiring managers and administrators on the motivation, means, and strategies for building and sustaining successful research programs and rewarding career paths for data scientists and research software engineers. It also serves as a reference for data scientists and RSEs for how to best engage in a productive and fulfilling career in data science or research software engineering.Support & AdvocacyEnable translational activities and partnerships across academia, other community organizations, foundations, and private and public sectors_2b8d7e12-7934-11ee-bfbf-3d202b83ea002AcademiaCommunity OrganizationsFoundationsPrivate SectorPublic SectorADSA is also a support and advocacy organization enabling translational activities and partnerships across academia, other community organizations, foundations, and private and public sectors. ADSA actively supports activities that bridge methodology and application fields, emphasizing the value that all fields have to contribute to the development and evolution of data-driven research practices. By connecting data science communities across different domains, ADSA hopes to accelerate the advancement and uptake of data science innovation and best practices. Just one example: The ADSA community wrote a letter to the US Department of Homeland Security requesting the CIP code for data science be added to the STEM OPT eligibility list. The letter was signed by 84 individuals representing 49 different institutions and cited in the Federal Register when the data science CIP code was added.Data Science EthosDescribe the stages of research in a typical data science project_2b8d7f48-7934-11ee-bfbf-3d202b83ea002.1Data Science Ethos Working GroupADSA's Data Science Ethos Working Group has designed this new lifecycle model, including example case studies, with participation from community members who are experts in data science and the social sciences. Our model has applications in formal and informal training, guidance for research administrators seeking to move beyond regulatory frameworks for research compliance, and as reference for practicing data scientists.UC Berkeley Human Contexts and Ethics ProgramElements of the Data Science Lifecycle Ethos Tool have already been piloted in classrooms at multiple institutions, and in a workshop for the UC Berkeley Human Contexts and Ethics Program.Journal of Statistics and Data Science EducationA description of the tool is currently under review at the Journal of Statistics and Data Science Education. The next major phase of this project is to continue to gather and refine an initial set of case studies to be included at launch, to complete the build-out of the interactive tool, and to partner with community members for piloting in classroom or research group settings. Efforts are underway for a third case-study focused on clean water to be included in the initial launch of the lifecycle tool.The data science lifecycle model is a ubiquitous tool for describing the stages of research in a typical data science project. While helpful for illustrating parts of the research process, lifecycle tools almost universally omit ethical considerations and societal contexts. By abstracting away the broader societal contexts, these lifecycle models do not adequately capture the way in which data scientists think, and the kinds of questions they must address while doing real world data science work. We see a need for a data science framework that includes explicit societal contexts and makes questions of social good actionable. The result will be a more true-to-life model of the data science lifecycle that shows how societal questions are a constitutive part of the day-to-day work of a data scientist... ^^ ADSA's vision for this project is that it will become a common touchpoint for data science instruction in higher education, starting with members of the ADSA community. To make the tool relevant to the needs of instructors and practitioners, future development of the tool should allow submission of new case studies, allow users to match curricular artifacts to case studies, and offer versions of the tool that can be used off-line.Data Science DegreesProvide a venue for data science programs to illustrate how their programs meet core needs for data science masters degrees_042cfa9e-79bd-11ee-8c78-bbcf0183ea002.2Data Science ProgramsStandardization and Transparency for Data Science Degrees (STIDS) Working GroupOver the past year, the STIDS Working Group has sought to define a roadmap for program transparency to better enable students, employers, and the universities themselves to understand the skills, student learning outcomes, and program best practices available across the spectrum of programs and degrees. The working group started by surveying the landscape of Masters in Data Science programs and educational frameworks, and turned the resultant information into a survey for those implementing Masters programs in data science (or similarly named degrees).Cathy AndersonCONTRIBUTOR | University of VirginiaPurush PapatlaCONTRIBUTOR | University of Wisconsin, MilwaukeeBrian WrightCONTRIBUTOR | University of VirginiaAlexandra JohnsonCONTRIBUTOR | Washington State UniversityMicaela ParkerCONTRIBUTOR | ADSASungjune ParkCONTRIBUTOR | UNC CharlotteH. V. JagadishCONTRIBUTOR | University of MichiganNairanjana DasguptaCONTRIBUTOR | Washington State UniversityAbani PatraCONTRIBUTOR | Tufts UniversityStandardization and Transparency in Data Science Masters Degree Programs ~ With the rapid emergence of data science degree programs it has become challenging for students, faculty, staff, and administrators to meaningfully compare program offerings. To start to address this challenge, ADSA established the Standardization and Transparency for Data Science Degrees (STIDS) Working Group to begin to characterize data science education programs at a variety of levels and create a careful taxonomy for data science competencies. ^^ The ultimate goal of the survey and this project is to provide a venue for data science programs to illustrate how their programs meet the core needs described by existing frameworks, and to highlight what makes their programs unique. Providing this information in an open venue can create a dialog among programs, and with learners and employers, about best practices for data science education.DatasetsProvide a space for curated datasets and related curricula for data science education_042cfd78-79bd-11ee-8c78-bbcf0183ea002.3Ajay AnandCONTRIBUTOR | University of RochesterMelissa CraginCONTRIBUTOR | San Diego Supercomputer CenterPeter FreemanCONTRIBUTOR | Carnegie Mellon UniversityRachel HendricksCONTRIBUTOR | RECODEStephanie HicksCONTRIBUTOR | Johns HopkinsYekaterina KharitonovaCONTRIBUTOR | UC Santa BarbaraBrian MacdonaldCONTRIBUTOR | Yale UniversityPamela ReynoldsCONTRIBUTOR | UC DavisOpen Datasets for Data Science Education ~ Teaching data science often requires instructors to provide example datasets to students who are learning to use tools and methods. While open datasets are increasingly common, open data that is prepared for specific use cases, such as teaching specific tools or methods, is less common. The ADSA community has expressed interest in curating a corpus of open datasets that could be used for data science education, capstone projects, and pedagogical research. While many open datasets exist for teaching and research, the ADSA community plans to provide a space for curated datasets and related curricula that others can modify and share at will. ADSA is also interested in partnering with organizations with similar interest in data science instruction who may benefit from such an open data corpus, such as The Carpentries. ^^ LONG TERM VISION ~ Beyond the creation of an initial corpus of open data, the working group has discussed the possibility of building out infrastructure that would allow users to submit new datasets to the corpus, along with supporting materials such as lesson plans and other curricular elements. Allowing user-submitted content will also require a team (formal or informal) of curators to assist with management of the corpus.2023-11-02https://academicdatascience.org/data-science/about/OwenAmburOwen.Ambur@verizon.net