---
objectId: 8e817ec2-2369-4001-8230-dcfee5eb6fab
title: Metadata
description: Metadata is data about data. It’s quite simple, really. Learn more about how it’s used within.
slug: metadata
tags: ["Data Science", "Standards"]
---

Metadata is data about data. For any piece of data, there is typically lots of metadata. Some common types of metadata found on HASH include:

- **Provenance metadata** – information such as the timestamp of creation, the datetime a file was last modified, the identity of a file’s creator, other information about the data’s origin
- **Quality metadata** – a margin of error, confidence interval, or probability score that indicates the likely accuracy of the data
- **Statistical metadata** – information that describes the process (e.g. [data pipeline](https://hash.ai/glossary/data-pipelines)) that produced data
- **Legal metadata** – the copyright holder, and any licensing terms data may be available under
- **Security metadata** – logs recording attempts to access data, and information regarding authorized users

Metadata is useful because it provides context to the data we use.

Some metadata is attached automatically by systems such as HASH when users perform certain actions (e.g. creating a file, connecting a datasource, constructing a flow, or editing a row in a dataset).

Other metadata can be added manually. For example, mapping data to schemas within HASH is an example of purposefully attaching metadata that describes the **type** of columns in a dataset, and the **properties** of the agents or events those columns represent.

A multitude of standards exist for describing different possible and expected types of metadata, which can in turn be used by business software to provide improved functionality or interoperability.


Metadata is data about data. It’s quite simple, really. Learn more about how it’s used within.

Metadata

There are two main approaches to building agent-based simulations: object-oriented programming and the actor-based model.

Actor Model

ABMs simulate entities in virtual environments, or digital twins, in order to help better understand both entities and their environments.

Agent-Based Modeling

Applicant tracking systems help employers manage recruitment and hiring.

Applicant Tracking System

Autocorrelation is a measure of the degree of similarity between any time series and a lagged or offset version of itself over successive time intervals.

Autocorrelation

The open Block Protocol standardizes the means by which blocks and the applications that embed them communicate.

Block Protocol

Business Intelligence allows companies to make data-driven decisions.

Business Intelligence

Business Process Modeling (BPM) helps organizations catalog, understand and improve their processes.

Business Process Modeling

Content management systems allow you to build and manage websites.

Content Management System

Customer relationship management systems track and coordinate interactions between a company and its customers.

Customer Relationship Management System

If you don’t know your DAGs from your dogs, you can finally get some clarity and sleep easily tonight. Learn what makes a Directed Acyclic Graph a DAG.

Directed Acyclic Graphs

Data Drift is the phenomenon where changes to data degrade model performance.

Data Drift

Data meshes are decentralized database solutions.

Data Mesh

Data Mining is a process applied to find unknown patterns, correlations, and anomalies in data. Through mining, meaningful insights can be extracted from data.

Data Mining

Data pipelines are processes that result in the production of data products, including datasets and models.

Data Pipelines

Data types describe a space of possible values through the specification of constraints

Data Types

Datasets are collections of numbers or words, generally centered around a single topic or subject.

Datasets

DRL is a subset of Machine Learning in which agents are allowed to solve tasks on their own, and thus discover new solutions independent of human intuition.

Deep Reinforcement Learning

Diffs are used to track changes between different versions or forks of a project, providing an overview regarding files changed, and the nature of those changes.

Diffing

Digital twins are a detailed simulated analogue to a real-world system

Digital Twin

DES is a modeling approach that focuses on the occurrence of events in a simulation, separately and instantaneously, rather than on any chronological-scale.

Discrete Event Simulation

Ego networks are a framework for local analysis of larger graphs.

Ego Networks

Enterprise resource planning uses an integrated software system to manage a business' daily tasks.

Enterprise Resource Planning

Entities are individual ‘things’ with a distinct, independent existence.

Entities

Entity types represent commonly recurring classes of entities, and describe their properties.

Entity Types

An electronic healthcare standard for data interoperability.

Fast Healthcare Interoperability Resources

Forking something means to create a copy of it, allowing individual developers or teams to work on their own versions of it, in safe isolation.

Forking

Graph Databases are a type of database that emphasizes the relationships between data.

Graph Databases

Graph representation learning is a more tailored way of applying machine learning algorithms to graphs and networks.

Graph Representation Learning

A graph is a collection of entities which may be connected to other entities by links.

Graphs

Integrations allow information from different systems to be brought together, and actions coordinated across them.

Integrations

Knowledge graphs are information-dense inputs to machine learning algorithms, and can capture more human-readable outputs of algorithms.

Knowledge Graph Machine Learning

Knowledge Graphs contextualize data and power insight generation.

Knowledge Graphs

Links between different entities represent the relationships and connections between them.

Links

Properties store individual pieces of information about entities. All property fields on an entity are inferred from its entity type(s).

Properties

Machine Learning is a subfield of Artificial Intelligence where parameters of an algorithm are updated from data inputs or by interacting with an environment.

Machine Learning

Merging is the process of reconciling two projects together. In HASH merging projects is handled by submitting, reviewing and approving “merge requests”.

Merging

Models tend to become less accurate over time.

Model Drift

There are lots of ways to license simulation models. Here we outline some key considerations and things to be aware of.

Model Licensing

There are lots of ways to share simulation models: blackbox, greybox, closed, open, transparent, and output-only. Here we explain what these terms all mean.

Model Sharing

Multi-Agent Systems represent real-world systems as collections of intelligent agents.

Multi-Agent Systems

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Artificial Neural Networks

The key to finding the best solution to any problem.

Optimization Methods

Parameters control specific parts of a system's behavior.

Parameters

Process mining is an application of data mining with the purpose of mapping an organization’s processes. It is used to optimize operations, and identify weaknesses.

Process Mining

Project management software is used to manage teams completing complex projects.

Project Management Software

Robotic process automation uses software to perform repeatable business tasks.

Robotic Process Automation

Robustness is a measure of a model's accuracy when presented with novel data.

Robustness

Schemas are descriptions of things: agents in simulations, and the actions they take. They help make simulations interoperable, and data more easily understood.

Schemas

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Simulation Modeling

Single synthetic environments allow you to build, run, and analyze data-driven models and simulations.

Single Synthetic Environment

Stochasticity is a measure of randomness. The state of a stochastic system can be modeled but not precisely predicted.

Stochasticity

Generating data that mimics real data for use in machine learning.

Synthetic Data Generation

System Dynamics models represent a system as a set of stocks and the rates of flows between them.

System Dynamics

Time series data is data that has been indexed, listed, or graphed in time order. For example, the daily closing value of the NASDAQ, the price of a cryptocurrency per second, or a single step in a simulation run.

Time Series Data

Types describe the shape that information is expected to take, through rules and constraints associated with it.

Types

In continuous time, variables may have specific values for only infinitesimally short amounts of time. In discrete time, values are measured once per time interval.

Discrete vs Continuous Time

Values are the information contained within a single property on a specific instance of an entity.

Metadata

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