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# Citation | ||
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If your work uses the DataJoint API for Python, please cite the following manuscript and Research Resource Identifier (RRID): | ||
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- Yatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi: https://doi.org/10.1101/031658 | ||
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- DataJoint API for Python - [RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543) - Version `Enter version here` |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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# Make Method | ||
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For auto-populated *Imported* and *Computed* tables[^1], a `make` method gives exact | ||
instructions for generating the content. By making these steps explicit, we keep a | ||
careful record of data provenance and ensure reproducibility. Data should never be | ||
entered using the `insert` method directly. | ||
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[^1]: For information on differentiating these data tiers, see the Table Tier section on | ||
[Automation](../design/tables/tiers#automation-imported-and-computed). | ||
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The `make` method receives one argument: the *key*, which represents the upstream table | ||
entries that need populating. The `key` is a `dict` in Python. | ||
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A `make` function should do three things: | ||
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1. [Fetch](../query/common-commands#fetch) data from tables upstream in the | ||
pipeline using the key for restriction. | ||
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2. Compute and add any missing attributes to the fields already in the key. | ||
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3. [Inserts](../query/common-commands#insert) the entire entity into the | ||
triggering table. | ||
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## Populate | ||
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The `make` method is sometimes referred to as the `populate` function because this is | ||
the class method called to run the `make` method on all relevant keys[^2]. | ||
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[^2]: For information on reprocessing keys that resulted in an error, see information | ||
on the [Jobs table](./distributed). | ||
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``` python | ||
Segmentation.populate() | ||
``` |
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## Work in progress | ||
You may ask questions in the chat window below or | ||
refer to [legacy documentation](https://docs.datajoint.org/) |
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<!-- markdownlint-disable MD013 --> | ||
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# Glossary | ||
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We've taken careful consideration to use consistent terminology. | ||
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<!-- Contributors: Please keep this table in alphabetical order --> | ||
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| Term | Definition | | ||
| --- | --- | | ||
| <span id="DAG">DAG</span> | directed acyclic graph (DAG) is a set of nodes and connected with a set of directed edges that form no cycles. This means that there is never a path back to a node after passing through it by following the directed edges. Formal workflow management systems represent workflows in the form of DAGs. | | ||
| <span id="data-pipeline">data pipeline</span> | A sequence of data transformation steps from data sources through multiple intermediate structures. More generally, a data pipeline is a directed acyclic graph. In DataJoint, each step is represented by a table in a relational database. | | ||
| <span id="datajoint">DataJoint</span> | a software framework for database programming directly from matlab and python. Thanks to its support of automated computational dependencies, DataJoint serves as a workflow management system. | | ||
| <span id="datajoint-elements">DataJoint Elements</span> | software modules implementing portions of experiment workflows designed for ease of integration into diverse custom workflows. | | ||
| <span id="datajoint-pipeline">DataJoint pipeline</span> | the data schemas and transformations underlying a DataJoint workflow. DataJoint allows defining code that specifies both the workflow and the data pipeline, and we have used the words "pipeline" and "workflow" almost interchangeably. | | ||
| <span id="datajoint-schema">DataJoint schema</span> | a software module implementing a portion of an experiment workflow. Includes database table definitions, dependencies, and associated computations. | | ||
| <span id="foreign-key">foreign key</span> | a field that is linked to another table's primary key. | | ||
| <span id="primary-key">primary key</span> | the subset of table attributes that uniquely identify each entity in the table. | | ||
| <span id="secondary-attribute">secondray attribute</span> | any field in a table not in the primary key. | | ||
| <span id="workflow">workflow</span> | a formal representation of the steps for executing an experiment from data collection to analysis. Also the software configured for performing these steps. A typical workflow is composed of tables with inter-dependencies and processes to compute and insert data into the tables. | |
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