材料数据科学本体学 (MDS-Onto):统一材料和应用数据科学领域的知识
Balashanmuga Priyan Rajamohan1,2, Alexander C Harding Bradley1,2, Van D Tran2,3
1Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Scientific data
|April 15, 2025
概括
我们开发了一个统一的框架,用于创建标准化的材料数据科学本体. 这一框架提高了数据的互操作性,并简化了术语匹配,以便更好地管理科学数据.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 语义网络技术 语义网络技术
背景情况:
- 本体学越来越多地用于标准化科学数据术语.
- 当前的材料科学本体学缺乏标准化,导致互操作性问题.
- 现有的框架为开发一致和可重复使用的本体论提供了有限的指导.
研究的目的:
- 提出一个统一的,自动化的框架 (MDS-Onto) 用于在材料数据科学中开发可互操作和模块化本体.
- 建立一个语义桥梁到基本形式本体 (BFO) 简化本体术语匹配.
- 提供关于本体学定位,知识表示语言和出版的建议,以提高可查和互操作性.
主要方法:
- 开发了MDS-Onto框架,其中有两个核心组件:FAIR材料用于本体结构创建和FAIRLinked用于FAIR数据创建.
- 建立了语义网络集成的建议,推的知识表示语言和在线出版策略.
- 使用FAIR材料包创建了用于同步射线X射线衍射和光伏的示例域本体.
主要成果:
- MDS-Onto框架提供了一种标准化的方法来开发材料数据科学本体学.
- FAIRmaterials促进了双语本体论的创建,提高了可访问性和可用性.
- 范例的实体学展示了框架的实际应用和增强数据互操作性的潜力.
结论:
- 拟议的框架解决了在材料数据科学中对标准化,互操作和模块化本体学的需求.
- 实施MDS-Onto,FAIRmaterials和FAIRLinked可以显著提高材料数据的可查找性,可访问性,互操作性和可重复使用性 (FAIR).
- 这项工作为材料科学社区中更一致和协作的本体论发展提供了基础.
相关概念视频
01:05Collisions in Multiple Dimensions: Introduction
4.4K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
4.4K
01:06Collisions in Multiple Dimensions: Problem Solving
3.5K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.5K
03:40Dimensional Analysis
44.2K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
Conversion Factors and Dimensional Analysis
The unit...
44.2K
01:08Problem Solving: Dimensional Analysis
3.2K
Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...
3.2K
01:16Deductive Reasoning
54.7K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
For example, a researcher can deduce specific predictions...
54.7K
01:19Data: Types and Distribution
647
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Distributions in...
647