Harvard data science review最新文献_第9页

How Can Data Science Revolutionize Humanitarian Crises? 数据科学如何改变人道主义危机？

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.57898732

L. Vittert, Rita Ko

引用次数: 0

Data Science and Energy: Some Lessons from Europe on Higher Education Course Design and Delivery 数据科学与能源:欧洲高等教育课程设计与教学的经验教训

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.fd504fc4

H. Kazmi, Ingrid Munné-Collado, Khaoula Tidriri, L. Nordström, F. Gielen, J. Driesen

{"title":"Data Science and Energy: Some Lessons from Europe on Higher Education Course Design and Delivery","authors":"H. Kazmi, Ingrid Munné-Collado, Khaoula Tidriri, L. Nordström, F. Gielen, J. Driesen","doi":"10.1162/99608f92.fd504fc4","DOIUrl":"https://doi.org/10.1162/99608f92.fd504fc4","url":null,"abstract":"Data science is seen as a key enabler for technologies that help decarbonize global energy use. However, the energy sector continues to struggle to attract and train enough data scientists. The primary reason for this is the lack of emphasis on data science in most graduate programs in energy engineering, and the high barriers of entry for data scientists from other sectors. In this article, we present a snapshot of the data science–related curriculum being taught in graduate energy programs in four different European universities as well as include feedback we received from students and alumni of these programs. While knowledge of data science remains low across the board, students in these programs already recognize data science as an important element of their future professional careers. We also present findings from running three separate iterations of an energy data science course we developed in light of this feedback—one of these iterations was offered only in KU Leuven (Belgium), while the other two were accessible to students at all four universities. In the article, we also discuss challenges and opportunities arising from designing and delivering courses in a cross-university context. This foundational course and others like it are seen as a necessary means to enable students to take more specialized courses in data science, and eventually contribute toward realizing a sustainable energy transition and meeting climate change mitigation objectives.","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42542270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When Lions Write: An American Immigrant Story 当狮子写作:一个美国移民的故事

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.08b08a97

T. Olubunmi

引用次数: 0

When Quantum Computation Meets Data Science: Making Data Science Quantum 当量子计算遇到数据科学:使数据科学量子化

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.ef5d8928

Yazhen Wang

引用次数: 10

Data Science on a Future Quantum Internet 未来量子互联网上的数据科学

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.32fa682f

Nana Liu

引用次数: 0

The Development of Quantum Machine Learning 量子机器学习的发展

Harvard data science review Pub Date : 2022-01-27 DOI: 10.1162/99608f92.5a9fd72c

K. Najafi, S. Yelin, Xun Gao

引用次数: 4

Data Flush. 数据刷新。

Harvard data science review Pub Date : 2022-01-01 DOI: 10.1162/99608f92.681fe3bd

Xiaotong Shen, Xuan Bi, Rex Shen

引用次数: 2

The Family of Single-Case Experimental Designs. 单例实验设计系列。

Harvard data science review Pub Date : 2022-01-01 Epub Date: 2022-09-08 DOI: 10.1162/99608f92.ff9300a8

Leonard H Epstein, Jesse Dallery

{"title":"The Family of Single-Case Experimental Designs.","authors":"Leonard H Epstein, Jesse Dallery","doi":"10.1162/99608f92.ff9300a8","DOIUrl":"10.1162/99608f92.ff9300a8","url":null,"abstract":"Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case-which can be an individual, clinic, or community-ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design-including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs-are also discussed.","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"4 SI3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016625/pdf/nihms-1842588.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10536718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity. 以患者为中心的多病治疗的个性化(N-of-1)试验

Harvard data science review Pub Date : 2022-01-01 Epub Date: 2022-09-08 DOI: 10.1162/99608f92.d99e6ff5

Jerry M Suls, Catherine Alfano, Christina Yap

{"title":"Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity.","authors":"Jerry M Suls, Catherine Alfano, Christina Yap","doi":"10.1162/99608f92.d99e6ff5","DOIUrl":"10.1162/99608f92.d99e6ff5","url":null,"abstract":"Treatment of patients who suffer from concurrent health conditions is not well served by (1) evidence-based clinical guidelines that mainly specify treatment of single conditions and (2) conventional randomized controlled trials (RCTs) that identify treatments as safe and effective on average. Clinical decision-making based on the average patient effect may be inappropriate for treatment of those with multimorbidity who experience burdens and obstacles that may be unique to their personal situation. We describe how the personalized (N-of-1) trials can be integrated with an automatic platform and virtual/remote technologies to improve patient-centered care for those living with multimorbidity. To illustrate, we present a hypothetical clinical scenario-survivors of both coronavirus disease 2019 (COVID-19) and cancer who chronically suffer from sleeplessness and fatigue. Then, we will describe how the four standard phases of conventional RCT development can be modified for personalized trials and applied to the multimorbidity clinical scenario, outline how personalized trials can be adapted and extended to compare the benefits of personalized trials versus between-subject trial design, and explain how personalized trials can address special problems associated with multimorbidity for which conventional trials are poorly suited.","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47706300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse. 共享始于家庭：持续和无处不在的 FAIRness 如何提高研究效率和数据再利用。

Harvard data science review Pub Date : 2022-01-01 Epub Date: 2022-07-28 DOI: 10.1162/99608f92.44d21b86

William Dempsey, Ian Foster, Scott Fraser, Carl Kesselman

{"title":"Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.","authors":"William Dempsey, Ian Foster, Scott Fraser, Carl Kesselman","doi":"10.1162/99608f92.44d21b86","DOIUrl":"10.1162/99608f92.44d21b86","url":null,"abstract":"The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications-by which time essential details are no longer accessible. Thus, we propose an approach to research informatics in which FAIR principles are applied continuously, from the inception of a research project and ubiquitously, to every data asset produced by experiment or computation. We suggest that this seemingly challenging task can be made feasible by the adoption of simple tools, such as lightweight identifiers (to ensure that every data asset is findable), packaging methods (to facilitate understanding of data contents), data access methods, and metadata organization and structuring tools (to support schema development and evolution). We use an example from experimental neuroscience to illustrate how these methods can work in practice.","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410569/pdf/nihms-1829357.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33444431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0