SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

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Against generalisation: Data-driven decisions need context to be human-compatible

Business Information Review ◽

10.1177/02663821211061986 ◽

2021 ◽

pp. 026638212110619

Author(s):

Sharon Richardson

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Data Science ◽

Machine Learning Algorithms ◽

Data Driven ◽

Theoretical Frameworks ◽

The Past ◽

Real World Applications ◽

Contextual Framework ◽

Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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Salaries, Dollars, and Sense: Making Data Driven Decisions

Case Studies in Sport Management ◽

10.1123/cssm.2014-0009 ◽

2014 ◽

Vol 3 (1) ◽

pp. 29-32

Author(s):

Stacy Warner ◽

Emily S. Sparvero

Keyword(s):

Data Driven ◽

Sense Making ◽

Data Driven Decisions

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On Leading and Making Data-Driven Decisions, or Not

Developing Informed Intuition for Decision-Making ◽

10.1201/9780429298097-10 ◽

2019 ◽

pp. 161-173

Author(s):

Kenneth Carling

Keyword(s):

Data Driven ◽

Data Driven Decisions

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Bias in smart city governance: How socio-spatial disparities in 311 complaint behavior impact the fairness of data-driven decisions

Sustainable Cities and Society ◽

10.1016/j.scs.2020.102503 ◽

2021 ◽

Vol 64 ◽

pp. 102503

Author(s):

Constantine E. Kontokosta ◽

Boyeong Hong

Keyword(s):

Smart City ◽

Data Driven ◽

Spatial Disparities ◽

City Governance ◽

Data Driven Decisions ◽

Complaint Behavior

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Building strong health supply chain systems: the visibility and analytics network approach to improving the Nigeria immunisation supply chain

BMJ Health & Care Informatics ◽

10.14236/jhi.v25i4.944 ◽

2018 ◽

Vol 25 (4) ◽

pp. 199-206 ◽

Cited By ~ 3

Author(s):

Chibuzo Ottih ◽

Kevin Cussen ◽

Mahmud Mustafa

Keyword(s):

Decision Making ◽

Supply Chain ◽

Data Analytics ◽

Data Driven ◽

Evidence Based ◽

Type Method ◽

Effective Operation ◽

Data Driven Decisions ◽

Vaccine Availability ◽

Evidence Based Decision Making

BackgroundHealth supply chain managers are unable to effectively monitor the performance of the immunisation supply chain in Nigeria. As a result, they are unable to make effective, data-driven decisions. This results in poor vaccine availability at some service delivery points. A lack of reliable data for evidence-based decision making is a significant contributor to this challenge.MethodThe visibility and analytics network (VAN) principles were introduced to enable end-to-end visibility in the immunisation supply chain and logistics (ISCL) system and make more accurate data available to health supply chain managers.ResultsThe application of the VAN principles has led to improved data collection, real-time stock visibility and enhanced data analytics framework. This enhanced visibility has promoted a culture of accountability and data-driven decision-making, previously unattainable. Health supply chain managers are now equipped with better skills and tools to promote effective operation of the immunisation supply chain.ConclusionThe introduction of VAN principles has been an effective approach to improving data visibility and creating incremental improvements in the ISCL in Nigeria.

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A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0285.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 837-843 ◽

Cited By ~ 15

Author(s):

Randal D. Koster ◽

Rolf H. Reichle ◽

Sarith P. P. Mahanama

Keyword(s):

United States ◽

Soil Moisture ◽

Real Time ◽

Surface Soil ◽

Data Driven ◽

Surface Soil Moisture ◽

Near Surface ◽

Time Estimates ◽

Data Driven Approach ◽

Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

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